hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d86a7610b205b3e4cc679d4d420398b2a7ef8963 | 4,872 | py | Python | TorPool/tor_method.py | SUN-PEI-YUAN/TorPool | 6e77e3b4c0b1e370e6b9417e94dc358b1d25f365 | [
"MIT"
] | 1 | 2022-01-29T22:24:07.000Z | 2022-01-29T22:24:07.000Z | TorPool/tor_method.py | SUN-PEI-YUAN/TorPool | 6e77e3b4c0b1e370e6b9417e94dc358b1d25f365 | [
"MIT"
] | null | null | null | TorPool/tor_method.py | SUN-PEI-YUAN/TorPool | 6e77e3b4c0b1e370e6b9417e94dc358b1d25f365 | [
"MIT"
] | null | null | null | # coding: utf-8
import subprocess
import shutil
import sys
import os
class TorMethod(object):
'''tor 代理伺服器製作
:::參數說明:::
torrc_dir: torrc要儲存的位置(必要)
tordata_dir: torrc內DataDirectory的資訊(必要)
__process: tor的process控制器, 可以使用TorMethod.get_process取得
__torname: torrc檔案和資料夾的名稱, 可以使用TorMethod.get_toruuid取得
__torrcfile: torrc檔案路徑, 可以使用TorMethod.get_torrcpath取得
__tordatafile: torrc內DataDirectory的資訊, 可以使用TorMethod.get_torrcpath取得
__socksport: Tor opens a SOCKS proxy on port [socksport]
__controlport: The port on which Tor will listen for local connections from Tor
controller applications, as documented in control-spec.txt.
'''
def __init__(self, torrc_dir, tordata_dir, hashedcontrolpassword):
from . import _TOR_EXE
if sys.platform is 'win32':
self.__tor_exe = _TOR_EXE
else:
self.__tor_exe = os.popen('which tor').read().rstrip('\n')
if self.__tor_exe is '':
error_msg = (
"\'Tor client\' is not installed. Please insatll tor client first!\n"
"\t If Your system is debian or ubuntu, please execute \'sudo apt install tor -y\'.\n"
"\t If Your system is macOS, please install homebrew and execute \'brew install tor -y\'.\n"
)
raise OSError(error_msg)
self.torrc_dir = torrc_dir #
self.tordata_dir = tordata_dir #
self.hashedcontrolpassword = hashedcontrolpassword
import uuid
self.__process = None #
self.__torname = str(uuid.uuid4()) #
self.__torrcfile = os.path.join(self.torrc_dir, self.__torname + '.conf') #
self.__tordatafile = os.path.join(self.tordata_dir, self.__torname)
self.__socksport = None #
self.__controlport = None #
self.__hashed = self.__tor_hashpasswd()
if os.path.exists(self.torrc_dir):
shutil.rmtree(self.torrc_dir)
os.makedirs(self.torrc_dir)
if os.path.exists(self.tordata_dir):
shutil.rmtree(self.tordata_dir)
os.makedirs(self.tordata_dir)
@property
def get_status(self):
if self.__process is None:
pid = None
else:
pid = self.__process.pid
return {
'tor_exe': self.__tor_exe,
'socksport': self.__socksport,
'process': pid,
'tor_uuid': self.__torname,
'torrc_path': self.__torrcfile,
'torrcdata_path': self.__tordatafile,
}
def __tor_hashpasswd(self):
process = subprocess.Popen(self.__tor_exe + ' --hash-password ' + str(self.hashedcontrolpassword), shell=True, stdout=subprocess.PIPE)
return str(process.stdout.readline().decode('utf-8')).rstrip('\n')
def get_free_port(self):
'''找閒置port'''
from socket import socket
port = None
with socket() as s:
s.bind(('',0))
port = s.getsockname()[1]
s.close()
return port
def make_torrc(self):
'''寫出torrc'''
if not os.path.exists(self.torrc_dir):
os.makedirs(self.torrc_dir)
if not os.path.exists(self.tordata_dir):
os.makedirs(self.tordata_dir)
with open(self.__torrcfile, 'w') as f:
torrc = self.torrc()
f.write(torrc)
def torrc(self):
'''torrc格式'''
if self.__socksport is None:
self.__socksport = self.get_free_port()
if self.__controlport is None:
self.__controlport = self.get_free_port()
torrc_file = (
'HashedControlPassword {hashedcontrolpassword}\n'
'SocksPort {socksport}\n'
'ControlPort {controlport}\n'
'DataDirectory {tordatafile}\n'
)
return torrc_file.format(
hashedcontrolpassword = self.__hashed,
socksport = self.__socksport,
controlport = self.__controlport,
tordatafile = self.__tordatafile
)
def start_tor(self):
'''啟動tor'''
if self.__process is not None:
self.__process.kill()
else:
process = subprocess.Popen(self.__tor_exe + ' -f ' + self.__torrcfile, shell=True)
self.__process = process
def restart_tor(self):
'''若proxy被封鎖，殺掉程序重新執行tor'''
self.__process.kill()
self.start_tor()
def kill_process(self):
'''殺死利用套件啟動的tor程序'''
self.__process.kill()
shutil.rmtree(self.torrcfile)
shutil.rmtree(self.tordatafile)
self.__process = None
def kill_all_tor(self):
'''殺死系統所有存在的tor'''
if sys.platform is 'win32':
os.system('TASKKILL /F /IM tor.exe /T')
else:
os.system('killall -9 tor')
self.pool = [] | 36.088889 | 142 | 0.594212 | 535 | 4,872 | 5.127103 | 0.28785 | 0.029165 | 0.034998 | 0.023332 | 0.13416 | 0.118848 | 0.053956 | 0.053956 | 0.026249 | 0 | 0 | 0.002933 | 0.300082 | 4,872 | 135 | 143 | 36.088889 | 0.801466 | 0.129105 | 0 | 0.142857 | 0 | 0.019048 | 0.12503 | 0.011082 | 0 | 0 | 0 | 0 | 0 | 1 | 0.095238 | false | 0.066667 | 0.066667 | 0 | 0.209524 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
d86e898eaf189c08d400028fbb8d2971795df000 | 5,107 | py | Python | core/management/commands/gendoc.py | klebed/esdc-ce | 2c9e4591f344247d345a83880ba86777bb794460 | [
"Apache-2.0"
] | 97 | 2016-11-15T14:44:23.000Z | 2022-03-13T18:09:15.000Z | core/management/commands/gendoc.py | klebed/esdc-ce | 2c9e4591f344247d345a83880ba86777bb794460 | [
"Apache-2.0"
] | 334 | 2016-11-17T19:56:57.000Z | 2022-03-18T10:45:53.000Z | core/management/commands/gendoc.py | klebed/esdc-ce | 2c9e4591f344247d345a83880ba86777bb794460 | [
"Apache-2.0"
] | 33 | 2017-01-02T16:04:13.000Z | 2022-02-07T19:20:24.000Z | import os
import re
import shutil
from ._base import DanubeCloudCommand, CommandOption, CommandError, lcd
class Command(DanubeCloudCommand):
help = 'Generate documentation files displayed in GUI.'
DOC_REPO = 'https://github.com/erigones/esdc-docs.git'
DOC_TMP_DIR = '/var/tmp/esdc-docs'
options = (
CommandOption('--api', '--api-only', action='store_true', dest='api_only', default=False,
help='Generate only the API documentation.'),
CommandOption('--user-guide', '--user-guide-only', action='store_true', dest='user_guide_only', default=False,
help='Generate only the User Guide.'),
)
def gendoc_api(self):
"""Generate api documentation"""
with lcd(self.PROJECT_DIR):
doc_dir = self._path(self.PROJECT_DIR, 'doc', 'api')
doc_dst = self._path(self.PROJECT_DIR, 'api', 'static', 'api', 'doc')
bin_dst = self._path(self.PROJECT_DIR, 'api', 'static', 'api', 'bin')
# Build sphinx docs
with lcd(doc_dir):
self.local('make esdc-clean; make esdc ESDOCDIR="%s"' % doc_dst)
# Create es script suitable for download
es_src = self._path(self.PROJECT_DIR, 'bin', 'es')
es_dst = self._path(bin_dst, 'es')
es_current = os.path.join(self.settings.PROJECT_DIR, 'var', 'www', 'static', 'api', 'bin', 'es')
api_url = "API_URL = '%s'" % (self.settings.SITE_LINK + '/api')
if os.path.isfile(es_current):
with open(es_current, 'r') as es0:
for line in es0:
if line.startswith("API_URL = '"):
api_url = line
break
with open(es_src) as es1:
with os.fdopen(os.open(es_dst, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o644), 'w') as es2:
es2.write(es1.read().replace("API_URL = 'http://127.0.0.1:8000/api'", api_url))
# Copy es_bash_completion.sh to download location
es_bc_src = self._path(doc_dir, 'es_bash_completion.sh')
self.local('cp %s %s' % (es_bc_src, bin_dst))
self.display('API documentation built successfully.', color='green')
def gendoc_user_guide(self, fallback_branch='master'):
"""Generate user guide"""
doc_dst = self._path(self.PROJECT_DIR, 'gui', 'static', 'user-guide')
with lcd(self.PROJECT_DIR):
try:
branch = self.get_git_version()[0] # Git tag or branch name
except CommandError:
self.display('Could not determine our branch or tag', color='yellow')
branch = fallback_branch
self.display('Falling back to "%s" branch' % branch, color='yellow')
else:
self.display('We are on branch "%s"' % branch)
if self._path_exists(self.DOC_TMP_DIR, 'user-guide', 'conf.py'):
existing_repo = True
self.display('%s already exists in %s' % (self.DOC_REPO, self.DOC_TMP_DIR), color='yellow')
with lcd(self.DOC_TMP_DIR):
self.local('git fetch')
self.display('%s has been successfully updated.' % self.DOC_REPO, color='green')
else:
if self._path_exists(self.DOC_TMP_DIR):
self.display('Removing stale %s', self.DOC_TMP_DIR, color='yellow')
shutil.rmtree(self.DOC_TMP_DIR)
existing_repo = False
self.local('git clone %s %s' % (self.DOC_REPO, self.DOC_TMP_DIR))
self.display('%s has been successfully cloned.' % self.DOC_TMP_DIR, color='green')
with lcd(self.DOC_TMP_DIR):
if self.local('git checkout %s' % branch, raise_on_error=False) != 0:
self.display('Could not checkout esdc-docs branch "%s"' % branch, color='yellow')
branch = fallback_branch
self.display('Falling back to "%s" branch' % branch, color='yellow')
self.local('git checkout %s' % branch)
self.display('Checked out esdc-docs branch "%s"' % branch, color='green')
# If the branch is no a tag name, then we need to merge/pull
if existing_repo and not re.search('^v[0-9]', branch):
self.local('git merge --ff-only origin/%s' % branch)
self.display('Merged esdc-docs branch "%s"' % branch, color='green')
# Build sphinx docs
with lcd(self._path(self.DOC_TMP_DIR, 'user-guide')):
self.local('make esdc-clean; make esdc ESDOCDIR="%s"' % doc_dst)
self.display('User guide built successfully.', color='green')
def handle(self, api_only=False, user_guide_only=False, **options):
if api_only and user_guide_only:
pass
elif api_only:
self.gendoc_api()
return
elif user_guide_only:
self.gendoc_user_guide()
return
self.gendoc_api()
self.display('\n\n', stderr=True)
self.gendoc_user_guide()
| 44.798246 | 118 | 0.577247 | 658 | 5,107 | 4.305471 | 0.261398 | 0.047653 | 0.034945 | 0.045888 | 0.390046 | 0.298976 | 0.209319 | 0.151783 | 0.11366 | 0.08754 | 0 | 0.006639 | 0.292148 | 5,107 | 113 | 119 | 45.19469 | 0.77704 | 0.049344 | 0 | 0.209302 | 1 | 0 | 0.22608 | 0.00434 | 0 | 0 | 0 | 0 | 0 | 1 | 0.034884 | false | 0.011628 | 0.046512 | 0 | 0.162791 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8723e31987ced651ec6ee7cac0b7d24f592d4fe | 1,332 | py | Python | analytics_management/models.py | mattiolato98/reservation-ninja | 0e50b218dd9d90f134868bade2ec2934283c12b5 | [
"MIT"
] | 1 | 2022-03-10T11:34:14.000Z | 2022-03-10T11:34:14.000Z | analytics_management/models.py | mattiolato98/reservation-ninja | 0e50b218dd9d90f134868bade2ec2934283c12b5 | [
"MIT"
] | null | null | null | analytics_management/models.py | mattiolato98/reservation-ninja | 0e50b218dd9d90f134868bade2ec2934283c12b5 | [
"MIT"
] | null | null | null | from django.contrib.auth import get_user_model
from django.db import models
class Log(models.Model):
"""
Model that describe a Log object, it contains information about daily
executions.
"""
execution_time = models.FloatField()
users = models.IntegerField()
lessons = models.IntegerField()
date = models.DateField(auto_now_add=True)
def __str__(self):
return f"{self.date}"
@property
def average_user_execution_time(self):
return self.execution_time / self.users if self.users > 0 else 0
@property
def average_lesson_execution_time(self):
"""
This property returns a useful data about the average execution time of
a lesson.
Returns:
float: average time resulted
"""
return self.execution_time / self.lessons if self.lessons > 0 else 0
class Feedback(models.Model):
"""
Model that describe a user feedback of the daily reservations.
"""
user = models.ForeignKey(get_user_model(), on_delete=models.SET_NULL, related_name='feedbacks', null=True)
ok = models.BooleanField()
date = models.DateField(auto_now_add=True)
def __str__(self):
return f'{self.user.username} {self.ok}'
class Stats(models.Model):
unsubscribed_users = models.IntegerField(default=0)
| 27.75 | 110 | 0.680931 | 170 | 1,332 | 5.164706 | 0.405882 | 0.088838 | 0.077449 | 0.045558 | 0.250569 | 0.189066 | 0.123007 | 0.123007 | 0.123007 | 0.123007 | 0 | 0.004854 | 0.226727 | 1,332 | 47 | 111 | 28.340426 | 0.847573 | 0.201952 | 0 | 0.26087 | 0 | 0 | 0.050968 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.173913 | false | 0 | 0.086957 | 0.130435 | 0.913043 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
d877c087af72ca345542775e075546a6694a0d12 | 1,422 | py | Python | examples/s2_extensions.py | ChubV/oop-di | 3de449e3c209529bd2554187aa9857bf729841b8 | [
"MIT"
] | null | null | null | examples/s2_extensions.py | ChubV/oop-di | 3de449e3c209529bd2554187aa9857bf729841b8 | [
"MIT"
] | null | null | null | examples/s2_extensions.py | ChubV/oop-di | 3de449e3c209529bd2554187aa9857bf729841b8 | [
"MIT"
] | null | null | null | from abc import ABC, abstractmethod
from oop_di import ContainerDefinition, Extension
# #############Mailer bounded context###############
class MailerInterface(ABC):
@abstractmethod
def send_mail(self):
...
class Mailer(MailerInterface):
def __init__(self, from_email):
self.from_email = from_email
def send_mail(self):
print(f"Sending from {self.from_email}...")
print("Sent")
class MailExtension(Extension):
def define(self):
self.add_param("from_email", "test@example.com")
self.add_named_service(MailerInterface, Mailer)
# ############Product bounded context###########
class ProductService:
def __init__(self, mailer: MailerInterface):
self.mailer = mailer
def process_product(self):
print("processing product")
self.mailer.send_mail()
class ProductExtension(Extension):
def define(self):
self.add_service(ProductService)
# #################Application
container_definition = ContainerDefinition()
container_definition.add_extension(ProductExtension())
container_definition.add_extension(MailExtension())
container = container_definition.compile()
@container.inject()
def process_product_endpoint(something, *, product_service: ProductService):
print(something)
product_service.process_product()
process_product_endpoint("doing something before calling product service")
| 22.571429 | 76 | 0.699015 | 145 | 1,422 | 6.613793 | 0.324138 | 0.046924 | 0.040667 | 0.031283 | 0.06048 | 0.06048 | 0 | 0 | 0 | 0 | 0 | 0 | 0.164557 | 1,422 | 62 | 77 | 22.935484 | 0.807239 | 0.040788 | 0 | 0.117647 | 0 | 0 | 0.098297 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.235294 | false | 0 | 0.058824 | 0 | 0.441176 | 0.117647 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d881534818a726ed66374fd1b791859515909369 | 2,122 | py | Python | cmsplugin_svg/migrations/0001_initial.py | parthenon/cmsplugin-svg | bb89705002cd3557f074f3f063a3ec251ca0a605 | [
"BSD-3-Clause"
] | null | null | null | cmsplugin_svg/migrations/0001_initial.py | parthenon/cmsplugin-svg | bb89705002cd3557f074f3f063a3ec251ca0a605 | [
"BSD-3-Clause"
] | null | null | null | cmsplugin_svg/migrations/0001_initial.py | parthenon/cmsplugin-svg | bb89705002cd3557f074f3f063a3ec251ca0a605 | [
"BSD-3-Clause"
] | null | null | null | # Generated by Django 3.1.13 on 2021-08-03 22:33
from django.db import migrations, models
import django.db.models.deletion
import filer.fields.file
class Migration(migrations.Migration):
initial = True
dependencies = [
('filer', '0012_file_mime_type'),
('cms', '0022_auto_20180620_1551'),
]
operations = [
migrations.CreateModel(
name='SvgImage',
fields=[
('cmsplugin_ptr', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, related_name='+', serialize=False, to='cms.cmsplugin')),
('label', models.CharField(blank=True, help_text='Optional label for this plugin.', max_length=128, verbose_name='label')),
('id_name', models.CharField(blank=True, max_length=50, verbose_name='id name')),
('tag_type', models.CharField(blank=True, choices=[('', ''), ('figure', 'figure')], max_length=50, null=True, verbose_name='tag Type')),
('additional_class_names', models.TextField(blank=True, help_text='Comma separated list of additional classes to apply to tag_type', verbose_name='additional classes')),
('alignment', models.CharField(blank=True, choices=[('left', 'left'), ('right', 'right'), ('center', 'center')], max_length=10, null=True, verbose_name='image alignment')),
('width', models.PositiveIntegerField(blank=True, null=True, verbose_name='width')),
('height', models.PositiveIntegerField(blank=True, null=True, verbose_name='height')),
('caption_text', models.CharField(blank=True, max_length=255, null=True, verbose_name='caption text')),
('alt_text', models.CharField(blank=True, max_length=255, null=True, verbose_name='alt text')),
('svg_image', filer.fields.file.FilerFileField(null=True, on_delete=django.db.models.deletion.SET_NULL, to='filer.file', verbose_name='file')),
],
options={
'abstract': False,
},
bases=('cms.cmsplugin',),
),
] | 55.842105 | 192 | 0.633836 | 246 | 2,122 | 5.308943 | 0.394309 | 0.084227 | 0.091884 | 0.11026 | 0.29173 | 0.244257 | 0.173047 | 0.173047 | 0.090352 | 0.090352 | 0 | 0.030448 | 0.21065 | 2,122 | 38 | 193 | 55.842105 | 0.749254 | 0.021678 | 0 | 0 | 1 | 0 | 0.207711 | 0.021687 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.096774 | 0 | 0.225806 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d881b477a22ed1f78da11df06776a0e9cf84193c | 302 | py | Python | 5-gui.py | theseana/pesteh | 1125dc1055e3b8466c3c539c4afc2149d663dd46 | [
"MIT"
] | 1 | 2022-01-16T00:33:57.000Z | 2022-01-16T00:33:57.000Z | 5-gui.py | theseana/pesteh | 1125dc1055e3b8466c3c539c4afc2149d663dd46 | [
"MIT"
] | null | null | null | 5-gui.py | theseana/pesteh | 1125dc1055e3b8466c3c539c4afc2149d663dd46 | [
"MIT"
] | null | null | null | from tkinter import *
root = Tk()
root.config(bg='yellow')
l1 = Label(root, text='Hello World!', bg='magenta')
l1.pack(side=LEFT)
b1 = Button(root, text='Click Me Please!', bg='cyan')
b1.pack(side=LEFT)
l2 = Label(root, text='Ta-Da!', bg='green')
l2.pack(side=LEFT)
root.mainloop()
| 18.875 | 54 | 0.629139 | 48 | 302 | 3.958333 | 0.583333 | 0.126316 | 0.189474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02381 | 0.165563 | 302 | 15 | 55 | 20.133333 | 0.730159 | 0 | 0 | 0 | 0 | 0 | 0.195122 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.1 | 0 | 0.1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d883b9402b2e431043b91f8f29e4ee4248eaa1ba | 20,375 | py | Python | venv/lib/python3.6/site-packages/ansible_collections/google/cloud/plugins/modules/gcp_redis_instance.py | usegalaxy-no/usegalaxy | 75dad095769fe918eb39677f2c887e681a747f3a | [
"MIT"
] | 7 | 2021-11-16T04:05:42.000Z | 2022-02-19T21:14:29.000Z | venv/lib/python3.6/site-packages/ansible_collections/google/cloud/plugins/modules/gcp_redis_instance.py | usegalaxy-no/usegalaxy | 75dad095769fe918eb39677f2c887e681a747f3a | [
"MIT"
] | 12 | 2020-02-21T07:24:52.000Z | 2020-04-14T09:54:32.000Z | venv/lib/python3.6/site-packages/ansible_collections/google/cloud/plugins/modules/gcp_redis_instance.py | usegalaxy-no/usegalaxy | 75dad095769fe918eb39677f2c887e681a747f3a | [
"MIT"
] | 1 | 2022-03-01T05:43:07.000Z | 2022-03-01T05:43:07.000Z | #!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2017 Google
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
# ----------------------------------------------------------------------------
#
# *** AUTO GENERATED CODE *** AUTO GENERATED CODE ***
#
# ----------------------------------------------------------------------------
#
# This file is automatically generated by Magic Modules and manual
# changes will be clobbered when the file is regenerated.
#
# Please read more about how to change this file at
# https://www.github.com/GoogleCloudPlatform/magic-modules
#
# ----------------------------------------------------------------------------
from __future__ import absolute_import, division, print_function
__metaclass__ = type
################################################################################
# Documentation
################################################################################
ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ["preview"], 'supported_by': 'community'}
DOCUMENTATION = '''
---
module: gcp_redis_instance
description:
- A Google Cloud Redis instance.
short_description: Creates a GCP Instance
author: Google Inc. (@googlecloudplatform)
requirements:
- python >= 2.6
- requests >= 2.18.4
- google-auth >= 1.3.0
options:
state:
description:
- Whether the given object should exist in GCP
choices:
- present
- absent
default: present
type: str
alternative_location_id:
description:
- Only applicable to STANDARD_HA tier which protects the instance against zonal
failures by provisioning it across two zones.
- If provided, it must be a different zone from the one provided in [locationId].
required: false
type: str
auth_enabled:
description:
- Optional. Indicates whether OSS Redis AUTH is enabled for the instance. If set
to "true" AUTH is enabled on the instance.
- Default value is "false" meaning AUTH is disabled.
required: false
default: 'false'
type: bool
authorized_network:
description:
- The full name of the Google Compute Engine network to which the instance is
connected. If left unspecified, the default network will be used.
required: false
type: str
connect_mode:
description:
- The connection mode of the Redis instance.
- 'Some valid choices include: "DIRECT_PEERING", "PRIVATE_SERVICE_ACCESS"'
required: false
default: DIRECT_PEERING
type: str
display_name:
description:
- An arbitrary and optional user-provided name for the instance.
required: false
type: str
labels:
description:
- Resource labels to represent user provided metadata.
required: false
type: dict
redis_configs:
description:
- Redis configuration parameters, according to U(http://redis.io/topics/config).
- 'Please check Memorystore documentation for the list of supported parameters:
U(https://cloud.google.com/memorystore/docs/redis/reference/rest/v1/projects.locations.instances#Instance.FIELDS.redis_configs)
.'
required: false
type: dict
location_id:
description:
- The zone where the instance will be provisioned. If not provided, the service
will choose a zone for the instance. For STANDARD_HA tier, instances will be
created across two zones for protection against zonal failures. If [alternativeLocationId]
is also provided, it must be different from [locationId].
required: false
type: str
name:
description:
- The ID of the instance or a fully qualified identifier for the instance.
required: true
type: str
memory_size_gb:
description:
- Redis memory size in GiB.
required: true
type: int
redis_version:
description:
- 'The version of Redis software. If not provided, latest supported version will
be used. Currently, the supported values are: - REDIS_5_0 for Redis 5.0 compatibility
- REDIS_4_0 for Redis 4.0 compatibility - REDIS_3_2 for Redis 3.2 compatibility
.'
required: false
type: str
reserved_ip_range:
description:
- The CIDR range of internal addresses that are reserved for this instance. If
not provided, the service will choose an unused /29 block, for example, 10.0.0.0/29
or 192.168.0.0/29. Ranges must be unique and non-overlapping with existing subnets
in an authorized network.
required: false
type: str
tier:
description:
- 'The service tier of the instance. Must be one of these values: - BASIC: standalone
instance - STANDARD_HA: highly available primary/replica instances .'
- 'Some valid choices include: "BASIC", "STANDARD_HA"'
required: false
default: BASIC
type: str
region:
description:
- The name of the Redis region of the instance.
required: true
type: str
project:
description:
- The Google Cloud Platform project to use.
type: str
auth_kind:
description:
- The type of credential used.
type: str
required: true
choices:
- application
- machineaccount
- serviceaccount
service_account_contents:
description:
- The contents of a Service Account JSON file, either in a dictionary or as a
JSON string that represents it.
type: jsonarg
service_account_file:
description:
- The path of a Service Account JSON file if serviceaccount is selected as type.
type: path
service_account_email:
description:
- An optional service account email address if machineaccount is selected and
the user does not wish to use the default email.
type: str
scopes:
description:
- Array of scopes to be used
type: list
elements: str
env_type:
description:
- Specifies which Ansible environment you're running this module within.
- This should not be set unless you know what you're doing.
- This only alters the User Agent string for any API requests.
type: str
notes:
- 'API Reference: U(https://cloud.google.com/memorystore/docs/redis/reference/rest/)'
- 'Official Documentation: U(https://cloud.google.com/memorystore/docs/redis/)'
- for authentication, you can set service_account_file using the C(gcp_service_account_file)
env variable.
- for authentication, you can set service_account_contents using the C(GCP_SERVICE_ACCOUNT_CONTENTS)
env variable.
- For authentication, you can set service_account_email using the C(GCP_SERVICE_ACCOUNT_EMAIL)
env variable.
- For authentication, you can set auth_kind using the C(GCP_AUTH_KIND) env variable.
- For authentication, you can set scopes using the C(GCP_SCOPES) env variable.
- Environment variables values will only be used if the playbook values are not set.
- The I(service_account_email) and I(service_account_file) options are mutually exclusive.
'''
EXAMPLES = '''
- name: create a network
google.cloud.gcp_compute_network:
name: network-instance
project: "{{ gcp_project }}"
auth_kind: "{{ gcp_cred_kind }}"
service_account_file: "{{ gcp_cred_file }}"
state: present
register: network
- name: create a instance
google.cloud.gcp_redis_instance:
name: instance37
tier: STANDARD_HA
memory_size_gb: 1
region: us-central1
location_id: us-central1-a
redis_version: REDIS_3_2
display_name: Ansible Test Instance
reserved_ip_range: 192.168.0.0/29
labels:
my_key: my_val
other_key: other_val
project: test_project
auth_kind: serviceaccount
service_account_file: "/tmp/auth.pem"
state: present
'''
RETURN = '''
alternativeLocationId:
description:
- Only applicable to STANDARD_HA tier which protects the instance against zonal
failures by provisioning it across two zones.
- If provided, it must be a different zone from the one provided in [locationId].
returned: success
type: str
authEnabled:
description:
- Optional. Indicates whether OSS Redis AUTH is enabled for the instance. If set
to "true" AUTH is enabled on the instance.
- Default value is "false" meaning AUTH is disabled.
returned: success
type: bool
authorizedNetwork:
description:
- The full name of the Google Compute Engine network to which the instance is connected.
If left unspecified, the default network will be used.
returned: success
type: str
connectMode:
description:
- The connection mode of the Redis instance.
returned: success
type: str
createTime:
description:
- The time the instance was created in RFC3339 UTC "Zulu" format, accurate to nanoseconds.
returned: success
type: str
currentLocationId:
description:
- The current zone where the Redis endpoint is placed.
- For Basic Tier instances, this will always be the same as the [locationId] provided
by the user at creation time. For Standard Tier instances, this can be either
[locationId] or [alternativeLocationId] and can change after a failover event.
returned: success
type: str
displayName:
description:
- An arbitrary and optional user-provided name for the instance.
returned: success
type: str
host:
description:
- Hostname or IP address of the exposed Redis endpoint used by clients to connect
to the service.
returned: success
type: str
labels:
description:
- Resource labels to represent user provided metadata.
returned: success
type: dict
redisConfigs:
description:
- Redis configuration parameters, according to U(http://redis.io/topics/config).
- 'Please check Memorystore documentation for the list of supported parameters:
U(https://cloud.google.com/memorystore/docs/redis/reference/rest/v1/projects.locations.instances#Instance.FIELDS.redis_configs)
.'
returned: success
type: dict
locationId:
description:
- The zone where the instance will be provisioned. If not provided, the service
will choose a zone for the instance. For STANDARD_HA tier, instances will be created
across two zones for protection against zonal failures. If [alternativeLocationId]
is also provided, it must be different from [locationId].
returned: success
type: str
name:
description:
- The ID of the instance or a fully qualified identifier for the instance.
returned: success
type: str
memorySizeGb:
description:
- Redis memory size in GiB.
returned: success
type: int
port:
description:
- The port number of the exposed Redis endpoint.
returned: success
type: int
persistenceIamIdentity:
description:
- Output only. Cloud IAM identity used by import / export operations to transfer
data to/from Cloud Storage. Format is "serviceAccount:".
- The value may change over time for a given instance so should be checked before
each import/export operation.
returned: success
type: str
redisVersion:
description:
- 'The version of Redis software. If not provided, latest supported version will
be used. Currently, the supported values are: - REDIS_5_0 for Redis 5.0 compatibility
- REDIS_4_0 for Redis 4.0 compatibility - REDIS_3_2 for Redis 3.2 compatibility
.'
returned: success
type: str
reservedIpRange:
description:
- The CIDR range of internal addresses that are reserved for this instance. If not
provided, the service will choose an unused /29 block, for example, 10.0.0.0/29
or 192.168.0.0/29. Ranges must be unique and non-overlapping with existing subnets
in an authorized network.
returned: success
type: str
tier:
description:
- 'The service tier of the instance. Must be one of these values: - BASIC: standalone
instance - STANDARD_HA: highly available primary/replica instances .'
returned: success
type: str
region:
description:
- The name of the Redis region of the instance.
returned: success
type: str
'''
################################################################################
# Imports
################################################################################
from ansible_collections.google.cloud.plugins.module_utils.gcp_utils import navigate_hash, GcpSession, GcpModule, GcpRequest, replace_resource_dict
import json
import time
################################################################################
# Main
################################################################################
def main():
"""Main function"""
module = GcpModule(
argument_spec=dict(
state=dict(default='present', choices=['present', 'absent'], type='str'),
alternative_location_id=dict(type='str'),
auth_enabled=dict(type='bool'),
authorized_network=dict(type='str'),
connect_mode=dict(default='DIRECT_PEERING', type='str'),
display_name=dict(type='str'),
labels=dict(type='dict'),
redis_configs=dict(type='dict'),
location_id=dict(type='str'),
name=dict(required=True, type='str'),
memory_size_gb=dict(required=True, type='int'),
redis_version=dict(type='str'),
reserved_ip_range=dict(type='str'),
tier=dict(default='BASIC', type='str'),
region=dict(required=True, type='str'),
)
)
if not module.params['scopes']:
module.params['scopes'] = ['https://www.googleapis.com/auth/cloud-platform']
state = module.params['state']
fetch = fetch_resource(module, self_link(module))
changed = False
if fetch:
if state == 'present':
if is_different(module, fetch):
update(module, self_link(module), fetch)
fetch = fetch_resource(module, self_link(module))
changed = True
else:
delete(module, self_link(module))
fetch = {}
changed = True
else:
if state == 'present':
fetch = create(module, create_link(module))
changed = True
else:
fetch = {}
fetch.update({'changed': changed})
module.exit_json(**fetch)
def create(module, link):
auth = GcpSession(module, 'redis')
return wait_for_operation(module, auth.post(link, resource_to_request(module)))
def update(module, link, fetch):
auth = GcpSession(module, 'redis')
params = {'updateMask': updateMask(resource_to_request(module), response_to_hash(module, fetch))}
request = resource_to_request(module)
del request['name']
return wait_for_operation(module, auth.patch(link, request, params=params))
def updateMask(request, response):
update_mask = []
if request.get('authEnabled') != response.get('authEnabled'):
update_mask.append('authEnabled')
if request.get('displayName') != response.get('displayName'):
update_mask.append('displayName')
if request.get('labels') != response.get('labels'):
update_mask.append('labels')
if request.get('redisConfigs') != response.get('redisConfigs'):
update_mask.append('redisConfigs')
if request.get('memorySizeGb') != response.get('memorySizeGb'):
update_mask.append('memorySizeGb')
return ','.join(update_mask)
def delete(module, link):
auth = GcpSession(module, 'redis')
return wait_for_operation(module, auth.delete(link))
def resource_to_request(module):
request = {
u'alternativeLocationId': module.params.get('alternative_location_id'),
u'authEnabled': module.params.get('auth_enabled'),
u'authorizedNetwork': module.params.get('authorized_network'),
u'connectMode': module.params.get('connect_mode'),
u'displayName': module.params.get('display_name'),
u'labels': module.params.get('labels'),
u'redisConfigs': module.params.get('redis_configs'),
u'locationId': module.params.get('location_id'),
u'name': module.params.get('name'),
u'memorySizeGb': module.params.get('memory_size_gb'),
u'redisVersion': module.params.get('redis_version'),
u'reservedIpRange': module.params.get('reserved_ip_range'),
u'tier': module.params.get('tier'),
}
return_vals = {}
for k, v in request.items():
if v or v is False:
return_vals[k] = v
return return_vals
def fetch_resource(module, link, allow_not_found=True):
auth = GcpSession(module, 'redis')
return return_if_object(module, auth.get(link), allow_not_found)
def self_link(module):
return "https://redis.googleapis.com/v1/projects/{project}/locations/{region}/instances/{name}".format(**module.params)
def collection(module):
return "https://redis.googleapis.com/v1/projects/{project}/locations/{region}/instances".format(**module.params)
def create_link(module):
return "https://redis.googleapis.com/v1/projects/{project}/locations/{region}/instances?instanceId={name}".format(**module.params)
def return_if_object(module, response, allow_not_found=False):
# If not found, return nothing.
if allow_not_found and response.status_code == 404:
return None
# If no content, return nothing.
if response.status_code == 204:
return None
try:
module.raise_for_status(response)
result = response.json()
except getattr(json.decoder, 'JSONDecodeError', ValueError):
module.fail_json(msg="Invalid JSON response with error: %s" % response.text)
if navigate_hash(result, ['error', 'errors']):
module.fail_json(msg=navigate_hash(result, ['error', 'errors']))
return result
def is_different(module, response):
request = resource_to_request(module)
response = response_to_hash(module, response)
# Remove all output-only from response.
response_vals = {}
for k, v in response.items():
if k in request:
response_vals[k] = v
request_vals = {}
for k, v in request.items():
if k in response:
request_vals[k] = v
return GcpRequest(request_vals) != GcpRequest(response_vals)
# Remove unnecessary properties from the response.
# This is for doing comparisons with Ansible's current parameters.
def response_to_hash(module, response):
return {
u'alternativeLocationId': module.params.get('alternative_location_id'),
u'authEnabled': response.get(u'authEnabled'),
u'authorizedNetwork': module.params.get('authorized_network'),
u'connectMode': module.params.get('connect_mode'),
u'createTime': response.get(u'createTime'),
u'currentLocationId': response.get(u'currentLocationId'),
u'displayName': response.get(u'displayName'),
u'host': response.get(u'host'),
u'labels': response.get(u'labels'),
u'redisConfigs': response.get(u'redisConfigs'),
u'locationId': module.params.get('location_id'),
u'name': module.params.get('name'),
u'memorySizeGb': response.get(u'memorySizeGb'),
u'port': response.get(u'port'),
u'persistenceIamIdentity': response.get(u'persistenceIamIdentity'),
u'redisVersion': module.params.get('redis_version'),
u'reservedIpRange': module.params.get('reserved_ip_range'),
u'tier': module.params.get('tier'),
}
def async_op_url(module, extra_data=None):
if extra_data is None:
extra_data = {}
url = "https://redis.googleapis.com/v1/{op_id}"
combined = extra_data.copy()
combined.update(module.params)
return url.format(**combined)
def wait_for_operation(module, response):
op_result = return_if_object(module, response)
if op_result is None:
return {}
status = navigate_hash(op_result, ['done'])
wait_done = wait_for_completion(status, op_result, module)
raise_if_errors(wait_done, ['error'], module)
return navigate_hash(wait_done, ['response'])
def wait_for_completion(status, op_result, module):
op_id = navigate_hash(op_result, ['name'])
op_uri = async_op_url(module, {'op_id': op_id})
while not status:
raise_if_errors(op_result, ['error'], module)
time.sleep(1.0)
op_result = fetch_resource(module, op_uri, False)
status = navigate_hash(op_result, ['done'])
return op_result
def raise_if_errors(response, err_path, module):
errors = navigate_hash(response, err_path)
if errors is not None:
module.fail_json(msg=errors)
if __name__ == '__main__':
main()
| 34.533898 | 147 | 0.672442 | 2,552 | 20,375 | 5.256661 | 0.174373 | 0.020872 | 0.023481 | 0.022959 | 0.495043 | 0.434141 | 0.405442 | 0.377861 | 0.357585 | 0.349981 | 0 | 0.007179 | 0.200098 | 20,375 | 589 | 148 | 34.59253 | 0.815928 | 0.046822 | 0 | 0.455466 | 0 | 0.020243 | 0.646854 | 0.035431 | 0 | 0 | 0 | 0 | 0 | 1 | 0.034413 | false | 0 | 0.012146 | 0.008097 | 0.082996 | 0.002024 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d884d18fcb8bab9fdae1894792a48243cc8a96d8 | 690 | py | Python | tests/test_blogs.py | MichelAtieno/Personal-Blog | 16657391b968e644b99fa0dde5d5a443881698da | [
"Unlicense"
] | null | null | null | tests/test_blogs.py | MichelAtieno/Personal-Blog | 16657391b968e644b99fa0dde5d5a443881698da | [
"Unlicense"
] | null | null | null | tests/test_blogs.py | MichelAtieno/Personal-Blog | 16657391b968e644b99fa0dde5d5a443881698da | [
"Unlicense"
] | null | null | null | import unittest
from app.models import BlogPost
from app import db
class CommentTest(unittest.TestCase):
def setUp(self):
self.new_blog = BlogPost(title='New Blog',blog_post='This is the content')
def tearDown(self):
db.session.delete(self.new_blog)
db.session.commit()
def test_instance(self):
self.assertTrue(isinstance(self.new_blog,BlogPost))
def test_check_instance_variables(self):
self.assertEquals(self.new_blog.title,'New Blog')
self.assertEquals(self.new_blog.blog_post,'This is the content')
def test_save_blog(self):
self.new_blog.save_blog()
self.assertTrue(len(BlogPost.query.all())>0) | 28.75 | 82 | 0.7 | 96 | 690 | 4.875 | 0.385417 | 0.119658 | 0.141026 | 0.064103 | 0.245727 | 0.145299 | 0.145299 | 0.145299 | 0.145299 | 0 | 0 | 0.001786 | 0.188406 | 690 | 24 | 83 | 28.75 | 0.833929 | 0 | 0 | 0 | 0 | 0 | 0.078148 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 1 | 0.294118 | false | 0 | 0.176471 | 0 | 0.529412 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d885bcf7c2809e705cb09676abf33bf651c041a9 | 603 | py | Python | numero_parole.py | mecroby/test_pi_learning | 5e32b768968b523445578f8dc33dd720930c72e7 | [
"Unlicense"
] | null | null | null | numero_parole.py | mecroby/test_pi_learning | 5e32b768968b523445578f8dc33dd720930c72e7 | [
"Unlicense"
] | null | null | null | numero_parole.py | mecroby/test_pi_learning | 5e32b768968b523445578f8dc33dd720930c72e7 | [
"Unlicense"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 15 20:33:49 2017
@author: roby
"""
#dato un numero n, restituisce le prime n parole più usate
import sys
from collections import Counter
try:
num_words=int(sys.argv[1])
except:
print "usage: nomefile.py numero_parole"
sys.exit(1)
counter= Counter(word.lower() for line in sys.stdin for word in line.strip().split() if word)
for word, count in counter.most_common(num_words):
sys.stdout.write(str(count))
sys.stdout.write("\t")
sys.stdout.write(word)
sys.stdout.write("\n")
| 22.333333 | 94 | 0.630182 | 91 | 603 | 4.131868 | 0.626374 | 0.095745 | 0.148936 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.032751 | 0.240464 | 603 | 26 | 95 | 23.192308 | 0.78821 | 0.129353 | 0 | 0 | 0 | 0 | 0.082192 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.153846 | null | null | 0.076923 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d88a2d7db32e6f798da168a5ca1a0ee451bd035f | 3,973 | py | Python | nodes/swagger_server/models/job_simulator_opts.py | rdbox-intec/r2s2_for_rostest | 42b70d3ba72cdae08e9fd5fcdce9ddaeca37297f | [
"MIT"
] | null | null | null | nodes/swagger_server/models/job_simulator_opts.py | rdbox-intec/r2s2_for_rostest | 42b70d3ba72cdae08e9fd5fcdce9ddaeca37297f | [
"MIT"
] | null | null | null | nodes/swagger_server/models/job_simulator_opts.py | rdbox-intec/r2s2_for_rostest | 42b70d3ba72cdae08e9fd5fcdce9ddaeca37297f | [
"MIT"
] | null | null | null | # coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from swagger_server.models.base_model_ import Model
from swagger_server import util
class JobSimulatorOpts(Model):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, display_no: int=None, screent_no: int=None, vnc_password: str=None, no_vnc_port: int=None): # noqa: E501
"""JobSimulatorOpts - a model defined in Swagger
:param display_no: The display_no of this JobSimulatorOpts. # noqa: E501
:type display_no: int
:param screent_no: The screent_no of this JobSimulatorOpts. # noqa: E501
:type screent_no: int
:param vnc_password: The vnc_password of this JobSimulatorOpts. # noqa: E501
:type vnc_password: str
:param no_vnc_port: The no_vnc_port of this JobSimulatorOpts. # noqa: E501
:type no_vnc_port: int
"""
self.swagger_types = {
'display_no': int,
'screent_no': int,
'vnc_password': str,
'no_vnc_port': int
}
self.attribute_map = {
'display_no': 'display_no',
'screent_no': 'screent_no',
'vnc_password': 'vnc_password',
'no_vnc_port': 'no_vnc_port'
}
self._display_no = display_no
self._screent_no = screent_no
self._vnc_password = vnc_password
self._no_vnc_port = no_vnc_port
@classmethod
def from_dict(cls, dikt) -> 'JobSimulatorOpts':
"""Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The Job_simulator_opts of this JobSimulatorOpts. # noqa: E501
:rtype: JobSimulatorOpts
"""
return util.deserialize_model(dikt, cls)
@property
def display_no(self) -> int:
"""Gets the display_no of this JobSimulatorOpts.
:return: The display_no of this JobSimulatorOpts.
:rtype: int
"""
return self._display_no
@display_no.setter
def display_no(self, display_no: int):
"""Sets the display_no of this JobSimulatorOpts.
:param display_no: The display_no of this JobSimulatorOpts.
:type display_no: int
"""
self._display_no = display_no
@property
def screent_no(self) -> int:
"""Gets the screent_no of this JobSimulatorOpts.
:return: The screent_no of this JobSimulatorOpts.
:rtype: int
"""
return self._screent_no
@screent_no.setter
def screent_no(self, screent_no: int):
"""Sets the screent_no of this JobSimulatorOpts.
:param screent_no: The screent_no of this JobSimulatorOpts.
:type screent_no: int
"""
self._screent_no = screent_no
@property
def vnc_password(self) -> str:
"""Gets the vnc_password of this JobSimulatorOpts.
:return: The vnc_password of this JobSimulatorOpts.
:rtype: str
"""
return self._vnc_password
@vnc_password.setter
def vnc_password(self, vnc_password: str):
"""Sets the vnc_password of this JobSimulatorOpts.
:param vnc_password: The vnc_password of this JobSimulatorOpts.
:type vnc_password: str
"""
self._vnc_password = vnc_password
@property
def no_vnc_port(self) -> int:
"""Gets the no_vnc_port of this JobSimulatorOpts.
:return: The no_vnc_port of this JobSimulatorOpts.
:rtype: int
"""
return self._no_vnc_port
@no_vnc_port.setter
def no_vnc_port(self, no_vnc_port: int):
"""Sets the no_vnc_port of this JobSimulatorOpts.
:param no_vnc_port: The no_vnc_port of this JobSimulatorOpts.
:type no_vnc_port: int
"""
self._no_vnc_port = no_vnc_port
| 27.783217 | 128 | 0.63403 | 500 | 3,973 | 4.756 | 0.146 | 0.04836 | 0.083263 | 0.100925 | 0.571068 | 0.433978 | 0.286796 | 0.200168 | 0.164844 | 0.04037 | 0 | 0.008806 | 0.285427 | 3,973 | 142 | 129 | 27.978873 | 0.828813 | 0.435691 | 0 | 0.24 | 0 | 0 | 0.077957 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0.18 | 0.1 | 0 | 0.42 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
d88b2dca04f46637500ddd52a9ae3a5f2b3f87ce | 43,776 | py | Python | code/CCU004-2-run-models-[4].py | BHFDSC/CCU004_02 | 81c645d9877addfb500374c53689f13be6b56726 | [
"Apache-2.0"
] | null | null | null | code/CCU004-2-run-models-[4].py | BHFDSC/CCU004_02 | 81c645d9877addfb500374c53689f13be6b56726 | [
"Apache-2.0"
] | null | null | null | code/CCU004-2-run-models-[4].py | BHFDSC/CCU004_02 | 81c645d9877addfb500374c53689f13be6b56726 | [
"Apache-2.0"
] | 2 | 2022-01-04T17:04:45.000Z | 2022-02-02T10:17:56.000Z | # Databricks notebook source
# MAGIC %md
# MAGIC **Description** This notebook runs the model analysis pipeline for CCU004-2
# MAGIC
# MAGIC **Project(s)** CCU004-2 - A nationwide deep learning pipeline to predict stroke and COVID-19 death in atrial fibrillation
# MAGIC
# MAGIC **Author(s)** Alex Handy
# MAGIC
# MAGIC **Reviewer(s)** Chris Tomlinson, Hiu Yan (Samantha) Ip
# MAGIC
# MAGIC **Date last updated** 24-01-2022
# COMMAND ----------
# MAGIC %run /Workspaces/dars_nic_391419_j3w9t_collab/CCU004/CCU004_2/CCU004-2-global-helper-functions
# COMMAND ----------
#set upfront parameters
#ALL SCENARIOS
outcomes = ["stroke", "covid_death"]
max_seq_lens = [100]
sample_ratios = [1]
runs = [1,2,3]
input_run_date = "240122"
output_run_date = "240122"
scenarios = len(outcomes) * len(max_seq_lens) * len(sample_ratios) * len(runs)
print(scenarios)
SUB_GROUPS = ["female", "male", "lt_65", "gte_65", "white", "asian_or_asian_british", "black_or_black_british", "mixed", "other_ethnic_groups"]
# COMMAND ----------
#helper functions and packages
from datetime import datetime
import math
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, roc_auc_score, confusion_matrix, recall_score, precision_score, accuracy_score
import time
import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
import torch.optim as optim
from torch import Tensor
from torch.utils.data import dataset
from typing import Tuple
import xgboost as xgb
#to stop setting copy warning output - consider reviewing in refactoring
pd.set_option('chained',None)
##MACHINE LEARNING METHODS
def create_ml_features(x, feature_list, codelist, target_field, outcome):
entry = {}
for code in codelist:
if code in x[target_field]:
entry[code] = 1
else:
entry[code] = 0
if outcome == "stroke":
entry["age_at_af_diagnosis"] = x["age_at_af_diagnosis"]
else:
entry["age_at_cohort_start"] = x["age_at_cohort_start"]
entry["female"] = x["female"]
entry["white"] = x["white"]
entry["asian_or_asian_british"] = x["asian_or_asian_british"]
entry["black_or_black_british"] = x["black_or_black_british"]
entry["mixed"] = x["mixed"]
entry["other_ethnic_groups"] = x["other_ethnic_groups"]
entry[outcome] = x[outcome]
feature_list.append(entry)
def calc_ml_metrics(prediction, target):
try:
tn, fp, fn, tp = confusion_matrix(target, prediction).ravel()
accuracy = (tp + tn) / (tp + tn + fp + fn)
auc = roc_auc_score(target, prediction)
sensitivity = tp / (tp + fn)
specificity = tn / (tn + fp)
precision = tp / (tp + fp)
except ValueError:
print("Predicting all one class")
accuracy = 0
auc = 0
sensitivity = 0
specificity = 0
precision = 0
return accuracy, auc, sensitivity, specificity, precision
def evaluate_ml_models(models, x_train, x_val, y_train, y_val, cohort_test_sub_non_n, all_codes_non_n, num_static_features, outcome, summary_data, summary_data_sub, sub_groups):
for i, model in enumerate(models):
entry = {}
md = model.fit(x_train, y_train)
pred = md.predict(x_val)
if isinstance(model,sklearn.linear_model._logistic.LogisticRegression):
model_name = "Logistic Regression"
elif isinstance(model,sklearn.ensemble._forest.RandomForestClassifier):
model_name = "Random Forest"
elif isinstance(model, xgb.XGBRegressor):
model_name = "XG Boost"
pred = [ 1 if p >= 0.5 else 0 for p in pred ]
else:
print("Incorrect model type")
print("Validation sample results")
accuracy, auc, sensitivity, specificity, precision = calc_ml_metrics(pred, y_val)
print("Model:", model_name)
print("Accuracy (val): ", accuracy)
print("Auc (val): ", auc)
print("Sensitivity (val):", sensitivity)
print("Specificity (val):", specificity)
print("Precision (val):", precision)
print("Test sample results - whole group")
pred_test = md.predict(cohort_test_sub_non_n.iloc[:, :(len(all_codes_non_n)+num_static_features)])
y_test = cohort_test_sub_non_n[outcome]
if isinstance(model, xgb.XGBRegressor):
pred_test = [ 1 if p >= 0.5 else 0 for p in pred_test ]
accuracy_test, auc_test, sensitivity_test, specificity_test, precision_test = calc_ml_metrics(pred_test, y_test)
entry["model"] = model_name
entry["accuracy"] = accuracy_test
entry["auc"] = auc_test
entry["sensitivity"] = sensitivity_test
entry["specificity"] = specificity_test
entry["precision"] = precision_test
print("Accuracy (test): ", accuracy_test)
print("Auc (test): ", auc_test)
print("Sensitivity (test):", sensitivity_test)
print("Specificity (test):", specificity_test)
print("Precision (test):", precision_test)
summary_data.append(entry)
print("Test sample results - sub groups")
entry_sub = {}
entry_sub["model"] = model_name
if outcome == "stroke":
age_col = "age_at_af_diagnosis"
else:
age_col = "age_at_cohort_start"
for sub_group in sub_groups:
if sub_group == "male":
sub_group_test_df = cohort_test_sub_non_n[cohort_test_sub_non_n["female"] == 0]
elif sub_group == "gte_65":
sub_group_test_df = cohort_test_sub_non_n[cohort_test_sub_non_n[age_col] >=65]
elif sub_group == "lt_65":
sub_group_test_df = cohort_test_sub_non_n[cohort_test_sub_non_n[age_col] <65]
else:
sub_group_test_df = cohort_test_sub_non_n[cohort_test_sub_non_n[sub_group] == 1]
pred_test_sub = md.predict(sub_group_test_df.iloc[:, :(len(all_codes_non_n)+num_static_features)])
y_test_sub = sub_group_test_df[outcome]
if isinstance(model, xgb.XGBRegressor):
pred_test_sub = [ 1 if p >= 0.5 else 0 for p in pred_test_sub ]
accuracy_test_sub, auc_test_sub, sensitivity_test_sub, specificity_test_sub, precision_test_sub = calc_ml_metrics(pred_test_sub, y_test_sub)
entry_sub[str("accuracy" + "_" + sub_group)] = accuracy_test_sub
entry_sub[str("auc" + "_" + sub_group)] = auc_test_sub
entry_sub[str("sensitivity" + "_" + sub_group)] = sensitivity_test_sub
entry_sub[str("specificity" + "_" + sub_group)] = specificity_test_sub
entry_sub[str("precision" + "_" + sub_group)] = precision_test_sub
summary_data_sub.append(entry_sub)
##DEEP LEARNING METHODS
#data preparation
def lookup_embeddings(x, code_to_ix, target_field):
med_hist_entry = []
for code in x[target_field]:
emb = code_to_ix[code]
med_hist_entry.append(emb)
return med_hist_entry
def add_label(x, target_field):
if x[target_field] == 1:
return target_field
else:
return "No " + target_field
def create_dl_features(cohort_train_sub, cohort_test_sub, code_to_ix_nn, TARGET_FIELD, STATIC_FEATURES, outcome, run):
#NOTE: ASSUMES SET PADDING IDX TO ZERO IN EMBEDDING LAYER
seq_features_nn = cohort_train_sub.apply(lookup_embeddings, args=(code_to_ix_nn, TARGET_FIELD), axis=1)
seq_features_nn_tn = [ torch.tensor(seq) for seq in seq_features_nn ]
seq_features_nn_tn_pd = pad_sequence(seq_features_nn_tn, batch_first=True)
#add static features
static_df = cohort_train_sub[STATIC_FEATURES]
static_features_nn = torch.tensor([ row for row in static_df.values ])
#generate outcome labels for training data
OUTCOME_CAT = outcome + "_cat"
cohort_train_sub[OUTCOME_CAT] = cohort_train_sub.apply(add_label, args=(outcome,), axis=1)
all_categories = list(cohort_train_sub[OUTCOME_CAT].unique())
all_categories.sort()
#CHECK SORT SO LABELLING MAKES SENSE FOR OUTCOMES e.g. 0=No stroke, 1=stroke
print("DL categories: ", all_categories)
n_categories = len(all_categories)
labels_nn = cohort_train_sub[OUTCOME_CAT].apply(lambda x: all_categories.index(x))
labels_nn_tn = torch.tensor(labels_nn)
#create train, validation and test dataset
x_seq_train, x_seq_val, x_static_train, x_static_val, y_train, y_val = train_test_split(seq_features_nn_tn_pd, static_features_nn, labels_nn_tn,test_size=0.2, random_state=run)
#test sample
seq_features_test_nn = cohort_test_sub.apply(lookup_embeddings, args=(code_to_ix_nn, TARGET_FIELD), axis=1)
seq_features_test_nn_tn = [ torch.tensor(seq) for seq in seq_features_test_nn ]
seq_features_test_nn_tn_pd = pad_sequence(seq_features_test_nn_tn, batch_first=True)
x_seq_test = seq_features_test_nn_tn_pd
static_df_test = cohort_test_sub[STATIC_FEATURES]
static_features_test_nn = torch.tensor([ row for row in static_df_test.values ])
x_static_test = static_features_test_nn
cohort_test_sub[OUTCOME_CAT] = cohort_test_sub.apply(add_label, args=(outcome,), axis=1)
labels_test_nn = cohort_test_sub[OUTCOME_CAT].apply(lambda x: all_categories.index(x))
labels_test_nn_tn = torch.tensor(labels_test_nn)
y_test = labels_test_nn_tn
print("x seq train", x_seq_train.size())
print("x static train", x_static_train.size())
print("y train", y_train.size())
print("x seq val", x_seq_val.size())
print("x static val", x_static_val.size())
print("y val", y_val.size())
print("x seq test", x_seq_test.size())
print("x static test", x_static_test.size())
print("y test", y_test.size())
return x_seq_train, x_seq_val, x_static_train, x_static_val, y_train, y_val, x_seq_test, x_static_test, y_test, all_categories, n_categories, OUTCOME_CAT
def create_dl_sub_sample(cohort_test_sub, sub_group, code_to_ix_nn, TARGET_FIELD, STATIC_FEATURES, outcome):
if outcome == "stroke":
age_col = "age_at_af_diagnosis"
else:
age_col = "age_at_cohort_start"
if sub_group == "male":
cohort_test_sub_sample_df = cohort_test_sub[cohort_test_sub["female"] == 0]
elif sub_group == "gte_65":
cohort_test_sub_sample_df = cohort_test_sub[cohort_test_sub[age_col] >=65]
elif sub_group == "lt_65":
cohort_test_sub_sample_df = cohort_test_sub[cohort_test_sub[age_col] <65]
else:
cohort_test_sub_sample_df = cohort_test_sub[cohort_test_sub[sub_group] == 1]
seq_features_test_nn_sub = cohort_test_sub_sample_df.apply(lookup_embeddings, args=(code_to_ix_nn, TARGET_FIELD), axis=1)
seq_features_test_nn_tn_sub = [ torch.tensor(seq) for seq in seq_features_test_nn_sub ]
seq_features_test_nn_tn_pd_sub = pad_sequence(seq_features_test_nn_tn_sub, batch_first=True)
x_seq_test_sub = seq_features_test_nn_tn_pd_sub
static_df_test_sub = cohort_test_sub_sample_df[STATIC_FEATURES]
static_features_test_nn_sub = torch.tensor([ row for row in static_df_test_sub.values ])
x_static_test_sub = static_features_test_nn_sub
OUTCOME_CAT = outcome + "_cat"
cohort_test_sub_sample_df[OUTCOME_CAT] = cohort_test_sub_sample_df.apply(add_label, args=(outcome,), axis=1)
labels_test_nn_sub = cohort_test_sub_sample_df[OUTCOME_CAT].apply(lambda x: all_categories.index(x))
labels_test_nn_tn_sub = torch.tensor(labels_test_nn_sub.values)
y_test_sub = labels_test_nn_tn_sub
return x_seq_test_sub, x_static_test_sub, y_test_sub
def create_dl_batches(batch_size, val_batch_size, x_seq_train, x_static_train, y_train, x_seq_val, x_static_val, y_val):
train_data_nn = torch.utils.data.TensorDataset(
x_seq_train, x_static_train,
y_train)
val_data_nn = torch.utils.data.TensorDataset(
x_seq_val, x_static_val,
y_val)
train_loader_nn = torch.utils.data.DataLoader(
train_data_nn, shuffle=True,
batch_size=batch_size, drop_last=True)
val_loader_nn = torch.utils.data.DataLoader(
val_data_nn, shuffle=False,
batch_size=val_batch_size, drop_last=True)
return train_loader_nn, val_loader_nn
#training and evaluation
def categoryFromOutput(output, all_categories):
top_n, top_i = output.topk(1)
category_i = top_i[0].item()
return all_categories[category_i]
def get_pred_label(output):
top_n, top_i = output.topk(1)
category_i = top_i[0].item()
return category_i
def calc_dl_metrics(prediction, target, all_categories):
predictions = []
targets = []
for i, sample in enumerate(prediction):
pred_label = categoryFromOutput(sample, all_categories)
pred = get_pred_label(sample)
target_label = target[i].item()
predictions.append(get_pred_label(sample))
targets.append(target[i].item())
try:
tn, fp, fn, tp = confusion_matrix(targets, predictions).ravel()
accuracy = accuracy_score(targets, predictions)
auc = roc_auc_score(targets, predictions)
sensitivity = recall_score(targets, predictions)
specificity = tn / (tn + fp)
precision = precision_score(targets, predictions, zero_division=0)
except ValueError:
print("Predicting all one class")
accuracy = 0
auc = 0
sensitivity = 0
specificity = 0
precision = 0
return accuracy, auc, sensitivity, specificity, precision
def run_dl_training_and_evaluation(net, opt, criterion, summary_data, max_seq_len, all_categories, epochs, train_loader_nn, val_batch_size, val_loader_nn, x_seq_test, x_static_test, y_test, cohort_test_sub, sub_groups, code_to_ix_nn, TARGET_FIELD, STATIC_FEATURES, outcome, summary_data_sub):
losses_train = []
accs_train = []
aucs_train = []
sens_train = []
specs_train = []
precs_train = []
accs_val = []
aucs_val = []
sens_val = []
specs_val = []
precs_val = []
accs_test = []
aucs_test = []
sens_test = []
specs_test = []
precs_test = []
sub_group_res = []
net_start = time.time()
print(net.model_name, " started ", datetime.fromtimestamp(net_start))
#loop through epochs
for e in range(1,epochs+1):
net.train()
epoch_start = time.time()
print("Epoch: ", str(e), " started ", datetime.fromtimestamp(epoch_start))
#setup evaluation metrics
epoch_loss = 0
epoch_acc_train = 0
epoch_auc_train = 0
epoch_sen_train = 0
epoch_spec_train = 0
epoch_prec_train = 0
epoch_acc_val = 0
epoch_auc_val = 0
epoch_sen_val = 0
epoch_spec_val = 0
epoch_prec_val = 0
epoch_sub_group_res = {}
if net.model_name == "LSTM":
#initialize hidden layers
h = net.init_hidden(batch_size)
#training batches
for batch_index, batch in enumerate(train_loader_nn):
x_batch_seq = batch[0]
x_batch_static = batch[1]
y_batch = batch[2]
if net.model_name == "LSTM":
#generates hidden layer input for lstm
h = tuple([l.data for l in h])
elif net.model_name == "Transformer":
#generates [max_seq_len, max_seq_len] square for transformer
src_mask = generate_square_subsequent_mask(max_seq_len)
else:
print("Error, model type not available")
#zero the gradient
opt.zero_grad()
#predict the output
if net.model_name == "LSTM":
y_batch_pred = net(x_batch_seq, x_batch_static, h)
elif net.model_name == "Transformer":
y_batch_pred = net(x_batch_seq, x_batch_static, src_mask)
else:
print("Error, model type not available")
#calculate the loss
loss = criterion(y_batch_pred, y_batch)
#calculate evaluation metrics
accuracy_train, auc_train, sensitivity_train, specificity_train, precision_train = calc_dl_metrics(y_batch_pred, y_batch, all_categories)
#compute the gradient
loss.backward()
#update the weights
opt.step()
epoch_loss += loss.item()
epoch_acc_train += accuracy_train
epoch_auc_train += auc_train
epoch_sen_train += sensitivity_train
epoch_spec_train += specificity_train
epoch_prec_train += precision_train
#validation and test
net.eval()
with torch.no_grad():
#validation
if net.model_name == "LSTM":
h_val = net.init_hidden(val_batch_size)
for batch_val_index, batch_val in enumerate(val_loader_nn):
x_batch_seq_val = batch_val[0]
x_batch_static_val = batch_val[1]
y_batch_val = batch_val[2]
if net.model_name == "LSTM":
#generates hidden layer input for lstm
h_val = tuple([l_v.data for l_v in h_val])
y_batch_pred_val = net(x_batch_seq_val, x_batch_static_val, h_val)
elif net.model_name == "Transformer":
#generates [max_seq_len, max_seq_len] square for transformer
src_mask_val = generate_square_subsequent_mask(max_seq_len)
y_batch_pred_val = net(x_batch_seq_val, x_batch_static_val, src_mask_val)
else:
print("Error, model type not available")
accuracy_val, auc_val, sensitivity_val, specificity_val, precision_val = calc_dl_metrics(y_batch_pred_val, y_batch_val, all_categories)
epoch_acc_val += accuracy_val
epoch_auc_val += auc_val
epoch_sen_val += sensitivity_val
epoch_spec_val += specificity_val
epoch_prec_val += precision_val
#test the output - whole group
if net.model_name == "LSTM":
#test batch prep lstm
h_test = net.init_hidden(len(x_seq_test))
h_test = tuple([l_t.data for l_t in h_test])
y_pred_test = net(x_seq_test, x_static_test, h_test)
elif net.model_name == "Transformer":
#test batch prep transformer
src_mask_test = generate_square_subsequent_mask(max_seq_len)
y_pred_test = net(x_seq_test, x_static_test, src_mask_test)
else:
print("Error, model type not available")
accuracy_test, auc_test, sensitivity_test, specificity_test, precision_test = calc_dl_metrics(y_pred_test, y_test, all_categories)
#test the output - sub groups
epoch_sub_group_res["model"] = net.model_name
for sub_group in sub_groups:
x_seq_test_sub, x_static_test_sub, y_test_sub = create_dl_sub_sample(cohort_test_sub, sub_group, code_to_ix_nn, TARGET_FIELD, STATIC_FEATURES, outcome)
if net.model_name == "LSTM":
#test batch prep lstm
h_test_sub = net.init_hidden(len(x_seq_test_sub))
h_test_sub = tuple([l_t.data for l_t in h_test_sub])
y_pred_test_sub = net(x_seq_test_sub, x_static_test_sub, h_test_sub)
elif net.model_name == "Transformer":
#test batch prep transformer
#NOTE: in sub groups, there is greater possibility that sample does not have an individual with max seq len (e.g. black british error) so mask with size of longest length
mask_len = x_seq_test_sub.size()[1]
src_mask_test_sub = generate_square_subsequent_mask(mask_len)
y_pred_test_sub = net(x_seq_test_sub, x_static_test_sub, src_mask_test_sub)
else:
print("Error, model type not available")
accuracy_test_sub, auc_test_sub, sensitivity_test_sub, specificity_test_sub, precision_test_sub = calc_dl_metrics(y_pred_test_sub, y_test_sub, all_categories)
epoch_sub_group_res[str("accuracy" + "_" + sub_group)] = accuracy_test_sub
epoch_sub_group_res[str("auc" + "_" + sub_group)] = auc_test_sub
epoch_sub_group_res[str("sensitivity" + "_" + sub_group)] = sensitivity_test_sub
epoch_sub_group_res[str("specificity" + "_" + sub_group)] = specificity_test_sub
epoch_sub_group_res[str("precision" + "_" + sub_group)] = precision_test_sub
#accumulate metrics at epoch level (for charts and model reporting)
reported_loss_train = epoch_loss / len(train_loader_nn)
reported_acc_train = epoch_acc_train / len(train_loader_nn)
reported_auc_train = epoch_auc_train / len(train_loader_nn)
reported_sen_train = epoch_sen_train / len(train_loader_nn)
reported_spec_train = epoch_spec_train / len(train_loader_nn)
reported_prec_train = epoch_prec_train / len(train_loader_nn)
reported_acc_val = epoch_acc_val / len(val_loader_nn)
reported_auc_val = epoch_auc_val / len(val_loader_nn)
reported_sen_val = epoch_sen_val / len(val_loader_nn)
reported_spec_val = epoch_spec_val / len(val_loader_nn)
reported_prec_val = epoch_prec_val / len(val_loader_nn)
losses_train.append(epoch_loss)
accs_train.append(reported_acc_train)
aucs_train.append(reported_auc_train)
sens_train.append(reported_sen_train)
specs_train.append(reported_spec_train)
precs_train.append(reported_prec_train)
accs_val.append(reported_acc_val)
aucs_val.append(reported_auc_val)
sens_val.append(reported_sen_val)
specs_val.append(reported_spec_val)
precs_val.append(reported_prec_val)
accs_test.append(accuracy_test)
aucs_test.append(auc_test)
sens_test.append(sensitivity_test)
specs_test.append(specificity_test)
precs_test.append(precision_test)
sub_group_res.append(epoch_sub_group_res)
epoch_end = time.time()
print("Epoch: " + str(e) + " completed in %s seconds" % ( round(epoch_end - epoch_start,2) ) )
#present epoch outputs
print("Epoch: " + str(e) + " | Training Loss: " + str(round(reported_loss_train, 3)) + " | Training Accuracy: " + str(round(reported_acc_train, 3)) + " | Training AUC: " + str(round(reported_auc_train, 3)))
print("Epoch: " + str(e) + " | Training Sensitivity: " + str(round(reported_sen_train, 3)) + " | Training Specificity: " + str(round(reported_spec_train, 3)) + " | Training Precision: " + str(round(reported_prec_train, 3)))
print("Epoch: " + str(e) + "| Validation Accuracy: " + str(round(reported_acc_val, 3)) + " | Validation AUC: " + str(round(reported_auc_val, 3)))
print("Epoch: " + str(e) + " | Validation Sensitivity: " + str(round(reported_sen_val, 3)) + " | Validation Specificity: " + str(round(reported_spec_val, 3)) + " | Validation Precision: " + str(round(reported_prec_val, 3)))
print("Epoch: " + str(e) + "| Test Accuracy: " + str(round(accuracy_test, 3)) + " | Test AUC: " + str(round(auc_test, 3)))
print("Epoch: " + str(e) + " | Test Sensitivity: " + str(round(sensitivity_test, 3)) + " | Test Specificity: " + str(round(specificity_test, 3)) + " | Test Precision: " + str(round(precision_test, 3)))
net_end = time.time()
print("Training completed in %s minutes" % ( round(net_end - net_start,2) / 60) )
print("Get the summary results")
max_auc_val = max(aucs_val)
print("Max auc val", max_auc_val)
max_auc_epoch_idx = aucs_val.index(max_auc_val)
print("Max auc epoch", max_auc_epoch_idx)
#load into summary data
entry = {}
entry["model"] = net.model_name
entry["accuracy"] = accs_test[max_auc_epoch_idx]
entry["auc"] = aucs_test[max_auc_epoch_idx]
entry["sensitivity"] = sens_test[max_auc_epoch_idx]
entry["specificity"] = specs_test[max_auc_epoch_idx]
entry["precision"] = precs_test[max_auc_epoch_idx]
summary_data.append(entry)
summary_data_sub_entry = sub_group_res[max_auc_epoch_idx]
summary_data_sub.append(summary_data_sub_entry)
##CHADSVASC
def list_medcodes(codelist_column_df):
codelist = [item.code for item in codelist_column_df.select('code').collect()]
return codelist
def load_chads_codelists(components):
for comp in components:
spark.sql(f"""CREATE OR REPLACE GLOBAL TEMP VIEW {comp}_codelist AS
SELECT * FROM dars_nic_391419_j3w9t_collab.ccu020_20210816_2020_01_01_codelists WHERE codelist = '{comp}_chads'
""")
comp_table = 'global_temp.' + comp + '_codelist'
comp_codelist = spark.table(comp_table)
comp_codelist_py = list_medcodes(comp_codelist)
component_codelists.append(comp_codelist_py)
def create_features_chads(x, feature_list, codelists, outcome):
entry = {}
#populate component fields
for idx, codelist in enumerate(codelists):
if idx == 0:
comp_name = "vascular_disease"
elif idx == 1:
comp_name = "congestive_heart_failure"
elif idx == 2:
comp_name = "diabetes"
else:
comp_name = "hypertension"
#NOTE: this field is different than ML and DL models which use most recent 100 codes as did not want to artificially disadvantage chadsvasc that does not use high dimensional sequence data
for code in x["med_hist_uniq"]:
if code in codelist:
entry[comp_name] = 1
break
else:
entry[comp_name] = 0
if outcome == "stroke":
entry["age"] = x["age_at_af_diagnosis"]
else:
entry["age"] = x["age_at_cohort_start"]
entry["female"] = x["female"]
entry["white"] = x["white"]
entry["asian_or_asian_british"] = x["asian_or_asian_british"]
entry["black_or_black_british"] = x["black_or_black_british"]
entry["mixed"] = x["mixed"]
entry["other_ethnic_groups"] = x["other_ethnic_groups"]
entry[outcome] = x[outcome]
feature_list.append(entry)
def create_chads_score(x):
if x["age"] >=75:
age = 2
elif (x["age"] >=65) & (x["age"] <75):
age = 1
else:
age = 0
score = (x["vascular_disease"] + x["congestive_heart_failure"] + x["diabetes"] + x["hypertension"] + age + x["female"])
return score
def run_chads_evaluation(cohort_test_sub_chads, outcome, summary_data, summary_data_sub, sub_groups):
#whole group metrics
y = cohort_test_sub_chads[outcome].values
pred = cohort_test_sub_chads["pred_chads2"].values
accuracy, auc, sensitivity, specificity, precision = calc_ml_metrics(pred, y)
print("Accuracy: ", accuracy)
print("Auc : ", auc)
print("Sensitivity:", sensitivity)
print("Specificity:", specificity)
print("Precision:", precision)
#load into summary data
entry = {}
entry["model"] = "CHA2DS2-VASc >=2"
entry["accuracy"] = accuracy
entry["auc"] = auc
entry["sensitivity"] = sensitivity
entry["specificity"] = specificity
entry["precision"] = precision
summary_data.append(entry)
#sub group metrics
entry_sub = {}
entry_sub["model"] = "CHA2DS2-VASc >=2"
#NOTE: different age interface for chads as the age parameter is already adjusted for stroke vs covid death in chads features (opportunity for tidying)
for sub_group in sub_groups:
if sub_group == "male":
sub_group_test_df = cohort_test_sub_chads[cohort_test_sub_chads["female"] == 0]
elif sub_group == "gte_65":
sub_group_test_df = cohort_test_sub_chads[cohort_test_sub_chads["age"] >=65]
elif sub_group == "lt_65":
sub_group_test_df = cohort_test_sub_chads[cohort_test_sub_chads["age"] <65]
else:
sub_group_test_df = cohort_test_sub_chads[cohort_test_sub_chads[sub_group] == 1]
y_test_sub = sub_group_test_df[outcome].values
pred_test_sub = sub_group_test_df["pred_chads2"].values
accuracy_test_sub, auc_test_sub, sensitivity_test_sub, specificity_test_sub, precision_test_sub = calc_ml_metrics(pred_test_sub, y_test_sub)
entry_sub[str("accuracy" + "_" + sub_group)] = accuracy_test_sub
entry_sub[str("auc" + "_" + sub_group)] = auc_test_sub
entry_sub[str("sensitivity" + "_" + sub_group)] = sensitivity_test_sub
entry_sub[str("specificity" + "_" + sub_group)] = specificity_test_sub
entry_sub[str("precision" + "_" + sub_group)] = precision_test_sub
summary_data_sub.append(entry_sub)
# COMMAND ----------
#LSTM model class
class MyLSTM(nn.Module):
def __init__(self, output_size, vocab_size, embedding_dim, hidden_dim, n_layers, static_features_n, fc1_dim, dropout=0.2):
super(MyLSTM, self).__init__()
self.model_name = "LSTM"
self.embeddings = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, batch_first=True)
self.fc_static = nn.Linear(static_features_n, hidden_dim)
self.fc1 = nn.Linear((hidden_dim*2),fc1_dim)
self.fc_out = nn.Linear(fc1_dim, output_size)
self.softmax = nn.LogSoftmax(dim=1)
self.dropout = nn.Dropout(dropout)
def forward(self, seq_batch, static_batch, hidden):
embeds = self.embeddings(seq_batch)
lstm_out, (ht, ct) = self.lstm(embeds, hidden)
lstm_ht = lstm_out[:,-1,:]
lstm_ht_drop = self.dropout(lstm_ht)
static = F.relu(self.fc_static(static_batch.float()))
comb = torch.cat([lstm_ht_drop, static], dim=1)
comb_drop = self.dropout(comb)
fc1_out = F.relu(self.fc1(comb_drop))
out = self.fc_out(fc1_out)
out = self.softmax(out)
return out
def init_hidden(self, batch_size):
weight = next(self.parameters()).data
#initializes hidden state and cell state
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())
return hidden
# COMMAND ----------
class MyTransformer(nn.Module):
def __init__(self, output_size, vocab_size, embedding_dim, hidden_dim, n_head, n_layers, n_static_features, static_dim, combo_dim, fc_int_dim, dropout = 0.2):
super().__init__()
self.model_name = "Transformer"
self.pos_encoder = PositionalEncoding(embedding_dim, dropout)
encoder_layers = TransformerEncoderLayer(embedding_dim, n_head, hidden_dim, dropout, batch_first=True)
self.transformer_encoder = TransformerEncoder(encoder_layers, n_layers)
self.encoder = nn.Embedding(vocab_size, embedding_dim)
self.fc_static = nn.Linear(n_static_features, static_dim)
self.fc_int = nn.Linear(combo_dim, fc_int_dim)
self.embedding_dim = embedding_dim
self.decoder = nn.Linear(fc_int_dim, output_size)
self.dropout = nn.Dropout(dropout)
self.softmax = nn.LogSoftmax(dim=1)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
def forward(self, src_seq, src_static, src_mask):
src_1 = self.encoder(src_seq) * math.sqrt(self.embedding_dim)
src_2 = self.pos_encoder(src_1)
seq_output = self.transformer_encoder(src_2, src_mask)
seq_output_drop = self.dropout(seq_output)
seq_sum_output = seq_output_drop.sum(dim=1) # pool over the time dimension
static_output_1 = F.relu(self.fc_static(src_static.float()))
comb_output_1 = torch.cat([static_output_1, seq_sum_output], dim=1)
comb_output_2 = F.relu(self.fc_int(comb_output_1))
comb_output_2_drop = self.dropout(comb_output_2)
decoder_output = self.decoder(comb_output_2_drop)
output = self.softmax(decoder_output)
return output
def generate_square_subsequent_mask(sz):
return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)
class PositionalEncoding(nn.Module):
def __init__(self, embedding_dim, dropout = 0.2, max_len = 5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, embedding_dim, 2) * (-math.log(10000.0) / embedding_dim))
pe = torch.zeros(max_len, 1, embedding_dim)
pe[:, 0, 0::2] = torch.sin(position * div_term)
pe[:, 0, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x):
#permute to change from seq_len first to batch size first
pos_add = self.pe[:x.size(1)].permute(1,0,2)
x = x + pos_add
return self.dropout(x)
# COMMAND ----------
#main script
start = time.time()
print("Script started ", datetime.fromtimestamp(start))
for outcome in outcomes:
print("Outcome: ", outcome)
#define static features
if outcome == "stroke":
STATIC_FEATURES = ["age_at_af_diagnosis", "female", "white", "asian_or_asian_british", "black_or_black_british", "mixed", "other_ethnic_groups"]
else:
STATIC_FEATURES = ["age_at_cohort_start", "female", "white", "asian_or_asian_british", "black_or_black_british", "mixed", "other_ethnic_groups"]
NUM_STATIC_FEATURES = len(STATIC_FEATURES)
print("Number of static features", NUM_STATIC_FEATURES)
for run in runs:
print("Run: ", run)
#load the test table
print("Load the test table for: ", outcome, " and run ", run)
cohort_test_sub_py_export_table_name = "ccu004_2_cohort_" + outcome + "_seq_len_all_run_" + str(run) + "_test_sub_" + input_run_date
cohort_test_sub_py = spark.table("dars_nic_391419_j3w9t_collab." + cohort_test_sub_py_export_table_name)
cohort_test_sub = cohort_test_sub_py.toPandas()
print("Test sub rows", len(cohort_test_sub))
for sample_ratio in sample_ratios:
print("Sample ratio: ", sample_ratio)
#load the train table
print("Load the train table for: ", outcome, " and run ", run, "and sample ratio ", sample_ratio)
cohort_train_sub_py_export_table_name = "ccu004_2_cohort_" + outcome + "_seq_len_all_sr_" + str(sample_ratio) + "_run_" + str(run) + "_train_sub_" + input_run_date
cohort_train_sub_py = spark.table("dars_nic_391419_j3w9t_collab." + cohort_train_sub_py_export_table_name)
cohort_train_sub = cohort_train_sub_py.toPandas()
print("Train sub rows", len(cohort_train_sub))
for max_seq_len in max_seq_lens:
print("Max seq len: ", max_seq_len)
#setup summary data for each scenario
print("Setup summary data for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, "and max seq len ", max_seq_len)
summary_data = []
summary_data_sub = []
#define max seq len field
if max_seq_len == 60:
TARGET_FIELD = "med_hist_target_60"
else:
TARGET_FIELD = "med_hist_target_100"
print("Target field: ", TARGET_FIELD)
#create universal vocab of medical codes
training_codes_non_n = [item for sublist in cohort_train_sub[TARGET_FIELD].values for item in sublist]
training_codelist_non_n = list(set(training_codes_non_n))
print("Codelist vocab length training non-neural", len(training_codelist_non_n))
test_codes_non_n = [item for sublist in cohort_test_sub[TARGET_FIELD].values for item in sublist]
test_codelist_non_n = list(set(test_codes_non_n))
print("Codelist vocab length test non-neural", len(test_codelist_non_n))
all_codes_non_n = list(set(training_codelist_non_n + test_codelist_non_n))
print("Codelist vocab length non-neural", len(all_codes_non_n))
# use same approach to cover training and test sub samples
all_codes_nn = all_codes_non_n
print("Codelist vocab length neural nets", len(all_codes_nn))
code_to_ix_nn = {code: i+1 for i, code in enumerate(all_codes_nn)}
#create features for ml models
print("Create ml features")
start_ml = time.time()
print("ML started ", datetime.fromtimestamp(start_ml))
train_features_non_n = []
test_features_non_n = []
cohort_train_sub.apply(create_ml_features, args=(train_features_non_n,all_codes_non_n, TARGET_FIELD, outcome), axis=1)
cohort_test_sub.apply(create_ml_features, args=(test_features_non_n,all_codes_non_n, TARGET_FIELD, outcome), axis=1)
cohort_train_sub_non_n = pd.DataFrame(train_features_non_n)
cohort_test_sub_non_n = pd.DataFrame(test_features_non_n)
print("Check dimensions of ml features")
print("Ml train features shape: ", cohort_train_sub_non_n.shape)
print("Ml test features shape: ", cohort_test_sub_non_n.shape)
#train, evaluate and report on ml models
ml_models = [LogisticRegression(max_iter=3000, random_state=run), RandomForestClassifier(random_state=run),xgb.XGBRegressor(objective="binary:logistic", random_state=run)]
x_train, x_val, y_train, y_val = train_test_split(cohort_train_sub_non_n.iloc[:, :(len(all_codes_non_n)+NUM_STATIC_FEATURES)], cohort_train_sub_non_n[outcome], test_size=0.20, random_state=run)
evaluate_ml_models(ml_models, x_train, x_val, y_train, y_val, cohort_test_sub_non_n, all_codes_non_n, NUM_STATIC_FEATURES, outcome, summary_data, summary_data_sub, SUB_GROUPS)
#NOTE: aim to free up memory here
cohort_train_sub_non_n = None
cohort_test_sub_non_n = None
summary_data_df = pd.DataFrame(summary_data)
print("Summary data after ML models for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, " and max seq len ", max_seq_len, "\n", summary_data_df)
summary_data_sub_df = pd.DataFrame(summary_data_sub)
print("Summary data for sub groups after ML models for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, " and max seq len ", max_seq_len, "\n", summary_data_sub_df)
end_ml = time.time()
print("ML completed in %s minutes" % ( round(end_ml - start_ml,2) / 60) )
#setup features for dl models
print("Create dl features")
start_dl = time.time()
x_seq_train, x_seq_val, x_static_train, x_static_val, y_train, y_val, x_seq_test, x_static_test, y_test, all_categories, n_categories, OUTCOME_CAT = create_dl_features(cohort_train_sub, cohort_test_sub, code_to_ix_nn, TARGET_FIELD, STATIC_FEATURES, outcome, run)
#train, evaluate and report on dl models
batch_size = 64
val_batch_size = len(y_val)
train_loader_nn, val_loader_nn = create_dl_batches(batch_size, val_batch_size, x_seq_train, x_static_train, y_train, x_seq_val, x_static_val, y_val)
#setup dl model parameters
output_size = n_categories
vocab_size = len(all_codes_nn)+1
embedding_dim = 200
hidden_dim = 128
n_layers = 2
static_features_n = NUM_STATIC_FEATURES
dropout = 0.2
fc1_dim = 64 # lstm
n_head = 2 # number of heads in nn.MultiheadAttention transformer
static_dim = 64 # dimension for the transformer static data feedforward layer
combo_dim = (static_dim + embedding_dim) #dimension for feedforward layer after concatenation in transformer
fc_int_dim = int((combo_dim / 2)) #dimension for feedforward layer prior to decoder in transformer
#setup training parameters
epochs = 10
learning_rate = 0.001
iterations = int((len(y_train) / batch_size) * epochs)
print("Number of iterations: ", iterations)
lstm = MyLSTM(output_size, vocab_size, embedding_dim, hidden_dim, n_layers, static_features_n, fc1_dim, dropout)
transformer = MyTransformer(output_size, vocab_size, embedding_dim, hidden_dim, n_head, n_layers, static_features_n, static_dim, combo_dim, fc_int_dim, dropout)
nets = [lstm, transformer]
#nets = [transformer]
for net in nets:
print(net.model_name)
opt = optim.Adam(net.parameters(), lr=learning_rate)
criterion = nn.NLLLoss()
run_dl_training_and_evaluation(net, opt, criterion, summary_data, max_seq_len, all_categories, epochs, train_loader_nn, val_batch_size, val_loader_nn, x_seq_test, x_static_test, y_test, cohort_test_sub, SUB_GROUPS, code_to_ix_nn, TARGET_FIELD, STATIC_FEATURES, outcome, summary_data_sub)
summary_data_df = pd.DataFrame(summary_data)
print("Summary data after DL models for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, " and max seq len ", max_seq_len, "\n", summary_data_df)
summary_data_sub_df = pd.DataFrame(summary_data_sub)
print("Summary data for sub groups after DL models for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, " and max seq len ", max_seq_len, "\n", summary_data_sub_df)
end_dl = time.time()
print("DL completed in %s minutes" % ( round(end_dl - start_dl,2) / 60) )
#setup features for chadsvasc
print("Create chadsvasc baseline")
chads_components = ["vascular_disease", "congestive_heart_failure", "diabetes", "hypertension"]
component_codelists = []
load_chads_codelists(chads_components)
train_features_chads = []
test_features_chads = []
cohort_train_sub.apply(create_features_chads, args=(train_features_chads,component_codelists, outcome), axis=1)
cohort_test_sub.apply(create_features_chads, args=(test_features_chads,component_codelists, outcome), axis=1)
cohort_train_sub_chads = pd.DataFrame(train_features_chads)
cohort_test_sub_chads = pd.DataFrame(test_features_chads)
cohort_train_sub_chads["chads_score"] = cohort_train_sub_chads.apply(create_chads_score, axis=1)
cohort_test_sub_chads["chads_score"] = cohort_test_sub_chads.apply(create_chads_score, axis=1)
cohort_train_sub_chads["pred_chads2"] = np.where(cohort_train_sub_chads["chads_score"] >=2, 1, 0)
cohort_test_sub_chads["pred_chads2"] = np.where(cohort_test_sub_chads["chads_score"] >=2, 1, 0)
#evaluate and report on chadsvasc
run_chads_evaluation(cohort_test_sub_chads, outcome, summary_data, summary_data_sub, SUB_GROUPS)
#save summary tables
summary_data_df = pd.DataFrame(summary_data)
print("Final summary data table for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, " and max seq len ", max_seq_len, "\n", summary_data_df)
summary_data_sub_df = pd.DataFrame(summary_data_sub)
print("Final summary data table for sub groups for ", outcome, " and run ", run, " and sample ratio ", sample_ratio, " and max seq len ", max_seq_len, "\n", summary_data_sub_df)
summary_data_py = spark.createDataFrame(summary_data_df)
summary_data_py_table_name = "ccu004_2_cohort_" + outcome + "_seq_len_" + str(max_seq_len) + "_sr_" + str(sample_ratio) + "_run_" + str(run) + "_summary_data_" + output_run_date
create_table_pyspark(summary_data_py, summary_data_py_table_name)
summary_data_sub_py = spark.createDataFrame(summary_data_sub_df)
summary_data_sub_py_table_name = "ccu004_2_cohort_" + outcome + "_seq_len_" + str(max_seq_len) + "_sr_" + str(sample_ratio) + "_run_" + str(run) + "_summary_data_sub_" + output_run_date
create_table_pyspark(summary_data_sub_py, summary_data_sub_py_table_name)
end = time.time()
print("Script completed in %s minutes" % ( round(end - start,2) / 60) )
| 42.295652 | 297 | 0.689053 | 6,228 | 43,776 | 4.466121 | 0.087508 | 0.035484 | 0.032249 | 0.011001 | 0.516268 | 0.440302 | 0.3762 | 0.336257 | 0.297825 | 0.283696 | 0 | 0.010938 | 0.210595 | 43,776 | 1,034 | 298 | 42.336557 | 0.793964 | 0.075429 | 0 | 0.191667 | 0 | 0 | 0.112787 | 0.012458 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.029167 | null | null | 0.118056 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d88dbbafb09f9e5620ebb0c0a2726af886fe10c5 | 25,165 | py | Python | hanibal/crm_gestion_faces/report/cheques_gir_no_cob_reporte.py | Christian-Castro/castro_odoo8 | 8247fdb20aa39e043b6fa0c4d0af509462ab3e00 | [
"Unlicense"
] | null | null | null | hanibal/crm_gestion_faces/report/cheques_gir_no_cob_reporte.py | Christian-Castro/castro_odoo8 | 8247fdb20aa39e043b6fa0c4d0af509462ab3e00 | [
"Unlicense"
] | null | null | null | hanibal/crm_gestion_faces/report/cheques_gir_no_cob_reporte.py | Christian-Castro/castro_odoo8 | 8247fdb20aa39e043b6fa0c4d0af509462ab3e00 | [
"Unlicense"
] | null | null | null | # -*- encoding: utf-8 -*-
from openerp.report import report_sxw
import openerp.pooler
class conciliacion_bancaria_c(report_sxw.rml_parse):
ESTADOS = {
'draft':'Borrador',
'proforma':'Pro-Forma',
'posted':'Contabilizado',
'cancel':'Cancelado',
'open':'Abierto',
'confirmed':'Confirmado'
}
ESTADO = {
'no':'Movimientos',
'estado_ch':'Custodios VL',
'estado_ch_otros':'Custodios Otros',
'estado_ch_rise':'Custodios RISE'
}
def __init__(self, cr, uid, name, context=None):
if context is None:
context = {}
super(conciliacion_bancaria_c, self).__init__(cr, uid, name, context=context)
self.localcontext.update({
'cuentas' :self._cuentas,
'get_cont':self._get_cont,
'resultante' :self._resultante,
'resultante_todo' :self._resultante_todo,
'proyectos':self._proyectos,
'get_desde':self._get_desde,
'get_hasta':self._get_hasta,
'get_desdeu':self._get_desdeu,
'get_hastau':self._get_hastau,
'get_corte':self._get_corte,
'get_retorno':self._get_retorno,
'valores':self._valor,
'get_saldo':self._get_salini,
'reset_total_cheques':self._reset_total_cheques,
'suma_total':self._suma_total,
'convert':self._convert,
'convierto':self._convierto,
'get_cuenta': self._get_cuenta,
'get_banco': self._get_banco,
'deposito':self._deposito,
'conver': self._convertir_estado,
'tra':self._tra,
'total_ch':self._total_ch,
'total_che':self._total_che,
'reset_t_ch':self._reset_t_ch,
'total_cheques':self._total_cheques,
'total_reporte':self._total,
'saldo_final':self._saldo_final,
})
lineas = {}
def _cuentas(self, data ):
banco= False
if data.get('form', False) and data['form'].get('bank', False):
banco = data['form']['bank'][0]
cuenta = False
if data.get('form', False) and data['form'].get('cuenta', False):
cuenta = data['form']['cuenta'][0]
estado = False
if data.get('form', False) and data['form'].get('estado', False):
estado = data['form']['estado']
tipodiario= False
if data.get('form', False) and data['form'].get('tipo_diario', False):
tipodiario = data['form']['tipo_diario'][0]
confirmados= False
if data.get('form', False) and data['form'].get('confirmados', False):
confirmados = data['form']['confirmados']
desde = False
if data.get('form', False) and data['form'].get('desde', False):
desde = data['form']['desde']
hasta = False
if data.get('form', False) and data['form'].get('hasta', False):
hasta = data['form']['hasta']
desdeu = False
if data.get('form', False) and data['form'].get('desdeu', False):
desdeu = data['form']['desdeu']
hastau = False
if data.get('form', False) and data['form'].get('hastau', False):
hastau = data['form']['hastau']
parametros = []
param = []
if banco:
parametros.append("b.id = %s")
param.append(banco)
if confirmados:
if confirmados == 'si':
parametros.append("v.ver_banco = true")
else:
if confirmados == 'no':
parametros.append("v.ver_banco = false")
else:
parametros.append("v.ver_banco is not null ")
if cuenta:
parametros.append("c.id = %s")
param.append(cuenta)
if tipodiario:
parametros.append("m.id = %s")
param.append(tipodiario)
if estado:
parametros.append("v.state = %s")
param.append(estado)
if desde and hasta:
parametros.append("v.date between %s and %s ")
param.append(desde)
param.append(hasta)
elif desde:
parametros.append(" v.date >= %s ")
param.append(desde)
elif hasta:
parametros.append("v.date <= %s ")
param.append(hasta)
if desdeu and hastau:
parametros.append(" v.fecha_cobro between %s and %s ")
param.append(desdeu)
param.append(hastau)
elif desdeu:
parametros.append(" v.fecha_cobro >= %s ")
param.append(desdeu)
elif hastau:
parametros.append(" v.fecha_cobro <= %s ")
param.append(hastau)
principal = """ select distinct v.ver_banco as verificacion , count (*) as contador
from account_voucher as v
left join payment_mode as m on m.journal = v.journal_id
left join account_journal as j on j.id = v.journal_id
left join res_partner_bank as c on c.id = m.bank_id
left join res_bank as b on b.id = c.bank
left join res_partner as p on p.id = v.partner_id """
groupby = "\n group by v.ver_banco "
where = "\n where "
query=''
if (not parametros) or ( len(parametros) == 0 ):
query = principal + groupby
else:
i=0
for g in parametros:
if i==0:
where = where + g
else:
where = where +' and '+ g
i=1
query = principal + where + groupby
param = tuple(param)
self.cr.execute(query,param)
lineas = self.cr.dictfetchall()
return lineas
def _resultante(self, data, confirmado_id ):
banco= False
if data.get('form', False) and data['form'].get('bank', False):
banco = data['form']['bank'][0]
cuenta = False
if data.get('form', False) and data['form'].get('cuenta', False):
cuenta = data['form']['cuenta'][0]
tipodiario = False
if data.get('form', False) and data['form'].get('tipo_diario', False):
tipodiario = data['form']['tipo_diario'][0]
desde = False
if data.get('form', False) and data['form'].get('desde', False):
desde = data['form']['desde']
hasta = False
if data.get('form', False) and data['form'].get('hasta', False):
hasta = data['form']['hasta']
desdeu = False
if data.get('form', False) and data['form'].get('desdeu', False):
desdeu = data['form']['desdeu']
hastau = False
if data.get('form', False) and data['form'].get('hastau', False):
hastau = data['form']['hastau']
estado = False
if data.get('form', False) and data['form'].get('estado', False):
estado = data['form']['estado']
parametros = []
param = []
if desde and hasta:
parametros.append(" date between %s and %s ")
param.append(desde)
param.append(hasta)
elif desde:
parametros.append(" date >= %s ")
param.append(desde)
elif hasta:
parametros.append(" date <= %s ")
param.append(hasta)
#---------------------------------------------
if desdeu and hastau:
parametros.append(" fec_giro between %s and %s ")
param.append(desdeu)
param.append(hastau)
elif desdeu:
parametros.append(" fec_giro >= %s ")
param.append(desdeu)
elif hastau:
parametros.append(" fec_giro <= %s ")
param.append(hastau)
if estado:
parametros.append(" state = %s ")
param.append(estado)
if banco:
parametros.append("b_id = %s")
param.append(banco)
if cuenta:
parametros.append("c_id = %s")
param.append(cuenta)
if tipodiario:
parametros.append("m_id = %s")
param.append(tipodiario)
#orderby = "\n ORDER BY 10,5,11 "
#principal = """ select distinct estado_ch from conciliacion_bancaria """
#where = "\n where ver_banco = " +str(confirmado_id) + ' '
#for g in parametros:
# where = where +' and '+ g
#query = principal + where + str('order by 1 desc')
#self.cr.execute(query ,param)
#lineas = self.cr.dictfetchall()
print confirmado_id,'<-------------'
principal = """ select * from conciliacion_bancaria where estado_ch = 't' """
where = "\n and ver_banco = " +str(confirmado_id) + ' '
for g in parametros:
where = where +' and '+ g
query = principal + where +' '+ str('order by 1 desc')
self.cr.execute(query ,param)
lineas = self.cr.dictfetchall()
principal = """ select * from conciliacion_bancaria where estado_ch_rise = 't' """
where = "\n and ver_banco = " +str(confirmado_id) + ' '
for g in parametros:
where = where +' and '+ g
query = principal + where +' '+ str('order by 1 desc')
self.cr.execute(query ,param)
lineas_a = self.cr.dictfetchall()
principal = """ select * from conciliacion_bancaria where estado_ch_otros = 't' """
where = "\n and ver_banco = " +str(confirmado_id) + ' '
for g in parametros:
where = where +' and '+ g
query = principal + where +' '+ str('order by 1 desc')
self.cr.execute(query ,param)
lineas_b = self.cr.dictfetchall()
principal = """ select * from conciliacion_bancaria where estado_ch_rise = 'f' and estado_ch_otros = 'f' and estado_ch = 'f' """
where = "\n and ver_banco = " +str(confirmado_id) + ' '
for g in parametros:
where = where +' and '+ g
query = principal + where +' '+ str('order by 1 desc')
self.cr.execute(query ,param)
lineas_c = self.cr.dictfetchall()
dct=[]
if len(lineas) > 0 :
r = {'custodio': 'estado_ch' }
dct.append(r)
if len(lineas_a) > 0 :
s = {'custodio': 'estado_ch_rise' }
dct.append(s)
if len(lineas_b) > 0 :
t = {'custodio': 'estado_ch_otros' }
dct.append(t)
if len(lineas_c) > 0 :
u = {'custodio': 'no' }
dct.append(u)
return dct
#----------------------------------------------------------------------------------
def _resultante_todo(self, data, confirmado_id,estados_cheques ):
if estados_cheques == 'no':
estados_cheques = "estado_ch = 'f' and estado_ch_rise = 'f' and estado_ch_otros = 'f' "
if estados_cheques == 'estado_ch':
estados_cheques = "estado_ch = 't' "
if estados_cheques == 'estado_ch_otros':
estados_cheques = "estado_ch_otros = 't' "
if estados_cheques == 'estado_ch_rise':
estados_cheques = "estado_ch_rise = 't' "
banco= False
if data.get('form', False) and data['form'].get('bank', False):
banco = data['form']['bank'][0]
cuenta = False
if data.get('form', False) and data['form'].get('cuenta', False):
cuenta = data['form']['cuenta'][0]
tipodiario = False
if data.get('form', False) and data['form'].get('tipo_diario', False):
tipodiario = data['form']['tipo_diario'][0]
desde = False
if data.get('form', False) and data['form'].get('desde', False):
desde = data['form']['desde']
hasta = False
if data.get('form', False) and data['form'].get('hasta', False):
hasta = data['form']['hasta']
desdeu = False
if data.get('form', False) and data['form'].get('desdeu', False):
desdeu = data['form']['desdeu']
hastau = False
if data.get('form', False) and data['form'].get('hastau', False):
hastau = data['form']['hastau']
estado = False
if data.get('form', False) and data['form'].get('estado', False):
estado = data['form']['estado']
parametros = []
param = []
if desde and hasta:
parametros.append(" date between %s and %s ")
param.append(desde)
param.append(hasta)
elif desde:
parametros.append(" date >= %s ")
param.append(desde)
elif hasta:
parametros.append(" date <= %s ")
param.append(hasta)
if desdeu and hastau:
parametros.append(" fec_giro between %s and %s ")
param.append(desdeu)
param.append(hastau)
elif desdeu:
parametros.append(" fec_giro >= %s ")
param.append(desdeu)
elif hastau:
parametros.append(" fec_giro <= %s ")
param.append(hastau)
if estado:
parametros.append(" state = %s ")
param.append(estado)
if banco:
parametros.append("b_id = %s")
param.append(banco)
if cuenta:
parametros.append("c_id = %s")
param.append(cuenta)
if tipodiario:
parametros.append("m_id = %s")
param.append(tipodiario)
orderby = "\n ORDER BY 10,5,11 "
principal = """ select * from conciliacion_bancaria """
where = "\n where ver_banco = '"+str(confirmado_id)+"' and "+str(estados_cheques)+" "
for g in parametros:
where = where +' and '+ g
query = principal + where + orderby
self.cr.execute(query ,param)
lineas = self.cr.dictfetchall()
return lineas
#----------------------------------------------------------------------------------
def _proyectos(self, cheque_id ):
query = """ SELECT analytics_id as id
from account_invoice_line where invoice_id in (
select id
from account_invoice
where replace(number,'/','') in (
select ref from account_move_line where id in (
select move_line_id
from account_voucher_line
where voucher_id = %s
) ) )
group by analytics_id """
self.cr.execute(query,(cheque_id,))
res = self.cr.dictfetchall()
mod_nom = []
c = len(res)
i = 0
while i < c :
nom_u = str(''+self._consulta_nombre(res[i]['id']) )
mod_nom.append(nom_u)
i += 1
TEXT = str("//".join(mod_nom))
return TEXT
def _consulta_nombre(self, pr_id ):
self.cr.execute("select name from account_analytic_plan_instance where id = %s ",( pr_id ,))
nom = self.cr.dictfetchall()
return nom[0]['name']
#----------------------------------------------------------------------------------
def _deposito(self, data ):
banco= False
if data.get('form', False) and data['form'].get('bank', False):
banco = data['form']['bank'][0]
cuenta = False
if data.get('form', False) and data['form'].get('cuenta', False):
cuenta = data['form']['cuenta'][0]
corte = False
if data.get('form', False) and data['form'].get('f_corte', False):
corte = data['form']['f_corte']
parametros = []
param = []
if corte:
parametros.append(" registrobanco <= %s ")
param.append(corte)
if banco:
parametros.append("b_id = %s")
param.append(banco)
if cuenta:
parametros.append("c_id = %s")
param.append(cuenta)
ejecutar = """ CREATE OR REPLACE VIEW conciliacion_bancaria AS
SELECT
v.id AS ninterno,
p.name AS proveedor,
v.amount AS total,
v.ver_banco AS verificacion,
v.veri_fecha AS fechaverifi,
v.ver_regbanco AS registrobanco,
v.number AS numero,
v.pospago AS posfechado,
v.amount * (-1)::numeric AS valor,
v.date AS movi,
v.fecha_cobro as fec_giro,
'CHEQUES'::character varying AS tipomovimiento,
v.state AS estado,
p.id as p_id,
b.id as b_id,
c.id as c_id,
m.id as m_id,
v.ver_banco as ver_banco,
v.date date,
v.state state,
coalesce(v.estado_ch,'f') estado_ch,
coalesce(v.estado_ch_otros,'f') estado_ch_otros,
coalesce(v.estado_ch_rise,'f') estado_ch_rise
FROM account_voucher v
JOIN payment_mode m ON m.journal = v.journal_id
JOIN res_partner_bank c ON c.id = m.bank_id
JOIN res_bank b ON b.id = c.bank
JOIN res_partner p ON p.id = v.partner_id
where v.state in ( 'posted','draft')
and number not like '%/%'
and number not like '%B%'
UNION all
SELECT
v.id AS ninterno,
COALESCE(p.name, ' - '::character varying) AS proveedor,
det.amount AS total,
true AS verificacion,
det.date AS fechaverifi,
det.date AS registrobanco,
det.ref AS numero,
false AS posfechado,
det.amount AS valor,
det.date AS movi,
det.date as fec_giro,
upper(det.name::text) AS tipomovimiento,
v.state AS estado,
p.id AS p_id ,
b.id AS b_id,
c.id AS c_id,
m.id AS m_id,
'True' AS ver_banco,
v.date date,
v.state as state,
'f' as estado_ch,
'f' as estado_ch_otros,
'f' as estado_ch_rise
FROM account_bank_statement v
LEFT JOIN account_journal tdiario ON tdiario.id = v.journal_id
LEFT JOIN account_bank_statement_line det ON det.statement_id = v.id
LEFT JOIN res_partner p ON p.id = det.partner_id
JOIN payment_mode m ON m.journal = tdiario.id
JOIN res_partner_bank c ON m.bank_id = c.id
JOIN res_bank b ON b.id = c.bank
where v.state in ('open','confirm') """
query= ''
if parametros:
query = ejecutar
else:
query = ejecutar
self.cr.execute(query)
principal = """ select
sum(valor) as saldoini
from conciliacion_bancaria """
principalr = """ select
( sum(total) - sum(total) ) as saldoini
from conciliacion_bancaria """
where = "\n where ver_banco = true "
query=''
if banco and cuenta and not corte:
query = principalr
else:
i=0
for g in parametros:
if i==0:
where = where +' and '+ g
else:
where = where +' and '+ g
i=1
query = principal + where
param = tuple(param)
self.cr.execute(query,param)
lineas = self.cr.dictfetchall()
return lineas
def _get_banco(self, data):
if data.get('form', False) and data['form'].get('bank', False):
id = data['form']['bank'][0]
return openerp.pooler.get_pool(self.cr.dbname).get('res.bank').browse(self.cr, self.uid, id).name
return False
def _get_cont(self,est):
if est == 'posted':
val = 'SI'
else:
val = 'NO'
return val
def _get_cuenta(self, data):
if data.get('form', False) and data['form'].get('cuenta', False):
id = data['form']['cuenta'][0]
return openerp.pooler.get_pool(self.cr.dbname).get('res.partner.bank').browse(self.cr, self.uid, id).acc_number
return False
def _get_desde(self, data):
if data.get('form', False) and data['form'].get('desde', False):
return data['form']['desde']
return False
def _get_hasta(self, data):
if data.get('form', False) and data['form'].get('hasta', False):
return data['form']['hasta']
return False
#------------------------------------------------------------
def _get_desdeu(self, data):
if data.get('form', False) and data['form'].get('desdeu', False):
return data['form']['desdeu']
return False
def _get_hastau(self, data):
if data.get('form', False) and data['form'].get('hastau', False):
return data['form']['hastau']
return False
def _get_corte(self, data):
if data.get('form', False) and data['form'].get('f_corte', False):
return data['form']['f_corte']
return False
def _convert(self , estado ):
if not estado:
valor = 'No'
else:
valor = 'SI'
return valor
def _convierto(self , estados ):
if not estados:
valores = 'no Confirmados '
else:
valores = 'Confirmados'
return valores
def _convertir_estado(self,tipo):
if self.ESTADOS.has_key(tipo):
return self.ESTADOS[tipo]
return 'Otros'
def _tra(self,tipo):
if self.ESTADO.has_key(tipo):
return self.ESTADO[tipo]
total_cheques = 0.00
total = 0.00
tota_saldo = 0.00
saldo = 0.00
pri = 0
seg = 0
suma = 0
sumado = 0.00
total_c = 0.0
def _valor(self, valor, cheque,cheque_a,cheque_b ):
if str(cheque) == 'False' and str(cheque_a) == 'False' and str(cheque_b) == 'False' :
self.total_cheques = self.total_cheques + valor
def _total_cheques(self):
return self.total_cheques
def _reset_total_cheques(self):
self.total_cheques = 0.0
def _suma_total(self, valor ):
self.total = self.total - valor
def _total(self):
return self.total
def _get_salini(self , valor ):
self.total_saldo = valor
def _saldo_final(self):
self.saldo = self.total_saldo - self.total
return self.saldo
def _get_retorno(self,valor):
self.suma += valor
return self.suma
def _total_che(self,val):
self.total_c = self.total_c + val
def _total_ch(self):
return self.total_c
def _reset_t_ch(self):
self.total_c = 0.0
report_sxw.report_sxw(
'report.conciliacion.bancaria',
'rt.conciliacion.bancaria',
'addons/rt_verificacionbancaria/report/cheques_gir_no_cob_reporte.rml',
parser=conciliacion_bancaria_c,header=False)
| 32.85248 | 137 | 0.476972 | 2,680 | 25,165 | 4.340672 | 0.08694 | 0.048139 | 0.027078 | 0.039113 | 0.589616 | 0.551105 | 0.534944 | 0.512078 | 0.492908 | 0.484828 | 0 | 0.004472 | 0.395788 | 25,165 | 765 | 138 | 32.895425 | 0.760605 | 0.027657 | 0 | 0.47331 | 0 | 0.001779 | 0.296045 | 0.020287 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.003559 | null | null | 0.001779 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d88fd615e27e9bedcbbc112337a612f942bcf04c | 400 | py | Python | images/migrations/0002_auto_20180226_2100.py | andykimchris/Unsplash | dc0395a30ad983ea4620c30889cdb4b0ef6d707e | [
"MIT"
] | null | null | null | images/migrations/0002_auto_20180226_2100.py | andykimchris/Unsplash | dc0395a30ad983ea4620c30889cdb4b0ef6d707e | [
"MIT"
] | 9 | 2019-08-06T01:57:09.000Z | 2021-09-16T16:04:04.000Z | images/migrations/0002_auto_20180226_2100.py | andykimchris/Unsplash | dc0395a30ad983ea4620c30889cdb4b0ef6d707e | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# Generated by Django 1.11.1 on 2018-02-26 18:00
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('images', '0001_initial'),
]
operations = [
migrations.AlterModelOptions(
name='gallery',
options={'ordering': ['image']},
),
]
| 20 | 48 | 0.6 | 41 | 400 | 5.707317 | 0.829268 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071672 | 0.2675 | 400 | 19 | 49 | 21.052632 | 0.726962 | 0.17 | 0 | 0 | 1 | 0 | 0.115502 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.166667 | 0 | 0.416667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d894c6184e52a7c8ac071f8f156fcf53e4231961 | 6,277 | py | Python | pythonLoops.py | SandraCoburn/python-code-challenges | 52ca026c02a45cadc890d01fc095d39d42b36d4c | [
"MIT"
] | null | null | null | pythonLoops.py | SandraCoburn/python-code-challenges | 52ca026c02a45cadc890d01fc095d39d42b36d4c | [
"MIT"
] | null | null | null | pythonLoops.py | SandraCoburn/python-code-challenges | 52ca026c02a45cadc890d01fc095d39d42b36d4c | [
"MIT"
] | null | null | null | '''
We can use two types of loops in Python, a for loop and a while loop. A for loop iterates over a given sequence(iterator expression)
A while loop repeats as long as a boolean context evaluates to True.
the break statement terminates the loop containing it. Control of the program flows to the statement immediately after
the body of the loop. If the break statement is inside a nested loop(loop inside another loop), the break statement will only terminate
the innermost loop.
You can use the continue statement to skp the rest of the code inside a loop for the current iretation only. The loop
does not terminate entirely but continues with the next iteration
'''
#Accesing the index in "for" loops
ints = [8,23,45,12,78]
for i, n in enumerate(ints):
print(f"item #{i} = {n}")
for x in range(5):
print(x) # 0,1,2,3,4
for x in range(2,7):
print("starts at two stops before 7",x) # 2,3,4,5,6
for x in range(1,8,2):
print("starts at 1, before 8 by 2",x) # 1,3,5,7
# while loops to print the same values as the for loops above
count = 0
while count < 5:
print(count)
count += 1
count = 2
while count < 7:
print("starts at two stops before 7",count)
count += 1
count = 1
while count < 8:
print("starts at 1, before 8 by 2",count)
count += 2
# You can use a break statement to exit a for loop or a while loop
count = 0
while True:
print(count)
count += 1
if count >= 5:
break
# You can use continue statement to skp the current block but not exit the loop entirely
for x in range(8):
# if x is even, skip this block and do not print
if x % 2 == 0:
continue
print(x)
"""
Write Python code below to loop through and print out all the odd numbers from the numbers
list in the same order they are received. Don't print any numbers that come after 600 in the sequence.
"""
numbers_list = [
951, 402, 984, 651, 360, 69, 408, 319, 601, 485, 980, 507, 725, 547, 544, 615, 83, 165, 141, 501, 263, 617, 865, 575, 219, 390, 984, 592, 236, 105, 942, 941, 386, 462, 47, 418, 907, 344, 236, 375, 823, 566, 597, 978, 328, 615, 953, 345, 399, 162, 758, 219, 918, 237, 412, 566, 826, 248, 866, 950, 626, 949, 687, 217, 815, 67, 104, 58, 512, 24, 892, 894, 767, 553, 81, 379, 843, 831, 445, 742, 717, 958, 609, 842, 451, 688, 753, 854, 685, 93, 857, 440, 380, 126, 721, 328, 753, 470, 743, 527
]
for num in numbers_list:
if num % 2 != 0 and num < 600:
print("odds", num)
import string
print("whitespace",string.whitespace)
# Basic types
my_int = 3
print(float(my_int)) #3.0
#modulo operator
my_remainder = 9 % 4
print(my_remainder) #1
# exponentiation operator
two_squared = 2 ** 2
print(two_squared) #4
two_cubed = 2 ** 3
print(two_cubed) #8
# Using multiplication operator to create a new list of string that repeats the original sequence:
my_string = "Python"
repeated = my_string * 3
print(repeated) #PythonPythonPython
my_list = [1,2,3]
repeated_list = my_list * 3
print( repeated_list) #[1, 2, 3, 1, 2, 3, 1, 2, 3]
a = object()
b = object()
a_list = [a] * 5
b_list = [b] * 5
combined_a_and_b = a_list + b_list
print(len(combined_a_and_b)) #10
# To forat a string in Python, you use the % operator to format a set of stored variables in a tuple. You also include
# argument specifieres in your string with special symbols like %s and %d
name = "Sandra"
formatted_string = "hello, %s!" %name
print(formatted_string) # hello, Sandra!
# If you have more than one argument specifier, you need to enclose your arguments ina tuple:
name2 = "Jackie"
year = 2021
print("Hey %s! It's the year %d." % (name2, year)) #Hey Jackie! It's the year 2021.
# Any object that is not a string can also be formatted using the %s operator
my_sample_list = [1,2,3]
print("my_list: %s" % my_sample_list)#my_list: [1, 2, 3]
'''
#Common argument specifiers:
- %s - String (or any object with a string representation)
- %d - Integers
- %f - Floating point numbers
- %.<number of digits>f - Floating point numbers with a fixed amount of digits to the dot's right
- %x/%X - Integers in hexadecimals(lowercase/uppercase)
'''
product_name = "bananas"
price = 1.23
product_id = 123456
print("%s (id: %d) are currently $%.2f." % (product_name, product_id, price)) #bananas (id: 123456) are currently $1.23.
# The len() method prints out the number of characters in the string
my_string_sample = "Hello, World"
print(len(my_string_sample)) #12
#The index() method prints out theindex of the substring argument's first occurrence
print(my_string_sample.index("o")) #4
print(my_string_sample.index(", W")) #5
# The count() method returns the number of occurrences of the substring argument
print(my_string_sample.count("ll"))#1
print(my_string_sample.count('o'))# 2
# to slice a string, you can use this syntax: [start:stop:step]. To reverse the string's order, you can set the step value to be -1
print(my_string_sample[3:7]) #lo,
print(my_string_sample[3:7:2])# start at index 3 stop before 7, we skip every 2 letters -> l,
print(my_string_sample[::-1]) #dlroW ,olleH
#uppercase and lowercase
print(my_string_sample.upper()) #HELLO, WORLD
print(my_string_sample.lower()) #hello, world
#starts with
print(my_string_sample.startswith("H")) #True
print(my_string_sample.endswith("W")) #False
#Split string, The split() method allows you to split up a strint into a list. The default separator is any whtespace
print(my_string_sample.split(" ")) #['Hello,', 'World']
print(my_string_sample.split(", ")) #['Hello', 'World']
print(my_string_sample.split("l")) #['He', '', 'o, Wor', 'd']
#Anytime you have an iterable object(like a list) you can check if a specific item exists inside that iterable by using the `in` operator
years = [2018, 2019,2020,2021]
year = 2020
if year in years:
print("%s is in the years collection" % year) # 2020 is in the years collection
#If we want to determine if two objects are actually the same instance in memory, we use the `is` operator instead of value comparison operator ==
d = [1,2,3,4,5]
e = [1,2,3,4,5]
print(a == b) #True because a and b have teh same value
print(a is b) #False because a and b reference two different list object
x = [1,2,3]
y = x
print(x == y) #True because x and y have the same value
print(x is y) #True because x and y reference the same list object | 33.388298 | 492 | 0.70368 | 1,130 | 6,277 | 3.846903 | 0.326549 | 0.033126 | 0.05153 | 0.061192 | 0.122383 | 0.071774 | 0.045549 | 0.045549 | 0.021164 | 0.021164 | 0 | 0.091636 | 0.18289 | 6,277 | 188 | 493 | 33.388298 | 0.755898 | 0.451968 | 0 | 0.090909 | 0 | 0 | 0.102048 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.010101 | 0 | 0.010101 | 0.424242 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
d896b92786c756abe5bfc350a2fe42f3f303203a | 1,322 | py | Python | hashdist/formats/templated_stream.py | krafczyk/hashdist | a322a66d4bcd4b989a6a163cd2569f3e71995f60 | [
"BSD-3-Clause"
] | 67 | 2015-01-21T14:16:20.000Z | 2022-03-31T23:21:09.000Z | hashdist/formats/templated_stream.py | dannygriffin000/hashdist | a322a66d4bcd4b989a6a163cd2569f3e71995f60 | [
"BSD-3-Clause"
] | 77 | 2015-01-01T00:38:55.000Z | 2020-06-15T22:04:42.000Z | hashdist/formats/templated_stream.py | dannygriffin000/hashdist | a322a66d4bcd4b989a6a163cd2569f3e71995f60 | [
"BSD-3-Clause"
] | 20 | 2015-01-22T16:17:49.000Z | 2021-02-11T21:35:25.000Z | """
A simple stream constructor that constructs a Stream by evaluating
parameter substitutions from a dictionary parameters. Finds tokens of the form
\{\{([a-zA-Z_-][\w-]*)\}\}
and replaces {{var}} with the contents of gettattr(parameters, var) in
the new stream.
"""
import re
from StringIO import StringIO
class TemplatedStream(StringIO):
"""
StringIO stream that expands template parameters of the form {{var}}
"""
dbrace_re = re.compile(r'\{\{([a-zA-Z_][\w-]*)\}\}')
def __init__(self, stream, parameters):
"""
Create a TemplatedStream by populating variables from the
parameters mapping. Silently passes matching strings that
do not have a corresponding key defined in parameters as empty strings.
"""
StringIO.__init__(self)
def dbrace_expand(match):
if match.group(1) in parameters:
# we may occassionally be handed non-string object in
# parameters. Just convert them to string, they will
# be re-run through the YAML parser anyway.
return str(parameters[match.group(1)])
else:
return ''
for line in stream:
self.write(self.dbrace_re.sub(dbrace_expand, line))
self.seek(0)
| 30.045455 | 79 | 0.618003 | 161 | 1,322 | 4.987578 | 0.565217 | 0.044832 | 0.022416 | 0.012453 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.003181 | 0.286687 | 1,322 | 43 | 80 | 30.744186 | 0.848356 | 0.503782 | 0 | 0 | 0 | 0 | 0.04223 | 0.04223 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0 | 0.142857 | 0 | 0.571429 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
d899d8df0f282d6997185cd206095a224300208b | 3,483 | py | Python | rl_agent/scripts/agents/test/gym_ddpg_server.py | alejodosr/drl-landing-iros | d2a221ef06332d36398c2a27008dfc4276d92af8 | [
"MIT"
] | 8 | 2018-07-12T11:12:36.000Z | 2021-07-12T13:37:51.000Z | rl_agent/scripts/agents/test/gym_ddpg_server.py | alejodosr/drl-landing-iros | d2a221ef06332d36398c2a27008dfc4276d92af8 | [
"MIT"
] | 3 | 2018-11-13T22:40:12.000Z | 2020-12-11T12:13:46.000Z | rl_agent/scripts/agents/test/gym_ddpg_server.py | alejodosr/drl-landing-iros | d2a221ef06332d36398c2a27008dfc4276d92af8 | [
"MIT"
] | 7 | 2018-07-12T11:12:39.000Z | 2021-03-09T21:52:30.000Z | import filter_env
import rospy
from rl_agent_environment_communication.srv import *
import cv2
from cv_bridge import CvBridge
import gym
import numpy as np
ENV_NAME = 'LunarLanderContinuous-v2'
#ENV_NAME = 'Pendulum-v0'
DEBUG_SERVICES_MODE = False
env = filter_env.makeFilteredEnv(gym.make(ENV_NAME))
#env = gym.wrappers.Monitor(env, 'experiments/' + ENV_NAME,force=True)
state = env.reset()
def handle_environment_reset(req):
print 'Reseting Env...'
state = env.reset()
resp = ResetEnvSrvResponse()
resp.state = state
return resp
def handle_environment_render(req):
print 'Rendering Env...'
image_state = env.render(mode='rgb_array')
print 'ENV RENDERED!'
#~ size = 400, 600, 3
#~ image_state = np.zeros(size, dtype=np.uint8)
#~ print 'image_state (type): ', type(image_state)
#~ cv2.imshow('image_state', image_state)
#~ cv2.waitKey(1)
print 'Creating CvBridge object...'
cv_bridge_obj = CvBridge()
img_msg = cv_bridge_obj.cv2_to_imgmsg(image_state, "bgr8")
print 'Sending response...'
resp = RenderEnvSrvResponse()
resp.img = img_msg
return resp
def handle_environment_step(req):
#env.render()
action_req = np.array(req.action)
if DEBUG_SERVICES_MODE:
print '++++++++++ Requested Action INFO ++++++++++'
print 'action (type): ', type(action_req)
print 'action (shape): ', action_req.shape
print 'action (length): ', len(action_req)
print 'action (dim): ', action_req.ndim
print 'action (values): ', action_req
next_state, reward, done, _ = env.step(action_req)
size = 5, 5, 3
img = np.zeros(size, dtype=np.uint8)
cv_bridge_obj = CvBridge()
img_msg = cv_bridge_obj.cv2_to_imgmsg(img, "bgr8")
resp = AgentSrvResponse()
resp.reward = reward
resp.obs_real = np.array(next_state)
resp.terminal_state = done
resp.img = img_msg
return resp
def handle_environment_dimensionality(req):
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
state_dim_low = env.observation_space.low
state_dim_high = env.observation_space.high
action_dim_low = env.action_space.low
action_dim_high = env.action_space.high
if DEBUG_SERVICES_MODE:
print '**** state_dim: ', state_dim
print '**** action_dim: ', action_dim
print '**** state_dim_low: ', state_dim_low
print '**** state_dim_low (type): ', type(state_dim_low)
print '**** state_dim_high: ', state_dim_high
print '**** action_dim_low: ', action_dim_low
print '**** action_dim_low (type): ', type(action_dim_low)
print '**** action_dim_high: ', action_dim_high
resp = EnvDimensionalitySrvResponse()
resp.state_dim_lowdim = state_dim
resp.state_dim_img = np.array([5, 5, 3], dtype=np.int)
resp.state_min = state_dim_low
resp.state_max = state_dim_high
resp.action_dim = action_dim
resp.action_min = action_dim_low
resp.action_max = action_dim_high
resp.num_iterations = env.spec.timestep_limit
return resp
def environment_server():
rospy.init_node('environment_step_server')
s1 = rospy.Service('environment_step', AgentSrv, handle_environment_step)
s2 = rospy.Service('environment_dimensionality', EnvDimensionalitySrv, handle_environment_dimensionality)
s3 = rospy.Service('environment_reset', ResetEnvSrv, handle_environment_reset)
s4 = rospy.Service('environment_render', RenderEnvSrv, handle_environment_render)
print "Ready to step the Environment..."
rospy.spin()
def main():
environment_server()
if __name__ == '__main__':
main()
| 27.210938 | 109 | 0.729543 | 483 | 3,483 | 4.950311 | 0.254658 | 0.060226 | 0.027604 | 0.023839 | 0.168967 | 0.136345 | 0.075282 | 0.075282 | 0.075282 | 0.039314 | 0 | 0.01042 | 0.145851 | 3,483 | 127 | 110 | 27.425197 | 0.793277 | 0.079242 | 0 | 0.139535 | 0 | 0 | 0.176673 | 0.022827 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.081395 | null | null | 0.232558 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d89b06fcef8d37cf29e8426902af9fd2fae016ee | 807 | py | Python | game/characters/enemy.py | FilippoLeone/pypega | 8dd3eee22dcac063d5de430a4c1e3e34a5cc5b85 | [
"MIT"
] | 4 | 2020-05-10T09:20:15.000Z | 2021-03-11T22:59:04.000Z | game/characters/enemy.py | FilippoLeone/pypega | 8dd3eee22dcac063d5de430a4c1e3e34a5cc5b85 | [
"MIT"
] | null | null | null | game/characters/enemy.py | FilippoLeone/pypega | 8dd3eee22dcac063d5de430a4c1e3e34a5cc5b85 | [
"MIT"
] | 1 | 2020-09-14T02:39:02.000Z | 2020-09-14T02:39:02.000Z | import pyxel
import constants as c
import random
class Gachi:
def __init__(self, x, y):
self.x = x
self.y = y
self.x_side = [-16, 16, 16, 16, 16, 16]
self.y_side = [16, -16, 16, 16, 16, 16, 16, 16, 16, 16]
self.hp = c.gachi_hp
def draw(self):
pyxel.blt(self.x, self.y, 0, 16, 48, self.x_side[random.randint(0,len(self.x_side) - 1)]
, self.y_side[random.randint(0,len(self.y_side) - 1)]
, 0)
def draw_hp_bar(self):
pyxel.text(17, 10, self.get_hp(), 8)
pyxel.blt(-2, 10, 0, 48, 48, 16, 16, 0)
pyxel.blt(-18, 10, 0, 48+16, 48, 16, 16, 0)
pyxel.blt(-34, 10, 0, 48+32, 48, 16, 16, 0)
pyxel.blt(-50, 10, 0, 48+48, 48, 16, 16, 0)
def get_hp(self):
return (f"{self.hp}") | 29.888889 | 96 | 0.519207 | 147 | 807 | 2.748299 | 0.244898 | 0.178218 | 0.178218 | 0.19802 | 0.381188 | 0.35396 | 0.118812 | 0.049505 | 0.049505 | 0 | 0 | 0.192171 | 0.303594 | 807 | 27 | 97 | 29.888889 | 0.52669 | 0 | 0 | 0 | 0 | 0 | 0.011139 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.181818 | false | 0 | 0.136364 | 0.045455 | 0.409091 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8a04a5bb0fbfb255a9d56aeb1bca04c5479f958 | 302 | py | Python | scripts/load_raw_data_into_db.py | netoferraz/acordaos-tcu | 67088d87d3ace7f85b8628955db7cf3ecdfaac45 | [
"MIT"
] | 21 | 2019-09-02T20:42:30.000Z | 2021-09-14T09:54:04.000Z | scripts/load_raw_data_into_db.py | netoferraz/acordaos-tcu | 67088d87d3ace7f85b8628955db7cf3ecdfaac45 | [
"MIT"
] | 2 | 2021-06-02T00:20:39.000Z | 2021-12-13T20:14:30.000Z | scripts/load_raw_data_into_db.py | netoferraz/acordaos-tcu | 67088d87d3ace7f85b8628955db7cf3ecdfaac45 | [
"MIT"
] | 2 | 2019-09-03T13:55:34.000Z | 2020-12-08T22:09:49.000Z | from scripts.funcs import initiate_db, load_csv_into_db, load_json_into_db
conn, cur = initiate_db("./db/acordaos-download.db")
#years = list(range(1992, 2000))
filename = "./data/api/raw/2018_2019.json"
load_json_into_db(filename, cursor=cur)
#load_csv_into_db(years, cur)
conn.commit()
conn.close()
| 30.2 | 74 | 0.774834 | 51 | 302 | 4.294118 | 0.54902 | 0.109589 | 0.100457 | 0.118721 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.057554 | 0.07947 | 302 | 9 | 75 | 33.555556 | 0.730216 | 0.195364 | 0 | 0 | 0 | 0 | 0.224066 | 0.224066 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.166667 | 0 | 0.166667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8a517012e48b5bb958e754dbf1e564082718cef | 2,759 | py | Python | tools/hippydebug.py | jweinraub/hippyvm | 09c7643aaa1c4ade566e8681abd2543f12bf874c | [
"MIT"
] | 289 | 2015-01-01T15:36:55.000Z | 2022-03-27T00:22:27.000Z | tools/hippydebug.py | jweinraub/hippyvm | 09c7643aaa1c4ade566e8681abd2543f12bf874c | [
"MIT"
] | 26 | 2015-01-21T16:34:41.000Z | 2020-08-26T15:12:54.000Z | tools/hippydebug.py | jweinraub/hippyvm | 09c7643aaa1c4ade566e8681abd2543f12bf874c | [
"MIT"
] | 35 | 2015-01-05T12:09:41.000Z | 2022-03-16T09:30:16.000Z | #!/usr/bin/env python
"""Hippy debugger.
Usage: hippydebug.py [debugger_options] ../hippy-c args...
(There are no debugger_options so far.)
"""
import sys, os, signal
import getopt
import subprocess
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from hippy.debugger import Connection, Message
def run_interactive(read_fd, write_fd):
import readline # for raw_input() below
con = Connection(read_fd, write_fd)
last_command = ''
while True:
try:
msg = con.read()
except EOFError:
break
if msg.command == '>':
line = raw_input('> ')
if not line: # Ctrl-D
break
line = line.strip()
if not line:
line = last_command
else:
last_command = line
lst = line.split(" ", 1)
if len(lst) == 1:
con.write(Message(lst[0], None))
else:
con.write(Message(lst[0], [lst[1]]))
else:
print msg.command, " ".join(msg.args)
con.write(Message(".", None))
def reopen_terminal():
f = open("/dev/tty", "r+", 0)
sys.stdin = sys.stdout = sys.stderr = f
os.dup2(f.fileno(), 0)
os.dup2(f.fileno(), 1)
os.dup2(f.fileno(), 2)
def printable_process_status(status):
if os.WIFEXITED(status):
return 'exit code %s' % (os.WEXITSTATUS(status),)
elif os.WIFSIGNALED(status):
return 'terminated by signal %s' % (os.WTERMSIG(status),)
else:
return 'unknown exit status 0x%x' % (status,)
def main(hippy_command, *hippy_args):
read_fd1, write_fd1 = os.pipe()
read_fd2, write_fd2 = os.pipe()
child_pid = os.fork()
if child_pid == 0: # in the child
os.close(read_fd1)
os.close(write_fd2)
hippy_command_list = [
hippy_command,
'--debugger_pipes', str(read_fd2), str(write_fd1),
] + list(hippy_args)
os.execvp(hippy_command, hippy_command_list)
# this point never reached
os.close(read_fd2)
os.close(write_fd1)
try:
reopen_terminal()
print >> sys.stderr, 'Hippy Debugger'
run_interactive(read_fd1, write_fd2)
finally:
os.kill(child_pid, signal.SIGQUIT)
print >> sys.stderr, 'Hippy finished:',
_, status = os.waitpid(child_pid, 0)
print >> sys.stderr, printable_process_status(status)
if __name__ == '__main__':
options, args = getopt.getopt(sys.argv[1:], '', [])
if not args:
print >> sys.stderr, __doc__
sys.exit(1)
if not os.path.isfile(args[0]):
print >> sys.stderr, '%s: No such file' % (args[0],)
sys.exit(1)
main(*args)
| 29.042105 | 79 | 0.573759 | 356 | 2,759 | 4.272472 | 0.351124 | 0.035503 | 0.046022 | 0.025641 | 0.08547 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016854 | 0.290323 | 2,759 | 94 | 80 | 29.351064 | 0.759959 | 0.031533 | 0 | 0.157895 | 0 | 0 | 0.056604 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.065789 | null | null | 0.092105 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8a71387fda8430f0022fe54e931176d01f5e1cf | 2,380 | py | Python | brewerslab-orig-master/pitmButtonv2.py | allena29/brewerslabng | f47e671971436b7af806b54f6019c5b185d7d194 | [
"Apache-2.0"
] | 1 | 2020-04-12T10:08:10.000Z | 2020-04-12T10:08:10.000Z | brewerslab-orig-master/pitmButtonv2.py | allena29/brewerslabng | f47e671971436b7af806b54f6019c5b185d7d194 | [
"Apache-2.0"
] | 2 | 2021-12-13T20:09:45.000Z | 2022-03-08T21:09:57.000Z | brewerslab-orig-master/pitmButtonv2.py | allena29/brewerslabng | f47e671971436b7af806b54f6019c5b185d7d194 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
import os
import sys
import threading
import time
from pitmMcastOperations import pitmMcast
from pitmLogHandler import pitmLogHandler
from gpiotools2 import gpiotools2
from pitmCfg import pitmCfg
class pitmButton:
"""
pitmButton manages the toggling of mode switches.
These switches may be
- Physcial, which are read from GPIO
- or fake buttons (ipc/manualswitch_<NAME>
In both cases if a physical button is on or the fakebutton is set
the status is written to an ipc/<NAME> file and broadcast via multicast
"""
def __init__(self, rpi=True):
self.cfg = pitmCfg()
self.groot = pitmLogHandler()
if rpi:
self.gpio = gpiotools2()
self.doMonitoring = False
def _check_a_single_button(self, button):
if os.path.exists('ipc/manualswitch_%s' % (button)):
return True
return self.gpio.input(button)
def _set_ipc_flag(self, button):
o = open("ipc/%s" % (button), "w")
o.close()
def _remove_ipc_flag(self, button):
try:
os.remove("ipc/%s" % (button))
except:
pass
def _build_button_control_message(self):
controlMessage = {}
controlMessage['_operation'] = 'button'
controlMessage['_checksum'] = " "
controlMessage['_button'] = {}
for button in ['swHlt', 'swFerm', 'swSparge', 'swMash', 'swBoil', 'swPump']:
button_state = self._check_a_single_button(button)
if button_state:
self._set_ipc_flag(button)
else:
self._remove_ipc_flag(button)
controlMessage['_button'][button] = button_state
return controlMessage
def broadcastButtonResult(self):
print "advertising our Button capabiltiies"
mcastHandler = pitmMcast()
while 1:
controlMessage = self._build_button_control_message()
mcastHandler.send_mcast_message(controlMessage, self.cfg.mcastButtonPort, 'button')
time.sleep(1)
if __name__ == '__main__':
buttonController = pitmButton()
broadcastResult = threading.Thread(target=buttonController.broadcastButtonResult)
broadcastResult.daemon = True
broadcastResult.start()
while 1:
time.sleep(5)
| 27.674419 | 95 | 0.62521 | 251 | 2,380 | 5.7251 | 0.462151 | 0.019485 | 0.016701 | 0.025052 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004115 | 0.285294 | 2,380 | 85 | 96 | 28 | 0.840682 | 0.006723 | 0 | 0.037037 | 0 | 0 | 0.097345 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.018519 | 0.148148 | null | null | 0.018519 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8a8be7176c225f7e17fbe31e29722d7b21628c6 | 1,167 | py | Python | muddery/mappings/event_action_set.py | noahzaozao/muddery | 294da6fb73cb04c62e5ba6eefe49b595ca76832a | [
"BSD-3-Clause"
] | null | null | null | muddery/mappings/event_action_set.py | noahzaozao/muddery | 294da6fb73cb04c62e5ba6eefe49b595ca76832a | [
"BSD-3-Clause"
] | null | null | null | muddery/mappings/event_action_set.py | noahzaozao/muddery | 294da6fb73cb04c62e5ba6eefe49b595ca76832a | [
"BSD-3-Clause"
] | null | null | null | """
All available event actions.
"""
from __future__ import print_function
from django.conf import settings
from evennia.utils import logger
from muddery.utils.exception import MudderyError
from muddery.utils.utils import classes_in_path
from muddery.events.base_event_action import BaseEventAction
class EventActionSet(object):
"""
All available event triggers.
"""
def __init__(self):
self.dict = {}
self.load()
def load(self):
"""
Add all event actions from the path.
"""
# load classes
for cls in classes_in_path(settings.PATH_EVENT_ACTION_BASE, BaseEventAction):
key = cls.key
if self.dict.has_key(key):
logger.log_infomsg("Event action %s is replaced by %s." % (key, cls))
self.dict[key] = cls()
def get(self, key):
"""
Get the function of the event action.
"""
action = self.dict.get(key, None)
if action:
return action.func
def all(self):
"""
Get all event types.
"""
return self.dict.keys()
EVENT_ACTION_SET = EventActionSet()
| 22.882353 | 85 | 0.608398 | 141 | 1,167 | 4.879433 | 0.390071 | 0.079942 | 0.049419 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.29563 | 1,167 | 50 | 86 | 23.34 | 0.836983 | 0.143959 | 0 | 0 | 0 | 0 | 0.037486 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.173913 | false | 0 | 0.26087 | 0 | 0.565217 | 0.043478 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
d8a9231416df4329b68705f67027a39e5c572589 | 3,401 | py | Python | bcml4pheno/ttbarzp.py | sheride/bcml4pheno | c9629dafcdbee0a4c28ceb7b28c9862de8479a24 | [
"Apache-2.0"
] | null | null | null | bcml4pheno/ttbarzp.py | sheride/bcml4pheno | c9629dafcdbee0a4c28ceb7b28c9862de8479a24 | [
"Apache-2.0"
] | null | null | null | bcml4pheno/ttbarzp.py | sheride/bcml4pheno | c9629dafcdbee0a4c28ceb7b28c9862de8479a24 | [
"Apache-2.0"
] | null | null | null | # AUTOGENERATED! DO NOT EDIT! File to edit: ttbarzp.ipynb (unless otherwise specified).
__all__ = ['get_elijah_ttbarzp_cs', 'get_manuel_ttbarzp_cs', 'import47Ddata', 'get47Dfeatures']
# Cell
import numpy as np
import tensorflow as tf
# Cell
def get_elijah_ttbarzp_cs():
r"""
Contains cross section information produced by Elijah for $pp \to t\overline{t} \; Z'$ collider phenomenology.
Returns list containing signal masses, signal cross sections (for those masses, in pb), and background cross sections
(also in pb)
"""
# Z' masses (GeV) for which Elijah created signal samples
elijah_masses = [10, 50, 100, 200, 350, 500, 1000, 2000, 5000]
# signal cross sections (pb)
elijah_sig_css = [9.801, 0.5445, 0.1442, 0.03622, 0.009998, 0.003802, 0.0003936, 2.034e-05, 2.748e-08]
# background cross sections (pb)
elijah_bg_css = [0.106, 0.0117, 5.58]
return [elijah_masses, elijah_sig_css, elijah_bg_css]
# Cell
def get_manuel_ttbarzp_cs():
r"""
Contains cross section information produced through MadGraph by Manuel for collider phenomenology regarding
the semihadronic, semileptonic $pp \to t\overline{t} \; Z', Z' \to b\overline{b}$ channel
"""
# Z' masses (GeV) for which I (Elijah) created signal samples
manuel_masses = [350, 500, 750, 1000, 2000, 3000, 4000]
# signal cross sections (pb)
manuel_sig_css = [0.001395, 0.0007823, 0.0003429, 0.0001692, 1.808e-05, 1.325e-06, 4.456e-07]
# background cross sections (pb)
manuel_bg_css = [0.1339, 0.01187, 5.603]
return [manuel_masses, manuel_sig_css, manuel_bg_css]
# Cell
def import47Ddata(name):
r"""
Imports `name.npy` file containing 47-dimensional data for training
Available files:
- bgh.npy (Standard Model background 1, $pp \to t\overline{t}h$)
- bg4t.npy (Standard Model background 2, $pp \to t\overline{t}t\overline{t}$)
- bgnoh.npy (Standard Model background 3, $pp \to t\overline{t} \; \setminus \; h$)
- sig350G.npy ($Z'$ signal, $m_{Z'} = 350$ GeV)
- sig500G.npy ($Z'$ signal, $m_{Z'} = 500$ GeV)
- sig1T.npy ($Z'$ signal, $m_{Z'} = 1$ TeV)
- sig2T.npy ($Z'$ signal, $m_{Z'} = 2$ TeV)
- sig4T.npy ($Z'$ signal, $m_{Z'} = 4$ TeV)
"""
if name[-4:] == '.npy':
name = name[:-4]
url = 'https://storage.googleapis.com/ttbarzp/47dim/'
try:
path = tf.keras.utils.get_file(f'{name}.npy', url + name + '.npy')
data = np.load(path)
return data
except:
print(f"{name}.npy doesn't appear to exist")
# Cell
def get47Dfeatures():
"""
Returns list containing the names of the 47 features found in the data accessible through
`ttbarzp.import47Ddata()`
"""
return [
'pT b1', 'pT b2', 'pT b3', 'pT b4',
'sdEta b1 b2', 'sdEta b1 b3', 'sdEta b1 b4', 'sdEta b2 b3', 'sdEta b2 b4', 'sdEta b3 b4',
'sdPhi b1 b2', 'sdPhi b1 b3', 'sdPhi b1 b4', 'sdPhi b2 b3', 'sdPhi b2 b4', 'sdPhi b3 b4',
'dR b1 b2', 'dR b1 b3', 'dR b1 b4', 'dR b2 b3', 'dR b2 b4', 'dR b3 b4',
'MET', 'pT l', 'MT l MET',
'M b1 b2', 'M b1 b3', 'M b1 b4', 'M b2 b3', 'M b2 b4', 'M b3 b4',
'MT b1 l MET', 'MT b2 l MET', 'MT b3 l MET', 'MT b4 l MET',
'M j1 j2', 'pT j1', 'pT j2', 'dR j1 j2',
'dR b1 l', 'dR b2 l', 'dR b3 l', 'dR b4 l',
'sdPhi b1 l', 'sdPhi b2 l', 'sdPhi b3 l', 'sdPhi b4 l'] | 41.987654 | 121 | 0.614525 | 540 | 3,401 | 3.792593 | 0.32037 | 0.026367 | 0.029297 | 0.031738 | 0.129883 | 0.0625 | 0.047852 | 0.047852 | 0 | 0 | 0 | 0.112132 | 0.231697 | 3,401 | 81 | 122 | 41.987654 | 0.671642 | 0.426051 | 0 | 0 | 1 | 0 | 0.306178 | 0.022963 | 0 | 0 | 0 | 0 | 0 | 1 | 0.105263 | false | 0 | 0.105263 | 0 | 0.315789 | 0.026316 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8ae73d38d5c3ddf758f8b307b86f0cea7f183a9 | 444 | py | Python | aiohttp_admin2/controllers/types.py | Arfey/aiohttp_admin2 | 2b3782389ec9e25809635811b76ef8111b27d8ba | [
"MIT"
] | 12 | 2021-10-15T11:48:12.000Z | 2022-03-24T07:31:43.000Z | aiohttp_admin2/controllers/types.py | Arfey/aiohttp_admin2 | 2b3782389ec9e25809635811b76ef8111b27d8ba | [
"MIT"
] | 2 | 2021-12-29T16:31:05.000Z | 2021-12-30T00:50:40.000Z | aiohttp_admin2/controllers/types.py | Arfey/aiohttp_admin2 | 2b3782389ec9e25809635811b76ef8111b27d8ba | [
"MIT"
] | null | null | null | import typing as t
__all__ = ["Cell", "ListObject", ]
class Cell(t.NamedTuple):
"""Field data representation for html template"""
value: t.Any
url: t.Tuple[str, t.Dict[str, t.Union[str, int]]]
is_safe: bool = False
class ListObject(t.NamedTuple):
rows: t.List[t.List[Cell]]
has_next: bool
has_prev: bool
count: t.Optional[int]
active_page: t.Optional[int]
per_page: int
next_id: t.Optional[int]
| 21.142857 | 53 | 0.650901 | 68 | 444 | 4.102941 | 0.558824 | 0.096774 | 0.129032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.209459 | 444 | 20 | 54 | 22.2 | 0.794872 | 0.096847 | 0 | 0 | 0 | 0 | 0.035443 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.071429 | 0 | 0.928571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
d8b1ac767220107dcae2af420dd45336ec49a8d3 | 9,548 | py | Python | sppas/sppas/src/ui/phoenix/page_files/refstreectrl.py | mirfan899/MTTS | 3167b65f576abcc27a8767d24c274a04712bd948 | [
"MIT"
] | null | null | null | sppas/sppas/src/ui/phoenix/page_files/refstreectrl.py | mirfan899/MTTS | 3167b65f576abcc27a8767d24c274a04712bd948 | [
"MIT"
] | null | null | null | sppas/sppas/src/ui/phoenix/page_files/refstreectrl.py | mirfan899/MTTS | 3167b65f576abcc27a8767d24c274a04712bd948 | [
"MIT"
] | null | null | null | # -*- coding: UTF-8 -*-
"""
..
---------------------------------------------------------------------
___ __ __ __ ___
/ | \ | \ | \ / the automatic
\__ |__/ |__/ |___| \__ annotation and
\ | | | | \ analysis
___/ | | | | ___/ of speech
http://www.sppas.org/
Use of this software is governed by the GNU Public License, version 3.
SPPAS 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.
SPPAS 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 SPPAS. If not, see <http://www.gnu.org/licenses/>.
This banner notice must not be removed.
---------------------------------------------------------------------
src.ui.phoenix..page_files.refstreectrl.py
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
import logging
import wx
import wx.dataview
from .basectrls import BaseTreeViewCtrl
from .refsviewmodel import ReferencesTreeViewModel
# ----------------------------------------------------------------------------
# Control to store the data matching the model
# ----------------------------------------------------------------------------
class ReferencesTreeViewCtrl(BaseTreeViewCtrl):
"""A control to display references in a tree-spreadsheet style.
:author: Brigitte Bigi
:organization: Laboratoire Parole et Langage, Aix-en-Provence, France
:contact: contact@sppas.org
:license: GPL, v3
:copyright: Copyright (C) 2011-2019 Brigitte Bigi
Columns of this class are defined by the model and created by the
constructor. No parent nor children will have the possibility to
Append/Insert/Prepend/Delete columns: such methods are disabled.
"""
def __init__(self, parent, name=wx.PanelNameStr):
"""Constructor of the ReferencesTreeViewCtrl.
:param parent: (wx.Window)
"""
super(ReferencesTreeViewCtrl, self).__init__(parent, name)
# Create an instance of our model and associate to the view.
self._model = ReferencesTreeViewModel()
self.AssociateModel(self._model)
self._model.DecRef()
# Create the columns that the model wants in the view.
for i in range(self._model.GetColumnCount()):
col = BaseTreeViewCtrl._create_column(self._model, i)
if i == self._model.GetExpanderColumn():
self.SetExpanderColumn(col)
wx.dataview.DataViewCtrl.AppendColumn(self, col)
# Bind events.
# Used to remember the expend/collapse status of items after a refresh.
self.Bind(wx.dataview.EVT_DATAVIEW_ITEM_EXPANDED, self._on_item_expanded)
self.Bind(wx.dataview.EVT_DATAVIEW_ITEM_COLLAPSED, self._on_item_collapsed)
self.Bind(wx.dataview.EVT_DATAVIEW_ITEM_ACTIVATED, self._on_item_activated)
self.Bind(wx.dataview.EVT_DATAVIEW_SELECTION_CHANGED, self._on_item_selection_changed)
self.Bind(wx.dataview.EVT_DATAVIEW_ITEM_EDITING_DONE, self._on_item_edited)
# ------------------------------------------------------------------------
# Public methods
# ------------------------------------------------------------------------
def get_data(self):
"""Return the data of the model."""
return self._model.get_data()
# ------------------------------------------------------------------------
def set_data(self, data):
self._model.set_data(data)
self.__refresh()
# ------------------------------------------------------------------------
def GetCheckedRefs(self):
"""Return checked references."""
return self._model.get_checked_refs()
# ------------------------------------------------------------------------
def HasCheckedRefs(self):
"""Return True if at least one reference is checked."""
return self._model.has_checked_refs()
# ------------------------------------------------------------------------
def CreateRef(self, ref_name, ref_type):
"""Create a new reference and add it into the tree.
:param ref_name: (str)
:param ref_type: (str) On of the accepted type of references
:raise: Exception
"""
item = self._model.create_ref(ref_name, ref_type)
if item is None:
raise Exception("An unexpected error occurred.")
logging.info('Reference created successfully: {:s}, {:d}'
''.format(ref_name, ref_type))
# ------------------------------------------------------------------------
def AddRefs(self, entries):
"""Add a list of references into the model.
:param entries: (str) List of references.
"""
nb = self._model.add_refs(entries)
if nb > 0:
logging.debug('Added {:d} references in the data.'.format(len(entries)))
self.__refresh()
return nb
# ------------------------------------------------------------------------
def RemoveCheckedRefs(self):
"""Remove all checked references."""
nb = self._model.remove_checked_refs()
if nb > 0:
logging.info('Removed {:d} references.'.format(nb))
self.__refresh()
return nb
# ------------------------------------------------------------------------
def RemoveAttribute(self, identifier):
"""Remove an attribute from the checked references.
:param identifier: (str)
:returns: Number of references in which the attribute were removed.
"""
nb = self._model.remove_attribute(identifier)
logging.info('Identifier {:s} removed of {:d} references.'
''.format(identifier, nb))
if nb > 0:
self.__refresh()
return nb
# ------------------------------------------------------------------------
def EditAttribute(self, identifier, value, att_type, description):
"""Add or modify an attribute into the checked references.
:param identifier: (str)
:param value: (str)
:param att_type: (str)
:param description: (str)
:returns: Number of references in which the attribute were added.
"""
nb = self._model.edit_attribute(identifier, value, att_type, description)
logging.info('Identifier {:s} added into {:d} references.'
''.format(identifier, nb))
if nb > 0:
self.__refresh()
return nb
# ------------------------------------------------------------------------
def update_data(self):
"""Overridden. Update the currently displayed data."""
self._model.update()
self.__refresh()
# ------------------------------------------------------------------------
# Callbacks to events
# ------------------------------------------------------------------------
def _on_item_expanded(self, evt):
"""Happens when the user checked the 1st column of the tree.
We have to update the corresponding object 'expand' value to True.
"""
self._model.expand(True, evt.GetItem())
# ------------------------------------------------------------------------
def _on_item_collapsed(self, evt):
"""Happens when the user checked the 1st column of the tree.
We have to update the corresponding object 'expand' value to False.
"""
self._model.expand(False, evt.GetItem())
# ------------------------------------------------------------------------
def _on_item_activated(self, event):
"""Happens when the user activated a cell (double-click).
This event is triggered by double clicking an item or pressing some
special key (usually "Enter") when it is focused.
"""
self._model.change_value(event.GetItem())
# ------------------------------------------------------------------------
def _on_item_selection_changed(self, event):
"""Happens when the user simple-click a cell.
"""
self._model.change_value(event.GetItem())
# ------------------------------------------------------------------------
def _on_item_edited(self, event):
"""Happens when the user modified the content of an editable cell.
Notice that on MacOS, the event.GetValue() method returns None, so
that the value can not be changed in that way. Use SetValue() of
the model instead.
"""
if wx.Platform != "__WXMAC__":
self._model.change_value(event.GetItem(),
event.GetColumn(),
event.GetValue())
# ------------------------------------------------------------------------
def __refresh(self):
for item in self._model.get_expanded_items(True):
self.Expand(item)
for item in self._model.get_expanded_items(False):
self.Collapse(item)
| 36.723077 | 94 | 0.506912 | 934 | 9,548 | 5.008565 | 0.307281 | 0.04425 | 0.010688 | 0.019239 | 0.252886 | 0.207567 | 0.149637 | 0.121419 | 0.106883 | 0.089354 | 0 | 0.002506 | 0.247801 | 9,548 | 259 | 95 | 36.864865 | 0.648844 | 0.529849 | 0 | 0.216867 | 0 | 0 | 0.055986 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.204819 | false | 0 | 0.060241 | 0 | 0.361446 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8bd9a8ceac50742b31415693499379e4a5d5377 | 8,540 | py | Python | RecoBTag/PerformanceDB/python/measure/Btag_btagTtbarWp0612.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 852 | 2015-01-11T21:03:51.000Z | 2022-03-25T21:14:00.000Z | RecoBTag/PerformanceDB/python/measure/Btag_btagTtbarWp0612.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 30,371 | 2015-01-02T00:14:40.000Z | 2022-03-31T23:26:05.000Z | RecoBTag/PerformanceDB/python/measure/Btag_btagTtbarWp0612.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 3,240 | 2015-01-02T05:53:18.000Z | 2022-03-31T17:24:21.000Z | import FWCore.ParameterSet.Config as cms
BtagPerformanceESProducer_TTBARWPBTAGCSVL = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGCSVL'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGCSVLtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGCSVLwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGCSVM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGCSVM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGCSVMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGCSVMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGCSVT = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGCSVT'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGCSVTtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGCSVTwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJPL = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJPL'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJPLtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJPLwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJPM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJPM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJPMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJPMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJPT = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJPT'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJPTtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJPTwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJBPL = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJBPL'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJBPLtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJBPLwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJBPM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJBPM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJBPMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJBPMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJBPT = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJBPT'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJBPTtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJBPTwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJBPL = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJBPL'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJBPLtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJBPLwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJBPM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJBPM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJBPMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJBPMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGJBPT = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGJBPT'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGJBPTtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGJBPTwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGSSVHEM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGSSVHEM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGSSVHEMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGSSVHEMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGSSVHET = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGSSVHET'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGSSVHETtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGSSVHETwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGSSVHPT = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGSSVHPT'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGSSVHPTtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGSSVHPTwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGTCHEL = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGTCHEL'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGTCHELtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGTCHELwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGTCHEM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGTCHEM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGTCHEMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGTCHEMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGTCHET = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGTCHET'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGTCHETtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGTCHETwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGTCHPL = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGTCHPL'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGTCHPLtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGTCHPLwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGTCHPM = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGTCHPM'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGTCHPMtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGTCHPMwp_v8_offline')
)
BtagPerformanceESProducer_TTBARWPBTAGTCHPT = cms.ESProducer("BtagPerformanceESProducer",
# this is what it makes available
ComponentName = cms.string('TTBARWPBTAGTCHPT'),
# this is where it gets the payload from
PayloadName = cms.string('BTagTTBARWPBTAGTCHPTtable_v8_offline'),
WorkingPointName = cms.string('BTagTTBARWPBTAGTCHPTwp_v8_offline')
)
| 57.315436 | 89 | 0.709133 | 741 | 8,540 | 8.031039 | 0.106613 | 0.095278 | 0.134095 | 0.14821 | 0.719039 | 0.633339 | 0.633339 | 0.633339 | 0.633339 | 0.633339 | 0 | 0.0063 | 0.219321 | 8,540 | 148 | 90 | 57.702703 | 0.886306 | 0.286885 | 0 | 0.226415 | 0 | 0 | 0.379063 | 0.325054 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.009434 | 0 | 0.009434 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8c3cd712d592f39d12428d51a6c41a7fc38afb8 | 9,316 | py | Python | qurkexp/join/pair-results.py | marcua/qurk_experiments | 453c207ff50e730aefb6e1118e0f93e33babdb0b | [
"BSD-3-Clause"
] | 1 | 2015-09-30T00:09:06.000Z | 2015-09-30T00:09:06.000Z | qurkexp/join/pair-results.py | marcua/qurk_experiments | 453c207ff50e730aefb6e1118e0f93e33babdb0b | [
"BSD-3-Clause"
] | null | null | null | qurkexp/join/pair-results.py | marcua/qurk_experiments | 453c207ff50e730aefb6e1118e0f93e33babdb0b | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
import sys, os
ROOT = os.path.abspath('%s/../..' % os.path.abspath(os.path.dirname(__file__)))
sys.path.append(ROOT)
os.environ['DJANGO_SETTINGS_MODULE'] = 'qurkexp.settings'
from django.core.management import setup_environ
from django.conf import settings
from qurkexp.join.models import *
from qurkexp.join.gal import getbtjoindata, getjoindata, run_gal
from qurkexp.hitlayer.models import HitLayer
from scipy import stats
#batch = (sys.argv[1] == "batch")
#num_to_compare = int(sys.argv[2])
#run_name = "joinpairs-actual-4" # match 6x6, 5 assignments each, 1 cent
#run_name = "joinpairs-30-2" # match 30x30, 5 assignments each, 1 cent
#run_name = "joinpairs-30-5" # match 30x30, 5 assignments each, 1 cent
#run_name = "joinpairs-20-2" # match 20x20, 5 assignments each, 1 cent
#run_name = "joinpairs-20-4" # match 20x20, 5 assignments each, 1 cent
#run_name = "joinpairs-15-1" # match 15x15, 5 assignments each, 1 cent
run_groups = [
# [False, "joinpairs-30-5",],
# [False, "joinpairs-30-2",],
[False, "joinpairs-30-2", "joinpairs-30-5",],
# [False, "joinpairs-20-2",],
# [False, "joinpairs-20-4",],
# [False, "joinpairs-20-2", "joinpairs-20-4",],
# [False, "joinpairs-15-1",],
# [True, "30-10-naive-ordered-1",], # match 30x30, batch size 10, 5 assignments each, 1 cent
# [True, "30-10-naive-ordered-20",], # match 30x30, batch size 10, 5 assignments each, 1 cent
# [True, "30-10-naive-ordered-1", "30-10-naive-ordered-20",],
# [True, "30-5-naive-ordered-1",], # match 30x30, batch size 5, 5 assignments each, 1 cent
# [True, "30-5-naive-ordered-20",], # match 30x30, batch size 5, 5 assignments each, 1 cent
# [True, "30-5-naive-ordered-1", "30-5-naive-ordered-20",],
# [True, "30-3-naive-ordered-1",], # match 30x30, batch size 3, 5 assignments each, 1 cent
# [True, "30-3-naive-ordered-20",], # match 30x30, batch size 3, 5 assignments each, 1 cent
# [True, "30-3-naive-ordered-1", "30-3-naive-ordered-20",],
# [True, "20-1-naive-ordered-3",], # match 20x20, batch size 3, 5 assignments each, 1 cent
# [True, "20-1-naive-ordered-4",], # match 20x20, batch size 3, 5 assignments each, 1 cent
# [True, "20-1-naive-ordered-3", "20-1-naive-ordered-4",],
# [True, "20-1-naive-ordered-1-ACTUALLYSMART",], # match 20x20, batch size 1, 5 assignments each, 1 cent
# [True, "20-1-naive-ordered-2-ACTUALLYSMART",], # match 20x20, batch size 1, 5 assignments each, 1 cent
# [True, "20-1-naive-ordered-1-ACTUALLYSMART", "20-1-naive-ordered-2-ACTUALLYSMART",],
# [True, "8-2-smart-ordered-1",], # match 8x8, batch size 2, 5 assignments each, 1 cent (bad join interface taint?)
# [True, "30-5-smart-ordered-1",], # match 30x30, batch size 5, 5 assignments each, 1 cent (bad join interface taint?)
# "30-2-smart-ordered-1", # match 30x30, batch size 2, 5 assignments each, 1 cent (bad join interface taint?)
# "20-1-smart-ordered-1", # match 20x20, batch size 1, 5 assignments each, 1 cent (bad join interface taint?)
# [True, "30-3-smart-ordered-1",], # match 30x30, batch size 3, 5 assignments each, 1 cent (fixed UI taint for IE8)
# [True, "30-3-smart-ordered-2",], # match 30x30, batch size 3, 5 assignments each, 1 cent (fixed UI taint for IE8)
# [True, "30-3-smart-ordered-1", "30-3-smart-ordered-2"],
# [True, "20-1-smart-ordered-3",], # match 20x20, batch size 1, 5 assignments each, 1 cent (fixed UI taint for IE8)
# [True, "30-2-smart-ordered-2",], # match 30x30, batch size 2, 5 assignments each, 1 cent (fixed UI taint for IE8)
# [True, "30-2-smart-ordered-3",], # match 30x30, batch size 2, 5 assignments each, 1 cent (fixed UI taint for IE8)
# [True, "30-2-smart-ordered-2", "30-2-smart-ordered-3"],
]
def update_matches(ismatch, foundtrue, fn, fp, tn, tp):
if ismatch and foundtrue:
tp += 1
elif ismatch and not foundtrue:
fn += 1
# fn_group.append("%d_%d %d %d" % (left, right, true_count, false_count))
elif not ismatch and foundtrue:
fp += 1
# fp_group.append("%d_%d %d %d" % (left, right, true_count, false_count))
elif not ismatch and not foundtrue:
tn += 1
return (fn, fp, tn, tp)
def main(batch, run_names):
if batch:
pairs = BPPair.objects.filter(bpbatch__experiment__run_name__in = run_names)
else:
pairs = Pair.objects.filter(run_name__in = run_names)
# if num_to_compare > 0:
# pairs = pairs.filter(left__lte = num_to_compare).filter(right__lte = num_to_compare)
print "num pairs", pairs.count()
#print "Turker histogram"
turkers = {}
for pair in pairs:
if batch:
resps = pair.bprespans_set.all()
else:
resps = pair.pairresp_set.all()
for resp in resps:
if batch:
wid = resp.bprm.wid
else:
wid = resp.wid
if wid not in turkers:
turkers[wid] = [0.0,0.0,[],[]]
# weight = num_to_compare if (pair.left == pair.right) else 1
# weight = 30 if (pair.left == pair.right) else 1
turkers[wid][0] += 1#weight#1
turkers[wid][1] += (1 if ((pair.left == pair.right) == resp.same) else 0)#*weight
turkers[wid][2].append(resp.same)
if batch:
worker_resps = BPRespAns.objects.filter(bprm__wid__in = turkers.keys()).filter(bprm__batch__experiment__run_name__in = run_names).order_by('bprm__submit_time')
else:
worker_resps = PairResp.objects.filter(wid__in = turkers.keys()).filter(pair__run_name__in = run_names).order_by('submit_time')
for resp in worker_resps:
if batch:
arr = turkers[resp.bprm.wid][3]
else:
arr = turkers[resp.wid][3]
actualres = resp.pair.left == resp.pair.right
if actualres:
if resp.same == actualres:
arr.append('a')
else:
arr.append('_')
else:
if resp.same == actualres:
arr.append('b')
else:
arr.append('-')
lturkers = list(turkers.items())
lturkers.sort(lambda x,y: x[1][0] < y[1][0] and -1 or 1)
for k,v in lturkers:
#print k,v[0],v[1]/v[0], "".join(v[3])
pass#print '%5f, %d, %s' % ((v[1]/v[0]), len(v[3]), "".join(v[3]))
xs = [v[1]/v[0] for k,v in lturkers]
print "len xs", len(xs)
ys = [len(v[3]) for k,v in lturkers]
print "len ys", len(ys)
(s, i, r, p, std) = stats.linregress(ys,xs)
print "regression---slope %f, intercept %f, R^2 %f, p %f" % (s, i, r*r, p)
#print "Accuracy printout"
pair_counts = {}
for pair in pairs:
counts = pair_counts.get((pair.left, pair.right), {})
if batch:
resps = pair.bprespans_set.all()
else:
resps = pair.pairresp_set.all()
for resp in resps:
if batch:
wid = resp.bprm.wid
else:
wid = resp.wid
# if turkers[wid][1] / turkers[wid][0] < 0.6:
# continue
if resp.same:
counts["true_count"] = counts.get("true_count", 0) + 1
else:
counts["false_count"] = counts.get("false_count", 0) + 1
pair_counts[(pair.left, pair.right)] = counts
if batch:
data = getbtjoindata(run_names)
exptype = "btjoin"
else:
data = getjoindata(run_names)
exptype = "join"
gal_w, gal_res = run_gal(exptype, data)
gal_res = dict(gal_res)
# fp_group = []
# fn_group = []
(fn_mv, fp_mv, tn_mv, tp_mv, fn_g, fp_g, tn_g, tp_g) = [0.0]*8
# tc_count = 0
for (left, right), counts in pair_counts.items():
(true_count, false_count) = (counts.get("true_count", 0), counts.get("false_count", 0))
foundtrue_mv = true_count > false_count
gal_dict = dict(gal_res["%d_%d" % (left, right)])
if gal_dict["True"] > .8:
foundtrue_g = True
else:
foundtrue_g = False
ismatch = (left == right)
# if ismatch:
# tc_count += counts.get("true_count", 0)
(fn_mv, fp_mv, tn_mv, tp_mv) = update_matches(ismatch, foundtrue_mv, fn_mv, fp_mv, tn_mv, tp_mv)
(fn_g, fp_g, tn_g, tp_g) = update_matches(ismatch, foundtrue_g, fn_g, fp_g, tn_g, tp_g)
# if not(ismatch and false_count == 0) and not(not ismatch and true_count == 0) and ismatch != foundtrue:
# print left, right, ismatch, true_count, false_count, foundtrue
# print "true pos, true neg, false pos, false neg"
print "%s\t%d\t%d\t%d\t%d\t%d\t%d\t%d\t%d" % ("+".join(run_names), fn_mv, fp_mv, tn_mv, tp_mv, fn_g, fp_g, tn_g, tp_g)
print "tc_count", tc_count
# fp_group.sort()
# fn_group.sort()
#print "fp_group", fp_group
#print "fn_group", fn_group
if __name__ == "__main__":
print "runs\tfn_mv\tfp_mv\ttn_mv\ttp_mv\tfn_g\tfp_g\ttn_g\ttp_g"
for runs in run_groups:
main(runs[0], runs[1:])
| 46.348259 | 167 | 0.584908 | 1,387 | 9,316 | 3.794521 | 0.129056 | 0.057002 | 0.076002 | 0.080752 | 0.545886 | 0.480144 | 0.424093 | 0.380581 | 0.375261 | 0.362721 | 0 | 0.061132 | 0.264277 | 9,316 | 200 | 168 | 46.58 | 0.706741 | 0.484006 | 0 | 0.29661 | 0 | 0.008475 | 0.077429 | 0.023759 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.008475 | 0.059322 | null | null | 0.059322 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8c54062ff8343ff9ddf5caf6e861fe863f75af6 | 8,057 | py | Python | plasmapy/utils/tests/test_checks.py | ludoro/PlasmaPy | 69712cb40b8b588400301edfd6925d41d2f13eac | [
"BSD-2-Clause-Patent",
"BSD-3-Clause"
] | 1 | 2020-04-28T23:04:41.000Z | 2020-04-28T23:04:41.000Z | plasmapy/utils/tests/test_checks.py | ludoro/PlasmaPy | 69712cb40b8b588400301edfd6925d41d2f13eac | [
"BSD-2-Clause-Patent",
"BSD-3-Clause"
] | null | null | null | plasmapy/utils/tests/test_checks.py | ludoro/PlasmaPy | 69712cb40b8b588400301edfd6925d41d2f13eac | [
"BSD-2-Clause-Patent",
"BSD-3-Clause"
] | null | null | null | """Tests for methods relating to quantities."""
import numpy as np
from astropy import units as u
import pytest
from ...constants import c
from ..checks import (
_check_quantity, _check_relativistic, check_relativistic,
check_quantity
)
# (value, units, error)
quantity_error_examples_default = [
# exceptions associated with the units keyword
(5*u.T, 5*u.T, TypeError),
(5*u.T, 5, TypeError),
(5*u.T, [u.T, 1], TypeError),
(5*u.T, [1, u.m], TypeError),
(u.T, u.J, TypeError),
(5.0, u.m, UserWarning),
(3*u.m/u.s, u.m, u.UnitConversionError),
(5j*u.K, u.K, ValueError),
]
# (value, units, can_be_negative, can_be_complex, can_be_inf, error)
quantity_error_examples_non_default = [
(-5*u.K, u.K, False, False, True, ValueError),
(np.inf*u.K, u.K, True, False, False, ValueError)
]
# (value, units)
quantity_valid_examples_default = [
# check basic functionality
(5*u.T, u.T),
(3*u.m/u.s, u.m/u.s),
(3*u.m/u.s, [u.m/u.s]),
(3*u.m/u.s**2, [u.m/u.s, u.m/(u.s**2)]),
(3*u.km/u.yr, u.m/u.s),
# check temperature in units of energy per particle (e.g., eV)
(5*u.eV, u.K),
(5*u.K, u.eV),
(5*u.keV, [u.m, u.K]),
# check keywords relating to numerical values
(np.inf*u.T, u.T)
]
# (value, units, can_be_negative, can_be_complex, can_be_inf)
quantity_valid_examples_non_default = [
(5j*u.m, u.m, True, True, True)
]
# Tests for _check_quantity
@pytest.mark.parametrize(
"value, units, can_be_negative, can_be_complex, can_be_inf, error",
quantity_error_examples_non_default)
def test__check_quantity_errors_non_default(
value, units, can_be_negative, can_be_complex, can_be_inf, error):
with pytest.raises(error):
_check_quantity(value, 'arg', 'funcname', units,
can_be_negative=can_be_negative,
can_be_complex=can_be_complex,
can_be_inf=can_be_inf)
@pytest.mark.parametrize(
"value, units, error", quantity_error_examples_default)
def test__check_quantity_errors_default(value, units, error):
with pytest.raises(error):
_check_quantity(value, 'arg', 'funcname', units)
@pytest.mark.parametrize(
"value, units, can_be_negative, can_be_complex, can_be_inf",
quantity_valid_examples_non_default)
def test__check_quantity_non_default(
value, units, can_be_negative, can_be_complex, can_be_inf):
_check_quantity(value, 'arg', 'funcname', units,
can_be_negative=can_be_negative,
can_be_complex=can_be_complex,
can_be_inf=can_be_inf)
@pytest.mark.parametrize("value, units", quantity_valid_examples_default)
def test__check_quantity_default(value, units):
_check_quantity(value, 'arg', 'funcname', units)
# Tests for check_quantity decorator
@pytest.mark.parametrize(
"value, units, error", quantity_error_examples_default)
def test_check_quantity_decorator_errors_default(value, units, error):
@check_quantity({
"x": {"units": units}
})
def func(x):
return x
with pytest.raises(error):
func(value)
@pytest.mark.parametrize(
"value, units, can_be_negative, can_be_complex, can_be_inf, error",
quantity_error_examples_non_default)
def test_check_quantity_decorator_errors_non_default(
value, units, can_be_negative, can_be_complex, can_be_inf, error):
@check_quantity({
"x": {"units": units, "can_be_negative": can_be_negative,
"can_be_complex": can_be_complex, "can_be_inf": can_be_inf}
})
def func(x):
return x
with pytest.raises(error):
func(value)
@pytest.mark.parametrize("value, units", quantity_valid_examples_default)
def test_check_quantity_decorator_default(value, units):
@check_quantity({
"x": {"units": units}
})
def func(x):
return x
func(value)
@pytest.mark.parametrize(
"value, units, can_be_negative, can_be_complex, can_be_inf",
quantity_valid_examples_non_default)
def test_check_quantity_decorator_non_default(
value, units, can_be_negative, can_be_complex, can_be_inf):
@check_quantity({
"x": {"units": units, "can_be_negative": can_be_negative,
"can_be_complex": can_be_complex, "can_be_inf": can_be_inf}
})
def func(x):
return x
func(value)
def test_check_quantity_decorator_missing_validated_params():
@check_quantity({
"x": {"units": u.m},
"y": {"units": u.s}
})
def func(x):
return x
with pytest.raises(TypeError) as e:
func(1*u.m)
assert "Call to func is missing validated params y" == str(e.value)
def test_check_quantity_decorator_two_args_default():
@check_quantity({
"x": {"units": u.m},
"y": {"units": u.s}
})
def func(x, y):
return x/y
func(1*u.m, 1*u.s)
def test_check_quantity_decorator_two_args_not_default():
@check_quantity({
"x": {"units": u.m, "can_be_negative": False},
"y": {"units": u.s}
})
def func(x, y):
return x/y
with pytest.raises(ValueError):
func(-1*u.m, 2*u.s)
def test_check_quantity_decorator_two_args_one_kwargs_default():
@check_quantity({
"x": {"units": u.m},
"y": {"units": u.s},
"z": {"units": u.eV}
})
def func(x, y, another, z=10*u.eV):
return x*y*z
func(1*u.m, 1*u.s, 10*u.T)
def test_check_quantity_decorator_two_args_one_kwargs_not_default():
@check_quantity({
"x": {"units": u.m},
"y": {"units": u.s, "can_be_negative": False},
"z": {"units": u.eV, "can_be_inf": False}
})
def func(x, y, z=10*u.eV):
return x*y*z
with pytest.raises(ValueError):
func(1*u.m, 1*u.s, z=np.inf*u.eV)
# (speed, betafrac)
non_relativistic_speed_examples = [
(0*u.m/u.s, 0.1),
(0.099999*c, 0.1),
(-0.09*c, 0.1),
(5*u.AA/u.Gyr, 0.1)
]
# (speed, betafrac, error)
relativisitc_error_examples = [
(0.11*c, 0.1, UserWarning),
(1.0*c, 0.1, UserWarning),
(1.1*c, 0.1, UserWarning),
(np.inf*u.cm/u.s, 0.1, UserWarning),
(-0.11*c, 0.1, UserWarning),
(-1.0*c, 0.1, UserWarning),
(-1.1*c, 0.1, UserWarning),
(-np.inf*u.cm/u.s, 0.1, UserWarning),
(2997924581*u.cm/u.s, 0.1, UserWarning),
(0.02*c, 0.01, UserWarning),
(u.m/u.s, 0.1, TypeError),
(51513.35, 0.1, TypeError),
(5*u.m, 0.1, u.UnitConversionError),
(np.nan*u.m/u.s, 0.1, ValueError)
]
# Tests for _check_relativistic
@pytest.mark.parametrize("speed, betafrac", non_relativistic_speed_examples)
def test__check_relativisitc_valid(speed, betafrac):
_check_relativistic(speed, 'f', betafrac=betafrac)
@pytest.mark.parametrize("speed, betafrac, error", relativisitc_error_examples)
def test__check_relativistic_errors(speed, betafrac, error):
with pytest.raises(error):
_check_relativistic(speed, 'f', betafrac=betafrac)
# Tests for check_relativistic decorator
@pytest.mark.parametrize("speed, betafrac", non_relativistic_speed_examples)
def test_check_relativistic_decorator(speed, betafrac):
@check_relativistic(betafrac=betafrac)
def speed_func():
return speed
speed_func()
@pytest.mark.parametrize(
"speed",
[item[0] for item in non_relativistic_speed_examples])
def test_check_relativistic_decorator_no_args(speed):
@check_relativistic
def speed_func():
return speed
speed_func()
@pytest.mark.parametrize(
"speed",
[item[0] for item in non_relativistic_speed_examples])
def test_check_relativistic_decorator_no_args_parentheses(speed):
@check_relativistic()
def speed_func():
return speed
speed_func()
@pytest.mark.parametrize("speed, betafrac, error", relativisitc_error_examples)
def test_check_relativistic_decorator_errors(speed, betafrac, error):
@check_relativistic(betafrac=betafrac)
def speed_func():
return speed
with pytest.raises(error):
speed_func()
| 26.767442 | 79 | 0.655827 | 1,183 | 8,057 | 4.19273 | 0.099746 | 0.05746 | 0.052419 | 0.058065 | 0.775 | 0.758871 | 0.691935 | 0.665121 | 0.623589 | 0.575202 | 0 | 0.019467 | 0.203053 | 8,057 | 300 | 80 | 26.856667 | 0.752998 | 0.068884 | 0 | 0.557143 | 0 | 0 | 0.092209 | 0 | 0 | 0 | 0 | 0 | 0.004762 | 1 | 0.152381 | false | 0 | 0.02381 | 0.061905 | 0.238095 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8d163fba6f3d79bbb87bf34a4ae8f44106e768f | 324 | py | Python | Python3/Tornado/apps/pg/PG_Deposit/test/test_request.py | youngqqcn/QBlockChainNotes | 85122049024dc5555705bf016312491a51966621 | [
"MIT"
] | 24 | 2018-11-01T03:36:43.000Z | 2022-03-28T08:20:30.000Z | Python3/Tornado/apps/pg/PG_Deposit/test/test_request.py | songning4/QBlockChainNotes | d65ede073f5a20f728f41cc6850409693820cdb1 | [
"MIT"
] | 57 | 2019-12-04T08:26:47.000Z | 2022-03-08T07:35:15.000Z | Python3/Tornado/apps/pg/PG_Deposit/test/test_request.py | youngqqcn/QBlockChainNotes | 85122049024dc5555705bf016312491a51966621 | [
"MIT"
] | 11 | 2019-01-04T08:41:57.000Z | 2022-03-16T03:51:36.000Z | #!coding:utf8
#author:yqq
#date:2020/8/14 0014 19:26
#description:
import requests
def main():
url = 'http://htdf2020-test01.orientwalt.cn:1317/block_detail/1009408'
r = requests.get(url=url)
r.encoding = 'utf8'
print(r.text)
pass
if __name__ == '__main__':
main() | 12.96 | 75 | 0.595679 | 42 | 324 | 4.380952 | 0.809524 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142259 | 0.262346 | 324 | 25 | 76 | 12.96 | 0.627615 | 0.182099 | 0 | 0 | 0 | 0 | 0.310924 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.111111 | false | 0.111111 | 0.111111 | 0 | 0.222222 | 0.111111 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
d8d4111b5c5d9efbe753fdccc4f3bae97104b8fb | 401 | py | Python | test_project/test_project/settings_pytest.py | mpasternak/django-reciprocity | 2bffe1ae6025675ae96bb9420e1f69cf48b414c6 | [
"MIT"
] | 1 | 2019-12-09T11:23:51.000Z | 2019-12-09T11:23:51.000Z | test_project/test_project/settings_pytest.py | mpasternak/django-reciprocity | 2bffe1ae6025675ae96bb9420e1f69cf48b414c6 | [
"MIT"
] | 7 | 2019-03-01T18:13:40.000Z | 2022-02-12T14:44:51.000Z | test_project/test_project/settings_pytest.py | mpasternak/django-reciprocity | 2bffe1ae6025675ae96bb9420e1f69cf48b414c6 | [
"MIT"
] | null | null | null | # Settings for testing with included docker-compose and pytest
from .settings import * # noqa
# Subscribe from remote selenium container to docker-compose nginx container
NGINX_PUSH_STREAM_PUB_HOST = "localhost"
NGINX_PUSH_STREAM_PUB_PORT = "9080"
# Subscribe from local TravisCI machine to docker-compose nginx container
NGINX_PUSH_STREAM_SUB_HOST = "webserver"
NGINX_PUSH_STREAM_SUB_PORT = "80"
| 33.416667 | 76 | 0.820449 | 57 | 401 | 5.491228 | 0.54386 | 0.115016 | 0.191693 | 0.127796 | 0.28115 | 0.28115 | 0.28115 | 0.28115 | 0 | 0 | 0 | 0.017143 | 0.127182 | 401 | 11 | 77 | 36.454545 | 0.877143 | 0.528678 | 0 | 0 | 0 | 0 | 0.130435 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.2 | 0 | 0.2 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8d857f5c61e30e7fbb6c701c98c28a407e889a3 | 2,184 | py | Python | UnitGenerServer.py | SachithS/UnitGener | 165912afd050e6bc20ac988291c3311e4d351c8f | [
"MIT"
] | null | null | null | UnitGenerServer.py | SachithS/UnitGener | 165912afd050e6bc20ac988291c3311e4d351c8f | [
"MIT"
] | null | null | null | UnitGenerServer.py | SachithS/UnitGener | 165912afd050e6bc20ac988291c3311e4d351c8f | [
"MIT"
] | null | null | null | """
UnitGenerCore.py - Server of the UnitGener
This file is responsible for creating the routes and the server of the UnitGener core module.
Will create all the needed routes with params and initiate the server.
@author Sachith Senarathne
@version 1.0
@maintainer Sachith Senarathne
@copyright Copyright 2017, The UnitGener Project
@license MIT
@version 1.0
@email sachith.senarathnes@gmail.com
@status Development
"""
from flask import Flask, Response
from flask import request
from pystruct.learners import FrankWolfeSSVM
from pystruct.models import GraphCRF
import urllib
from tokenizer import FunctionTokenizer as fT
from crfmodels import CRFPredictor as crf
from crfmodels import AssertionPredictor as ap
app = Flask(__name__)
tokenizer = fT.FunctionTokenizer()
crfpredictor = crf.CRFPredictor()
assert_pre = ap.AssertionPredictor()
model = GraphCRF(directed=True, inference_method="max-product")
ssvm = FrankWolfeSSVM(model=model, C=.1, max_iter=10)
@app.route('/status')
def unitgener_status():
print ssvm
return "Hello form UnitGener"
@app.route('/generate', methods=['POST'])
def get_unit_generated():
print request.data
_js_function = urllib.unquote_plus(urllib.unquote_plus(request.data))
print _js_function
# processed_function = process_function(_js_function)
line_f = _js_function.replace('/n', " ")
raw_tokens = tokenizer.init_processing_function(line_f)
tr_sets = crfpredictor.generate_type1_prediction(raw_tokens)
r_assert = ssvm.predict(tr_sets[0][0:1])
unit_test = assert_pre.unit_test_assembler(r_assert, raw_tokens, 2)
response = Response(str(unit_test))
response.headers["content-type"] = "text/plain"
return response
if __name__ == '__main__':
result = tokenizer.read_process_file()
train_sets = crfpredictor.generate_type1_prediction(result)
ssvm.fit(train_sets[0], train_sets[1])
result_assert = ssvm.predict(train_sets[0][0:1])
test = assert_pre.unit_test_assembler(result_assert, result, 2)
for f in test:
print f
print result_assert
app.run()
def process_function(_js_function):
pass
| 28.736842 | 96 | 0.742216 | 288 | 2,184 | 5.402778 | 0.440972 | 0.032134 | 0.014139 | 0.025707 | 0.088689 | 0.03856 | 0 | 0 | 0 | 0 | 0 | 0.012686 | 0.169872 | 2,184 | 75 | 97 | 29.12 | 0.84556 | 0.023352 | 0 | 0 | 0 | 0 | 0.050663 | 0 | 0 | 0 | 0 | 0 | 0.162791 | 0 | null | null | 0.023256 | 0.186047 | null | null | 0.116279 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8d8d2b359e3e55295ef066e6f3edd2c7fb3f2d5 | 288 | py | Python | setup.py | Califrais/lights | cb4e3a0cbe64606f071ed02c06a9fc5db2681c1d | [
"MIT"
] | 6 | 2021-01-15T14:23:33.000Z | 2022-02-01T12:25:24.000Z | setup.py | Califrais/lights | cb4e3a0cbe64606f071ed02c06a9fc5db2681c1d | [
"MIT"
] | 11 | 2020-12-18T13:16:34.000Z | 2021-11-02T08:27:02.000Z | setup.py | Califrais/lights | cb4e3a0cbe64606f071ed02c06a9fc5db2681c1d | [
"MIT"
] | 1 | 2021-08-12T23:07:07.000Z | 2021-08-12T23:07:07.000Z | from setuptools import setup
setup(
name='lights',
version='0.1',
author="Van-Tuan Nguyen",
description="ligths is a generalized joint model for high-dimensional multivariate longitudinal data and censored durations",
url="https://github.com/Califrais/lights",
) | 28.8 | 129 | 0.71875 | 36 | 288 | 5.75 | 0.944444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008403 | 0.173611 | 288 | 10 | 130 | 28.8 | 0.861345 | 0 | 0 | 0 | 0 | 0 | 0.584775 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.125 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8da595c175dc5992171e47a8fd68be6e3fb96a1 | 6,909 | py | Python | pycg/machinery/imports.py | WenJinfeng/PyCG | b45e8e04fe697d8301cf27222a8f37646d69f168 | [
"Apache-2.0"
] | 121 | 2020-12-16T20:31:37.000Z | 2022-03-21T20:32:43.000Z | pycg/machinery/imports.py | WenJinfeng/PyCG | b45e8e04fe697d8301cf27222a8f37646d69f168 | [
"Apache-2.0"
] | 24 | 2021-03-13T00:04:00.000Z | 2022-03-21T17:28:11.000Z | pycg/machinery/imports.py | WenJinfeng/PyCG | b45e8e04fe697d8301cf27222a8f37646d69f168 | [
"Apache-2.0"
] | 19 | 2021-03-23T10:58:47.000Z | 2022-03-24T19:46:50.000Z | #
# Copyright (c) 2020 Vitalis Salis.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
import sys
import ast
import os
import importlib
import copy
from pycg import utils
def get_custom_loader(ig_obj):
"""
Closure which returns a custom loader
that modifies an ImportManager object
"""
class CustomLoader(importlib.abc.SourceLoader):
def __init__(self, fullname, path):
self.fullname = fullname
self.path = path
ig_obj.create_edge(self.fullname)
if not ig_obj.get_node(self.fullname):
ig_obj.create_node(self.fullname)
ig_obj.set_filepath(self.fullname, self.path)
def get_filename(self, fullname):
return self.path
def get_data(self, filename):
return ""
return CustomLoader
class ImportManager(object):
def __init__(self):
self.import_graph = dict()
self.current_module = ""
self.input_file = ""
self.mod_dir = None
self.old_path_hooks = None
self.old_path = None
def set_pkg(self, input_pkg):
self.mod_dir = input_pkg
def get_mod_dir(self):
return self.mod_dir
def get_node(self, name):
if name in self.import_graph:
return self.import_graph[name]
def create_node(self, name):
if not name or not isinstance(name, str):
raise ImportManagerError("Invalid node name")
if self.get_node(name):
raise ImportManagerError("Can't create a node a second time")
self.import_graph[name] = {"filename": "", "imports": set()}
return self.import_graph[name]
def create_edge(self, dest):
if not dest or not isinstance(dest, str):
raise ImportManagerError("Invalid node name")
node = self.get_node(self._get_module_path())
if not node:
raise ImportManagerError("Can't add edge to a non existing node")
node["imports"].add(dest)
def _clear_caches(self):
importlib.invalidate_caches()
sys.path_importer_cache.clear()
# TODO: maybe not do that since it empties the whole cache
for name in self.import_graph:
if name in sys.modules:
del sys.modules[name]
def _get_module_path(self):
return self.current_module
def set_current_mod(self, name, fname):
self.current_module = name
self.input_file = os.path.abspath(fname)
def get_filepath(self, modname):
if modname in self.import_graph:
return self.import_graph[modname]["filename"]
def set_filepath(self, node_name, filename):
if not filename or not isinstance(filename, str):
raise ImportManagerError("Invalid node name")
node = self.get_node(node_name)
if not node:
raise ImportManagerError("Node does not exist")
node["filename"] = os.path.abspath(filename)
def get_imports(self, modname):
if not modname in self.import_graph:
return []
return self.import_graph[modname]["imports"]
def _is_init_file(self):
return self.input_file.endswith("__init__.py")
def _handle_import_level(self, name, level):
# add a dot for each level
package = self._get_module_path().split(".")
if level > len(package):
raise ImportError("Attempting import beyond top level package")
mod_name = ("." * level) + name
# When an __init__ file is analyzed, then the module name doesn't contain
# the __init__ part in it, so special care must be taken for levels.
if self._is_init_file() and level >= 1:
if level != 1:
level -= 1
package = package[:-level]
else:
package = package[:-level]
return mod_name, ".".join(package)
def _do_import(self, mod_name, package):
if mod_name in sys.modules:
self.create_edge(mod_name)
return sys.modules[mod_name]
return importlib.import_module(mod_name, package=package)
def handle_import(self, name, level):
# We currently don't support builtin modules because they're frozen.
# Add an edge and continue.
# TODO: identify a way to include frozen modules
root = name.split(".")[0]
if root in sys.builtin_module_names:
self.create_edge(root)
return
# Import the module
try:
mod_name, package = self._handle_import_level(name, level)
except ImportError:
return
parent = ".".join(mod_name.split(".")[:-1])
parent_name = ".".join(name.split(".")[:-1])
combos = [(mod_name, package),
(parent, package),
(utils.join_ns(package, name), ""),
(utils.join_ns(package, parent_name), "")]
mod = None
for mn, pkg in combos:
try:
mod = self._do_import(mn, pkg)
break
except:
continue
if not mod:
return
if not hasattr(mod, "__file__") or not mod.__file__:
return
if self.mod_dir not in mod.__file__:
return
fname = mod.__file__
if fname.endswith("__init__.py"):
fname = os.path.split(fname)[0]
return utils.to_mod_name(
os.path.relpath(fname, self.mod_dir))
def get_import_graph(self):
return self.import_graph
def install_hooks(self):
loader = get_custom_loader(self)
self.old_path_hooks = copy.deepcopy(sys.path_hooks)
self.old_path = copy.deepcopy(sys.path)
loader_details = loader, importlib.machinery.all_suffixes()
sys.path_hooks.insert(0, importlib.machinery.FileFinder.path_hook(loader_details))
sys.path.insert(0, os.path.abspath(self.mod_dir))
self._clear_caches()
def remove_hooks(self):
sys.path_hooks = self.old_path_hooks
sys.path = self.old_path
self._clear_caches()
class ImportManagerError(Exception):
pass
| 31.262443 | 90 | 0.625561 | 887 | 6,909 | 4.678692 | 0.251409 | 0.031807 | 0.039759 | 0.025301 | 0.134217 | 0.086506 | 0.056627 | 0.045301 | 0.026988 | 0.026988 | 0 | 0.003439 | 0.284412 | 6,909 | 220 | 91 | 31.404545 | 0.835963 | 0.179766 | 0 | 0.126761 | 0 | 0 | 0.047153 | 0 | 0 | 0 | 0 | 0.004545 | 0 | 1 | 0.161972 | false | 0.007042 | 0.288732 | 0.042254 | 0.619718 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
d8e3d77e68c95fabda4facfab3e43eb49416a652 | 923 | py | Python | mit_d3m/db.py | micahjsmith/mit-d3m | a8138b2cc6329545bf3204f47cb39fe8faf7a44f | [
"MIT"
] | 6 | 2018-11-26T09:48:49.000Z | 2019-06-15T15:49:00.000Z | mit_d3m/db.py | micahjsmith/mit-d3m | a8138b2cc6329545bf3204f47cb39fe8faf7a44f | [
"MIT"
] | 12 | 2019-01-21T19:07:33.000Z | 2020-05-24T19:06:37.000Z | mit_d3m/db.py | micahjsmith/mit-d3m | a8138b2cc6329545bf3204f47cb39fe8faf7a44f | [
"MIT"
] | 6 | 2018-11-26T09:48:52.000Z | 2020-02-20T11:46:54.000Z | # -*- coding: utf-8 -*-
import getpass
import json
import logging
from pymongo import MongoClient
LOGGER = logging.getLogger(__name__)
def get_db(database=None, config=None, **kwargs):
if config:
with open(config, 'r') as f:
config = json.load(f)
else:
config = kwargs
host = config.get('host', 'localhost')
port = config.get('port', 27017)
user = config.get('user')
password = config.get('password')
database = database or config.get('database', 'test')
auth_database = config.get('auth_database', 'admin')
if user and not password:
password = getpass.getpass(prompt='Please insert database password: ')
client = MongoClient(
host=host,
port=port,
username=user,
password=password,
authSource=auth_database
)
LOGGER.info("Setting up a MongoClient %s", client)
return client[database]
| 23.075 | 78 | 0.633803 | 109 | 923 | 5.293578 | 0.477064 | 0.093588 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008621 | 0.245937 | 923 | 39 | 79 | 23.666667 | 0.820402 | 0.022752 | 0 | 0 | 0 | 0 | 0.133333 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.035714 | false | 0.178571 | 0.142857 | 0 | 0.214286 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
d8e3de05ad69c6353ea7726191b1fe77d3b073d2 | 53,019 | py | Python | src/StandAlone/inputs/MPM/Arenisca/Arenisca3/AreniscaTestSuite_PostProc.py | abagusetty/Uintah | fa1bf819664fa6f09c5a7cd076870a40816d35c9 | [
"MIT"
] | 3 | 2020-06-10T08:21:31.000Z | 2020-06-23T18:33:16.000Z | src/StandAlone/inputs/MPM/Arenisca/Arenisca3/AreniscaTestSuite_PostProc.py | abagusetty/Uintah | fa1bf819664fa6f09c5a7cd076870a40816d35c9 | [
"MIT"
] | null | null | null | src/StandAlone/inputs/MPM/Arenisca/Arenisca3/AreniscaTestSuite_PostProc.py | abagusetty/Uintah | fa1bf819664fa6f09c5a7cd076870a40816d35c9 | [
"MIT"
] | 2 | 2019-12-30T05:48:30.000Z | 2020-02-12T16:24:16.000Z | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
import math
import tempfile
import numpy as np
import subprocess as sub_proc
#Plotting stuff below
from matplotlib import rc
import matplotlib.pyplot as plt
from matplotlib import ticker
SHOW_ON_MAKE = False
#Useful constants
sqrtThree = np.sqrt(3.0)
twoThirds = 2.0/3.0
threeHalves = 3.0/2.0
#Set matplotlib defaults to desired values
#Set the legend to best fit
fontSize = 16
markers = None
plt.rcParams['legend.loc']='best'
#Set font size
plt.rcParams['mathtext.it'] = 'serif:bold'
plt.rcParams['mathtext.rm'] = 'serif:bold'
plt.rcParams['mathtext.sf'] = 'serif:bold'
plt.rcParams['font.size']=fontSize
plt.rcParams['font.weight']='bold'
plt.rcParams['axes.labelsize']='medium'
#plt.rcParams['axes.labelweight']='bold'
plt.rcParams['legend.fontsize']='medium'
#Set linewidth
lineWidth = 2
plt.rcParams['lines.linewidth']=lineWidth
#Set markersize
plt.rcParams['lines.markersize'] = 8
#Set padding for tick labels and size
plt.rcParams['xtick.major.pad'] = 12
plt.rcParams['ytick.major.pad'] = 8
plt.rcParams['xtick.major.size'] = 6
plt.rcParams['xtick.minor.size'] = 3
plt.rcParams['ytick.major.size'] = 6
plt.rcParams['ytick.minor.size'] = 3
#resolution
plt.rcParams['figure.dpi']=120
font = {'family' : 'serif',
'weight' : 'bold',
'size' : fontSize}
rc('font', **font)
rc('text', usetex=True)
def savePNG(name,size='1920x1080'):
res = float(plt.rcParams['figure.dpi'])
#Add Check for file already existing as name.png
if size == '640x480':
size = [640/res,480/res]
if size == '1080x768':
size = [1080/res,768/res]
if size == '1152x768':
size = [1152/res,768/res]
if size == '1280x854':
size = [1280/res,854/res]
if size == '1280x960':
size = [1280/res,960/res]
if size == '1920x1080':
size = [1920/res,1080/res]
#set the figure size for saving
plt.gcf().set_size_inches(size[0],size[1])
#save at speciified resolution
plt.savefig(name+'.png', bbox_inches=0, dpi=plt.rcParams['figure.dpi'])
def str_to_mathbf(string):
#Only works with single spaces no leading space
string = string.split()
return_string = ''
for elem in string:
elem = r'$\mathbf{'+elem+'}$'
return_string+=elem+' '
return return_string[0:-1]
def sign(x,y):
if y>=0:
return abs(x)
else:
return -abs(x)
def sigma_iso(sigma):
return (np.trace(sigma)/3.0)*np.eye(3)
def sigma_dev(sigma):
return sigma-sigma_iso(sigma)
def sigma_I1(sigma):
return sigma.trace()
def sigma_J2(sigma):
return 0.5*np.dot(sigma_dev(sigma),sigma_dev(sigma)).trace()
def sigma_J3(sigma):
return (1/3.0)*np.dot(np.dot(sigma_dev(sigma),sigma_dev(sigma)),sigma_dev(sigma)).trace()
def sigma_mag(sigma):
#Returns the magnitude of a second-rank tensor
#return np.linalg.norm(sigma)
return np.sqrt(DblDot(sigma,sigma))
def DblDot(x,y):#Returns the double inner product of two second-rank tensors
val=0
for i in range(0,3):
for j in range(0,3):
val=val+(x[i][j]*y[i][j])
return val
def sigma_tau(sigma):
#return sign(np.sqrt(sigma_J2(sigma)),sigma_J3(sigma))
return sign(np.sqrt(sigma_J2(sigma)),sigma_J3(sigma))
def get_ps_and_qs(sigmas):
ps = []
qs = []
for sigma in sigmas:
qs.append(sign(sqrtThree*np.sqrt(sigma_J2(sigma)),sigma_J3(sigma)))
ps.append(sigma_I1(sigma)/3.0)
return ps,qs
def get_pStress(uda_path):
NAN_FAIL = False
#Extract stress history
print "Extracting stress history..."
args = ["partextract","-partvar","p.stress",uda_path]
F_stress = tempfile.TemporaryFile()
#F_stress = open("./tempStressFileOut.txt","w+")
#open(os.path.split(uda_path)[0]+'/stressHistory.dat',"w+")
tmp = sub_proc.Popen(args,stdout=F_stress,stderr=sub_proc.PIPE)
dummy = tmp.wait()
print('Done.')
#Read file back in
F_stress.seek(0)
times = []
sigmas = []
for line in F_stress:
line = line.strip().split()
times.append(float(line[0]))
S11 = np.float64(line[4])
S12 = np.float64(line[5])
S13 = np.float64(line[6])
S21 = np.float64(line[7])
S22 = np.float64(line[8])
S23 = np.float64(line[9])
S31 = np.float64(line[10])
S32 = np.float64(line[11])
S33 = np.float64(line[12])
sigmas.append(np.array([[S11,S12,S13],[S21,S22,S23],[S31,S32,S33]]))
for i in range(3):
for j in range(3):
if np.isnan(sigmas[-1][i][j]):
NAN_FAIL = True
F_stress.close()
if NAN_FAIL:
print "\nERROR: 'nan's found reading in stress. Will not plot correctly"
return times,sigmas
def get_pDeformationMeasure(uda_path):
NAN_FAIL = False
#Extract stress history
print "Extracting deformation history..."
args = ["partextract","-partvar","p.deformationMeasure",uda_path]
F_defMes = tempfile.TemporaryFile()
#open(os.path.split(uda_path)[0]+'/stressHistory.dat',"w+")
tmp = sub_proc.Popen(args,stdout=F_defMes,stderr=sub_proc.PIPE)
dummy = tmp.wait()
print('Done.')
#Read file back in
F_defMes.seek(0)
times = []
Fs = []
for line in F_defMes:
line = line.strip().split()
times.append(float(line[0]))
F11 = np.float64(line[4])
F12 = np.float64(line[5])
F13 = np.float64(line[6])
F21 = np.float64(line[7])
F22 = np.float64(line[8])
F23 = np.float64(line[9])
F31 = np.float64(line[10])
F32 = np.float64(line[11])
F33 = np.float64(line[12])
Fs.append(np.array([[F11,F12,F13],[F21,F22,F23],[F31,F32,F33]]))
for i in range(3):
for j in range(3):
if np.isnan(Fs[-1][i][j]):
NAN_FAIL = True
F_defMes.close()
if NAN_FAIL:
print "\nERROR: 'nan's found reading in stress. Will not plot correctly"
return times,Fs
def get_epsilons(uda_path):
#Assumes no shear strains
times,Fs = get_pDeformationMeasure(uda_path)
epsils = []
for F in Fs:
epsils.append(np.array([[np.log(F[0][0]),0,0],[0,np.log(F[1][1]),0],[0,0,np.log(F[2][2])]]))
return times,epsils
def get_pKappa(uda_path):
#Extract stress history
print "Extracting kappa history..."
args = ["partextract","-partvar","p.kappa",uda_path]
F_kappa = tempfile.TemporaryFile()
#open(os.path.split(uda_path)[0]+'/kappaHistory.dat',"w+")
tmp = sub_proc.Popen(args,stdout=F_kappa,stderr=sub_proc.PIPE)
dummy = tmp.wait()
print('Done.')
#Read file back in
F_kappa.seek(0)
times = []
kappas = []
for line in F_kappa:
line = line.strip().split()
times.append(float(line[0]))
kappas.append(float(line[4]))
F_kappa.close()
return times,kappas
def get_pPlasticStrainVol(uda_path):
FAIL_NAN = False
#Extract stress history
print "Extracting plasticStrainVol history..."
args = ["partextract","-partvar","p.evp",uda_path]
F_plasticStrainVol = tempfile.TemporaryFile()
#open(os.path.split(uda_path)[0]+'/plasticStrainVolHistory.dat',"w+")
tmp = sub_proc.Popen(args,stdout=F_plasticStrainVol,stderr=sub_proc.PIPE)
dummy = tmp.wait()
print('Done.')
#Read file back in
F_plasticStrainVol.seek(0)
times = []
plasticStrainVol = []
for line in F_plasticStrainVol:
line = line.strip().split()
times.append(float(line[0]))
plasticStrainVol.append(np.float64(line[4]))
if np.isnan(plasticStrainVol[-1]):
FAIL_NAN = True
F_plasticStrainVol.close()
if FAIL_NAN:
print "\ERROR: 'nan' encountered while retrieving p.evp, will not plot correctly."
return times,plasticStrainVol
def get_pElasticStrainVol(uda_path):
FAIL_NAN = False
#Extract elastic strain history
print "Extracting elasticStrainVol history..."
args = ["partextract","-partvar","p.eve",uda_path]
F_elasticStrainVol = tempfile.TemporaryFile()
#open(os.path.split(uda_path)[0]+'/elasticStrainVolHistory.dat',"w+")
tmp = sub_proc.Popen(args,stdout=F_elasticStrainVol,stderr=sub_proc.PIPE)
dummy = tmp.wait()
print('Done.')
#Read file back in
F_elasticStrainVol.seek(0)
times = []
elasticStrainVol = []
for line in F_elasticStrainVol:
line = line.strip().split()
times.append(float(line[0]))
elasticStrainVol.append(np.float64(line[4]))
if np.isnan(elasticStrainVol[-1]):
FAIL_NAN = True
F_elasticStrainVol.close()
if FAIL_NAN:
print "\ERROR: 'nan' encountered while retrieving p.eve, will not plot correctly."
return times,elasticStrainVol
def get_totalStrainVol(uda_path):
times,plasticStrainVol = get_pPlasticStrainVol(uda_path)
times,elasticStrainVol = get_pElasticStrainVol(uda_path)
print 'num plastic : ',len(plasticStrainVol)
print 'num elastic : ',len(elasticStrainVol)
totalStrainVol = np.array(plasticStrainVol)+np.array(elasticStrainVol)
return times,totalStrainVol
def get_defTable(uda_path,working_dir):
#Determine the defTable file
try:
ups_file = os.path.abspath(uda_path)+'/input.xml.orig'
F = open(ups_file,"r")
except:
ups_file = os.path.abspath(uda_path)+'/input.xml'
F = open(ups_file,"r")
for line in F:
if '<PrescribedDeformationFile>' in line and '</PrescribedDeformationFile>' in line:
def_file = line.split('<PrescribedDeformationFile>')[1].split('</PrescribedDeformationFile>')[0].strip()
F.close()
#Assumes the input deck and uda share the same parent folder.
def_file = working_dir+'/'+def_file
F = open(def_file,'r')
times = []
Fs = []
for line in F:
line = line.strip().split()
times.append(float(line[0]))
Fs.append(np.array([[float(line[1]),float(line[2]),float(line[3])],
[float(line[4]),float(line[5]),float(line[6])],
[float(line[7]),float(line[8]),float(line[9])]]))
F.close()
return times,Fs
def exp_fmt(x,loc):
tmp = format(x,'1.2e').split('e')
lead = tmp[0]
exp = str(int(tmp[1]))
if exp=='0' and lead=='0.00':
return r'$\mathbf{0.00}$'
else:
if int(exp)<10 and int(exp)>0:
exp = '+0'+exp
elif int(exp)>-10 and int(exp)<0:
exp = '-0'+exp.split('-')[1]
elif int(exp)>10:
exp = '+'+exp
return r'$\mathbf{'+lead+r'\cdot{}10^{'+exp+'}}$'
def eqShear_vs_meanStress(xs,ys,Xlims=False,Ylims=False,LINE_LABEL='Uintah',GRID=True):
ax1 = plt.subplot(111)
plt.plot(np.array(xs),np.array(ys),'-r',label=LINE_LABEL)
plt.xlabel(str_to_mathbf('Mean Stress, p (Pa)'))
plt.ylabel(str_to_mathbf('Equivalent Shear Stress, q, (Pa)'))
formatter_int = ticker.FormatStrFormatter('$\mathbf{%g}$')
formatter_exp = ticker.FuncFormatter(exp_fmt)
ax1.xaxis.set_major_formatter(formatter_exp)
ax1.yaxis.set_major_formatter(formatter_exp)
if Xlims:
ax1.set_xlim(Xlims[0],Xlims[1])
if Ylims:
ax1.set_ylim(Ylims[0],Ylims[1])
if GRID:
plt.grid(True)
return ax1
def get_yield_surface(uda_path):
#Reads in FSLOPE, FSLOPE_p, PEAKI1, CR, and P0
#WILL ONLY WORK FOR SINGLE ELEMENT TESTS OR DECKS
#HAVING ONLY ONE ARENISCA SPECIFICATION
try:
ups_file = os.path.abspath(uda_path)+'/input.xml.orig'
F_ups = open(ups_file,"r")
except:
ups_file = os.path.abspath(uda_path)+'/input.xml'
F_ups = open(ups_file,"r")
check_lines = False
already_read = False
material_dict = {}
for line in F_ups:
if '<constitutive_model' in line and 'type' in line and '"Arenisca3"' in line and not(already_read):
check_lines = True
if check_lines and not(already_read):
if '<B0>' in line:
material_dict['B0'] = float(line.split('<B0>')[1].split('</B0>')[0].strip())
if '<G0>' in line:
material_dict['G0'] = float(line.split('<G0>')[1].split('</G0>')[0].strip())
if '<FSLOPE>' in line:
material_dict['FSLOPE'] = float(line.split('<FSLOPE>')[1].split('</FSLOPE>')[0].strip())
if '<PEAKI1>' in line:
material_dict['PEAKI1'] = float(line.split('<PEAKI1>')[1].split('</PEAKI1>')[0].strip())
if '<STREN>' in line:
material_dict['STREN'] = float(line.split('<STREN>')[1].split('</STREN>')[0].strip())
if '<YSLOPE>' in line:
material_dict['YSLOPE'] = float(line.split('<YSLOPE>')[1].split('</YSLOPE>')[0].strip())
if '<CR>' in line:
material_dict['CR'] = float(line.split('<CR>')[1].split('</CR>')[0].strip())
if '<p0_crush_curve>' in line:
material_dict['P0'] = float(line.split('<p0_crush_curve>')[1].split('</p0_crush_curve>')[0].strip())
if '<p1_crush_curve>' in line:
material_dict['P1'] = float(line.split('<p1_crush_curve>')[1].split('</p1_crush_curve>')[0].strip())
if '<p3_crush_curve>' in line:
material_dict['P3'] = float(line.split('<p3_crush_curve>')[1].split('</p3_crush_curve>')[0].strip())
if '<fluid_B0>' in line:
material_dict['fluid_B0'] = float(line.split('<fluid_B0>')[1].split('</fluid_B0>')[0].strip())
if '<fluid_pressure_initial>' in line:
material_dict['P_f0'] = float(line.split('<fluid_pressure_initial>')[1].split('</fluid_pressure_initial>')[0].strip())
if '<subcycling_characteristic_number>' in line:
material_dict['subcycling char num'] = float(line.split('<subcycling_characteristic_number>')[1].split('</subcycling_characteristic_number>')[0].strip())
if '<T1_rate_dependence>' in line:
material_dict['T1'] = float(line.split('<T1_rate_dependence>')[1].split('</T1_rate_dependence>')[0].strip())
if '<T2_rate_dependence>' in line:
material_dict['T2'] = float(line.split('<T2_rate_dependence>')[1].split('</T2_rate_dependence>')[0].strip())
if '</constitutive_model>' in line:
already_read = True
check_lines = False
F_ups.close()
PRINTOUT = False
if PRINTOUT:
print '--Material Specification--'
for key in material_dict:
print key,':',material_dict[key]
#tmp_string = r'$\mathbf{\underline{Material}}$'+' '+r'$\mathbf{\underline{Properties:}}$'+'\n'
tmp_string = r'$\mathbf{\underline{Material\phantom{1}Properties:}}$'+'\n'
key_list = material_dict.keys()
key_list.sort()
for key in key_list:
if '_' in key:
tmp = key.split('_')
tmp = str_to_mathbf(tmp[0]+'_'+'{'+tmp[1]+'}')
tmp_string += tmp+str_to_mathbf(' = ')+str_to_mathbf(format(material_dict[key],'1.3e'))+'\n'
else:
tmp = key
if key == 'subcycling char num':
tmp_string += str_to_mathbf(tmp+' = '+format(material_dict[key],'4.1f'))+'\n'
else:
tmp_string += str_to_mathbf(tmp+' = '+format(material_dict[key],'1.3e'))+'\n'
material_dict['material string'] = tmp_string[0:-1]
if PRINTOUT:
print tmp_string
return material_dict
def get_kappa(PEAKI1,P0,CR):
PEAKI1,P0,CR
kappa = PEAKI1-CR*(PEAKI1-P0)
return kappa
def get_rs(nPoints,FSLOPE,PEAKI1,P0,CR):
kappa = get_kappa(PEAKI1,P0,CR)
I1s = np.linspace(PEAKI1,P0,nPoints)
rs = []
for I1 in I1s:
inner_root = (1.0-(pow(kappa-I1,2.0)/pow(kappa-P0,2.0)))
r = FSLOPE*(I1-PEAKI1)*np.sqrt(2.0*inner_root)
rs.append(r)
return I1s,rs
def I1_to_zbar(I1s):
sqrt_3 = np.sqrt(3.0)
if type(I1s) in [list,np.ndarray]:
zbars = []
for I1 in I1s:
zbars.append(-I1/sqrt_3)
return zbars
elif type(I1s) in [int,float,np.float64]:
return -I1s/sqrt_3
else:
print '\nERROR: cannot compute zbar from I1. Invalid type.\n\ttype(I1)\t:\t',type(I1s)
return None
def plot_crush_curve(uda_path,I1lims=[-10000,0]):
nPoints = 500
material_dict = get_yield_surface(uda_path)
P0 = material_dict['P0']
P1 = material_dict['P1']
P3 = material_dict['P3']
# Analytical solution for porosity vs. X for piece-wise crush curve
# compression
I1sC = np.linspace(I1lims[0],P0,nPoints)
porosityC = 1-np.exp(-P3*np.exp(P1*(I1sC-P0)))
plt.plot(I1sC,porosityC,'--g',linewidth=lineWidth+1,label='Analytical crush curve - Compression')
plt.hold(True)
# tension
I1sT = np.linspace(P0,I1lims[1],nPoints)
porosityT = 1-np.exp(-(I1sT/P0)**(P0*P1*P3)-P3+1)
plt.plot(I1sT,porosityT,'--b',linewidth=lineWidth+1,label='Analytical crush curve - Tension')
def plot_yield_surface_OLD(uda_path):
nPoints = 500
material_dict = get_yield_surface(uda_path)
FSLOPE = material_dict['FSLOPE']
#FSLOPE_p = material_dict['FSLOPE']
PEAKI1 = material_dict['PEAKI1']
CR = material_dict['CR']
P0 = material_dict['P0']
I1s,rs = get_rs(nPoints,FSLOPE,PEAKI1,P0,CR)
zbars = I1_to_zbar(I1s)
#WTF?
for i in range(len(rs)):
rs[i] = -rs[i]
#print zbars
#print rs
plt.plot(np.array(I1s)/3.0,rs,'--k',linewidth=lineWidth+1,label='Initial Yield Surface')
plt.plot(np.array(I1s)/3.0,-np.array(rs),'--k',linewidth=lineWidth+1)
def J2VM(epsil_dot,dt,sig_Beg,K,G,tau_y):
#J2 plasticity Von misses material model for 3D
#Inputs: epsil_dot, dt, sig_Beg, K, G, tau_y
#Outputs: epsil_Elastic_dot, epsil_Plastic_dot, sig_End
#Initialize the trial stress state
sig_Trial = sig_Beg+((2*G*sigma_dev(epsil_dot))+3*K*sigma_iso(epsil_dot))*dt
#Determine if this is below, on, or above the yeild surface
test = sigma_mag(sigma_dev(sig_Trial))/(np.sqrt(2.0)*tau_y)
if test<=1:
#Stress state is elastic
sig_End = sig_Trial
epsil_Plastic_dot = np.zeros((3,3))
epsil_Elastic_dot = epsil_dot
elif test>1:
#Stress state elastic-plastic
sig_End = (sigma_dev(sig_Trial)/test)#+sigma_iso(sig_Trial)
#Evaluate the consistent stress rate
#sig_dot = (sig_End-sig_Beg)/test
#Apply hookes law to get the elastic strain rate
#epsil_Elastic_dot = sigma_dev(sig_dot)/(2*G)# + sigma_iso(sig_dot)/(3*K)
#Apply strain rate decomposition relationship to get plastic strain rate
#epsil_Plastic_dot = epsil_dot-epsil_Elastic_dot
#Determine the equivalent stress and equivalent plastic strain rate
#sig_Eq = np.sqrt(3/2)*sigma_mag(sigma_dev(sig_End))
#epsil_Plastic_dot_Eq = np.sqrt(3/2)*sigma_mag(sigma_dev(epsil_Plastic_dot))
#ans={'Elastic dot':epsil_Elastic_dot,'Plastic dot':epsil_Plastic_dot,'Stress State':sig_End}
return sig_End
def defTable_to_J2Solution(def_times,Fs,bulk_mod,shear_mod,tau_yield,num_substeps=1000):
#Assumes:
print 'Solving for analytical solution...'
analytical_epsils = [np.array([[0,0,0],[0,0,0],[0,0,0]])]
analytical_sigmas = [np.array([[0,0,0],[0,0,0],[0,0,0]])]
analytical_times = [def_times[0]]
epsils = []
for F in Fs:
epsils.append(np.array([[np.log(sum(F[0])),0,0],[0,np.log(sum(F[1])),0],[0,0,np.log(sum(F[2]))]]))
for leg in range(len(def_times)-1):
t_start = def_times[leg]
leg_delT = def_times[leg+1]-t_start
leg_sub_delT = float(leg_delT)/float(num_substeps)
leg_del_epsil = (epsils[leg+1]-epsils[leg])
leg_epsil_dot = leg_del_epsil/leg_delT
for i in range(num_substeps):
t_now = t_start+float(i)*leg_sub_delT
analytical_times.append(t_now)
analytical_sigmas.append(J2VM(leg_epsil_dot,leg_sub_delT,analytical_sigmas[-1],bulk_mod,shear_mod,tau_yield))
analytical_epsils.append(analytical_epsils[-1]+(leg_epsil_dot*leg_sub_delT))
analytical_epsils.append(analytical_epsils[-1]+(leg_epsil_dot*leg_sub_delT))
analytical_sigmas.append(J2VM(leg_epsil_dot,leg_sub_delT,analytical_sigmas[-1],bulk_mod,shear_mod,tau_yield))
analytical_times.append(def_times[-1])
print 'Done.'
return analytical_times,analytical_sigmas,analytical_epsils
def J2_at_Yield(uda_path):
material_dict = get_yield_surface(uda_path)
B0 = material_dict['B0']
G0 = material_dict['G0']
FSLOPE = material_dict['FSLOPE']
#FSLOPE_p = material_dict['FSLOPE']
PEAKI1 = material_dict['PEAKI1']
CR = material_dict['CR']
P0 = material_dict['P0']
P1 = material_dict['P1']
P3 = material_dict['P3']
fluid_B0 = material_dict['fluid_B0']
Pf0 = material_dict['P_f0']
subcyc_char_num = material_dict['subcycling char num']
#hardening_const = material_dict['hardening_constant']
kappa_initial = get_kappa(PEAKI1,P0,CR)
I1 = 0
I1_plus3Pf0 = I1+3.0*Pf0
if I1_plus3Pf0 >= kappa_initial and I1_plus3Pf0<= PEAKI1:
J2 = (FSLOPE**2)*((I1-PEAKI1+3.0*Pf0)**2)
elif I1_plus3Pf0 >= P0 and I1_plus3Pf0 < kappa_initial:
J2 = ((FSLOPE**2)*((I1-PEAKI1+3.0*Pf0)**2))*(1.0-((I1+CR*FSLOPE*I1-P0-CR*FSLOPE*PEAKI1+3.0*Pf0+3.0*CR*FSLOPE*Pf0)**2/((CR**2)*(FSLOPE**2)*(P0-PEAKI1)**2)))
else:
J2 = 0.0
return J2
def plot_yield_surface(uda_path,PLOT_TYPE='J2_vs_I1'):
num_points = 500
material_dict = get_yield_surface(uda_path)
B0 = material_dict['B0']
G0 = material_dict['G0']
FSLOPE = material_dict['FSLOPE']
PEAKI1 = material_dict['PEAKI1']
CR = material_dict['CR']
P0 = material_dict['P0']
P1 = material_dict['P1']
P3 = material_dict['P3']
fluid_B0 = material_dict['fluid_B0']
Pf0 = material_dict['P_f0']
kappa_initial = get_kappa(PEAKI1,P0,CR)
I1s = np.linspace(P0-3.0*Pf0,PEAKI1-3.0*Pf0,num_points)
#print 'Region 1:: ','I1 >= kappa initial-3.0*Pf0 : ',kappa_initial-3.0*Pf0,' ','I1 <= PEAKI1-3*Pf0 : ',PEAKI1-3.0*Pf0
#print 'Region 2:: ','I1 >= P0-3*Pf0 : ',P0-3.0*Pf0,' ','I1 < kappa_initial-3*Pf0 : ',kappa_initial-3.0*Pf0
#print 'Region 3:: Not Region 1 or 2'
#J2 versus I1
J2s = []
PLOT = True
for I1 in I1s:
I1_plus3Pf0 = I1+3.0*Pf0
if I1_plus3Pf0 >= kappa_initial and I1_plus3Pf0<= PEAKI1:
J2 = (FSLOPE**2)*((I1-PEAKI1+3.0*Pf0)**2)
elif I1_plus3Pf0 >= P0 and I1_plus3Pf0 < kappa_initial:
Ff = FSLOPE*(PEAKI1-I1_plus3Pf0)
fc = np.sqrt(1.0 - ( (kappa_initial-I1_plus3Pf0)/(kappa_initial-P0) )**2)
J2 = (Ff*fc)**2
else:
J2 = 0.0
J2s.append(J2)
if PLOT_TYPE == 'J2_vs_I1':
xs = I1s
ys = np.array(J2s)
elif PLOT_TYPE == 'sqrtJ2_vs_I1':
xs = I1s
ys = np.sqrt(np.array(J2s))
elif PLOT_TYPE == 'r_vs_z':
xs = np.array(I1s)/np.sqrt(3.0)
ys = np.sqrt(2.0*np.array(J2s))
elif PLOT_TYPE == 'q_vs_I1':
xs = I1s
ys = np.sqrt(3.0*np.array(J2s))
elif PLOT_TYPE == 'q_vs_p':
xs = np.array(I1s)/3.0
ys = np.sqrt(3.0*np.array(J2s))
else:
PLOT = False
print '\nError: invalid plot type specified for initial yield surface plot.\n\tPLOT_TYPE:',PLOT_TYPE
if PLOT:
plt.plot(xs,ys,'--k',linewidth=lineWidth+1,label='Initial Yield Surface')
plt.plot(xs,-ys,'--k',linewidth=lineWidth+1)
def test_yield_surface(uda_path):
plot_yield_surface_2(uda_path,'J2_vs_I1')
plt.show()
plot_yield_surface_2(uda_path,'sqrtJ2_vs_I1')
plt.show()
plot_yield_surface_2(uda_path,'r_vs_z')
plt.show()
plot_yield_surface_2(uda_path,'q_vs_I1')
plt.show()
plot_yield_surface_2(uda_path,'q_vs_p')
plt.show()
### ----------
# Test Methods Below
### ----------
def test01_postProc(uda_path,save_path,**kwargs):
print "Post Processing Test: 01 - Uniaxial Compression With Rotation"
times,sigmas = get_pStress(uda_path)
material_dict = get_yield_surface(uda_path)
Sxx = []
Syy = []
for sigma in sigmas:
Sxx.append(sigma[0][0])
Syy.append(sigma[1][1])
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
plt.figure(1)
plt.clf()
plt.subplots_adjust(right=0.75)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
#Syy
ax2 = plt.subplot(212)
#without rotation
plt.plot([0,1],[0,0],'-b')
#simulation results
plt.plot(times,Syy,'-r')
#guide line
plt.plot([0,1],[0,-60],'--g')
#labels and limits
ax2.set_ylim(-70,10)
plt.grid(True)
ax2.xaxis.set_major_formatter(formatter)
ax2.yaxis.set_major_formatter(formatter)
plt.ylabel(str_to_mathbf('\sigma_{yy} (Pa)'))
plt.xlabel(str_to_mathbf('Time (s)'))
#Sxx
ax1 = plt.subplot(211,sharex=ax2,sharey=ax2)
plt.setp(ax1.get_xticklabels(), visible=False)
#without rotation
plt.plot([0,1],[0,-60],'-b',label='No rotation')
#simulation results
plt.plot(times,Sxx,'-r',label='Uintah')
#guide lines
plt.plot([0,1],[0,0],'--g',label='Guide lines')
#labels
ax1.set_ylim(-70,10)
plt.grid(True)
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
ax1.set_yticks([0,-20,-40,-60])
plt.ylabel(str_to_mathbf('\sigma_{xx} (Pa)'))
plt.title('AreniscaTest 01:\nUniaxial Compression With Rotation')
plt.legend()
savePNG(save_path+'/Test01_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test02_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 02 - Vertex Treatment"
times,sigmas = get_pStress(uda_path)
ps,qs = get_ps_and_qs(sigmas)
Sxx = []
Syy = []
Szz = []
for sigma in sigmas:
Sxx.append(sigma[0][0])
Syy.append(sigma[1][1])
Szz.append(sigma[2][2])
#Analytical Solutions
#Drucker-Prager constants
r0 = 50.0
z0 = 50.0*sqrtThree
#Solution From Brannon Leelavanichkul paper
analytical_times = [0,1,threeHalves,2.0,5.0/2.0,3.0]
analytical_S11 = np.array([0,-850.0/3.0,(-50.0/3.0)*(9.0+4.0*np.sqrt(6.0)),(-50.0/3.0)*(9.0+4.0*np.sqrt(6.0)),(50.0/3.0)*(2.0*np.sqrt(6)-3.0),160.0*np.sqrt(twoThirds)-110.0])
analytical_S22 = np.array([0,-850.0/3.0,(50.0/3.0)*(2.0*np.sqrt(6.0)-9.0),(50.0/3.0)*(2.0*np.sqrt(6.0)-9.0),(-50.0/3.0)*(3.0+np.sqrt(6.0)),(-10.0/3.0)*(33.0+8.0*np.sqrt(6.0))])
analytical_S33 = np.array([0,-850.0/3.0,(50.0/3.0)*(2.0*np.sqrt(6.0)-9.0),(50.0/3.0)*(2.0*np.sqrt(6.0)-9.0),(-50.0/3.0)*(3.0+np.sqrt(6.0)),(-10.0/3.0)*(33.0+8.0*np.sqrt(6.0))])
analytical_mean = (analytical_S11+analytical_S22+analytical_S33)/3.0
analytical_I1 = analytical_S11+analytical_S22+analytical_S33
tmp = (1.0/3.0)*analytical_I1
analytical_s1 = analytical_S11-tmp
analytical_s2 = analytical_S22-tmp
analytical_s3 = analytical_S33-tmp
analytical_J2 = (1.0/2.0)*(pow(analytical_s1,2)+pow(analytical_s2,2)+pow(analytical_s3,2))
analytical_J3 = (1.0/3.0)*(pow(analytical_s1,3)+pow(analytical_s2,3)+pow(analytical_s3,3))
analytical_z = analytical_I1/sqrtThree
analytical_q = []
for idx,J2 in enumerate(analytical_J2):
J3 = analytical_J3[idx]
analytical_q.append(sign(sqrtThree*np.sqrt(J2),J3))
#Drucker-Prager yield surface
yield_zs = np.array([z0,min(analytical_z)])
yield_rs = r0/z0*((get_yield_surface(uda_path)['PEAKI1']/sqrtThree)-yield_zs)
yield_ps = yield_zs*(sqrtThree/3.0)
yield_qs = yield_rs*np.sqrt(threeHalves)
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
##Plot a
plt.figure(1)
plt.clf()
plt.subplot(111)
plt.subplots_adjust(right=0.75)
material_dict = get_yield_surface(uda_path)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
plt.plot(analytical_mean,analytical_q,'-g',linewidth=lineWidth+1,label='Analytical')
plt.plot(yield_ps,yield_qs,'--k',linewidth=lineWidth+2,label='Yield surface')
plt.plot(yield_ps,-yield_qs,'--k',linewidth=lineWidth+2)
eqShear_vs_meanStress(ps,qs,(-300,60),(-300,300))
plt.title('AreniscaTest 02:\nVertex Treatment (plot a)')
plt.legend()
savePNG(save_path+'/Test02_verificationPlot_a','1280x960')
##Plot b
plt.figure(2)
plt.clf()
plt.subplots_adjust(right=0.75)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
endT = max(times)
#Sigma zz
ax3 = plt.subplot(313)
plt.plot(analytical_times,analytical_S33,'-g',linewidth=lineWidth+2)
plt.plot(times,np.array(Szz),'-r')
#Add Yield Surface
#Add Analytical
plt.xlabel(str_to_mathbf('Time (s)'))
plt.ylabel(str_to_mathbf('\sigma_{zz} (Pa)'))
ax3.yaxis.set_major_formatter(formatter)
ax3.set_xlim(0,endT)
ax3.set_ylim(-300,100)
ax3.set_yticks([-300,-200,-100,0,100])
plt.grid(True)
#Sigma xx
ax1 = plt.subplot(311,sharex=ax3)
plt.plot(analytical_times,analytical_S11,'-g',linewidth=lineWidth+2,label='Analytical')
plt.plot(times,np.array(Sxx),'-r',label='Uintah')
#Add Yield Surface
#Add Analytical
plt.legend()
plt.setp(ax1.get_xticklabels(), visible=False)
plt.ylabel(str_to_mathbf('\sigma_{xx} (Pa)'))
plt.title('AreniscaTest 02:\nVertex Treatment (plot b)')
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
ax1.set_xlim(0,endT)
ax1.set_ylim(-400,100)
ax1.set_yticks([-400,-300,-200,-100,0,100])
plt.grid(True)
#Sigma yy
ax2 = plt.subplot(312,sharex=ax3)
plt.plot(analytical_times,analytical_S22,'-g',linewidth=lineWidth+2)
plt.plot(times,np.array(Syy),'-r')
#Add Yield Surface
#Add Analytical
plt.setp(ax2.get_xticklabels(), visible=False)
plt.ylabel(str_to_mathbf('\sigma_{yy} (Pa)'))
ax2.yaxis.set_major_formatter(formatter)
ax2.set_xlim(0,endT)
ax2.set_ylim(-300,100)
ax2.set_yticks([-300,-200,-100,0,100])
plt.grid(True)
savePNG(save_path+'/Test02_verificationPlot_b','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test03_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 03 - Uniaxial Strain Without Hardening"
times,sigmas = get_pStress(uda_path)
ps,qs = get_ps_and_qs(sigmas)
material_dict = get_yield_surface(uda_path)
PEAKI1 = material_dict['PEAKI1']
J2Yield = J2_at_Yield(uda_path)
q_yield = np.sqrt(3.0*J2Yield)
#print 'J2Yield : ',J2Yield
#print 'q_yield : ',q_yield
###PLOTTING
Xlims = (-450,50)
Ylims = (-100,100)
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
material_dict = get_yield_surface(uda_path)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(ps,qs,Xlims,Ylims,)
plt.title('AreniscaTest 03:\nUniaxial Strain Without Hardening')
plt.plot(Xlims,(q_yield,q_yield),'--k',linewidth=lineWidth+1,label='Initial yield surface')
plt.plot(Xlims,(-q_yield,-q_yield),'--k',linewidth=lineWidth+1)
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
plt.legend()
savePNG(save_path+'/Test03_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test04_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 04 - Curved Yield Surface"
times,sigmas = get_pStress(uda_path)
ps,qs = get_ps_and_qs(sigmas)
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
##Plot a
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
material_dict = get_yield_surface(uda_path)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(ps,qs,(-700,300),(-200,200))
plt.title('AreniscaTest 04:\nCurved Yield Surface')
plot_yield_surface(uda_path,'q_vs_p')
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
#Add Analytical
plt.legend()
savePNG(save_path+'/Test04_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test05_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 05 - Hydrostatic Compression Fixed Cap"
times,sigmas = get_pStress(uda_path)
ps,qs = get_ps_and_qs(sigmas)
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
##Plot a
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
material_dict = get_yield_surface(uda_path)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(ps,qs,(-700,300),(-200,200))
plt.title('AreniscaTest 05:\nHydrostatic Compression Fixed Cap')
plot_yield_surface(uda_path,'q_vs_p')
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
#Add Analytical
plt.legend()
savePNG(save_path+'/Test05_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test06_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 06 - Uniaxial Strain Cap Evolution"
times,sigmas = get_pStress(uda_path)
ps,qs = get_ps_and_qs(sigmas)
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
##Plot a
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
material_dict = get_yield_surface(uda_path)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(ps,qs,(-800,300),(-200,200))
plt.title('AreniscaTest 06:\nUniaxial Strain Cap Evolution')
plot_yield_surface(uda_path,'q_vs_p')
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
#Add Analytical
plt.legend()
savePNG(save_path+'/Test06_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test07_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 07 - Hydrostatic Compression with Fixed Cap"
times,sigmas = get_pStress(uda_path)
I1s = []
for sigma in sigmas:
I1s.append(sigma_I1(sigma))
times,plasticStrainVol = get_pPlasticStrainVol(uda_path)
material_dict = get_yield_surface(uda_path)
P3 = material_dict['P3']
porosity = 1-np.exp(-(P3+np.array(plasticStrainVol)))
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
##Plot a
I1lims = (-8000,0)
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
ax1=eqShear_vs_meanStress(I1s,porosity,I1lims,(0,0.6))
plt.title('AreniscaTest 07:\nHydrostatic Compression with Fixed Cap')
plt.ylabel(str_to_mathbf('Porosity'))
plt.xlabel(str_to_mathbf('I_{1}:first invariant of stress tensor (Pa)'))
plot_crush_curve(uda_path,I1lims)
#ax1.set_xticks([-9000,-7000,-5000,-3000,-1000,0])
ax1.set_xticks([-8000,-6000,-4000,-2000,0])
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
plt.legend()
savePNG(save_path+'/Test07_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test08_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 08 - Loading/Unloading"
times,sigmas = get_pStress(uda_path)
I1s = []
ps = []
for sigma in sigmas:
I1s.append(sigma_I1(sigma))
ps.append(sigma_I1(sigma)/3.0)
times,plasticStrainVol = get_pPlasticStrainVol(uda_path)
times,elasticStrainVol = get_pElasticStrainVol(uda_path)
totalStrainVol = np.array(elasticStrainVol)+np.array(plasticStrainVol)
material_dict = get_yield_surface(uda_path)
P3 = material_dict['P3']
porosity = 1-np.exp(-(P3+np.array(plasticStrainVol)))
###PLOTTING
int_formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
exp_formatter = ticker.FuncFormatter(exp_fmt)
##Plot a
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75,left=0.15)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
ax1=eqShear_vs_meanStress(times,-np.array(ps),(0,3.5),(-500,2000))
plt.title('AreniscaTest 08:\nLoading/Unloading (plot a)')
plt.ylabel(str_to_mathbf('Pressure (Pa)'))
plt.xlabel(str_to_mathbf('Time (s)'))
ax1.xaxis.set_major_formatter(int_formatter)
ax1.yaxis.set_major_formatter(exp_formatter)
ax1.tick_params(axis='both',labelsize='small')
savePNG(save_path+'/Test08_verificationPlot_a','1280x960')
##Plot b
plt.figure(2)
plt.clf()
ax2 = plt.subplot(111)
plt.subplots_adjust(right=0.75,left=0.15)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
ax1=eqShear_vs_meanStress(times,totalStrainVol,(0,3.5),(-0.8,0.8))
plt.title('AreniscaTest 08:\nLoading/Unloading (plot b)')
plt.ylabel(str_to_mathbf('Total Volumetric Strain, \epsilon_{v}'))
plt.xlabel(str_to_mathbf('Time (s)'))
ax2.xaxis.set_major_formatter(int_formatter)
ax2.yaxis.set_major_formatter(int_formatter)
ax2.tick_params(axis='both',labelsize='small')
savePNG(save_path+'/Test08_verificationPlot_b','1280x960')
##Plot c
I1lims = (-10000,0)
plt.figure(3)
plt.clf()
ax3 = plt.subplot(111)
plt.subplots_adjust(right=0.75,left=0.15)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(I1s,porosity,I1lims,(0,1.25))
plt.title('AreniscaTest 08:\nLoading/Unloading (plot c)')
plt.ylabel(str_to_mathbf('Porosity'))
plt.xlabel(str_to_mathbf('I_{1}:first invariant of stress tensor (Pa)'))
plot_crush_curve(uda_path,I1lims)
#ax1.set_xticks([-9000,-7000,-5000,-3000,-1000,0])
ax3.set_xticks([-10000,-7500,-5000,-2500,0,1000])
ax3.set_yticks([0,0.2,0.4,0.6,0.8,1.0])
ax3.xaxis.set_major_formatter(exp_formatter)
ax3.yaxis.set_major_formatter(int_formatter)
ax3.tick_params(axis='both',labelsize='small')
plt.legend()
savePNG(save_path+'/Test08_verificationPlot_c','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test09_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 09 - Fluid Filled Pore Space"
times,sigmas = get_pStress(uda_path)
I1s = []
ps = []
for sigma in sigmas:
I1s.append(sigma_I1(sigma))
ps.append(sigma_I1(sigma)/3.0)
times,plasticStrainVol = get_pPlasticStrainVol(uda_path)
times,elasticStrainVol = get_pElasticStrainVol(uda_path)
totalStrainVol = np.array(elasticStrainVol)+np.array(plasticStrainVol)
material_dict = get_yield_surface(uda_path)
P3 = material_dict['P3']
porosity = 1-np.exp(-(P3+np.array(plasticStrainVol)))
###PLOTTING
int_formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
exp_formatter = ticker.FuncFormatter(exp_fmt)
##Plot a
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75,left=0.15)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
ax1=eqShear_vs_meanStress(times,-np.array(ps),(0,3.5),(-500,2000))
plt.title('AreniscaTest 09:\nFluid EFfects (plot a)')
plt.ylabel(str_to_mathbf('Pressure (Pa)'))
plt.xlabel(str_to_mathbf('Time (s)'))
ax1.xaxis.set_major_formatter(int_formatter)
ax1.yaxis.set_major_formatter(exp_formatter)
ax1.tick_params(axis='both',labelsize='small')
savePNG(save_path+'/Test09_verificationPlot_a','1280x960')
##Plot b
plt.figure(2)
plt.clf()
ax2 = plt.subplot(111)
plt.subplots_adjust(right=0.75,left=0.15)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
ax1=eqShear_vs_meanStress(times,totalStrainVol,(0,3.5),(-0.8,0.8))
plt.title('AreniscaTest 09:\nFluid EFfects (plot b)')
plt.ylabel(str_to_mathbf('Total Volumetric Strain, \epsilon_{v}'))
plt.xlabel(str_to_mathbf('Time (s)'))
ax2.xaxis.set_major_formatter(int_formatter)
ax2.yaxis.set_major_formatter(int_formatter)
ax2.tick_params(axis='both',labelsize='small')
savePNG(save_path+'/Test09_verificationPlot_b','1280x960')
##Plot c
I1lims = (-10000,0)
plt.figure(3)
plt.clf()
ax3 = plt.subplot(111)
plt.subplots_adjust(right=0.75,left=0.15)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(I1s,porosity,I1lims,(0,1.25))
plt.title('AreniscaTest 09:\nFluid EFfects (plot c)')
plt.ylabel(str_to_mathbf('Porosity'))
plt.xlabel(str_to_mathbf('I_{1}:first invariant of stress tensor (Pa)'))
plot_crush_curve(uda_path,I1lims)
#ax1.set_xticks([-9000,-7000,-5000,-3000,-1000,0])
ax3.set_xticks([-10000,-7500,-5000,-2500,0,1000])
ax3.set_yticks([0,0.2,0.4,0.6,0.8,1.0])
ax3.xaxis.set_major_formatter(exp_formatter)
ax3.yaxis.set_major_formatter(int_formatter)
ax3.tick_params(axis='both',labelsize='small')
plt.legend()
savePNG(save_path+'/Test09_verificationPlot_c','1280x960')
if SHOW_ON_MAKE:
plt.show()
def test10_postProc(uda_path,save_path,**kwargs):
if 'WORKING_PATH' in kwargs:
working_dir = kwargs['WORKING_PATH']
#Extract stress history
print "Post Processing Test: 10 - Transient Stress Eigenvalues with Constant Eigenvectors"
times,sigmas = get_pStress(uda_path)
Sxx = []
Syy = []
Szz = []
for sigma in sigmas:
Sxx.append(sigma[0][0])
Syy.append(sigma[1][1])
Szz.append(sigma[2][2])
#Analytical solution
material_dict = get_yield_surface(uda_path)
def_times,Fs = get_defTable(uda_path,working_dir)
tau_yield = material_dict['PEAKI1']/1e10
bulk_mod = material_dict['B0']
shear_mod = material_dict['G0']
analytical_times,analytical_sigmas,epsils=defTable_to_J2Solution(def_times,Fs,bulk_mod,shear_mod,tau_yield,num_substeps=10)
analytical_Sxx = []
analytical_Syy = []
analytical_Szz = []
for sigma in analytical_sigmas:
analytical_Sxx.append(sigma[0][0])
analytical_Syy.append(sigma[1][1])
analytical_Szz.append(sigma[2][2])
###PLOTTING
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
if BIG_FIGURE:
plt.subplots_adjust(right=0.75)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
else:
plt.subplots_adjust(left=0.15,top=0.96,bottom=0.15,right=0.96)
#analytical solution
plt.plot(analytical_times,np.array(analytical_Sxx)/1e6,':r',linewidth=lineWidth+2,label=str_to_mathbf('Analytical \sigma_{xx}'))
plt.plot(analytical_times,np.array(analytical_Syy)/1e6,'--g',linewidth=lineWidth+2,label=str_to_mathbf('Analytical \sigma_{yy}'))
plt.plot(analytical_times,np.array(analytical_Szz)/1e6,'-.b',linewidth=lineWidth+2,label=str_to_mathbf('Analytical \sigma_{zz}'))
#simulation results
plt.plot(times,np.array(Sxx)/1e6,'-r',label=str_to_mathbf('Uintah \sigma_{xx}'))
plt.plot(times,np.array(Syy)/1e6,'-g',label=str_to_mathbf('Uintah \sigma_{yy}'))
plt.plot(times,np.array(Szz)/1e6,'-b',label=str_to_mathbf('Uintah \sigma_{zz}'))
ax1.set_xlim(0,2.25)
ax1.xaxis.set_major_formatter(formatter_int)
ax1.yaxis.set_major_formatter(formatter_int)
#labels
plt.grid(True)
plt.xlabel(str_to_mathbf('Time (s)'))
plt.ylabel(str_to_mathbf('Stress (MPa)'))
if BIG_FIGURE:
plt.legend(loc='upper right', bbox_to_anchor=(1.38,1.12))
plt.title('AreniscaTest 10:\nTransient Stress Eigenvalues with Constant Eigenvectors')
saveIMG(save_path+'/Test10_verificationPlot','1280x960')
else:
tmp = plt.rcParams['legend.fontsize']
plt.rcParams['legend.fontsize']='x-small'
plt.legend(loc=7)
savePNG(save_path+'/Test10_verificationPlot','640x480')
plt.rcParams['legend.fontsize']=tmp
if SHOW_ON_MAKE:
plt.show()
else:
print '\nERROR: need working directory to post process this problem'
def test11_postProc(uda_path,save_path,**kwargs):
if 'WORKING_PATH' in kwargs:
working_dir = kwargs['WORKING_PATH']
#Extract stress and strain history
print "Post Processing Test: 11 - Uniaxial Strain J2 Plasticity"
times,sigmas = get_pStress(uda_path)
times,epsils = get_epsilons(uda_path)
exx = []
eyy = []
ezz = []
for epsil in epsils:
exx.append(epsil[0][0])
eyy.append(epsil[1][1])
ezz.append(epsil[2][2])
Sxx = []
Syy = []
Szz = []
for sigma in sigmas:
Sxx.append(sigma[0][0])
Syy.append(sigma[1][1])
Szz.append(sigma[2][2])
#Analytical solution
material_dict = get_yield_surface(uda_path)
def_times,Fs = get_defTable(uda_path,working_dir)
tau_yield = material_dict['PEAKI1']*material_dict['FSLOPE']
#tau_yield = material_dict['PEAKI1']
bulk_mod = material_dict['B0']
shear_mod = material_dict['G0']
analytical_times,analytical_sigmas,epsils=defTable_to_J2Solution(def_times,Fs,bulk_mod,shear_mod,tau_yield,num_substeps=1000)
analytical_e11 = []
analytical_e22 = []
analytical_e33 = []
for epsil in epsils:
analytical_e11.append(epsil[0][0])
analytical_e22.append(epsil[1][1])
analytical_e33.append(epsil[2][2])
analytical_Sxx = []
analytical_Syy = []
analytical_Szz = []
for sigma in analytical_sigmas:
analytical_Sxx.append(sigma[0][0])
analytical_Syy.append(sigma[1][1])
analytical_Szz.append(sigma[2][2])
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
plt.title('AreniscaTest 11:\nUniaxial Strain J2 Plasticity (plot a)')
plt.plot(np.array(analytical_e11),np.array(analytical_Sxx)/1e6,'--g',linewidth=lineWidth+1,label=str_to_mathbf('Analytical'))
plt.plot(np.array(exx),np.array(Sxx)/1e6,'-r',label=str_to_mathbf('Uintah'))
plt.xlabel(str_to_mathbf('\epsilon_{A}'))
plt.ylabel(str_to_mathbf('\sigma_{A} (Mpa)'))
plt.legend()
savePNG(save_path+'/Test11_verificationPlot_a','1280x960')
plt.figure(2)
plt.clf()
ax2 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
ax2.xaxis.set_major_formatter(formatter)
ax2.yaxis.set_major_formatter(formatter)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
plt.title('AreniscaTest 11:\nUniaxial Strain J2 Plasticity (plot b)')
plt.plot(np.array(analytical_e11),np.array(analytical_Syy)/1e6,'--g',linewidth=lineWidth+1,label=str_to_mathbf('Analytical'))
plt.plot(np.array(exx),np.array(Syy)/1e6,'-r',label=str_to_mathbf('Uintah'))
plt.xlabel(str_to_mathbf('\epsilon_{A}'))
plt.ylabel(str_to_mathbf('\sigma_{L} (Mpa)'))
plt.legend()
savePNG(save_path+'/Test11_verificationPlot_b','1280x960')
plt.figure(3)
plt.clf()
ax3 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
ax3.xaxis.set_major_formatter(formatter)
ax3.yaxis.set_major_formatter(formatter)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
plt.title('AreniscaTest 11:\nUniaxial Strain J2 Plasticity (plot c)')
plt.plot(analytical_times,np.array(analytical_e11),'-g',linewidth=lineWidth+1,label=str_to_mathbf('Analytical \epsilon_{xx}'))
plt.plot(analytical_times,np.array(analytical_e22),'-r',linewidth=lineWidth+1,label=str_to_mathbf('Analytical \epsilon_{yy}'))
plt.plot(analytical_times,np.array(analytical_e33),'-b',linewidth=lineWidth+1,label=str_to_mathbf('Analytical \epsilon_{zz}'))
plt.legend()
plt.xlabel(str_to_mathbf('Time (s)'))
plt.ylabel(str_to_mathbf('\epsilon'))
savePNG(save_path+'/Test11_verificationPlot_c','1280x960')
plt.figure(4)
plt.clf()
ax4 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
ax4.xaxis.set_major_formatter(formatter)
ax4.yaxis.set_major_formatter(formatter)
param_text = material_dict['material string']
plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
plt.title('AreniscaTest 11:\nUniaxial Strain J2 Plasticity (plot d)')
plt.plot(analytical_times,np.array(analytical_Sxx)/1e6,'-g',linewidth=lineWidth+1,label=str_to_mathbf('Analytical \sigma_{xx}'))
plt.plot(analytical_times,np.array(analytical_Syy)/1e6,'-r',linewidth=lineWidth+1,label=str_to_mathbf('Analytical \sigma_{yy}'))
plt.plot(analytical_times,np.array(analytical_Szz)/1e6,'-b',linewidth=lineWidth+1,label=str_to_mathbf('Analytical \sigma_{zz}'))
plt.legend()
plt.xlabel(str_to_mathbf('Time (s)'))
plt.ylabel(str_to_mathbf('\sigma (Mpa)'))
savePNG(save_path+'/Test11_verificationPlot_d','1280x960')
if SHOW_ON_MAKE:
plt.show()
else:
print '\nERROR: need working directory to post process this problem'
def test12_postProc(uda_path,save_path,**kwargs):
#Extract stress history
print "Post Processing Test: 12 - Nonlinear Elasticity"
times,sigmas = get_pStress(uda_path)
pressure = []
for sigma in sigmas:
pressure.append(-sigma_I1(sigma)/3.0)
times,plasticStrainVol = get_pPlasticStrainVol(uda_path)
times,elasticStrainVol = get_pElasticStrainVol(uda_path)
totalStrainVol = -np.array(elasticStrainVol)-np.array(plasticStrainVol)
###PLOTTING
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
##Plot a
evlims = (0.0,0025)
plt.figure(1)
plt.clf()
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
#param_text = material_dict['material string']
#plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
#ax1=eqShear_vs_meanStress(I1s,porosity,I1lims,(0,0.6))
plt.plot(totalStrainVol,pressure,'-b',label='Arenisca')
plt.title('AreniscaTest 12:\nNonlinear Elasticity')
plt.ylabel(str_to_mathbf('p: pressure (Pa)'))
plt.xlabel(str_to_mathbf('ev: compressive volumetric strain'))
ax1.set_xticks([0,0.005,0.010,0.015,0.020,0.025])
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
plt.legend()
savePNG(save_path+'/Test12_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
else:
print '\nERROR: need working directory to post process this problem'
def test13_postProc(uda_path,save_path,**kwargs):
COLORS = ['Black','Blue','Magenta','Red','Green']
if 'WORKING_PATH' in kwargs:
working_dir = kwargs['WORKING_PATH']
#Plot Constants
Xlims = (-450,50)
Ylims = (-100,100)
formatter = ticker.FormatStrFormatter('$\mathbf{%g}$')
plt.figure(1)
plt.hold(True)
plt.clf()
material_dict = get_yield_surface(uda_path)
PEAKI1 = material_dict['PEAKI1']
FSLOPE = material_dict['FSLOPE']
#STREN = material_dict['STREN']
STREN = PEAKI1*FSLOPE
T1 = material_dict['T1']
T2 = material_dict['T2']
def_times,Fs = get_defTable(uda_path,working_dir)
A = Fs[1][0][0]
#As = Fs[10][0][0]
K = material_dict['B0']
G = material_dict['G0']
C = K+(4.0/3.0)*G
Y = STREN*1.732
YS = STREN
#uniaxial strain (unscaled)
analytical_exx = [0.0,
(Y/(2.0*G)),
np.log(A),
]
analytical_Sxx=[0.0,
(C*Y)/(2.0*G),
((C-K)*Y)/(2*G)+K*np.log(A),
]
#uniaxial strain (scaled)
#analytical_exx = np.array([0.0,
#(Y/(2.0*G)),
#np.log(A),
#np.log(A)-(Y)/(G),
#0.0
#])/(Y/(2.0*G))
#analytical_Sxx = np.array([0.0,
#(C*Y)/(2.0*G),
#((C-K)*Y)/(2*G)+K*np.log(A),
#K*np.log(A)-((C+K)*Y)/(2*G),
#(K-C)*Y/(2*G)
#])/((C*Y)/(2.0*G))
#pure shear (unscaled)
#analytical_exx = np.array([0.0,
# (YS/(2.0*G)),
# np.log(As),
# ])
#analytical_Sxx = np.array([0.0,
# (YS),
# (YS),
# ])
#Extract stress history
print "Post Processing Test: 13 "
times,sigmas = get_pStress(uda_path)
times,epsils = get_epsilons(uda_path)
exx = []
eyy = []
ezz = []
exy = []
for epsil in epsils:
exx.append(epsil[0][0])
eyy.append(epsil[1][1])
ezz.append(epsil[2][2])
exy.append(epsil[0][1])
Sxx = []
Syy = []
Szz = []
Sxy = []
for sigma in sigmas:
Sxx.append(sigma[0][0])
Syy.append(sigma[1][1])
Szz.append(sigma[2][2])
Sxy.append(sigma[0][1])
scaled_exx = ((2.0*G)/Y)*np.array(exx)
scaled_Sxx = ((2.0*G)/(C*Y))*np.array(Sxx)
scaled_Syy = ((2.0*G)/(C*Y))*np.array(Syy)
#S = np.array(Sxx) - np.array(Syy)
S = np.array(Sxx)
#E = np.array(exy)
###PLOTTING
ax1 = plt.subplot(111)
plt.subplots_adjust(right=0.75)
#param_text = material_dict['material string']
#plt.figtext(0.77,0.70,param_text,ha='left',va='top',size='x-small')
eqShear_vs_meanStress(exx,S,LINE_LABEL = 'T1='+format(T1,'1.3e')+' T2='+format(T2,'1.3e'))
#eqShear_vs_meanStress(E,S,LINE_LABEL = 'T1='+format(T1,'1.3e')+' T2='+format(T2,'1.3e'),COLOR=COLORS[idx])
plt.plot(analytical_exx,analytical_Sxx,'--',color='Red',label='Analytical solution for rate independent case.')
plt.title('AreniscaTest 13:')
plt.ylabel(str_to_mathbf('\sigma_{xx}'))
plt.xlabel(str_to_mathbf('\epsilon_{xx}'))
#plt.ylabel(str_to_mathbf('\sigma_{xy}'))
#plt.xlabel(str_to_mathbf('\epsilon_{xy}'))
ax1.xaxis.set_major_formatter(formatter)
ax1.yaxis.set_major_formatter(formatter)
plt.legend()
savePNG(save_path+'/Test13_verificationPlot','1280x960')
if SHOW_ON_MAKE:
plt.show()
else:
print '\nERROR: need working directory to post process this problem'
| 35.607119 | 178 | 0.679304 | 8,104 | 53,019 | 4.270854 | 0.08465 | 0.038832 | 0.019387 | 0.025541 | 0.662708 | 0.615007 | 0.570454 | 0.541389 | 0.524689 | 0.471382 | 0 | 0.055805 | 0.152681 | 53,019 | 1,488 | 179 | 35.631048 | 0.714629 | 0.096833 | 0 | 0.496575 | 0 | 0.000856 | 0.15687 | 0.021867 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.006849 | null | null | 0.034247 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8f1098b74f0861cbf64404f75ae544f49b9969a | 1,617 | py | Python | src/products/migrations/0006_ShippableFullName.py | denkasyanov/education-backend | c796b6f2f1cc1cd09f83cab2ca0cc45344906ef5 | [
"MIT"
] | 151 | 2020-04-21T09:58:57.000Z | 2021-09-12T09:01:21.000Z | src/products/migrations/0006_ShippableFullName.py | tlgtaa/education-backend | 86f8af315f9cff2c1fd19406899d593fc0852124 | [
"MIT"
] | 163 | 2020-05-29T20:52:00.000Z | 2021-09-11T12:44:56.000Z | src/products/migrations/0006_ShippableFullName.py | boochamoocha/education-backend | c6ffb0c00bc066c8f1e0a8c0ffe4d0215c7c416a | [
"MIT"
] | 39 | 2020-04-21T12:28:16.000Z | 2021-09-12T15:33:47.000Z | # Generated by Django 2.2.7 on 2019-11-15 21:48
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('products', '0005_ClickMeeetingRoomURL'),
]
operations = [
migrations.AddField(
model_name='course',
name='full_name',
field=models.CharField(default='', help_text='Билет на мастер-класс о TDD или «запись курсов кройки и шитья»', max_length=255, verbose_name='Full name for letters'),
preserve_default=False,
),
migrations.AddField(
model_name='record',
name='full_name',
field=models.CharField(default='', help_text='«Запись мастер-класса о TDD»', max_length=255, verbose_name='Full name for letters'),
preserve_default=False,
),
migrations.AlterField(
model_name='course',
name='name_genitive',
field=models.CharField(help_text='«мастер-класса о TDD». К примеру для записей.', max_length=255, verbose_name='Genitive name'),
),
migrations.AlterField(
model_name='course',
name='name_receipt',
field=models.CharField(help_text='«посещение мастер-класса по TDD» или «Доступ к записи курсов кройки и шитья»', max_length=255, verbose_name='Name for receipts'),
),
migrations.AlterField(
model_name='record',
name='name_receipt',
field=models.CharField(help_text='«Доступ к записи курсов кройки и шитья»', max_length=255, verbose_name='Name for receipts'),
),
]
| 39.439024 | 177 | 0.622758 | 198 | 1,617 | 5.005051 | 0.348485 | 0.045409 | 0.100908 | 0.095863 | 0.643794 | 0.587286 | 0.558022 | 0.489405 | 0.489405 | 0.306761 | 0 | 0.0285 | 0.262214 | 1,617 | 40 | 178 | 40.425 | 0.792121 | 0.027829 | 0 | 0.617647 | 1 | 0 | 0.291083 | 0.015924 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.029412 | 0 | 0.117647 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8f21d41b8c07b30108c551d5f9b8476610f53e3 | 2,286 | py | Python | convert_xml_to_xls.py | CharlesBuy/pyxmlspreadsheet | 9ac2064c5c44a3800c3ff8892292d12fbd57d27b | [
"MIT"
] | null | null | null | convert_xml_to_xls.py | CharlesBuy/pyxmlspreadsheet | 9ac2064c5c44a3800c3ff8892292d12fbd57d27b | [
"MIT"
] | null | null | null | convert_xml_to_xls.py | CharlesBuy/pyxmlspreadsheet | 9ac2064c5c44a3800c3ff8892292d12fbd57d27b | [
"MIT"
] | null | null | null | #
# - Very simple code to convert from office spreadsheet to xls
#
# Sneldev.com
#
import sys
import xml.etree.ElementTree as ET
import xlwt
from dateutil.parser import parse
class xml_workbook():
#hard code a tag here
_tag_prefix = "{urn:schemas-microsoft-com:office:spreadsheet}"
def _wt(self, key):
return self._tag_prefix + key
def __init__(self, path):
tree = ET.parse(path)
self.root = tree.getroot()
def get_worksheets(self):
for ws in self.root.findall(self._wt('Worksheet')):
yield ws.findall(self._wt('Table'))[0]
def get_rows(self, ws):
return ws.findall(self._wt('Row'))
def get_cells(self, row):
def create_cell(c):
data = c.findall(self._wt('Data'))[0]
text = data.text
data_type = data.get(self._wt('Type'),"String")
return {'text' : text, 'type' : data_type}
cells=row.findall(self._wt('Cell'))
return [create_cell(c) for c in cells]
#This Small Class convert a xml cell to a text and a style
#as can be put in an xlwt worksheet
class cell_converter():
def __init__(self):
self.cell_convert = {
"String" : lambda value : value,
"Number" : lambda value : float(value),
"DateTime" : lambda value : value and parse(value),
}
number_style = xlwt.XFStyle()
number_style.num_format_str = "0.00"
date_style = xlwt.XFStyle()
date_style.num_format_str = 'dd/mm/yyyy'
self.cell_styles = {
"DateTime" : date_style,
"Number" : number_style,
}
self.default_format = xlwt.XFStyle()
def get_text(self, cell):
return self.cell_convert[cell['type']](cell['text'])
def get_style(self, cell):
return self.cell_styles.get(cell['type'],self.default_format)
def convert_xml_spreadsheet_to_xls(in_path, out_path):
wb_o = xlwt.Workbook(encoding="UTF-8")
wb = xml_workbook(in_path)
conv = cell_converter()
for ws_idx, ws in enumerate(wb.get_worksheets()):
ws_o = wb_o.add_sheet("Converted Data%d" % ws_idx)
for row_idx, row in enumerate(wb.get_rows(ws)):
for cell_idx, cell in enumerate(wb.get_cells(row)):
ws_o.write(row_idx, cell_idx, conv.get_text(cell), conv.get_style(cell))
wb_o.save(out_path)
if __name__ == '__main__':
if (len(sys.argv) < 3):
print "me.py input_spreadsheet.xml output.xls"
sys.exit()
convert_xml_spreadsheet_to_xls(sys.argv[1],sys.argv[2])
| 26.275862 | 76 | 0.701225 | 367 | 2,286 | 4.133515 | 0.316076 | 0.023731 | 0.042848 | 0.031641 | 0.063283 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004678 | 0.158355 | 2,286 | 86 | 77 | 26.581395 | 0.783784 | 0.08049 | 0 | 0 | 0 | 0 | 0.103448 | 0.032088 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.067797 | null | null | 0.016949 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
d8ff6bd51bd1471d74da2d3d069253e4211178d6 | 861 | py | Python | AutoRecon-main/autorecon/default-plugins/nmap-oracle.py | Nano-Techx/nano-tool | 6907cbaa3251ba9e47ab634b6e1a656002b3672b | [
"Apache-2.0"
] | null | null | null | AutoRecon-main/autorecon/default-plugins/nmap-oracle.py | Nano-Techx/nano-tool | 6907cbaa3251ba9e47ab634b6e1a656002b3672b | [
"Apache-2.0"
] | null | null | null | AutoRecon-main/autorecon/default-plugins/nmap-oracle.py | Nano-Techx/nano-tool | 6907cbaa3251ba9e47ab634b6e1a656002b3672b | [
"Apache-2.0"
] | null | null | null | from autorecon.plugins import ServiceScan
class NmapOracle(ServiceScan):
def __init__(self):
super().__init__()
self.name = "Nmap Oracle"
self.tags = ['default', 'safe', 'databases']
def configure(self):
self.match_service_name('^oracle')
def manual(self, service, plugin_was_run):
service.add_manual_command('Brute-force SIDs using Nmap:', 'nmap {nmap_extra} -sV -p {port} --script="banner,oracle-sid-brute" -oN "{scandir}/{protocol}_{port}_oracle_sid-brute_nmap.txt" -oX "{scandir}/xml/{protocol}_{port}_oracle_sid-brute_nmap.xml" {address}')
async def run(self, service):
await service.execute('nmap {nmap_extra} -sV -p {port} --script="banner,(oracle* or ssl*) and not (brute or broadcast or dos or external or fuzzer)" -oN "{scandir}/{protocol}_{port}_oracle_nmap.txt" -oX "{scandir}/xml/{protocol}_{port}_oracle_nmap.xml" {address}')
| 47.833333 | 266 | 0.727062 | 123 | 861 | 4.845528 | 0.455285 | 0.080537 | 0.120805 | 0.050336 | 0.375839 | 0.315436 | 0.251678 | 0.251678 | 0.127517 | 0 | 0 | 0 | 0.10453 | 861 | 17 | 267 | 50.647059 | 0.773022 | 0 | 0 | 0 | 0 | 0.166667 | 0.586527 | 0.310105 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.083333 | 0 | 0.416667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b01580378afa92e53e9be55b04e4282dd5ebf24 | 2,128 | py | Python | sweden_crs_transformations/transformation/_transform_strategy_from_sweref99_or_rt90_to_wgs84_and_then_to_real_target.py | TomasJohansson/sweden_crs_transformations_4python | d4c0ae1bab2e0f505f3e6f948c1b31b3bf397f04 | [
"MIT"
] | 1 | 2021-10-02T20:05:59.000Z | 2021-10-02T20:05:59.000Z | sweden_crs_transformations/transformation/_transform_strategy_from_sweref99_or_rt90_to_wgs84_and_then_to_real_target.py | TomasJohansson/sweden_crs_transformations_4python | d4c0ae1bab2e0f505f3e6f948c1b31b3bf397f04 | [
"MIT"
] | null | null | null | sweden_crs_transformations/transformation/_transform_strategy_from_sweref99_or_rt90_to_wgs84_and_then_to_real_target.py | TomasJohansson/sweden_crs_transformations_4python | d4c0ae1bab2e0f505f3e6f948c1b31b3bf397f04 | [
"MIT"
] | null | null | null | """
| Copyright (c) Tomas Johansson , http://www.programmerare.com
| The code in this library is licensed with MIT.
| The library is based on the C#.NET library 'sweden_crs_transformations_4net' (https://github.com/TomasJohansson/sweden_crs_transformations_4net)
| which in turn is based on 'MightyLittleGeodesy' (https://github.com/bjornsallarp/MightyLittleGeodesy/)
| which is also released with MIT.
| License information about 'sweden_crs_transformations_4python' and 'MightyLittleGeodesy':
| https://github.com/TomasJohansson/sweden_crs_transformations_4python/blob/python_SwedenCrsTransformations/LICENSE
| For more information see the webpage below.
| https://github.com/TomasJohansson/sweden_crs_transformations_4python
"""
from sweden_crs_transformations.crs_coordinate import CrsCoordinate
from sweden_crs_transformations.crs_projection import CrsProjection
from sweden_crs_transformations.transformation._transform_strategy import _TransformStrategy
class _TransFormStrategy_From_Sweref99OrRT90_to_WGS84_andThenToRealTarget(_TransformStrategy):
# Precondition: sourceCoordinate must be CRS SWEREF99 or RT90, and the target too
def transform(self,
source_coordinate: CrsCoordinate,
final_target_crs_projection: CrsProjection
) -> CrsCoordinate:
from sweden_crs_transformations.transformation._transformer import _Transformer
source_coordinate_projection: CrsProjection = source_coordinate.get_crs_projection()
if (not (
(source_coordinate_projection.is_sweref99() or source_coordinate_projection.is_rt90())
and
(final_target_crs_projection.is_sweref99() or final_target_crs_projection.is_rt90())
)):
_Transformer._throwExceptionMessage(source_coordinate.get_crs_projection(), final_target_crs_projection)
intermediate_crs_projection = CrsProjection.WGS84
intermediate_wgs84_coordinate = _Transformer.transform(source_coordinate, intermediate_crs_projection)
return _Transformer.transform(intermediate_wgs84_coordinate, final_target_crs_projection)
| 60.8 | 146 | 0.795113 | 230 | 2,128 | 7 | 0.356522 | 0.080745 | 0.134161 | 0.074534 | 0.284472 | 0.10559 | 0.10559 | 0.073292 | 0 | 0 | 0 | 0.015873 | 0.141447 | 2,128 | 34 | 147 | 62.588235 | 0.864806 | 0.037124 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.210526 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b066e4df5230c4aa951723634c68a3d9f727def | 1,430 | py | Python | vibromaf/signal/spectrum.py | hofbi/vibromaf | 7678042d18fa3b4ab006283bdbd1b1cc6d84e822 | [
"MIT"
] | 1 | 2022-03-11T19:56:59.000Z | 2022-03-11T19:56:59.000Z | vibromaf/signal/spectrum.py | hofbi/vibromaf | 7678042d18fa3b4ab006283bdbd1b1cc6d84e822 | [
"MIT"
] | null | null | null | vibromaf/signal/spectrum.py | hofbi/vibromaf | 7678042d18fa3b4ab006283bdbd1b1cc6d84e822 | [
"MIT"
] | null | null | null | """Spectrum module"""
import numpy as np
from scipy.stats import norm
def pow2db(power: np.array) -> np.array:
"""
Convert power to decibels
https://de.mathworks.com/help/signal/ref/pow2db.html
"""
return 10.0 * np.log10(power)
def db2pow(decibel: np.array) -> np.array:
"""
Convert decibel to power
https://de.mathworks.com/help/signal/ref/db2pow.html
"""
return np.power(10.0, decibel / 10.0)
def mag2db(power: np.array) -> np.array:
"""
Convert magnitude to decibels
https://de.mathworks.com/help/signal/ref/mag2db.html
"""
return 2 * pow2db(power)
def signal_energy(signal: np.array) -> np.array:
"""Calculate the signal energy"""
return np.sum(np.square(signal, dtype=np.float64))
def compute_normalized_spectral_difference(
reference_spectrum: np.array, distorted_spectrum: np.array
) -> np.array:
"""Compute the normalized difference of two spectra"""
difference = np.sum(
np.abs(db2pow(reference_spectrum) - db2pow(distorted_spectrum)), axis=1
)
return pow2db(
difference
/ (np.sum(np.abs(db2pow(reference_spectrum)), axis=1) + np.finfo(float).eps)
)
def compute_spectral_support(spectrum: np.array, scale: float = 12) -> np.array:
"""Compute the spectral support of perceptual spectrum using a normal distribution cdf"""
return np.apply_along_axis(norm.cdf, 1, spectrum, scale=scale)
| 27.5 | 93 | 0.678322 | 196 | 1,430 | 4.882653 | 0.341837 | 0.095089 | 0.047022 | 0.073145 | 0.287356 | 0.265413 | 0.211076 | 0.177638 | 0.087774 | 0 | 0 | 0.025795 | 0.186713 | 1,430 | 51 | 94 | 28.039216 | 0.797077 | 0.290909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.272727 | false | 0 | 0.090909 | 0 | 0.636364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b0823806306e95de80b37510219b6b55f6cacc5 | 1,484 | py | Python | src/condor_tests/ornithology/io.py | sridish123/htcondor | 481d975fd8602242f6a052aab04e20b0b560db89 | [
"Apache-2.0"
] | 217 | 2015-01-08T04:49:42.000Z | 2022-03-27T10:11:58.000Z | src/condor_tests/ornithology/io.py | sridish123/htcondor | 481d975fd8602242f6a052aab04e20b0b560db89 | [
"Apache-2.0"
] | 185 | 2015-05-03T13:26:31.000Z | 2022-03-28T03:08:59.000Z | src/condor_tests/ornithology/io.py | sridish123/htcondor | 481d975fd8602242f6a052aab04e20b0b560db89 | [
"Apache-2.0"
] | 133 | 2015-02-11T09:17:45.000Z | 2022-03-31T07:28:54.000Z | # Copyright 2019 HTCondor Team, Computer Sciences Department,
# University of Wisconsin-Madison, WI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import textwrap
from pathlib import Path
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# TODO: does this way of doing permissions work on Windows?
def write_file(path: Path, text: str, permissions: int = 0o777) -> Path:
"""
Write the given ``text`` to a new file at the given ``path``, stomping
anything that might exist there.
Parameters
----------
path
The path to write to.
text
The text to write.
permissions
The permissions to give the file.
Returns
-------
path : pathlib.Path
The path the file was written to (as an absolute path).
"""
path = Path(path).absolute()
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(textwrap.dedent(text))
path.chmod(permissions)
return path
| 29.68 | 74 | 0.703504 | 210 | 1,484 | 4.938095 | 0.547619 | 0.057859 | 0.025072 | 0.030858 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010239 | 0.210243 | 1,484 | 49 | 75 | 30.285714 | 0.874573 | 0.679919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.020408 | 0 | 1 | 0.090909 | false | 0 | 0.272727 | 0 | 0.454545 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b121fd3a533e1e023e1b1b4e531b0bbc5c02d6c | 621 | py | Python | main.py | m-zakeri/ADAFEST | d13f73682aecded34b4e8fa203435e56dd7a280a | [
"MIT"
] | 2 | 2022-01-04T14:47:35.000Z | 2022-02-23T07:14:11.000Z | main.py | m-zakeri/ADAFEST | d13f73682aecded34b4e8fa203435e56dd7a280a | [
"MIT"
] | 1 | 2021-03-20T07:25:30.000Z | 2021-03-20T07:25:30.000Z | main.py | m-zakeri/ADAFEST | d13f73682aecded34b4e8fa203435e56dd7a280a | [
"MIT"
] | 1 | 2022-02-23T07:14:13.000Z | 2022-02-23T07:14:13.000Z | """
The main module of DAFEST project.
ADAFEST is an abbreviation: 'A Data-Driven Approach to Estimating / Evaluating Software Testability'
The full version of source code will be available
as soon as the relevant paper(s) are published.
"""
class Main():
"""Welcome to project ADAFEST
This file contains the main script
"""
@classmethod
def print_welcome(cls, name) -> None:
"""
Print welcome message
:param name:
:return:
"""
print(f'Welcome to the project {name}.')
# Main driver
if __name__ == '__main__':
Main.print_welcome('ADAFEST')
| 20.7 | 101 | 0.648953 | 78 | 621 | 5.038462 | 0.653846 | 0.091603 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.256039 | 621 | 29 | 102 | 21.413793 | 0.850649 | 0.571659 | 0 | 0 | 0 | 0 | 0.220588 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0 | 0 | 0.333333 | 0.5 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
2b143c83842d129d2bd45478b75903d5f38b5a19 | 2,652 | py | Python | src/gruenbeck/requests/__init__.py | xancoder/gruenbeck | f6d6e8a27156d56017227a5ed3b79e22eacd8cda | [
"MIT"
] | null | null | null | src/gruenbeck/requests/__init__.py | xancoder/gruenbeck | f6d6e8a27156d56017227a5ed3b79e22eacd8cda | [
"MIT"
] | null | null | null | src/gruenbeck/requests/__init__.py | xancoder/gruenbeck | f6d6e8a27156d56017227a5ed3b79e22eacd8cda | [
"MIT"
] | null | null | null | import random
import xml.etree.ElementTree
import requests
def get_data(host, parameters):
result_data = {}
url = f'http://{host}/mux_http'
request_id = random.randint(4000, 6000)
payload_header = f'id={request_id}&show='
data = '|'.join(parameters)
payload = f'{payload_header}{data}~'
headers = {'Content-Type': 'application/x-www-form-urlencoded'}
response_data = requests.post(url, data=payload, headers=headers)
try:
root = xml.etree.ElementTree.fromstring(response_data.text)
except xml.etree.ElementTree.ParseError as err:
raise ValueError(f"[-] failed to parse xml: {err}")
for child in root:
if child.tag == 'code':
continue
result_data.update({
child.tag: child.text
})
return result_data
if __name__ == '__main__':
host_device = 'softliq-sc-ae-85-48'
input_data = {
'D_Y_2_1': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'gestern'},
'D_Y_2_2': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 2 Tagen'},
'D_Y_2_3': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 3 Tagen'},
'D_Y_2_4': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 4 Tagen'},
'D_Y_2_5': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 5 Tagen'},
'D_Y_2_6': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 6 Tagen'},
'D_Y_2_7': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 7 Tagen'},
'D_Y_2_8': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 8 Tagen'},
'D_Y_2_9': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 9 Tagen'},
'D_Y_2_10': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 10 Tagen'},
'D_Y_2_11': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 11 Tagen'},
'D_Y_2_12': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 12 Tagen'},
'D_Y_2_13': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 13 Tagen'},
'D_Y_2_14': {'access': 'read', 'device': '', 'value': 'Int', 'unit': '[l]', 'code': '', 'note': 'vor 14 Tagen'},
}
result = get_data(host_device, input_data)
print(result)
| 47.357143 | 120 | 0.519608 | 345 | 2,652 | 3.802899 | 0.269565 | 0.021341 | 0.032012 | 0.224085 | 0.424543 | 0.424543 | 0.424543 | 0.424543 | 0.424543 | 0.396341 | 0 | 0.029929 | 0.206259 | 2,652 | 55 | 121 | 48.218182 | 0.593349 | 0 | 0 | 0 | 0 | 0 | 0.368401 | 0.029035 | 0 | 0 | 0 | 0 | 0 | 1 | 0.023256 | false | 0 | 0.069767 | 0 | 0.116279 | 0.023256 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b1fa39d20f7750128e2a06b8ef41f905c26ad90 | 4,000 | py | Python | scripts/run_scripts/main.py | ECP-copa/CabanaPIC | ac83e086cad3b6a3307abec26fb276ce7307143e | [
"BSD-3-Clause"
] | 10 | 2019-07-30T18:52:55.000Z | 2021-02-08T07:22:33.000Z | scripts/run_scripts/main.py | ECP-copa/CabanaPIC | ac83e086cad3b6a3307abec26fb276ce7307143e | [
"BSD-3-Clause"
] | 28 | 2019-04-16T21:15:34.000Z | 2021-02-08T20:16:44.000Z | scripts/run_scripts/main.py | ECP-copa/CabanaPIC | ac83e086cad3b6a3307abec26fb276ce7307143e | [
"BSD-3-Clause"
] | 2 | 2019-05-08T17:30:01.000Z | 2019-06-27T16:12:15.000Z | from git import Repo
import subprocess
import os, shutil
# I use this later to lazily generate an error with a message
class CustomError(Exception):
pass
repo_path = "../../"
r = Repo(repo_path)
repo_heads = r.heads # or it's alias: r.branches
repo_heads_names = [h.name for h in repo_heads]
#kokkos_src = '/Users/bird/kokkos/'
#kokkos_install = '/Users/bird/kokkos/build/install'
#cabana_install = '/Users/bird/Cabana/build/build/install' # not a typo, it's in a dumb path
#platforms = ["Serial", "CPU", "GPU", "UVM"]
platforms = ["Serial", "CPU", "GPU"]
#platforms = ["CPU", "GPU"]
#platforms = ["GPU"]
#platforms = ["CPU"]
CXX = "g++"
#arch = 'Volta70'
arch = 'Kepler35'
subprocess.check_call(['./timing_lib.sh'])
this_build_dir = 'build'
kokkos_dirs = {}
cabana_dirs = {}
home_dir = os.environ['HOME']
# Build Dependencies
# TODO: make this configurable
kokkos_root = os.path.join(home_dir,'kokkos')
cabana_root = os.path.join(home_dir,'Cabana')
# Check we can find Kokkos and Cabana
if not os.path.isdir(kokkos_root):
raise CustomError("Can't find kokkos")
if not os.path.isdir(cabana_root):
raise CustomError("Can't find Cabana")
# Copy Kokkos and Cabana to be inside this dir
def copy_and_overwrite(from_path, to_path):
if os.path.exists(to_path):
shutil.rmtree(to_path)
shutil.copytree(from_path, to_path)
def copy_if_safe(from_path, to_path):
if not os.path.isdir(to_path):
shutil.copytree(from_path, to_path)
# only copy if they don't exist already
kokkos_new = os.path.join(this_build_dir,'kokkos')
copy_if_safe(kokkos_root, kokkos_new)
cabana_new = os.path.join(this_build_dir,'cabana')
copy_if_safe(cabana_root, cabana_new)
# Build Dependencies
for plat in platforms:
install_dir = "build-" + plat
# Do Build
print("build_kokkos.sh " + CXX + " " + kokkos_new + " " + install_dir + " " + plat + " " + arch)
subprocess.check_call(['./build_kokkos.sh', CXX, kokkos_new, install_dir, plat, arch])
print("./build_cabana.sh " + " " + CXX + " " + os.path.join(kokkos_new,install_dir,'install') + " " + cabana_new + " " + install_dir + " " + plat)
subprocess.check_call(['./build_cabana.sh', CXX, os.path.join(kokkos_new,install_dir,'install'), cabana_new, install_dir, plat])
# Save dirs, relative to root
cabana_dirs[plat] = install_dir
kokkos_dirs[plat] = install_dir
# Iterate over *local* git branches
for branch in repo_heads_names:
print("Working on branch " + branch)
for plat in platforms:
print(plat)
# TODO: throughout these scripts we assume ./instal is the install dir! abstract it.
cabana_install = os.path.join( cabana_dirs[plat], 'install')
kokkos_install = os.path.join( kokkos_dirs[plat], 'install')
# For each repo, check it out into a new folder and build it
#clone_path = './' + branch
clone_path = os.path.join('./', this_build_dir, branch)
print("!!!! WORKING ON " + clone_path)
# look to see if the folder already exists:
if not os.path.isdir(clone_path):
# if it does... delete it (!)
#print("Deleting " + clone_path)
# We need to delete where it will build only one platforms worth,
# or hoist the clone
#shutil.rmtree(clone_path + build??)
# OR if it does... skip
#continue
# clone it
cloned = Repo.clone_from(
repo_path,
clone_path,
branch=branch
)
pwd = os.getcwd()
kokkos_full_path = os.path.join(pwd, kokkos_new, kokkos_install)
cabana_full_path = os.path.join(pwd, cabana_new, cabana_install)
print("kk full path " + kokkos_full_path)
print("./build_and_run.sh " + clone_path + " g++ " + kokkos_full_path + " " + cabana_full_path + " " + plat)
subprocess.check_call(['./build_and_run.sh', clone_path, "g++", kokkos_full_path, cabana_full_path, plat])
| 32.258065 | 150 | 0.6525 | 569 | 4,000 | 4.383128 | 0.240773 | 0.038492 | 0.044106 | 0.017642 | 0.303929 | 0.251002 | 0.186047 | 0.165998 | 0.138733 | 0.138733 | 0 | 0.001278 | 0.2175 | 4,000 | 123 | 151 | 32.520325 | 0.795527 | 0.2665 | 0 | 0.064516 | 1 | 0 | 0.109655 | 0 | 0 | 0 | 0 | 0.00813 | 0 | 1 | 0.032258 | false | 0.016129 | 0.048387 | 0 | 0.096774 | 0.112903 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b221e9fcd75b4225d8486366160ca6b79866358 | 566 | py | Python | misago/misago/socialauth/admin/tests/test_providers_list.py | vascoalramos/misago-deployment | 20226072138403108046c0afad9d99eb4163cedc | [
"MIT"
] | 2 | 2021-03-06T21:06:13.000Z | 2021-03-09T15:05:12.000Z | misago/misago/socialauth/admin/tests/test_providers_list.py | vascoalramos/misago-deployment | 20226072138403108046c0afad9d99eb4163cedc | [
"MIT"
] | null | null | null | misago/misago/socialauth/admin/tests/test_providers_list.py | vascoalramos/misago-deployment | 20226072138403108046c0afad9d99eb4163cedc | [
"MIT"
] | null | null | null | from django.urls import reverse
admin_link = reverse("misago:admin:settings:socialauth:index")
def test_providers_list_renders(admin_client):
response = admin_client.get(admin_link)
assert response.status_code == 200
def test_providers_list_renders_with_active_provider(admin_client, provider):
response = admin_client.get(admin_link)
assert response.status_code == 200
def test_providers_list_renders_with_disabled_provider(admin_client, disabled_provider):
response = admin_client.get(admin_link)
assert response.status_code == 200
| 28.3 | 88 | 0.805654 | 76 | 566 | 5.618421 | 0.355263 | 0.154567 | 0.112412 | 0.140515 | 0.653396 | 0.590164 | 0.590164 | 0.590164 | 0.590164 | 0.590164 | 0 | 0.018072 | 0.120141 | 566 | 19 | 89 | 29.789474 | 0.839357 | 0 | 0 | 0.545455 | 0 | 0 | 0.067138 | 0.067138 | 0 | 0 | 0 | 0 | 0.272727 | 1 | 0.272727 | false | 0 | 0.090909 | 0 | 0.363636 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b2a2cb2f442f0c61a738971056437e441c211fd | 340 | py | Python | project_name/urls.py | hotbaby/django-project-skeleton | 41ee89fa96e0df26157d5aea92ce9bcf731c0e13 | [
"MIT"
] | 1 | 2019-01-11T10:10:43.000Z | 2019-01-11T10:10:43.000Z | project_name/urls.py | hotbaby/django-project-skeleton | 41ee89fa96e0df26157d5aea92ce9bcf731c0e13 | [
"MIT"
] | 3 | 2018-12-18T12:15:28.000Z | 2020-06-05T19:38:46.000Z | project_name/urls.py | hotbaby/django-project-skeleton | 41ee89fa96e0df26157d5aea92ce9bcf731c0e13 | [
"MIT"
] | null | null | null | """{{ project_name }} URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
"""
# Django imports
from django.conf.urls import include, url
urlpatterns = [
# Examples:
# url(r'^blog/', include('blog.urls', namespace='blog')),
]
| 26.153846 | 77 | 0.694118 | 45 | 340 | 5.222222 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010381 | 0.15 | 340 | 12 | 78 | 28.333333 | 0.802768 | 0.758824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
2b32f528d435bacb92f58da33b3b9a36bf562ce4 | 449 | py | Python | Problema 2/Problema2.py | ItaloCcosccoAlvis/ADA-UNSA---Lab2 | 00b221f66d1a2149c83b4eb79bbedf3b35a1a2a9 | [
"MIT"
] | null | null | null | Problema 2/Problema2.py | ItaloCcosccoAlvis/ADA-UNSA---Lab2 | 00b221f66d1a2149c83b4eb79bbedf3b35a1a2a9 | [
"MIT"
] | null | null | null | Problema 2/Problema2.py | ItaloCcosccoAlvis/ADA-UNSA---Lab2 | 00b221f66d1a2149c83b4eb79bbedf3b35a1a2a9 | [
"MIT"
] | 1 | 2021-11-01T17:22:48.000Z | 2021-11-01T17:22:48.000Z | # Problema 2
# Generar una arreglo invertido de n números y después buscar un elemento
def generateList(size):
return list(range(size,0,-1))
def searchInvertArray(array, element):
for i in range(0,len(array)):
if array[i] == element:
return True
return False
# Casos de prueba
test1 = generateList(18)
test2 = generateList(89)
print(searchInvertArray(test1,78))
print(searchInvertArray(test2,15)) | 24.944444 | 74 | 0.681514 | 59 | 449 | 5.186441 | 0.694915 | 0.143791 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045714 | 0.22049 | 449 | 18 | 75 | 24.944444 | 0.828571 | 0.218263 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.181818 | false | 0 | 0 | 0.090909 | 0.454545 | 0.181818 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b3da381c58b4f82260a14be2bde2c3527be2594 | 442 | py | Python | src/rmi/bibliotheque.py | e-yuzo/distributed-systems-for-fun | 54e605265a4d78656aba815184b869b96227d3a9 | [
"Apache-2.0"
] | null | null | null | src/rmi/bibliotheque.py | e-yuzo/distributed-systems-for-fun | 54e605265a4d78656aba815184b869b96227d3a9 | [
"Apache-2.0"
] | null | null | null | src/rmi/bibliotheque.py | e-yuzo/distributed-systems-for-fun | 54e605265a4d78656aba815184b869b96227d3a9 | [
"Apache-2.0"
] | null | null | null | from __future__ import print_function
import Pyro4
@Pyro4.expose
class Bibliotheque(object):
books = []
def __init__(self):
self.books = []
def add(self, book):
self.books.append(book)
print("added")
def rm(self, book):
for b in self.books:
if b.title == book.title:
self.books.remove(b)
print("removed")
def ls(self):
return self.books | 19.217391 | 37 | 0.561086 | 54 | 442 | 4.425926 | 0.518519 | 0.188285 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006711 | 0.325792 | 442 | 23 | 38 | 19.217391 | 0.795302 | 0 | 0 | 0 | 0 | 0 | 0.027088 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.235294 | false | 0 | 0.117647 | 0.058824 | 0.529412 | 0.176471 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b3e182cdb9374aefd88508287e3f0812c0864ba | 634 | py | Python | onepanman_api/migrations/0014_auto_20200319_1740.py | Capstone-onepanman/api-server | 1a5174fbc441d2718f3963863590f634ba2014e1 | [
"MIT"
] | null | null | null | onepanman_api/migrations/0014_auto_20200319_1740.py | Capstone-onepanman/api-server | 1a5174fbc441d2718f3963863590f634ba2014e1 | [
"MIT"
] | 12 | 2020-03-24T18:09:30.000Z | 2022-03-12T00:15:07.000Z | onepanman_api/migrations/0014_auto_20200319_1740.py | Capstone-onepanman/api-server | 1a5174fbc441d2718f3963863590f634ba2014e1 | [
"MIT"
] | null | null | null | # Generated by Django 2.2.10 on 2020-03-19 08:40
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('onepanman_api', '0013_auto_20200319_1714'),
]
operations = [
migrations.AlterField(
model_name='problem',
name='rule',
field=models.TextField(db_column='RULE', default='{"obj_num": ,"placement" : , "action" : , "ending": ,}', verbose_name='문제 규칙'),
),
migrations.DeleteModel(
name='ProblemRuleInfo',
),
migrations.DeleteModel(
name='RuleInfo',
),
]
| 25.36 | 141 | 0.570978 | 61 | 634 | 5.803279 | 0.754098 | 0.118644 | 0.141243 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.071111 | 0.290221 | 634 | 24 | 142 | 26.416667 | 0.715556 | 0.072555 | 0 | 0.277778 | 1 | 0 | 0.226962 | 0.039249 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.055556 | 0 | 0.222222 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b4181e01e4e874885f0067383123bf864197817 | 7,231 | py | Python | cinderlib/tests/unit/objects/test_snapshot.py | Akrog/cinderlib | 6481cd9a34744f80bdba130fe9089f1b8b7cb327 | [
"Apache-2.0"
] | 15 | 2018-01-04T13:46:59.000Z | 2020-07-06T13:27:57.000Z | cinderlib/tests/unit/objects/test_snapshot.py | Akrog/cinderlib | 6481cd9a34744f80bdba130fe9089f1b8b7cb327 | [
"Apache-2.0"
] | 12 | 2018-06-13T10:57:55.000Z | 2019-04-04T09:31:44.000Z | cinderlib/tests/unit/objects/test_snapshot.py | Akrog/cinderlib | 6481cd9a34744f80bdba130fe9089f1b8b7cb327 | [
"Apache-2.0"
] | 7 | 2018-03-12T22:41:30.000Z | 2019-01-17T23:16:40.000Z | # Copyright (c) 2018, Red Hat, Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import mock
from cinderlib import exception
from cinderlib import objects
from cinderlib.tests.unit import base
class TestSnapshot(base.BaseTest):
def setUp(self):
super(TestSnapshot, self).setUp()
self.vol = objects.Volume(self.backend_name, size=10,
extra_specs={'e': 'v'},
qos_specs={'q': 'qv'})
self.snap = objects.Snapshot(self.vol,
name='my_snap', description='my_desc')
self.vol._snapshots.append(self.snap)
self.vol._ovo.snapshots.objects.append(self.snap._ovo)
def test_init_from_volume(self):
self.assertIsNotNone(self.snap.id)
self.assertEqual(self.backend, self.snap.backend)
self.assertEqual('my_snap', self.snap.name)
self.assertEqual('my_snap', self.snap.display_name)
self.assertEqual('my_desc', self.snap.description)
self.assertEqual(self.vol.user_id, self.snap.user_id)
self.assertEqual(self.vol.project_id, self.snap.project_id)
self.assertEqual(self.vol.id, self.snap.volume_id)
self.assertEqual(self.vol.size, self.snap.volume_size)
self.assertEqual(self.vol._ovo, self.snap._ovo.volume)
self.assertEqual(self.vol.volume_type_id, self.snap.volume_type_id)
self.assertEqual(self.vol, self.snap.volume)
def test_init_from_ovo(self):
snap2 = objects.Snapshot(None, __ovo=self.snap._ovo)
self.assertEqual(self.snap.backend, snap2.backend)
self.assertEqual(self.snap._ovo, snap2._ovo)
self.assertEqual(self.vol, self.snap.volume)
def test_create(self):
update_vol = {'provider_id': 'provider_id'}
self.backend.driver.create_snapshot.return_value = update_vol
self.snap.create()
self.assertEqual('available', self.snap.status)
self.assertEqual('provider_id', self.snap.provider_id)
self.backend.driver.create_snapshot.assert_called_once_with(
self.snap._ovo)
self.persistence.set_snapshot.assert_called_once_with(self.snap)
def test_create_error(self):
self.backend.driver.create_snapshot.side_effect = exception.NotFound
with self.assertRaises(exception.NotFound) as assert_context:
self.snap.create()
self.assertEqual(self.snap, assert_context.exception.resource)
self.backend.driver.create_snapshot.assert_called_once_with(
self.snap._ovo)
self.assertEqual('error', self.snap.status)
self.persistence.set_snapshot.assert_called_once_with(self.snap)
def test_delete(self):
with mock.patch.object(
self.vol, '_snapshot_removed',
wraps=self.vol._snapshot_removed) as snap_removed_mock:
self.snap.delete()
snap_removed_mock.assert_called_once_with(self.snap)
self.backend.driver.delete_snapshot.assert_called_once_with(
self.snap._ovo)
self.persistence.delete_snapshot.assert_called_once_with(self.snap)
self.assertEqual([], self.vol.snapshots)
self.assertEqual([], self.vol._ovo.snapshots.objects)
self.assertEqual('deleted', self.snap._ovo.status)
@mock.patch('cinderlib.objects.Volume._snapshot_removed')
def test_delete_error(self, snap_removed_mock):
self.backend.driver.delete_snapshot.side_effect = exception.NotFound
with self.assertRaises(exception.NotFound) as assert_context:
self.snap.delete()
self.assertEqual(self.snap, assert_context.exception.resource)
self.backend.driver.delete_snapshot.assert_called_once_with(
self.snap._ovo)
snap_removed_mock.assert_not_called()
self.persistence.delete_snapshot.assert_not_called()
self.assertEqual([self.snap], self.vol.snapshots)
self.assertEqual([self.snap._ovo], self.vol._ovo.snapshots.objects)
self.assertEqual('error_deleting', self.snap._ovo.status)
def test_create_volume(self):
create_mock = self.backend.driver.create_volume_from_snapshot
create_mock.return_value = None
vol2 = self.snap.create_volume(name='new_name', description='new_desc')
create_mock.assert_called_once_with(vol2._ovo, self.snap._ovo)
self.assertEqual('available', vol2.status)
self.assertEqual(1, len(self.backend._volumes))
self.assertEqual(vol2, self.backend._volumes[0])
self.persistence.set_volume.assert_called_once_with(vol2)
self.assertEqual(self.vol.id, self.vol.volume_type_id)
self.assertNotEqual(self.vol.id, vol2.id)
self.assertEqual(vol2.id, vol2.volume_type_id)
self.assertEqual(self.vol.volume_type.extra_specs,
vol2.volume_type.extra_specs)
self.assertEqual(self.vol.volume_type.qos_specs.specs,
vol2.volume_type.qos_specs.specs)
def test_create_volume_error(self):
create_mock = self.backend.driver.create_volume_from_snapshot
create_mock.side_effect = exception.NotFound
with self.assertRaises(exception.NotFound) as assert_context:
self.snap.create_volume()
self.assertEqual(1, len(self.backend._volumes_inflight))
vol2 = list(self.backend._volumes_inflight.values())[0]
self.assertEqual(vol2, assert_context.exception.resource)
create_mock.assert_called_once_with(vol2, self.snap._ovo)
self.assertEqual('error', vol2.status)
self.persistence.set_volume.assert_called_once_with(mock.ANY)
def test_get_by_id(self):
mock_get_snaps = self.persistence.get_snapshots
mock_get_snaps.return_value = [mock.sentinel.snap]
res = objects.Snapshot.get_by_id(mock.sentinel.snap_id)
mock_get_snaps.assert_called_once_with(
snapshot_id=mock.sentinel.snap_id)
self.assertEqual(mock.sentinel.snap, res)
def test_get_by_id_not_found(self):
mock_get_snaps = self.persistence.get_snapshots
mock_get_snaps.return_value = None
self.assertRaises(exception.SnapshotNotFound,
objects.Snapshot.get_by_id, mock.sentinel.snap_id)
mock_get_snaps.assert_called_once_with(
snapshot_id=mock.sentinel.snap_id)
def test_get_by_name(self):
res = objects.Snapshot.get_by_name(mock.sentinel.name)
mock_get_snaps = self.persistence.get_snapshots
mock_get_snaps.assert_called_once_with(
snapshot_name=mock.sentinel.name)
self.assertEqual(mock_get_snaps.return_value, res)
| 47.261438 | 79 | 0.696584 | 935 | 7,231 | 5.136898 | 0.168984 | 0.074953 | 0.079117 | 0.062461 | 0.550281 | 0.47512 | 0.394337 | 0.320841 | 0.287945 | 0.27004 | 0 | 0.005383 | 0.203568 | 7,231 | 152 | 80 | 47.572368 | 0.828616 | 0.083529 | 0 | 0.254098 | 0 | 0 | 0.029794 | 0.006352 | 0 | 0 | 0 | 0 | 0.491803 | 1 | 0.098361 | false | 0 | 0.032787 | 0 | 0.139344 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b4db1ded88b7c952c097270d8ab5f711e96a0de | 627 | py | Python | python/tvm/tensor_graph/testing/datasets/mnist.py | QinHan-Erin/AMOS | 634bf48edf4015e4a69a8c32d49b96bce2b5f16f | [
"Apache-2.0"
] | 22 | 2022-03-18T07:29:31.000Z | 2022-03-23T14:54:32.000Z | python/tvm/tensor_graph/testing/datasets/mnist.py | QinHan-Erin/AMOS | 634bf48edf4015e4a69a8c32d49b96bce2b5f16f | [
"Apache-2.0"
] | null | null | null | python/tvm/tensor_graph/testing/datasets/mnist.py | QinHan-Erin/AMOS | 634bf48edf4015e4a69a8c32d49b96bce2b5f16f | [
"Apache-2.0"
] | 2 | 2022-03-18T08:26:34.000Z | 2022-03-20T06:02:48.000Z | import torch
import torchvision
from torchvision import transforms
def load_mnist_dataset(train_batch_size, test_batch_size=1):
train_set = torchvision.datasets.MNIST(".", train=True, transform=transforms.Compose([transforms.ToTensor()]), download=True)
test_set = torchvision.datasets.MNIST(".", train=False, transform=transforms.Compose([transforms.ToTensor()]), download=True)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=train_batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=test_batch_size, shuffle=False)
return train_loader, test_loader
| 52.25 | 129 | 0.795853 | 82 | 627 | 5.841463 | 0.341463 | 0.112735 | 0.058455 | 0.075157 | 0.584551 | 0.23382 | 0.23382 | 0 | 0 | 0 | 0 | 0.001757 | 0.092504 | 627 | 11 | 130 | 57 | 0.84007 | 0 | 0 | 0 | 0 | 0 | 0.00319 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.111111 | false | 0 | 0.333333 | 0 | 0.555556 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
2b53d1c33b0ce215b092396a108e1d3388123180 | 6,028 | py | Python | MLE.py | tutan0558/Stevens-FE800-Group16-18Spring | 8683fe4e1b7971403d3c5db16ebf5ba5a0f75af4 | [
"BSD-2-Clause"
] | null | null | null | MLE.py | tutan0558/Stevens-FE800-Group16-18Spring | 8683fe4e1b7971403d3c5db16ebf5ba5a0f75af4 | [
"BSD-2-Clause"
] | null | null | null | MLE.py | tutan0558/Stevens-FE800-Group16-18Spring | 8683fe4e1b7971403d3c5db16ebf5ba5a0f75af4 | [
"BSD-2-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Wed Feb 21 18:22:32 2018
@author: 79127
"""
import numpy as np
from scipy.optimize import minimize
import math
import pandas as pd
minimize?
def LL(params, data):
'''
params is a ndarray, [mean, variance, skew, kurt]
'''
mean = params[0]
sig = params[1]
skew = params[2]
kurt = params[3]
x = data
norm = 1/(np.sqrt(2*np.pi)*sig) * np.exp(-(x-mean)**2 / (2*sig**2))
H3 = ((x - mean)/sig)**3 - 3*((x- mean)/sig)
H4 = ((x - mean)/sig)**4 - 6*((x - mean)/sig)**2 + 3
temp1 = 1+skew/(6*sig**3)*H3 + (kurt-3)/(24*sig**4)*H4
temp2 = 1+skew**2/(6*sig**3) + (kurt-3)**2/(24*sig**4)
f = norm*temp1**2/temp2
ll = np.sum(np.log(f))
return -ll
def LL(params, data):
# Params
mean = params[0]
sig = params[1]
skew = params[2]
kurt = params[3]
# Standardize data
x = (data - mean) / sig
# Compose PDF
norm = 1 / (np.sqrt(2*np.pi)) * np.exp(-x**2 / 2)
temp1 = 1+skew/6 * (x**3 - 3*x) + (kurt-3)/24 * (x**4 - 6*x**2 + 3)
temp2 = 1+skew**2 / 6 + (kurt-3)**2 / 24
f = norm * temp1**2 / temp2
# Log Maximum Likelihood Function
ll = np.sum(np.log(f))
return -ll
def MY_GC(x,params):
mean = params[0]
sig = params[1]
skew = params[2]
kurt = params[3]
norm = 1 / (np.sqrt(2*np.pi)) * np.exp(-x**2 / 2)
temp1 = 1+skew/6 * (x**3 - 3*x) + (kurt-3)/24 * (x**4 - 6*x**2 + 3)
temp2 = 1+skew**2 / 6 + (kurt-3)**2 / 24
f = norm * temp1**2 / temp2
return f
MY_GC(1, MLE_result)
x = np.linspace(-7, 7, 10000)
plt.figure(figsize = (15,10))
plt.title('Gram-Charlier With Positive Constraints')
plt.plot(x, MY_GC(x, MLE_result))
SPY100 = pd.read_clipboard()
SPY100.rename(columns = {'V1' : 'SPY_Positive'}, inplace = True)
SPY100.SPY_Positive = SPY100.SPY_Positive/100
SPY100.plot(secondary_y = ('SPY'),)
SPY100.hist(bins = 30, figsize = (15,10))
SPY100.hist(bins = 30, figsize = (7, 10))
SPY100.info()
SPY100
SPY100.info()
SPY100Var1 = SPY100Var1.reset_index()
Var_100Day.reset_index(inplace = True)
SPY100 = SPY100.join(SPY100Var1.SPY)
SPY100.info()
SPY100.SPY_Positive = SPY100.SPY_Positive/100
mean, sig, skew, kurt = np.array([0,1,1,4])
LL(np.array([1,1,1,3]), spy)
spy = np.log(data.SPY / data.SPY.shift(1))
x = spy
MLE_result = minimize(LL, x0=np.array([0,1,1,4]), args=spy, method = 'L-BFGS-B', bounds = ((-0.5, 0.5), (0.5, 1.1), (-200, 200), (0, 10)))['x']
minimize(LL, x0=np.array([0,1,1,4]), args=spy, method = 'L-BFGS-B', bounds = ((-0.5, 0.5), (0.5, 1.1), (-200, 200), (0, 10)))
def Positive(x, params):
mean = params[0]
sig = params[1]
skew = params[2]
kurt = params[3]
norm = 1/(np.sqrt(2*np.pi)*sig) * np.exp(-(x-mean)**2 / (2*sig**2))
H3 = ((x - mean)/sig)**3 - 3*((x - mean)/sig)
H4 = ((x- mean)/sig)**4 - 6*((x - mean)/sig)**2 + 3
temp1 = 1+skew/(6*sig**3)*H3 + (kurt-3)/(24*sig**4)*H4
temp2 = 1+skew**2/(6*sig**3) + (kurt-3)**2/(24*sig**4)
return norm*temp1**2/temp2
x = 5
mean = MLE_result[0]
sig = MLE_result[1]
skew = MLE_result[2]
kurt = MLE_result[3]
norm = 1/(np.sqrt(2*np.pi)*sig) * np.exp(-(x-mean)**2 / (2*sig**2))
H3 = ((data - mean)/sig)**3 - 3*((data - mean)/sig)
H4 = ((data - mean)/sig)**4 - 6*((data - mean)/sig)**2 + 3
temp1 = 1+skew/(6*sig**3)*H3 + (kurt-3)/(24*sig**4)*H4
temp2 = 1+skew**2/(6*sig**3) + (kurt-3)**2/(24*sig**4)
norm*temp1**2/temp2
Positive(1, MLE_result)
x = np.linspace(-0.03, 0.03, 100000)
plt.plot(x, Positive(x, MLE_result))
ss = pd.read_csv('ss.csv')
ss
def integrate(b):
x = np.linspace(-10, b, 1000000)
fx = Positive(x, params)
area = np.sum(fx)*(b+10)/1000000
return area -0.05
Positive(2, params)
integrate(2)
params = np.array(ss.iloc[1,1:5])
bisect(integrate, -20,-2)
params[1]
for i in range(11):
params = np.array(ss.iloc[i,1:5])
print(bisect(integrate, -20, 2))
def LL(params,data):
# Params
skew = params[0]
kurt = params[1]
# Standardize data
# Compose PDF
norm = 1 / (np.sqrt(2*np.pi)) * np.exp(-x**2 / 2)
temp1 = 1+skew/6 * (x**3 - 3*x) + (kurt-3)/24 * (x**4 - 6*x**2 + 3)
temp2 = 1+skew**2 / 6 + (kurt-3)**2 / 24
f = norm * temp1**2 / temp2
# Log Maximum Likelihood Function
ll = np.sum(np.log(f))
return -ll
skew, kurt = minimize(LL, x0=np.array([-1,4]), args=spy, method = 'L-BFGS-B', bounds = ( (-200, 200), (0, 10)))['x']
skew, kurt = minimize(LL, x0=np.array([-1,4]), args=spy[:50], method = 'L-BFGS-B')['x']
def GC_Positive(x, skew, kurt):
norm = 1 / (np.sqrt(2*np.pi)) * np.exp(-x**2 / 2)
temp1 = 1+skew/6 * (x**3 - 3*x) + (kurt-3)/24 * (x**4 - 6*x**2 + 3)
temp2 = 1+skew**2 / 6 + (kurt-3)**2 / 24
return norm *temp1**2 / temp2
x = np.linspace(-5, 5, 100000)
plt.figure(figsize = (15,10))
plt.plot(x,GC_Positive(x, skew, kurt))
area = 0
sets = np.linspace(-5, 5, 100000)
i = 0
while abs(area - 0.05) >= 0.001:
a, b = GC_Positive(sets[i], skew, kurt), GC_Positive(sets[i+1], skew, kurt)
area += (a+b)*(1/10000)
i += 1
def GC_VaR(data):
# Normalize Data
x = (data - np.mean(data)) / np.std(data)
# Log-Likelyhood Estimation
skew, kurt = minimize(LL, x0 = np.array([-1,4]), args = x, method = 'L-BFGS-B')['x']
# Compute VaR
area = 0
sets = np.linspace(-5, 5, 100000)
i = 0
while abs(area - 0.05) >= 0.001:
a, b = GC_Positive(sets[i], skew, kurt), GC_Positive(sets[i+1], skew, kurt)
area += (a+b)*(1/10000)
i += 1
return a
GC_SPY_50Day = spy.rolling(1900).apply(GC_VaR)
Log_Return = np.log(data/data.shift(1))
GC_SPY_100Day = pd.DataFrame()
for ETF in data.columns:
GC_SPY_100Day[ETF] = | 24.806584 | 144 | 0.532349 | 1,035 | 6,028 | 3.063768 | 0.143961 | 0.033113 | 0.020183 | 0.024283 | 0.626616 | 0.548723 | 0.503627 | 0.48029 | 0.48029 | 0.466099 | 0 | 0.121057 | 0.253152 | 6,028 | 243 | 145 | 24.806584 | 0.583296 | 0.034837 | 0 | 0.52027 | 0 | 0 | 0.019485 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.027027 | null | null | 0.006757 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b554d39cace94bec033a5b3f6828f64cc59bedf | 450 | py | Python | setup.py | Nathaniel-Rodriguez/reservoirlib | 367261d17fa762375ae40b4cc2ffab1de2113858 | [
"MIT"
] | 4 | 2018-07-01T20:17:08.000Z | 2020-04-19T06:56:43.000Z | setup.py | Nathaniel-Rodriguez/reservoirlib | 367261d17fa762375ae40b4cc2ffab1de2113858 | [
"MIT"
] | null | null | null | setup.py | Nathaniel-Rodriguez/reservoirlib | 367261d17fa762375ae40b4cc2ffab1de2113858 | [
"MIT"
] | 5 | 2019-08-20T02:04:54.000Z | 2021-08-16T22:11:38.000Z | from setuptools import setup
setup(name='reservoirlib',
version='0.1',
description='Python 3 library that provides utilities for creating and'
' training reservoir computers.',
author='Nathaniel Rodriguez',
packages=['reservoirlib'],
url='https://github.com/Nathaniel-Rodriguez/reservoirlib.git',
install_requires=[
'numpy',
'scipy'
],
include_package_data=True)
| 30 | 77 | 0.633333 | 44 | 450 | 6.409091 | 0.886364 | 0.12766 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008955 | 0.255556 | 450 | 14 | 78 | 32.142857 | 0.832836 | 0 | 0 | 0 | 0 | 0 | 0.44 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.076923 | 0 | 0.076923 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b59f33e04abb7e7a0a838142a38b316dd787aec | 2,349 | py | Python | Python/Deep Learning/Krish Naik/How to Select how many hidden layer and neurons in a neural network.py | omkarsutar1255/Python-Data | 169d0c54b23d9dd5a7f1aea41ab385121c3b3c63 | [
"CC-BY-3.0"
] | null | null | null | Python/Deep Learning/Krish Naik/How to Select how many hidden layer and neurons in a neural network.py | omkarsutar1255/Python-Data | 169d0c54b23d9dd5a7f1aea41ab385121c3b3c63 | [
"CC-BY-3.0"
] | null | null | null | Python/Deep Learning/Krish Naik/How to Select how many hidden layer and neurons in a neural network.py | omkarsutar1255/Python-Data | 169d0c54b23d9dd5a7f1aea41ab385121c3b3c63 | [
"CC-BY-3.0"
] | null | null | null | # todo: How to Select how many hidden layer and neurons in a neural network
# Importing the libraries
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Activation
# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
dataset.head()
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features=[1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
def create_model(layers, activation):
model = Sequential()
for i, nodes in enumerate(layers):
if i == 0:
model.add(Dense(nodes, input_dim=X_train.shape[1]))
model.add(Activation(activation))
else:
model.add(Dense(nodes))
model.add(Activation(activation))
model.add(Dense(1)) # Note: no activation beyond this point
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
model = KerasClassifier(build_fn=create_model, verbose=0)
print(model)
layers = [[20], [40, 20], [45, 30, 15]]
activations = ['sigmoid', 'relu']
param_grid = dict(layers=layers, activation=activations, batch_size=[128, 256], epochs=[30])
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(X_train, y_train)
print([grid_result.best_score_, grid_result.best_params_])
pred_y = grid.predict(X_test)
y_pred = (pred_y > 0.5)
print(y_pred)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)
from sklearn.metrics import accuracy_score
score = accuracy_score(y_test, y_pred)
print(score)
| 30.506494 | 92 | 0.747126 | 340 | 2,349 | 4.973529 | 0.382353 | 0.03903 | 0.030751 | 0.026611 | 0.10408 | 0.049675 | 0 | 0 | 0 | 0 | 0 | 0.021782 | 0.14006 | 2,349 | 76 | 93 | 30.907895 | 0.815347 | 0.108983 | 0 | 0.038462 | 0 | 0 | 0.029257 | 0 | 0 | 0 | 0 | 0.013158 | 0 | 1 | 0.019231 | false | 0 | 0.192308 | 0 | 0.230769 | 0.096154 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b604002fb5b796426d2078ad7992b9e321f4415 | 804 | py | Python | A3C/agent.py | gungui98/deeprl-a3c-ai2thor | 5a2699f0a4bc5fe8cd54b0f38b898e023d163e29 | [
"MIT"
] | 1 | 2020-12-14T13:11:24.000Z | 2020-12-14T13:11:24.000Z | A3C/agent.py | gungui98/deeprl-a3c-ai2thor | 5a2699f0a4bc5fe8cd54b0f38b898e023d163e29 | [
"MIT"
] | 13 | 2020-01-28T22:42:44.000Z | 2022-03-11T23:47:10.000Z | A3C/agent.py | gungui98/deeprl-a3c-ai2thor | 5a2699f0a4bc5fe8cd54b0f38b898e023d163e29 | [
"MIT"
] | 2 | 2019-06-26T05:03:12.000Z | 2021-03-29T08:26:07.000Z | import numpy as np
from keras.optimizers import RMSprop
class Agent:
""" Agent Generic Class
"""
def __init__(self, inp_dim, out_dim, lr, tau = 0.001):
self.inp_dim = inp_dim
self.out_dim = out_dim
self.tau = tau
self.rms_optimizer = RMSprop(lr=lr, epsilon=0.1, rho=0.99)
def fit(self, inp, targ):
""" Perform one epoch of training
"""
self.model.fit(self.reshape(inp), targ, epochs=1, verbose=0)
def predict(self, inp):
""" Critic Value Prediction
"""
return self.model.predict(self.reshape(inp))
def reshape(self, x):
if len(x.shape) < 4 and len(self.inp_dim) > 2: return np.expand_dims(x, axis=0)
elif len(x.shape) < 2: return np.expand_dims(x, axis=0)
else: return x
| 28.714286 | 87 | 0.599502 | 122 | 804 | 3.836066 | 0.45082 | 0.074786 | 0.064103 | 0.064103 | 0.106838 | 0.106838 | 0.106838 | 0.106838 | 0 | 0 | 0 | 0.02735 | 0.272388 | 804 | 27 | 88 | 29.777778 | 0.77265 | 0.108209 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0.125 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b6092497777272f3c9eff71d04f360c6510a763 | 12,676 | py | Python | genestack_client/scripts/genestack_user_setup.py | genestack/python-client | 083eb0508dc99c7575ba7f115595f2535f007583 | [
"MIT"
] | 2 | 2017-08-30T22:32:59.000Z | 2021-07-20T10:08:23.000Z | genestack_client/scripts/genestack_user_setup.py | genestack/python-client | 083eb0508dc99c7575ba7f115595f2535f007583 | [
"MIT"
] | 58 | 2015-10-19T08:36:00.000Z | 2020-12-07T13:48:17.000Z | genestack_client/scripts/genestack_user_setup.py | genestack/python-client | 083eb0508dc99c7575ba7f115595f2535f007583 | [
"MIT"
] | 6 | 2015-10-21T21:43:45.000Z | 2021-01-06T20:33:53.000Z | #!python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from future import standard_library
standard_library.install_aliases()
from builtins import input
from builtins import *
import os
import re
import sys
from argparse import ArgumentParser
from getpass import getpass
from operator import attrgetter
from genestack_client import GenestackAuthenticationException
from genestack_client.genestack_shell import Command, GenestackShell
from genestack_client.settings import DEFAULT_HOST, User, config
from genestack_client.utils import interactive_select
def input_host():
host = input('host [%s]: ' % DEFAULT_HOST).strip()
return host or DEFAULT_HOST
def validate_alias(alias):
expression = re.compile('[a-zA-Z0-9_@\-]+$')
return bool(alias and expression.match(alias))
def input_alias(existing):
print('Please input alias. (Alias can contain: letters (a-z, A-Z), '
'digits (0-9), at-sign (@), underscore (_), hyphen (-))')
while True:
alias = input('alias: ').strip()
if not alias:
print('Alias cannot be empty')
continue
if not validate_alias(alias):
print('Restricted symbols message')
continue
if alias in existing:
print('Alias must be unique')
continue
return alias
def create_user_from_input(host, alias):
"""
Ask credentials interactively and return user that can login to platform.
:param host: server host
:type host: basestring
:param alias: user alias
:type alias: basestring
:return: user
:rtype: User
"""
by_token = 'by token'
items = [by_token, 'by email and password']
use_token = interactive_select(items, 'Select authentication') == by_token
if use_token:
return create_user_from_token(host, alias=alias)
else:
return create_user_from_input_email_and_password(host, alias=alias)
def create_user_from_input_email_and_password(host, alias=None):
"""
Ask email and password, check that it is possible to login with this credentials
and return user.
:param host: server host
:type host: basestring
:param alias: user alias
:type alias: basestring
:return: user
:rtype: User
"""
print('Specify email and password for host: "%s"' % host, end=' ')
if alias:
print('and alias: "%s"' % alias)
else:
print()
user_login = None
while True:
if user_login:
res = input('Please specify your user login (email) [%s]: ' % user_login).strip()
if res:
user_login = res
else:
user_login = input('Please specify your user login (email): ').strip()
if not user_login:
print('Login cannot be empty')
continue
user_password = getpass('Please specify your password for %s: ' % user_login)
if not user_password:
print('Password cannot be empty')
continue
if not user_login or not user_password:
print()
continue
user = User(user_login, host=host, password=user_password, alias=alias)
try:
user.get_connection()
break
except GenestackAuthenticationException:
print('Your username or password was incorrect, please try again')
return user
def create_user_from_token(host, alias=None):
print('Host: %s' % host)
msg = 'Please specify Genestack API token%s: '
with_alias = '' if not alias else ' for "%s"' % alias
msg = msg % with_alias
while True:
token = getpass(msg)
if not token:
print('Token cannot be empty')
continue
user = User(email=None, host=host, password=None, alias=alias, token=token)
try:
user.get_connection()
break
except GenestackAuthenticationException:
print('Could not login with given token, please try again')
return user
def check_config():
config_path = config.get_settings_file()
if not os.path.exists(config_path):
print('You do not seem to have a config file yet. '
'Please run `genestack-user-setup init`. Exiting')
exit(1)
class AddUser(Command):
COMMAND = 'add'
DESCRIPTION = 'Add new user.'
OFFLINE = True
def run(self):
alias = input_alias(config.users.keys())
host = input_host()
user = create_user_from_input(host, alias)
config.add_user(user)
print('User "%s" has been created' % user.alias)
def select_user(users, selected=None):
"""
Choose user from users stored in config.
:param users:
:param selected:
:return:
:rtype: User
"""
user_list = sorted(users.values(), key=lambda x: x.alias)
return interactive_select(user_list, 'Select user', to_string=attrgetter('alias'), selected=selected)
class ChangePassword(Command):
COMMAND = 'change-password'
DESCRIPTION = 'Change password for user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias for user to change password', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users)
if not user.email:
print('User without email could be authorized only by token')
return
while True:
user.password = getpass('Input password for %s: ' % user.alias.encode('utf-8'))
try:
user.get_connection()
break
except GenestackAuthenticationException:
continue
config.change_password(user.alias, user.password)
print('Password has been changed successfully')
class ChangeToken(Command):
COMMAND = 'change-token'
DESCRIPTION = 'Change token for user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>',
help='Alias for user to change token for', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users)
new_user = create_user_from_token(user.host, alias=user.alias)
user.token = new_user.token
config.change_token(user.alias, user.token)
print('Token has been changed successfully')
class SetDefault(Command):
COMMAND = 'default'
DESCRIPTION = 'Set default user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias for user to change password', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users, selected=config.default_user)
if user.alias != config.default_user.alias:
config.set_default_user(user)
print('Default user has been set to "%s"' % user.alias)
else:
print('Default user has not been changed')
class Remove(Command):
COMMAND = 'remove'
DESCRIPTION = 'Remove user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias for user to change password', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
user = select_user(users)
if user.alias == config.default_user.alias:
print('Cannot delete default user')
return
config.remove_user(user)
print('"%s" has been removed from config' % user.alias)
class RenameUser(Command):
COMMAND = 'rename'
DESCRIPTION = 'Rename user.'
OFFLINE = True
def update_parser(self, parent):
parent.add_argument('alias', metavar='<alias>', help='Alias to be renamed', nargs='?')
parent.add_argument('new_alias', metavar='<new_alias>', help='New alias', nargs='?')
def run(self):
check_config()
users = config.users
user = users.get(self.args.alias)
if not user:
print('Select user to rename')
user = select_user(users)
if not self.args.new_alias or not validate_alias(self.args.new_alias):
print('Enter new alias')
new_alias = input_alias(users.keys())
else:
new_alias = self.args.new_alias
new_user = User(email=user.email, alias=new_alias, host=user.host, password=user.password,
token=user.token)
config.add_user(new_user, save=False)
if user.alias == config.default_user.alias:
config.set_default_user(new_user, save=False)
config.remove_user(user)
print('"%s" alias changed to "%s"' % (user.alias, new_user.alias))
class List(Command):
COMMAND = 'list'
DESCRIPTION = 'List all users.'
OFFLINE = True
def run(self):
check_config()
users = sorted(config.users.items())
default_user_alias = config.default_user and config.default_user.alias
for key, user in users:
print()
print('%s%s:' % (key, ' (default)' if default_user_alias == key else ''))
print(' %-10s%s' % ('email', user.email))
print(' %-10s%s' % ('host', user.host))
class Path(Command):
COMMAND = 'path'
DESCRIPTION = 'Show path to configuration file.'
OFFLINE = True
def run(self):
print(config.get_settings_file())
class Init(Command):
COMMAND = 'init'
DESCRIPTION = 'Create default settings.'
OFFLINE = True
def get_command_parser(self, parser=None):
parser = parser or ArgumentParser(description=self.DESCRIPTION)
parser.description = self.DESCRIPTION
group = parser.add_argument_group('command arguments')
self.update_parser(group)
group.add_argument('-H', '--host', default=DEFAULT_HOST,
help='Genestack host, '
'change it to connect somewhere else than %s' % DEFAULT_HOST,
metavar='<host>')
return parser
def run(self):
"""
Create config file if it is not present.
Catch ``KeyboardInterrupt`` and ``EOFError`` is required here for case
when this command is run for first time and in shell mode.
If we don't quit here, shell will continue execution and ask credentials once more.
"""
# Hardcoded alias that created for the first user only.
# Normal usecase is when user have single account and don't care about alias name.
# Advanced users can rename alias.
default_alias = 'Default'
try:
config_path = config.get_settings_file()
if os.path.exists(config_path):
print('A config file already exists at %s' % config_path)
return
print('If you do not have a Genestack account, you need to create one first')
user = create_user_from_input(self.args.host, default_alias)
config.add_user(user) # adding first user make him default.
print('Config file at "%s" has been created successfully' % config_path)
except (KeyboardInterrupt, EOFError):
sys.stdout.flush()
sys.stderr.write('\nError: Init is not finished\n')
exit(1)
class UserManagement(GenestackShell):
DESCRIPTION = 'Genestack user management application.'
COMMAND_LIST = [
Init,
List,
AddUser,
SetDefault,
ChangePassword,
ChangeToken,
Path,
Remove,
RenameUser
]
intro = "User setup shell.\nType 'help' for list of available commands.\n\n"
prompt = 'user_setup> '
def set_shell_user(self, args):
config_path = config.get_settings_file()
if not os.path.exists(config_path):
print('No config file was found, creating one interactively')
self.process_command(Init(), ['--host', args.host or DEFAULT_HOST], False)
args.host = None # do not provide host for future use of arguments
def main():
shell = UserManagement()
shell.cmdloop()
if __name__ == '__main__':
main()
| 31.376238 | 108 | 0.620069 | 1,535 | 12,676 | 4.991531 | 0.170033 | 0.023493 | 0.010572 | 0.014096 | 0.32746 | 0.283738 | 0.23597 | 0.209997 | 0.192247 | 0.180762 | 0 | 0.001316 | 0.28053 | 12,676 | 403 | 109 | 31.454094 | 0.838816 | 0.083465 | 0 | 0.352941 | 0 | 0 | 0.185835 | 0.001834 | 0 | 0 | 0 | 0 | 0 | 1 | 0.086505 | false | 0.083045 | 0.058824 | 0 | 0.32872 | 0.124567 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
2b619974b791b3dc319a188eb0b8ea38e14618c6 | 386 | py | Python | build_scripts/install_deps.py | kamranrad1993/ngfx | eeef60e3419a88371a97e8bc3109d2b35b82cc89 | [
"Apache-2.0",
"MIT-0",
"MIT"
] | 12 | 2021-04-03T16:50:22.000Z | 2022-03-18T07:14:14.000Z | build_scripts/install_deps.py | kamranrad1993/ngfx | eeef60e3419a88371a97e8bc3109d2b35b82cc89 | [
"Apache-2.0",
"MIT-0",
"MIT"
] | 6 | 2021-05-06T21:02:19.000Z | 2022-02-14T11:57:27.000Z | build_scripts/install_deps.py | kamranrad1993/ngfx | eeef60e3419a88371a97e8bc3109d2b35b82cc89 | [
"Apache-2.0",
"MIT-0",
"MIT"
] | 5 | 2021-06-11T20:15:37.000Z | 2022-03-18T07:14:21.000Z | from common import *
os.chdir(EXTERNAL_DIR)
PKGS = env('PKGS', 'all')
ninja_template = read_file(f'{SCRIPT_DIR}/install_deps_{OS_LOWER_CASE}.ninja.in')
write_file(f'install_deps_{OS_LOWER_CASE}.ninja', ninja_template.format(**ENV_PARAMS))
for key, val in ENV_PARAMS.items():
print(f'{key} = {val}')
shell(f'ninja -v -j 1 -f install_deps_{OS_LOWER_CASE}.ninja {PKGS}')
| 32.166667 | 87 | 0.715026 | 64 | 386 | 4 | 0.515625 | 0.128906 | 0.152344 | 0.210938 | 0.324219 | 0.324219 | 0.21875 | 0 | 0 | 0 | 0 | 0.00295 | 0.121762 | 386 | 11 | 88 | 35.090909 | 0.752212 | 0 | 0 | 0 | 0 | 0 | 0.432 | 0.314667 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.125 | 0 | 0.125 | 0.125 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b632497a6ea6fdf0bb92e774fc99e0e6b5fa507 | 957 | py | Python | en16931/tests/test_tax.py | invinet/python-en16931 | f6671f86e8d578c3c82a48134426f89ec13b160c | [
"Apache-2.0"
] | 9 | 2018-07-09T10:34:27.000Z | 2021-10-13T20:11:04.000Z | en16931/tests/test_tax.py | invinet/python-en16931 | f6671f86e8d578c3c82a48134426f89ec13b160c | [
"Apache-2.0"
] | null | null | null | en16931/tests/test_tax.py | invinet/python-en16931 | f6671f86e8d578c3c82a48134426f89ec13b160c | [
"Apache-2.0"
] | 1 | 2022-02-07T15:30:53.000Z | 2022-02-07T15:30:53.000Z | import pytest
from collections import Hashable
from en16931.tax import Tax
class TestTaxes:
def test_initialization(self):
t = Tax(0.21, "S", "IVA")
assert t
def test_hashable(self):
t = Tax(0.21, "S", "IVA")
assert isinstance(t, Hashable)
def test_percent_less_than_one(self):
t = Tax(0.21, "S", "IVA")
assert t.percent == 0.21
def test_percent_more_than_one(self):
t = Tax(21, "S", "IVA")
assert t.percent == 0.21
def test_percent_string(self):
t = Tax("21", "S", "IVA")
assert t.percent == 0.21
def test_cmp_with_None(self):
t = Tax("21", "S", "IVA")
assert not (t == None)
def test_value_error_bad_percent(self):
with pytest.raises(ValueError):
t = Tax("asdf", "S", "IVA")
def test_value_error_bad_category(self):
with pytest.raises(ValueError):
t = Tax("21", "asd", "IVA")
| 23.925 | 44 | 0.572623 | 136 | 957 | 3.860294 | 0.279412 | 0.106667 | 0.091429 | 0.137143 | 0.598095 | 0.495238 | 0.495238 | 0.327619 | 0.287619 | 0.228571 | 0 | 0.045322 | 0.285266 | 957 | 39 | 45 | 24.538462 | 0.722222 | 0 | 0 | 0.357143 | 0 | 0 | 0.045977 | 0 | 0 | 0 | 0 | 0 | 0.214286 | 1 | 0.285714 | false | 0 | 0.107143 | 0 | 0.428571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b6800c9e35b30d919c5fb6ac7e188c956250fd2 | 1,338 | py | Python | qgisserver/migrations/0012_auto_20190305_1011.py | aroiginfraplan/giscube-admin | b7f3131b0186f847f3902df97f982cb288b16a49 | [
"BSD-3-Clause"
] | 5 | 2018-06-07T12:54:35.000Z | 2022-01-14T10:38:38.000Z | qgisserver/migrations/0012_auto_20190305_1011.py | aroiginfraplan/giscube-admin | b7f3131b0186f847f3902df97f982cb288b16a49 | [
"BSD-3-Clause"
] | 140 | 2018-06-18T10:27:28.000Z | 2022-03-23T09:53:15.000Z | qgisserver/migrations/0012_auto_20190305_1011.py | aroiginfraplan/giscube-admin | b7f3131b0186f847f3902df97f982cb288b16a49 | [
"BSD-3-Clause"
] | 1 | 2021-04-13T11:20:54.000Z | 2021-04-13T11:20:54.000Z | # Generated by Django 2.1.7 on 2019-03-05 10:11
from django.db import migrations, models
import qgisserver.models
class Migration(migrations.Migration):
dependencies = [
('qgisserver', '0011_auto_20180803_0824'),
]
operations = [
migrations.AlterField(
model_name='service',
name='visibility',
field=models.CharField(choices=[('private', 'Private'), ('public', 'Public')], default='private', max_length=10),
),
migrations.AlterField(
model_name='service',
name='wms_buffer_enabled',
field=models.BooleanField(default=False, verbose_name='buffer enabled'),
),
migrations.AlterField(
model_name='service',
name='wms_buffer_size',
field=models.CharField(blank=True, help_text='Buffer around tiles, e.g. 64,64', max_length=12, null=True, validators=[qgisserver.models.validate_integer_pair], verbose_name='buffer size'),
),
migrations.AlterField(
model_name='service',
name='wms_tile_sizes',
field=models.TextField(blank=True, help_text='Integer pairs in different lines e.g.<br/>256,256<br/>512,512', null=True, validators=[qgisserver.models.validate_integer_pair_list], verbose_name='tile sizes'),
),
]
| 38.228571 | 219 | 0.637519 | 150 | 1,338 | 5.52 | 0.48 | 0.096618 | 0.120773 | 0.140097 | 0.346618 | 0.346618 | 0.298309 | 0.246377 | 0 | 0 | 0 | 0.049659 | 0.232436 | 1,338 | 34 | 220 | 39.352941 | 0.756573 | 0.033632 | 0 | 0.428571 | 1 | 0 | 0.215337 | 0.039504 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.071429 | 0 | 0.178571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b6933c7fbf6d07d10feccd70de52addfefb38ae | 658 | py | Python | python/tests/structural/test_decorator.py | harkhuang/designpatterns | dfd6623976410882753913498158dcb0ea70c1d2 | [
"Apache-2.0"
] | null | null | null | python/tests/structural/test_decorator.py | harkhuang/designpatterns | dfd6623976410882753913498158dcb0ea70c1d2 | [
"Apache-2.0"
] | null | null | null | python/tests/structural/test_decorator.py | harkhuang/designpatterns | dfd6623976410882753913498158dcb0ea70c1d2 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from patterns.structural.decorator import TextTag, BoldWrapper, ItalicWrapper
class TestTextWrapping(unittest.TestCase):
def setUp(self):
self.raw_string = TextTag('raw but not cruel')
def test_italic(self):
self.assertEqual(ItalicWrapper(self.raw_string).render(), '<i>raw but not cruel</i>')
def test_bold(self):
self.assertEqual(BoldWrapper(self.raw_string).render(), '<b>raw but not cruel</b>')
def test_mixed_bold_and_italic(self):
self.assertEqual(BoldWrapper(ItalicWrapper(self.raw_string)).render(), '<b><i>raw but not cruel</i></b>')
| 34.631579 | 113 | 0.700608 | 89 | 658 | 5.067416 | 0.404494 | 0.070953 | 0.115299 | 0.124169 | 0.259424 | 0.070953 | 0 | 0 | 0 | 0 | 0 | 0.001789 | 0.150456 | 658 | 18 | 114 | 36.555556 | 0.805009 | 0.06383 | 0 | 0 | 0 | 0 | 0.156352 | 0 | 0 | 0 | 0 | 0 | 0.272727 | 1 | 0.363636 | false | 0 | 0.181818 | 0 | 0.636364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b6da2f0c9b5f0cefbd0075526e9eb021fc084e9 | 2,092 | py | Python | dosu/utils.py | tsandrini/DoSU | 312afb1c1dccaf5088b8d5818adf08d5406076ae | [
"MIT"
] | null | null | null | dosu/utils.py | tsandrini/DoSU | 312afb1c1dccaf5088b8d5818adf08d5406076ae | [
"MIT"
] | 2 | 2017-02-07T08:23:34.000Z | 2017-09-09T08:46:25.000Z | dosu/utils.py | tsandrini/dosu | 312afb1c1dccaf5088b8d5818adf08d5406076ae | [
"MIT"
] | null | null | null | """
__/\\\\\\\\\\\\______________________/\\\\\\\\\\\____/\\\________/\\\_
_\/\\\////////\\\__________________/\\\/////////\\\_\/\\\_______\/\\\_
_\/\\\______\//\\\________________\//\\\______\///__\/\\\_______\/\\\_
_\/\\\_______\/\\\_____/\\\\\______\////\\\_________\/\\\_______\/\\\_
_\/\\\_______\/\\\___/\\\///\\\_______\////\\\______\/\\\_______\/\\\_
_\/\\\_______\/\\\__/\\\__\//\\\_________\////\\\___\/\\\_______\/\\\_
_\/\\\_______/\\\__\//\\\__/\\\___/\\\______\//\\\__\//\\\______/\\\__
_\/\\\\\\\\\\\\/____\///\\\\\/___\///\\\\\\\\\\\/____\///\\\\\\\\\/___
_\////////////________\/////_______\///////////________\/////////_____
Created by Tomáš Sandrini
"""
import yaml
import os
from .settings import HOME
class Config:
config_paths = (
HOME + '/.config/dosu/config.yml',
HOME + '/.dosu.yml',
)
def __init__(self):
self.config = self.load_raw_config()
self.subjects = self.load_subjects()
def load_raw_config(self):
for config_path in self.config_paths:
try:
with open(config_path, 'r') as ymlfile:
return yaml.load(ymlfile)
except IOError as e:
continue
else:
return None
def load_subjects(self):
if not self.config:
return None
base = self.get('general.root_dir')
if not base:
print ("DoSU root dir is not defined in config file")
sys.exit(2)
return set([name for name in os.listdir(base) if os.path.isdir(base + '/' + name)])
def get(self, key, fallback=None):
"""
Gets a cached value by its key using dotted notation
"""
try:
tmp = self.config
for fragment in key.split('.'):
tmp = tmp[fragment]
return tmp
except KeyError as e:
return fallback if fallback != None else key
def load_file(path):
with open(path, 'r') as f:
data = f.read()
return data
config = Config()
| 27.168831 | 91 | 0.498566 | 175 | 2,092 | 4.274286 | 0.411429 | 0.053476 | 0.034759 | 0.045455 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.000653 | 0.268164 | 2,092 | 76 | 92 | 27.526316 | 0.487916 | 0.32696 | 0 | 0.097561 | 0 | 0 | 0.074216 | 0.018363 | 0 | 0 | 0 | 0 | 0 | 1 | 0.121951 | false | 0 | 0.073171 | 0 | 0.414634 | 0.02439 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b6dbaaeee834ce10b296c5ec51a9014d0fbab68 | 2,113 | py | Python | mod/bad_images.py | lokal-profil/isfdb_site | 0ce20d6347849926d4eda961ea9249c31519eea5 | [
"BSD-3-Clause"
] | null | null | null | mod/bad_images.py | lokal-profil/isfdb_site | 0ce20d6347849926d4eda961ea9249c31519eea5 | [
"BSD-3-Clause"
] | null | null | null | mod/bad_images.py | lokal-profil/isfdb_site | 0ce20d6347849926d4eda961ea9249c31519eea5 | [
"BSD-3-Clause"
] | null | null | null | #!_PYTHONLOC
#
# (C) COPYRIGHT 2014-2021 Ahasuerus
# ALL RIGHTS RESERVED
#
# The copyright notice above does not evidence any actual or
# intended publication of such source code.
#
# Version: $Revision$
# Date: $Date$
from isfdb import *
from common import *
from isfdblib import *
from SQLparsing import *
from library import *
def PrintTableHeaders():
print '<table class="generic_table">'
print '<tr class="generic_table_header">'
for column in ('#', 'Publication', 'Suspect URL', 'Click Once Resolved'):
print '<th>%s</th>' % column
print '</tr>'
def PrintPubRecord(count, pub_id, url, pub_title, bgcolor):
if bgcolor:
print '<tr align=left class="table1">'
else:
print '<tr align=left class="table2">'
print '<td>%d</td>' % (count)
print '<td>%s</td>' % ISFDBLink('pl.cgi', pub_id, pub_title)
print '<td>%s</td>' % (url)
print '<td>%s</td>' % ISFDBLink('mod/resolve_bad_url.cgi', pub_id, 'Click Once Resolved')
print '</tr>'
if __name__ == '__main__':
PrintPreMod('Publications with Suspect Images')
PrintNavBar()
query = """select bad_images.pub_id, bad_images.image_url, pubs.pub_title
from bad_images, pubs
where pubs.pub_id=bad_images.pub_id
order by pubs.pub_title"""
db.query(query)
result = db.store_result()
num = result.num_rows()
if num:
PrintTableHeaders()
record = result.fetch_row()
bgcolor = 1
count = 1
while record:
pub_id = record[0][0]
url = record[0][1]
pub_title = record[0][2]
PrintPubRecord(count, pub_id, url, pub_title, bgcolor)
record = result.fetch_row()
bgcolor ^= 1
count += 1
print '</table>'
else:
print '<h2>No publications with bad images found</h2>'
PrintPostMod(0)
| 30.185714 | 97 | 0.54567 | 242 | 2,113 | 4.61157 | 0.421488 | 0.035842 | 0.021505 | 0.026882 | 0.207885 | 0.136201 | 0.136201 | 0.136201 | 0 | 0 | 0 | 0.01637 | 0.335069 | 2,113 | 69 | 98 | 30.623188 | 0.777936 | 0.10885 | 0 | 0.125 | 0 | 0 | 0.300587 | 0.066204 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.104167 | null | null | 0.270833 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b7661631e26d7c909ec01b827d25a4ce6c56fb8 | 1,096 | py | Python | nabu/postprocessing/reconstructors/weighted_kmeans.py | Darleen2019/Nabu-MSSS | 5e862cbf846d45b8a317f87588533f3fde9f0726 | [
"MIT"
] | 18 | 2017-10-16T13:12:46.000Z | 2022-02-15T01:20:00.000Z | nabu/postprocessing/reconstructors/weighted_kmeans.py | Darleen2019/Nabu-MSSS | 5e862cbf846d45b8a317f87588533f3fde9f0726 | [
"MIT"
] | null | null | null | nabu/postprocessing/reconstructors/weighted_kmeans.py | Darleen2019/Nabu-MSSS | 5e862cbf846d45b8a317f87588533f3fde9f0726 | [
"MIT"
] | 9 | 2017-10-03T18:10:10.000Z | 2020-11-13T08:26:31.000Z | # Based on: https://towardsdatascience.com/clustering-the-us-population-observation-weighted-k-means-f4d58b370002
import random
import numpy as np
import scipy.spatial
def distance(p1,p2):
return np.linalg.norm(p1,p2)
def cluster_centroids(data,weights, clusters, k):
results=[]
for i in range(k):
results.append( np.average(data[clusters == i],weights=weights[clusters == i],axis=0))
return np.array(results)
def kmeans(data,weights, k, steps=20):
if(np.shape(data)[0] != np.shape(weights)[0]):
print "Dimension data and weights don't match"
# Forgy initialization method: choose k data points randomly.
centroids = data[np.random.choice(np.arange(len(data)), k, False)]
for _ in range(max(steps, 1)):
sqdists = scipy.spatial.distance.cdist(centroids, data, 'euclidean')
# Index of the closest centroid to each data point.
clusters = np.argmin(sqdists, axis=0)
new_centroids = cluster_centroids(data,weights, clusters, k)
if np.array_equal(new_centroids, centroids):
break
centroids = new_centroids
return clusters, centroids
| 29.621622 | 113 | 0.720803 | 157 | 1,096 | 4.987261 | 0.509554 | 0.066411 | 0.051086 | 0.068966 | 0.091954 | 0.091954 | 0 | 0 | 0 | 0 | 0 | 0.021575 | 0.154197 | 1,096 | 36 | 114 | 30.444444 | 0.823085 | 0.201642 | 0 | 0 | 0 | 0 | 0.053961 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.136364 | null | null | 0.045455 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2b7ab663452a80c4be262712173532cb7f06dc77 | 2,339 | py | Python | Web.PY/client-post.py | Phoebus-Ma/Python-Helper | d880729f0bbfbc2b1503602fd74c9177ecd4e970 | [
"MIT"
] | null | null | null | Web.PY/client-post.py | Phoebus-Ma/Python-Helper | d880729f0bbfbc2b1503602fd74c9177ecd4e970 | [
"MIT"
] | null | null | null | Web.PY/client-post.py | Phoebus-Ma/Python-Helper | d880729f0bbfbc2b1503602fd74c9177ecd4e970 | [
"MIT"
] | null | null | null | ###
# Python http post example.
#
# License - MIT.
###
import os
# pip install requests.
import requests
# pip install lxml
# pip install beautifulsoup4
from bs4 import BeautifulSoup
# login github class.
class login_github():
# {
# Initialization function.
def __init__(self):
# {
# Chromium core browser user agent.
self._headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.74 Safari/537.36'
}
self._login_page = 'https://github.com/login'
self._session_page = 'https://github.com/session'
self._session = requests.Session()
# }
# Close session.
def close(self):
# {
self._session.close()
# }
# Get html data.
def datas(self, url_path):
# {
datas = self._session.get(url = url_path, headers = self._headers)
return datas
# }
# Http Get.
def get(self):
# {
html = requests.get(url = self._login_page, headers = self._headers)
soup = BeautifulSoup(html.text, 'lxml')
tokens = soup.find_all('input', type="hidden")[1]
attrs = tokens.attrs['value']
return attrs
# }
# Http Post.
def post(self, Username, Password):
# {
data = {
'commit' : 'Sign in',
'utf8' : ' ✓',
'authenticity_token': self.get(),
'login' : Username,
'password' : Password,
'webauthn-support': ' supported'
}
# Post.
res = self._session.post(
url = self._session_page,
data = data,
headers = self._headers
)
print(res.status_code)
# }
# }
# Main function.
def main():
# {
test_page = 'https://github.com/torvalds/linux'
print('Login Github !')
Username = input('Username or email address: ')
Password = input('Password: ')
login = login_github()
# login.
login.post(Username, Password)
# get data.
datas = login.datas(test_page)
with open('test.html', 'wb') as fd:
fd.write(datas.content)
# close.
login.close()
# }
# Program entry.
if '__main__' == __name__:
main()
| 20.163793 | 128 | 0.535271 | 246 | 2,339 | 4.943089 | 0.430894 | 0.054276 | 0.037007 | 0.044408 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.020566 | 0.334758 | 2,339 | 115 | 129 | 20.33913 | 0.760283 | 0.138948 | 0 | 0 | 0 | 0.019608 | 0.188956 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.117647 | false | 0.078431 | 0.058824 | 0 | 0.235294 | 0.039216 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
2b7b4342609278d1a046c18ba64c0b30f5c732af | 7,772 | py | Python | wqxlib/wqx_v3_0/BiologicalHabitatCollectionInformation.py | FlippingBinary/wqxlib-python | 5aa1d41384928f1faca47d5984485e2efa93174c | [
"MIT"
] | null | null | null | wqxlib/wqx_v3_0/BiologicalHabitatCollectionInformation.py | FlippingBinary/wqxlib-python | 5aa1d41384928f1faca47d5984485e2efa93174c | [
"MIT"
] | null | null | null | wqxlib/wqx_v3_0/BiologicalHabitatCollectionInformation.py | FlippingBinary/wqxlib-python | 5aa1d41384928f1faca47d5984485e2efa93174c | [
"MIT"
] | null | null | null | from yattag import Doc
from .CollectionEffort import CollectionEffort
from .MeasureCompact import MeasureCompact
from .NetInformation import NetInformation
from .SimpleContent import CollectionDescriptionText, PassCount
class BiologicalHabitatCollectionInformation:
"""
Allows for the reporting of biological habitat sample collection information.
"""
__collectionDuration: MeasureCompact
__collectionArea: MeasureCompact
__collectionEffort: CollectionEffort
__reachLengthMeasure: MeasureCompact
__reachWidthMeasure: MeasureCompact
__collectionDescriptionText: CollectionDescriptionText
__passCount: PassCount
__netInformation: NetInformation
def __init__(
self,
o: dict = None,
*,
collectionDuration: MeasureCompact = None,
collectionArea: MeasureCompact = None,
collectionEffort: CollectionEffort = None,
reachLengthMeasure: MeasureCompact = None,
reachWidthMeasure: MeasureCompact = None,
collectionDescriptionText: CollectionDescriptionText = None,
passCount: PassCount = None,
netInformation: NetInformation = None
):
if isinstance(o, BiologicalHabitatCollectionInformation):
# Assign attributes from objects without typechecking
self.__collectionDuration = o.collectionDuration
self.__collectionArea = o.collectionArea
self.__collectionEffort = o.collectionEffort
self.__reachLengthMeasure = o.reachLengthMeasure
self.__reachWidthMeasure = o.reachWidthMeasure
self.__collectionDescriptionText = o.collectionDescriptionText
self.__passCount = o.passCount
self.__netInformation = o.netInformation
elif isinstance(o, dict):
# Assign attributes from dictionary with typechecking
self.collectionDuration = o.get("collectionDuration")
self.collectionArea = o.get("collectionArea")
self.collectionEffort = o.get("collectionEffort")
self.reachLengthMeasure = o.get("reachLengthMeasure")
self.reachWidthMeasure = o.get("reachWidthMeasure")
self.collectionDescriptionText = o.get("collectionDescriptionText")
self.passCount = o.get("passCount")
self.netInformation = o.get("netInformation")
else:
# Assign attributes from named keywords with typechecking
self.collectionDuration = collectionDuration
self.collectionArea = collectionArea
self.collectionEffort = collectionEffort
self.reachLengthMeasure = reachLengthMeasure
self.reachWidthMeasure = reachWidthMeasure
self.collectionDescriptionText = collectionDescriptionText
self.passCount = passCount
self.netInformation = netInformation
@property
def collectionDuration(self) -> MeasureCompact:
"""
The length of time a collection procedure or protocol was performed (e.g. total
energized time for electrofishing, or total time kick net used).
"""
return self.__collectionDuration
@collectionDuration.setter
def collectionDuration(self, val: MeasureCompact) -> None:
"""
The length of time a collection procedure or protocol was performed (e.g. total
energized time for electrofishing, or total time kick net used).
"""
self.__collectionDuration = None if val is None else MeasureCompact(val)
@property
def collectionArea(self) -> MeasureCompact:
"""
The area of a collection procedure or protocol was performed (e.g. total area
coverage for electrofishing, or total area kick net used).
"""
return self.__collectionArea
@collectionArea.setter
def collectionArea(self, val: MeasureCompact) -> None:
"""
The area of a collection procedure or protocol was performed (e.g. total area
coverage for electrofishing, or total area kick net used).
"""
self.__collectionArea = None if val is None else MeasureCompact(val)
@property
def collectionEffort(self) -> CollectionEffort:
return self.__collectionEffort
@collectionEffort.setter
def collectionEffort(self, val: CollectionEffort) -> None:
self.__collectionEffort = None if val is None else CollectionEffort(val)
@property
def reachLengthMeasure(self) -> MeasureCompact:
"""
A measurement of the water body length distance in which the procedure or
protocol was performed.
"""
return self.__reachLengthMeasure
@reachLengthMeasure.setter
def reachLengthMeasure(self, val: MeasureCompact) -> None:
"""
A measurement of the water body length distance in which the procedure or
protocol was performed.
"""
self.__reachLengthMeasure = None if val is None else MeasureCompact(val)
@property
def reachWidthMeasure(self) -> MeasureCompact:
"""
A measurement of the reach width during collection procedures.
"""
return self.__reachWidthMeasure
@reachWidthMeasure.setter
def reachWidthMeasure(self, val: MeasureCompact) -> None:
"""
A measurement of the reach width during collection procedures.
"""
self.__reachWidthMeasure = None if val is None else MeasureCompact(val)
@property
def collectionDescriptionText(self) -> CollectionDescriptionText:
return self.__collectionDescriptionText
@collectionDescriptionText.setter
def collectionDescriptionText(self, val: CollectionDescriptionText) -> None:
self.__collectionDescriptionText = (
None if val is None else CollectionDescriptionText(val)
)
@property
def passCount(self) -> PassCount:
return self.__passCount
@passCount.setter
def passCount(self, val: PassCount) -> None:
self.__passCount = None if val is None else PassCount(val)
@property
def netInformation(self) -> NetInformation:
return self.__netInformation
@netInformation.setter
def netInformation(self, val: NetInformation) -> None:
self.__netInformation = None if val is None else NetInformation(val)
def generateXML(self, name: str = "BiologicalHabitatCollectionInformation") -> str:
doc = Doc()
asis = doc.asis
line = doc.line
tag = doc.tag
with tag(name):
if self.__collectionDuration is not None:
asis(self.__collectionDuration.generateXML("CollectionDuration"))
if self.__collectionArea is not None:
asis(self.__collectionArea.generateXML("CollectionArea"))
if self.__collectionEffort is not None:
asis(self.__collectionEffort.generateXML("CollectionEffort"))
if self.__reachLengthMeasure is not None:
asis(self.__reachLengthMeasure.generateXML("ReachLengthMeasure"))
if self.__reachWidthMeasure is not None:
asis(self.__reachWidthMeasure.generateXML("ReachWidthMeasure"))
if self.__collectionDescriptionText is not None:
line("CollectionDescriptionText", self.__collectionDescriptionText)
if self.__passCount is not None:
line("PassCount", self.__passCount)
if self.__netInformation is not None:
asis(self.__netInformation.generateXML("NetInformation"))
return doc.getvalue()
| 41.340426 | 88 | 0.665208 | 665 | 7,772 | 7.62406 | 0.130827 | 0.010651 | 0.014201 | 0.017357 | 0.244576 | 0.210651 | 0.18856 | 0.18856 | 0.178698 | 0.157791 | 0 | 0 | 0.270458 | 7,772 | 187 | 89 | 41.561497 | 0.89418 | 0.144879 | 0 | 0.062992 | 0 | 0 | 0.048317 | 0.014173 | 0 | 0 | 0 | 0 | 0 | 1 | 0.141732 | false | 0.102362 | 0.03937 | 0.031496 | 0.322835 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
2b7b76037a4e898e16254c3e5645a41b057d31df | 2,434 | py | Python | Python/Search/Search-1-Billion-Users.py | sethmh82/SethDevelopment | 08f3bd22923b652f9d676ffa2af3dc037eed6d73 | [
"MIT"
] | null | null | null | Python/Search/Search-1-Billion-Users.py | sethmh82/SethDevelopment | 08f3bd22923b652f9d676ffa2af3dc037eed6d73 | [
"MIT"
] | null | null | null | Python/Search/Search-1-Billion-Users.py | sethmh82/SethDevelopment | 08f3bd22923b652f9d676ffa2af3dc037eed6d73 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Created on Tue Dec 29 15:50:49 2020
@author: SethHarden
"""
import math
# Add any extra import statements you may need here
"""
We have N different apps with differnt user growth rates.
At a given time (t)
Measure in days (d)
the number of users using an app is g ^ t
can be a float
G = growth rate
Apps launch at same time user can only use 1 app at a time
We want to know
How many total users there are when you add together the number of users from each
After how many full days will we have 1 billion total users across the N apps?
We are sliding
1.0 < GR < 2.0
1 <= N <= 1,000
"""
# Add any helper functions you may need here
# lets create a function for the passing of time
billion = 1000000000
# set the lower boundry
def getDays(arr, t): #O(log n)
days_passed = 0
for g in arr:
days_passed += (g ** t)
return days_passed
# find the upper boundry
def boundry(arr, low, high): #O(log n)
while low < high:
mid = low + (high - low) // 2
if getDays(arr, mid) < billion:
low = mid + 1
else:
high = mid
return high
def getBillionUsersDay(growthRates):
i = 1
app_users = getDays(growthRates, i) #we inherit the results
if app_users >= billion:
return 1 #return 1 day if we're already over.
while app_users < billion:
i *= 2
app_users = getDays(growthRates, i)
print(growthRates)
print(boundry(growthRates, i // 2, i))
return boundry(growthRates, i // 2, i)
# These are the tests we use to determine if the solution is correct.
# You can add your own at the bottom, but they are otherwise not editable!
def printInteger(n):
print('[', n, ']', sep='', end='')
test_case_number = 1
def check(expected, output):
global test_case_number
result = False
if expected == output:
result = True
rightTick = '\u2713'
wrongTick = '\u2717'
if result:
print(rightTick, 'Test #', test_case_number, sep='')
else:
print(wrongTick, 'Test #', test_case_number, ': Expected ', sep='', end='')
printInteger(expected)
print(' Your output: ', end='')
printInteger(output)
print()
test_case_number += 1
if __name__ == "__main__":
test_1 = [1.1, 1.2, 1.3]
expected_1 = 79
output_1 = getBillionUsersDay(test_1)
check(expected_1, output_1)
test_2 = [1.01, 1.02]
expected_2 = 1047
output_2 = getBillionUsersDay(test_2)
check(expected_2, output_2)
# Add your own test cases here | 22.962264 | 82 | 0.670501 | 391 | 2,434 | 4.079284 | 0.398977 | 0.037618 | 0.043887 | 0.017555 | 0.060188 | 0 | 0 | 0 | 0 | 0 | 0 | 0.043985 | 0.224733 | 2,434 | 106 | 83 | 22.962264 | 0.801272 | 0.208299 | 0 | 0.074074 | 0 | 0 | 0.040972 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.092593 | false | 0.055556 | 0.018519 | 0 | 0.185185 | 0.185185 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
990d999fd69482a15fa5ce17aef975e84d69d8f0 | 1,833 | py | Python | app/grandchallenge/workstations/urls.py | njmhendrix/grand-challenge.org | 9bc36f5e26561a78bd405e8ea5e4c0f86c95f011 | [
"Apache-2.0"
] | 1 | 2021-02-09T10:30:44.000Z | 2021-02-09T10:30:44.000Z | app/grandchallenge/workstations/urls.py | njmhendrix/grand-challenge.org | 9bc36f5e26561a78bd405e8ea5e4c0f86c95f011 | [
"Apache-2.0"
] | null | null | null | app/grandchallenge/workstations/urls.py | njmhendrix/grand-challenge.org | 9bc36f5e26561a78bd405e8ea5e4c0f86c95f011 | [
"Apache-2.0"
] | null | null | null | from django.urls import path
from grandchallenge.workstations.views import (
SessionCreate,
WorkstationCreate,
WorkstationDetail,
WorkstationEditorsUpdate,
WorkstationImageCreate,
WorkstationImageDetail,
WorkstationImageUpdate,
WorkstationList,
WorkstationUpdate,
WorkstationUsersAutocomplete,
WorkstationUsersUpdate,
)
app_name = "workstations"
urlpatterns = [
path("", WorkstationList.as_view(), name="list"),
path(
"users-autocomplete/",
WorkstationUsersAutocomplete.as_view(),
name="users-autocomplete",
),
path("create/", WorkstationCreate.as_view(), name="create"),
# TODO - add region
path(
"sessions/create/",
SessionCreate.as_view(),
name="default-session-create",
),
path(
"<slug>/sessions/create/",
SessionCreate.as_view(),
name="workstation-session-create",
),
path(
"<slug>/<uuid:pk>/sessions/create/",
SessionCreate.as_view(),
name="workstation-image-session-create",
),
path(
"<slug>/editors/update/",
WorkstationEditorsUpdate.as_view(),
name="editors-update",
),
path(
"<slug>/users/update/",
WorkstationUsersUpdate.as_view(),
name="users-update",
),
path("<slug>/", WorkstationDetail.as_view(), name="detail"),
path("<slug>/update/", WorkstationUpdate.as_view(), name="update"),
path(
"<slug>/images/create/",
WorkstationImageCreate.as_view(),
name="image-create",
),
path(
"<slug>/images/<uuid:pk>/",
WorkstationImageDetail.as_view(),
name="image-detail",
),
path(
"<slug>/images/<uuid:pk>/update/",
WorkstationImageUpdate.as_view(),
name="image-update",
),
]
| 25.816901 | 71 | 0.608838 | 153 | 1,833 | 7.202614 | 0.281046 | 0.07078 | 0.117967 | 0.078947 | 0.156987 | 0.12069 | 0.087114 | 0 | 0 | 0 | 0 | 0 | 0.242226 | 1,833 | 70 | 72 | 26.185714 | 0.793377 | 0.009274 | 0 | 0.318182 | 0 | 0 | 0.237596 | 0.128997 | 0 | 0 | 0 | 0.014286 | 0 | 1 | 0 | false | 0 | 0.030303 | 0 | 0.030303 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
990e9f8c1c95014b70321c97ccabc51b75a24bab | 8,829 | py | Python | Project10- Bank Marketing.py | vaibhav162/Banking-Marketing-Project | 1255338b0a844a6a662e3b1887e9cef1a9edc834 | [
"Unlicense"
] | null | null | null | Project10- Bank Marketing.py | vaibhav162/Banking-Marketing-Project | 1255338b0a844a6a662e3b1887e9cef1a9edc834 | [
"Unlicense"
] | null | null | null | Project10- Bank Marketing.py | vaibhav162/Banking-Marketing-Project | 1255338b0a844a6a662e3b1887e9cef1a9edc834 | [
"Unlicense"
] | null | null | null | #!/usr/bin/env python
# coding: utf-8
# # Importing Libraries and Dataset
# In[1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# In[2]:
bank= pd.read_csv(r"C:\Users\shruti\Desktop\Decodr\Project\Decodr Project\Bank marketing project\bank.csv", delimiter=";")
# In[3]:
bank.head()
# In[4]:
bank.tail()
# In[5]:
# Renaming "y" column with "deposit"
bank.rename(columns={"y":"deposit"}, inplace=True)
# In[6]:
bank.head()
# # Data Exploration
# In[7]:
# To get total number of rows
print("Bank Marketing Dataset contains {rows} rows.".format(rows=len(bank)))
# In[8]:
# To get percentage of missing values in each columns
missing_values= bank.isnull().mean()*100
missing_values.sum()
# ### Categorical Columns Exploration
# In[9]:
cat_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month','poutcome']
fig, axs = plt.subplots(3, 3, sharex=False, sharey=False, figsize=(10, 8))
counter = 0
for cat_column in cat_columns:
value_counts = bank[cat_column].value_counts()
trace_x = counter // 3
trace_y = counter % 3
x_pos = np.arange(0, len(value_counts))
axs[trace_x, trace_y].bar(x_pos, value_counts.values, tick_label = value_counts.index)
axs[trace_x, trace_y].set_title(cat_column)
for tick in axs[trace_x, trace_y].get_xticklabels():
tick.set_rotation(90)
counter += 1
plt.show()
# ### Numerical Columns Exploration
# In[10]:
num_columns = ['balance', 'day', 'duration', 'campaign', 'pdays', 'previous']
fig, axs = plt.subplots(2, 3, sharex=False, sharey=False, figsize=(10, 8))
counter = 0
for num_column in num_columns:
trace_x = counter // 3
trace_y = counter % 3
axs[trace_x, trace_y].bar(x_pos, value_counts.values, tick_label = value_counts.index)
axs[trace_x, trace_y].set_title(num_column)
counter += 1
plt.show()
# In[11]:
bank[["pdays", "campaign", "previous"]].describe()
# In[12]:
len(bank[bank["pdays"]> 400])/ len(bank)*100
# In[13]:
len(bank[bank["campaign"]> 34])/ len(bank)*100
# In[14]:
len(bank[bank["previous"]> 34])/ len(bank)*100
# ## Analysis of Categorical columns
# In[15]:
value_counts= bank["deposit"].value_counts()
value_counts.plot.bar(title= "Deposit value counts")
# In[16]:
# Plotting Deposit Vs Jobs
j_bank= pd.DataFrame()
j_bank["yes"]= bank[bank["deposit"] == "yes"]["job"].value_counts()
j_bank["no"]= bank[bank["deposit"] == "no"]["job"].value_counts()
j_bank.plot.bar(title= "Job & Deposit")
# In[17]:
# Plotting Deposit Vs Marital Status
j_bank= pd.DataFrame()
j_bank["yes"]= bank[bank["deposit"] == "yes"]["marital"].value_counts()
j_bank["no"]= bank[bank["deposit"] == "no"]["marital"].value_counts()
j_bank.plot.bar(title= "Marital Status & Deposit")
# In[18]:
# Plotting Deposite Vs Education
j_bank= pd.DataFrame()
j_bank["yes"]= bank[bank["deposit"] == "yes"]["education"].value_counts()
j_bank["no"]= bank[bank["deposit"] == "no"]["education"].value_counts()
j_bank.plot.bar(title= "Education & Deposit")
# In[19]:
# Plotting Deposit Vs Contact
j_bank= pd.DataFrame()
j_bank["yes"]= bank[bank["deposit"] == "yes"]["contact"].value_counts()
j_bank["no"]= bank[bank["deposit"] == "no"]["contact"].value_counts()
j_bank.plot.bar(title= "Contact & Deposit")
# ## Analysis of Numeric columns
# In[20]:
# Balance & Deposit
b_bank= pd.DataFrame()
b_bank['balance_yes'] = (bank[bank['deposit'] == 'yes'][['deposit','balance']].describe())['balance']
b_bank['balance_no'] = (bank[bank['deposit'] == 'no'][['deposit','balance']].describe())['balance']
b_bank
# In[21]:
b_bank.drop(["count", "25%", "50%", "75%"]).plot.bar(title= "Balance & Deposit Statistics")
# In[22]:
# Age & Deposit
b_bank= pd.DataFrame()
b_bank['age_yes'] = (bank[bank['deposit'] == 'yes'][['deposit','age']].describe())['age']
b_bank['age_no'] = (bank[bank['deposit'] == 'no'][['deposit','age']].describe())['age']
b_bank
# In[23]:
b_bank.drop(["count", "25%", "50%", "75%"]).plot.bar(title= "Age & Deposit Statistics")
# In[24]:
# Campaign & Deposit
b_bank= pd.DataFrame()
b_bank['campaign_yes'] = (bank[bank['deposit'] == 'yes'][['deposit','campaign']].describe())['campaign']
b_bank['campaign_no'] = (bank[bank['deposit'] == 'no'][['deposit','campaign']].describe())['campaign']
b_bank
# In[25]:
b_bank.drop(["count", "25%", "50%", "75%"]).plot.bar(title= "Campaign & Deposit Statistics")
# In[26]:
# Previous Campaign & Deposit
b_bank= pd.DataFrame()
b_bank['previous_yes'] = (bank[bank['deposit'] == 'yes'][['deposit','previous']].describe())['previous']
b_bank['previous_no'] = (bank[bank['deposit'] == 'no'][['deposit','previous']].describe())['previous']
b_bank
# In[27]:
b_bank.drop(["count", "25%", "50%", "75%"]).plot.bar(title= "Previous Campaign & Deposit Statistics")
# # Data Cleaning
# In[28]:
def get_dummy_from_bool(row, column_name):
"""Returns 0 if value in column_name is no, returns 1 if value in column_name is yes"""
return 1 if row[column_name] == "yes" else 0
def get_correct_values(row, column_name, threshold, bank):
"""Returns mean value if value in column_name is above threshold"""
if row[column_name] <= threshold:
return row[column_name]
else:
mean= bank[bank[column_name] <= threshold][column_name].mean()
return mean
def clean_data(bank):
'''
INPUT
df - pandas dataframe containing bank marketing campaign dataset
OUTPUT
df - cleaned dataset:
1. columns with 'yes' and 'no' values are converted into boolean variables;
2. categorical columns are converted into dummy variables;
3. drop irrelevant columns.
4. impute incorrect values
'''
cleaned_bank = bank.copy()
# Converting columns containing 'yes' and 'no' values to boolean variables and drop original columns
bool_columns = ['default', 'housing', 'loan', 'deposit']
for bool_col in bool_columns:
cleaned_bank[bool_col + '_bool'] = bank.apply(lambda row: get_dummy_from_bool(row, bool_col),axis=1)
cleaned_bank = cleaned_bank.drop(columns = bool_columns)
# Converting categorical columns to dummies
cat_columns = ['job', 'marital', 'education', 'contact', 'month', 'poutcome']
for col in cat_columns:
cleaned_bank = pd.concat([cleaned_bank.drop(col, axis=1),
pd.get_dummies(cleaned_bank[col], prefix=col, prefix_sep='_',
drop_first=True, dummy_na=False)], axis=1)
# Dropping irrelevant columns
cleaned_bank = cleaned_bank.drop(columns = ['pdays'])
# Imputing incorrect values and drop original columns
cleaned_bank['campaign_cleaned'] = bank.apply(lambda row: get_correct_values(row, 'campaign', 34, cleaned_bank),axis=1)
cleaned_bank['previous_cleaned'] = bank.apply(lambda row: get_correct_values(row, 'previous', 34, cleaned_bank),axis=1)
cleaned_bank = cleaned_bank.drop(columns = ['campaign', 'previous'])
return cleaned_bank
# In[29]:
cleaned_bank= clean_data(bank)
cleaned_bank.head()
# # Predicting Campaign Model
# ### Classification Model
# In[30]:
X= cleaned_bank.drop(columns= "deposit_bool")
y= cleaned_bank[["deposit_bool"]]
# In[31]:
TEST_SIZE = 0.3
RAND_STATE= 42
# In[41]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= TEST_SIZE, random_state= RAND_STATE)
# In[42]:
pip install xgboost
# In[43]:
import xgboost
import warnings
xgb = xgboost.XGBClassifier(n_estimators=100, learning_rate=0.08, gamma=0, subsample=0.75,
colsample_bytree=1, max_depth=7)
# In[44]:
xgb.fit(X_train, y_train.squeeze().values)
# In[45]:
y_train_preds= xgb.predict(X_train)
y_test_preds= xgb.predict(X_test)
# In[47]:
from sklearn.metrics import accuracy_score
print("XGB accuracy score for train data : %.3f and for test data : %.3f" % (accuracy_score(y_train, y_train_preds),
accuracy_score(y_test, y_test_preds)))
# # Get Feature Importance from Trained Model
# In[50]:
headers= ["name", "score"]
values= sorted(zip(X_train.columns, xgb.feature_importances_), key= lambda x: x[1]*-1)
xgb_feature_importances_=pd.DataFrame(values,columns=headers)
xgb_feature_importances_
# In[52]:
x_pos= np.arange(0, len(xgb_feature_importances_))
plt.figure(figsize=(10,8))
plt.bar(x_pos, xgb_feature_importances_["score"])
plt.xticks(x_pos, xgb_feature_importances_["name"])
plt.xticks(rotation=90)
plt.title("Feature Importance (XGB)")
plt.show()
# In[ ]:
| 19.930023 | 123 | 0.654094 | 1,235 | 8,829 | 4.501215 | 0.211336 | 0.043533 | 0.043173 | 0.025904 | 0.356719 | 0.315165 | 0.215866 | 0.17809 | 0.141392 | 0.100378 | 0 | 0.02429 | 0.174652 | 8,829 | 442 | 124 | 19.975113 | 0.738576 | 0.131725 | 0 | 0.204545 | 0 | 0.007576 | 0.181793 | 0.006351 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.113636 | null | null | 0.015152 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
990ee382aab29a71c3680acb076db25c10304e41 | 829 | py | Python | backend/recotem/recotem/api/serializers/project.py | codelibs/recotem | 383ccdd6e1e9feb59bc3adb2543c00b08277317a | [
"Apache-2.0"
] | 7 | 2021-05-15T05:43:36.000Z | 2022-01-06T16:08:06.000Z | backend/recotem/recotem/api/serializers/project.py | codelibs/recotem | 383ccdd6e1e9feb59bc3adb2543c00b08277317a | [
"Apache-2.0"
] | 5 | 2021-09-25T13:30:38.000Z | 2022-01-09T12:59:03.000Z | backend/recotem/recotem/api/serializers/project.py | codelibs/recotem | 383ccdd6e1e9feb59bc3adb2543c00b08277317a | [
"Apache-2.0"
] | 1 | 2021-11-02T12:49:06.000Z | 2021-11-02T12:49:06.000Z | from rest_framework import serializers
from recotem.api.models import Project, TrainingData
class ProjectSerializer(serializers.ModelSerializer):
class Meta:
model = Project
fields = "__all__"
class TrainingDataForSummarySerializer(serializers.ModelSerializer):
n_parameter_tuning_jobs = serializers.IntegerField(
source="parametertuningjob_set.count"
)
n_trained_models = serializers.IntegerField(source="trainedmodel_set.count")
class Meta:
model = TrainingData
fields = ["id", "n_parameter_tuning_jobs", "n_trained_models"]
class ProjectSummarySerializer(serializers.Serializer):
n_data = serializers.IntegerField()
n_complete_jobs = serializers.IntegerField()
n_models = serializers.IntegerField()
ins_datetime = serializers.DateTimeField()
| 29.607143 | 80 | 0.756333 | 79 | 829 | 7.658228 | 0.468354 | 0.190083 | 0.046281 | 0.066116 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.165259 | 829 | 27 | 81 | 30.703704 | 0.874277 | 0 | 0 | 0.105263 | 0 | 0 | 0.118215 | 0.088058 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.105263 | 0 | 0.684211 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
9915ead35e605389439f3f75b603bcbfca8137f3 | 401 | py | Python | Leetcode/Move Zeroes/Move Zeroes.py | rahil-1407/Data-Structure-and-Algorithms | ea3eb9849aeb2716ef5812a0b5621a28120b1880 | [
"MIT"
] | 51 | 2021-01-14T04:05:55.000Z | 2022-01-25T11:25:37.000Z | Leetcode/Move Zeroes/Move Zeroes.py | rahil-1407/Data-Structure-and-Algorithms | ea3eb9849aeb2716ef5812a0b5621a28120b1880 | [
"MIT"
] | 638 | 2020-12-27T18:49:53.000Z | 2021-11-21T05:22:52.000Z | Leetcode/Move Zeroes/Move Zeroes.py | rahil-1407/Data-Structure-and-Algorithms | ea3eb9849aeb2716ef5812a0b5621a28120b1880 | [
"MIT"
] | 124 | 2021-01-30T06:40:20.000Z | 2021-11-21T15:14:40.000Z | class Solution:
def moveZeroes(self, nums: List[int]) -> None:
non_zeros = [i for i in range(len(nums)) if nums[i] != 0] # List comprehension to keep only numbers that are non -zero
nz = len(non_zeros)
nums[:nz] = [nums[i] for i in non_zeros] # edit the list to add non zero numbers to the list
nums[nz:] = [0] *(len(nums)-nz) #dd zeroes at the end | 57.285714 | 126 | 0.598504 | 67 | 401 | 3.537313 | 0.507463 | 0.101266 | 0.042194 | 0.059072 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006944 | 0.281796 | 401 | 7 | 127 | 57.285714 | 0.815972 | 0.326683 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
9918396b139801392f0a7cf201d6cd0639fc9b91 | 4,629 | py | Python | sandbox/test/test_misc.py | yingted/pysandbox | cb20c202459fc1b22a81e879c0efafc66e1ddd8a | [
"BSD-2-Clause-FreeBSD"
] | 1 | 2019-02-05T13:13:09.000Z | 2019-02-05T13:13:09.000Z | sandbox/test/test_misc.py | yingted/pysandbox | cb20c202459fc1b22a81e879c0efafc66e1ddd8a | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | sandbox/test/test_misc.py | yingted/pysandbox | cb20c202459fc1b22a81e879c0efafc66e1ddd8a | [
"BSD-2-Clause-FreeBSD"
] | null | null | null | from __future__ import with_statement
from sandbox import Sandbox, SandboxError, SandboxConfig, Timeout
from sandbox.test import createSandbox, createSandboxConfig, SkipTest
from sandbox.test.tools import capture_stdout
def test_valid_code():
def valid_code():
assert 1+2 == 3
createSandbox().call(valid_code)
def test_exit():
def exit_noarg():
try:
exit()
except SandboxError as err:
assert str(err) == "exit() function blocked by the sandbox"
else:
assert False
createSandbox().call(exit_noarg)
config = createSandboxConfig("exit")
def exit_1():
try:
exit(1)
except SystemExit as err:
assert err.args[0] == 1
else:
assert False
import sys
try:
sys.exit("bye")
except SystemExit as err:
assert err.args[0] == "bye"
else:
assert False
Sandbox(config).call(exit_1)
def test_sytem_exit():
def system_exit_denied():
try:
raise SystemExit()
except NameError as err:
assert str(err) == "global name 'SystemExit' is not defined"
except:
assert False
createSandbox().call(system_exit_denied)
config = createSandboxConfig("exit")
def system_exit_allowed():
try:
raise SystemExit()
except SystemExit:
pass
else:
assert False
Sandbox(config).call(system_exit_allowed)
try:
raise SystemExit()
except SystemExit:
pass
else:
assert False
def test_stdout():
import sys
config = createSandboxConfig(disable_debug=True)
with capture_stdout() as stdout:
def print_denied():
print "Hello Sandbox 1"
try:
Sandbox(config).call(print_denied)
except SandboxError:
pass
else:
assert False
def print_allowed():
print "Hello Sandbox 2"
config2 = createSandboxConfig('stdout')
Sandbox(config2).call(print_allowed)
print "Hello Sandbox 3"
sys.stdout.flush()
stdout.seek(0)
output = stdout.read()
assert output == "Hello Sandbox 2\nHello Sandbox 3\n"
def test_traceback():
def check_frame_filename():
import sys
frame = sys._getframe(1)
frame_code = frame.f_code
frame_filename = frame_code.co_filename
# it may ends with .py or .pyc
assert __file__.startswith(frame_filename)
config = createSandboxConfig('traceback')
config.allowModule('sys', '_getframe')
Sandbox(config).call(check_frame_filename)
check_frame_filename()
def test_regex():
def check_regex():
import re
assert re.escape('+') == '\\+'
assert re.match('a+', 'aaaa').group(0) == 'aaaa'
# FIXME: Remove this workaround: list(...)
assert list(re.findall('.', 'abc')) == ['a', 'b', 'c']
assert re.search('a+', 'aaaa').group(0) == 'aaaa'
# FIXME: Remove this workaround: list(...)
assert list(re.split(' +', 'a b c')) == ['a', 'b', 'c']
assert re.sub('a+', '#', 'a b aa c') == '# b # c'
sandbox = createSandbox('regex')
sandbox.call(check_regex)
check_regex()
def test_timeout_while_1():
if not createSandboxConfig.use_subprocess:
raise SkipTest("timeout is only supported with subprocess")
def denial_of_service():
while 1:
pass
config = createSandboxConfig()
config.timeout = 0.1
try:
Sandbox(config).call(denial_of_service)
except Timeout:
pass
else:
assert False
def test_timeout_cpu_intensive():
if not createSandboxConfig.use_subprocess:
raise SkipTest("timeout is only supported with subprocess")
def denial_of_service():
sum(2**x for x in range(100000))
config = createSandboxConfig()
config.timeout = 0.1
try:
Sandbox(config).call(denial_of_service)
except Timeout:
pass
else:
assert False
def test_crash():
if not createSandboxConfig.use_subprocess:
raise SkipTest("catching a crash is only supported with subprocess")
def crash():
import _sandbox
_sandbox._test_crash()
config = createSandboxConfig()
config.allowSafeModule("_sandbox", "_test_crash")
sand = Sandbox(config)
try:
sand.call(crash)
except SandboxError as err:
assert str(err) == 'subprocess killed by signal 11', str(err)
else:
assert False
| 26.301136 | 76 | 0.600994 | 522 | 4,629 | 5.176245 | 0.243295 | 0.040711 | 0.049963 | 0.035159 | 0.409326 | 0.357513 | 0.324944 | 0.280533 | 0.254626 | 0.254626 | 0 | 0.010753 | 0.296824 | 4,629 | 175 | 77 | 26.451429 | 0.819355 | 0.023763 | 0 | 0.454545 | 0 | 0 | 0.098582 | 0 | 0 | 0 | 0 | 0.005714 | 0.167832 | 0 | null | null | 0.041958 | 0.062937 | null | null | 0.048951 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
9929ca378f85f069f43b01b2e6b9a5f840475d81 | 466 | py | Python | setup.py | spyoungtech/commander | 891172d31dc93b9e34c5e38097159141c863d75e | [
"MIT"
] | 3 | 2018-08-14T20:42:43.000Z | 2020-03-04T05:18:47.000Z | setup.py | spyoungtech/commander | 891172d31dc93b9e34c5e38097159141c863d75e | [
"MIT"
] | 2 | 2020-07-08T12:30:08.000Z | 2022-02-02T14:59:23.000Z | setup.py | spyoungtech/commander | 891172d31dc93b9e34c5e38097159141c863d75e | [
"MIT"
] | null | null | null | from setuptools import setup
setup(
name='voice-commander',
version='0.0.2a',
packages=['voice_commander'],
install_requires=['fuzzywuzzy', 'fuzzywuzzy[speedup]', 'keyboard', 'easygui', 'pyaudio', 'SpeechRecognition'],
url='https://github.com/spyoungtech/voice-commander',
license='MIT',
author='Spencer Young',
author_email='spencer.young@spyoung.coom',
description='cross-platform voice-activation hooks and keyboard macros'
)
| 33.285714 | 114 | 0.708155 | 51 | 466 | 6.411765 | 0.764706 | 0.12844 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007444 | 0.135193 | 466 | 13 | 115 | 35.846154 | 0.80397 | 0 | 0 | 0 | 0 | 0 | 0.534335 | 0.055794 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.083333 | 0 | 0.083333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99323e8a12cde588dd2bc1c30a2dbda9df176374 | 7,804 | py | Python | neatest/genome.py | goktug97/NEATEST | f35f355fd896c8f9ab88d411752324ebcc836d71 | [
"MIT"
] | 13 | 2021-09-25T19:52:38.000Z | 2021-09-28T09:42:22.000Z | neatest/genome.py | goktug97/NEATEST | f35f355fd896c8f9ab88d411752324ebcc836d71 | [
"MIT"
] | null | null | null | neatest/genome.py | goktug97/NEATEST | f35f355fd896c8f9ab88d411752324ebcc836d71 | [
"MIT"
] | null | null | null | from typing import List
import math
import os
from .connection import Connection, GeneRate, Weight
from .node import Node, NodeType, group_nodes
from .version import VERSION
import cloudpickle # type: ignore
try:
disable_mpi = os.environ.get('NEATEST_DISABLE_MPI')
if disable_mpi and disable_mpi != '0':
raise ImportError
from mpi4py import MPI # type: ignore
except ImportError:
from .MPI import MPI
MPI = MPI()
class Genome(object):
def __init__(self, nodes: List[Node], connections: List[Connection]):
self.connections = connections
self.nodes = nodes
self.version = VERSION
grouped_nodes = group_nodes(self.nodes, 'type')
self.input_size = len(grouped_nodes[0])
self.output_size = len(grouped_nodes[-1])
self.outputs = grouped_nodes[-1]
def __call__(self, inputs: List[float]) -> List[float]:
self.nodes.sort(key=lambda x: x.depth)
for node in self.nodes:
value = 0.0
if node.type == NodeType.INPUT:
value += inputs[node.id]
elif node.type == NodeType.BIAS:
continue
for connection in node.inputs:
if connection.enabled:
value += connection.in_node.value * connection.weight.value
node.value = node.activation(value)
return [node.value for node in self.outputs]
def copy(self):
connections: List[Connection] = []
nodes: List[Node] = []
for idx in range(len(self.connections)):
connection = self.connections[idx]
in_node = Node(connection.in_node.id, connection.in_node.type,
connection.in_node.activation,
depth=connection.in_node.depth)
out_node = Node(connection.out_node.id, connection.out_node.type,
connection.out_node.activation,
depth=connection.out_node.depth)
nodes_dict = dict(zip(nodes, range(len(nodes))))
if in_node not in nodes_dict:
nodes.append(in_node)
nodes_dict[in_node] = len(nodes)-1
if out_node not in nodes_dict:
nodes.append(out_node)
nodes_dict[out_node] = len(nodes)-1
new_connection = Connection(nodes[nodes_dict[in_node]],
nodes[nodes_dict[out_node]],
innovation=connection.innovation,
dominant_gene_rate=connection.dominant_gene_rate,
weight=connection.weight)
new_connection.enabled = connection.enabled
connections.append(new_connection)
new_genome = Genome(nodes, connections)
return new_genome
def deepcopy(self):
connections: List[Connection] = []
nodes: List[Node] = []
for idx in range(len(self.connections)):
connection = self.connections[idx]
in_node = Node(connection.in_node.id, connection.in_node.type,
connection.in_node.activation,
depth=connection.in_node.depth)
out_node = Node(connection.out_node.id, connection.out_node.type,
connection.out_node.activation,
depth=connection.out_node.depth)
nodes_dict = dict(zip(nodes, range(len(nodes))))
if in_node not in nodes_dict:
nodes.append(in_node)
nodes_dict[in_node] = len(nodes)-1
if out_node not in nodes_dict:
nodes.append(out_node)
nodes_dict[out_node] = len(nodes)-1
new_connection = Connection(nodes[nodes_dict[in_node]],
nodes[nodes_dict[out_node]],
innovation=connection.innovation,
dominant_gene_rate=GeneRate(
connection.dominant_gene_rate.value),
weight=Weight(connection.weight.value))
new_connection.enabled = connection.enabled
connections.append(new_connection)
new_genome = Genome(nodes, connections)
return new_genome
def draw(self, node_radius: float = 0.05,
vertical_distance: float = 0.25,
horizontal_distance: float = 0.25) -> None:
draw_genome(self, node_radius, vertical_distance, horizontal_distance)
def save(self, filename: str) -> None:
if MPI.COMM_WORLD.rank == 0:
with open(filename, 'wb') as output:
cloudpickle.dump(self, output)
@classmethod
def load(cls, filename: str) -> 'Genome':
print(f"\033[33;1mLoading: {filename}\033[0m")
with open(filename, 'rb') as f:
genome = cloudpickle.load(f)
if genome.version != VERSION:
print("\033[31;1mWarning: Genome version mismatch!\n"
f"Current Version: {VERSION.major}.{VERSION.minor}.{VERSION.patch}\n"
"Checkpoint Version:"
f" {genome.version.major}.{genome.version.minor}."
f"{genome.version.patch}\033[0m")
return genome
def __str__(self):
string = ''
string = f'{string}NODES:\n'
for node in self.nodes:
string = f'{string}{node}\n'
string = f'{string}\n\nCONNECTIONS:\n'
for connection in self.connections:
string = f'{string}{connection}\n'
return string
def draw_genome(genome: Genome,
node_radius: float = 0.05,
vertical_distance: float = 0.25,
horizontal_distance: float = 0.25) -> None:
'''Draw the genome to a matplotlib figure but do not show it.'''
import matplotlib.pyplot as plt # type: ignore
import matplotlib.patches as patches # type: ignore
plt.gcf().canvas.set_window_title('float')
positions = {}
node_groups = group_nodes(genome.nodes, 'depth')
for group_idx, nodes in enumerate(node_groups):
y_position = -vertical_distance * (len(nodes)-1)/2
for i, node in enumerate(nodes):
positions[f'{node.id}'] = (group_idx * horizontal_distance,
y_position + i*vertical_distance)
circle = plt.Circle(positions[f'{node.id}'],
node_radius, color='r', fill=False)
plt.gcf().gca().text(*positions[f'{node.id}'], node.id,
horizontalalignment='center',
verticalalignment='center',
fontsize=10.0)
plt.gcf().gca().add_artist(circle)
for connection in genome.connections:
if connection.enabled:
node1_x = positions[f'{connection.in_node.id}'][0]
node2_x = positions[f'{connection.out_node.id}'][0]
node1_y = positions[f'{connection.in_node.id}'][1]
node2_y = positions[f'{connection.out_node.id}'][1]
angle = math.atan2(node2_x - node1_x, node2_y - node1_y)
x_adjustment = node_radius * math.sin(angle)
y_adjustment = node_radius * math.cos(angle)
arrow = patches.FancyArrowPatch(
(node1_x + x_adjustment,
node1_y + y_adjustment),
(node2_x - x_adjustment,
node2_y - y_adjustment),
arrowstyle="Simple,tail_width=0.5,head_width=3,head_length=5",
color="k", antialiased=True)
plt.gcf().gca().add_patch(arrow)
plt.axis('scaled')
| 43.597765 | 89 | 0.563813 | 871 | 7,804 | 4.881745 | 0.202067 | 0.031044 | 0.045155 | 0.016933 | 0.407338 | 0.389464 | 0.362653 | 0.362653 | 0.362653 | 0.362653 | 0 | 0.014649 | 0.335213 | 7,804 | 178 | 90 | 43.842697 | 0.804934 | 0.014223 | 0 | 0.35 | 1 | 0 | 0.072228 | 0.040864 | 0 | 0 | 0 | 0 | 0 | 1 | 0.05625 | false | 0 | 0.08125 | 0 | 0.175 | 0.0125 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
993374093a84570c0c1ef8f2f30f5090353300ca | 558 | py | Python | release/stubs.min/System/Windows/Forms/__init___parts/FormClosedEventArgs.py | YKato521/ironpython-stubs | b1f7c580de48528490b3ee5791b04898be95a9ae | [
"MIT"
] | null | null | null | release/stubs.min/System/Windows/Forms/__init___parts/FormClosedEventArgs.py | YKato521/ironpython-stubs | b1f7c580de48528490b3ee5791b04898be95a9ae | [
"MIT"
] | null | null | null | release/stubs.min/System/Windows/Forms/__init___parts/FormClosedEventArgs.py | YKato521/ironpython-stubs | b1f7c580de48528490b3ee5791b04898be95a9ae | [
"MIT"
] | null | null | null | class FormClosedEventArgs(EventArgs):
"""
Provides data for the System.Windows.Forms.Form.FormClosed event.
FormClosedEventArgs(closeReason: CloseReason)
"""
@staticmethod
def __new__(self, closeReason):
""" __new__(cls: type,closeReason: CloseReason) """
pass
CloseReason = property(
lambda self: object(), lambda self, v: None, lambda self: None
)
"""Gets a value that indicates why the form was closed.
Get: CloseReason(self: FormClosedEventArgs) -> CloseReason
"""
| 20.666667 | 71 | 0.645161 | 54 | 558 | 6.518519 | 0.648148 | 0.085227 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.252688 | 558 | 26 | 72 | 21.461538 | 0.844125 | 0.283154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142857 | false | 0.142857 | 0 | 0 | 0.428571 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
9939051042bf55dc56637d71a73ebeb04bd0f880 | 1,085 | py | Python | doc/source/EXAMPLES/mu_reproj_interact.py | kapteyn-astro/kapteyn | f12332cfd567c7c0da40628dcfc7b297971ee636 | [
"BSD-3-Clause"
] | 3 | 2016-04-28T08:55:33.000Z | 2018-07-23T18:35:58.000Z | doc/source/EXAMPLES/mu_reproj_interact.py | kapteyn-astro/kapteyn | f12332cfd567c7c0da40628dcfc7b297971ee636 | [
"BSD-3-Clause"
] | 2 | 2020-07-23T12:28:37.000Z | 2021-07-13T18:26:06.000Z | doc/source/EXAMPLES/mu_reproj_interact.py | kapteyn-astro/kapteyn | f12332cfd567c7c0da40628dcfc7b297971ee636 | [
"BSD-3-Clause"
] | 3 | 2017-05-03T14:01:08.000Z | 2020-07-23T12:23:28.000Z | from kapteyn import maputils
from matplotlib import pyplot as plt
import numpy
# Read first image as base
Basefits = maputils.FITSimage(promptfie=maputils.prompt_fitsfile)
print(type(Basefits), isinstance(Basefits, maputils.FITSimage))
# Get data from a second image. This is the data that
# should be reprojected to fit the header of Basefits.
Secondfits = maputils.FITSimage(promptfie=maputils.prompt_fitsfile)
#Secondfits.set_imageaxes(promptfie=maputils.prompt_imageaxes)
#Secondfits.set_limits(promptfie=maputils.prompt_box)
# Now we want to overlay the data of this Base fits object onto
# the wcs of the second fits object. This is done with the
# reproject_to() method of
# the first FITSimage object (the data object) with the second
# FITSimage object as parameter. This results in a new fits file
#Reprojfits = Basefits.reproject_to(Secondfits.hdr, plimlo=(2,1), plimhi=(2,1))
#Reprojfits = Basefits.reproject_to(Secondfits.hdr, pxlim=(100,400), pylim=(100,400))
Reprojfits = Basefits.reproject_to(Secondfits.hdr)
Reprojfits.writetofits("reproj.fits", clobber=True)
| 43.4 | 86 | 0.795392 | 158 | 1,085 | 5.398734 | 0.455696 | 0.079719 | 0.107855 | 0.101993 | 0.260258 | 0.260258 | 0 | 0 | 0 | 0 | 0 | 0.016719 | 0.117972 | 1,085 | 24 | 87 | 45.208333 | 0.874608 | 0.623041 | 0 | 0 | 0 | 0 | 0.027778 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.375 | 0 | 0.375 | 0.125 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
9939f19eab14d0c7a3b637eb8dcb7c8e88f38637 | 381 | py | Python | python/Container With Most Water.py | kuwarkapur/Hacktoberfest-2022 | efaafeba5ce51d8d2e2d94c6326cc20bff946f17 | [
"MIT"
] | 1 | 2021-12-03T09:23:41.000Z | 2021-12-03T09:23:41.000Z | python/Container With Most Water.py | kuwarkapur/Hacktoberfest-2022 | efaafeba5ce51d8d2e2d94c6326cc20bff946f17 | [
"MIT"
] | null | null | null | python/Container With Most Water.py | kuwarkapur/Hacktoberfest-2022 | efaafeba5ce51d8d2e2d94c6326cc20bff946f17 | [
"MIT"
] | null | null | null | class Solution:
def maxArea(self, height: List[int]) -> int:
i = 0
j = len(height)-1
res = 0
area = 0
while i < j:
area = min(height[i],height[j])*(j-i)
#print(area)
res = max(res,area)
if height[i]<height[j]:
i+=1
else:
j-=1
return res
| 23.8125 | 49 | 0.393701 | 48 | 381 | 3.125 | 0.458333 | 0.093333 | 0.173333 | 0.186667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.030151 | 0.47769 | 381 | 15 | 50 | 25.4 | 0.723618 | 0.028871 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.071429 | false | 0 | 0 | 0 | 0.214286 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
993e1a819f6f487b24a2d7f90f46158e52218a76 | 584 | py | Python | lib/hyperparams.py | J-Moravec/pairtree | 91cbba628b78aea31034efb080976fdb47d83976 | [
"MIT"
] | 15 | 2021-01-19T21:13:50.000Z | 2022-02-02T00:01:33.000Z | lib/hyperparams.py | J-Moravec/pairtree | 91cbba628b78aea31034efb080976fdb47d83976 | [
"MIT"
] | 17 | 2020-11-25T09:41:03.000Z | 2022-03-28T04:52:14.000Z | lib/hyperparams.py | J-Moravec/pairtree | 91cbba628b78aea31034efb080976fdb47d83976 | [
"MIT"
] | 6 | 2021-01-01T06:00:31.000Z | 2021-06-29T15:03:11.000Z | explanations = {
'gamma': '''
Proportion of tree modifications that should use mutrel-informed choice for
node to move, rather than uniform choice
''',
'zeta': '''
Proportion of tree modifications that should use mutrel-informed choice for
destination to move node to, rather than uniform choice
''',
'iota': '''
Probability of initializing with mutrel-informed tree rather than fully
branching tree when beginning chain
'''
}
defaults = {
'gamma': 0.7,
'zeta': 0.7,
'iota': 0.7,
}
assert set(explanations.keys()) == set(defaults.keys())
| 23.36 | 79 | 0.674658 | 74 | 584 | 5.324324 | 0.486486 | 0.106599 | 0.081218 | 0.147208 | 0.329949 | 0.329949 | 0.329949 | 0.329949 | 0.329949 | 0.329949 | 0 | 0.013015 | 0.210616 | 584 | 24 | 80 | 24.333333 | 0.841649 | 0 | 0 | 0.2 | 0 | 0 | 0.712329 | 0 | 0 | 0 | 0 | 0 | 0.05 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
9943e8ba1fe715b4c9d83b3026d9f901028d6a2e | 359 | py | Python | app/database/seed/seeds/real_madrid/team.py | batistado/FlaskFootball | 4cd57edca35a7ce9864201f7ae0fc8af55a2724f | [
"MIT"
] | null | null | null | app/database/seed/seeds/real_madrid/team.py | batistado/FlaskFootball | 4cd57edca35a7ce9864201f7ae0fc8af55a2724f | [
"MIT"
] | null | null | null | app/database/seed/seeds/real_madrid/team.py | batistado/FlaskFootball | 4cd57edca35a7ce9864201f7ae0fc8af55a2724f | [
"MIT"
] | null | null | null | import os
import app.database.seed.seed_helper as helper
from app.translation.deserializer import Deserializer
from app.extensions import db
real_madrid = {
'name': 'Real Madrid CF',
'players': helper.read_csv_file(os.path.join(os.path.dirname(__file__), 'players.csv')),
}
team = Deserializer().deserialize_team(real_madrid)
db.session.add(team)
| 23.933333 | 92 | 0.763231 | 51 | 359 | 5.176471 | 0.529412 | 0.113636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.114206 | 359 | 14 | 93 | 25.642857 | 0.830189 | 0 | 0 | 0 | 0 | 0 | 0.100279 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.4 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
9946ecb403a384000ba7abec0b673e218cfb79aa | 436 | py | Python | examples/loop.py | letmaik/exhaust | 7c3b4f1dfab95192968b0bcb49bbb5574e1ac83b | [
"MIT"
] | 1 | 2021-12-04T21:37:41.000Z | 2021-12-04T21:37:41.000Z | examples/loop.py | letmaik/exhaust | 7c3b4f1dfab95192968b0bcb49bbb5574e1ac83b | [
"MIT"
] | null | null | null | examples/loop.py | letmaik/exhaust | 7c3b4f1dfab95192968b0bcb49bbb5574e1ac83b | [
"MIT"
] | null | null | null | # This example shows how a space can be modelled with loops.
import exhaust
def generate_numbers(state: exhaust.State):
numbers = []
for _ in range(5):
numbers.append(state.randint(1, 5))
return numbers
for numbers in exhaust.space(generate_numbers):
print(numbers)
# Output:
# [1, 1, 1, 1, 1]
# [1, 1, 1, 1, 2]
# [1, 1, 1, 1, 3]
# [1, 1, 1, 1, 4]
# [1, 1, 1, 1, 5]
# [1, 1, 1, 2, 1]
# ...
# [5, 5, 5, 5, 5]
| 18.956522 | 60 | 0.568807 | 77 | 436 | 3.181818 | 0.38961 | 0.155102 | 0.171429 | 0.146939 | 0.065306 | 0.036735 | 0.036735 | 0.036735 | 0 | 0 | 0 | 0.115502 | 0.245413 | 436 | 22 | 61 | 19.818182 | 0.629179 | 0.417431 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.125 | false | 0 | 0.125 | 0 | 0.375 | 0.125 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
994a38b1a44b5485284d61f6f94e7e736096f6bb | 488 | py | Python | app/api/v1/models/meetups_model.py | MRichardN/Questioner-api | 2697ccc976c6f4896246f7f817aab7d12f1d606c | [
"MIT"
] | null | null | null | app/api/v1/models/meetups_model.py | MRichardN/Questioner-api | 2697ccc976c6f4896246f7f817aab7d12f1d606c | [
"MIT"
] | 2 | 2019-01-08T06:58:47.000Z | 2019-01-08T08:46:16.000Z | app/api/v1/models/meetups_model.py | MRichardN/Questioner-api | 2697ccc976c6f4896246f7f817aab7d12f1d606c | [
"MIT"
] | null | null | null | from datetime import datetime
from ..utils.utils import idGenerator
from .base_model import Model
meetups = []
class Meetup(Model):
""" This class represents the meetup model."""
def __init__(self):
super().__init__(meetups)
def save(self, data):
""" Save a meetup."""
data['id'] = idGenerator(self.collection)
data['createdOn'] = datetime.now()
data['happeningOn'] = datetime.now()
return super().save(data)
| 17.428571 | 50 | 0.612705 | 54 | 488 | 5.37037 | 0.481481 | 0.075862 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.254098 | 488 | 27 | 51 | 18.074074 | 0.796703 | 0.110656 | 0 | 0 | 0 | 0 | 0.052632 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | false | 0 | 0.25 | 0 | 0.583333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
994c3e8c1a0f1cd2339af26e2dce6708adab8045 | 469 | py | Python | FootageOverview/FootageManager/migrations/0003_footage_staticpath.py | nylser/FootageOverview | 921e003550ba445d5a3308dee231a2d92e642b01 | [
"Unlicense"
] | null | null | null | FootageOverview/FootageManager/migrations/0003_footage_staticpath.py | nylser/FootageOverview | 921e003550ba445d5a3308dee231a2d92e642b01 | [
"Unlicense"
] | null | null | null | FootageOverview/FootageManager/migrations/0003_footage_staticpath.py | nylser/FootageOverview | 921e003550ba445d5a3308dee231a2d92e642b01 | [
"Unlicense"
] | null | null | null | # Generated by Django 2.1.1 on 2018-09-20 07:56
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('FootageManager', '0002_footage_length'),
]
operations = [
migrations.AddField(
model_name='footage',
name='staticpath',
field=models.CharField(default='', max_length=200, verbose_name='static_path'),
preserve_default=False,
),
]
| 23.45 | 91 | 0.616205 | 49 | 469 | 5.755102 | 0.77551 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06414 | 0.268657 | 469 | 19 | 92 | 24.684211 | 0.758017 | 0.095949 | 0 | 0 | 1 | 0 | 0.14455 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.076923 | 0 | 0.307692 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99528f1bcb117738e7d9a6979b7ae04eff5afc1b | 429 | py | Python | equinox/__init__.py | marcelroed/equinox | 3804a8d60217bde685bee0a893a7bd55b1e63c26 | [
"Apache-2.0"
] | null | null | null | equinox/__init__.py | marcelroed/equinox | 3804a8d60217bde685bee0a893a7bd55b1e63c26 | [
"Apache-2.0"
] | null | null | null | equinox/__init__.py | marcelroed/equinox | 3804a8d60217bde685bee0a893a7bd55b1e63c26 | [
"Apache-2.0"
] | null | null | null | from . import experimental, nn
from .filters import (
combine,
filter,
is_array,
is_array_like,
is_inexact_array,
is_inexact_array_like,
partition,
)
from .grad import filter_custom_vjp, filter_grad, filter_value_and_grad
from .jit import filter_jit
from .module import Module, static_field
from .tree import tree_at, tree_equal, tree_pformat
from .update import apply_updates
__version__ = "0.3.1"
| 22.578947 | 71 | 0.762238 | 63 | 429 | 4.809524 | 0.507937 | 0.046205 | 0.092409 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008475 | 0.174825 | 429 | 18 | 72 | 23.833333 | 0.847458 | 0 | 0 | 0 | 0 | 0 | 0.011655 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.4375 | 0 | 0.4375 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
9956e73164bd63500cea24e33dc9bcf6d9f73e4e | 4,311 | py | Python | scp.py | KvantPro/SCP | 1304d03007992f223d319d41037e2b32c9fbf934 | [
"Unlicense"
] | 1 | 2021-11-12T19:28:16.000Z | 2021-11-12T19:28:16.000Z | scp.py | KvantPro/SCP | 1304d03007992f223d319d41037e2b32c9fbf934 | [
"Unlicense"
] | null | null | null | scp.py | KvantPro/SCP | 1304d03007992f223d319d41037e2b32c9fbf934 | [
"Unlicense"
] | null | null | null | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'SCP.ui'
#
# Created by: PyQt5 UI code generator 5.15.2
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import QMessageBox
import random, time
def animate():
a = 0
while a <= 100:
ui.progressBar.setValue(a)
time.sleep(0.01)
a += 1
def wt(w):
sg = QMessageBox()
sg.resize(100, 100)
if w == 'w':
te = 'Вы выиграли!'
elif w == 'p':
te = 'Вы проиграли'
else:
te = 'Ничья'
sg.setWindowTitle('Результат')
sg.setText(str(te))
x = sg.exec_()
def pr(x, win):
if win == 'S':
if x == 0:
wt = 'n'
elif x == 1:
wt = 'w'
else:
wt = 'p'
if win == 'C':
if x == 0:
wt = 'p'
elif x == 1:
wt = 'n'
else:
wt = 'w'
if win == 'P':
if x == 0:
wt = 'w'
elif x == 1:
wt = 'p'
else:
wt = 'n'
return wt
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(396, 257)
self.S = QtWidgets.QPushButton(Form)
self.S.setGeometry(QtCore.QRect(70, 180, 75, 71))
self.S.setObjectName("pushButton")
self.C = QtWidgets.QPushButton(Form)
self.C.setGeometry(QtCore.QRect(160, 180, 75, 71))
self.C.setObjectName("pushButton_2")
self.P = QtWidgets.QPushButton(Form)
self.P.setGeometry(QtCore.QRect(250, 180, 75, 71))
self.P.setObjectName("pushButton_3")
self.ME = QtWidgets.QPushButton(Form)
self.ME.setGeometry(QtCore.QRect(320, 10, 75, 31))
self.ME.setObjectName("pushButton_4")
self.label = QtWidgets.QLabel(Form)
self.label.setGeometry(QtCore.QRect(80, 120, 211, 31))
self.label.setStyleSheet("font-size: 25px;")
self.label.setObjectName("label")
self.label_2 = QtWidgets.QLabel(Form)
self.label_2.setGeometry(QtCore.QRect(80, 70, 211, 31))
self.label_2.setStyleSheet("font-size: 25px;")
self.label_2.setObjectName("label_2")
self.progressBar = QtWidgets.QProgressBar(Form)
self.progressBar.setGeometry(QtCore.QRect(10, 10, 301, 31))
self.progressBar.setProperty("value", 0)
self.progressBar.setObjectName("progressBar")
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
_translate = QtCore.QCoreApplication.translate
Form.setWindowTitle(_translate("Form", "Камень, ножницы, бумага!"))
self.S.setText(_translate("Form", "Камень"))
self.C.setText(_translate("Form", "Ножницы"))
self.P.setText(_translate("Form", "Бумага"))
self.ME.setText(_translate("Form", "О нас"))
self.label.setText(_translate("Form", "Бот:"))
self.label_2.setText(_translate("Form", "Вы:"))
def start(winh):
ui.label.setText("Бот: ")
winb = ['Камень', 'Ножницы', 'Бумага']
x = random.randint(0, 2)
bot = winb[x]
win = pr(x, winh)
if winh == 'S':
winh = 'Камень'
elif winh == 'C':
winh = 'Ножницы'
else:
winh = 'Бумага'
ui.label_2.setText("Вы: " + winh)
animate()
ui.label.setText("Бот: " + bot)
wt(win)
def ME():
msg = QMessageBox()
msg.setWindowTitle('О нас')
msg.setText("Мы, компания @Kvant`s studios\nЯвляемся разработчиками игры:\nКамень, ножницы, бумага.\nДля связи с нами пишите на почту \nили в ВК:\n\nkvantgd@gmail.com\nvk.com/kvantgd")
x = msg.exec_()
if __name__ == "__main__":
import sys
app = QtWidgets.QApplication(sys.argv)
Form = QtWidgets.QWidget()
ui = Ui_Form()
ui.setupUi(Form)
Form.show()
ui.S.clicked.connect( lambda: start("S") )
ui.C.clicked.connect( lambda: start("C") )
ui.P.clicked.connect( lambda: start("P") )
ui.ME.clicked.connect( ME )
sys.exit(app.exec_())
| 31.23913 | 189 | 0.566922 | 536 | 4,311 | 4.501866 | 0.324627 | 0.037298 | 0.063821 | 0.046415 | 0.051388 | 0.028181 | 0 | 0 | 0 | 0 | 0 | 0.038562 | 0.290188 | 4,311 | 137 | 190 | 31.467153 | 0.75 | 0.062167 | 0 | 0.172414 | 1 | 0.008621 | 0.124166 | 0.010262 | 0 | 0 | 0 | 0 | 0 | 1 | 0.060345 | false | 0 | 0.034483 | 0 | 0.112069 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
995a079b3191ebb48a0a239dc759e1d69f8990ae | 9,682 | py | Python | vendor/istio.io/api/python/istio_api/networking/v1alpha3/service_dependency_pb2.py | octarinesec/istio | 913b459130045fc4846a36c46c05a48da88776bb | [
"Apache-2.0"
] | 2 | 2020-07-20T06:35:29.000Z | 2021-01-22T03:35:38.000Z | vendor/istio.io/api/python/istio_api/networking/v1alpha3/service_dependency_pb2.py | octarinesec/istio | 913b459130045fc4846a36c46c05a48da88776bb | [
"Apache-2.0"
] | null | null | null | vendor/istio.io/api/python/istio_api/networking/v1alpha3/service_dependency_pb2.py | octarinesec/istio | 913b459130045fc4846a36c46c05a48da88776bb | [
"Apache-2.0"
] | 1 | 2021-01-22T03:35:42.000Z | 2021-01-22T03:35:42.000Z | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: networking/v1alpha3/service_dependency.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='networking/v1alpha3/service_dependency.proto',
package='istio.networking.v1alpha3',
syntax='proto3',
serialized_pb=_b('\n,networking/v1alpha3/service_dependency.proto\x12\x19istio.networking.v1alpha3\"\x92\x03\n\x11ServiceDependency\x12M\n\x0c\x64\x65pendencies\x18\x01 \x03(\x0b\x32\x37.istio.networking.v1alpha3.ServiceDependency.Dependency\x1a)\n\x06Import\x12\x11\n\tnamespace\x18\x01 \x01(\t\x12\x0c\n\x04host\x18\x02 \x01(\t\x1a\x82\x02\n\nDependency\x12q\n\x16source_workload_labels\x18\x01 \x03(\x0b\x32Q.istio.networking.v1alpha3.ServiceDependency.Dependency.SourceWorkloadLabelsEntry\x12\x44\n\x07imports\x18\x02 \x03(\x0b\x32\x33.istio.networking.v1alpha3.ServiceDependency.Import\x1a;\n\x19SourceWorkloadLabelsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01*&\n\x0b\x43onfigScope\x12\n\n\x06PUBLIC\x10\x00\x12\x0b\n\x07PRIVATE\x10\x01\x42\"Z istio.io/api/networking/v1alpha3b\x06proto3')
)
_CONFIGSCOPE = _descriptor.EnumDescriptor(
name='ConfigScope',
full_name='istio.networking.v1alpha3.ConfigScope',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='PUBLIC', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='PRIVATE', index=1, number=1,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=480,
serialized_end=518,
)
_sym_db.RegisterEnumDescriptor(_CONFIGSCOPE)
ConfigScope = enum_type_wrapper.EnumTypeWrapper(_CONFIGSCOPE)
PUBLIC = 0
PRIVATE = 1
_SERVICEDEPENDENCY_IMPORT = _descriptor.Descriptor(
name='Import',
full_name='istio.networking.v1alpha3.ServiceDependency.Import',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='namespace', full_name='istio.networking.v1alpha3.ServiceDependency.Import.namespace', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='host', full_name='istio.networking.v1alpha3.ServiceDependency.Import.host', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=176,
serialized_end=217,
)
_SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY = _descriptor.Descriptor(
name='SourceWorkloadLabelsEntry',
full_name='istio.networking.v1alpha3.ServiceDependency.Dependency.SourceWorkloadLabelsEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='key', full_name='istio.networking.v1alpha3.ServiceDependency.Dependency.SourceWorkloadLabelsEntry.key', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='value', full_name='istio.networking.v1alpha3.ServiceDependency.Dependency.SourceWorkloadLabelsEntry.value', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=_descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001')),
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=419,
serialized_end=478,
)
_SERVICEDEPENDENCY_DEPENDENCY = _descriptor.Descriptor(
name='Dependency',
full_name='istio.networking.v1alpha3.ServiceDependency.Dependency',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='source_workload_labels', full_name='istio.networking.v1alpha3.ServiceDependency.Dependency.source_workload_labels', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='imports', full_name='istio.networking.v1alpha3.ServiceDependency.Dependency.imports', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=220,
serialized_end=478,
)
_SERVICEDEPENDENCY = _descriptor.Descriptor(
name='ServiceDependency',
full_name='istio.networking.v1alpha3.ServiceDependency',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='dependencies', full_name='istio.networking.v1alpha3.ServiceDependency.dependencies', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[_SERVICEDEPENDENCY_IMPORT, _SERVICEDEPENDENCY_DEPENDENCY, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=76,
serialized_end=478,
)
_SERVICEDEPENDENCY_IMPORT.containing_type = _SERVICEDEPENDENCY
_SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY.containing_type = _SERVICEDEPENDENCY_DEPENDENCY
_SERVICEDEPENDENCY_DEPENDENCY.fields_by_name['source_workload_labels'].message_type = _SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY
_SERVICEDEPENDENCY_DEPENDENCY.fields_by_name['imports'].message_type = _SERVICEDEPENDENCY_IMPORT
_SERVICEDEPENDENCY_DEPENDENCY.containing_type = _SERVICEDEPENDENCY
_SERVICEDEPENDENCY.fields_by_name['dependencies'].message_type = _SERVICEDEPENDENCY_DEPENDENCY
DESCRIPTOR.message_types_by_name['ServiceDependency'] = _SERVICEDEPENDENCY
DESCRIPTOR.enum_types_by_name['ConfigScope'] = _CONFIGSCOPE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
ServiceDependency = _reflection.GeneratedProtocolMessageType('ServiceDependency', (_message.Message,), dict(
Import = _reflection.GeneratedProtocolMessageType('Import', (_message.Message,), dict(
DESCRIPTOR = _SERVICEDEPENDENCY_IMPORT,
__module__ = 'networking.v1alpha3.service_dependency_pb2'
# @@protoc_insertion_point(class_scope:istio.networking.v1alpha3.ServiceDependency.Import)
))
,
Dependency = _reflection.GeneratedProtocolMessageType('Dependency', (_message.Message,), dict(
SourceWorkloadLabelsEntry = _reflection.GeneratedProtocolMessageType('SourceWorkloadLabelsEntry', (_message.Message,), dict(
DESCRIPTOR = _SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY,
__module__ = 'networking.v1alpha3.service_dependency_pb2'
# @@protoc_insertion_point(class_scope:istio.networking.v1alpha3.ServiceDependency.Dependency.SourceWorkloadLabelsEntry)
))
,
DESCRIPTOR = _SERVICEDEPENDENCY_DEPENDENCY,
__module__ = 'networking.v1alpha3.service_dependency_pb2'
# @@protoc_insertion_point(class_scope:istio.networking.v1alpha3.ServiceDependency.Dependency)
))
,
DESCRIPTOR = _SERVICEDEPENDENCY,
__module__ = 'networking.v1alpha3.service_dependency_pb2'
# @@protoc_insertion_point(class_scope:istio.networking.v1alpha3.ServiceDependency)
))
_sym_db.RegisterMessage(ServiceDependency)
_sym_db.RegisterMessage(ServiceDependency.Import)
_sym_db.RegisterMessage(ServiceDependency.Dependency)
_sym_db.RegisterMessage(ServiceDependency.Dependency.SourceWorkloadLabelsEntry)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('Z istio.io/api/networking/v1alpha3'))
_SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY.has_options = True
_SERVICEDEPENDENCY_DEPENDENCY_SOURCEWORKLOADLABELSENTRY._options = _descriptor._ParseOptions(descriptor_pb2.MessageOptions(), _b('8\001'))
# @@protoc_insertion_point(module_scope)
| 40.008264 | 837 | 0.777629 | 1,070 | 9,682 | 6.749533 | 0.160748 | 0.072279 | 0.063694 | 0.099695 | 0.596511 | 0.476599 | 0.44946 | 0.402935 | 0.368457 | 0.352534 | 0 | 0.035445 | 0.108345 | 9,682 | 241 | 838 | 40.174274 | 0.801112 | 0.058356 | 0 | 0.589623 | 1 | 0.004717 | 0.237923 | 0.210145 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.09434 | 0 | 0.09434 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
995ff73160e804154606204631367eefc3fb8d9c | 1,522 | py | Python | notifications/sms.py | HexNumbers/OctoPrint | ba01fdd0c625150bdbe09a2ba965e30b7f434e4a | [
"MIT"
] | null | null | null | notifications/sms.py | HexNumbers/OctoPrint | ba01fdd0c625150bdbe09a2ba965e30b7f434e4a | [
"MIT"
] | null | null | null | notifications/sms.py | HexNumbers/OctoPrint | ba01fdd0c625150bdbe09a2ba965e30b7f434e4a | [
"MIT"
] | null | null | null | import smtplib, sys, urllib2
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from email.mime.image import MIMEImage
from PIL import Image
import io
GMAIL_USERNAME = '*******@gmail.com'
GMAIL_PASS = '*******'
RECEPIENT = '*******@tmomail.net'
SNAP_URL='http://127.0.0.1:8080/?action=snapshot'
MESSAGE = "Print complete! Here is your thingy:"
ROTATE_IMAGE = False
def send():
email = MIMEMultipart()
envelope = MIMEMultipart('alternative')
msg_text = MIMEText(MESSAGE, 'plain')
msg_html = MIMEText(MESSAGE, 'html')
u = urllib2.urlopen(SNAP_URL)
image = Image.open(u)
fp = io.BytesIO()
if ROTATE_IMAGE:
image.rotate(180).save(fp, Image.registered_extensions()['.jpg'])
else:
image.save(fp, Image.registered_extensions()['.jpg'])
img = MIMEImage(fp.getvalue(), 'jpeg; name="print.jpg"')
img.add_header('Content-Disposition', 'attachment; filename="print.jpg"')
img.add_header('Content-ID', '<thingy>')
img.add_header('X-Attachment-Id', 'thingy')
email['From'] = GMAIL_USERNAME
email['To'] = RECEPIENT
envelope.attach(msg_text)
envelope.attach(msg_html)
email.attach(envelope)
email.attach(img)
server = smtplib.SMTP( "smtp.gmail.com", 587 )
server.starttls()
server.login(GMAIL_USERNAME, GMAIL_PASS)
server.sendmail( GMAIL_USERNAME, RECEPIENT, email.as_string())
send()
| 35.395349 | 81 | 0.636662 | 181 | 1,522 | 5.243094 | 0.447514 | 0.054795 | 0.041096 | 0.044257 | 0.128556 | 0.128556 | 0 | 0 | 0 | 0 | 0 | 0.015113 | 0.217477 | 1,522 | 42 | 82 | 36.238095 | 0.781696 | 0 | 0 | 0 | 0 | 0 | 0.181997 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025641 | false | 0.051282 | 0.153846 | 0 | 0.179487 | 0.051282 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
9962163f869a0d942c76685500a8b2f453af027d | 6,306 | py | Python | dendropy/test/test_dataio_nexml_reader_chars.py | EnjoyLifeFund/macHighSierra-py36-pkgs | 5668b5785296b314ea1321057420bcd077dba9ea | [
"BSD-3-Clause",
"BSD-2-Clause",
"MIT"
] | null | null | null | dendropy/test/test_dataio_nexml_reader_chars.py | EnjoyLifeFund/macHighSierra-py36-pkgs | 5668b5785296b314ea1321057420bcd077dba9ea | [
"BSD-3-Clause",
"BSD-2-Clause",
"MIT"
] | null | null | null | dendropy/test/test_dataio_nexml_reader_chars.py | EnjoyLifeFund/macHighSierra-py36-pkgs | 5668b5785296b314ea1321057420bcd077dba9ea | [
"BSD-3-Clause",
"BSD-2-Clause",
"MIT"
] | null | null | null | # !/usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this work or any portion thereof in published work,
## please cite it as:
##
## Sukumaran, J. and M. T. Holder. 2010. DendroPy: a Python library
## for phylogenetic computing. Bioinformatics 26: 1569-1571.
##
##############################################################################
"""
Tests for general NEXUS character matrix reading.
"""
import unittest
import dendropy
from dendropy.utility import error
from dendropy.test.support import dendropytest
from dendropy.test.support import pathmap
from dendropy.test.support import standard_file_test_chars
from dendropy.test.support import compare_and_validate
from dendropy.dataio import nexmlreader
from dendropy.utility import messaging
_LOG = messaging.get_logger(__name__)
class NexmlCharactersReaderDnaTestCase(
standard_file_test_chars.DnaTestChecker,
dendropytest.ExtendedTestCase):
@classmethod
def setUpClass(cls):
cls.build()
def test_basic_nexml(self):
src_filenames = [
"standard-test-chars-dna.as_cells.nexml",
"standard-test-chars-dna.as_seqs.nexml",
]
for src_idx, src_filename in enumerate(src_filenames):
# print(src_idx, src_filename)
src_path = pathmap.char_source_path(src_filename)
self.verify_get_from(
matrix_type=dendropy.DnaCharacterMatrix,
src_filepath=src_path,
schema="nexml",
factory_kwargs={},
check_taxon_annotations=False,
check_matrix_annotations=False,
check_sequence_annotations=False,
check_column_annotations=False,
check_cell_annotations=False)
class NexmlCharactersReaderRnaTestCase(
standard_file_test_chars.RnaTestChecker,
dendropytest.ExtendedTestCase):
@classmethod
def setUpClass(cls):
cls.build()
def test_basic_nexml(self):
src_filenames = [
"standard-test-chars-rna.as_cells.nexml",
"standard-test-chars-rna.as_seqs.nexml",
]
for src_idx, src_filename in enumerate(src_filenames):
# print(src_idx, src_filename)
src_path = pathmap.char_source_path(src_filename)
self.verify_get_from(
matrix_type=dendropy.RnaCharacterMatrix,
src_filepath=src_path,
schema="nexml",
factory_kwargs={},
check_taxon_annotations=False,
check_matrix_annotations=False,
check_sequence_annotations=False,
check_column_annotations=False,
check_cell_annotations=False)
class NexmlCharactersReaderProteinTestCase(
standard_file_test_chars.ProteinTestChecker,
dendropytest.ExtendedTestCase):
@classmethod
def setUpClass(cls):
cls.build()
def test_basic_nexml(self):
src_filenames = [
"standard-test-chars-protein.as_cells.nexml",
"standard-test-chars-protein.as_seqs.nexml",
]
for src_idx, src_filename in enumerate(src_filenames):
# print(src_idx, src_filename)
src_path = pathmap.char_source_path(src_filename)
self.verify_get_from(
matrix_type=dendropy.ProteinCharacterMatrix,
src_filepath=src_path,
schema="nexml",
factory_kwargs={},
check_taxon_annotations=False,
check_matrix_annotations=False,
check_sequence_annotations=False,
check_column_annotations=False,
check_cell_annotations=False)
class NexmlCharactersContinuousTestCase(
standard_file_test_chars.ContinuousTestChecker,
dendropytest.ExtendedTestCase):
@classmethod
def setUpClass(cls):
cls.build()
def test_basic_nexml(self):
src_filenames = [
"standard-test-chars-continuous.as_cells.nexml",
"standard-test-chars-continuous.as_seqs.nexml",
]
for src_idx, src_filename in enumerate(src_filenames):
# print(src_idx, src_filename)
src_path = pathmap.char_source_path(src_filename)
self.verify_get_from(
matrix_type=dendropy.ContinuousCharacterMatrix,
src_filepath=src_path,
schema="nexml",
factory_kwargs={},
check_taxon_annotations=False,
check_matrix_annotations=False,
check_sequence_annotations=False,
check_column_annotations=False,
check_cell_annotations=False)
class NexmlStandardCharacters01234TestCase(
standard_file_test_chars.Standard01234TestChecker,
dendropytest.ExtendedTestCase):
@classmethod
def setUpClass(cls):
cls.build()
def test_basic_nexml(self):
src_filenames = [
"standard-test-chars-generic.as_cells.nexml",
"standard-test-chars-generic.as_seqs.nexml",
]
for src_idx, src_filename in enumerate(src_filenames):
# print(src_idx, src_filename)
src_path = pathmap.char_source_path(src_filename)
self.verify_get_from(
matrix_type=dendropy.StandardCharacterMatrix,
src_filepath=src_path,
schema="nexml",
factory_kwargs={},
check_taxon_annotations=False,
check_matrix_annotations=False,
check_sequence_annotations=False,
check_column_annotations=False,
check_cell_annotations=False)
if __name__ == "__main__":
unittest.main()
| 36.877193 | 78 | 0.599746 | 598 | 6,306 | 6.016722 | 0.220736 | 0.111173 | 0.116732 | 0.047248 | 0.704836 | 0.65592 | 0.61562 | 0.61562 | 0.61562 | 0.61562 | 0 | 0.007304 | 0.305265 | 6,306 | 170 | 79 | 37.094118 | 0.813969 | 0.09594 | 0 | 0.669291 | 0 | 0 | 0.07955 | 0.073556 | 0 | 0 | 0 | 0 | 0 | 1 | 0.07874 | false | 0 | 0.070866 | 0 | 0.188976 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
9966d637d34484011d82a970b82967f225c9276e | 2,067 | py | Python | lib.py | miami-acm/unit-testing | d074c680805f49848991bdfeab43537785238560 | [
"MIT"
] | null | null | null | lib.py | miami-acm/unit-testing | d074c680805f49848991bdfeab43537785238560 | [
"MIT"
] | null | null | null | lib.py | miami-acm/unit-testing | d074c680805f49848991bdfeab43537785238560 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
def sleep_in(weekday, vacation):
"""
The parameter weekday is True if it is a weekday, and the parameter
vacation is True if we are on vacation. We sleep in if it is not a
weekday or we're on vacation. Return True if we sleep in.
sleep_in(False, False) → True
sleep_in(True, False) → False
sleep_in(False, True) → True
"""
return False
def monkey_trouble(a_smile, b_smile):
"""
We have two monkeys, a and b, and the parameters a_smile and b_smile
indicate if each is smiling. We are in trouble if they are both smiling or
if neither of them is smiling. Return True if we are in trouble.
monkey_trouble(True, True) → True
monkey_trouble(False, False) → True
monkey_trouble(True, False) → False
"""
return False
def sum_double(a, b):
"""
Given two int values, return their sum. Unless the two values are the same,
then return double their sum.
sum_double(1, 2) → 3
sum_double(3, 2) → 5
sum_double(2, 2) → 8
"""
return 0
def diff21(n):
"""
Given an int n, return the absolute difference between n and 21, except
return double the absolute difference if n is over 21.
diff21(19) → 2
diff21(10) → 11
diff21(21) → 0
"""
return 0
def count_evens(nums):
"""
Return the number of even ints in the given array. Note: the % "mod"
operator computes the remainder, e.g. 5 % 2 is 1.
count_evens([2, 1, 2, 3, 4]) → 3
count_evens([2, 2, 0]) → 3
count_evens([1, 3, 5]) → 0
"""
return 0
def xyz_there(s):
"""
Return True if the given string contains an appearance of "xyz" where the
xyz is not directly preceeded by a period (.). So "xxyz" counts but "x.xyz"
does not.
xyz_there('abcxyz') → True
xyz_there('abc.xyz') → False
xyz_there('xyz.abc') → True
"""
return False
def is_prime(n):
"""
Return True if the given number is prime.
is_prime(2) → True
is_prime(4) → False
is_prime(11) → True
"""
return False
| 23.488636 | 79 | 0.627963 | 357 | 2,067 | 3.616247 | 0.291317 | 0.030984 | 0.03718 | 0.03718 | 0.079009 | 0 | 0 | 0 | 0 | 0 | 0 | 0.037259 | 0.272859 | 2,067 | 87 | 80 | 23.758621 | 0.807718 | 0.734398 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
9968ab6b834c0b7f00339801637e6217641653ab | 246 | py | Python | gvars.py | MattSkiff/cow_flow | 6354842fbe3ceccc3648d956987b391670476292 | [
"MIT"
] | null | null | null | gvars.py | MattSkiff/cow_flow | 6354842fbe3ceccc3648d956987b391670476292 | [
"MIT"
] | 1 | 2022-03-12T01:03:01.000Z | 2022-03-12T01:03:01.000Z | gvars.py | MattSkiff/cow_flow | 6354842fbe3ceccc3648d956987b391670476292 | [
"MIT"
] | null | null | null | import config as c
FEAT_MOD_DIR = './models/feat_extractors/'
VIZ_DIR = './viz'
WEIGHT_DIR = './weights'
MODEL_DIR = './models'
LOG_DIR = './logs'
C_DIR = './cstates'
DMAP_DIR = './data/precompute/size_{}_sigma_{}/'.format(c.filter_size,c.sigma) | 27.333333 | 78 | 0.695122 | 37 | 246 | 4.27027 | 0.621622 | 0.113924 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.101626 | 246 | 9 | 78 | 27.333333 | 0.714932 | 0 | 0 | 0 | 0 | 0 | 0.392713 | 0.242915 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.125 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
996bd5432ca4d3076b30cd7035a3ff74a9f4d4d5 | 2,220 | py | Python | elationmagic.py | lordjabez/light-maestro | ddc8a6398f818bc531f5c809ab00e69e121e25ad | [
"Apache-2.0"
] | 1 | 2015-08-20T08:05:41.000Z | 2015-08-20T08:05:41.000Z | elationmagic.py | lordjabez/light-maestro | ddc8a6398f818bc531f5c809ab00e69e121e25ad | [
"Apache-2.0"
] | null | null | null | elationmagic.py | lordjabez/light-maestro | ddc8a6398f818bc531f5c809ab00e69e121e25ad | [
"Apache-2.0"
] | null | null | null | """
@copyright: 2013 Single D Software - All Rights Reserved
@summary: Elation Magic 260 MIDI interface for Light Maestro.
"""
# Standard library imports
import logging
# Additional library imports
import rtmidi
import rtmidi.midiconstants
# Application imports
import console
# Named logger for this module
_logger = logging.getLogger(__name__)
class ElationMagic(console.Console):
"""The console class that communicates with the Elation Magic 260."""
def _sendmidi(self, channel, note):
try:
self._midi.send_message((rtmidi.midiconstants.NOTE_ON | channel, note, 127))
_logger.debug('Sent note {0} to channel {1}'.format(note, channel))
except RuntimeError:
raise console.CommunicationError
def getstatus(self):
"""
Provide status information for the connection to the console.
@return: Dictionary containing status information
"""
status = super().getstatus()
status['condition'] = 'operational' if self._midi else 'nonoperational'
return status
def getchannels(self):
raise console.NotSupportedError
def loadchannels(self, data, sceneid=None):
raise console.NotSupportedError
def getscenes(self):
raise console.NotSupportedError
def getscene(self, sceneid):
raise console.NotSupportedError
def loadscene(self, sceneid):
try:
channel, note = divmod(int(sceneid) - 1, 72)
self._sendmidi(channel, note)
except ValueError:
_logger.warning('Non-numeric scenes are not supported.')
def savescene(self, sceneid, fade=5, scene=None):
raise console.NotSupportedError
def deletescene(self, sceneid):
raise console.NotSupportedError
def __init__(self, parameter='USB'):
self._midi = rtmidi.MidiOut()
for p, portname in enumerate(self._midi.get_ports()):
if parameter in portname:
self._midi.open_port(p)
_logger.info('Connected to MIDI device "{0}"'.format(self._midi.get_port_name(p)))
super().__init__()
return
_logger.warning('No USB MIDI device found')
| 29.6 | 98 | 0.656757 | 243 | 2,220 | 5.872428 | 0.473251 | 0.058865 | 0.121934 | 0.134548 | 0.161177 | 0.060266 | 0 | 0 | 0 | 0 | 0 | 0.012048 | 0.252252 | 2,220 | 74 | 99 | 30 | 0.84759 | 0.178378 | 0 | 0.186047 | 0 | 0 | 0.087788 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.232558 | false | 0 | 0.093023 | 0 | 0.395349 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
996e1f99f31c354cf76b0192bd90d53f495fec14 | 194 | py | Python | 6 kyu/Caesar Cipher Encryption Variation.py | mwk0408/codewars_solutions | 9b4f502b5f159e68024d494e19a96a226acad5e5 | [
"MIT"
] | 6 | 2020-09-03T09:32:25.000Z | 2020-12-07T04:10:01.000Z | 6 kyu/Caesar Cipher Encryption Variation.py | mwk0408/codewars_solutions | 9b4f502b5f159e68024d494e19a96a226acad5e5 | [
"MIT"
] | 1 | 2021-12-13T15:30:21.000Z | 2021-12-13T15:30:21.000Z | 6 kyu/Caesar Cipher Encryption Variation.py | mwk0408/codewars_solutions | 9b4f502b5f159e68024d494e19a96a226acad5e5 | [
"MIT"
] | null | null | null | def caesar_encode(phrase, shift):
res=[]
for i,j in enumerate(phrase.split()):
res.append("".join(chr(ord("a")+(ord(k)-ord("a")+shift+i)%26) for k in j))
return " ".join(res) | 38.8 | 82 | 0.582474 | 33 | 194 | 3.393939 | 0.606061 | 0.071429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012579 | 0.180412 | 194 | 5 | 83 | 38.8 | 0.691824 | 0 | 0 | 0 | 0 | 0 | 0.015385 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0 | 0 | 0.4 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99729efd1ed3e15b30cbb89f9e80540b94f9b6f9 | 557 | py | Python | python/setup.py | dune-mirrors/dune-python | e83ac8c8e6eb7c4f6e72a21b1efde8e674a226bd | [
"BSD-3-Clause"
] | null | null | null | python/setup.py | dune-mirrors/dune-python | e83ac8c8e6eb7c4f6e72a21b1efde8e674a226bd | [
"BSD-3-Clause"
] | null | null | null | python/setup.py | dune-mirrors/dune-python | e83ac8c8e6eb7c4f6e72a21b1efde8e674a226bd | [
"BSD-3-Clause"
] | null | null | null | from setuptools import setup, find_packages
setup(name='dune.common',
namespace_packages=['dune'],
version='2.4',
description='Python package accompanying the DUNE project',
url='http://www.dune-project.org',
author='Dominic Kempf',
author_email='dominic.kempf@iwr.uni-heidelberg.de',
license='BSD',
packages=['dune.common',
'dune.common.parametertree',
'dune.common.modules',
],
install_requires=['pyparsing>=2.1.10',
],
)
| 30.944444 | 65 | 0.572711 | 57 | 557 | 5.526316 | 0.701754 | 0.126984 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015075 | 0.285458 | 557 | 17 | 66 | 32.764706 | 0.776382 | 0 | 0 | 0.125 | 0 | 0 | 0.38061 | 0.10772 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.0625 | 0 | 0.0625 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
998087f02dbab8dae6c25cacb1cd710f8eb57923 | 345 | py | Python | plugins/broadcast.py | DeveloperNoob/BAD_LOKI_FACE | 75acea0e9c2403bfd7b732153a0bef284d12b16e | [
"MIT"
] | null | null | null | plugins/broadcast.py | DeveloperNoob/BAD_LOKI_FACE | 75acea0e9c2403bfd7b732153a0bef284d12b16e | [
"MIT"
] | null | null | null | plugins/broadcast.py | DeveloperNoob/BAD_LOKI_FACE | 75acea0e9c2403bfd7b732153a0bef284d12b16e | [
"MIT"
] | null | null | null | #By @Joel_Noob
from pyrogram import Client, filters
from config import Config
@Client.on_message(
filters.private
& filters.command("broadcast")
& filters.user(Config.ADMINS)
& filters.reply
)
async def broadcast_(c, m):
await c.start_broadcast(
broadcast_message=m.reply_to_message, admin_id=m.from_user.id
)
| 20.294118 | 69 | 0.715942 | 47 | 345 | 5.06383 | 0.553191 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.185507 | 345 | 16 | 70 | 21.5625 | 0.846975 | 0.037681 | 0 | 0 | 0 | 0 | 0.02719 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.166667 | 0 | 0.166667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
998f551a05620bbe51cf037cfa0f6b3d026b742d | 486 | py | Python | recipes/migrations/0004_auto_20210308_0628.py | PavelYasukevich/foodgram-project | d03af25d8fd0cbf1eec03467a95620b89993c9fd | [
"MIT"
] | null | null | null | recipes/migrations/0004_auto_20210308_0628.py | PavelYasukevich/foodgram-project | d03af25d8fd0cbf1eec03467a95620b89993c9fd | [
"MIT"
] | null | null | null | recipes/migrations/0004_auto_20210308_0628.py | PavelYasukevich/foodgram-project | d03af25d8fd0cbf1eec03467a95620b89993c9fd | [
"MIT"
] | 1 | 2021-03-27T16:34:07.000Z | 2021-03-27T16:34:07.000Z | # Generated by Django 3.1.7 on 2021-03-08 06:28
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('recipes', '0003_auto_20210304_1233'),
]
operations = [
migrations.AlterField(
model_name='recipe',
name='fav_counter',
field=models.PositiveSmallIntegerField(default=0, help_text='Счетчик добавлений в избранное', verbose_name='Добавлений в избранное'),
),
]
| 25.578947 | 145 | 0.650206 | 53 | 486 | 5.830189 | 0.811321 | 0.071197 | 0.12945 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.086957 | 0.242798 | 486 | 18 | 146 | 27 | 0.752717 | 0.092593 | 0 | 0 | 1 | 0 | 0.225513 | 0.052392 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.083333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
9993ad6982945646f5058571579df16c0d7ea0c7 | 438 | py | Python | board_app/migrations/0004_alter_boardmodel_snsimage.py | OkuboAtsushi/board-project | c73beab6ad9525f1fe31d8e9b987476e4b45fd18 | [
"MIT"
] | null | null | null | board_app/migrations/0004_alter_boardmodel_snsimage.py | OkuboAtsushi/board-project | c73beab6ad9525f1fe31d8e9b987476e4b45fd18 | [
"MIT"
] | null | null | null | board_app/migrations/0004_alter_boardmodel_snsimage.py | OkuboAtsushi/board-project | c73beab6ad9525f1fe31d8e9b987476e4b45fd18 | [
"MIT"
] | null | null | null | # Generated by Django 3.2.3 on 2021-08-01 12:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('board_app', '0003_alter_boardmodel_snsimage'),
]
operations = [
migrations.AlterField(
model_name='boardmodel',
name='snsimage',
field=models.ImageField(blank=True, default=None, null=True, upload_to=''),
),
]
| 23.052632 | 87 | 0.621005 | 48 | 438 | 5.541667 | 0.791667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.058642 | 0.260274 | 438 | 18 | 88 | 24.333333 | 0.762346 | 0.10274 | 0 | 0 | 1 | 0 | 0.14578 | 0.076726 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.083333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99a086af551d96b57462192e6864be3a7cb52597 | 2,049 | py | Python | smartlearner/status.py | MarcCote/smartlearner | 0afdcd3b38dddfee16330b8324eb3b0e224f1c2b | [
"BSD-3-Clause"
] | null | null | null | smartlearner/status.py | MarcCote/smartlearner | 0afdcd3b38dddfee16330b8324eb3b0e224f1c2b | [
"BSD-3-Clause"
] | null | null | null | smartlearner/status.py | MarcCote/smartlearner | 0afdcd3b38dddfee16330b8324eb3b0e224f1c2b | [
"BSD-3-Clause"
] | null | null | null | from os.path import join as pjoin
from .utils import save_dict_to_json_file, load_dict_from_json_file
class Status(object):
def __init__(self, trainer=None, starting_epoch=0, starting_update=0):
self.current_epoch = starting_epoch
self.current_update = starting_update
self.current_update_in_epoch = 1
self.trainer = trainer
self.training_time = 0
self.done = False
self.extra = {}
def increment_update(self):
self.current_update += 1
self.current_update_in_epoch += 1
def increment_epoch(self):
self.current_epoch += 1
self.current_update_in_epoch = 0
def __repr__(self):
return ('Status object with state :\n' +\
' current_epoch = {!r}\n' +\
' current_update = {!r}\n' +\
' current_update_in_epoch = {!r}\n' +\
' trainer = {!r}\n' +\
' done = {!r}\n' +\
' extra = {!r}\n').format(self.current_epoch, self.current_update, self.current_update_in_epoch,
self.trainer, self.training_time, self.done, self.extra)
def save(self, savedir="./"):
state = {"version": 1,
"current_epoch": self.current_epoch,
"current_update": self.current_update,
"current_update_in_epoch": self.current_update_in_epoch,
"training_time": self.training_time,
"done": self.done,
"extra": self.extra,
}
save_dict_to_json_file(pjoin(savedir, 'status.json'), state)
def load(self, loaddir="./"):
state = load_dict_from_json_file(pjoin(loaddir, 'status.json'))
self.current_epoch = state["current_epoch"]
self.current_update = state["current_update"]
self.current_update_in_epoch = state["current_update_in_epoch"]
self.training_time = state["training_time"]
self.done = state["done"]
self.extra = state["extra"]
| 37.254545 | 113 | 0.585163 | 240 | 2,049 | 4.666667 | 0.1875 | 0.197321 | 0.166964 | 0.160714 | 0.338393 | 0.139286 | 0.066071 | 0 | 0 | 0 | 0 | 0.006272 | 0.299658 | 2,049 | 54 | 114 | 37.944444 | 0.774216 | 0 | 0 | 0 | 0 | 0 | 0.164959 | 0.033675 | 0 | 0 | 0 | 0 | 0 | 1 | 0.136364 | false | 0 | 0.045455 | 0.022727 | 0.227273 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99a24f7bca5db37447ff93cc105a33b663fe4185 | 9,991 | py | Python | 10601-hws/HW 6/hw6/python/hwTestLeNet.py | dfreilich/machine-learning-workspace | a1b6e5bd84a4f5708461f3827d64e2bf5a32dffa | [
"MIT"
] | null | null | null | 10601-hws/HW 6/hw6/python/hwTestLeNet.py | dfreilich/machine-learning-workspace | a1b6e5bd84a4f5708461f3827d64e2bf5a32dffa | [
"MIT"
] | null | null | null | 10601-hws/HW 6/hw6/python/hwTestLeNet.py | dfreilich/machine-learning-workspace | a1b6e5bd84a4f5708461f3827d64e2bf5a32dffa | [
"MIT"
] | null | null | null | import numpy as np
import cnn_lenet
import pickle
import copy
import random
import matplotlib as mp
import matplotlib.pyplot as plt
import math
def get_lenet():
"""Define LeNet
Explanation of parameters:
type: layer type, supports convolution, pooling, relu
channel: input channel
num: output channel
k: convolution kernel width (== height)
group: split input channel into several groups, not used in this assignment
"""
layers = {}
layers[1] = {}
layers[1]['type'] = 'DATA'
layers[1]['height'] = 28
layers[1]['width'] = 28
layers[1]['channel'] = 1
layers[1]['batch_size'] = 1
layers[2] = {}
layers[2]['type'] = 'CONV'
layers[2]['num'] = 20
layers[2]['k'] = 5
layers[2]['stride'] = 1
layers[2]['pad'] = 0
layers[2]['group'] = 1
layers[3] = {}
layers[3]['type'] = 'POOLING'
layers[3]['k'] = 2
layers[3]['stride'] = 2
layers[3]['pad'] = 0
layers[4] = {}
layers[4]['type'] = 'CONV'
layers[4]['num'] = 50
layers[4]['k'] = 5
layers[4]['stride'] = 1
layers[4]['pad'] = 0
layers[4]['group'] = 1
layers[5] = {}
layers[5]['type'] = 'POOLING'
layers[5]['k'] = 2
layers[5]['stride'] = 2
layers[5]['pad'] = 0
layers[6] = {}
layers[6]['type'] = 'IP'
layers[6]['num'] = 500
layers[6]['init_type'] = 'uniform'
layers[7] = {}
layers[7]['type'] = 'RELU'
layers[8] = {}
layers[8]['type'] = 'LOSS'
layers[8]['num'] = 10
return layers
def trainNet():
# define lenet
layers = get_lenet()
# load data
# change the following value to true to load the entire dataset
fullset = True
print("Loading MNIST Dataset...")
xtrain, ytrain, xval, yval, xtest, ytest = cnn_lenet.load_mnist(fullset)
print("MNIST Dataset Loading Complete!\n")
xtrain = np.hstack([xtrain, xval])
ytrain = np.hstack([ytrain, yval])
m_train = xtrain.shape[1]
# cnn parameters
batch_size = 64
mu = 0.9
epsilon = 0.01
gamma = 0.0001
power = 0.75
weight_decay = 0.0005
w_lr = 1
b_lr = 2
test_interval = 100
display_interval = 100
snapshot = 5000
max_iter = 10000
# Lets it run the entire way
# initialize parameters
print("Initializing Parameters...")
# You can make the params your params, and not the initialized ones, in order to visualize the results
params = cnn_lenet.init_convnet(layers)
param_winc = copy.deepcopy(params)
print("Initilization Complete!\n")
for l_idx in range(1, len(layers)):
param_winc[l_idx]['w'] = np.zeros(param_winc[l_idx]['w'].shape)
param_winc[l_idx]['b'] = np.zeros(param_winc[l_idx]['b'].shape)
# learning iterations
random.seed(100000)
indices = range(m_train)
random.shuffle(indices)
train_cost = np.array([])
train_accuracy = np.array([])
test_cost = np.array([])
test_accuracy = np.array([])
print("Training Started. Printing report on training data every " + str(display_interval) + " steps.")
print("Printing report on test data every " + str(test_interval) + " steps.\n")
for step in range(max_iter):
# get mini-batch and setup the cnn with the mini-batch
start_idx = step * batch_size % m_train
end_idx = (step+1) * batch_size % m_train
if start_idx > end_idx:
random.shuffle(indices)
continue
idx = indices[start_idx: end_idx]
[cp, param_grad] = cnn_lenet.conv_net(params,
layers,
xtrain[:, idx],
ytrain[idx], True)
# True there is to get backtracking, but you can just use it for forward, to visualize
# You have to make the function return output for you, so that you can reshape it into an image matrix, to show the image
# we have different epsilons for w and b
w_rate = cnn_lenet.get_lr(step, epsilon*w_lr, gamma, power)
b_rate = cnn_lenet.get_lr(step, epsilon*b_lr, gamma, power)
params, param_winc = cnn_lenet.sgd_momentum(w_rate,
b_rate,
mu,
weight_decay,
params,
param_winc,
param_grad)
# display training loss
if (step+1) % display_interval == 0:
print 'training_cost = %f training_accuracy = %f' % (cp['cost'], cp['percent']) + ' current_step = ' + str(step + 1)
train_cost = np.append(train_cost, cp['cost'])
train_accuracy = np.append(train_accuracy, cp['percent'])
# display test accuracy
if (step+1) % test_interval == 0:
layers[1]['batch_size'] = xtest.shape[1]
cptest, _ = cnn_lenet.conv_net(params, layers, xtest, ytest, False)
layers[1]['batch_size'] = 64
print 'test_cost = %f test_accuracy = %f' % (cptest['cost'], cptest['percent']) + ' current_step = ' + str(step + 1) + '\n'
test_cost = np.append(test_cost, cptest['cost'])
test_accuracy = np.append(test_accuracy, cptest['percent'])
# save params peridocally to recover from any crashes
if (step+1) % snapshot == 0:
pickle_path = 'lenet.mat'
pickle_file = open(pickle_path, 'wb')
pickle.dump(params, pickle_file)
pickle_file.close()
# Saves params at 30 for Question 4
if (step+1) == 30:
pickle_path = 'lenetAt30Iterations.mat'
pickle_file = open(pickle_path, 'wb')
pickle.dump(params, pickle_file)
pickle_file.close()
if (step+1) == max_iter:
np.savetxt('trainCost.txt', train_cost)
np.savetxt('trainAccuracy.txt', train_accuracy)
np.savetxt('testCost.txt', test_cost)
np.savetxt('testAccuracy.txt', test_accuracy)
# np.savetxt('costsStacked.txt', np.column_stack(train_cost, test_cost))
# np.savetxt('accuracyStacked.txt', np.column_stack(train_accuracy, test_accuracy))
pickle_path = 'lenetAt10000Iterations.mat'
pickle_file = open(pickle_path, 'wb')
pickle.dump(params, pickle_file)
pickle_file.close()
if (step) == max_iter:
np.savetxt('trainCost1.txt', train_cost)
np.savetxt('trainAccuracy1.txt', train_accuracy)
np.savetxt('testCost1.txt', test_cost)
np.savetxt('testAccuracy1.txt', test_accuracy)
# np.savetxt('costsStacked1.txt', np.column_stack(train_cost, test_cost))
# np.savetxt('accuracyStacked1.txt', np.column_stack(train_accuracy, test_accuracy))
pickle_path = 'lenetAtMAXPLUSONEIterations.mat'
pickle_file = open(pickle_path, 'wb')
pickle.dump(params, pickle_file)
pickle_file.close()
def visualizeOutputOfSecondLayer(givenParams):
# define lenet
layers = get_lenet()
# load data
# change the following value to true to load the entire dataset
fullset = True
print("Loading MNIST Dataset...")
xtrain, ytrain, xval, yval, xtest, ytest = cnn_lenet.load_mnist(fullset)
print("MNIST Dataset Loading Complete!\n")
xtrain = np.hstack([xtrain, xval])
ytrain = np.hstack([ytrain, yval])
m_train = xtrain.shape[1]
# cnn parameters
batch_size = 1
# initialize parameters
print("Initializing Parameters from given params")
# You can make the params your params, and not the initialized ones, in order to visualize the results
params = givenParams
param_winc = copy.deepcopy(params)
print("Initilization Complete!\n")
for l_idx in range(1, len(layers)):
param_winc[l_idx]['w'] = np.zeros(param_winc[l_idx]['w'].shape)
param_winc[l_idx]['b'] = np.zeros(param_winc[l_idx]['b'].shape)
# learning iterations
random.seed(100000)
indices = range(m_train)
random.shuffle(indices)
max_iter = 1
# get mini-batch and setup the cnn with the mini-batch
for step in range(max_iter):
# get mini-batch and setup the cnn with the mini-batch
start_idx = step * batch_size % m_train
end_idx = (step + 1) * batch_size % m_train
if start_idx > end_idx:
random.shuffle(indices)
continue
idx = indices[start_idx: end_idx]
[cp, param_grad, output] = cnn_lenet.conv_net(params,
layers,
xtrain[:, 0:1],
ytrain[0:1], False)
# conv_out = output[2]['data'].reshape(24,24,20)
# plotNNFilter(conv_out)
conv_out = output[1]['data'].reshape(28,28,1)
plotNNFilter(conv_out)
# for j in range(20):
# plt.figure()
# print j
# plt.imshow(conv_out[:,:,j], cmap="gray")
# plt.show()
# plotNNFilter(additionalReturn['data'].reshape(24,24,20))
# plotNNFilter(additionalReturn['data'].reshape())
# You have to make the function return output for you, so that you can reshape it into an image matrix, to show the image
def plotNNFilter(units):
filters = 1
plt.figure(1, figsize=(24,24))
n_columns = 4
n_rows = math.ceil(filters / n_columns) + 1
for i in range(filters):
plt.subplot(n_rows, n_columns, i+1)
plt.title('Filter ' + str(i+1))
plt.imshow(units[:,:,i], interpolation="nearest", cmap="gray")
plt.pause(100)
def visualizeCost():
train_Cost = np.genfromtxt('trainCost.txt')
test_Cost = np.genfromtxt('testCost.txt')
plt.gca().set_color_cycle(['red', 'green'])
plt.plot(train_Cost, train_Cost)
plt.plot(train_Cost, test_Cost)
# plt.axis([0,1,0,10000])
plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
plt.show()
def visualizeAccuracy():
train_Cost = np.genfromtxt('trainAccuracy.txt')
test_Cost = np.genfromtxt('testAccuracy.txt')
plt.gca().set_color_cycle(['red', 'green'])
plt.plot(train_Cost, train_Cost)
plt.plot(train_Cost, test_Cost)
# plt.axis([0,1,0,10000])
plt.legend(['Train Accuracy', 'Test Accuracy'], loc='upper left')
plt.show()
if __name__ == '__main__':
# params = pickle.load(open("lenetAt30Iterations.mat", "rb"))
# params2 = pickle.load(open("lenetAt10000Iterations.mat", "rb"))
# visualizeOutputOfSecondLayer(params2)
visualizeAccuracy()
# print(params2)
# visualizeOutputOfSecondLayer()
# main()
| 31.319749 | 129 | 0.636273 | 1,366 | 9,991 | 4.513177 | 0.207906 | 0.023358 | 0.012976 | 0.016869 | 0.511436 | 0.453366 | 0.431144 | 0.411354 | 0.411354 | 0.411354 | 0 | 0.028686 | 0.225403 | 9,991 | 318 | 130 | 31.418239 | 0.767929 | 0.201481 | 0 | 0.326923 | 0 | 0 | 0.141084 | 0.010451 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.038462 | null | null | 0.057692 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99a673617423652de49110e1613b793a9d706888 | 644 | py | Python | conf/urls.py | crisariasgg/RepinSolution | 27e9b04ccc887b4300d77dda8657e761f9523123 | [
"MIT"
] | null | null | null | conf/urls.py | crisariasgg/RepinSolution | 27e9b04ccc887b4300d77dda8657e761f9523123 | [
"MIT"
] | null | null | null | conf/urls.py | crisariasgg/RepinSolution | 27e9b04ccc887b4300d77dda8657e761f9523123 | [
"MIT"
] | 1 | 2021-12-09T21:27:35.000Z | 2021-12-09T21:27:35.000Z | """URL Configuration"""
from django.contrib import admin
from django.urls import path, include
from django.conf import settings
from django.views.generic import TemplateView
from django.conf.urls.static import static
urlpatterns = [
path('admin/', admin.site.urls),
path('', include(('apps.users.urls', 'users'), namespace='users')),
path('', include(('apps.import_excel.urls', 'import_excel'), namespace='import_excel')),
]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
if settings.DEBUG:
import debug_toolbar
urlpatterns += [
path('__debug__/', include(debug_toolbar.urls)),
]
| 25.76 | 96 | 0.706522 | 78 | 644 | 5.679487 | 0.358974 | 0.112867 | 0.063205 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150621 | 644 | 24 | 97 | 26.833333 | 0.809872 | 0.026398 | 0 | 0 | 0 | 0 | 0.140097 | 0.035427 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.466667 | 0 | 0.466667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
99a8de2271c3c05622470fceff6ae843c59d9535 | 1,524 | py | Python | pages/migrations/0001_initial.py | mixnix/subject_rate | 224fdc7c17afd972596c628bda65a384274ed4a1 | [
"MIT"
] | null | null | null | pages/migrations/0001_initial.py | mixnix/subject_rate | 224fdc7c17afd972596c628bda65a384274ed4a1 | [
"MIT"
] | null | null | null | pages/migrations/0001_initial.py | mixnix/subject_rate | 224fdc7c17afd972596c628bda65a384274ed4a1 | [
"MIT"
] | null | null | null | # Generated by Django 2.1.3 on 2018-11-22 04:31
import django.core.validators
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='CourseName',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('course_name', models.CharField(max_length=225)),
],
),
migrations.CreateModel(
name='Professor',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('professor_name', models.CharField(default='', max_length=255)),
],
),
migrations.CreateModel(
name='Review',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('how_easy', models.IntegerField(default=0, validators=[django.core.validators.MaxValueValidator(100), django.core.validators.MinValueValidator(0)])),
('how_interesting', models.IntegerField(default=0, validators=[django.core.validators.MaxValueValidator(100), django.core.validators.MinValueValidator(0)])),
('creation_date', models.DateTimeField(auto_now_add=True)),
('review_body', models.TextField()),
],
),
]
| 38.1 | 173 | 0.603018 | 148 | 1,524 | 6.081081 | 0.418919 | 0.055556 | 0.111111 | 0.076667 | 0.516667 | 0.516667 | 0.516667 | 0.516667 | 0.516667 | 0.516667 | 0 | 0.027605 | 0.263123 | 1,524 | 39 | 174 | 39.076923 | 0.77382 | 0.029528 | 0 | 0.46875 | 1 | 0 | 0.073798 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.0625 | 0 | 0.1875 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
99a91fc05ae7123630e16e93bde5cb1b4970dfad | 570 | py | Python | src/spaceone/inventory/error/custom.py | jihyungSong/plugin-azure-power-state | d66bd5dfafa01659c877da11c0d18de6e55cb5ab | [
"Apache-2.0"
] | 1 | 2020-12-04T01:37:15.000Z | 2020-12-04T01:37:15.000Z | src/spaceone/inventory/error/custom.py | jihyungSong/plugin-azure-power-state | d66bd5dfafa01659c877da11c0d18de6e55cb5ab | [
"Apache-2.0"
] | null | null | null | src/spaceone/inventory/error/custom.py | jihyungSong/plugin-azure-power-state | d66bd5dfafa01659c877da11c0d18de6e55cb5ab | [
"Apache-2.0"
] | 2 | 2020-12-04T01:37:18.000Z | 2020-12-28T02:53:39.000Z | from spaceone.core.error import ERROR_BASE
class ERROR_REPOSITORY_BACKEND(ERROR_BASE):
status_code = 'INTERNAL'
message = 'Repository backend has problem. ({host})'
class ERROR_DRIVER(ERROR_BASE):
status_code = 'INTERNAL'
message = '{message}'
class ERROR_NOT_INITIALIZED_EXCEPTION(ERROR_BASE):
status_code = 'INTERNAL'
message = 'Collector is not initialized. Please call initialize() method before using it.'
class ERROR_ATHENTICATION_VERIFY(ERROR_BASE):
message = 'Connection failed. Please check your authentication information.'
| 27.142857 | 94 | 0.759649 | 68 | 570 | 6.132353 | 0.544118 | 0.107914 | 0.107914 | 0.136691 | 0.244604 | 0.244604 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15614 | 570 | 20 | 95 | 28.5 | 0.866944 | 0 | 0 | 0.25 | 0 | 0 | 0.377193 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.083333 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
99aacf62d8ba4b992323536e4d23c55bfd7baf04 | 706 | py | Python | db_server.py | YashithaNadiranga/MysqlFlask | 1576abc388666f3e7e5ff288d1d221a4012f991b | [
"MIT"
] | null | null | null | db_server.py | YashithaNadiranga/MysqlFlask | 1576abc388666f3e7e5ff288d1d221a4012f991b | [
"MIT"
] | null | null | null | db_server.py | YashithaNadiranga/MysqlFlask | 1576abc388666f3e7e5ff288d1d221a4012f991b | [
"MIT"
] | null | null | null | from flask import Flask
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql://root:pass@localhost/student_details'
db = SQLAlchemy(app)
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(80), unique=True, nullable=False)
email = db.Column(db.String(120), unique=True, nullable=False)
def __repr__(self):
return '<User %r>' % self.username
db.create_all()
admin = User(username='admin', email='admin@example.com')
db.session.add(admin)
db.session.commit()
print(User.query.all())
@app.route("/")
def index():
pass
if __name__ == "__main__":
app.run(debug=True)
| 22.0625 | 85 | 0.70255 | 100 | 706 | 4.74 | 0.54 | 0.050633 | 0.063291 | 0.067511 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008237 | 0.140227 | 706 | 31 | 86 | 22.774194 | 0.772652 | 0 | 0 | 0 | 0 | 0 | 0.150142 | 0.093484 | 0 | 0 | 0 | 0 | 0 | 1 | 0.095238 | false | 0.095238 | 0.095238 | 0.047619 | 0.428571 | 0.047619 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
99ac99fa9ef1118415e2e909b4f4d2bb005ce536 | 1,397 | py | Python | relay_daemon/logger.py | vt-gs/relay_daemon | 9d77cd3222a3fe3e588f7c2196a4a06e8a73a471 | [
"MIT"
] | null | null | null | relay_daemon/logger.py | vt-gs/relay_daemon | 9d77cd3222a3fe3e588f7c2196a4a06e8a73a471 | [
"MIT"
] | null | null | null | relay_daemon/logger.py | vt-gs/relay_daemon | 9d77cd3222a3fe3e588f7c2196a4a06e8a73a471 | [
"MIT"
] | null | null | null | #!/usr/bin/env python2
# Logger utilities
import math, sys, os, time, struct, traceback, binascii, logging
import datetime as dt
class MyFormatter(logging.Formatter):
#Overriding formatter for datetime
converter=dt.datetime.utcfromtimestamp
def formatTime(self, record, datefmt=None):
ct = self.converter(record.created)
if datefmt:
s = ct.strftime(datefmt)
else:
t = ct.strftime("%Y%m%d_%H:%M:%S")
s = "%s,%03d" % (t, record.msecs)
return s
def setup_logger(log_name, level=logging.DEBUG, ts = None, log_path = None):
l = logging.getLogger(log_name)
if ts == None: ts = str(get_uptime())
log_file = "relayd_{:s}_{:s}.log".format(log_name, ts)
if log_path == None: log_path = '.'
log_path = log_path + '/' + log_file
#log_path = os.getcwd() + '/log/' + log_file
print log_path
formatter = MyFormatter(fmt='%(asctime)s UTC | %(threadName)14s | %(levelname)8s | %(message)s',datefmt='%Y%m%d %H:%M:%S.%f')
#fileHandler = logging.FileHandler(log_path, mode='w')
fileHandler = logging.FileHandler(log_path)
fileHandler.setFormatter(formatter)
#streamHandler = logging.StreamHandler()
#streamHandler.setFormatter(formatter)
l.setLevel(level)
l.addHandler(fileHandler)
l.info('Logger Initialized')
#l.addHandler(streamHandler)
return fileHandler
| 34.073171 | 129 | 0.655691 | 179 | 1,397 | 5.005587 | 0.446927 | 0.070313 | 0.033482 | 0.008929 | 0.120536 | 0.013393 | 0 | 0 | 0 | 0 | 0 | 0.005386 | 0.202577 | 1,397 | 40 | 130 | 34.925 | 0.798923 | 0.193271 | 0 | 0 | 0 | 0 | 0.12958 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.076923 | null | null | 0.038462 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
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