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5ee5cb9bf9402ad2216dd4aa9568e06ed20148e8 | fc3ffd1a5f4f229bc585f62fe8ae0db55c8a435a | /ml4rt/jtech2021/make_site_figure.py | 4ec16416b1ffe3a449e986c5e9e41a278f8de660 | []
| no_license | thunderhoser/ml4rt | b587d96ae7094e672d0445458e7b812c33941fc6 | 517d7cb2008a0ff06014c81e158c13bf8e17590a | refs/heads/master | 2023-08-05T04:28:29.691564 | 2023-07-31T22:25:50 | 2023-07-31T22:25:50 | 270,113,792 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,692 | py | """Creates paneled figure with different views of sites."""
import os
import argparse
from gewittergefahr.gg_utils import file_system_utils
from gewittergefahr.plotting import imagemagick_utils
PATHLESS_INPUT_FILE_NAMES = [
'all_sites.jpg', 'tropical_sites.jpg', 'assorted2_sites.jpg'
]
CONVERT_EXE_NAME = '/usr/bin/convert'
TITLE_FONT_SIZE = 100
TITLE_FONT_NAME = 'DejaVu-Sans-Bold'
PANEL_SIZE_PX = int(5e6)
CONCAT_FIGURE_SIZE_PX = int(2e7)
INPUT_DIR_ARG_NAME = 'input_dir_name'
OUTPUT_DIR_ARG_NAME = 'output_dir_name'
INPUT_DIR_HELP_STRING = (
'Name of input directory, containing images to be paneled together.'
)
OUTPUT_DIR_HELP_STRING = (
'Name of output directory. Output images (paneled figure and temporary '
'figures) will be saved here.'
)
INPUT_ARG_PARSER = argparse.ArgumentParser()
INPUT_ARG_PARSER.add_argument(
'--' + INPUT_DIR_ARG_NAME, type=str, required=True,
help=INPUT_DIR_HELP_STRING
)
INPUT_ARG_PARSER.add_argument(
'--' + OUTPUT_DIR_ARG_NAME, type=str, required=True,
help=OUTPUT_DIR_HELP_STRING
)
def _overlay_text(
image_file_name, x_offset_from_left_px, y_offset_from_top_px,
text_string):
"""Overlays text on image.
:param image_file_name: Path to image file.
:param x_offset_from_left_px: Left-relative x-coordinate (pixels).
:param y_offset_from_top_px: Top-relative y-coordinate (pixels).
:param text_string: String to overlay.
:raises: ValueError: if ImageMagick command (which is ultimately a Unix
command) fails.
"""
command_string = (
'"{0:s}" "{1:s}" -pointsize {2:d} -font "{3:s}" '
'-fill "rgb(0, 0, 0)" -annotate {4:+d}{5:+d} "{6:s}" "{1:s}"'
).format(
CONVERT_EXE_NAME, image_file_name, TITLE_FONT_SIZE, TITLE_FONT_NAME,
x_offset_from_left_px, y_offset_from_top_px, text_string
)
exit_code = os.system(command_string)
if exit_code == 0:
return
raise ValueError(imagemagick_utils.ERROR_STRING)
def _run(input_dir_name, output_dir_name):
"""Creates paneled figure with different views of sites.
This is effectively the main method.
:param input_dir_name: See documentation at top of file.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name
)
panel_file_names = [
'{0:s}/{1:s}'.format(input_dir_name, p)
for p in PATHLESS_INPUT_FILE_NAMES
]
resized_panel_file_names = [
'{0:s}/{1:s}'.format(output_dir_name, p)
for p in PATHLESS_INPUT_FILE_NAMES
]
letter_label = None
for i in range(len(panel_file_names)):
print('Resizing panel and saving to: "{0:s}"...'.format(
resized_panel_file_names[i]
))
imagemagick_utils.trim_whitespace(
input_file_name=panel_file_names[i],
output_file_name=resized_panel_file_names[i],
border_width_pixels=TITLE_FONT_SIZE + 75
)
if letter_label is None:
letter_label = 'a'
else:
letter_label = chr(ord(letter_label) + 1)
_overlay_text(
image_file_name=resized_panel_file_names[i],
x_offset_from_left_px=0, y_offset_from_top_px=TITLE_FONT_SIZE + 150,
text_string='({0:s})'.format(letter_label)
)
imagemagick_utils.trim_whitespace(
input_file_name=resized_panel_file_names[i],
output_file_name=resized_panel_file_names[i]
)
imagemagick_utils.resize_image(
input_file_name=resized_panel_file_names[i],
output_file_name=resized_panel_file_names[i],
output_size_pixels=PANEL_SIZE_PX
)
concat_figure_file_name = '{0:s}/sites_concat.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(concat_figure_file_name))
imagemagick_utils.concatenate_images(
input_file_names=resized_panel_file_names,
output_file_name=concat_figure_file_name,
num_panel_rows=len(resized_panel_file_names), num_panel_columns=1
)
imagemagick_utils.trim_whitespace(
input_file_name=concat_figure_file_name,
output_file_name=concat_figure_file_name
)
imagemagick_utils.resize_image(
input_file_name=concat_figure_file_name,
output_file_name=concat_figure_file_name,
output_size_pixels=CONCAT_FIGURE_SIZE_PX
)
if __name__ == '__main__':
INPUT_ARG_OBJECT = INPUT_ARG_PARSER.parse_args()
_run(
input_dir_name=getattr(INPUT_ARG_OBJECT, INPUT_DIR_ARG_NAME),
output_dir_name=getattr(INPUT_ARG_OBJECT, OUTPUT_DIR_ARG_NAME)
)
| [
"[email protected]"
]
| |
b32f5035cf85169d11f1cb0b73654819c498f5d6 | 15fae17aadc1ff83ad84ad2ee3db14ec40c6ffce | /app/articles/admin.py | c6bc56c32480f9df92b025a4cb61be49c96cb0c5 | []
| no_license | elmcrest/feincms3-example | 2eaaed3bd2bb68b9cfa6c21c9e60b190c193e08f | 3ec92b1bb23656d52c3cb46f4a0c8a138a088cbf | refs/heads/master | 2020-03-21T23:29:33.704979 | 2017-08-18T10:08:59 | 2017-08-18T10:08:59 | 139,190,393 | 0 | 0 | null | 2018-06-29T19:59:32 | 2018-06-29T19:59:32 | null | UTF-8 | Python | false | false | 954 | py | from __future__ import unicode_literals
from django.contrib import admin
from feincms3.plugins.versatileimage import AlwaysChangedModelForm
from . import models
class ImageInline(admin.TabularInline):
form = AlwaysChangedModelForm
model = models.Image
extra = 0
@admin.register(models.Article)
class ArticleAdmin(admin.ModelAdmin):
date_hierarchy = 'publication_date'
inlines = [ImageInline]
list_display = [
'title', 'is_active', 'publication_date', 'category']
list_editable = ['is_active']
list_filter = ['is_active', 'category']
prepopulated_fields = {
'slug': ('title',),
}
radio_fields = {
'category': admin.HORIZONTAL,
}
fieldsets = [
(None, {
'fields': (
('is_active',),
('title', 'slug'),
'publication_date',
'category',
'body',
)
}),
]
| [
"[email protected]"
]
| |
681140367c0aacb15268f68df64bf7845fba3c57 | b0f0473f10df2fdb0018165785cc23c34b0c99e7 | /Peach.Core/Lib/nntplib.py | d3212d2505d233f917f2760ad751ae01e67b3489 | []
| no_license | wimton/Meter-peach | d9294a56ec0c1fb2d1a2a4acec1c2bf47b0932df | af0302d1789a852746a3c900c6129ed9c15fb0f4 | refs/heads/master | 2023-04-25T22:54:31.696184 | 2021-05-19T13:14:55 | 2021-05-19T13:14:55 | 355,202,202 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 21,500 | py | """An NNTP client class based on RFC 977: Network News Transfer Protocol.
Example:
>>> from nntplib import NNTP
>>> s = NNTP('news')
>>> resp, count, first, last, name = s.group('comp.lang.python')
>>> print 'Group', name, 'has', count, 'articles, range', first, 'to', last
Group comp.lang.python has 51 articles, range 5770 to 5821
>>> resp, subs = s.xhdr('subject', first + '-' + last)
>>> resp = s.quit()
>>>
Here 'resp' is the server response line.
Error responses are turned into exceptions.
To post an article from a file:
>>> f = open(filename, 'r') # file containing article, including header
>>> resp = s.post(f)
>>>
For descriptions of all methods, read the comments in the code below.
Note that all arguments and return values representing article numbers
are strings, not numbers, since they are rarely used for calculations.
"""
# RFC 977 by Brian Kantor and Phil Lapsley.
# xover, xgtitle, xpath, date methods by Kevan Heydon
# Imports
import re
import socket
__all__ = ["NNTP","NNTPReplyError","NNTPTemporaryError",
"NNTPPermanentError","NNTPProtocolError","NNTPDataError",
"error_reply","error_temp","error_perm","error_proto",
"error_data",]
# maximal line length when calling readline(). This is to prevent
# reading arbitrary length lines. RFC 3977 limits NNTP line length to
# 512 characters, including CRLF. We have selected 2048 just to be on
# the safe side.
_MAXLINE = 2048
# Exceptions raised when an error or invalid response is received
class NNTPError(Exception):
"""Base class for all nntplib exceptions"""
def __init__(self, *args):
Exception.__init__(self, *args)
try:
self.response = args[0]
except IndexError:
self.response = 'No response given'
class NNTPReplyError(NNTPError):
"""Unexpected [123]xx reply"""
pass
class NNTPTemporaryError(NNTPError):
"""4xx errors"""
pass
class NNTPPermanentError(NNTPError):
"""5xx errors"""
pass
class NNTPProtocolError(NNTPError):
"""Response does not begin with [1-5]"""
pass
class NNTPDataError(NNTPError):
"""Error in response data"""
pass
# for backwards compatibility
error_reply = NNTPReplyError
error_temp = NNTPTemporaryError
error_perm = NNTPPermanentError
error_proto = NNTPProtocolError
error_data = NNTPDataError
# Standard port used by NNTP servers
NNTP_PORT = 119
# Response numbers that are followed by additional text (e.g. article)
LONGRESP = ['100', '215', '220', '221', '222', '224', '230', '231', '282']
# Line terminators (we always output CRLF, but accept any of CRLF, CR, LF)
CRLF = '\r\n'
# The class itself
class NNTP:
def __init__(self, host, port=NNTP_PORT, user=None, password=None,
readermode=None, usenetrc=True):
"""Initialize an instance. Arguments:
- host: hostname to connect to
- port: port to connect to (default the standard NNTP port)
- user: username to authenticate with
- password: password to use with username
- readermode: if true, send 'mode reader' command after
connecting.
readermode is sometimes necessary if you are connecting to an
NNTP server on the local machine and intend to call
reader-specific commands, such as `group'. If you get
unexpected NNTPPermanentErrors, you might need to set
readermode.
"""
self.host = host
self.port = port
self.sock = socket.create_connection((host, port))
self.file = self.sock.makefile('rb')
self.debugging = 0
self.welcome = self.getresp()
# 'mode reader' is sometimes necessary to enable 'reader' mode.
# However, the order in which 'mode reader' and 'authinfo' need to
# arrive differs between some NNTP servers. Try to send
# 'mode reader', and if it fails with an authorization failed
# error, try again after sending authinfo.
readermode_afterauth = 0
if readermode:
try:
self.welcome = self.shortcmd('mode reader')
except NNTPPermanentError:
# error 500, probably 'not implemented'
pass
except NNTPTemporaryError as e:
if user and e.response[:3] == '480':
# Need authorization before 'mode reader'
readermode_afterauth = 1
else:
raise
# If no login/password was specified, try to get them from ~/.netrc
# Presume that if .netc has an entry, NNRP authentication is required.
try:
if usenetrc and not user:
import netrc
credentials = netrc.netrc()
auth = credentials.authenticators(host)
if auth:
user = auth[0]
password = auth[2]
except IOError:
pass
# Perform NNRP authentication if needed.
if user:
resp = self.shortcmd('authinfo user '+user)
if resp[:3] == '381':
if not password:
raise NNTPReplyError(resp)
else:
resp = self.shortcmd(
'authinfo pass '+password)
if resp[:3] != '281':
raise NNTPPermanentError(resp)
if readermode_afterauth:
try:
self.welcome = self.shortcmd('mode reader')
except NNTPPermanentError:
# error 500, probably 'not implemented'
pass
# Get the welcome message from the server
# (this is read and squirreled away by __init__()).
# If the response code is 200, posting is allowed;
# if it 201, posting is not allowed
def getwelcome(self):
"""Get the welcome message from the server
(this is read and squirreled away by __init__()).
If the response code is 200, posting is allowed;
if it 201, posting is not allowed."""
if self.debugging: print(('*welcome*', repr(self.welcome)))
return self.welcome
def set_debuglevel(self, level):
"""Set the debugging level. Argument 'level' means:
0: no debugging output (default)
1: print commands and responses but not body text etc.
2: also print raw lines read and sent before stripping CR/LF"""
self.debugging = level
debug = set_debuglevel
def putline(self, line):
"""Internal: send one line to the server, appending CRLF."""
line = line + CRLF
if self.debugging > 1: print(('*put*', repr(line)))
self.sock.sendall(line)
def putcmd(self, line):
"""Internal: send one command to the server (through putline())."""
if self.debugging: print(('*cmd*', repr(line)))
self.putline(line)
def getline(self):
"""Internal: return one line from the server, stripping CRLF.
Raise EOFError if the connection is closed."""
line = self.file.readline(_MAXLINE + 1)
if len(line) > _MAXLINE:
raise NNTPDataError('line too long')
if self.debugging > 1:
print(('*get*', repr(line)))
if not line: raise EOFError
if line[-2:] == CRLF: line = line[:-2]
elif line[-1:] in CRLF: line = line[:-1]
return line
def getresp(self):
"""Internal: get a response from the server.
Raise various errors if the response indicates an error."""
resp = self.getline()
if self.debugging: print(('*resp*', repr(resp)))
c = resp[:1]
if c == '4':
raise NNTPTemporaryError(resp)
if c == '5':
raise NNTPPermanentError(resp)
if c not in '123':
raise NNTPProtocolError(resp)
return resp
def getlongresp(self, file=None):
"""Internal: get a response plus following text from the server.
Raise various errors if the response indicates an error."""
openedFile = None
try:
# If a string was passed then open a file with that name
if isinstance(file, str):
openedFile = file = open(file, "w")
resp = self.getresp()
if resp[:3] not in LONGRESP:
raise NNTPReplyError(resp)
list = []
while 1:
line = self.getline()
if line == '.':
break
if line[:2] == '..':
line = line[1:]
if file:
file.write(line + "\n")
else:
list.append(line)
finally:
# If this method created the file, then it must close it
if openedFile:
openedFile.close()
return resp, list
def shortcmd(self, line):
"""Internal: send a command and get the response."""
self.putcmd(line)
return self.getresp()
def longcmd(self, line, file=None):
"""Internal: send a command and get the response plus following text."""
self.putcmd(line)
return self.getlongresp(file)
def newgroups(self, date, time, file=None):
"""Process a NEWGROUPS command. Arguments:
- date: string 'yymmdd' indicating the date
- time: string 'hhmmss' indicating the time
Return:
- resp: server response if successful
- list: list of newsgroup names"""
return self.longcmd('NEWGROUPS ' + date + ' ' + time, file)
def newnews(self, group, date, time, file=None):
"""Process a NEWNEWS command. Arguments:
- group: group name or '*'
- date: string 'yymmdd' indicating the date
- time: string 'hhmmss' indicating the time
Return:
- resp: server response if successful
- list: list of message ids"""
cmd = 'NEWNEWS ' + group + ' ' + date + ' ' + time
return self.longcmd(cmd, file)
def list(self, file=None):
"""Process a LIST command. Return:
- resp: server response if successful
- list: list of (group, last, first, flag) (strings)"""
resp, list = self.longcmd('LIST', file)
for i in range(len(list)):
# Parse lines into "group last first flag"
list[i] = tuple(list[i].split())
return resp, list
def description(self, group):
"""Get a description for a single group. If more than one
group matches ('group' is a pattern), return the first. If no
group matches, return an empty string.
This elides the response code from the server, since it can
only be '215' or '285' (for xgtitle) anyway. If the response
code is needed, use the 'descriptions' method.
NOTE: This neither checks for a wildcard in 'group' nor does
it check whether the group actually exists."""
resp, lines = self.descriptions(group)
if len(lines) == 0:
return ""
else:
return lines[0][1]
def descriptions(self, group_pattern):
"""Get descriptions for a range of groups."""
line_pat = re.compile("^(?P<group>[^ \t]+)[ \t]+(.*)$")
# Try the more std (acc. to RFC2980) LIST NEWSGROUPS first
resp, raw_lines = self.longcmd('LIST NEWSGROUPS ' + group_pattern)
if resp[:3] != "215":
# Now the deprecated XGTITLE. This either raises an error
# or succeeds with the same output structure as LIST
# NEWSGROUPS.
resp, raw_lines = self.longcmd('XGTITLE ' + group_pattern)
lines = []
for raw_line in raw_lines:
match = line_pat.search(raw_line.strip())
if match:
lines.append(match.group(1, 2))
return resp, lines
def group(self, name):
"""Process a GROUP command. Argument:
- group: the group name
Returns:
- resp: server response if successful
- count: number of articles (string)
- first: first article number (string)
- last: last article number (string)
- name: the group name"""
resp = self.shortcmd('GROUP ' + name)
if resp[:3] != '211':
raise NNTPReplyError(resp)
words = resp.split()
count = first = last = 0
n = len(words)
if n > 1:
count = words[1]
if n > 2:
first = words[2]
if n > 3:
last = words[3]
if n > 4:
name = words[4].lower()
return resp, count, first, last, name
def help(self, file=None):
"""Process a HELP command. Returns:
- resp: server response if successful
- list: list of strings"""
return self.longcmd('HELP',file)
def statparse(self, resp):
"""Internal: parse the response of a STAT, NEXT or LAST command."""
if resp[:2] != '22':
raise NNTPReplyError(resp)
words = resp.split()
nr = 0
id = ''
n = len(words)
if n > 1:
nr = words[1]
if n > 2:
id = words[2]
return resp, nr, id
def statcmd(self, line):
"""Internal: process a STAT, NEXT or LAST command."""
resp = self.shortcmd(line)
return self.statparse(resp)
def stat(self, id):
"""Process a STAT command. Argument:
- id: article number or message id
Returns:
- resp: server response if successful
- nr: the article number
- id: the message id"""
return self.statcmd('STAT ' + id)
def __next__(self):
"""Process a NEXT command. No arguments. Return as for STAT."""
return self.statcmd('NEXT')
def last(self):
"""Process a LAST command. No arguments. Return as for STAT."""
return self.statcmd('LAST')
def artcmd(self, line, file=None):
"""Internal: process a HEAD, BODY or ARTICLE command."""
resp, list = self.longcmd(line, file)
resp, nr, id = self.statparse(resp)
return resp, nr, id, list
def head(self, id):
"""Process a HEAD command. Argument:
- id: article number or message id
Returns:
- resp: server response if successful
- nr: article number
- id: message id
- list: the lines of the article's header"""
return self.artcmd('HEAD ' + id)
def body(self, id, file=None):
"""Process a BODY command. Argument:
- id: article number or message id
- file: Filename string or file object to store the article in
Returns:
- resp: server response if successful
- nr: article number
- id: message id
- list: the lines of the article's body or an empty list
if file was used"""
return self.artcmd('BODY ' + id, file)
def article(self, id):
"""Process an ARTICLE command. Argument:
- id: article number or message id
Returns:
- resp: server response if successful
- nr: article number
- id: message id
- list: the lines of the article"""
return self.artcmd('ARTICLE ' + id)
def slave(self):
"""Process a SLAVE command. Returns:
- resp: server response if successful"""
return self.shortcmd('SLAVE')
def xhdr(self, hdr, str, file=None):
"""Process an XHDR command (optional server extension). Arguments:
- hdr: the header type (e.g. 'subject')
- str: an article nr, a message id, or a range nr1-nr2
Returns:
- resp: server response if successful
- list: list of (nr, value) strings"""
pat = re.compile('^([0-9]+) ?(.*)\n?')
resp, lines = self.longcmd('XHDR ' + hdr + ' ' + str, file)
for i in range(len(lines)):
line = lines[i]
m = pat.match(line)
if m:
lines[i] = m.group(1, 2)
return resp, lines
def xover(self, start, end, file=None):
"""Process an XOVER command (optional server extension) Arguments:
- start: start of range
- end: end of range
Returns:
- resp: server response if successful
- list: list of (art-nr, subject, poster, date,
id, references, size, lines)"""
resp, lines = self.longcmd('XOVER ' + start + '-' + end, file)
xover_lines = []
for line in lines:
elem = line.split("\t")
try:
xover_lines.append((elem[0],
elem[1],
elem[2],
elem[3],
elem[4],
elem[5].split(),
elem[6],
elem[7]))
except IndexError:
raise NNTPDataError(line)
return resp,xover_lines
def xgtitle(self, group, file=None):
"""Process an XGTITLE command (optional server extension) Arguments:
- group: group name wildcard (i.e. news.*)
Returns:
- resp: server response if successful
- list: list of (name,title) strings"""
line_pat = re.compile("^([^ \t]+)[ \t]+(.*)$")
resp, raw_lines = self.longcmd('XGTITLE ' + group, file)
lines = []
for raw_line in raw_lines:
match = line_pat.search(raw_line.strip())
if match:
lines.append(match.group(1, 2))
return resp, lines
def xpath(self,id):
"""Process an XPATH command (optional server extension) Arguments:
- id: Message id of article
Returns:
resp: server response if successful
path: directory path to article"""
resp = self.shortcmd("XPATH " + id)
if resp[:3] != '223':
raise NNTPReplyError(resp)
try:
[resp_num, path] = resp.split()
except ValueError:
raise NNTPReplyError(resp)
else:
return resp, path
def date (self):
"""Process the DATE command. Arguments:
None
Returns:
resp: server response if successful
date: Date suitable for newnews/newgroups commands etc.
time: Time suitable for newnews/newgroups commands etc."""
resp = self.shortcmd("DATE")
if resp[:3] != '111':
raise NNTPReplyError(resp)
elem = resp.split()
if len(elem) != 2:
raise NNTPDataError(resp)
date = elem[1][2:8]
time = elem[1][-6:]
if len(date) != 6 or len(time) != 6:
raise NNTPDataError(resp)
return resp, date, time
def post(self, f):
"""Process a POST command. Arguments:
- f: file containing the article
Returns:
- resp: server response if successful"""
resp = self.shortcmd('POST')
# Raises error_??? if posting is not allowed
if resp[0] != '3':
raise NNTPReplyError(resp)
while 1:
line = f.readline()
if not line:
break
if line[-1] == '\n':
line = line[:-1]
if line[:1] == '.':
line = '.' + line
self.putline(line)
self.putline('.')
return self.getresp()
def ihave(self, id, f):
"""Process an IHAVE command. Arguments:
- id: message-id of the article
- f: file containing the article
Returns:
- resp: server response if successful
Note that if the server refuses the article an exception is raised."""
resp = self.shortcmd('IHAVE ' + id)
# Raises error_??? if the server already has it
if resp[0] != '3':
raise NNTPReplyError(resp)
while 1:
line = f.readline()
if not line:
break
if line[-1] == '\n':
line = line[:-1]
if line[:1] == '.':
line = '.' + line
self.putline(line)
self.putline('.')
return self.getresp()
def quit(self):
"""Process a QUIT command and close the socket. Returns:
- resp: server response if successful"""
resp = self.shortcmd('QUIT')
self.file.close()
self.sock.close()
del self.file, self.sock
return resp
# Test retrieval when run as a script.
# Assumption: if there's a local news server, it's called 'news'.
# Assumption: if user queries a remote news server, it's named
# in the environment variable NNTPSERVER (used by slrn and kin)
# and we want readermode off.
if __name__ == '__main__':
import os
newshost = 'news' and os.environ["NNTPSERVER"]
if newshost.find('.') == -1:
mode = 'readermode'
else:
mode = None
s = NNTP(newshost, readermode=mode)
resp, count, first, last, name = s.group('comp.lang.python')
print(resp)
print(('Group', name, 'has', count, 'articles, range', first, 'to', last))
resp, subs = s.xhdr('subject', first + '-' + last)
print(resp)
for item in subs:
print(("%7s %s" % item))
resp = s.quit()
print(resp)
| [
"[email protected]"
]
| |
486fda6e1753ed2136f830174096f2c571d665ad | 29f830670675cea44bf3aad6e50e98e5b1692f70 | /scripts/import_permissions_and_roles.py | 867979ebca2c717d403bdf57b45d34d2dce26019 | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
]
| permissive | forksbot/byceps | 02db20149f1f0559b812dacad276e4210993e300 | ac29a0cb50e2ef450d4e5ebd33419ed490c96e4f | refs/heads/main | 2023-03-04T05:55:07.743161 | 2021-02-14T06:03:37 | 2021-02-14T06:19:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 798 | py | #!/usr/bin/env python
"""Import permissions, roles, and their relations from a TOML file.
:Copyright: 2006-2021 Jochen Kupperschmidt
:License: Revised BSD (see `LICENSE` file for details)
"""
import click
from byceps.services.authorization import impex_service
from byceps.util.system import get_config_filename_from_env_or_exit
from _util import app_context
@click.command()
@click.argument('data_file', type=click.File())
def execute(data_file):
permission_count, role_count = impex_service.import_from_file(data_file)
click.secho(
f'Imported {permission_count} permissions and {role_count} roles.',
fg='green',
)
if __name__ == '__main__':
config_filename = get_config_filename_from_env_or_exit()
with app_context(config_filename):
execute()
| [
"[email protected]"
]
| |
b936ffc9e6684b9c97da4d88a0f2e59e3e42aab1 | cc101e71d4b47e1ade22159bc3273aab5386a49e | /integration-tests/fake_spine/fake_spine/vnp_request_matcher_wrappers.py | 3591870d8d4679c49b285dbbc7a413cba93a0ceb | [
"Apache-2.0"
]
| permissive | nhsconnect/integration-adaptors | 20f613f40562a79428e610df916835f4e3c3e455 | 8420d9d4b800223bff6a648015679684f5aba38c | refs/heads/develop | 2023-02-22T22:04:31.193431 | 2022-03-15T16:01:25 | 2022-03-15T16:01:25 | 179,653,046 | 15 | 7 | Apache-2.0 | 2023-08-23T14:52:10 | 2019-04-05T09:18:56 | Python | UTF-8 | Python | false | false | 803 | py | from fake_spine.request_matching import RequestMatcher
def async_express():
return RequestMatcher('async-express-vnp',
lambda request: '<eb:Action>QUPC_IN160101UK05</eb:Action>' in request.body.decode())
def async_reliable():
return RequestMatcher('async-reliable-vnp',
lambda request: '<eb:Action>REPC_IN150016UK05</eb:Action>' in request.body.decode())
def sync():
return RequestMatcher('sync-vnp',
lambda request: '<wsa:Action>urn:nhs:names:services:pdsquery/QUPA_IN040000UK32</wsa:Action>' in request.body.decode())
def forward_reliable():
return RequestMatcher('forward-reliable-vnp',
lambda request: '<eb:Action>COPC_IN000001UK01</eb:Action>' in request.body.decode())
| [
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]
| |
a2dcbf9b1d89748680c0d4367c744d3038686cc4 | 8e86b14e153a6c626739d12666d0131c5fcc24fd | /requirements.py | af2e789d7fa56fb50e86563d6fbef6b454a4caeb | [
"MIT"
]
| permissive | Kromey/err-nanobot | 16e4db659edc142df33fcb48aa637d3f6cc1756a | af07232512b2fc04efb19d5271064decd4c14d08 | refs/heads/master | 2021-05-04T10:11:47.659386 | 2017-11-07T21:49:29 | 2017-11-07T21:49:29 | 45,268,537 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14 | py | pynano==0.1.1
| [
"[email protected]"
]
| |
525929dc1eeca4dacff44536fcb21918ee9ee501 | 3f41bafb8012f264605724dbe9b1a6ee11a1f767 | /competitions/EMNIST/resize_380_B4.py | 80995b99e649cb8fcb0364b6c8c07df6932621e3 | []
| no_license | pervin0527/pervinco | 6d0c9aad8dbf6d944960b2e2c963054d1d91b29a | 9ced846438130341726e31954cc7e45a887281ef | refs/heads/master | 2022-11-26T02:11:00.848871 | 2022-11-24T00:56:14 | 2022-11-24T00:56:14 | 223,062,903 | 5 | 3 | null | null | null | null | UTF-8 | Python | false | false | 6,169 | py | import cv2, pathlib, datetime, os
import numpy as np
import pandas as pd
import tensorflow as tf
from matplotlib import pyplot as plt
from functools import partial
from tqdm import tqdm
from sklearn.model_selection import KFold
# GPU setup
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 1:
try:
print("Activate Multi GPU")
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
except RuntimeError as e:
print(e)
else:
try:
print("Activate Sigle GPU")
tf.config.experimental.set_memory_growth(gpus[0], True)
strategy = tf.distribute.experimental.CentralStorageStrategy()
except RuntimeError as e:
print(e)
# Disable AutoShard.
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
def get_dataset(df):
CLASSES = [c for c in df]
CLASSES = CLASSES[1:]
# print(len(df))
X = np.zeros([len(df), IMG_SIZE, IMG_SIZE, 3], dtype=np.uint8)
y = np.zeros([len(df), len(CLASSES)], dtype=np.uint8)
for idx in tqdm(range(len(df))):
file_name = str(df.iloc[idx, 0]).zfill(5)
image = cv2.imread(f'{TRAIN_DS_PATH}/{file_name}.png')
image2 = np.where((image <= 254) & (image != 0), 0, image)
X[idx] = image2
label = df.iloc[idx, 1:].values.astype('float')
y[idx] = label
return X, y, CLASSES
def normalize_image(image, label):
image = tf.image.resize(image, [RE_SIZE, RE_SIZE])
image = tf.cast(image, tf.float32)
image = tf.keras.applications.resnet.preprocess_input(image)
label = tf.cast(label, tf.float32)
return image, label
def make_tf_dataset(images, labels):
images = tf.data.Dataset.from_tensor_slices(images)
labels = tf.data.Dataset.from_tensor_slices(labels)
dataset = tf.data.Dataset.zip((images, labels))
dataset = dataset.repeat()
dataset = dataset.map(normalize_image, num_parallel_calls=AUTOTUNE)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(AUTOTUNE)
dataset = dataset.with_options(options)
return dataset
def get_model():
with strategy.scope():
base_model = tf.keras.applications.EfficientNetB4(input_shape=(RE_SIZE, RE_SIZE, 3),
weights='imagenet', # noisy-student
include_top=False)
base_model.trainable = True
avg = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
output = tf.keras.layers.Dense(26, activation="sigmoid")(avg)
model = tf.keras.Model(inputs=base_model.input, outputs=output)
model.compile(optimizer='adam', loss = 'binary_crossentropy', metrics = ['binary_accuracy'])
return model
def split_dataset():
df = pd.read_csv(f'{DS_PATH}/dirty_mnist_2nd_answer.csv')
kfold = KFold(n_splits=N_FOLD)
for fold, (train, valid) in enumerate(kfold.split(df, df.index)):
df.loc[valid, 'kfold'] = int(fold)
if not(os.path.isdir(f'{DS_PATH}/custom_split')):
os.makedirs(f'{DS_PATH}/custom_split')
df.to_csv(f'{DS_PATH}/custom_split/split_kfold.csv', index=False)
def train_cross_validate():
split_dataset()
df = pd.read_csv(f'{DS_PATH}/custom_split/split_kfold.csv')
if not(os.path.isdir(f'/{SAVED_PATH}/{LOG_TIME}')):
os.makedirs(f'/{SAVED_PATH}/{LOG_TIME}')
os.system('clear')
for i in range(N_FOLD):
df_train = df[df['kfold'] != i].reset_index(drop=True)
df_valid = df[df['kfold'] == i].reset_index(drop=True)
df_train.drop(['kfold'], axis=1).to_csv(f'{DS_PATH}/custom_split/train-kfold-{i}.csv', index=False)
df_valid.drop(['kfold'], axis=1).to_csv(f'{DS_PATH}/custom_split/valid-kfold-{i}.csv', index=False)
df_train = pd.read_csv(f'{DS_PATH}/custom_split/train-kfold-{i}.csv')
df_valid = pd.read_csv(f'{DS_PATH}/custom_split/valid-kfold-{i}.csv')
train_x, train_y, _ = get_dataset(df_train)
valid_x, valid_y, _ = get_dataset(df_valid)
print('FOLD', i + 1)
output_path = f'/{SAVED_PATH}/{LOG_TIME}/{i+1}'
os.makedirs(output_path)
print(train_x.shape, train_y.shape, valid_x.shape, valid_y.shape)
WEIGHT_FNAME = '{epoch:02d}-{val_binary_accuracy:.2f}.hdf5'
checkpoint_path = f'{output_path}/{i+1}-{WEIGHT_FNAME}'
cb_checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
monitor='val_binary_accuracy',
save_best_only=True,
mode='max')
cb_early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)
TRAIN_STEPS_PER_EPOCH = int(tf.math.ceil(len(train_x) / BATCH_SIZE).numpy())
VALID_STEPS_PER_EPOCH = int(tf.math.ceil(len(valid_x) / BATCH_SIZE).numpy())
model = get_model()
model.fit(make_tf_dataset(train_x, train_y),
steps_per_epoch = TRAIN_STEPS_PER_EPOCH,
epochs = EPOCHS,
validation_data = make_tf_dataset(valid_x, valid_y),
validation_steps = VALID_STEPS_PER_EPOCH,
verbose=1,
callbacks = [cb_checkpointer, cb_early_stopping])
model.save(f'{output_path}/{i+1}_dmnist.h5')
del train_x, train_y
del valid_x, valid_y
if __name__ == "__main__":
EPOCHS = 100
IMG_SIZE = 256
RE_SIZE = IMG_SIZE + 124
AUTOTUNE = tf.data.experimental.AUTOTUNE
BATCH_SIZE = 5 * strategy.num_replicas_in_sync
N_FOLD = 5
DS_PATH = '/data/tf_workspace/datasets/dirty_mnist_2'
SAVED_PATH = '/data/tf_workspace/model/dirty_mnist'
TRAIN_DS_PATH = f'{DS_PATH}/dirty_mnist_2nd'
LOG_TIME = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
train_cross_validate()
| [
"[email protected]"
]
| |
1446845eeccf87263d870b37805acf3b3c96d21d | 4c8c0f857500b5f4b572f139602e46a6c813f6e3 | /Polymorhphism_and_Magic_methods_exercises/project/cat.py | 12c5083c335f08076e5581860eca49af0764f67d | []
| no_license | svetoslavastoyanova/Python_OOP | 3d21fb0480c088ecad11211c2d9a01139cde031f | 518f73ecc8a39e7085d4b8bf5657a1556da3dcfa | refs/heads/main | 2023-08-04T19:46:58.906739 | 2021-09-18T07:46:02 | 2021-09-18T07:46:02 | 352,304,158 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 250 | py | from project.animal import Animal
class Cat(Animal):
def __repr__(self):
return f"This is {self.name}. {self.name} is a {self.age} year old {self.gender} {self.__class__.__name__}"
def make_sound(self):
return f"Meow meow!" | [
"[email protected]"
]
| |
5efcb7fb86370b09e864d1c00759871aabe142ae | 593b23cd61932e8206d89e43925f038c86758288 | /covid19_pipeline/engine/module.py | c9e80824df08107c21b3a61deabe4888864d0db2 | []
| no_license | HDU-DSRC-AI/HKBU_HPML_COVID-19 | ad4f311777176d469b07c155e252df26d57f5056 | 0f685312d26c0b50fffb433408a913243638a14a | refs/heads/master | 2022-10-12T09:32:15.635509 | 2020-06-09T13:53:42 | 2020-06-09T13:53:42 | 271,016,743 | 1 | 0 | null | 2020-06-09T13:55:31 | 2020-06-09T13:55:28 | null | UTF-8 | Python | false | false | 6,807 | py | import os
from collections import OrderedDict
import numpy as np
import torch
from sklearn import metrics
from torchline.engine import MODULE_REGISTRY, DefaultModule, build_module
from torchline.utils import AverageMeterGroup, topk_acc
from .utils import mixup_data, mixup_loss_fn
__all__ = [
'CTModule'
]
@MODULE_REGISTRY.register()
class CTModule(DefaultModule):
def __init__(self, cfg):
super(CTModule, self).__init__(cfg)
h, w = self.cfg.input.size
self.example_input_array = torch.rand(1, 3, 2, h, w)
self.crt_batch_idx = 0
self.inputs = self.example_input_array
def training_step_end(self, output):
self.print_log(self.trainer.batch_idx, True, self.inputs, self.train_meters)
return output
def validation_step_end(self, output):
self.crt_batch_idx += 1
self.print_log(self.crt_batch_idx, False, self.inputs, self.valid_meters)
return output
def training_step(self, batch, batch_idx):
"""
Lightning calls this inside the training loop
:param batch:
:return:
"""
try:
# forward pass
inputs, gt_labels, paths = batch
self.crt_batch_idx = batch_idx
self.inputs = inputs
if self.cfg.mixup.enable:
inputs, gt_labels_a, gt_labels_b, lam = mixup_data(inputs, gt_labels, self.cfg.mixup.alpha)
mixup_y = [gt_labels_a, gt_labels_b, lam]
predictions = self.forward(inputs)
# calculate loss
if self.cfg.mixup.enable:
loss_val = mixup_loss_fn(self.loss, predictions, *mixup_y)
else:
loss_val = self.loss(predictions, gt_labels)
# acc
acc_results = topk_acc(predictions, gt_labels, self.cfg.topk)
tqdm_dict = {}
if self.on_gpu:
acc_results = [torch.tensor(x).to(loss_val.device.index) for x in acc_results]
# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
if self.trainer.use_dp or self.trainer.use_ddp2:
loss_val = loss_val.unsqueeze(0)
acc_results = [x.unsqueeze(0) for x in acc_results]
tqdm_dict['train_loss'] = loss_val
for i, k in enumerate(self.cfg.topk):
tqdm_dict[f'train_acc_{k}'] = acc_results[i]
output = OrderedDict({
'loss': loss_val,
'progress_bar': tqdm_dict,
'log': tqdm_dict
})
self.train_meters.update({key: val.item() for key, val in tqdm_dict.items()})
# can also return just a scalar instead of a dict (return loss_val)
return output
except Exception as e:
print(str(e))
print(batch_idx, paths)
pass
def validation_step(self, batch, batch_idx):
"""
Lightning calls this inside the validation loop
:param batch:
:return:
"""
inputs, gt_labels, paths = batch
self.inputs = inputs
predictions = self.forward(inputs)
loss_val = self.loss(predictions, gt_labels)
# acc
val_acc_1, val_acc_k = topk_acc(predictions, gt_labels, self.cfg.topk)
if self.on_gpu:
val_acc_1 = val_acc_1.cuda(loss_val.device.index)
val_acc_k = val_acc_k.cuda(loss_val.device.index)
# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
if self.trainer.use_dp or self.trainer.use_ddp2:
loss_val = loss_val.unsqueeze(0)
val_acc_1 = val_acc_1.unsqueeze(0)
val_acc_k = val_acc_k.unsqueeze(0)
output = OrderedDict({
'valid_loss': torch.tensor(loss_val),
'valid_acc_1': torch.tensor(val_acc_1),
f'valid_acc_{self.cfg.topk[-1]}': val_acc_k,
})
tqdm_dict = {k: v for k, v in dict(output).items()}
self.valid_meters.update({key: val.item() for key, val in tqdm_dict.items()})
# self.print_log(batch_idx, False, inputs, self.valid_meters)
if self.cfg.module.analyze_result:
output.update({
'predictions': predictions.detach(),
'gt_labels': gt_labels.detach(),
})
# can also return just a scalar instead of a dict (return loss_val)
return output
def validation_epoch_end(self, outputs):
"""
Called at the end of validation to aggregate outputs
:param outputs: list of individual outputs of each validation step
:return:
"""
# if returned a scalar from validation_step, outputs is a list of tensor scalars
# we return just the average in this case (if we want)
# return torch.stack(outputs).mean()
self.crt_batch_idx = 0
tqdm_dict = {key: val.avg for key, val in self.valid_meters.meters.items()}
valid_loss = torch.tensor(self.valid_meters.meters['valid_loss'].avg)
valid_acc_1 = torch.tensor(self.valid_meters.meters['valid_acc_1'].avg)
result = {'progress_bar': tqdm_dict, 'log': tqdm_dict,
'valid_loss': valid_loss,
'valid_acc_1': valid_acc_1}
if self.cfg.module.analyze_result:
predictions = []
gt_labels = []
for output in outputs:
predictions.append(output['predictions'])
gt_labels.append(output['gt_labels'])
predictions = torch.cat(predictions)
gt_labels = torch.cat(gt_labels)
analyze_result = self.analyze_result(gt_labels, predictions)
self.log_info(analyze_result)
result.update({'analyze_result': analyze_result, 'predictions': predictions, 'gt_labels': gt_labels})
return result
def test_step(self, batch, batch_idx):
return self.validation_step(batch, batch_idx)
def test_epoch_end(self, outputs):
result = self.validation_epoch_end(outputs)
predictions = result['predictions'].cpu().detach().numpy()
gt_labels = result['gt_labels'].cpu().detach().numpy()
path = self.cfg.log.path
np.save(os.path.join(path,'predictions.npy'), predictions)
np.save(os.path.join(path,'gt_labels.npy'), gt_labels)
result = {key:val for key, val in result.items() if key not in ['predictions', 'gt_labels']}
return result
def analyze_result(self, gt_labels, predictions):
'''
Args:
gt_lables: tensor (N)
predictions: tensor (N*C)
'''
return str(metrics.classification_report(gt_labels.cpu(), predictions.cpu().argmax(1), digits=4))
| [
"[email protected]"
]
| |
80ff391e57858bfe6654bb74b3b2aad7a68da33c | ab5ef28065b0ad3f8d86fc894be569074a4569ea | /mirari/SV/migrations/0020_auto_20190321_1210.py | 063b11284a810c9cdddd97f51c5c6e61e556b605 | [
"MIT"
]
| permissive | gcastellan0s/mirariapp | 1b30dce3ac2ee56945951f340691d39494b55e95 | 24a9db06d10f96c894d817ef7ccfeec2a25788b7 | refs/heads/master | 2023-01-22T22:21:30.558809 | 2020-09-25T22:37:24 | 2020-09-25T22:37:24 | 148,203,907 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 522 | py | # Generated by Django 2.0.5 on 2019-03-21 18:10
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('SV', '0019_auto_20190315_1330'),
]
operations = [
migrations.AlterModelOptions(
name='ticket',
options={'default_permissions': [], 'ordering': ['-id'], 'permissions': [('Can_View__Ticket', 'Ve tickets'), ('Can_Delete__Ticket', 'Elimina tickets')], 'verbose_name': 'Ticket', 'verbose_name_plural': 'Tickets'},
),
]
| [
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]
| |
ba6e5b9e1e9ff2e71c68568c7835e0414609b61d | 0062ceae0071aaa3e4e8ecd9025e8cc9443bcb3b | /solved/2579.py | 90890849ba15de80636cc12fe760fd9e34b65942 | []
| no_license | developyoun/AlgorithmSolve | 8c7479082528f67be9de33f0a337ac6cc3bfc093 | 5926924c7c44ffab2eb8fd43290dc6aa029f818d | refs/heads/master | 2023-03-28T12:02:37.260233 | 2021-03-24T05:05:48 | 2021-03-24T05:05:48 | 323,359,039 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 286 | py | N = int(input())
arr = [int(input()) for _ in range(N)]
dp = [[0, 0] for _ in range(N)]
dp[0][0] = arr[0]
if N != 1:
dp[1][0] = arr[1]
dp[1][1] = arr[0] + arr[1]
for i in range(2, N):
dp[i][0] = max(dp[i-2]) + arr[i]
dp[i][1] = dp[i-1][0] + arr[i]
print(max(dp[N-1])) | [
"[email protected]"
]
| |
39a0c47ca4248562ba9920e235e06a51a43f6be8 | aa76391d5789b5082702d3f76d2b6e13488d30be | /BOJ-Solution/2440.py | 442fb8f075508e999d021ff816b95a6a2762b386 | []
| no_license | B2SIC/python_playground | 118957fe4ca3dc9395bc78b56825b9a014ef95cb | 14cbc32affbeec57abbd8e8c4ff510aaa986874e | refs/heads/master | 2023-02-28T21:27:34.148351 | 2021-02-12T10:20:49 | 2021-02-12T10:20:49 | 104,154,645 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 59 | py | n = int(input())
for i in range(0, n):
print("*"*(n-i)) | [
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]
| |
190cb5e443625842236cc6cbe8c93583f288a126 | 6c524d7c4114531dd0b9872090bd7389a3cd3fd8 | /poems/migrations/0003_auto_20200731_1245.py | ec917ae86031cdc2623eba1b2a2431d466946ae0 | []
| no_license | cement-hools/poems_project | e33bcd03ca8b2b1f1fa558d1036928aee73c87c9 | 493e6d517b65faab6b25a9fda485e165b6eea03d | refs/heads/master | 2022-11-28T02:11:50.837816 | 2020-08-01T10:12:16 | 2020-08-01T10:12:16 | 284,234,726 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,340 | py | # Generated by Django 2.2.14 on 2020-07-31 09:45
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('poems', '0002_auto_20200731_1219'),
]
operations = [
migrations.AlterModelOptions(
name='poem',
options={'ordering': ('title',), 'verbose_name': 'Стихотворение'},
),
migrations.AddField(
model_name='poem',
name='author',
field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='poems.Poet', verbose_name='Автор(ша)'),
preserve_default=False,
),
migrations.AddField(
model_name='poem',
name='text',
field=models.TextField(default=1, verbose_name='Текст'),
preserve_default=False,
),
migrations.AddField(
model_name='poem',
name='title',
field=models.CharField(default=1, max_length=250, verbose_name='Название'),
preserve_default=False,
),
migrations.AddField(
model_name='poem',
name='year',
field=models.CharField(blank=True, max_length=50, null=True, verbose_name='Год(ы)'),
),
]
| [
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]
| |
a15de310a575dd2dfa1b63f03ee73cd8ee65edf5 | 13084338fa9d1c72fe32d323bcd2df1417b98e83 | /src/bxcommon/models/blockchain_peer_info.py | 7ad272ad39cc2493b806aa95ec618225f48830a7 | [
"MIT"
]
| permissive | bloXroute-Labs/bxcommon | ad45e3a060a7d1afd119513248da036818c7f885 | 03c4cc5adab1ae182e59a609eff273957499ba5d | refs/heads/master | 2023-02-22T00:10:46.755175 | 2022-08-16T19:38:22 | 2022-08-16T19:38:22 | 220,556,144 | 14 | 7 | MIT | 2023-02-07T22:58:14 | 2019-11-08T22:16:37 | Python | UTF-8 | Python | false | false | 896 | py | from dataclasses import dataclass
from typing import Optional
from bxcommon.utils.blockchain_utils.eth import eth_common_constants
@dataclass
class BlockchainPeerInfo:
ip: str
port: int
node_public_key: Optional[str] = None
blockchain_protocol_version: int = eth_common_constants.ETH_PROTOCOL_VERSION
connection_established: bool = False
def __repr__(self):
return f"BlockchainPeerInfo(ip address: {self.ip}, " \
f"port: {self.port}, " \
f"node public key: {self.node_public_key}, " \
f"blockchain protocol version: {self.blockchain_protocol_version})"
def __eq__(self, other) -> bool:
return (
isinstance(other, BlockchainPeerInfo)
and other.port == self.port
and other.ip == self.ip
)
def __hash__(self):
return hash(f"{self.ip}:{self.port}")
| [
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]
| |
b137928399ca34af62b565e767f81889f316ac21 | 2af28d499c4865311d7b350d7b8f96305af05407 | /model-optimizer/mo/front/mxnet/extractor.py | bce78e371dcb4aa4d043ad108a22efe1cbaf7f3d | [
"Apache-2.0"
]
| permissive | Dipet/dldt | cfccedac9a4c38457ea49b901c8c645f8805a64b | 549aac9ca210cc5f628a63174daf3e192b8d137e | refs/heads/master | 2021-02-15T11:19:34.938541 | 2020-03-05T15:12:30 | 2020-03-05T15:12:30 | 244,893,475 | 1 | 0 | Apache-2.0 | 2020-03-04T12:22:46 | 2020-03-04T12:22:45 | null | UTF-8 | Python | false | false | 2,912 | py | """
Copyright (c) 2018-2019 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from mo.front.mxnet.extractors.batchnorm import batch_norm_ext
from mo.front.mxnet.extractors.concat import concat_ext
from mo.front.mxnet.extractors.crop import crop_ext
from mo.front.mxnet.extractors.l2_normalization import l2_normalization_ext
from mo.front.mxnet.extractors.lrn import lrn_ext
from mo.front.mxnet.extractors.multibox_detection import multi_box_detection_ext
from mo.front.mxnet.extractors.multibox_prior import multi_box_prior_ext
from mo.front.mxnet.extractors.null import null_ext
from mo.front.mxnet.extractors.scaleshift import scale_shift_ext
from mo.front.mxnet.extractors.slice_axis import slice_axis_ext
from mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
from mo.graph.graph import Node
from mo.utils.error import Error
from mo.utils.utils import refer_to_faq_msg
def extractor_wrapper(mxnet_extractor):
return lambda node: mxnet_extractor(get_mxnet_layer_attrs(node.symbol_dict))
mxnet_op_extractors = {
'BatchNorm': extractor_wrapper(batch_norm_ext),
'ScaleShift': extractor_wrapper(scale_shift_ext),
'slice_axis': extractor_wrapper(slice_axis_ext),
'null': lambda node: null_ext(node.symbol_dict),
'Concat': extractor_wrapper(concat_ext),
'LRN': extractor_wrapper(lrn_ext),
'L2Normalization': extractor_wrapper(l2_normalization_ext),
'_contrib_MultiBoxPrior': extractor_wrapper(multi_box_prior_ext),
'_contrib_MultiBoxDetection': extractor_wrapper(multi_box_detection_ext),
}
def common_mxnet_fields(node: Node):
return {
'kind': 'op',
'name': node.id,
'type': node['symbol_dict']['op'],
'op': node['symbol_dict']['op'],
'infer': None,
'precision': 'FP32'
}
def mxnet_op_extractor(node: Node):
result = common_mxnet_fields(node)
op = result['op']
if op not in mxnet_op_extractors:
raise Error(
"Operation '{}' not supported. Please register it as custom op. " +
refer_to_faq_msg(86),
op)
result_attr = mxnet_op_extractors[op](node)
if result_attr is None:
raise Error('Model Optimizer does not support layer "{}". Please, implement extension. '.format(node.name) +
refer_to_faq_msg(45))
result.update(result_attr)
supported = bool(result_attr)
return supported, result
| [
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]
| |
49881c0afd820a8d03c284c032931f34cb14c3ef | e23a4f57ce5474d468258e5e63b9e23fb6011188 | /_use_in_my_scripts/switch/008_decorators_template_Simulating a simple Switch in Python with dict.py | 00900f8959a0911755babf3db801ea1c231eaa31 | []
| no_license | syurskyi/Python_Topics | 52851ecce000cb751a3b986408efe32f0b4c0835 | be331826b490b73f0a176e6abed86ef68ff2dd2b | refs/heads/master | 2023-06-08T19:29:16.214395 | 2023-05-29T17:09:11 | 2023-05-29T17:09:11 | 220,583,118 | 3 | 2 | null | 2023-02-16T03:08:10 | 2019-11-09T02:58:47 | Python | UTF-8 | Python | false | false | 459 | py | def dow_switch_dict(dow):
dow_dict = {
1: lambda: print('Monday'),
2: lambda: print('Tuesday'),
3: lambda: print('Wednesday'),
4: lambda: print('Thursday'),
5: lambda: print('Friday'),
6: lambda: print('Saturday'),
7: lambda: print('Sunday'),
'default': lambda: print('Invalid day of week')
}
return dow_dict.get(dow, dow_dict['default'])()
dow_switch_dict(1)
dow_switch_dict(100) | [
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]
| |
16ca6ca8294bb2452eda7a18ad8e9c5ef5fc649e | a12bd907b26934978a09173039e7eed361d09670 | /nbs/models/supplier.py | c1c899d7caac32a40f373b6ffc86e1e8f7ad3a0f | [
"MIT"
]
| permissive | coyotevz/nobix-app | 489e2dc8cafc40a3022ef02913461e324bc9f752 | 9523d150e0299b851779f42927992810184e862d | refs/heads/master | 2020-12-20T23:22:10.302025 | 2015-12-18T22:06:44 | 2015-12-18T22:06:44 | 32,998,125 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,051 | py | # -*- coding: utf-8 -*-
from sqlalchemy.ext.associationproxy import association_proxy
from nbs.models import db
from nbs.models.entity import Entity
class Supplier(Entity):
__tablename__ = 'supplier'
__mapper_args__ = {'polymorphic_identity': u'supplier'}
FREIGHT_SUPPLIER = 'FREIGHT_SUPPLIER'
FREIGHT_CUSTOMER = 'FREIGHT_CUSTOMER'
_freight_types = {
FREIGHT_SUPPLIER: 'Flete de proveedor',
FREIGHT_CUSTOMER: 'Flete de cliente',
}
supplier_id = db.Column(db.Integer, db.ForeignKey('entity.id'),
primary_key=True)
name = Entity._name_1
fiscal_data_id = db.Column(db.Integer, db.ForeignKey('fiscal_data.id'))
fiscal_data = db.relationship('FiscalData',
backref=db.backref('supplier',
uselist=False))
#: our number as customer with this supplier
customer_no = db.Column(db.Unicode)
payment_term = db.Column(db.Integer) # in days
freight_type = db.Column(db.Enum(*_freight_types.keys(),
name='freight_type'), default=FREIGHT_CUSTOMER)
leap_time = db.Column(db.Integer) # in days
supplier_contacts = db.relationship('SupplierContact',
cascade='all,delete-orphan',
backref='supplier')
contacts = association_proxy('supplier_contacts', 'contact')
#: 'bank_accounts' attribute added by BankAccount.supplier relation
#: 'purchases' attribute added by PurchaseDocument.supplier relation
#: 'orders' attribute added by PurchaseOrder.supplier relation
#: Inherited from Entity
#: - address (collection)
#: - phone (collection)
#: - email (collection)
#: - extrafield (collection)
@property
def freight_type_str(self):
return self._freight_types[self.freight_type]
def add_contact(self, contact, role):
self.supplier_contacts.append(SupplierContact(contact, role))
| [
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]
| |
90ad9fcdb2334a3144853bebdcabed989714fc08 | d54e1b89dbd0ec5baa6a018464a419e718c1beac | /Python from others/文件/wk_03_分行读取文件.py | ae79c246fb71c837847b4312137a77ae4ae62097 | []
| no_license | cjx1996/vscode_Pythoncode | eda438279b7318e6cb73211e26107c7e1587fdfb | f269ebf7ed80091b22334c48839af2a205a15549 | refs/heads/master | 2021-01-03T19:16:18.103858 | 2020-05-07T13:51:31 | 2020-05-07T13:51:31 | 240,205,057 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 207 | py | # 1. 打开文件
file = open("README")
# 2. 读取文件内容
while True:
text = file.readline()
# 判断时候有内容
if not text:
break
print(text)
# 3. 关闭
file.close()
| [
"[email protected]"
]
| |
bfd6fab30015421b87ddfbb130b4c0fda5ced7dd | 8e474edd3954c4679061bb95970ba40e20c39c2d | /pre_analysis/observable_analysis/qtq0eff_mass_mc_intervals.py | 83a06fd90bb8b0503b0d5ac9de8309a5479a28ad | [
"MIT"
]
| permissive | JinKiwoog/LatticeAnalyser | c12f5c11f2777c343a2e1e1cd4e70e91471b4e79 | 6179263e30555d14192e80d94121f924a37704c9 | refs/heads/master | 2020-04-17T18:35:24.240467 | 2019-01-21T11:25:19 | 2019-01-21T11:25:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,597 | py | from pre_analysis.observable_analysis import QtQ0EffectiveMassAnalyser
import copy
import numpy as np
import os
from tools.folderreadingtools import check_folder
import statistics.parallel_tools as ptools
class QtQ0EffectiveMassMCAnalyser(QtQ0EffectiveMassAnalyser):
"""Correlator of <QtQ0> in euclidean time analysis class."""
observable_name = r""
observable_name_compact = "qtq0effmc"
x_label = r"$t_e[fm]$"
y_label = r"$am_\textrm{eff} = \ln \frac{\langle Q_{t_e} Q_0 \rangle}{\langle Q_{t_e+1} Q_0 \rangle}$"
mark_interval = 1
error_mark_interval = 1
def __str__(self):
def info_string(s1, s2): return "\n{0:<20s}: {1:<20s}".format(s1, s2)
return_string = ""
return_string += "\n" + self.section_seperator
return_string += info_string("Data batch folder",
self.batch_data_folder)
return_string += info_string("Batch name", self.batch_name)
return_string += info_string("Observable",
self.observable_name_compact)
return_string += info_string("Beta", "%.2f" % self.beta)
return_string += info_string("Flow time t0",
"%.2f" % self.q0_flow_time)
return_string += info_string("MC-interval: ",
"[%d,%d)" % self.mc_interval)
return_string += "\n" + self.section_seperator
return return_string
def main():
exit("Module QtQ0EffectiveMassAnalyser not intended for standalone usage.")
if __name__ == '__main__':
main()
| [
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]
| |
455467f723018a27dbe6a7830158e27d70b9d9a8 | 7a915ae2c07c652cb3abffccd3b1b54c04fd2918 | /main/views.py | 26333397c360b25743f2035cebddc66815dfc322 | []
| no_license | YUNKWANGYOU/healingWheel | 410135bd21f9a4f6922051e63bc50fcf090edc3c | 416434e5ee8f79b366cdee7b81d58382e073020e | refs/heads/master | 2022-10-18T17:59:33.272890 | 2020-06-14T04:10:36 | 2020-06-14T04:10:36 | 264,862,011 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,211 | py | from django.shortcuts import render,redirect
from .forms import DrivingTimeForm
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User
from datetime import timedelta
def index(request):
return render(request,'main/index.html',)
def aboutus(request):
return render(request,'main/aboutus.html',)
def loc_ren(request):
return render(request,'main/loc_ren.html',)
def how_to(request):
return render(request,'main/how_to.html')
def contact(request):
return render(request,'main/contact.html')
@login_required
def charge(request):
if request.method == 'POST':
us = request.user
profile = us.profile
if request.POST['detail_menu'] == '10':
profile.duration += timedelta(minutes = 10)
elif request.POST['detail_menu'] == '30':
profile.duration += timedelta(minutes = 30)
else:
profile.duration += timedelta(minutes = 60)
profile.save()
return redirect('profile')
return render(request,'main/charge.html',{
})
@login_required
def profile(request):
us = request.user
return render(request,'main/profile.html',{
'user' : us
})
| [
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]
| |
abe9c326d87ac84ae84d1b1abc67baad6d8cd389 | 657d549ffa47c4ef599aa5e0f5760af8de77fec4 | /src/runner/predictors/base_predictor.py | 865569b96277b12446c2ca90d1e3595b99495a53 | []
| no_license | Tung-I/Incremental_Learning | 68357d3db5a646aa6b3df844b85e12fa45e3eb3e | 95602f404ab8dd627c5dd5fcc94a4a071ad330ab | refs/heads/master | 2021-01-14T15:18:21.941132 | 2020-03-30T04:04:13 | 2020-03-30T04:04:13 | 242,659,450 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,531 | py | import logging
import torch
from tqdm import tqdm
from src.runner.utils import EpochLog
LOGGER = logging.getLogger(__name__.split('.')[-1])
class BasePredictor:
"""The base class for all predictors.
Args:
device (torch.device): The device.
test_dataloader (Dataloader): The testing dataloader.
net (BaseNet): The network architecture.
loss_fns (LossFns): The loss functions.
loss_weights (LossWeights): The corresponding weights of loss functions.
metric_fns (MetricFns): The metric functions.
"""
def __init__(self, saved_dir, device, test_dataloader, net, loss_fns, loss_weights, metric_fns):
self.saved_dir = saved_dir
self.device = device
self.test_dataloader = test_dataloader
self.net = net.to(device)
self.loss_fns = loss_fns
self.loss_weights = loss_weights
self.metric_fns = metric_fns
def predict(self):
"""The testing process.
"""
self.net.eval()
dataloader = self.test_dataloader
pbar = tqdm(dataloader, desc='test', ascii=True)
epoch_log = EpochLog()
for i, batch in enumerate(pbar):
with torch.no_grad():
test_dict = self._test_step(batch)
loss = test_dict['loss']
losses = test_dict.get('losses')
metrics = test_dict.get('metrics')
if (i + 1) == len(dataloader) and not dataloader.drop_last:
batch_size = len(dataloader.dataset) % dataloader.batch_size
else:
batch_size = dataloader.batch_size
epoch_log.update(batch_size, loss, losses, metrics)
pbar.set_postfix(**epoch_log.on_step_end_log)
test_log = epoch_log.on_epoch_end_log
LOGGER.info(f'Test log: {test_log}.')
def _test_step(self, batch):
"""The user-defined testing logic.
Args:
batch (dict or sequence): A batch of the data.
Returns:
test_dict (dict): The computed results.
test_dict['loss'] (torch.Tensor)
test_dict['losses'] (dict, optional)
test_dict['metrics'] (dict, optional)
"""
raise NotImplementedError
def load(self, path):
"""Load the model checkpoint.
Args:
path (Path): The path to load the model checkpoint.
"""
checkpoint = torch.load(path, map_location='cpu')
self.net.load_state_dict(checkpoint['net'])
| [
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]
| |
0f0b1fd4c55e34e3152eb2d5b014c46a1f681429 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03125/s620551773.py | 2fa87780ec501f96d186ec29b97ba692ec344e65 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 105 | py | A, B = map(int, raw_input().split())
if B % A == 0:
ans = A + B
else:
ans = B - A
print str(ans) | [
"[email protected]"
]
| |
dfe4cdb4b2cd3b16377380d041e286d9589ad92f | a26b8a208259b61ad5d77a9092d0edf02a7f5fe3 | /爬虫/py/zeran_answer.py | 67edd655f6fbf8abedfe14a057a2ad70c2e5c55f | []
| no_license | yiyue21/PythonCode | e088aa8661c0c88bd9971a2f1f7cd1cac9eaf1c6 | 39445a0898c404836f3c60fd067d8489ab804fb4 | refs/heads/master | 2020-06-19T04:23:28.019604 | 2019-06-09T17:08:45 | 2019-06-09T17:08:45 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 89,180 | py | #coding:utf-8
import requests
import re
text = u"{"paging": {"is_end": false, "totals": 2628, "previous": "https://www.zhihu.com/api/v4/members/ze.ran/answers?sort_by=created&include=data%5B%2A%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Cmark_infos%2Ccreated_time%2Cupdated_time%2Creview_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cvoting%2Cis_author%2Cis_thanked%2Cis_nothelp%2Cupvoted_followees%3Bdata%5B%2A%5D.author.badge%5B%3F%28type%3Dbest_answerer%29%5D.topics&limit=20&offset=20", "is_start": false, "next": "https://www.zhihu.com/api/v4/members/ze.ran/answers?sort_by=created&include=data%5B%2A%5D.is_normal%2Cadmin_closed_comment%2Creward_info%2Cis_collapsed%2Cannotation_action%2Cannotation_detail%2Ccollapse_reason%2Ccollapsed_by%2Csuggest_edit%2Ccomment_count%2Ccan_comment%2Ccontent%2Cvoteup_count%2Creshipment_settings%2Ccomment_permission%2Cmark_infos%2Ccreated_time%2Cupdated_time%2Creview_info%2Cquestion%2Cexcerpt%2Crelationship.is_authorized%2Cvoting%2Cis_author%2Cis_thanked%2Cis_nothelp%2Cupvoted_followees%3Bdata%5B%2A%5D.author.badge%5B%3F%28type%3Dbest_answerer%29%5D.topics&limit=20&offset=60"}, "data": [{"suggest_edit": {"status": false, "reason": "", "title": "", "url": "", "unnormal_details": {}, "tip": ""}, "relationship": {"upvoted_followees": [], "is_author": false, "is_nothelp": false, "is_authorized": false, "voting": 0, "is_thanked": false}, "mark_infos": [], "excerpt": "\u5411\u5f80\u4e8b\u81f4\u656c\uff0c\u4e3a\u9752\u6625\u4e70\u5355\u3002", "annotation_action": [], 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"\u9886\u5bfc\u53d1\u8a00\uff0c\u5c4f\u6c14\u51dd\u795e\u7684\u8046\u542c\uff1b<br>\u9886\u5bfc\u63d0\u95ee\uff0c\u4e89\u5148\u6050\u540e\u7684\u56de\u7b54\u3002", "comment_count": 29, "extras": "", "reshipment_settings": "allowed", "reward_info": {"reward_member_count": 0, "is_rewardable": false, "reward_total_money": 0, "can_open_reward": false, "tagline": ""}, "is_copyable": true, "type": "answer", "thumbnail": "", "is_normal": true}, {"suggest_edit": {"status": false, "reason": "", "title": "", "url": "", "unnormal_details": {}, "tip": ""}, "relationship": {"upvoted_followees": [], "is_author": false, "is_nothelp": false, "is_authorized": false, "voting": 0, "is_thanked": false}, "mark_infos": [], "excerpt": 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"comment_count": 50, "extras": "", "reshipment_settings": "need_payment", "reward_info": {"reward_member_count": 0, "is_rewardable": false, "reward_total_money": 0, "can_open_reward": false, "tagline": ""}, "is_copyable": false, "type": "answer", "thumbnail": "", "is_normal": true}]}
"
| [
"[email protected]"
]
| |
6a6f7df26cb37b166a82c2a0b11ebe4621986391 | d554b1aa8b70fddf81da8988b4aaa43788fede88 | /5 - Notebooks e Data/1 - Análises numéricas/Arquivos David/Atualizados/logDicas-master/data/2019-1/225/users/4009/codes/1590_1014.py | a4d00150034f869d011a6a04ebe840fb0ed78141 | []
| no_license | JosephLevinthal/Research-projects | a3bc3ca3b09faad16f5cce5949a2279cf14742ba | 60d5fd6eb864a5181f4321e7a992812f3c2139f9 | refs/heads/master | 2022-07-31T06:43:02.686109 | 2020-05-23T00:24:26 | 2020-05-23T00:24:26 | 266,199,309 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 45 | py | y=float(input("valor:" ))
x=y*30/100
print(x) | [
"[email protected]"
]
| |
c3771eaa61b07b3cd184d7bdca9ff13270cbd4b8 | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2577/60660/296683.py | 4283bd8bc19eca118b313934e87233f0cbfd48c0 | []
| no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 668 | py | def jobScheduling(startTime, endTime, profit):
n = len(startTime)
# 按结束时间排序
work = sorted(zip(startTime, endTime, profit))
# 计算OPT数组
dp = [0] * (n + 1)
pos=0#记录与当前不重合的最大区间序号，减少循环量
s=0
for i in range(n):
for j in range(pos, i+1):
# 区间不重合
if work[i][0] >= work[j][1]:
if j == pos:
pos += 1
s = max(s, dp[j])
dp[i]=s+work[i][2]
print(dp[n-1])
st=eval('['+input()+']')
et=eval('['+input()+']')
pf=eval('['+input()+']')
jobScheduling(st,et,pf) | [
"[email protected]"
]
| |
0569a5ee3ff6e13a8e55562b2dde689b330181d1 | 945b3c14b5a58f8d98955cdf27aef9469e21523c | /flod_matrikkel_address_restapi/matrikkel.py | 370a48d6bcdd2bec075df00225106e533e8fb18f | [
"BSD-2-Clause-Views"
]
| permissive | Trondheim-kommune/Bookingbasen | 34e595e9c57ea6428406b2806559aab17e9a3031 | 58235a5a1fd6ad291cb237e6ec9a67bfe8c463c6 | refs/heads/master | 2022-11-29T00:20:18.681549 | 2017-05-29T19:33:43 | 2017-05-29T19:33:43 | 49,863,780 | 1 | 1 | NOASSERTION | 2022-11-22T00:27:34 | 2016-01-18T08:47:46 | JavaScript | UTF-8 | Python | false | false | 6,031 | py | #!/usr/bin/python
# -*- coding: utf-8 -*-
from suds.client import Client
import suds
import re
import logging
import httplib
import ssl
import urllib2
import socket
import base64
logging.basicConfig(level=logging.INFO)
class HTTPSConnectionV3(httplib.HTTPSConnection):
def __init__(self, *args, **kwargs):
httplib.HTTPSConnection.__init__(self, *args, **kwargs)
def connect(self):
sock = socket.create_connection((self.host, self.port), self.timeout)
if self._tunnel_host:
self.sock = sock
self._tunnel()
try:
self.sock = ssl.wrap_socket(
sock, self.key_file,
self.cert_file,
ssl_version=ssl.PROTOCOL_SSLv3
)
except ssl.SSLError:
print("Trying SSLv23.")
self.sock = ssl.wrap_socket(
sock,
self.key_file,
self.cert_file,
ssl_version=ssl.PROTOCOL_SSLv23
)
class HTTPSHandlerV3(urllib2.HTTPSHandler):
def https_open(self, req):
return self.do_open(HTTPSConnectionV3, req)
class MatrikkelService(object):
def __init__(self, url, wsdl_url, username, password):
self.url = url
self.wsdl_url = wsdl_url
self.username = username
self.password = password
# install opener
opener = urllib2.build_opener(HTTPSHandlerV3())
self.transport = suds.transport.https.HttpAuthenticated(
username=username,
password=password
)
self.transport.urlopener = opener
self.client = self.create_client()
def create_client(self):
base64string = base64.encodestring(
'%s:%s' % (self.username, self.password)
).replace('\n', '')
authentication_header = {
"WWW-Authenticate": "https://www.test.matrikkel.no",
"Authorization": "Basic %s" % base64string
}
client = Client(
url=self.wsdl_url,
location=self.url,
transport=self.transport,
username=self.username,
password=self.password
)
client.set_options(headers=authentication_header)
return client
def serialize_ident(ident):
dict = {
"kommunenr": str(ident.kommunenr),
"gardsnr": ident.gardsnr,
"bruksnr": ident.bruksnr
}
try:
dict["festenr"] = ident.festenr
except AttributeError:
pass
try:
dict["seksjonsnr"] = ident.seksjonsnr
except AttributeError:
pass
return dict
def get_number_and_letter(query):
#finds out if query ends in number or number + character
match = re.search(r'\d+(\s+)?([A-Za-z]?)$', query)
number = None
letter = None
if match:
number_and_letter = match.group()
query = query.replace(number_and_letter, "")
number_match = re.search(r'\d+', number_and_letter)
if number_match:
number = number_match.group()
letter_match = re.search(r'[A-Za-z]$', number_and_letter)
if letter_match:
letter = letter_match.group()
return query, number, letter
class MatrikkelAdressService(MatrikkelService):
def search_address(self, query, municipality_number):
matrikkel_context = self.client.factory.create('ns2:MatrikkelContext')
query, search_number, search_letter = get_number_and_letter(query)
try:
adresses = self.client.service.findAdresserForVeg(
query,
municipality_number,
matrikkel_context
)
except Exception, e:
print type(e)
adresses = []
result = []
for address in adresses:
address_ident = address.vegadresseIdent
if search_number and int(search_number) != int(address_ident.nr):
continue
try:
letter = address_ident.bokstav
except AttributeError:
letter = None
if search_letter and letter.lower() != search_letter.lower():
continue
address_response = {
"name": "%s %s" % (address.adressenavn, address_ident.nr)
}
if letter:
address_response["name"] += letter
try:
address_response["matrikkel_ident"] = serialize_ident(
address.matrikkelenhetIdent
)
result.append(address_response)
except AttributeError:
pass
return result
def create_point_dict(point):
coord_string = point.point.coordinates.value.split(" ")
return {
"lon": float(coord_string[0]),
"lat": float(coord_string[1])
}
class MatrikkelBuildingService(MatrikkelService):
def find_buildings(self,
kommunenr,
gardsnr,
bruksnr,
festenr=None,
seksjonsnr=None):
matrikkelenhetident = self.client.factory.create('ns5:MatrikkelenhetIdent')
matrikkelenhetident.kommunenr = kommunenr
matrikkelenhetident.gardsnr = gardsnr
matrikkelenhetident.bruksnr = bruksnr
matrikkelenhetident.festenr = festenr
matrikkelenhetident.seksjonsnr = seksjonsnr
matrikkel_context = self.client.factory.create('ns2:MatrikkelContext')
#EPSG:4326
matrikkel_context.sosiKode = 84
buildings = self.client.service.findBygningerForMatrikkelenhet(
matrikkelenhetident,
matrikkel_context
)
return [
{
"position": create_point_dict(building.representasjonspunkt),
"building_number": building.bygningIdent.bygningsnr
}
for building in buildings if str(building.__class__) == "suds.sudsobject.Bygning"
]
| [
"[email protected]"
]
| |
aa8b90ef142a6c6eab8212204d6d4306724706ae | 19bc8a9343aa4120453abeff3deddda7d900f774 | /ProgrammingInterviewQuestions/24_DynamicProgrammingFibonacci.py | da078e3a21ff91ec68517a6074e80e997e529813 | []
| no_license | ArunkumarRamanan/CLRS-1 | 98643cde2f561d9960c26378ae29dd92b4c3fc89 | f085db885bcee8d09c1e4f036517acdbd3a0918e | refs/heads/master | 2020-06-28T08:30:44.029970 | 2016-11-19T15:27:55 | 2016-11-19T15:27:55 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 721 | py | # -*- coding: utf-8 -*-
"""
Created on Sun Sep 25 10:29:52 2016
@author: Rahul Patni
"""
# Dynamic Programming Fibonacci
def recursiveApproach(n):
if n == 0 or n == 1:
return 1
return recursiveApproach(n - 1) + recursiveApproach(n - 2)
def iterativeApproach(n):
x1 = 0
x2 = 1
for i in range(1, n + 1):
x3 = x1 + x2
x1 = x2
x2 = x3
return x2
def dynamicApproach(n):
fib = dict()
fib[0] = 1
fib[1] = 1
for i in range(2, n + 1):
fib[i] = fib[i - 1] + fib[i - 2]
# print fib
return fib[n]
def main():
x = 10
print recursiveApproach(x)
print iterativeApproach(x)
print dynamicApproach(x)
main() | [
"[email protected]"
]
| |
7bdbc7a11bdde9e5916deb7091b35bd212766c1d | 08db28fa3836c36433aa105883a762396d4883c6 | /spider/learning/day_01/01_url.py | 1359c9c42a557bf47dfc6cf4ab93d3ca22994db6 | []
| no_license | xieyipeng/FaceRecognition | 1127aaff0dd121319a8652abcfe8a59a7beaaf43 | dede5b181d6b70b87ccf00052df8056a912eff0f | refs/heads/master | 2022-09-19T07:02:33.624410 | 2020-06-02T03:03:58 | 2020-06-02T03:03:58 | 246,464,586 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 808 | py | import urllib.request
# 学习网址 https://www.bilibili.com/video/av68030937?p=26
def load_data():
url = "http://www.baidu.com/"
# get请求
# http请求
# response：http相应对象
response = urllib.request.urlopen(url)
print(response)
# 读取内容 byte类型
data = response.read()
print(data)
# 将文件获取的内容转换成字符串
str_data = data.decode("utf-8")
print(str_data)
# 将数据写入文件
with open("01-baidu.html", "w", encoding="utf-8")as f:
f.write(str_data)
# 将字符串类型转换为bytes
str_name="baidu"
byte_name=str_name.encode("utf-8")
print(byte_name)
# 如果爬取bytes，类型，要写入str： decode
# 如果爬取str，类型，要写入bytes： encode
load_data()
| [
"[email protected]"
]
| |
0d3a0639593a2f61d15a0d586b1eec308bd1662b | 933a4f98b3ab1df987bce525d20ca904b225140f | /scripts/slave/recipe_modules/chromium/tests/run_gn.py | 1fa3b5362367efa0f9276e1b2c2d605e9d943b9e | [
"BSD-3-Clause"
]
| permissive | mcgreevy/chromium-build | 3881c489b4d9be2f113da755487808b3593f8156 | f8e42c70146c1b668421ee6358dc550a955770a3 | refs/heads/master | 2020-12-30T12:32:15.685191 | 2017-05-17T06:58:18 | 2017-05-17T06:58:18 | 91,419,271 | 0 | 2 | NOASSERTION | 2020-07-22T09:27:35 | 2017-05-16T05:52:45 | Python | UTF-8 | Python | false | false | 1,002 | py | # Copyright 2017 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
DEPS = [
'chromium',
'recipe_engine/platform',
'recipe_engine/properties',
]
def RunSteps(api):
api.chromium.set_config(
api.properties.get('chromium_config', 'chromium'),
BUILD_CONFIG=api.properties.get('build_config', 'Release'),
TARGET_PLATFORM=api.properties.get('target_platform', 'linux'))
api.chromium.run_gn(use_goma=True, gn_path=api.properties.get('gn_path'))
def GenTests(api):
yield api.test('basic')
yield (
api.test('custom_gn_path') +
api.properties(gn_path='some/other/path/gn')
)
yield (
api.test('mac') +
api.platform('mac', 64) +
api.properties(target_platform='mac')
)
yield (
api.test('android') +
api.properties(target_platform='android')
)
yield (
api.test('debug') +
api.properties(build_config='Debug')
)
| [
"[email protected]"
]
| |
e146a9201f1636b25f025374ad8d9c41871ad505 | f0581fa08ef790606ca019890a2233f91b1c42a7 | /PythonSrc/Unused/Rotations/vector3d.py | 23e7968dd55e8668927443f19b0b467adbd8ada3 | []
| no_license | jaycoskey/IntroToPythonCourse | de758f0dd0a1b541edb2ef4dcc20950a8d8788bb | d1373ec6602584a6791fd48d37ae66ff5f104487 | refs/heads/master | 2023-02-22T16:32:50.533091 | 2021-01-27T08:22:14 | 2021-01-27T08:22:14 | 333,007,314 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,986 | py | package rotations
import unittest
class Vector3d:
ZERO = Vector3d(0.0, 0.0, 0.0)
XUNIT = Vector3d(1.0, 0.0, 0.0)
YUNIT = Vector3d(0.0, 1.0, 0.0)
ZUNIT = Vector3d(0.0, 0.0, 1.0)
def __init__(self, *args):
if len(args) == 0:
self.x = self.y = self.z = 0.0
elif len(args) == 1:
self.x = args[0].x
self.y = args[0].y
self.z = args[0].z
elif len(args) == 3:
self.x = args[0]
self.y = args[1]
self.z = args[2]
else:
raise ValueError('Vector3d() requires 0, 1, or 3 arguments')
def __add__(other):
return Vector3d(self.x + other.x, self.y + other.y, self.z + other.z)
def __mul__(other):
return Vector3d(other * v.x, other * v.y, other * v.z)
def __rmul__(other):
return Vector3d(other * v.x, other * v.y, other * v.z)
def __str__():
return '({0:f}, {1:f}, {2:f})'.format(self.x, self.y, self.z)
def __sub__(other):
return Vector3d(self.x - other.x, self.y - other.y, self.z - other.z)
def __truediv__(other):
return Vector3d(self.x / other, self.y / other, self.z / other)
def as_quaternion():
'''Identifies the imaginary subspace of quaternionic space with R^3.'''
return quaternion(0.0, self.x, self.y, self.z)
@staticmethod
def cross(a, b):
result = Vector3d(
a.Y * b.Z - a.Z * b.Y,
a.Z * b.X - a.X * b.Z,
a.X * b.Y - a.Y * b.X )
return result
def cross(self, b):
return Vector3d.cross(self, b)
@staticmethod
def dot(a, b):
return(a.x * b.x + a.y * b.y + a.z * b.z)
def interpolate(a, b, t):
return (1 - t) * a + t * b
def norm(self):
return math.sqrt(self.norm2())
def norm2(self):
return self.x ** 2 + self.y ** 2 + self.z ** 2
| [
"[email protected]"
]
| |
7b8e5a0b99e1f8250761f4bebafb28c015e5515a | b47f2e3f3298388b1bcab3213bef42682985135e | /experiments/heat-3d/tmp_files/1618.py | 79d9cc6c1db159c2a66cbdad061c4da75f285850 | [
"BSD-2-Clause"
]
| permissive | LoopTilingBenchmark/benchmark | 29cc9f845d323431e3d40e878cbfc6d1aad1f260 | 52a3d2e70216552a498fd91de02a2fa9cb62122c | refs/heads/master | 2020-09-25T09:45:31.299046 | 2019-12-04T23:25:06 | 2019-12-04T23:25:06 | 225,975,074 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 375 | py | from chill import *
source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/heat-3d/kernel.c')
destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/heat-3d/tmp_files/1618.c')
procedure('kernel_heat_3d')
loop(0)
tile(0,2,16,2)
tile(0,4,16,4)
tile(0,6,32,6)
tile(1,2,16,2)
tile(1,4,16,4)
tile(1,6,32,6)
| [
"[email protected]"
]
| |
d929851f0eb039c525508cc703f4ebab44b783d8 | b44960d60c4969b2f2014cfc978a9a8ba1e584fa | /122.py | 789526cc3e7c00dc524292c8a662260111466325 | []
| no_license | sushmithasushi/player | 83650582689c9f5a9edc3aee3c46c40a051368c4 | 949672a23ee2dc31b4748fa221640fe6e85b5324 | refs/heads/master | 2020-06-17T12:43:39.010920 | 2019-07-22T01:28:19 | 2019-07-22T01:28:19 | 195,928,088 | 0 | 0 | null | 2019-07-09T03:45:57 | 2019-07-09T03:45:56 | null | UTF-8 | Python | false | false | 70 | py | n=int(input())
s=list(map(int,input().split()[:n]))
print(n*(n+1)//2)
| [
"[email protected]"
]
| |
9395a2ed51c260190fff3c4e43d459356f20f233 | ce9d22c3e0e06d5543b404d0c254a582231a0f4b | /tensorflow_federated/python/aggregators/measurements.py | fbf0d5aae3e8289aff9de732e98c67698bdac830 | [
"Apache-2.0"
]
| permissive | stjordanis/federated | d9da8c68072a4eb7871f8e293dafebd7584a00c4 | 6819c65eb823dcb7f3f5666051529b9e2346cb28 | refs/heads/master | 2021-09-08T21:41:56.552453 | 2021-09-02T23:45:17 | 2021-09-02T23:46:21 | 191,418,366 | 0 | 0 | Apache-2.0 | 2019-06-11T17:25:27 | 2019-06-11T17:25:26 | null | UTF-8 | Python | false | false | 7,531 | py | # Copyright 2021, The TensorFlow Federated Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Aggregation factory for adding custom measurements."""
import inspect
from typing import Any, Callable, Dict, Optional
from tensorflow_federated.python.aggregators import factory
from tensorflow_federated.python.common_libs import py_typecheck
from tensorflow_federated.python.core.api import computations
from tensorflow_federated.python.core.impl.federated_context import intrinsics
from tensorflow_federated.python.core.impl.types import computation_types
from tensorflow_federated.python.core.templates import aggregation_process
from tensorflow_federated.python.core.templates import measured_process
def add_measurements(
inner_agg_factory: factory.AggregationFactory,
*,
client_measurement_fn: Optional[Callable[..., Dict[str, Any]]] = None,
server_measurement_fn: Optional[Callable[..., Dict[str, Any]]] = None,
) -> factory.AggregationFactory:
"""Wraps `AggregationFactory` to report additional measurements.
The function `client_measurement_fn` should be a Python callable that will be
called as `client_measurement_fn(value)` or `client_measurement_fn(value,
weight)` depending on whether `inner_agg_factory` is weighted or unweighted.
It must be traceable by TFF and expect `tff.Value` objects placed at `CLIENTS`
as inputs, and return a `collections.OrderedDict` mapping string names to
tensor values placed at `SERVER`, which will be added to the measurement dict
produced by the `inner_agg_factory`.
Similarly, `server_measurement_fn` should be a Python callable that will be
called as `server_measurement_fn(result)` where `result` is the result (on
server) of the inner aggregation.
One or both of `client_measurement_fn` and `server_measurement_fn` must be
specified.
Args:
inner_agg_factory: The factory to wrap and add measurements.
client_measurement_fn: A Python callable that will be called on `value`
(and/or `weight`) provided to the `next` function to compute additional
measurements of the client values/weights.
server_measurement_fn: A Python callable that will be called on the `result`
of aggregation at server to compute additional measurements of the result.
Returns:
An `AggregationFactory` that reports additional measurements.
"""
py_typecheck.check_type(inner_agg_factory,
factory.AggregationFactory.__args__)
if not (client_measurement_fn or server_measurement_fn):
raise ValueError('Must specify one or both of `client_measurement_fn` or '
'`server_measurement_fn`.')
if client_measurement_fn:
py_typecheck.check_callable(client_measurement_fn)
if isinstance(inner_agg_factory, factory.UnweightedAggregationFactory):
if len(inspect.signature(client_measurement_fn).parameters) != 1:
raise ValueError(
'`client_measurement_fn` must take a single parameter if '
'`inner_agg_factory` is unweighted.')
elif isinstance(inner_agg_factory, factory.WeightedAggregationFactory):
if len(inspect.signature(client_measurement_fn).parameters) != 2:
raise ValueError(
'`client_measurement_fn` must take a two parameters if '
'`inner_agg_factory` is weighted.')
if server_measurement_fn:
py_typecheck.check_callable(server_measurement_fn)
if len(inspect.signature(server_measurement_fn).parameters) != 1:
raise ValueError('`server_measurement_fn` must take a single parameter.')
@computations.tf_computation()
def dict_update(orig_dict, new_values):
if not orig_dict:
return new_values
orig_dict.update(new_values)
return orig_dict
if isinstance(inner_agg_factory, factory.WeightedAggregationFactory):
class WeightedWrappedFactory(factory.WeightedAggregationFactory):
"""Wrapper for `WeightedAggregationFactory` that adds new measurements."""
def create(
self, value_type: factory.ValueType, weight_type: factory.ValueType
) -> aggregation_process.AggregationProcess:
py_typecheck.check_type(value_type, factory.ValueType.__args__)
py_typecheck.check_type(weight_type, factory.ValueType.__args__)
inner_agg_process = inner_agg_factory.create(value_type, weight_type)
init_fn = inner_agg_process.initialize
@computations.federated_computation(
init_fn.type_signature.result,
computation_types.at_clients(value_type),
computation_types.at_clients(weight_type))
def next_fn(state, value, weight):
inner_agg_output = inner_agg_process.next(state, value, weight)
measurements = inner_agg_output.measurements
if client_measurement_fn:
client_measurements = client_measurement_fn(value, weight)
measurements = intrinsics.federated_map(
dict_update, (measurements, client_measurements))
if server_measurement_fn:
server_measurements = server_measurement_fn(inner_agg_output.result)
measurements = intrinsics.federated_map(
dict_update, (measurements, server_measurements))
return measured_process.MeasuredProcessOutput(
state=inner_agg_output.state,
result=inner_agg_output.result,
measurements=measurements)
return aggregation_process.AggregationProcess(init_fn, next_fn)
return WeightedWrappedFactory()
else:
class UnweightedWrappedFactory(factory.UnweightedAggregationFactory):
"""Wrapper for `UnweightedAggregationFactory` that adds new measurements."""
def create(
self, value_type: factory.ValueType
) -> aggregation_process.AggregationProcess:
py_typecheck.check_type(value_type, factory.ValueType.__args__)
inner_agg_process = inner_agg_factory.create(value_type)
init_fn = inner_agg_process.initialize
@computations.federated_computation(
init_fn.type_signature.result,
computation_types.at_clients(value_type))
def next_fn(state, value):
inner_agg_output = inner_agg_process.next(state, value)
measurements = inner_agg_output.measurements
if client_measurement_fn:
client_measurements = client_measurement_fn(value)
measurements = intrinsics.federated_map(
dict_update, (measurements, client_measurements))
if server_measurement_fn:
server_measurements = server_measurement_fn(inner_agg_output.result)
measurements = intrinsics.federated_map(
dict_update, (measurements, server_measurements))
return measured_process.MeasuredProcessOutput(
state=inner_agg_output.state,
result=inner_agg_output.result,
measurements=measurements)
return aggregation_process.AggregationProcess(init_fn, next_fn)
return UnweightedWrappedFactory()
| [
"[email protected]"
]
| |
9b029ba1462be18dcc18bfc84ccc15d1ca07a792 | 0c2583011200a5bed73315fde7ef30678075fce7 | /modules/db/entities/US_TOIMP.py | c7bdec4c93c6cdf081aa1872ccff8557411b87aa | []
| no_license | enzococca/pyarchinit_3 | 3d3b5784a3b2e4b753581f28064748043f8c47fe | 00626ba5c24d447fc54c267071f0584a2962182c | refs/heads/master | 2020-03-09T16:59:21.853411 | 2018-03-12T14:49:51 | 2018-03-12T14:49:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,869 | py | '''
Created on 19 feb 2018
@author: Serena Sensini
'''
class US_TOIMP(object):
#def __init__"
def __init__(self,
id_us,
sito,
area,
us,
d_stratigrafica,
d_interpretativa,
descrizione,
interpretazione,
periodo_iniziale,
fase_iniziale,
periodo_finale,
fase_finale,
scavato,
attivita,
anno_scavo,
metodo_di_scavo,
inclusi,
campioni,
rapporti,
data_schedatura,
schedatore,
formazione,
stato_di_conservazione,
colore,
consistenza,
struttura
):
self.id_us = id_us #0
self.sito = sito #1
self.area = area #2
self.us = us #3
self.d_stratigrafica = d_stratigrafica #4
self.d_interpretativa = d_interpretativa #5
self.descrizione = descrizione #6
self.interpretazione = interpretazione #7
self.periodo_iniziale = periodo_iniziale #8
self.fase_iniziale = fase_iniziale #9
self.periodo_finale = periodo_finale #10
self.fase_finale = fase_finale #11
self.scavato = scavato #12
self.attivita = attivita #13
self.anno_scavo = anno_scavo #14
self.metodo_di_scavo = metodo_di_scavo #15
self.inclusi = inclusi #16
self.campioni = campioni #17
self.rapporti = rapporti #18
self.data_schedatura = data_schedatura #19
self.schedatore = schedatore #20
self.formazione = formazione #21
self.stato_di_conservazione = stato_di_conservazione #22
self.colore = colore #23
self.consistenza = consistenza #24
self.struttura = struttura #25
#def __repr__"
def __repr__(self):
return "<US_TOIMP('%d', '%s', '%s', '%d','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')>" % (
self.id_us,
self.sito,
self.area,
self.us,
self.d_stratigrafica,
self.d_interpretativa,
self.descrizione,
self.interpretazione,
self.periodo_iniziale,
self.fase_iniziale,
self.periodo_finale,
self.fase_finale,
self.scavato,
self.attivita,
self.anno_scavo,
self.metodo_di_scavo,
self.inclusi,
self.campioni,
self.rapporti,
self.data_schedatura,
self.schedatore,
self.formazione,
self.stato_di_conservazione,
self.colore,
self.consistenza,
self.struttura
) | [
"[email protected]"
]
| |
4dffd211b37de445ce2265d53a8f960213309ae9 | fc2d1f44ec35577b0e291f403907ccc8c7859edf | /docs/conf.py | d59a6ebf0dd656cf813d7cab8dbcd6f4446c78ff | [
"MIT"
]
| permissive | sobolevn/python-humanfriendly | 35403b4e611f0f95ad474de8e8efd354f12b5369 | 03d1db48e8ab4539403a58d7dea7ef0bd6672ae3 | refs/heads/master | 2020-04-26T10:52:16.294536 | 2019-02-21T20:21:43 | 2019-02-21T20:21:43 | 173,498,753 | 0 | 0 | MIT | 2019-03-02T21:04:23 | 2019-03-02T21:04:23 | null | UTF-8 | Python | false | false | 2,325 | py | # -*- coding: utf-8 -*-
"""Documentation build configuration file for the `humanfriendly` package."""
import os
import sys
# Add the 'humanfriendly' source distribution's root directory to the module path.
sys.path.insert(0, os.path.abspath('..'))
# -- General configuration -----------------------------------------------------
# Sphinx extension module names.
extensions = [
'sphinx.ext.doctest',
'sphinx.ext.autodoc',
'sphinx.ext.intersphinx',
'humanfriendly.sphinx',
]
# Configuration for the `autodoc' extension.
autodoc_member_order = 'bysource'
# Paths that contain templates, relative to this directory.
templates_path = ['templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'humanfriendly'
copyright = u'2018, Peter Odding'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
# Find the package version and make it the release.
from humanfriendly import __version__ as humanfriendly_version # noqa
# The short X.Y version.
version = '.'.join(humanfriendly_version.split('.')[:2])
# The full version, including alpha/beta/rc tags.
release = humanfriendly_version
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
language = 'en'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['build']
# If true, '()' will be appended to :func: etc. cross-reference text.
add_function_parentheses = True
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# Refer to the Python standard library.
# From: http://twistedmatrix.com/trac/ticket/4582.
intersphinx_mapping = dict(
python2=('https://docs.python.org/2', None),
python3=('https://docs.python.org/3', None),
coloredlogs=('https://coloredlogs.readthedocs.io/en/latest/', None),
)
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'nature'
| [
"[email protected]"
]
| |
5ef4c9611c9a041497ebe1c6441f572a0ea7280a | feca498db2f7819320bdc177fe6f9368fbd83ab9 | /loop_script_morph.py | e9bea5d9d9a3400aba9e49b6443b049e63d89e4a | []
| no_license | ModelDBRepository/241932 | a98155010f46f068533f746d08061540ebaf9a84 | ea9aabbbb29f086e924f837832b99cac1961e1cb | refs/heads/master | 2020-03-19T10:07:19.457538 | 2018-06-06T19:32:32 | 2018-06-06T19:32:32 | 136,344,942 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 46,194 | py | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 2 10:32:29 2015
@author: rennocosta
This script is part of the publication Renno-Costa & Tort, 2017, JNeurosci
This script relates to the data presented in the Figure 5, 6, 7 and 8
Will run a single experiment with specific parameters determined below
Output data will be saved in file direction defined at support_filename.py script
"""
import sys, argparse
import numpy as np
from numpy import *
import gzip
import pickle
import support_filename as rfn
import copy
# acessory functions
def normalize_weight(www,www_mean):
www /= np.tile(np.mean(www,axis=0),(www.shape[0],1))
return www
def learn_weight(www,activity_pre,activity_pos,lrate):
#www += lrate*(np.tile(activity_pre,(activity_pos.shape[0],1)).transpose()-www) * (np.tile(activity_pos,(activity_pre.shape[0],1)))
www += lrate*(np.tile(activity_pre,(activity_pos.shape[0],1)).transpose()) * (np.tile(activity_pos,(activity_pre.shape[0],1)))
return www
def lec_whichone(lectype,change,ccc,sss):
saida = np.zeros(lectype.shape)
saida[np.logical_and(lectype==1,change<ccc)] = 2
saida[np.logical_and(lectype==1,change>=ccc)] = 1
saida[np.logical_and(lectype==0,change<sss)] = 2
saida[np.logical_and(lectype==0,change>=sss)] = 1
saida[lectype==2] = 1
return saida
def main(argv):
parser = argparse.ArgumentParser(description='Will run a simulation instance.')
# seeds for the random number generator
# seed for the input activity pattern
parser.add_argument('seed_input', metavar='seed_input', type=int, nargs=1,
help='seed_input number')
# seed for the initial synaptic weights
parser.add_argument('seed_www', metavar='seed_www', type=int, nargs=1,
help='seed_www')
# seed for the trajectories
parser.add_argument('seed_path', metavar='seed_path', type=int, nargs=1,
help='seed_path')
# variables of the session
# number of theta cycles simulated for each position
parser.add_argument('theta_cycles', metavar='theta_cycles', type=int, nargs=1,
help='theta_cycles')
# number of times that the agent makes a full exploration of the arena for each session
parser.add_argument('arena_runs', metavar='arena_runs', type=int, nargs=1,
help='arena_runs')
# number of sessions with plasticity run before collecting data
parser.add_argument('pre_runs', metavar='pre_runs', type=int, nargs=1,
help='pre_runs')
# learning rates from place to grid cells, grid to place cells and from lec to place cells...
parser.add_argument('lrate_hpc_mec', metavar='lrate_hpc_mec', type=int, nargs=1,
help='lrate_hpc_mec')
parser.add_argument('lrate_mec_hpc', metavar='lrate_mec_hpc', type=int, nargs=1,
help='lrate_mec_hpc')
parser.add_argument('lrate_lec_hpc', metavar='lrate_lec_hpc', type=int, nargs=1,
help='lrate_lec_hpc')
# ... relative number of place cells vs recurrent grid cells ...
# sensibility of pattern completion algorithm
# relative number of grid cells vs lec cells as place cell input (0 to 100)
parser.add_argument('mec_ratio', metavar='mec_ratio', type=int, nargs=1,
help='MEC ratio (x100)')
# relative number of place cells vs recurrent grid cells as grid cell input (0 to 100)
parser.add_argument('hpc_ratio', metavar='hpc_ratio', type=int, nargs=1,
help='HPC ratio (x100)')
# relative number of place cells vs recurrent grid cells as grid cell input (0 to 100)
parser.add_argument('hpc_pcompl_th', metavar='hpc_pcompl_th', type=int, nargs=1,
help='HPC pattern completion th (x100)')
# sensibility of pattern completion algorithm
parser.add_argument('morph_per', metavar='morph_per', type=int, nargs=1,
help='morph_per')
# flag to save the activity of cells... use with caution
parser.add_argument('-a', '--activity',dest='actsave',action='store_const',default="no",const="yes")
# flags to set the place to save the files
# parser.add_argument('-w', '--windows',dest='envir',action='store_const',default="default",const="windows")
# parser.add_argument('-u', '--ufrgs',dest='envir',action='store_const',default="default",const="UFRGS")
parser.add_argument('-s', '--cluster',dest='envir',action='store_const',default="default",const="cluster")
# flag to overwrite previous simulation
parser.add_argument('-k', '--KILL',dest='tokill',action='store_const',default="no",const="yes")
# for conencted environments, not implemented
#parser.add_argument('-c', '--connected',dest='conntype',action='store_const',default="no",const="yes")
# process the arguments
args = parser.parse_args()
envir = args.envir
# conntype = args.conntype
actsave = args.actsave
tokill = args.tokill;
ct = 0
conna = False
# if(conntype=="yes"):
# conna = True
# ct = 1
# else:
# ct = 0
# conna = False
if(actsave=="yes"):
acts = True
else:
acts = False
seed_input = args.seed_input[0]
seed_www = args.seed_www[0]
seed_path = args.seed_path[0]
mec_ratio = float(args.mec_ratio[0])/100
hpc_ratio = float(args.hpc_ratio[0])/100
hpc_pcompl_th = float(args.hpc_pcompl_th[0])/100
morphing_per = float(args.morph_per[0])/100
lrate_hpc_mec = float(args.lrate_hpc_mec[0])/1000
lrate_mec_hpc = float(args.lrate_mec_hpc[0])/1000
lrate_lec_hpc = float(args.lrate_lec_hpc[0])/1000
theta_cycles = args.theta_cycles[0]
arena_runs = args.arena_runs[0]
pre_runs = args.pre_runs[0]
simulation_num = 69;
listofvalues = [ct,args.seed_input[0],args.seed_www[0],args.seed_path[0],args.theta_cycles[0],args.arena_runs[0],args.pre_runs[0],args.lrate_hpc_mec[0],args.lrate_mec_hpc[0],args.lrate_lec_hpc[0],args.mec_ratio[0],args.hpc_ratio[0],args.hpc_pcompl_th[0],args.morph_per[0]]
filenames = rfn.remappingFileNames(envir)
filenames.prepareSimulation(listofvalues,simulation_num)
if (tokill == "no"):
try:
tosee = 0;
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,0)+'z', 'rb') as ff:
tosee = tosee + 1
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,1)+'z', 'rb') as ff:
tosee = tosee + 1
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,2)+'z', 'rb') as ff:
tosee = tosee + 1
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,3)+'z', 'rb') as ff:
tosee = tosee + 1
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,4)+'z', 'rb') as ff:
tosee = tosee + 1
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,5)+'z', 'rb') as ff:
tosee = tosee + 1
print("File exist. Will exit!")
torun = 0;
except:
print("File does not existe. Will do!",flush=True)
print("... %s" % (filenames.fileRunPickle(listofvalues,simulation_num,0)))
torun = 1;
else:
print("Will do anyway!",flush=True)
torun = 0;
if(torun == 0):
sys.exit();
# %%
#
#
# MAKE ALL INITIALIZATIONS
#
#
#
#set the arena size
arena_binsize = [4,4]
context_per = 0
# set the patterns of input activity
np.random.seed(seed_input)
lec_numcells = 500
lec_activity = []
lec_activity.append(pow(np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1])),2))
lec_activity.append(pow(np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1])),2))
lec_type = np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
lec_type[lec_type>(1-context_per)] = 1
lec_type[lec_type<morphing_per] = 0
lec_type[np.logical_and(lec_type<=(1-context_per),lec_type>=morphing_per)]=2
lec_change = np.random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
# number of place cells cells
hpc_numcells = 5000
hpc_memories = []
# set the grid cells topology
mec_blocksize = [2,4,6,8,10,12,14,16]
mec_blocks = len(mec_blocksize)
mec_numcells = np.sum(np.power(mec_blocksize,2))
mec_indexlist = []
init_val = 0
for ii in arange(mec_blocks):
mec_indexlist.append((init_val+arange(pow(mec_blocksize[ii],2))).reshape((mec_blocksize[ii],mec_blocksize[ii])))
init_val = np.max(mec_indexlist[ii])+1
del(init_val)
random.uniform(0,1,(lec_numcells,arena_binsize[0],arena_binsize[1]))
# set the different trajectories
np.random.seed(seed_path)
xxx,yyy = np.meshgrid(arange(arena_binsize[0]),arange(arena_binsize[1]))
xxx = xxx.ravel()
popo = []
for ii in arange(100):
popo.append(np.random.permutation(len(xxx)))
#set the initial synaptic weights
np.random.seed(seed_www)
lec_hpc_weights_mean = 1
lec_hpc_weights = np.random.lognormal(1.0,1.0,(lec_numcells,hpc_numcells))
lec_hpc_weights[lec_hpc_weights<0] = 0
lec_hpc_weights = normalize_weight(lec_hpc_weights,lec_hpc_weights_mean)
mec_hpc_weights_mean = 1
mec_hpc_weights = np.random.lognormal(1.0,1.0,(mec_numcells,hpc_numcells))
mec_hpc_weights[mec_hpc_weights<0] = 0
mec_hpc_weights = normalize_weight(mec_hpc_weights,mec_hpc_weights_mean)
hpc_mec_weights_mean = 1
hpc_mec_weights = np.random.lognormal(1.0,1.0,(hpc_numcells,mec_numcells))
hpc_mec_weights[hpc_mec_weights<0] = 0
hpc_mec_weights = normalize_weight(hpc_mec_weights,hpc_mec_weights_mean)
# set the E%MAX
current_emax = 0.90
current_emax_plast = 0
current_lrate_hpc_mec = lrate_hpc_mec
current_lrate_mec_hpc = lrate_mec_hpc
current_lrate_lec_hpc = lrate_lec_hpc
lec_hpc_weights_mean = 1
mec_hpc_weights_mean = 1
hpc_mec_weights_mean = 1
lec_noise = 0
mec_noise = 0
hpc_noise = 0
# %%
#
#
# PRE-LEARN - SIMULATIONS WITH LEARNING BEFORE SAVING THE DATA
# - THIS PART SIMULATES THE HABITUATION OF THE ANIMAL PRIOR TO IMPLANT
#
#
lllf = [1.0,1.0]
mooo = [mec_ratio,mec_ratio]
shape_vec = [0.0,1.0]
context_vec = [0.0,0.0]
pzzz = [0,0]
for sessions in arange(pre_runs):
print("session %d of %d" % (sessions,pre_runs),flush=True)
hhhr = hpc_ratio * (sessions/(pre_runs-1))
lllf[0] = 1.0
lllf[1] = 1.0
pzzz[0] = sessions + 16
pzzz[1] = sessions + 16 + pre_runs
lec_act_vect = []
mec_act_vect = []
hpc_act_vect = []
mec_inact_vect = np.zeros((len(shape_vec),mec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_inact_vect = np.zeros((len(shape_vec),hpc_numcells,arena_binsize[0],arena_binsize[1]))
lec_inact_vect = np.zeros((len(shape_vec),lec_numcells,arena_binsize[0],arena_binsize[1]))
for ii in arange(len(shape_vec),dtype=int):
print("shape %d of %d" % (ii,len(shape_vec)),flush=True)
mec_ratio = mooo[ii]
lec_act = zeros((lec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_act = zeros((hpc_numcells,arena_binsize[0],arena_binsize[1]))
mec_act = zeros((mec_numcells,arena_binsize[0],arena_binsize[1]))
xxx,yyy = np.meshgrid(arange(arena_binsize[0]),arange(arena_binsize[0]))
xxx = xxx.ravel()
yyy = yyy.ravel()
#ppp = np.random.permutation(len(xxx))
ppp = popo[pzzz[ii]]
xxx = xxx[ppp].astype(int)
yyy = yyy[ppp].astype(int)
#
# xxx = xxx + 4
if (lllf[ii]>0):
xxxr = []
yyyr = []
for arena_runss in arange(arena_runs):
xxxr = concatenate([xxx,xxxr])
yyyr = concatenate([yyy,yyyr])
xxx = xxxr
yyy = yyyr
if((conna == True) and (ii > 0)):
current_pos = current_pos - array((4,0))
else:
current_pos = array((xxx[0],yyy[0])).astype(int)
current_hpc_activity = np.zeros(hpc_numcells)
if ((ii<1) or (conna == False)):
current_mec_activity = np.zeros(mec_numcells)
current_context = context_vec[ii]
current_shape = shape_vec[ii]
current_vector = lec_whichone(lec_type,lec_change,current_context,current_shape)
base_lec = np.zeros(current_vector.shape)
base_lec = lec_activity[0].copy()
base_lec[current_vector==2] = lec_activity[1][current_vector==2]
for pp in arange(len(xxx),dtype=int):
print("aaa %d of %d" % (pp,len(xxx)),flush=True)
current_pos_old = current_pos
current_pos = array((xxx[pp],yyy[pp])).astype(int)
current_speed = current_pos - current_pos_old
current_lec_activity = base_lec[:,current_pos[0],current_pos[1]]
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
lec_inact_vect[ii.astype(int) ,:,xxx[pp].astype(int) ,yyy[pp].astype(int) ] = current_lec_activity
for kk in arange(theta_cycles):
if (kk>0): current_speed = array((0,0))
current_mec_input = (current_mec_activity+current_mec_noise)
if (mec_ratio>0.0):
for jj in arange(mec_blocks):
gxx,gyy = meshgrid(arange(mec_blocksize[jj])+(-1)*current_speed[0],arange(mec_blocksize[jj])+(-1)*current_speed[1])
gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] = gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] + floor(mec_blocksize[jj]/2)
gxx = int0(mod(gxx,mec_blocksize[jj]))
gyy = int0(mod(gyy,mec_blocksize[jj]))
current_mec_input[mec_indexlist[jj]] = current_mec_input[mec_indexlist[jj]][gyy,gxx]
#mec_input_vect[ii,kk,mec_indexlist[jj],xxx[pp],yyy[pp]] = current_mec_input[mec_indexlist[jj]]
h_h = np.dot(current_hpc_activity+current_hpc_noise,hpc_mec_weights)
if(np.max(h_h)>0.0):
h_h = h_h/np.max(h_h)
h_h[isnan(h_h)] = 0.0
current_mec_input = (1-mec_ratio)*h_h + mec_ratio*current_mec_input
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
for jj in arange(mec_blocks):
current_mec_activity[mec_indexlist[jj]] = (current_mec_input[mec_indexlist[jj]] - current_emax*np.max(current_mec_input[mec_indexlist[jj]]))
current_mec_activity[current_mec_activity<0] = 0.0
current_mec_activity[mec_indexlist[jj]] /= np.max(current_mec_activity[mec_indexlist[jj]])
current_mec_activity[isnan(current_mec_activity)] = 0.0
mec_inact_vect[ii.astype(int) ,mec_indexlist[jj.astype(int) ].astype(int) ,xxx[pp].astype(int) ,yyy[pp].astype(int) ] = current_mec_activity[mec_indexlist[jj.astype(int) ].astype(int) ]
h_l = np.dot(current_lec_activity+current_lec_noise,lec_hpc_weights)
h_l = h_l/np.max(h_l)
h_l[isnan(h_l)] = 0.0
if(hhhr>0):
h_m = np.dot(current_mec_activity+current_mec_noise,mec_hpc_weights)
if(np.max(h_m)>0.0):
h_m = h_m/np.max(h_m)
h_m[isnan(h_m)] = 0.0
current_hpc_input = (1-hhhr)*h_l + hhhr*h_m
else:
current_hpc_input = h_l;
if (kk>0):
ddd = current_hpc_activity * 0
for mm in arange(len(hpc_memories)):
ccc = corrcoef(hpc_memories[mm],current_hpc_activity+current_hpc_noise)[0][1]
if ccc<hpc_pcompl_th:
ccc=0
else:
ddd += hpc_memories[mm]
if (np.max(ddd) > 0):
ddd = ddd/np.max(ddd)
ddd[isnan(ddd)] = 0.0
current_hpc_input = (1-mec_ratio)*current_hpc_input + mec_ratio*ddd
current_hpc_activity = (current_hpc_input - current_emax*np.max(current_hpc_input))
current_hpc_activity[current_hpc_activity<0] = 0.0
current_hpc_activity /= np.max(current_hpc_activity)
current_hpc_activity[current_hpc_activity<current_emax_plast] = 0
hpc_inact_vect[ii.astype(int),:,xxx[pp].astype(int),yyy[pp].astype(int)] = current_hpc_activity
if (lllf[ii]>0):
lec_hpc_weights = normalize_weight(learn_weight(lec_hpc_weights,current_lec_activity+current_lec_noise,current_hpc_activity+current_hpc_noise,current_lrate_lec_hpc),lec_hpc_weights_mean)
mec_hpc_weights = normalize_weight(learn_weight(mec_hpc_weights,current_mec_activity+current_mec_noise,current_hpc_activity+current_hpc_noise,current_lrate_mec_hpc),mec_hpc_weights_mean)
hpc_mec_weights = normalize_weight(learn_weight(hpc_mec_weights,current_hpc_activity+current_hpc_noise,current_mec_activity+current_mec_noise,current_lrate_hpc_mec),hpc_mec_weights_mean)
if ((lllf[ii]>0) and (hpc_pcompl_th<1.0)):
hpc_memories.append(current_hpc_activity)
lec_act[:,xxx[pp].astype(int),yyy[pp].astype(int)] = current_lec_activity
mec_act[:,xxx[pp].astype(int),yyy[pp].astype(int)] = current_mec_activity
hpc_act[:,xxx[pp].astype(int),yyy[pp].astype(int)] = current_hpc_activity
mec_act_vect.append(mec_act)
lec_act_vect.append(lec_act)
hpc_act_vect.append(hpc_act)
# %%
#
#
# PREPARE THE SIMULATION... WILL SET THE ENVIRONMENTAL VARIABLES FOR EACH SESSION OF THE PROTOCOL
# EMULATES THE MORPHING PROTOCOL
#
#
mooo = 0.9999 * np.ones((108))
hooo = hpc_ratio * np.ones((108))
shape_vec = 0.0 * np.ones((108))
context_vec = 0.0 * np.ones((108))
lllf = 0.0 * np.ones((108))
pzzz = np.concatenate((arange(1),arange(1),arange(0,16),arange(0,16),arange(0,16),arange(0,16),arange(0,21),arange(0,21),arange(0,21),arange(0,21),arange(0,21),arange(0,21)))
lllf[0] = 0.0
mooo[0] = mec_ratio
mooo[18:34] = mec_ratio
lllf[1] = 0.0
mooo[1] = mec_ratio
mooo[50:67] = mec_ratio
mooo[66:] = mec_ratio
hooo[87:108] = 0.0
shape_vec[1] = 1.0
shape_vec[34:66] = 1.0
shape_vec[66:87]=np.linspace(0.0,1.0,21)
shape_vec[87:108]=np.linspace(0.0,1.0,21)
nono = 0.0 * np.ones((108))
# %%
#
#
# RUN THE SIMULATION...
#
#
#
num_runsss = 1
corrVectMEC1 = -1* ones(num_runsss)
corrVectHPC1 = -1* ones(num_runsss)
corrVectMECGRID1 = -1* ones(num_runsss)
corrVectHPCGRID1 = -1* ones(num_runsss)
corrVectMECvsGRID1 = -1* ones(num_runsss)
corrVectMEC2 = -1* ones(num_runsss)
corrVectHPC2 = -1* ones(num_runsss)
corrVectMECGRID2 = -1* ones(num_runsss)
corrVectHPCGRID2 = -1* ones(num_runsss)
corrVectMECvsGRID2 = -1* ones(num_runsss)
corrVectMECx = -1* ones(num_runsss)
corrVectHPCx = -1* ones(num_runsss)
corrVectMECGRIDx = -1* ones(num_runsss)
corrVectHPCGRIDx = -1* ones(num_runsss)
dist_pf1 = -1*ones((num_runsss,16))
dist_pf2 = -1*ones((num_runsss,16))
pvCorrelationCurveHPC1 = -1*ones((num_runsss,21))
pvCorrelationCurveMEC1 = -1*ones((num_runsss,21))
pvCorrelationCurveHPC2 = -1*ones((num_runsss,21))
pvCorrelationCurveMEC2 = -1*ones((num_runsss,21))
pvCorrelationCurveHPC = -1*ones((num_runsss,21))
pvCorrelationCurveMEC = -1*ones((num_runsss,21))
pvCorrelationCurveHPC1Lesion = -1*ones((num_runsss,21))
pvCorrelationCurveMEC1Lesion = -1*ones((num_runsss,21))
pvCorrelationCurveHPC2Lesion = -1*ones((num_runsss,21))
pvCorrelationCurveMEC2Lesion = -1*ones((num_runsss,21))
pvCorrelationCurveHPCLesion = -1*ones((num_runsss,21))
pvCorrelationCurveMECLesion = -1*ones((num_runsss,21))
# LOOP FOR EACH PROTOCOL DEFINED EARLIER
for sessions in arange(num_runsss):
print("session %d of %d" % (sessions,num_runsss),flush=True)
pzzz[0] = sessions + 16 + pre_runs
pzzz[1] = sessions + 16 + pre_runs + num_runsss
lec_act_vect = []
mec_act_vect = []
hpc_act_vect = []
mec_inact_vect = np.zeros((len(shape_vec),mec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_inact_vect = np.zeros((len(shape_vec),hpc_numcells,arena_binsize[0],arena_binsize[1]))
lec_inact_vect = np.zeros((len(shape_vec),lec_numcells,arena_binsize[0],arena_binsize[1]))
# RUN FOR EACH SESSION
for ii in arange(len(shape_vec)):
print("shape %d of %d" % (ii,len(shape_vec)),flush=True)
mec_ratio = mooo[ii]
hpc_ratio = hooo[ii]
lec_act = zeros((lec_numcells,arena_binsize[0],arena_binsize[1]))
mec_act = zeros((mec_numcells,arena_binsize[0],arena_binsize[1]))
hpc_act = zeros((hpc_numcells,arena_binsize[0],arena_binsize[1]))
# SET TRAJECTORY
xxx,yyy = np.meshgrid(arange(arena_binsize[0]),arange(arena_binsize[0]))
xxx = xxx.ravel()
yyy = yyy.ravel()
ppp = popo[pzzz[ii]]
xxx = xxx[ppp]
yyy = yyy[ppp]
if((conna == True) and (ii == 1)):
current_pos = current_pos - array((5,0))
else:
current_pos = array((xxx[0],yyy[0]))
current_hpc_activity = np.zeros(hpc_numcells)
if ((ii!=1) or (conna == False)):
current_mec_activity = np.zeros(mec_numcells)
current_hpc_activity = np.zeros(hpc_numcells)
current_mec_activity = np.zeros(mec_numcells)
current_context = context_vec[ii]
current_shape = shape_vec[ii]
current_vector = lec_whichone(lec_type,lec_change,current_context,current_shape)
base_lec = np.zeros(current_vector.shape)
base_lec = lec_activity[0].copy()
base_lec[current_vector==2] = lec_activity[1][current_vector==2]
#set the random seed
np.random.seed(seed_path+ii)
if (nono[ii]>0.0):
ttt = floor(lec_numcells*nono[ii]);
base_lec[:ttt,:,:] = pow(np.random.uniform(0,1,(ttt,arena_binsize[0],arena_binsize[1])),2)
# RUN FOR EACH POSITION OF THE TRAJECTORY
for pp in arange(len(xxx)):
print("aaa %d of %d" % (pp,len(xxx)),flush=True)
# COMPUTE SPEED AND LEC ACTIVITY
current_pos_old = current_pos
current_pos = array((xxx[pp],yyy[pp]))
current_speed = current_pos - current_pos_old
current_lec_activity = base_lec[:,current_pos[0],current_pos[1]]
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
lec_inact_vect[ii,:,xxx[pp],yyy[pp]] = current_lec_activity
# RUN FOR EACH THETA CYCLE
for kk in arange(theta_cycles):
# SET SPEED ZERO FOR THE FIRST POSITION
if (kk>0): current_speed = array((0,0))
# COMPUTE THE RECURRENT GRID CELL ACTIVITY - TWISTED TOURUS
current_mec_input = (current_mec_activity+current_mec_noise)
if(mec_ratio>0.0):
for jj in arange(mec_blocks):
gxx,gyy = meshgrid(arange(mec_blocksize[jj])+(-1)*current_speed[0],arange(mec_blocksize[jj])+(-1)*current_speed[1])
gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] = gyy[mod(divide(gxx-mod(gxx,mec_blocksize[jj]),mec_blocksize[jj]),2)>0] + floor(mec_blocksize[jj]/2)
gxx = int0(mod(gxx,mec_blocksize[jj]))
gyy = int0(mod(gyy,mec_blocksize[jj]))
current_mec_input[mec_indexlist[jj]] = current_mec_input[mec_indexlist[jj]][gyy,gxx]
# COMPUTE INPUT TO GRID CELLS
h_h = np.dot(current_hpc_activity+current_hpc_noise,hpc_mec_weights)
h_h = h_h/np.max(h_h)
h_h[isnan(h_h)] = 0.0
current_mec_input = (1-mec_ratio)*h_h + mec_ratio*current_mec_input
current_lec_noise = np.random.uniform(0.0,lec_noise,current_lec_activity.shape)
current_mec_noise = np.random.uniform(0.0,mec_noise,current_mec_activity.shape)
current_hpc_noise = np.random.uniform(0.0,hpc_noise,current_hpc_activity.shape)
# COMPUTE e% MAX OF GRID CELLS
for jj in arange(mec_blocks):
current_mec_activity[mec_indexlist[jj]] = (current_mec_input[mec_indexlist[jj]] - current_emax*np.max(current_mec_input[mec_indexlist[jj]]))
current_mec_activity[current_mec_activity<0] = 0.0
current_mec_activity[mec_indexlist[jj]] /= np.max(current_mec_activity[mec_indexlist[jj]])
current_mec_activity[isnan(current_mec_activity)] = 0.0
mec_inact_vect[ii,mec_indexlist[jj],xxx[pp],yyy[pp]] = current_mec_activity[mec_indexlist[jj]]
# COMPUTE THE PLACE CELLS INPUT
h_l = np.dot(current_lec_activity+current_lec_noise,lec_hpc_weights)
h_l = h_l/np.max(h_l)
h_l[isnan(h_l)] = 0.0
h_m = np.dot(current_mec_activity+current_mec_noise,mec_hpc_weights)
h_m = h_m/np.max(h_m)
h_m[isnan(h_m)] = 0.0
current_hpc_input = (1-hpc_ratio)*h_l + hpc_ratio*h_m
# THE PATTERN COMPLETION ALGORITHM
if (kk>0):
ddd = current_hpc_activity * 0
for mm in arange(len(hpc_memories)):
ccc = corrcoef(hpc_memories[mm],current_hpc_activity+current_hpc_noise)[0][1]
if ccc<hpc_pcompl_th:
ccc=0
else:
ddd += hpc_memories[mm]
if (np.max(ddd) > 0):
ddd = ddd/np.max(ddd)
ddd[isnan(ddd)] = 0.0
current_hpc_input = (1-mec_ratio)*current_hpc_input + mec_ratio*ddd
# COMPUTE THE e% MAX OF PLACE CELLS
current_hpc_activity = (current_hpc_input - current_emax*np.max(current_hpc_input))
current_hpc_activity[current_hpc_activity<0] = 0.0
current_hpc_activity /= np.max(current_hpc_activity)
current_hpc_activity[current_hpc_activity<current_emax_plast] = 0
hpc_inact_vect[ii,:,xxx[pp],yyy[pp]] = current_hpc_activity
# IF LEARNING IS SET, UPDATE THE WEIGHTS
if (lllf[ii]>0):
lec_hpc_weights = normalize_weight(learn_weight(lec_hpc_weights,current_lec_activity+current_lec_noise,current_hpc_activity+current_hpc_noise,current_lrate_lec_hpc),lec_hpc_weights_mean)
mec_hpc_weights = normalize_weight(learn_weight(mec_hpc_weights,current_mec_activity+current_mec_noise,current_hpc_activity+current_hpc_noise,current_lrate_mec_hpc),mec_hpc_weights_mean)
hpc_mec_weights = normalize_weight(learn_weight(hpc_mec_weights,current_hpc_activity+current_hpc_noise,current_mec_activity+current_mec_noise,current_lrate_hpc_mec),hpc_mec_weights_mean)
# SAVE PATTERN COMPLETION PATTERN
if ((lllf[ii]>0) and (hpc_pcompl_th<1.0)):
hpc_memories.append(current_hpc_activity)
lec_act[:,xxx[pp],yyy[pp]] = current_lec_activity
mec_act[:,xxx[pp],yyy[pp]] = current_mec_activity
hpc_act[:,xxx[pp],yyy[pp]] = current_hpc_activity
mec_act_vect.append(mec_act)
lec_act_vect.append(lec_act)
hpc_act_vect.append(hpc_act)
# COLLECT THE STATISTICS AND SAVE
ooo1a = np.zeros((16,16))
ooo2a = np.zeros((16,16))
ooo3a = np.zeros((16,16))
ooo4a = np.zeros((16,16))
ooo5a = np.zeros((16))
ooo1b = np.zeros((16,16))
ooo2b = np.zeros((16,16))
ooo3b = np.zeros((16,16))
ooo4b = np.zeros((16,16))
ooo5b = np.zeros((16))
ooo1c = np.zeros((16,16))
ooo2c = np.zeros((16,16))
ooo3c = np.zeros((16,16))
ooo4c = np.zeros((16,16))
pfdist1 = np.zeros((16))
pfdist2 = np.zeros((16))
for xx in arange(16):
pfdist1 = pfdist1 + np.histogram(np.sum(np.sum(hpc_inact_vect[xx+18,:,:,:]>0,axis=1),axis=1),arange(17))[0]
pfdist2 = pfdist2 + np.histogram(np.sum(np.sum(hpc_inact_vect[xx+50,:,:,:]>0,axis=1),axis=1),arange(17))[0]
vvv5a = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv5b = np.zeros((arena_binsize[0],arena_binsize[1]))
for ii in arange(arena_binsize[0]):
for jj in arange(arena_binsize[1]):
vvv5a[ii,jj] = np.corrcoef(mec_inact_vect[xx+2,:,ii,jj],mec_inact_vect[xx+18,:,ii,jj])[0,1]
vvv5b[ii,jj] = np.corrcoef(mec_inact_vect[xx+34,:,ii,jj],mec_inact_vect[xx+50,:,ii,jj])[0,1]
ooo5a[xx] = np.mean(vvv5a)
ooo5b[xx] = np.mean(vvv5b)
for yy in arange(xx,16):
vvv1a = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv2a = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv3a = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv4a = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv1b = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv2b = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv3b = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv4b = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv1c = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv2c = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv3c = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv4c = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv1d = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv2d = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv3d = np.zeros((arena_binsize[0],arena_binsize[1]))
vvv4d = np.zeros((arena_binsize[0],arena_binsize[1]))
for ii in arange(arena_binsize[0]):
for jj in arange(arena_binsize[1]):
vvv1a[ii,jj] = np.corrcoef(mec_inact_vect[xx+2,:,ii,jj],mec_inact_vect[yy+2,:,ii,jj])[0,1]
vvv2a[ii,jj] = np.corrcoef(hpc_inact_vect[xx+2,:,ii,jj],hpc_inact_vect[yy+2,:,ii,jj])[0,1]
vvv3a[ii,jj] = np.corrcoef(mec_inact_vect[xx+18,:,ii,jj],mec_inact_vect[yy+18,:,ii,jj])[0,1]
vvv4a[ii,jj] = np.corrcoef(hpc_inact_vect[xx+18,:,ii,jj],hpc_inact_vect[yy+18,:,ii,jj])[0,1]
vvv1b[ii,jj] = np.corrcoef(mec_inact_vect[xx+34,:,ii,jj],mec_inact_vect[yy+34,:,ii,jj])[0,1]
vvv2b[ii,jj] = np.corrcoef(hpc_inact_vect[xx+34,:,ii,jj],hpc_inact_vect[yy+34,:,ii,jj])[0,1]
vvv3b[ii,jj] = np.corrcoef(mec_inact_vect[xx+50,:,ii,jj],mec_inact_vect[yy+50,:,ii,jj])[0,1]
vvv4b[ii,jj] = np.corrcoef(hpc_inact_vect[xx+50,:,ii,jj],hpc_inact_vect[yy+50,:,ii,jj])[0,1]
vvv1c[ii,jj] = np.corrcoef(mec_inact_vect[xx+2,:,ii,jj],mec_inact_vect[yy+34,:,ii,jj])[0,1]
vvv2c[ii,jj] = np.corrcoef(hpc_inact_vect[xx+2,:,ii,jj],hpc_inact_vect[yy+34,:,ii,jj])[0,1]
vvv3c[ii,jj] = np.corrcoef(mec_inact_vect[xx+18,:,ii,jj],mec_inact_vect[yy+50,:,ii,jj])[0,1]
vvv4c[ii,jj] = np.corrcoef(hpc_inact_vect[xx+18,:,ii,jj],hpc_inact_vect[yy+50,:,ii,jj])[0,1]
vvv1d[ii,jj] = np.corrcoef(mec_inact_vect[xx+34,:,ii,jj],mec_inact_vect[yy+2,:,ii,jj])[0,1]
vvv2d[ii,jj] = np.corrcoef(hpc_inact_vect[xx+34,:,ii,jj],hpc_inact_vect[yy+2,:,ii,jj])[0,1]
vvv3d[ii,jj] = np.corrcoef(mec_inact_vect[xx+50,:,ii,jj],mec_inact_vect[yy+18,:,ii,jj])[0,1]
vvv4d[ii,jj] = np.corrcoef(hpc_inact_vect[xx+50,:,ii,jj],hpc_inact_vect[yy+18,:,ii,jj])[0,1]
ooo1a[xx,yy] = np.mean(vvv1a)
ooo1a[yy,xx] = np.mean(vvv1a)
ooo2a[xx,yy] = np.mean(vvv2a)
ooo2a[yy,xx] = np.mean(vvv2a)
ooo3a[xx,yy] = np.mean(vvv3a)
ooo3a[yy,xx] = np.mean(vvv3a)
ooo4a[xx,yy] = np.mean(vvv4a)
ooo4a[yy,xx] = np.mean(vvv4a)
ooo1b[xx,yy] = np.mean(vvv1b)
ooo1b[yy,xx] = np.mean(vvv1b)
ooo2b[xx,yy] = np.mean(vvv2b)
ooo2b[yy,xx] = np.mean(vvv2b)
ooo3b[xx,yy] = np.mean(vvv3b)
ooo3b[yy,xx] = np.mean(vvv3b)
ooo4b[xx,yy] = np.mean(vvv4b)
ooo4b[yy,xx] = np.mean(vvv4b)
ooo1c[xx,yy] = np.mean(vvv1c)
ooo1c[yy,xx] = np.mean(vvv1d)
ooo2c[xx,yy] = np.mean(vvv2c)
ooo2c[yy,xx] = np.mean(vvv2d)
ooo3c[xx,yy] = np.mean(vvv3c)
ooo3c[yy,xx] = np.mean(vvv3d)
ooo4c[xx,yy] = np.mean(vvv4c)
ooo4c[yy,xx] = np.mean(vvv4d)
corrVectMECGRID1[sessions] = np.mean(ooo1a)
corrVectHPCGRID1[sessions] = np.mean(ooo2a)
corrVectMEC1[sessions] = np.mean(ooo3a)
corrVectHPC1[sessions] = np.mean(ooo4a)
corrVectMECvsGRID1[sessions] = np.mean(ooo5a)
corrVectMECGRID2[sessions] = np.mean(ooo1b)
corrVectHPCGRID2[sessions] = np.mean(ooo2b)
corrVectMEC2[sessions] = np.mean(ooo3b)
corrVectHPC2[sessions] = np.mean(ooo4b)
corrVectMECvsGRID2[sessions] = np.mean(ooo5b)
corrVectMECGRIDx[sessions] = np.mean(ooo1c)
corrVectHPCGRIDx[sessions] = np.mean(ooo2c)
corrVectMECx[sessions] = np.mean(ooo3c)
corrVectHPCx[sessions] = np.mean(ooo4c)
dist_pf1[sessions,:] = pfdist1
dist_pf2[sessions,:] = pfdist2
for xx in arange(21):
ooo1a = np.zeros(arena_binsize)
ooo2a = np.zeros(arena_binsize)
ooo1b = np.zeros(arena_binsize)
ooo2b = np.zeros(arena_binsize)
ooo3a = np.zeros(arena_binsize)
ooo3b = np.zeros(arena_binsize)
for ii in arange(arena_binsize[0]):
for jj in arange(arena_binsize[1]):
ooo1a[ii,jj] = np.corrcoef(hpc_inact_vect[66,:,ii,jj],hpc_inact_vect[xx+66,:,ii,jj])[0,1]
ooo1b[ii,jj] = np.corrcoef(mec_inact_vect[66,:,ii,jj],mec_inact_vect[xx+66,:,ii,jj])[0,1]
ooo2a[ii,jj] = np.corrcoef(hpc_inact_vect[86,:,ii,jj],hpc_inact_vect[86-xx,:,ii,jj])[0,1]
ooo2b[ii,jj] = np.corrcoef(mec_inact_vect[86,:,ii,jj],mec_inact_vect[86-xx,:,ii,jj])[0,1]
ooo3a[ii,jj] = np.mean((ooo1a[ii,jj],ooo2a[ii,jj]))
ooo3b[ii,jj] = np.mean((ooo1b[ii,jj],ooo2b[ii,jj]))
pvCorrelationCurveHPC1[sessions,xx] = np.mean(ooo1a)
pvCorrelationCurveMEC1[sessions,xx] = np.mean(ooo1b)
pvCorrelationCurveHPC2[sessions,xx] = np.mean(ooo2a)
pvCorrelationCurveMEC2[sessions,xx] = np.mean(ooo2b)
pvCorrelationCurveHPC[sessions,xx] = np.mean(ooo3a)
pvCorrelationCurveMEC[sessions,xx] = np.mean(ooo3b)
if (acts==True):
actvLec1 = lec_inact_vect[66,0:100,:,:]
actvLec2 = lec_inact_vect[86,0:100,:,:]
actvMec1 = mec_inact_vect[66,0:100,:,:]
actvMec2 = mec_inact_vect[86,0:100,:,:]
actvHpc1 = hpc_inact_vect[66,:,:,:]
actvHpc2 = hpc_inact_vect[86,:,:,:]
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,9)+'z', 'wb') as ff:
pickle.dump([actvLec1,actvLec2,actvMec1,actvMec2,actvHpc1,actvHpc2] , ff)
for xx in arange(21):
ooo1a = np.zeros(arena_binsize)
ooo2a = np.zeros(arena_binsize)
ooo1b = np.zeros(arena_binsize)
ooo2b = np.zeros(arena_binsize)
ooo3a = np.zeros(arena_binsize)
ooo3b = np.zeros(arena_binsize)
for ii in arange(arena_binsize[0]):
for jj in arange(arena_binsize[1]):
ooo1a[ii,jj] = np.corrcoef(hpc_inact_vect[87,:,ii,jj],hpc_inact_vect[xx+87,:,ii,jj])[0,1]
ooo1b[ii,jj] = np.corrcoef(mec_inact_vect[87,:,ii,jj],mec_inact_vect[xx+87,:,ii,jj])[0,1]
ooo2a[ii,jj] = np.corrcoef(hpc_inact_vect[107,:,ii,jj],hpc_inact_vect[107-xx,:,ii,jj])[0,1]
ooo2b[ii,jj] = np.corrcoef(mec_inact_vect[107,:,ii,jj],mec_inact_vect[107-xx,:,ii,jj])[0,1]
ooo3a[ii,jj] = np.mean((ooo1a[ii,jj],ooo2a[ii,jj]))
ooo3b[ii,jj] = np.mean((ooo1b[ii,jj],ooo2b[ii,jj]))
pvCorrelationCurveHPC1Lesion[sessions,xx] = np.mean(ooo1a)
pvCorrelationCurveMEC1Lesion[sessions,xx] = np.mean(ooo1b)
pvCorrelationCurveHPC2Lesion[sessions,xx] = np.mean(ooo2a)
pvCorrelationCurveMEC2Lesion[sessions,xx] = np.mean(ooo2b)
pvCorrelationCurveHPCLesion[sessions,xx] = np.mean(ooo3a)
pvCorrelationCurveMECLesion[sessions,xx] = np.mean(ooo3b)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,0)+'z', 'wb') as ff:
pickle.dump([corrVectMECGRID1,corrVectHPCGRID1,corrVectMEC1,corrVectHPC1,corrVectMECvsGRID1] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,1)+'z', 'wb') as ff:
pickle.dump([corrVectMECGRID2,corrVectHPCGRID2,corrVectMEC2,corrVectHPC2,corrVectMECvsGRID2] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,2)+'z', 'wb') as ff:
pickle.dump([corrVectMECGRIDx,corrVectHPCGRIDx,corrVectMECx,corrVectHPCx] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,3)+'z', 'wb') as ff:
pickle.dump([dist_pf1,dist_pf2] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,4)+'z', 'wb') as ff:
pickle.dump([pvCorrelationCurveHPC,pvCorrelationCurveHPC1,pvCorrelationCurveHPC2,pvCorrelationCurveMEC,pvCorrelationCurveMEC1,pvCorrelationCurveMEC2] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,5)+'z', 'wb') as ff:
pickle.dump([pvCorrelationCurveHPCLesion,pvCorrelationCurveHPC1Lesion,pvCorrelationCurveHPC2Lesion,pvCorrelationCurveMECLesion,pvCorrelationCurveMEC1Lesion,pvCorrelationCurveMEC2Lesion] , ff)
if(sessions>5):
if(np.max(np.abs(np.diff(corrVectMECGRID1[(sessions)-3:sessions])))==0.0):
if(np.max(np.abs(np.diff(corrVectMECGRID2[(sessions)-3:sessions])))==0.0):
if(np.max(np.abs(np.diff(corrVectMECGRIDx[(sessions)-3:sessions])))==0.0):
corrVectMECGRID1[(sessions+1):] = -2
corrVectHPCGRID1[(sessions+1):] = -2
corrVectMEC1[(sessions+1):] = -2
corrVectHPC1[(sessions+1):] = -2
corrVectMECvsGRID1[(sessions+1):] = -2
corrVectMECGRID2[(sessions+1):] = -2
corrVectHPCGRID2[(sessions+1):] = -2
corrVectMEC2[(sessions+1):] = -2
corrVectHPC2[(sessions+1):] = -2
corrVectMECvsGRID2[(sessions+1):] = -2
corrVectMECGRIDx[(sessions+1):] = -2
corrVectHPCGRIDx[(sessions+1):] = -2
corrVectMECx[(sessions+1):] = -2
corrVectHPCx[(sessions+1):] = -2
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,0)+'z', 'wb') as ff:
pickle.dump([corrVectMECGRID1,corrVectHPCGRID1,corrVectMEC1,corrVectHPC1,corrVectMECvsGRID1] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,1)+'z', 'wb') as ff:
pickle.dump([corrVectMECGRID2,corrVectHPCGRID2,corrVectMEC2,corrVectHPC2,corrVectMECvsGRID2] , ff)
with gzip.open(filenames.fileRunPickle(listofvalues,simulation_num,2)+'z', 'wb') as ff:
pickle.dump([corrVectMECGRIDx,corrVectHPCGRIDx,corrVectMECx,corrVectHPCx] , ff)
return
if __name__ == "__main__":
main(sys.argv[1:])
| [
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]
| |
cb7af099f940f5ba112e7f53d43e231bfca9550a | 3b9b4049a8e7d38b49e07bb752780b2f1d792851 | /src/third_party/catapult/tracing/bin/run_py_tests | d5cf781888af6fb4e715bae9e5e21c30164b2f3a | [
"BSD-3-Clause",
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0"
]
| permissive | webosce/chromium53 | f8e745e91363586aee9620c609aacf15b3261540 | 9171447efcf0bb393d41d1dc877c7c13c46d8e38 | refs/heads/webosce | 2020-03-26T23:08:14.416858 | 2018-08-23T08:35:17 | 2018-09-20T14:25:18 | 145,513,343 | 0 | 2 | Apache-2.0 | 2019-08-21T22:44:55 | 2018-08-21T05:52:31 | null | UTF-8 | Python | false | false | 1,115 | #!/usr/bin/env python
# Copyright (c) 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import os
import platform
import sys
_CATAPULT_PATH = os.path.abspath(os.path.join(
os.path.dirname(__file__), os.path.pardir, os.path.pardir))
_TRACING_PATH = os.path.join(_CATAPULT_PATH, 'tracing')
def _RunTestsOrDie(top_level_dir):
exit_code = run_with_typ.Run(top_level_dir, path=[_TRACING_PATH])
if exit_code:
sys.exit(exit_code)
def _AddToPathIfNeeded(path):
if path not in sys.path:
sys.path.insert(0, path)
if __name__ == '__main__':
_AddToPathIfNeeded(_CATAPULT_PATH)
from hooks import install
if '--no-install-hooks' in sys.argv:
sys.argv.remove('--no-install-hooks')
else:
install.InstallHooks()
from catapult_build import run_with_typ
# https://github.com/catapult-project/catapult/issues/2050
if platform.system() != 'Windows':
_RunTestsOrDie(os.path.join(_TRACING_PATH, 'tracing'))
_RunTestsOrDie(os.path.join(_TRACING_PATH, 'tracing_build'))
sys.exit(0)
| [
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]
| ||
ad40c75916ec2d3d84483c9477a39ee50804f258 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03089/s954098144.py | 4781af4d76b7517d6d52dd675494067d64a378b7 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 272 | py | n = int(input())
B = [int(i) for i in input().split()]
ans = []
while B:
L = []
for i in range(len(B)):
if B[i] == i+1:
L.append(B[i])
if L:
ans.append(L[-1])
B.pop(L[-1]-1)
else:
print(-1)
exit()
for i in ans[::-1]:
print(i) | [
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]
| |
75ca957fcc6b0ca47831ce5f73d697ab54cb8436 | 1d0a4750e216f301ec49a247bf7bf07cd61fa29f | /app/views/commuter/company_commuter_plan_view.py | ec9cd4dcec20664a89e39d135f1290141e2b2699 | []
| no_license | smoothbenefits/BenefitMY_Python | 52745a11db2cc9ab394c8de7954974e6d5a05e13 | b7e8474a728bc22778fd24fe88d1918945a8cfc8 | refs/heads/master | 2021-03-27T15:57:34.798289 | 2018-04-29T19:04:04 | 2018-04-29T19:04:04 | 24,351,568 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,178 | py | from rest_framework.views import APIView
from django.http import Http404
from rest_framework.response import Response
from rest_framework import status
from app.models.commuter.company_commuter_plan import CompanyCommuterPlan
from app.serializers.commuter.company_commuter_plan_serializer import (
CompanyCommuterPlanSerializer,
CompanyCommuterPlanPostSerializer)
class CompanyCommuterPlanView(APIView):
def _get_object(self, pk):
try:
return CompanyCommuterPlan.objects.get(pk=pk)
except CompanyCommuterPlan.DoesNotExist:
raise Http404
def get(self, request, pk, format=None):
plan = self._get_object(pk)
serializer = CompanyCommuterPlanSerializer(plan)
return Response(serializer.data)
def delete(self, request, pk, format=None):
plan = self._get_object(pk)
plan.delete()
return Response(status=status.HTTP_204_NO_CONTENT)
def put(self, request, pk, format=None):
plan = self._get_object(pk)
serializer = CompanyCommuterPlanSerializer(plan, data=request.DATA)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
def post(self, request, pk, format=None):
serializer = CompanyCommuterPlanPostSerializer(data=request.DATA)
if serializer.is_valid():
serializer.save()
response_serializer = CompanyCommuterPlanSerializer(serializer.object)
return Response(response_serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class CompanyCommuterPlanByCompanyView(APIView):
def _get_object(self, company_id):
try:
return CompanyCommuterPlan.objects.filter(company=company_id)
except CompanyCommuterPlan.DoesNotExist:
raise Http404
def get(self, request, company_id, format=None):
plans = self._get_object(company_id)
serializer = CompanyCommuterPlanSerializer(plans, many=True)
return Response(serializer.data)
| [
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]
| |
a4b03be3990b0a990a9b9b5921833e1949890b55 | 27b4d1b7723845812111a0c6c659ef87c8da2755 | /PythonCookBook/1_数据结构和算法/查找最大或者最小的N个元素列表/03.py | de7b91bd9c47a32638d00d17ac4d93dab161ccb6 | []
| no_license | NAMEs/Python_Note | 59a6eff7b4287aaef04bd69fbd4af3faf56cccb4 | f560e00af37c4f22546abc4c2756e7037adcc40c | refs/heads/master | 2022-04-11T09:32:17.512962 | 2020-03-17T09:30:58 | 2020-03-17T09:30:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,882 | py | '''
如果你想在一个集合中查找最小或最大的 N 个元素，并且 N 小于集合元素数量，
那么这些函数提供了很好的性能。因为在底层实现里面，首先会先将集合数据进行堆排
序后放入一个列表中
堆数据结构最重要的特征是 heap[0] 永远是最小的元素。并且剩余的元素可以很
容易的通过调用 heapq.heappop() 方法得到，该方法会先将第一个元素弹出来，然后
用下一个最小的元素来取代被弹出元素（这种操作时间复杂度仅仅是 O(log N)，N 是
堆大小）。比如，如果想要查找最小的 3 个元素，你可以这样做：
当要查找的元素个数相对比较小的时候，函数 nlargest() 和 nsmallest() 是很
合适的。如果你仅仅想查找唯一的最小或最大（N=1）的元素的话，那么使用 min() 和
max() 函数会更快些。类似的，如果 N 的大小和集合大小接近的时候，通常先排序这个
集合然后再使用切片操作会更快点（sorted(items)[:N] 或者是 sorted(items)[-N:]
）。需要在正确场合使用函数 nlargest() 和 nsmallest() 才能发挥它们的优势（如果
N 快接近集合大小了，那么使用排序操作会更好些）。
尽管你没有必要一定使用这里的方法，但是堆数据结构的实现是一个很有趣并且
值得你深入学习的东西。基本上只要是数据结构和算法书籍里面都会有提及到。heapq
模块的官方文档里面也详细的介绍了堆数据结构底层的实现细节。
'''
import heapq
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
print("nums:",nums)
heap = list(nums)
print("heap:",heap)
# 堆排序
heapq.heapify(heap)
print("heap:",heap)
for i in range(1,len(heap)+1):
num = heapq.heappop(heap)
print("{0} --- {1}".format(i,num))
print(heap) | [
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]
| |
c22b288eeec61c012ac1e9fb29b0cd92193615b1 | 1adc05008f0caa9a81cc4fc3a737fcbcebb68995 | /hardhat/recipes/libsigc++.py | bd7663119f55fe29bf5236253c373c8e8888cf25 | [
"MIT",
"BSD-3-Clause"
]
| permissive | stangelandcl/hardhat | 4aa995518697d19b179c64751108963fa656cfca | 1ad0c5dec16728c0243023acb9594f435ef18f9c | refs/heads/master | 2021-01-11T17:19:41.988477 | 2019-03-22T22:18:44 | 2019-03-22T22:18:52 | 79,742,340 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 541 | py | from .base import GnuRecipe
class LibSigCppRecipe(GnuRecipe):
def __init__(self, *args, **kwargs):
super(LibSigCppRecipe, self).__init__(*args, **kwargs)
self.sha256 = '774980d027c52947cb9ee4fac6ffe2ca' \
'60cc2f753068a89dfd281c83dbff9651'
self.name = 'libsigc++'
self.version = '2.8.0'
short_version = '.'.join(self.version.split('.')[:2])
self.url = 'http://ftp.gnome.org/pub/gnome/sources/$name/' \
'%s/$name-$version.tar.xz' % short_version
| [
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]
| |
bdaca89a365a3445264646da386645b9b5fad002 | 03a2c1eb549a66cc0cff72857963eccb0a56031d | /acmicpc/14427.py | 133779d5c5f1c126eba92eb72a510a05dea57127 | []
| no_license | nobe0716/problem_solving | c56e24564dbe3a8b7093fb37cd60c9e0b25f8e59 | cd43dc1eddb49d6b5965419e36db708c300dadf5 | refs/heads/master | 2023-01-21T14:05:54.170065 | 2023-01-15T16:36:30 | 2023-01-15T16:36:30 | 80,906,041 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,103 | py | import math
import sys
input = sys.stdin.readline
_DEFAULT = (10 ** 9 + 1, 1)
_GET = lambda x, y: min(x, y)
_SET = lambda i, v: (v, i)
n = int(input())
a = [int(x) for x in input().split()]
a = [_SET(i, v) for i, v in enumerate(a, start=1)]
m = int(input())
BASE = 2 ** math.ceil(math.log(n, 2))
st = [_DEFAULT] * BASE * 2
st[BASE:BASE + n] = a
for i in range(BASE - 1, 0, -1):
st[i] = _GET(st[i * 2], st[i * 2 + 1])
def get(lo: int, hi: int) -> int:
lo += BASE - 1
hi += BASE - 1
v = _DEFAULT
while lo < hi:
if lo % 2 == 1:
v = _GET(v, st[lo])
lo += 1
if hi % 2 == 0:
v = _GET(v, st[hi])
hi -= 1
lo //= 2
hi //= 2
if lo == hi:
v = _GET(v, st[lo])
return v
def set(i: int, v: int):
st[BASE + i - 1] = _SET(i, v)
i = (i + BASE - 1) // 2
while i > 0:
st[i] = _GET(st[i * 2], st[i * 2 + 1])
i //= 2
for _ in range(m):
line = list(map(int, input().split()))
if line[0] == 2:
print(get(1, n)[1])
else:
set(line[1], line[2])
| [
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]
| |
3e0fc28c46e9bd40233e17d0b10f99cee105f0c6 | b2ed893d04f04eeaf7209187133de7431c476a96 | /icc/merge_data.py | 7e76b3267853a20f5be07f2f3caa9dc3cd1a9150 | []
| no_license | liruikaiyao/workshop | 4b5221259f59ad504d87d73c31f5fa0e58d4a1f0 | 6dbde74e35ef02f5e92c76dcdd1909f1d0afb89e | refs/heads/master | 2021-01-17T16:09:13.248109 | 2015-08-05T09:43:21 | 2015-08-05T09:43:21 | 23,420,887 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 290 | py | #coding:utf-8
from config.db import bda
all_cluster=bda['all_cluster']
db_list=bda.collection_names()
a=db_list
db_list=[]
for elem in a:
if len(elem)==32:
db_list.append(elem)
for elem in db_list:
db=bda[elem]
for item in db.find():
all_cluster.insert(item)
| [
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]
| |
aa63d3f03980b5759d81dab4f148f013d82a0cab | f62fd455e593a7ad203a5c268e23129473d968b6 | /python-watcher-1.0.1/watcher/tests/decision_engine/model/faker_cluster_and_metrics.py | e0664158a07fed9442d6ba6ed109f10802e82eff | [
"Apache-2.0",
"CC-BY-3.0"
]
| permissive | MinbinGong/OpenStack-Ocata | 5d17bcd47a46d48ff9e71e2055f667836174242f | 8b7650128cfd2fdf5d6c8bc4613ac2e396fb2fb3 | refs/heads/master | 2021-06-23T05:24:37.799927 | 2017-08-14T04:33:05 | 2017-08-14T04:33:05 | 99,709,985 | 0 | 2 | null | 2020-07-22T22:06:22 | 2017-08-08T15:48:44 | Python | UTF-8 | Python | false | false | 5,643 | py | # -*- encoding: utf-8 -*-
#
# Authors: Vojtech CIMA <[email protected]>
# Bruno GRAZIOLI <[email protected]>
# Sean MURPHY <[email protected]>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import mock
from watcher.decision_engine.model.collector import base
from watcher.decision_engine.model import model_root as modelroot
class FakerModelCollector(base.BaseClusterDataModelCollector):
def __init__(self, config=None, osc=None):
if config is None:
config = mock.Mock()
super(FakerModelCollector, self).__init__(config)
@property
def notification_endpoints(self):
return []
def execute(self):
return self.generate_scenario_1()
def load_data(self, filename):
cwd = os.path.abspath(os.path.dirname(__file__))
data_folder = os.path.join(cwd, "data")
with open(os.path.join(data_folder, filename), 'rb') as xml_file:
xml_data = xml_file.read()
return xml_data
def load_model(self, filename):
return modelroot.ModelRoot.from_xml(self.load_data(filename))
def generate_scenario_1(self):
"""Simulates cluster with 2 nodes and 2 instances using 1:1 mapping"""
return self.load_model('scenario_1_with_metrics.xml')
def generate_scenario_2(self):
"""Simulates a cluster
With 4 nodes and 6 instances all mapped to a single node
"""
return self.load_model('scenario_2_with_metrics.xml')
def generate_scenario_3(self):
"""Simulates a cluster
With 4 nodes and 6 instances all mapped to one node
"""
return self.load_model('scenario_3_with_metrics.xml')
def generate_scenario_4(self):
"""Simulates a cluster
With 4 nodes and 6 instances spread on all nodes
"""
return self.load_model('scenario_4_with_metrics.xml')
class FakeCeilometerMetrics(object):
def __init__(self, model):
self.model = model
def mock_get_statistics(self, resource_id, meter_name, period=3600,
aggregate='avg'):
if meter_name == "compute.node.cpu.percent":
return self.get_node_cpu_util(resource_id)
elif meter_name == "cpu_util":
return self.get_instance_cpu_util(resource_id)
elif meter_name == "memory.usage":
return self.get_instance_ram_util(resource_id)
elif meter_name == "disk.root.size":
return self.get_instance_disk_root_size(resource_id)
def get_node_cpu_util(self, r_id):
"""Calculates node utilization dynamicaly.
node CPU utilization should consider
and corelate with actual instance-node mappings
provided within a cluster model.
Returns relative node CPU utilization <0, 100>.
:param r_id: resource id
"""
node_uuid = '%s_%s' % (r_id.split('_')[0], r_id.split('_')[1])
node = self.model.get_node_by_uuid(node_uuid)
instances = self.model.get_node_instances(node)
util_sum = 0.0
for instance_uuid in instances:
instance = self.model.get_instance_by_uuid(instance_uuid)
total_cpu_util = instance.vcpus * self.get_instance_cpu_util(
instance.uuid)
util_sum += total_cpu_util / 100.0
util_sum /= node.vcpus
return util_sum * 100.0
@staticmethod
def get_instance_cpu_util(r_id):
instance_cpu_util = dict()
instance_cpu_util['INSTANCE_0'] = 10
instance_cpu_util['INSTANCE_1'] = 30
instance_cpu_util['INSTANCE_2'] = 60
instance_cpu_util['INSTANCE_3'] = 20
instance_cpu_util['INSTANCE_4'] = 40
instance_cpu_util['INSTANCE_5'] = 50
instance_cpu_util['INSTANCE_6'] = 100
instance_cpu_util['INSTANCE_7'] = 100
instance_cpu_util['INSTANCE_8'] = 100
instance_cpu_util['INSTANCE_9'] = 100
return instance_cpu_util[str(r_id)]
@staticmethod
def get_instance_ram_util(r_id):
instance_ram_util = dict()
instance_ram_util['INSTANCE_0'] = 1
instance_ram_util['INSTANCE_1'] = 2
instance_ram_util['INSTANCE_2'] = 4
instance_ram_util['INSTANCE_3'] = 8
instance_ram_util['INSTANCE_4'] = 3
instance_ram_util['INSTANCE_5'] = 2
instance_ram_util['INSTANCE_6'] = 1
instance_ram_util['INSTANCE_7'] = 2
instance_ram_util['INSTANCE_8'] = 4
instance_ram_util['INSTANCE_9'] = 8
return instance_ram_util[str(r_id)]
@staticmethod
def get_instance_disk_root_size(r_id):
instance_disk_util = dict()
instance_disk_util['INSTANCE_0'] = 10
instance_disk_util['INSTANCE_1'] = 15
instance_disk_util['INSTANCE_2'] = 30
instance_disk_util['INSTANCE_3'] = 35
instance_disk_util['INSTANCE_4'] = 20
instance_disk_util['INSTANCE_5'] = 25
instance_disk_util['INSTANCE_6'] = 25
instance_disk_util['INSTANCE_7'] = 25
instance_disk_util['INSTANCE_8'] = 25
instance_disk_util['INSTANCE_9'] = 25
return instance_disk_util[str(r_id)]
| [
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]
| |
75508448eb04949efc0a5950f4ce7749c1dfc7fe | 2b16a66bfc186b52ed585081ae987e97cab8223b | /script/document_classification/import_lr_wiki_classification_result.py | d1509efa93e4dbe0e8ffc75c8f82eb869f157495 | []
| no_license | OldPickles/SKnowledgeGraph | d334000c7a41dd5014fd59154bbe070fcc754e4c | 6d131ad6bf3a09a5ce6461fa03690117d703c9e8 | refs/heads/master | 2022-01-09T11:27:00.043712 | 2019-06-06T07:57:06 | 2019-06-06T07:57:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 259 | py | from db_importer.wiki_classification_result import WikiClassificationResultDBImporter
if __name__ == "__main__":
importer = WikiClassificationResultDBImporter()
importer.import_lr_wiki_classification_result(result_json_file_name="lr_result.v2.json")
| [
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]
| |
7ec0f70abd775cb479257cc103252641eda0f42f | 5b93930ce8280b3cbc7d6b955df0bfc5504ee99c | /nodes/Ramsundar18TensorFlow/D_Chapter3/A_MathematicalReview/B_LossFunctions/index.py | 97543b4baacbc2c725c33517fa3f7795429b1173 | []
| no_license | nimra/module_gen | 8749c8d29beb700cac57132232861eba4eb82331 | 2e0a4452548af4fefd4cb30ab9d08d7662122cf4 | refs/heads/master | 2022-03-04T09:35:12.443651 | 2019-10-26T04:40:49 | 2019-10-26T04:40:49 | 213,980,247 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 15,909 | py | # Lawrence McAfee
# ~~~~~~~~ import ~~~~~~~~
from modules.node.HierNode import HierNode
from modules.node.LeafNode import LeafNode
from modules.node.Stage import Stage
from modules.node.block.CodeBlock import CodeBlock as cbk
from modules.node.block.ImageBlock import ImageBlock as ibk
from modules.node.block.MarkdownBlock import MarkdownBlock as mbk
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The key advantage of differentiable functions is that we can use the slope of the func‐
# tion at a particular point as a guide to find places where the function is higher or
# lower than our current position. This allows us to find the minima of the function.
# The derivative of differentiable function f, denoted f ′, is another function that pro‐
# vides the slope of the original function at all points. The conceptual idea is that the
# derivative of a function at a given point gives a signpost pointing to directions where
# the function is higher or lower than its current value. An optimization algorithm can
# follow this signpost to move closer to a minima of f. At the minima itself, the function
# will have derivative zero.
# The power of derivative-driven optimization isn’t apparent at first. Generations of
# calculus students have suffered through stultifying exercises minimizing tiny func‐
# tions on paper. These exercises aren’t useful since finding the minima of a function
# with only a small number of input parameters is a trivial exercise best done graphi‐
# cally. The power of derivative-driven optimization only becomes evident when there
# are hundreds, thousands, millions, or billions of variables. At these scales, under‐
# standing the function analytically is nigh impossible, and all visualizations are fraught
# exercises that may well miss the key attributes of the function. At these scales, the
# gradient of the function ∇ f , a generalization of f ′ to multivariate functions, is likely
# the most powerful mathematical tool to understand the function and its behavior. We
# will dig into gradients in more depth later in this chapter. (Conceptually that is; we
# won’t cover the technical details of gradients in this work.)
# At a very high level, machine learning is simply the act of function minimization:
# learning algorithms are nothing more than minima finders for suitably defined func‐
# tions. This definition has the advantage of mathematical simplicity. But, what are
# these special differentiable functions that encode useful solutions in their minima and
# how can we find them?
#
# Loss Functions
# In order to solve a given machine learning problem, a data scientist must find a way
# of constructing a function whose minima encode solutions to the real-world problem
# at hand. Luckily for our hapless data scientist, the machine learning literature has
# built up a rich history of loss functions that perform such encodings. Practical
# machine learning boils down to understanding the different types of loss functions
# available and knowing which loss function should be applied to which problems. Put
# another way, the loss function is the mechanism by which a data science project is
# transmuted into mathematics. All of machine learning, and much of artificial intelli‐
# gence, boils down to the creation of the right loss function to solve the problem at
# hand. We will give you a whirlwind tour of some common families of loss functions.
# We start by noting that a loss function ℒ must satisfy some mathematical properties
# to be meaningful. First ℒ must use both datapoints x and labels y. We denote this by
#
#
# Mathematical Review | 45
#
# writing the loss function as ℒ x, y . Using our language from the previous chapter,
# both x and y are tensors, and ℒ is a function from pairs of tensors to scalars. What
# should the functional form of the loss function be? A common assumption that peo‐
# ple use is to make loss functions additive. Suppose that xi, yi are the data available
# for example i and that there are N total examples. Then the loss function can be
# decomposed as
#
# N
# ℒ x, y = ∑ ℒ i xi, yi
# i=1
#
#
# (In practice ℒ i is the same for every datapoint.) This additive decomposition allows
# for many useful advantages. The first is that derivatives factor through addition, so
# computing the gradient of the total loss simplifies as follows:
#
# N
# ∇ℒ x, y = ∑ ∇ℒ i xi, yi
# i=1
#
#
# This mathematical trick means that so long as the smaller functions ℒ i are differen‐
# tiable, so too will the total loss function be. It follows that the problem of designing
# loss functions resolves into the problem of designing smaller functions ℒ i xi, yi .
# Before we dive into designing the ℒ i, it will be convenient to take a small detour that
# explains the difference between classification and regression problems.
#
# Classification and regression
# Machine learning algorithms can be broadly categorized as supervised or unsuper‐
# vised problems. Supervised problems are those for which both datapoints x and labels
# y are available, while unsupervised problems have only datapoints x without labels y.
# In general, unsupervised machine learning is much harder and less well-defined
# (what does it mean to “understand” datapoints x?). We won’t delve into unsupervised
# loss functions at this point since, in practice, most unsupervised losses are cleverly
# repurposed supervised losses.
# Supervised machine learning can be broken up into the two subproblems of classifi‐
# cation and regression. A classification problem is one in which you seek to design a
# machine learning system that assigns a discrete label, say 0/1 (or more generally
# 0, ⋯, n) to a given datapoint. Regression is the problem of designing a machine learn‐
# ing system that attaches a real valued label (in ℝ) to a given datapoint.
# At a high level, these problems may appear rather different. Discrete objects and con‐
# tinuous objects are typically treated differently by mathematics and common sense.
# However, part of the trickery used in machine learning is to use continuous, differen‐
#
#
#
# 46 | Chapter 3: Linear and Logistic Regression with TensorFlow
#
# tiable loss functions to encode both classification and regression problems. As we’ve
# mentioned previously, much of machine learning is simply the art of turning compli‐
# cated real-world systems into suitably simple differentiable functions.
# In the following sections, we will introduce you to a pair of mathematical functions
# that will prove very useful for transforming classification and regression tasks into
# suitable loss functions.
#
# L2 Loss
# The L2 loss (pronounced ell-two loss) is commonly used for regression problems. The
# L2 loss (or L2-norm as it’s commonly called elsewhere) provides for a measure of the
# magnitude of a vector:
#
#
# ∥ a ∥2 = ∑Ni = 1 a2i
#
# Here, a is assumed to be a vector of length N. The L2 norm is commonly used to
# define the distance between two vectors:
#
#
# ∥ a − b ∥2 = ∑Ni = 1 ai − bi 2
#
#
#
# This idea of L2 as a distance measurement is very useful for solving regression prob‐
# lems in supervised machine learning. Suppose that x is a collection of data and y the
# associated labels. Let f be some differentiable function that encodes our machine
# learning model. Then to encourage f to predict y, we create the L2 loss function
#
# ℒ x, y = ∥ f x − y ∥2
#
# As a quick note, it’s common in practice to not use the L2 loss directly, but rather its
# square
#
# N
# 2
# ∥ a − b ∥2 = ∑
# i=1
# ai − bi 2
#
#
#
# in order to avoid dealing with terms of the form 1/ x in the gradient. We will use
# the squared L2 loss repeatedly in the remainder of this chapter and book.
#
#
#
#
# Mathematical Review | 47
#
# Failure Modes of L2 Loss
# The L2 sharply penalizes large-scale deviances from true labels, but doesn’t do a great
# job of rewarding exact matches for real-valued labels. We can understand this dis‐
# crepancy mathematically, by studying the behavior of the functions x2 and x near the
# origin (Figure 3-3).
#
#
#
#
# Figure 3-3. A comparison of the square and identity functions near the origin.
#
# Notice how x2 dwindles rapidly to 0 for small values of x. As a result, small deviations
# aren’t penalized heavily by the L2 loss. In low-dimensional regression, this isn’t a
# major issue, but in high-dimensional regression, the L2 becomes a poor loss function
# since there may be many small deviations that together make the regression output
# poor. For example, in image prediction, L2 loss creates blurry images that are not vis‐
# ually appealing. Recent progress in machine learning has devised ways to learn loss
# functions. These learned loss functions, commonly styled Generative Adversarial
# Networks or GANs, are much more suitable for high-dimensional regression and are
# capable of generating nonblurry images.
#
#
# Probability distributions
# Before introducing loss functions for classification problems, it will be useful to take a
# quick aside to introduce probability distributions. To start, what is a probability dis‐
# tribution and why should we care about it for the purposes of machine learning?
# Probability is a deep subject, so we will only delve far enough into it for you to gain
# the required minimal understanding. At a high level, probability distributions pro‐
# vide a mathematical trick that allows you to relax a discrete set of choices into a con‐
#
#
# 48 | Chapter 3: Linear and Logistic Regression with TensorFlow
#
# tinuum. Suppose, for example, you need to design a machine learning system that
# predicts whether a coin will fall heads up or heads down. It doesn’t seem like heads
# up/down can be encoded as a continuous function, much less a differentiable one.
# How can you then use the machinery of calculus or TensorFlow to solve problems
# involving discrete choices?
# Enter the probability distribution. Instead of hard choices, make the classifier predict
# the chance of getting heads up or heads down. For example, the classifier may learn
# to predict that heads has probability 0.75 and tails has probability 0.25. Note that
# probabilities vary continuously! Consequently by working with the probabilities of
# discrete events rather than with the events themselves, you can neatly sidestep the
# issue that calculus doesn’t really work with discrete events.
# A probability distribution p is simply a listing of the probabilities for the possible dis‐
# crete events at hand. In this case, p = (0.75, 0.25). Note, alternatively, you can view
# p: 0, 1 ℝ as a function from the set of two elements to the real numbers. This
# viewpoint will be useful notationally at times.
# We briefly note that the technical definition of a probability distribution is more
# involved. It is feasible to assign probability distributions to real-valued events. We will
# discuss such distributions later in the chapter.
#
# Cross-entropy loss
# Cross-entropy is a mathematical method for gauging the distance between two prob‐
# ability distributions:
#
# H p, q = − ∑ p x log q x
# x
#
#
# Here p and q are two probability distributions. The notation p(x) denotes the proba‐
# bility p accords to event x. This definition is worth discussing carefully. Like the L2
# norm, H provides a notion of distance. Note that in the case where p = q,
#
# H p, p = − ∑ p x log p x
# x
#
#
# This quantity is the entropy of p and is usually written simply H(p). It’s a measure of
# how disordered the distribution is; the entropy is maximized when all events are
# equally likely. H(p) is always less than or equal to H(p, q). In fact, the “further away”
# distribution q is from p, the larger the cross-entropy gets. We won’t dig deeply into
# the precise meanings of these statements, but the intuition of cross-entropy as a dis‐
# tance mechanism is worth remembering.
#
#
#
#
# Mathematical Review | 49
#
# As an aside, note that unlike L2 norm, H is asymmetric! That is, H p, q ≠ H q, p .
# For this reason, reasoning with cross-entropy can be a little tricky and is best done
# with some caution.
# Returning to concrete matters, now suppose that p = y, 1 − y is the true data distri‐
# bution for a discrete system with two outcomes, and q = ypred, 1 − ypred is that pre‐
# dicted by a machine learning system. Then the cross-entropy loss is
#
# H p, q = y log ypred + 1 − y log 1 − ypred
#
# This form of the loss is used widely in machine learning systems to train classifiers.
# Empirically, minimizing H(p, q) seems to construct classifiers that reproduce pro‐
# vided training labels well.
#
# Gradient Descent
# So far in this chapter, you have learned about the notion of function minimization as
# a proxy for machine learning. As a short recap, minimizing a suitable function is
# often sufficient to learn to solve a desired task. In order to use this framework, you
# need to use suitable loss functions, such as the L2 or H(p, q) cross-entropy in order to
# transform classification and regression problems into suitable loss functions.
#
# Learnable Weights
# So far in this chapter, we’ve explained that machine learning is the
# act of minimizing suitably defined loss function ℒ x, y . That is, we
# attempt to find arguments to the loss function ℒ that minimize it.
# However, careful readers will recall that (x,y) are fixed quantities
# that cannot be changed. What arguments to ℒ are we changing
# during learning then?
# Enter learnable weights W. Suppose f(x) is a differentiable function
# we wish to fit with our machine learning model. We will dictate
# that f be parameterized by choice of W. That is, our function
# actually has two arguments f(W, x). Fixing the value of W results in
# a function that depends solely on datapoints x. These learnable
# weights are the quantities actually selected by minimization of the
# loss function. We will see later in the chapter how TensorFlow can
# be used to encode learnable weights using tf.Variable.
#
#
#
#
# 50 | Chapter 3: Linear and Logistic Regression with TensorFlow
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class Content(LeafNode):
def __init__(self):
super().__init__(
"Loss Functions",
# Stage.CROP_TEXT,
# Stage.CODE_BLOCKS,
# Stage.MARKDOWN_BLOCKS,
# Stage.FIGURES,
# Stage.EXERCISES,
# Stage.CUSTOMIZED,
)
self.add(mbk("# Loss Functions"))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class LossFunctions(HierNode):
def __init__(self):
super().__init__("Loss Functions")
self.add(Content())
# eof
| [
"[email protected]"
]
| |
b311deac9287395ba96912fa77bba8b7069189ba | 1e1c85d0d74bc1b111e77f082cd24c94219d7eb0 | /VE-Tests/tests/KD/test_device_logout.py | 4fcd4b6840117995b378d5aa39690bbfe93167a1 | []
| no_license | anshsaikia/GSSDeliverables-YesProject | b6f5e4de8d853ce21dfe7401c4b9179c40f32a89 | ed786ccfd7b8c344802c7ff6d0cfd4afbffe015e | refs/heads/master | 2020-04-06T04:07:49.034461 | 2017-02-24T13:39:48 | 2017-02-24T13:39:48 | 83,044,504 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,896 | py | import pytest
__author__ = 'srevg'
from tests_framework.ve_tests.ve_test import VeTestApi
from vgw_test_utils.IHmarks import IHmark
@IHmark.LV_L2
@IHmark.O_iOS
@IHmark.O_Android
@IHmark.MF1530
@IHmark.MF1342
@pytest.mark.commit
@pytest.mark.MF1530_LogOut
@pytest.mark.MF1342_LogOut
@pytest.mark.level2
def test_log_out():
ve_test = VeTestApi("log_out_feature")
ve_test.begin()
device_details_milestones = ve_test.milestones.getDeviceDetails()
deviceId_1 = device_details_milestones['drm-device-id']
hh_id = ve_test.configuration["he"]["generated_household"]
user_name = ve_test.configuration["he"]["generated_username"]
ve_test.wait(7)
ve_test.screens.settings.log_out()
# Re Sign In with Same User Name and verify if the device id which is used is same:
login_screen = ve_test.screens.login_screen
login_screen.sign_in(hh_id,user_name)
device_details_milestones = ve_test.milestones.getDeviceDetails()
deviceId_2 = device_details_milestones['drm-device-id']
ve_test.log_assert(deviceId_1 == deviceId_2, "Device ids are different")
ve_test.wait(7)
ve_test.screens.settings.log_out()
#Query from upm and see if the device id is still present in the household
d = ve_test.he_utils.getDeviceIdFromDeviceAndHH(deviceId_2, hh_id)
ve_test.log_assert(deviceId_2.upper() == d, "device id deleted in upm")
#Re Sign In with different User Name and verify if the device id which is used is different:
hhId, login = ve_test.he_utils.createTestHouseHold()
ve_test.he_utils.setHHoffers(hhId)
ve_test.screens.login_screen.sign_in(hhId, user_name=hhId, password='123')
device_details_milestones = ve_test.milestones.getDeviceDetails()
deviceId_3 = device_details_milestones['drm-device-id']
ve_test.log_assert(deviceId_3 is not deviceId_2,"Device ids are same")
ve_test.wait(7)
ve_test.end() | [
"[email protected]"
]
| |
fd30b078e6d7cffb844e3d1190637df352e04368 | ce76b3ef70b885d7c354b6ddb8447d111548e0f1 | /able_hand_and_little_case/own_group.py | b3f4f5a358d913248b2a972a20c5acd10078ea22 | []
| no_license | JingkaiTang/github-play | 9bdca4115eee94a7b5e4ae9d3d6052514729ff21 | 51b550425a91a97480714fe9bc63cb5112f6f729 | refs/heads/master | 2021-01-20T20:18:21.249162 | 2016-08-19T07:20:12 | 2016-08-19T07:20:12 | 60,834,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 201 | py |
#! /usr/bin/env python
def be_point(str_arg):
do_new_part(str_arg)
print('right_case')
def do_new_part(str_arg):
print(str_arg)
if __name__ == '__main__':
be_point('eye_or_person')
| [
"[email protected]"
]
| |
e07a8919ecdfb3638a538d4e5a1d875b6b48b2b3 | bf20548c143fdaecc1d8b5746dab142414b27786 | /galaxy-tool-BLAST/utilities/bold/add_taxonomy_bold.py | 1f03eabf49e5f4c97606b6edbf03d50fbf3cf580 | []
| no_license | zeromtmu/galaxy-tool-temp-2019 | e9f58956b014e2e4e9260b028c14549f90756f05 | 704c3b850e8ddf5420dc458a0282717ab2268c40 | refs/heads/master | 2021-10-25T05:02:55.328975 | 2019-04-01T11:40:41 | 2019-04-01T11:40:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,294 | py | """
"""
from Bio import SeqIO
import argparse
parser = argparse.ArgumentParser(description='Add taxonomy to BOLD fasta file')
parser.add_argument('-t', '--taxonomy', dest='taxonomy', type=str, required=True)
parser.add_argument('-g', '--gbif_taxonomy', dest='gbif', type=str, required=True)
parser.add_argument('-b', '--bold_fasta', dest='bold', type=str, required=True)
parser.add_argument('-o', '--output', dest='output', type=str, required=True)
args = parser.parse_args()
def make_taxon_dict():
taxonDict = {}
with open(args.taxonomy,"r") as taxonomy:
for x in taxonomy:
x = x.strip().split("\t")
unknowns = ["unknown kingdom", "unknown phylum", "unknown class", "unknown order", "unknown family", "unknown genus", "unknown species"]
for known in unknowns[len(x):]:
x.append(known)
valueCount = 0
for value in x:
if not value:
x[valueCount] = unknowns[valueCount]
valueCount += 1
taxonDict[x[0]] = x
return taxonDict
def make_kingdom_dict():
kingdomDict = {}
with open(args.gbif,"r") as gbif:
for x in gbif:
x = x.split("\t")
if x[1] not in kingdomDict:
kingdomDict[x[1]] = x[0]
if x[2] not in kingdomDict:
kingdomDict[x[2]] = x[0]
if x[3] not in kingdomDict:
kingdomDict[x[3]] = x[0]
if x[4] not in kingdomDict:
kingdomDict[x[4]] = x[0]
if x[5] not in kingdomDict:
kingdomDict[x[5]] = x[0]
return kingdomDict
def add_taxonomy(taxonDict, kingdomDict):
with open(args.bold, "r") as bold, open(args.output,"a") as output:
for record in SeqIO.parse(bold, "fasta"):
accession = str(record.description).split("|")[0]
if accession in taxonDict:
if taxonDict[accession][1] in kingdomDict:
kingdom = kingdomDict[taxonDict[accession][1]]
elif taxonDict[accession][2] in kingdomDict:
kingdom = kingdomDict[taxonDict[accession][2]]
elif taxonDict[accession][3] in kingdomDict:
kingdom = kingdomDict[taxonDict[accession][3]]
elif taxonDict[accession][4] in kingdomDict:
kingdom = kingdomDict[taxonDict[accession][4]]
elif taxonDict[accession][5] in kingdomDict:
kingdom = kingdomDict[taxonDict[accession][5]]
else:
#print accession+" no kingdom"
kingdom = "unknown kingdom"
output.write(">BOLD|"+accession+"|"+taxonDict[accession][-1]+"|"+kingdom+"|"+taxonDict[accession][1]+"|"+taxonDict[accession][2]+"|"+taxonDict[accession][3]+"|"+taxonDict[accession][4]+"|"+taxonDict[accession][5]+"|"+taxonDict[accession][-1]+"\n")
output.write(str(record.seq)+"\n")
else:
print accession+" no taxonomy"
def main():
taxonDict = make_taxon_dict()
kingdomDict = make_kingdom_dict()
add_taxonomy(taxonDict, kingdomDict)
if __name__=="__main__":
main()
| [
"[email protected]"
]
| |
a22cfb2be3ed7a20604c5c82392355d9e69ae696 | 008bc57ad937f0d76edbe29376220b33ff2fddc1 | /CRC/crc_report_regression_testing.py | b48f74725a8653f7462fc73b88bfeed1032599aa | []
| no_license | chetandg123/cQubeTesting-2.0 | f1b15d77401e677a6e4d2e9e497a364e3dd001b2 | bd3ab2b6c8be65bfc1aef3a42585360d70483bd5 | refs/heads/master | 2023-07-12T22:10:51.705709 | 2021-08-11T11:20:51 | 2021-08-11T11:20:51 | 374,532,154 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,482 | py | import unittest
from CRC.check_clusterwise_records import crc_schoolevel_records
from CRC.check_crc_tabledata_by_selecting_districts import districtwise_tabledata
from CRC.check_districtwise_records import test_crc_report_districtwise
from CRC.check_homebtn import Homeicon
from CRC.check_table_data_order import Check_order_of_tabledata
from CRC.check_xaxis_and_yaxis_from_selectbox import plot_values
from CRC.click_on_hyperlink import click_on_hyperlinks
from CRC.download_blockwise_csv import donwload_blockwise_csv
from CRC.download_clusterwise_csv import load_clusterwise_csv
from CRC.download_districtwise_csv import Districtwise_donwload
from CRC.download_schoolwise_csv import school_wise_download
from CRC.navigate_to_crc_and_click_on_logout import Logout_function
from CRC.navigate_to_crc_report import loading_crc
from reuse_func import GetData
class cQube_CRC_Report(unittest.TestCase):
@classmethod
def setUpClass(self):
self.data = GetData()
self.driver = self.data.get_driver()
self.data.open_cqube_appln(self.driver)
self.data.login_cqube(self.driver)
self.data.navigate_to_crc_report()
self.data.page_loading(self.driver)
self.driver.implicitly_wait(100)
def test_navigate_crc(self):
b = loading_crc(self.driver)
res = b.test_crc()
if "crc-report" in self.driver.current_url:
print("Navigated back to crc report")
else:
print("CRC report is not loaded ")
self.data.page_loading(self.driver)
def test_download_districtwise(self):
b = Districtwise_donwload(self.driver)
result = b.test_districtwise()
self.assertEqual(0, result, msg="File is not downloaded")
print("district wise csv file is downloaded ")
self.data.page_loading(self.driver)
def test_download_blockwise_csv(self):
b = donwload_blockwise_csv(self.driver)
result = b.test_blockwise()
self.assertEqual(0,result, msg="File is not downloaded")
print("blockwise csv file is downloaded ")
self.data.page_loading(self.driver)
def test_download_clusterwise_csv(self):
b = load_clusterwise_csv(self.driver)
result = b.test_clusterwise()
self.assertEqual(0, result, msg="File is not downloaded")
print("cluster wise csv file is downloaded ")
self.data.page_loading(self.driver)
def test_download_schoolwise(self):
b = school_wise_download(self.driver)
result = b.test_schoolwise()
self.assertEqual(0, result, msg="File is not downloaded")
print("district wise csv file is downloaded ")
self.data.page_loading(self.driver)
def test_crc_districtwise(self):
b = test_crc_report_districtwise(self.driver)
result = b.test_districtwise()
self.assertEqual(0, result, msg="File is not downloaded")
print('checked with districts records')
self.data.page_loading(self.driver)
def test_homeicon(self):
b = Homeicon(self.driver)
result = b.test_homeicon()
self.assertTrue(result, msg="Home button not working ")
print("checking with home icon and it is working ")
self.data.page_loading(self.driver)
def test_schools_per_cluster_csv_download1(self):
school = crc_schoolevel_records(self.driver)
result = school.check_csv_download()
self.assertEqual(result,0,msg='csv file is not downloaded')
self.data.page_loading(self.driver)
def test_districtwise_tabledata(self):
b = districtwise_tabledata(self.driver)
result = b.test_table_data()
if result != 0:
raise self.failureException('Data not found on table')
print("checked with districtwise table data")
self.data.page_loading(self.driver)
def test_logout(self):
b = Logout_function(self.driver)
res = b.test_logout()
if "crc-report" in self.driver.current_url:
print("Navigated back to crc report")
else:
print("CRC report is not loaded ")
self.data.page_loading(self.driver)
def test_crc_graph(self):
b = plot_values(self.driver)
res1, res2 = b.test_plots()
self.assertNotEqual(0, res1, msg="Xaxis options are not present")
self.assertNotEqual(0, res2, msg='Yaxis options are not present')
self.data.page_loading(self.driver)
print("checked graph x and y axis options")
def test_orderwise_tabledata(self):
b = Check_order_of_tabledata(self.driver)
result = b.test_order()
self.assertEqual(result, "menu", msg="Menu is not exist")
print("check order of table records is working ")
self.data.page_loading(self.driver)
def test_on_clusterlevel_to_hyperlinks(self):
b = click_on_hyperlinks(self.driver)
result = b.test_hyperlink()
print("checking hyperlink from cluster levels ")
self.data.page_loading(self.driver)
def test_homebutton(self):
b = Homeicon(self.driver)
result = b.test_homebutton()
self.assertEqual(0,result,msg="Home button is not working ")
print("checking with home icon and it is working ")
self.data.page_loading(self.driver)
@classmethod
def tearDownClass(cls):
cls.driver.close()
| [
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]
| |
38ba24a26d1eec490788d33c922c28a26fe279e3 | 5dc0534f17d34562f5eb90061bd77844db3110d9 | /misc/deep_learning_notes/Ch4_Recurrent_Networks/002_vanila_RNN_with_edf/edf.py | 7f18aacd0cfd85d08e6a79111b1cccd0674a4fce | [
"MIT"
]
| permissive | johnnymck/MoocX | 202394d064e8a7ebd269876f473b1cef43104c1b | 52c8450ff7ecc8450a8adc2457233d5777a3d5bb | refs/heads/master | 2023-03-21T04:40:04.069791 | 2017-09-15T12:49:19 | 2017-09-15T12:49:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 19,492 | py | import numpy as np
DT = np.float32
eps = 1e-12
# Globals
components = []
params = []
# Global forward/backward
def Forward():
for c in components:
c.forward()
def Backward(loss):
for c in components:
if c.grad is not None: c.grad = DT(0)
loss.grad = np.ones_like(loss.value)
for c in components[::-1]:
c.backward()
# Optimization functions
def SGD(lr):
for p in params:
lrp = p.opts['lr'] * lr if 'lr' in p.opts.keys() else lr
p.value = p.value - lrp * p.grad
p.grad = DT(0)
# Values
class Value:
def __init__(self, value=None):
self.value = DT(value).copy()
self.grad = None
def set(self, value):
self.value = DT(value).copy()
# Parameters
class Param:
def __init__(self, value, opts={}):
self.value = DT(value).copy()
self.opts = {}
params.append(self)
self.grad = DT(0)
# Xavier initializer
def xavier(shape):
sq = np.sqrt(3.0 / np.prod(shape[:-1]))
return np.random.uniform(-sq, sq, shape)
# Utility function for shape inference with broadcasting
def bcast(x, y):
xs = np.array(x.shape)
ys = np.array(y.shape)
pad = len(xs) - len(ys)
if pad > 0:
ys = np.pad(ys, [[pad, 0]], 'constant')
elif pad < 0:
xs = np.pad(xs, [[-pad, 0]], 'constant')
os = np.maximum(xs, ys)
xred = tuple([idx for idx in np.where(xs < os)][0])
yred = tuple([idx for idx in np.where(ys < os)][0])
return xred, yred
#### Actual components
class Add: # Add with broadcasting
"""
Class name: Add
Class usage: add two matrices x, y with broadcasting supported by numpy "+" operation.
Class function:
forward: calculate x + y with possible broadcasting
backward: calculate derivative w.r.t to x and y, when calculate the derivative w.r.t to y, we sum up all the axis over grad except the last dimension.
"""
def __init__(self, x, y):
components.append(self)
self.x = x
self.y = y
self.grad = None if x.grad is None and y.grad is None else DT(0)
def forward(self):
self.value = self.x.value + self.y.value
def backward(self):
xred, yred = bcast(self.x.value, self.y.value)
if self.x.grad is not None:
self.x.grad = self.x.grad + np.reshape(
np.sum(self.grad, axis=xred, keepdims=True),
self.x.value.shape)
if self.y.grad is not None:
self.y.grad = self.y.grad + np.reshape(
np.sum(self.grad, axis=yred, keepdims=True),
self.y.value.shape)
class Mul: # Multiply with broadcasting
"""
Class Name: Mul
Class Usage: elementwise multiplication with two matrix
Class Functions:
forward: compute the result x*y
backward: compute the derivative w.r.t x and y
"""
def __init__(self, x, y):
components.append(self)
self.x = x
self.y = y
self.grad = None if x.grad is None and y.grad is None else DT(0)
def forward(self):
self.value = self.x.value * self.y.value
def backward(self):
xred, yred = bcast(self.x.value, self.y.value)
if self.x.grad is not None:
self.x.grad = self.x.grad + np.reshape(
np.sum(self.grad * self.y.value, axis=xred, keepdims=True),
self.x.value.shape)
if self.y.grad is not None:
self.y.grad = self.y.grad + np.reshape(
np.sum(self.grad * self.x.value, axis=yred, keepdims=True),
self.y.value.shape)
class VDot: # Matrix multiply (fully-connected layer)
"""
Class Name: VDot
Class Usage: matrix multiplication where x, y are matrices
y is expected to be a parameter and there is a convention that parameters come last. Typical usage is x is batch feature vector with shape (batch_size, f_dim), y a parameter with shape (f_dim, f_dim2).
Class Functions:
forward: compute the vector matrix multplication result
backward: compute the derivative w.r.t x and y, where derivative of x and y are both matrices
"""
def __init__(self, x, y):
components.append(self)
self.x = x
self.y = y
self.grad = None if x.grad is None and y.grad is None else DT(0)
def forward(self):
self.value = np.matmul(self.x.value, self.y.value)
def backward(self):
if self.x.grad is not None:
if len(self.y.value.shape) == 1:
nabla = np.matmul(self.y.value.reshape(list(self.y.value.shape) + [1]), self.grad.T).T
else:
nabla = np.matmul(self.y.value, self.grad.T).T
self.x.grad = self.x.grad + nabla
if self.y.grad is not None:
if len(self.x.value.shape) == 1:
nabla = np.matmul(self.x.value.T.reshape(list(self.x.value.shape) + [1]),
self.grad.reshape([1] + list(self.grad.shape)))
else:
nabla = np.matmul(self.x.value.T, self.grad)
self.y.grad = self.y.grad + nabla
class Log: # Elementwise Log
"""
Class Name: Log
Class Usage: compute the elementwise log(x) given x.
Class Functions:
forward: compute log(x)
backward: compute the derivative w.r.t input vector x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.log(self.x.value)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad / self.x.value
class Sigmoid:
"""
Class Name: Sigmoid
Class Usage: compute the elementwise sigmoid activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = 1/(1 + exp(-x_{i}))
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = 1. / (1. + np.exp(-self.x.value))
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * self.value * (1. - self.value)
class Tanh:
"""
Class Name: Tanh
Class Usage: compute the elementwise Tanh activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = (exp(x_{i}) - exp(-x_{i}))/(exp(x_{i}) + exp(-x_{i}))
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
x_exp = np.exp(self.x.value)
x_neg_exp = np.exp(-self.x.value)
self.value = (x_exp - x_neg_exp) / (x_exp + x_neg_exp)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * (1 - self.value * self.value)
class RELU:
"""
Class Name: RELU
Class Usage: compute the elementwise RELU activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = max(0, x_{i})
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.maximum(self.x.value, 0)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * (self.value > 0)
class LeakyRELU:
"""
Class Name: LeakyRELU
Class Usage: compute the elementwise LeakyRELU activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = 0.01*x_{i} for x_{i} < 0 and y_{i} = x_{i} for x_{i} > 0
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.maximum(self.x.value, 0.01 * self.x.value)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * np.maximum(0.01, self.value > 0)
class Softplus:
"""
Class Name: Softplus
Class Usage: compute the elementwise Softplus activation.
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.log(1. + np.exp(self.x.value))
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * 1. / (1. + np.exp(-self.x.value))
class SoftMax:
"""
Class Name: SoftMax
Class Usage: compute the softmax activation for each element in the matrix, normalization by each all elements in each batch (row). Specificaly, input is matrix [x_{00}, x_{01}, ..., x_{0n}, ..., x_{b0}, x_{b1}, ..., x_{bn}], output is a matrix [p_{00}, p_{01}, ..., p_{0n},...,p_{b0},,,p_{bn} ] where p_{bi} = exp(x_{bi})/(exp(x_{b0}) + ... + exp(x_{bn}))
Class Functions:
forward: compute probability p_{bi} for all b, i.
backward: compute the derivative w.r.t input matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
lmax = np.max(self.x.value, axis=-1, keepdims=True)
ex = np.exp(self.x.value - lmax)
self.value = ex / np.sum(ex, axis=-1, keepdims=True)
def backward(self):
if self.x.grad is None:
return
gvdot = np.matmul(self.grad[..., np.newaxis, :], self.value[..., np.newaxis]).squeeze(-1)
self.x.grad = self.x.grad + self.value * (self.grad - gvdot)
class LogLoss:
"""
Class Name: LogLoss
Class Usage: compute the elementwise -log(x) given matrix x. this is the loss function we use in most case.
Class Functions:
forward: compute -log(x)
backward: compute the derivative w.r.t input matrix x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = -np.log(np.maximum(eps, self.x.value))
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + (-1) * self.grad / np.maximum(eps, self.x.value)
class Mean:
"""
Class Name: Mean
Class Usage: compute the mean given a vector x.
Class Functions:
forward: compute (x_{0} + ... + x_{n})/n
backward: compute the derivative w.r.t input vector x
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.mean(self.x.value)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * np.ones_like(self.x.value) / self.x.value.shape[0]
class Sum:
"""
Class Name: Sum
Class Usage: compute the sum of a matrix.
"""
def __init__(self, x):
components.append(self)
self.x = x
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.sum(self.x.value)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * np.ones_like(self.x.value)
class MeanwithMask:
"""
Class Name: MeanwithMask
Class Usage: compute the mean given a vector x with mask.
Class Functions:
forward: compute x = x*mask and then sum over nonzeros in x/#(nozeros in x)
backward: compute the derivative w.r.t input vector matrix
"""
def __init__(self, x, mask):
components.append(self)
self.x = x
self.mask = mask
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.sum(self.x.value * self.mask.value) / np.sum(self.mask.value)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + self.grad * np.ones_like(self.x.value) * self.mask.value / np.sum(
self.mask.value)
class Aref: # out = x[idx]
"""
Class Name: Aref
Class Usage: get some specific entry in a matrix. x is the matrix with shape (batch_size, N) and idx
is vector contains the entry index and x is differentiable.
Class Functions:
forward: compute x[b, idx(b)]
backward: compute the derivative w.r.t input matrix x
"""
def __init__(self, x, idx):
components.append(self)
self.x = x
self.idx = idx
self.grad = None if x.grad is None else DT(0)
def forward(self):
xflat = self.x.value.reshape(-1)
iflat = self.idx.value.reshape(-1)
outer_dim = len(iflat)
inner_dim = len(xflat) / outer_dim
self.pick = np.int32(np.array(range(outer_dim)) * inner_dim + iflat)
self.value = xflat[self.pick].reshape(self.idx.value.shape)
def backward(self):
if self.x.grad is not None:
grad = np.zeros_like(self.x.value)
gflat = grad.reshape(-1)
gflat[self.pick] = self.grad.reshape(-1)
self.x.grad = self.x.grad + grad
class Accuracy:
"""
Class Name: Accuracy
Class Usage: check the predicted label is correct or not. x is the probability vector where each probability is for each class. idx is ground truth label.
Class Functions:
forward: find the label that has maximum probability and compare it with the ground truth label.
backward: None
"""
def __init__(self, x, idx):
components.append(self)
self.x = x
self.idx = idx
self.grad = None
def forward(self):
self.value = np.mean(np.argmax(self.x.value, axis=-1) == self.idx.value)
def backward(self):
pass
class Reshape:
"""
Class name: Reshape
Class usage: Reshape the tensor x to specific shape.
Class function:
forward: Reshape the tensor x to specific shape
backward: calculate derivative w.r.t to x, which is simply reshape the income gradient to x's original shape
"""
def __init__(self, x, shape):
components.append(self)
self.x = x
self.shape = shape
self.grad = None if x.grad is None else DT(0)
def forward(self):
self.value = np.reshape(self.x.value, self.shape)
def backward(self):
if self.x.grad is not None:
self.x.grad = self.x.grad + np.reshape(self.grad, self.x.value.shape)
def Momentum(lr, mom):
for p in params:
if not hasattr(p, 'grad_hist'):
p.grad_hist = DT(0)
p.grad_hist = mom * p.grad_hist + p.grad
p.grad = p.grad_hist
SGD(lr)
def AdaGrad(lr, ep=1e-8):
for p in params:
if not hasattr(p, 'grad_G'):
p.grad_G = DT(0)
p.grad_G = p.grad_G + p.grad * p.grad
p.grad = p.grad / np.sqrt(p.grad_G + DT(ep))
SGD(lr)
def RMSProp(lr, g=0.9, ep=1e-8):
for p in params:
if not hasattr(p, 'grad_hist'):
p.grad_hist = DT(0)
p.grad_hist = g * p.grad_hist + (1 - g) * p.grad * p.grad
p.grad = p.grad / np.sqrt(p.grad_hist + DT(ep))
SGD(lr)
def Adam(alpha=0.001, b1=0.9, b2=0.999, ep=1e-8):
b1 = DT(b1)
b2 = DT(b2)
ep = DT(ep)
_a_b1t = DT(1.0) * b1
_a_b2t = DT(1.0) * b2
for p in params:
if not hasattr(p, 'grad_hist'):
p.grad_hist = DT(0)
p.grad_h2 = DT(0)
p.grad_hist = b1 * p.grad_hist + (1. - b1) * p.grad
p.grad_h2 = b2 * p.grad_h2 + (1. - b2) * p.grad * p.grad
mhat = p.grad_hist / (1. - _a_b1t)
vhat = p.grad_h2 / (1. - _a_b2t)
p.grad = mhat / (np.sqrt(vhat) + ep)
SGD(alpha)
# clip the gradient if the norm of gradient is larger than some threshold, this is crucial for RNN.
def GradClip(grad_clip):
for p in params:
l2 = np.sqrt(np.sum(p.grad * p.grad))
if l2 >= grad_clip:
p.grad *= grad_clip / l2
##################################################### Recurrent Components ##############################################
class Embed:
"""
Class name: Embed
Class usage: Embed layer.
Class function:
forward: given the embeeding matrix w2v and word idx, return its corresponding embedding vector.
backward: calculate the derivative w.r.t to embedding matrix
"""
def __init__(self, idx, w2v):
components.append(self)
self.idx = idx
self.w2v = w2v
self.grad = None if w2v.grad is None else DT(0)
def forward(self):
self.value = self.w2v.value[np.int32(self.idx.value), :]
def backward(self):
if self.w2v.grad is not None:
self.w2v.grad = np.zeros(self.w2v.value.shape)
self.w2v.grad[np.int32(self.idx.value), :] = self.w2v.grad[np.int32(self.idx.value), :] + self.grad
class ConCat:
"""
Class name: ConCat
Class usage: ConCat layer.
Class function:
forward: concat two matrix along with the axis 1.
backward: calculate the derivative w.r.t to matrix a and y.
"""
def __init__(self, x, y):
components.append(self)
self.x = x
self.y = y
self.grad = None if x.grad is None and y.grad is None else DT(0)
def forward(self):
self.value = np.concatenate((self.x.value, self.y.value), axis=-1)
def backward(self):
dim_x = self.x.value.shape[-1]
dim_y = self.y.value.shape[-1]
if self.x.grad is not None:
if len(self.x.value.shape) == 2:
self.x.grad = self.x.grad + self.grad[:, 0:dim_x]
else:
self.x.grad = self.x.grad + self.grad[0:dim_x]
if self.y.grad is not None:
if len(self.y.value.shape) == 2:
self.y.grad = self.y.grad + self.grad[:, dim_x:dim_x + dim_y]
else:
self.y.grad = self.y.grad + self.grad[dim_x:dim_x + dim_y]
class ArgMax:
"""
Class name: ArgMax
Class usage: ArgMax layer.
Class function:
forward: given x, calculate the index which has the maximum value
backward: None
"""
def __init__(self, x):
components.append(self)
self.x = x
def forward(self):
self.value = np.argmax(self.x.value)
def backward(self):
pass
| [
"[email protected]"
]
| |
1c6210564d19565b0fb0d19d5a16faa49512c900 | f5c3841a08c3faa1818d3ee210c8b9921dc9499d | /parsing_JSON_1.py | e41a258c27dd0efb92e08a6fbfd4055cf60134e0 | []
| no_license | villancikos/realpython-book2 | a4e74b51fe1d3a8e5af206c2938ff4966ef00df6 | 6c9a2ef714531f1163f3c78c80fad335661dacf2 | refs/heads/master | 2016-09-06T10:06:49.227106 | 2014-09-22T18:56:58 | 2014-09-22T18:56:58 | 23,493,659 | 1 | 1 | null | 2014-09-19T23:35:40 | 2014-08-30T14:44:52 | Python | UTF-8 | Python | false | false | 193 | py | # JSON Parsing 1
import json
# decodes the json file
output = json.load(open('cars.json'))
# display output screen
print output
print " "
print json.dumps(output, indent=4, sort_keys=True)
| [
"[email protected]"
]
| |
354ea62ee68b6d87dc18276952c5f8d9254a2185 | 747f759311d404af31c0f80029e88098193f6269 | /addons/crm_claim_refund/__openerp__.py | bdb21add0a4b5d9b880d8bff8907d124c775c8d9 | []
| no_license | sgeerish/sirr_production | 9b0d0f7804a928c0c582ddb4ccb7fcc084469a18 | 1081f3a5ff8864a31b2dcd89406fac076a908e78 | refs/heads/master | 2020-05-19T07:21:37.047958 | 2013-09-15T13:03:36 | 2013-09-15T13:03:36 | 9,648,444 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 69 | py | /home/openerp/production/extra-addons/crm_claim_refund/__openerp__.py | [
"[email protected]"
]
| |
681a5376528ffab913b8a88d30fc3a66a36752f2 | 5456502f97627278cbd6e16d002d50f1de3da7bb | /chrome/test/mini_installer/verifier_runner.py | 9f3f99f54ca5533d699a6f03c95fc058d6f53633 | [
"BSD-3-Clause"
]
| permissive | TrellixVulnTeam/Chromium_7C66 | 72d108a413909eb3bd36c73a6c2f98de1573b6e5 | c8649ab2a0f5a747369ed50351209a42f59672ee | refs/heads/master | 2023-03-16T12:51:40.231959 | 2017-12-20T10:38:26 | 2017-12-20T10:38:26 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,389 | py | # Copyright 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import file_verifier
import process_verifier
import registry_verifier
class VerifierRunner:
"""Runs all Verifiers."""
def __init__(self):
"""Constructor."""
# TODO(sukolsak): Implement other verifiers
self._verifiers = {
'Files': file_verifier.FileVerifier(),
'Processes': process_verifier.ProcessVerifier(),
'RegistryEntries': registry_verifier.RegistryVerifier(),
}
def VerifyAll(self, property, variable_expander):
"""Verifies that the current machine states match the property dictionary.
A property dictionary is a dictionary where each key is a verifier's name
and the associated value is the input to that verifier. For details about
the input format for each verifier, take a look at http://goo.gl/1P85WL
Args:
property: A property dictionary.
variable_expander: A VariableExpander object.
"""
for verifier_name, verifier_input in property.iteritems():
if verifier_name not in self._verifiers:
raise KeyError('Unknown verifier %s' % verifier_name)
self._verifiers[verifier_name].VerifyInput(verifier_input,
variable_expander)
| [
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]
| |
e5d1941ea66a0350ed8fe6c0a7e0f6f1275e4f81 | 061c36c4b33dd0c47d9d62c2057559d4c5973681 | /hdfs_find_replication_factor_1.py | 8745848be2d4daa4237a72ad514a994daa990440 | [
"MIT"
]
| permissive | ashkankamyab/DevOps-Python-tools | 0847f9e1b74d7864d17b0a9833beeef1f149e5a5 | dc4b1ce2b2fbee3797b66501ba3918a900a79769 | refs/heads/master | 2022-10-09T15:23:31.108086 | 2022-09-01T14:32:56 | 2022-09-01T14:32:56 | 189,855,037 | 1 | 0 | NOASSERTION | 2019-06-02T14:15:18 | 2019-06-02T14:15:18 | null | UTF-8 | Python | false | false | 5,894 | py | #!/usr/bin/env python
# coding=utf-8
# vim:ts=4:sts=4:sw=4:et
#
# Author: Hari Sekhon
# Date: 2018-11-28 16:37:00 +0000 (Wed, 28 Nov 2018)
#
# https://github.com/HariSekhon/DevOps-Python-tools
#
# License: see accompanying Hari Sekhon LICENSE file
#
# If you're using my code you're welcome to connect with me on LinkedIn
# and optionally send me feedback to help steer this or other code I publish
#
# https://www.linkedin.com/in/HariSekhon
#
"""
Tool to find HDFS file with replication factor 1
These cause problems because taking a single datanode offline may result in alerts for files with missing blocks
Uses any arguments are directory tree paths to starting scanning down. If no argument paths are given, searches under
top level directory /
Uses Hadoop configuration files it expects to find in $HADOOP_HOME/conf to auto-detect
NameNodes HA, Kerberos etc (just kinit first)
Optionally resets such files back to replication factor 3 if specifying --set-replication-factor-3
Tested on Hadoop 2.7 on HDP 2.6 with Kerberos
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
import time
import traceback
import krbV
import snakebite
from snakebite.client import AutoConfigClient
srcdir = os.path.abspath(os.path.dirname(__file__))
libdir = os.path.join(srcdir, 'pylib')
sys.path.append(libdir)
try:
# pylint: disable=wrong-import-position
from harisekhon.utils import log, log_option, validate_int
from harisekhon import CLI
except ImportError as _:
print(traceback.format_exc(), end='')
sys.exit(4)
__author__ = 'Hari Sekhon'
__version__ = '0.3'
class HdfsFindReplicationFactor1(CLI):
def __init__(self):
# Python 2.x
super(HdfsFindReplicationFactor1, self).__init__()
# Python 3.x
# super().__init__()
self.path_list = None
self.replication_factor = None
def add_options(self):
super(HdfsFindReplicationFactor1, self).add_options()
self.add_opt('--hadoop-home',
help='Sets $HADOOP_HOME, expects to find config in $HADOOP_HOME/conf, ' + \
'otherwise inherits from environment or tries default paths')
self.add_opt('--set-replication', metavar='N', type=int,
help='Resets any files with replication factor 1 back to this replication factor (optional)')
def process_options(self):
super(HdfsFindReplicationFactor1, self).process_options()
self.path_list = self.args
if not self.path_list:
self.path_list = ['/']
self.replication_factor = self.get_opt('set_replication')
if self.replication_factor is not None:
validate_int(self.replication_factor, 'set replication', 2, 5)
hadoop_home = self.get_opt('hadoop_home')
if hadoop_home is not None:
os.environ['HADOOP_HOME'] = hadoop_home
hadoop_home_env = os.getenv('HADOOP_HOME')
log_option('HADOOP_HOME', hadoop_home_env)
if hadoop_home_env:
log.info('will search for Hadoop config in %s/conf', hadoop_home_env)
def run(self):
log.info('initiating snakebite hdfs client')
try:
client = AutoConfigClient()
except krbV.Krb5Error as _: # pylint: disable=no-member
if self.verbose:
print('', file=sys.stderr)
print(_, file=sys.stderr)
start_time = time.time()
dir_count = 0
file_count = 0
repl1_count = 0
for path in self.path_list:
try:
result_list = client.ls([path], recurse=True, include_toplevel=True, include_children=True)
for result in result_list:
if self.verbose and (dir_count + file_count) % 100 == 0:
print('.', file=sys.stderr, end='')
if result['block_replication'] == 0:
dir_count += 1
continue
file_count += 1
if result['block_replication'] == 1:
file_path = result['path']
repl1_count += 1
if self.verbose:
print('', file=sys.stderr)
print(file_path)
if self.replication_factor:
log.info('setting replication factor to %s on %s', self.replication_factor, file_path)
# returns a generator so must evaluate in order to actually execute
# otherwise you find there is no effect on the replication factor
for _ in client.setrep([file_path], self.replication_factor, recurse=False):
if 'result' not in _:
print('WARNING: result field not found in setrep result: {}'.format(_),
file=sys.stderr)
continue
if not _['result']:
print('WARNING: failed to setrep: {}'.format(_))
except (snakebite.errors.FileNotFoundException, snakebite.errors.RequestError) as _:
if self.verbose:
print('', file=sys.stderr)
print(_, file=sys.stderr)
if self.verbose:
print('', file=sys.stderr)
secs = int(time.time() - start_time)
print('\nCompleted in {} secs\n'.format(secs), file=sys.stderr)
print('{} files with replication factor 1 out of {} files in {} dirs'\
.format(repl1_count, file_count, dir_count), file=sys.stderr)
if __name__ == '__main__':
HdfsFindReplicationFactor1().main()
| [
"[email protected]"
]
| |
2602ad8b379a491de681ea75acdc8be364eec2e4 | c6aea4804eae5a9390f1b49ab430292b3ddadb5b | /ipython/ipython_config.py | 49f484b383369f26bfb8fe49ac6d72b9d21462c6 | []
| no_license | dandavison/dotfiles | eceb48a2e831a9cf792a5d8ca5434610b20d0151 | b3ee393ac9585ea22b3747d65f3674f7aeeac947 | refs/heads/main | 2023-08-28T16:34:25.815800 | 2023-08-13T23:16:59 | 2023-08-13T23:16:59 | 244,988,871 | 7 | 3 | null | null | null | null | UTF-8 | Python | false | false | 123 | py | # print('ipython_config.py')
c = get_config()
# (NoColor, Linux, LightBG)
# c.TerminalInteractiveShell.colors = 'NoColor'
| [
"[email protected]"
]
| |
74bb10c845c3b32c868125b06e7cbd85e69d75f9 | cf58c2c216f6c76c71b5a04f72d79fb1d58e4b64 | /homeassistant/components/recorder/db_schema.py | c97f99b9e8cf0d8a57ef2aa630bbb5f7e8a09669 | [
"Apache-2.0"
]
| permissive | whtsky/home-assistant | c301a7a0c2f8e94806d411b705c8f7b5939355d2 | 2ea5811e3a34e228908802e18c29af1c2fc249c5 | refs/heads/dev | 2023-08-19T07:37:29.365289 | 2023-02-17T22:21:28 | 2023-02-17T22:21:28 | 204,410,639 | 1 | 0 | Apache-2.0 | 2023-02-22T06:14:25 | 2019-08-26T06:30:12 | Python | UTF-8 | Python | false | false | 26,470 | py | """Models for SQLAlchemy."""
from __future__ import annotations
from collections.abc import Callable
from datetime import datetime, timedelta
import logging
import time
from typing import Any, cast
import ciso8601
from fnvhash import fnv1a_32
from sqlalchemy import (
JSON,
BigInteger,
Boolean,
ColumnElement,
DateTime,
Float,
ForeignKey,
Identity,
Index,
Integer,
SmallInteger,
String,
Text,
distinct,
type_coerce,
)
from sqlalchemy.dialects import mysql, oracle, postgresql, sqlite
from sqlalchemy.engine.interfaces import Dialect
from sqlalchemy.orm import DeclarativeBase, Mapped, aliased, mapped_column, relationship
from sqlalchemy.orm.query import RowReturningQuery
from sqlalchemy.orm.session import Session
from typing_extensions import Self
from homeassistant.const import (
MAX_LENGTH_EVENT_CONTEXT_ID,
MAX_LENGTH_EVENT_EVENT_TYPE,
MAX_LENGTH_EVENT_ORIGIN,
MAX_LENGTH_STATE_ENTITY_ID,
MAX_LENGTH_STATE_STATE,
)
from homeassistant.core import Context, Event, EventOrigin, State, split_entity_id
from homeassistant.helpers.json import JSON_DUMP, json_bytes, json_bytes_strip_null
import homeassistant.util.dt as dt_util
from homeassistant.util.json import (
JSON_DECODE_EXCEPTIONS,
json_loads,
json_loads_object,
)
from .const import ALL_DOMAIN_EXCLUDE_ATTRS, SupportedDialect
from .models import (
StatisticData,
StatisticDataTimestamp,
StatisticMetaData,
datetime_to_timestamp_or_none,
process_timestamp,
)
# SQLAlchemy Schema
# pylint: disable=invalid-name
class Base(DeclarativeBase):
"""Base class for tables."""
SCHEMA_VERSION = 35
_LOGGER = logging.getLogger(__name__)
TABLE_EVENTS = "events"
TABLE_EVENT_DATA = "event_data"
TABLE_STATES = "states"
TABLE_STATE_ATTRIBUTES = "state_attributes"
TABLE_RECORDER_RUNS = "recorder_runs"
TABLE_SCHEMA_CHANGES = "schema_changes"
TABLE_STATISTICS = "statistics"
TABLE_STATISTICS_META = "statistics_meta"
TABLE_STATISTICS_RUNS = "statistics_runs"
TABLE_STATISTICS_SHORT_TERM = "statistics_short_term"
STATISTICS_TABLES = ("statistics", "statistics_short_term")
MAX_STATE_ATTRS_BYTES = 16384
PSQL_DIALECT = SupportedDialect.POSTGRESQL
ALL_TABLES = [
TABLE_STATES,
TABLE_STATE_ATTRIBUTES,
TABLE_EVENTS,
TABLE_EVENT_DATA,
TABLE_RECORDER_RUNS,
TABLE_SCHEMA_CHANGES,
TABLE_STATISTICS,
TABLE_STATISTICS_META,
TABLE_STATISTICS_RUNS,
TABLE_STATISTICS_SHORT_TERM,
]
TABLES_TO_CHECK = [
TABLE_STATES,
TABLE_EVENTS,
TABLE_RECORDER_RUNS,
TABLE_SCHEMA_CHANGES,
]
LAST_UPDATED_INDEX_TS = "ix_states_last_updated_ts"
ENTITY_ID_LAST_UPDATED_INDEX_TS = "ix_states_entity_id_last_updated_ts"
EVENTS_CONTEXT_ID_INDEX = "ix_events_context_id"
STATES_CONTEXT_ID_INDEX = "ix_states_context_id"
class FAST_PYSQLITE_DATETIME(sqlite.DATETIME):
"""Use ciso8601 to parse datetimes instead of sqlalchemy built-in regex."""
def result_processor(self, dialect, coltype): # type: ignore[no-untyped-def]
"""Offload the datetime parsing to ciso8601."""
return lambda value: None if value is None else ciso8601.parse_datetime(value)
JSON_VARIANT_CAST = Text().with_variant(
postgresql.JSON(none_as_null=True), "postgresql" # type: ignore[no-untyped-call]
)
JSONB_VARIANT_CAST = Text().with_variant(
postgresql.JSONB(none_as_null=True), "postgresql" # type: ignore[no-untyped-call]
)
DATETIME_TYPE = (
DateTime(timezone=True)
.with_variant(mysql.DATETIME(timezone=True, fsp=6), "mysql") # type: ignore[no-untyped-call]
.with_variant(FAST_PYSQLITE_DATETIME(), "sqlite") # type: ignore[no-untyped-call]
)
DOUBLE_TYPE = (
Float()
.with_variant(mysql.DOUBLE(asdecimal=False), "mysql") # type: ignore[no-untyped-call]
.with_variant(oracle.DOUBLE_PRECISION(), "oracle")
.with_variant(postgresql.DOUBLE_PRECISION(), "postgresql")
)
TIMESTAMP_TYPE = DOUBLE_TYPE
class JSONLiteral(JSON):
"""Teach SA how to literalize json."""
def literal_processor(self, dialect: Dialect) -> Callable[[Any], str]:
"""Processor to convert a value to JSON."""
def process(value: Any) -> str:
"""Dump json."""
return JSON_DUMP(value)
return process
EVENT_ORIGIN_ORDER = [EventOrigin.local, EventOrigin.remote]
EVENT_ORIGIN_TO_IDX = {origin: idx for idx, origin in enumerate(EVENT_ORIGIN_ORDER)}
class Events(Base):
"""Event history data."""
__table_args__ = (
# Used for fetching events at a specific time
# see logbook
Index("ix_events_event_type_time_fired_ts", "event_type", "time_fired_ts"),
{"mysql_default_charset": "utf8mb4", "mysql_collate": "utf8mb4_unicode_ci"},
)
__tablename__ = TABLE_EVENTS
event_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
event_type: Mapped[str | None] = mapped_column(String(MAX_LENGTH_EVENT_EVENT_TYPE))
event_data: Mapped[str | None] = mapped_column(
Text().with_variant(mysql.LONGTEXT, "mysql")
)
origin: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_ORIGIN)
) # no longer used for new rows
origin_idx: Mapped[int | None] = mapped_column(SmallInteger)
time_fired: Mapped[datetime | None] = mapped_column(
DATETIME_TYPE
) # no longer used for new rows
time_fired_ts: Mapped[float | None] = mapped_column(TIMESTAMP_TYPE, index=True)
context_id: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_CONTEXT_ID), index=True
)
context_user_id: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_CONTEXT_ID)
)
context_parent_id: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_CONTEXT_ID)
)
data_id: Mapped[int | None] = mapped_column(
Integer, ForeignKey("event_data.data_id"), index=True
)
event_data_rel: Mapped[EventData | None] = relationship("EventData")
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
return (
"<recorder.Events("
f"id={self.event_id}, type='{self.event_type}', "
f"origin_idx='{self.origin_idx}', time_fired='{self._time_fired_isotime}'"
f", data_id={self.data_id})>"
)
@property
def _time_fired_isotime(self) -> str | None:
"""Return time_fired as an isotime string."""
date_time: datetime | None
if self.time_fired_ts is not None:
date_time = dt_util.utc_from_timestamp(self.time_fired_ts)
else:
date_time = process_timestamp(self.time_fired)
if date_time is None:
return None
return date_time.isoformat(sep=" ", timespec="seconds")
@staticmethod
def from_event(event: Event) -> Events:
"""Create an event database object from a native event."""
return Events(
event_type=event.event_type,
event_data=None,
origin_idx=EVENT_ORIGIN_TO_IDX.get(event.origin),
time_fired=None,
time_fired_ts=dt_util.utc_to_timestamp(event.time_fired),
context_id=event.context.id,
context_user_id=event.context.user_id,
context_parent_id=event.context.parent_id,
)
def to_native(self, validate_entity_id: bool = True) -> Event | None:
"""Convert to a native HA Event."""
context = Context(
id=self.context_id,
user_id=self.context_user_id,
parent_id=self.context_parent_id,
)
try:
return Event(
self.event_type or "",
json_loads_object(self.event_data) if self.event_data else {},
EventOrigin(self.origin)
if self.origin
else EVENT_ORIGIN_ORDER[self.origin_idx or 0],
dt_util.utc_from_timestamp(self.time_fired_ts or 0),
context=context,
)
except JSON_DECODE_EXCEPTIONS:
# When json_loads fails
_LOGGER.exception("Error converting to event: %s", self)
return None
class EventData(Base):
"""Event data history."""
__table_args__ = (
{"mysql_default_charset": "utf8mb4", "mysql_collate": "utf8mb4_unicode_ci"},
)
__tablename__ = TABLE_EVENT_DATA
data_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
hash: Mapped[int | None] = mapped_column(BigInteger, index=True)
# Note that this is not named attributes to avoid confusion with the states table
shared_data: Mapped[str | None] = mapped_column(
Text().with_variant(mysql.LONGTEXT, "mysql")
)
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
return (
"<recorder.EventData("
f"id={self.data_id}, hash='{self.hash}', data='{self.shared_data}'"
")>"
)
@staticmethod
def shared_data_bytes_from_event(
event: Event, dialect: SupportedDialect | None
) -> bytes:
"""Create shared_data from an event."""
if dialect == SupportedDialect.POSTGRESQL:
return json_bytes_strip_null(event.data)
return json_bytes(event.data)
@staticmethod
def hash_shared_data_bytes(shared_data_bytes: bytes) -> int:
"""Return the hash of json encoded shared data."""
return cast(int, fnv1a_32(shared_data_bytes))
def to_native(self) -> dict[str, Any]:
"""Convert to an event data dictionary."""
shared_data = self.shared_data
if shared_data is None:
return {}
try:
return cast(dict[str, Any], json_loads(shared_data))
except JSON_DECODE_EXCEPTIONS:
_LOGGER.exception("Error converting row to event data: %s", self)
return {}
class States(Base):
"""State change history."""
__table_args__ = (
# Used for fetching the state of entities at a specific time
# (get_states in history.py)
Index(ENTITY_ID_LAST_UPDATED_INDEX_TS, "entity_id", "last_updated_ts"),
{"mysql_default_charset": "utf8mb4", "mysql_collate": "utf8mb4_unicode_ci"},
)
__tablename__ = TABLE_STATES
state_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
entity_id: Mapped[str | None] = mapped_column(String(MAX_LENGTH_STATE_ENTITY_ID))
state: Mapped[str | None] = mapped_column(String(MAX_LENGTH_STATE_STATE))
attributes: Mapped[str | None] = mapped_column(
Text().with_variant(mysql.LONGTEXT, "mysql")
) # no longer used for new rows
event_id: Mapped[int | None] = mapped_column( # no longer used for new rows
Integer, ForeignKey("events.event_id", ondelete="CASCADE"), index=True
)
last_changed: Mapped[datetime | None] = mapped_column(
DATETIME_TYPE
) # no longer used for new rows
last_changed_ts: Mapped[float | None] = mapped_column(TIMESTAMP_TYPE)
last_updated: Mapped[datetime | None] = mapped_column(
DATETIME_TYPE
) # no longer used for new rows
last_updated_ts: Mapped[float | None] = mapped_column(
TIMESTAMP_TYPE, default=time.time, index=True
)
old_state_id: Mapped[int | None] = mapped_column(
Integer, ForeignKey("states.state_id"), index=True
)
attributes_id: Mapped[int | None] = mapped_column(
Integer, ForeignKey("state_attributes.attributes_id"), index=True
)
context_id: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_CONTEXT_ID), index=True
)
context_user_id: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_CONTEXT_ID)
)
context_parent_id: Mapped[str | None] = mapped_column(
String(MAX_LENGTH_EVENT_CONTEXT_ID)
)
origin_idx: Mapped[int | None] = mapped_column(
SmallInteger
) # 0 is local, 1 is remote
old_state: Mapped[States | None] = relationship("States", remote_side=[state_id])
state_attributes: Mapped[StateAttributes | None] = relationship("StateAttributes")
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
return (
f"<recorder.States(id={self.state_id}, entity_id='{self.entity_id}',"
f" state='{self.state}', event_id='{self.event_id}',"
f" last_updated='{self._last_updated_isotime}',"
f" old_state_id={self.old_state_id}, attributes_id={self.attributes_id})>"
)
@property
def _last_updated_isotime(self) -> str | None:
"""Return last_updated as an isotime string."""
date_time: datetime | None
if self.last_updated_ts is not None:
date_time = dt_util.utc_from_timestamp(self.last_updated_ts)
else:
date_time = process_timestamp(self.last_updated)
if date_time is None:
return None
return date_time.isoformat(sep=" ", timespec="seconds")
@staticmethod
def from_event(event: Event) -> States:
"""Create object from a state_changed event."""
entity_id = event.data["entity_id"]
state: State | None = event.data.get("new_state")
dbstate = States(
entity_id=entity_id,
attributes=None,
context_id=event.context.id,
context_user_id=event.context.user_id,
context_parent_id=event.context.parent_id,
origin_idx=EVENT_ORIGIN_TO_IDX.get(event.origin),
last_updated=None,
last_changed=None,
)
# None state means the state was removed from the state machine
if state is None:
dbstate.state = ""
dbstate.last_updated_ts = dt_util.utc_to_timestamp(event.time_fired)
dbstate.last_changed_ts = None
return dbstate
dbstate.state = state.state
dbstate.last_updated_ts = dt_util.utc_to_timestamp(state.last_updated)
if state.last_updated == state.last_changed:
dbstate.last_changed_ts = None
else:
dbstate.last_changed_ts = dt_util.utc_to_timestamp(state.last_changed)
return dbstate
def to_native(self, validate_entity_id: bool = True) -> State | None:
"""Convert to an HA state object."""
context = Context(
id=self.context_id,
user_id=self.context_user_id,
parent_id=self.context_parent_id,
)
try:
attrs = json_loads_object(self.attributes) if self.attributes else {}
except JSON_DECODE_EXCEPTIONS:
# When json_loads fails
_LOGGER.exception("Error converting row to state: %s", self)
return None
if self.last_changed_ts is None or self.last_changed_ts == self.last_updated_ts:
last_changed = last_updated = dt_util.utc_from_timestamp(
self.last_updated_ts or 0
)
else:
last_updated = dt_util.utc_from_timestamp(self.last_updated_ts or 0)
last_changed = dt_util.utc_from_timestamp(self.last_changed_ts or 0)
return State(
self.entity_id or "",
self.state, # type: ignore[arg-type]
# Join the state_attributes table on attributes_id to get the attributes
# for newer states
attrs,
last_changed,
last_updated,
context=context,
validate_entity_id=validate_entity_id,
)
class StateAttributes(Base):
"""State attribute change history."""
__table_args__ = (
{"mysql_default_charset": "utf8mb4", "mysql_collate": "utf8mb4_unicode_ci"},
)
__tablename__ = TABLE_STATE_ATTRIBUTES
attributes_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
hash: Mapped[int | None] = mapped_column(BigInteger, index=True)
# Note that this is not named attributes to avoid confusion with the states table
shared_attrs: Mapped[str | None] = mapped_column(
Text().with_variant(mysql.LONGTEXT, "mysql")
)
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
return (
f"<recorder.StateAttributes(id={self.attributes_id}, hash='{self.hash}',"
f" attributes='{self.shared_attrs}')>"
)
@staticmethod
def shared_attrs_bytes_from_event(
event: Event,
exclude_attrs_by_domain: dict[str, set[str]],
dialect: SupportedDialect | None,
) -> bytes:
"""Create shared_attrs from a state_changed event."""
state: State | None = event.data.get("new_state")
# None state means the state was removed from the state machine
if state is None:
return b"{}"
domain = split_entity_id(state.entity_id)[0]
exclude_attrs = (
exclude_attrs_by_domain.get(domain, set()) | ALL_DOMAIN_EXCLUDE_ATTRS
)
encoder = json_bytes_strip_null if dialect == PSQL_DIALECT else json_bytes
bytes_result = encoder(
{k: v for k, v in state.attributes.items() if k not in exclude_attrs}
)
if len(bytes_result) > MAX_STATE_ATTRS_BYTES:
_LOGGER.warning(
"State attributes for %s exceed maximum size of %s bytes. "
"This can cause database performance issues; Attributes "
"will not be stored",
state.entity_id,
MAX_STATE_ATTRS_BYTES,
)
return b"{}"
return bytes_result
@staticmethod
def hash_shared_attrs_bytes(shared_attrs_bytes: bytes) -> int:
"""Return the hash of json encoded shared attributes."""
return cast(int, fnv1a_32(shared_attrs_bytes))
def to_native(self) -> dict[str, Any]:
"""Convert to a state attributes dictionary."""
shared_attrs = self.shared_attrs
if shared_attrs is None:
return {}
try:
return cast(dict[str, Any], json_loads(shared_attrs))
except JSON_DECODE_EXCEPTIONS:
# When json_loads fails
_LOGGER.exception("Error converting row to state attributes: %s", self)
return {}
class StatisticsBase:
"""Statistics base class."""
id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
created: Mapped[datetime] = mapped_column(
DATETIME_TYPE, default=dt_util.utcnow
) # No longer used
created_ts: Mapped[float] = mapped_column(TIMESTAMP_TYPE, default=time.time)
metadata_id: Mapped[int | None] = mapped_column(
Integer,
ForeignKey(f"{TABLE_STATISTICS_META}.id", ondelete="CASCADE"),
index=True,
)
start: Mapped[datetime | None] = mapped_column(
DATETIME_TYPE, index=True
) # No longer used
start_ts: Mapped[float | None] = mapped_column(TIMESTAMP_TYPE, index=True)
mean: Mapped[float | None] = mapped_column(DOUBLE_TYPE)
min: Mapped[float | None] = mapped_column(DOUBLE_TYPE)
max: Mapped[float | None] = mapped_column(DOUBLE_TYPE)
last_reset: Mapped[datetime | None] = mapped_column(DATETIME_TYPE)
last_reset_ts: Mapped[float | None] = mapped_column(TIMESTAMP_TYPE)
state: Mapped[float | None] = mapped_column(DOUBLE_TYPE)
sum: Mapped[float | None] = mapped_column(DOUBLE_TYPE)
duration: timedelta
@classmethod
def from_stats(cls, metadata_id: int, stats: StatisticData) -> Self:
"""Create object from a statistics with datatime objects."""
return cls( # type: ignore[call-arg]
metadata_id=metadata_id,
created=None,
created_ts=time.time(),
start=None,
start_ts=dt_util.utc_to_timestamp(stats["start"]),
mean=stats.get("mean"),
min=stats.get("min"),
max=stats.get("max"),
last_reset=None,
last_reset_ts=datetime_to_timestamp_or_none(stats.get("last_reset")),
state=stats.get("state"),
sum=stats.get("sum"),
)
@classmethod
def from_stats_ts(cls, metadata_id: int, stats: StatisticDataTimestamp) -> Self:
"""Create object from a statistics with timestamps."""
return cls( # type: ignore[call-arg]
metadata_id=metadata_id,
created=None,
created_ts=time.time(),
start=None,
start_ts=stats["start_ts"],
mean=stats.get("mean"),
min=stats.get("min"),
max=stats.get("max"),
last_reset=None,
last_reset_ts=stats.get("last_reset_ts"),
state=stats.get("state"),
sum=stats.get("sum"),
)
class Statistics(Base, StatisticsBase):
"""Long term statistics."""
duration = timedelta(hours=1)
__table_args__ = (
# Used for fetching statistics for a certain entity at a specific time
Index(
"ix_statistics_statistic_id_start_ts",
"metadata_id",
"start_ts",
unique=True,
),
)
__tablename__ = TABLE_STATISTICS
class StatisticsShortTerm(Base, StatisticsBase):
"""Short term statistics."""
duration = timedelta(minutes=5)
__table_args__ = (
# Used for fetching statistics for a certain entity at a specific time
Index(
"ix_statistics_short_term_statistic_id_start_ts",
"metadata_id",
"start_ts",
unique=True,
),
)
__tablename__ = TABLE_STATISTICS_SHORT_TERM
class StatisticsMeta(Base):
"""Statistics meta data."""
__table_args__ = (
{"mysql_default_charset": "utf8mb4", "mysql_collate": "utf8mb4_unicode_ci"},
)
__tablename__ = TABLE_STATISTICS_META
id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
statistic_id: Mapped[str | None] = mapped_column(
String(255), index=True, unique=True
)
source: Mapped[str | None] = mapped_column(String(32))
unit_of_measurement: Mapped[str | None] = mapped_column(String(255))
has_mean: Mapped[bool | None] = mapped_column(Boolean)
has_sum: Mapped[bool | None] = mapped_column(Boolean)
name: Mapped[str | None] = mapped_column(String(255))
@staticmethod
def from_meta(meta: StatisticMetaData) -> StatisticsMeta:
"""Create object from meta data."""
return StatisticsMeta(**meta)
class RecorderRuns(Base):
"""Representation of recorder run."""
__table_args__ = (Index("ix_recorder_runs_start_end", "start", "end"),)
__tablename__ = TABLE_RECORDER_RUNS
run_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
start: Mapped[datetime] = mapped_column(DATETIME_TYPE, default=dt_util.utcnow)
end: Mapped[datetime | None] = mapped_column(DATETIME_TYPE)
closed_incorrect: Mapped[bool] = mapped_column(Boolean, default=False)
created: Mapped[datetime] = mapped_column(DATETIME_TYPE, default=dt_util.utcnow)
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
end = (
f"'{self.end.isoformat(sep=' ', timespec='seconds')}'" if self.end else None
)
return (
f"<recorder.RecorderRuns(id={self.run_id},"
f" start='{self.start.isoformat(sep=' ', timespec='seconds')}', end={end},"
f" closed_incorrect={self.closed_incorrect},"
f" created='{self.created.isoformat(sep=' ', timespec='seconds')}')>"
)
def entity_ids(self, point_in_time: datetime | None = None) -> list[str]:
"""Return the entity ids that existed in this run.
Specify point_in_time if you want to know which existed at that point
in time inside the run.
"""
session = Session.object_session(self)
assert session is not None, "RecorderRuns need to be persisted"
query: RowReturningQuery[tuple[str]] = session.query(distinct(States.entity_id))
query = query.filter(States.last_updated >= self.start)
if point_in_time is not None:
query = query.filter(States.last_updated < point_in_time)
elif self.end is not None:
query = query.filter(States.last_updated < self.end)
return [row[0] for row in query]
def to_native(self, validate_entity_id: bool = True) -> Self:
"""Return self, native format is this model."""
return self
class SchemaChanges(Base):
"""Representation of schema version changes."""
__tablename__ = TABLE_SCHEMA_CHANGES
change_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
schema_version: Mapped[int | None] = mapped_column(Integer)
changed: Mapped[datetime] = mapped_column(DATETIME_TYPE, default=dt_util.utcnow)
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
return (
"<recorder.SchemaChanges("
f"id={self.change_id}, schema_version={self.schema_version}, "
f"changed='{self.changed.isoformat(sep=' ', timespec='seconds')}'"
")>"
)
class StatisticsRuns(Base):
"""Representation of statistics run."""
__tablename__ = TABLE_STATISTICS_RUNS
run_id: Mapped[int] = mapped_column(Integer, Identity(), primary_key=True)
start: Mapped[datetime] = mapped_column(DATETIME_TYPE, index=True)
def __repr__(self) -> str:
"""Return string representation of instance for debugging."""
return (
f"<recorder.StatisticsRuns(id={self.run_id},"
f" start='{self.start.isoformat(sep=' ', timespec='seconds')}', )>"
)
EVENT_DATA_JSON = type_coerce(
EventData.shared_data.cast(JSONB_VARIANT_CAST), JSONLiteral(none_as_null=True)
)
OLD_FORMAT_EVENT_DATA_JSON = type_coerce(
Events.event_data.cast(JSONB_VARIANT_CAST), JSONLiteral(none_as_null=True)
)
SHARED_ATTRS_JSON = type_coerce(
StateAttributes.shared_attrs.cast(JSON_VARIANT_CAST), JSON(none_as_null=True)
)
OLD_FORMAT_ATTRS_JSON = type_coerce(
States.attributes.cast(JSON_VARIANT_CAST), JSON(none_as_null=True)
)
ENTITY_ID_IN_EVENT: ColumnElement = EVENT_DATA_JSON["entity_id"]
OLD_ENTITY_ID_IN_EVENT: ColumnElement = OLD_FORMAT_EVENT_DATA_JSON["entity_id"]
DEVICE_ID_IN_EVENT: ColumnElement = EVENT_DATA_JSON["device_id"]
OLD_STATE = aliased(States, name="old_state")
| [
"[email protected]"
]
| |
eae6bc8c7738ace5b044e7819c925a0627ad7571 | 8049911411405e9db412fe81a9ed8869f28907ba | /python test/5.py | 2df3a5715cfd2ed1d779d03bff8bed2a4fe0e68f | [
"MIT"
]
| permissive | merry-hyelyn/LIKE_LION | e55a7b10a7c3547cd051b1f1cbebee67f9db0649 | 26d6642a88d5c075447c60d43a70a7d0f082fb07 | refs/heads/master | 2020-05-04T09:12:35.787141 | 2019-07-25T14:36:07 | 2019-07-25T14:36:07 | 179,063,513 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 77 | py | list1 = ['a', 'c', 'd', 'b', 'e']
list1.sort()
list1.reverse()
print(list1)
| [
"[email protected]"
]
| |
9e6aaeb8d65495a48c801f2d753a356698b1c673 | bffc8513026009bf6f8bda93b8cc779d9cfefea0 | /parser_table.py | 1e198324d636de7b192f5d330ad329eeff99193c | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
]
| permissive | Stanford-PERTS/mindsetmeter | 9e1a764271c9eedc0637eb5a8ab95dd863a65af3 | 74c0c9a3e908f54a9cae07e171b572225290492a | refs/heads/master | 2022-10-25T14:22:37.916936 | 2020-06-14T17:37:10 | 2020-06-14T17:37:10 | 272,250,181 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 215,305 | py |
# parser_table.py
# This file is automatically generated. Do not edit.
_tabversion = '3.2'
_lr_method = 'LALR'
_lr_signature = b'\x14\xd0^\xea\x1d8\x17\x96\xba%\xf1\xb7P\xab\xe7\x0e'
_lr_action_items = 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_lr_action = { }
for _k, _v in _lr_action_items.items():
for _x,_y in zip(_v[0],_v[1]):
if not _x in _lr_action: _lr_action[_x] = { }
_lr_action[_x][_k] = _y
del _lr_action_items
_lr_goto_items = {'comma_import_as_name':([400,557,],[553,648,]),'comma_name_list':([153,160,],[377,377,]),'factor':([0,2,6,8,15,36,43,50,56,57,61,63,70,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,180,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[9,9,117,9,9,9,9,9,226,9,9,9,9,9,286,9,9,9,9,9,9,343,344,345,346,347,9,9,9,9,9,9,9,9,395,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,]),'elif_part':([552,641,],[642,688,]),'equals_yield_expr_or_testlist_list':([108,111,116,],[318,318,318,]),'file_stmts':([0,],[2,]),'del_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,]),'test_comma_list':([0,2,131,151,396,426,435,453,535,576,591,605,629,636,638,664,683,707,],[8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,]),'comma_subscript':([361,521,],[519,620,]),'import_as_name':([202,401,556,],[400,400,647,]),'small_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[11,11,349,11,11,11,11,11,11,11,11,11,11,11,11,11,11,]),'period_name':([277,471,],[472,599,]),'comma_vfpdef_list':([445,448,714,],[583,587,727,]),'period_or_ellipsis_list':([49,],[224,]),'atom':([0,2,6,8,15,36,43,50,56,57,61,63,70,76,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,180,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[17,17,17,17,17,17,17,17,17,17,17,17,17,263,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,17,]),'comma_item_list':([214,564,],[420,651,]),'trailer_list':([17,263,],[143,143,]),'tfpdef':([354,510,514,670,676,702,719,720,737,741,745,],[516,608,615,703,703,717,608,730,703,703,747,]),'sliceop_opt':([623,],[679,]),'async_funcdef':([0,2,28,535,629,],[25,25,156,25,25,]),'subproc_arg':([34,66,178,186,391,394,640,],[181,181,181,181,181,181,181,]),'and_not_test_list':([13,],[136,]),'with_item':([70,454,],[257,593,]),'comma_subscript_list_opt':([361,],[518,]),'pipe_xor_expr_list':([64,],[241,]),'comma_tfpdef_list':([607,616,729,],[666,673,735,]),'dotted_as_name':([86,477,],[278,601,]),'classdef_or_funcdef':([28,],[154,]),'equals_test_opt':([251,516,660,703,],[445,616,697,718,]),'trailer':([17,143,263,],[145,360,145,]),'equals_yield_expr_or_testlist_list_opt':([108,111,116,],[322,341,341,]),'comma_vfpdef':([445,448,583,587,714,727,],[580,580,659,659,580,659,]),'expr_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,39,]),'comma_tfpdef_list_opt':([616,729,],[675,736,]),'comma_name':([153,160,377,],[379,379,542,]),'yield_arg_opt':([63,],[239,]),'comp_op_expr_list':([107,534,],[309,309,]),'comp_op_expr':([107,309,534,],[313,496,313,]),'continue_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[42,42,42,42,42,42,42,42,42,42,42,42,42,42,42,42,42,]),'comma_item':([214,420,564,651,],[422,568,422,568,]),'raise_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[44,44,44,44,44,44,44,44,44,44,44,44,44,44,44,44,44,]),'subproc':([34,66,178,186,],[183,249,384,393,]),'vfpdef':([67,253,254,584,589,662,695,696,723,728,733,734,739,742,],[251,449,450,660,660,698,449,715,251,660,660,740,744,746,]),'else_part':([536,577,643,699,],[632,657,689,716,]),'subproc_atoms':([34,66,178,186,391,],[199,199,199,199,548,]),'classdef':([0,2,28,535,629,],[46,46,157,46,46,]),'item':([43,421,570,652,],[214,572,572,572,]),'stmt':([0,2,535,629,],[53,53,628,682,]),'comma_test_list':([84,212,218,236,564,],[275,275,425,275,275,]),'comp_iter':([693,725,],[713,713,]),'comma_argument_list':([366,],[526,]),'func_call_opt':([228,],[430,]),'attr_name':([41,],[208,]),'pm_term':([71,261,],[262,459,]),'and_expr':([0,2,8,15,36,43,50,57,61,63,70,78,94,96,97,98,101,110,131,137,141,146,148,151,164,180,205,210,229,238,242,244,274,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,433,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,]),'arglist_opt':([148,408,],[367,561,]),'atom_expr':([0,2,6,8,15,36,43,50,56,57,61,63,70,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,180,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,35,]),'trailer_list_opt':([17,263,],[144,460,]),'test_nocond':([711,738,],[725,743,]),'colon_test_opt':([512,],[613,]),'finally_part_opt':([536,632,],[633,684,]),'test_or_star_expr':([0,2,43,96,98,131,148,151,205,396,408,435,453,528,535,576,591,605,629,636,638,664,683,707,],[40,40,213,289,289,40,368,40,405,40,368,40,40,368,40,40,40,40,40,40,40,40,40,40,]),'subscript':([146,522,],[361,621,]),'attr_period_name':([209,409,],[411,562,]),'dotted_as_names':([86,],[276,]),'comma_dotted_as_name':([278,478,],[476,602,]),'break_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,]),'and_not_test':([13,136,],[139,355,]),'stmt_list':([535,],[629,]),'pass_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,]),'except_part_list':([376,],[536,]),'shift_arith_expr_list_opt':([87,],[283,]),'newline_or_stmt':([0,2,],[75,113,]),'comma_with_item':([257,451,],[452,590,]),'comp_op_expr_list_opt':([107,534,],[301,301,]),'comma_pow_vfpdef_opt':([448,587,],[586,661,]),'augassign':([108,],[320,]),'typedargslist':([354,],[515,]),'funcdef':([0,2,28,31,159,535,629,],[79,79,158,162,162,79,79,]),'comma_dotted_as_name_list_opt':([278,],[474,]),'comma_subscript_list':([361,],[521,]),'return_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,104,]),'except_clause':([376,536,],[538,538,]),'pipe':([183,249,384,393,],[391,391,391,391,]),'comma_dotted_as_name_list':([278,],[478,]),'period_or_ellipsis':([49,224,],[222,428,]),'varargslist':([67,723,],[252,252,]),'as_expr':([258,],[456,]),'op_factor_list':([9,],[128,]),'colon_test':([512,],[614,]),'yield_expr_or_testlist_comp_opt':([96,],[294,]),'string_literal':([0,2,6,8,15,20,34,36,43,50,56,57,61,63,66,70,76,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,178,180,186,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,391,394,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,640,644,652,653,654,664,677,683,707,711,738,],[92,92,92,92,92,152,174,92,92,92,92,92,92,92,174,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,174,92,174,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,174,174,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,174,92,92,92,92,92,92,92,92,92,92,]),'rarrow_test_opt':([353,],[507,]),'string_literal_list':([0,2,6,8,15,36,43,50,56,57,61,63,70,76,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,180,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,]),'comma_expr_or_star_expr_list':([265,],[462,]),'single_input':([0,],[93,]),'star_expr':([0,2,43,47,78,96,97,98,131,148,151,205,396,408,417,435,453,461,528,535,576,591,605,629,636,638,664,683,707,],[77,77,77,218,264,77,264,77,77,77,77,77,77,77,264,77,77,264,77,77,77,77,77,77,77,77,77,77,77,]),'semi_small_stmt':([11,132,],[129,350,]),'subscriptlist':([146,],[363,]),'suite':([151,396,435,453,576,591,605,636,638,664,683,707,],[376,552,577,592,656,663,665,685,687,699,706,721,]),'import_from_post':([202,],[397,]),'semi_small_stmt_list':([11,],[132,]),'comma_expr_or_star_expr':([265,462,],[464,597,]),'pipe_xor_expr':([64,241,],[243,437,]),'elif_part_list_opt':([552,],[643,]),'newlines':([111,],[340,]),'async_with_stmt':([0,2,535,629,],[91,91,91,91,]),'lambdef':([0,2,8,15,36,43,57,61,63,70,94,96,98,110,131,146,148,151,164,180,205,238,274,314,320,369,372,396,408,413,414,421,424,426,429,435,442,444,453,454,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,654,664,677,683,707,],[103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,103,]),'empty':([0,2,9,11,13,17,40,43,49,60,63,65,67,81,87,96,98,107,108,110,111,116,131,132,146,148,151,153,160,204,213,218,228,251,253,263,265,278,279,287,289,353,354,361,366,396,400,402,408,416,420,426,435,445,448,453,462,491,510,512,516,518,522,524,526,534,535,536,552,555,576,582,587,591,605,607,616,623,629,632,636,637,638,651,660,664,666,675,677,683,693,695,703,707,714,719,723,725,729,],[85,114,123,130,135,147,207,215,220,230,237,245,256,270,281,293,298,307,332,335,338,332,114,130,365,373,114,381,381,207,207,207,432,446,447,147,207,475,480,486,207,509,513,520,207,114,554,480,373,207,207,114,114,581,585,114,207,207,609,611,446,207,365,365,207,307,114,634,645,207,114,207,585,114,114,671,674,678,114,634,114,480,114,207,446,114,671,207,365,114,709,447,446,114,581,609,256,709,674,]),'compound_stmt':([0,2,535,629,],[109,115,115,115,]),'comma_pow_vfpdef':([448,587,],[588,588,]),'eval_input':([0,],[112,]),'argument':([148,408,528,],[366,366,626,]),'assert_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,]),'comparison':([0,2,8,15,36,43,57,61,63,70,94,96,98,101,110,131,137,141,146,148,151,164,180,205,238,244,274,314,320,369,372,396,408,413,414,421,424,426,429,435,442,444,453,454,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,]),'newlines_opt':([111,],[337,]),'comma_test_opt':([287,],[487,]),'lambdef_nocond':([711,738,],[722,722,]),'xor_and_expr_list_opt':([60,],[232,]),'comp_iter_opt':([693,725,],[712,732,]),'ampersand_shift_expr_list':([81,],[269,]),'with_stmt':([0,2,31,535,629,],[10,10,163,10,10,]),'elif_part_list':([552,],[641,]),'and_test':([0,2,8,15,36,43,57,61,63,70,94,96,98,110,131,141,146,148,151,164,180,205,238,244,274,314,320,369,372,396,408,413,414,421,424,426,429,435,442,444,453,454,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,439,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,]),'yield_expr':([0,2,96,131,151,314,320,396,435,453,535,576,591,605,629,636,638,664,683,707,],[106,106,288,106,106,499,499,106,106,106,106,106,106,106,106,106,106,106,106,106,]),'not_test':([0,2,8,15,36,43,57,61,63,70,94,96,98,101,110,131,137,141,146,148,151,164,180,205,238,244,274,314,320,369,372,396,408,413,414,421,424,426,429,435,442,444,453,454,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[13,13,13,13,13,13,13,13,13,13,13,13,13,299,13,13,356,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,13,]),'import_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,14,]),'expr_or_star_expr':([78,97,417,461,],[265,265,265,595,]),'comma_argument':([366,526,],[527,625,]),'or_test':([0,2,8,15,36,43,57,61,63,70,94,96,98,110,131,141,146,148,151,164,180,205,238,274,314,320,369,372,396,408,413,414,421,424,426,429,435,442,444,453,454,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,358,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,693,16,16,16,16,16,724,724,]),'power':([0,2,6,8,15,36,43,50,56,57,61,63,70,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,180,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,32,]),'try_stmt':([0,2,535,629,],[19,19,19,19,]),'tfpdef_opt':([510,719,],[607,729,]),'shift_arith_expr':([87,280,],[282,483,]),'async_stmt':([0,2,535,629,],[24,24,24,24,]),'comma_pow_tfpdef':([607,666,],[669,669,]),'dictorsetmaker':([43,],[211,]),'for_stmt':([0,2,31,535,629,],[23,23,161,23,23,]),'nonlocal_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,]),'subproc_atom':([34,66,178,186,391,394,640,],[173,173,173,173,173,551,551,]),'shift_expr':([0,2,8,15,36,43,50,57,61,63,70,78,94,96,97,98,101,110,131,137,141,146,148,151,164,180,205,210,229,238,242,244,271,274,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,467,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,]),'comma_vfpdef_list_opt':([445,714,],[582,726,]),'testlist_comp_opt':([98,],[297,]),'arglist':([148,408,],[371,371,]),'period_or_ellipsis_list_opt':([49,],[221,]),'or_and_test':([65,246,],[247,440,]),'import_as_names':([202,401,],[399,558,]),'import_from_pre':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,38,]),'test_opt':([146,522,524,677,],[364,364,623,705,]),'decorated':([0,2,535,629,],[22,22,22,22,]),'comma_import_as_name_list':([400,],[557,]),'comma_test_or_star_expr':([40,204,213,289,416,491,],[203,403,203,203,403,403,]),'equals_yield_expr_or_testlist':([108,111,116,318,425,574,],[316,316,316,502,573,655,]),'varargslist_opt':([67,723,],[250,731,]),'ampersand_shift_expr_list_opt':([81,],[272,]),'yield_expr_or_testlist':([314,320,],[500,503,]),'comma_with_item_list':([257,],[451,]),'if_stmt':([0,2,535,629,],[45,45,45,45,]),'comma_tfpdef':([607,616,666,673,729,735,],[668,668,701,701,668,701,]),'test_comma_list_opt':([0,2,131,151,396,426,435,453,535,576,591,605,629,636,638,664,683,707,],[47,47,47,47,47,574,47,47,47,47,47,47,47,47,47,47,47,47,]),'xor_and_expr_list':([60,],[231,]),'global_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,]),'op_factor':([9,128,],[121,348,]),'or_and_test_list_opt':([65,],[248,]),'exprlist':([78,97,417,],[266,295,567,]),'file_input':([0,],[54,]),'comma_test':([84,212,218,236,275,287,425,564,],[273,273,273,273,469,488,469,273,]),'yield_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,]),'flow_stmt':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,52,]),'dotted_name':([86,221,477,],[279,427,279,]),'test_comma':([0,2,8,131,151,396,426,435,453,535,576,591,605,629,636,638,664,683,707,],[37,37,118,37,37,37,37,37,37,37,37,37,37,37,37,37,37,37,37,]),'testlist_opt':([110,],[334,]),'ampersand_shift_expr':([81,269,],[268,466,]),'period_name_list':([277,],[471,]),'while_stmt':([0,2,535,629,],[69,69,69,69,]),'term':([0,2,8,15,36,43,50,57,61,63,70,78,94,96,97,98,101,110,131,137,141,146,148,151,164,180,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,457,458,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,]),'comma_pow_tfpdef_opt':([607,666,],[667,700,]),'as_name_opt':([279,402,637,],[481,559,686,]),'equals_test':([251,516,660,703,],[443,443,443,443,]),'pm_term_list':([71,],[261,]),'testlist_comp':([96,98,],[291,296,]),'rarrow_test':([353,],[506,]),'parameters':([134,],[353,]),'sliceop':([623,],[680,]),'decorators':([0,2,535,629,],[28,28,28,28,]),'xor_and_expr':([60,231,],[233,434,]),'yield_arg':([63,],[235,]),'import_name':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,]),'comma_name_list_opt':([153,160,],[378,382,]),'start_symbols':([0,],[83,]),'test':([0,2,8,15,36,43,57,61,63,70,94,96,98,110,131,146,148,151,164,180,205,238,274,314,320,369,372,396,408,413,414,421,424,426,429,435,442,444,453,454,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,654,664,677,683,707,],[84,84,119,140,201,212,227,234,236,258,287,292,292,236,84,362,370,84,383,385,292,436,468,236,236,530,533,84,370,564,468,571,468,119,575,84,578,579,84,258,236,468,468,606,617,362,622,370,627,84,637,571,84,84,84,672,84,84,84,690,692,694,84,622,84,84,]),'testlist_star_expr':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[108,108,108,108,108,108,108,108,108,108,108,108,108,108,108,108,108,]),'comp_for':([213,214,289,370,693,725,],[415,419,490,531,708,708,]),'arith_expr':([0,2,8,15,36,43,50,57,61,63,70,78,94,96,97,98,101,110,131,137,141,146,148,151,164,180,205,210,229,238,242,244,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,484,485,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,]),'comma_test_or_star_expr_list':([40,213,289,],[204,416,491,]),'finally_part':([376,536,632,],[537,630,630,]),'subproc_arg_part':([34,66,178,181,186,391,394,640,],[167,167,167,386,167,167,167,167,]),'comma_import_as_name_list_opt':([400,],[555,]),'comma_opt':([40,204,213,218,265,289,366,416,420,462,491,518,526,555,582,651,675,],[206,404,418,426,463,492,525,566,569,596,604,618,624,646,658,691,704,]),'dictorsetmaker_opt':([43,],[216,]),'decorator':([0,2,28,535,629,],[89,89,155,89,89,]),'shift_arith_expr_list':([87,],[280,]),'op_factor_list_opt':([9,],[126,]),'xor_expr':([0,2,8,15,36,43,50,57,61,63,70,78,94,96,97,98,101,110,131,137,141,146,148,151,164,180,205,210,238,242,244,274,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,438,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,]),'expr':([0,2,8,15,36,43,50,57,61,63,70,78,94,96,97,98,101,110,131,137,141,146,148,151,164,180,205,210,238,244,274,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[107,107,107,107,107,107,225,107,107,107,107,267,107,107,267,107,107,107,107,107,107,107,107,107,107,107,107,412,107,107,107,495,107,107,107,534,107,107,107,107,267,107,107,107,107,107,107,107,107,107,594,267,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,107,]),'async_for_stmt':([0,2,535,629,],[95,95,95,95,]),'yield_expr_or_testlist_comp':([96,],[290,]),'as_name':([279,402,637,],[482,482,482,]),'semi_opt':([11,132,],[133,351,]),'except_part':([376,536,],[540,635,]),'or_and_test_list':([65,],[246,]),'func_call':([208,228,],[407,431,]),'comp_if':([693,725,],[710,710,]),'comp_op':([107,309,534,],[302,302,302,]),'simple_stmt':([0,2,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[68,68,374,374,374,374,68,374,374,374,68,374,374,374,374,374,]),'import_from':([0,2,131,151,396,435,453,535,576,591,605,629,636,638,664,683,707,],[102,102,102,102,102,102,102,102,102,102,102,102,102,102,102,102,102,]),'typedargslist_opt':([354,],[511,]),'vfpdef_opt':([253,695,],[448,714,]),'attr_period_name_list':([209,],[409,]),'number':([0,2,6,8,15,36,43,50,56,57,61,63,70,76,78,90,94,96,97,98,101,110,120,122,124,125,127,131,137,141,146,148,151,164,180,200,205,210,229,238,242,244,259,260,271,274,284,285,302,314,320,369,372,396,408,413,414,417,421,424,426,429,435,442,444,453,454,455,461,465,470,489,508,517,522,524,528,532,535,539,570,576,591,605,612,629,636,638,644,652,653,654,664,677,683,707,711,738,],[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,]),'and_not_test_list_opt':([13,],[138,]),'testlist':([0,2,43,63,110,131,151,314,320,396,413,435,453,465,535,576,591,605,629,636,638,664,683,707,],[111,116,217,240,336,116,116,501,501,116,565,116,116,598,116,116,116,116,116,116,116,116,116,116,]),}
_lr_goto = { }
for _k, _v in _lr_goto_items.items():
for _x,_y in zip(_v[0],_v[1]):
if not _x in _lr_goto: _lr_goto[_x] = { }
_lr_goto[_x][_k] = _y
del _lr_goto_items
_lr_productions = [
("S' -> start_symbols","S'",1,None,None,None),
('ampersand_shift_expr_list_opt -> empty','ampersand_shift_expr_list_opt',1,'p_ampersand_shift_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('ampersand_shift_expr_list_opt -> ampersand_shift_expr_list','ampersand_shift_expr_list_opt',1,'p_ampersand_shift_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('and_not_test_list_opt -> empty','and_not_test_list_opt',1,'p_and_not_test_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('and_not_test_list_opt -> and_not_test_list','and_not_test_list_opt',1,'p_and_not_test_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('arglist_opt -> empty','arglist_opt',1,'p_arglist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('arglist_opt -> arglist','arglist_opt',1,'p_arglist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('as_name_opt -> empty','as_name_opt',1,'p_as_name_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('as_name_opt -> as_name','as_name_opt',1,'p_as_name_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('colon_test_opt -> empty','colon_test_opt',1,'p_colon_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('colon_test_opt -> colon_test','colon_test_opt',1,'p_colon_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_dotted_as_name_list_opt -> empty','comma_dotted_as_name_list_opt',1,'p_comma_dotted_as_name_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_dotted_as_name_list_opt -> comma_dotted_as_name_list','comma_dotted_as_name_list_opt',1,'p_comma_dotted_as_name_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_import_as_name_list_opt -> empty','comma_import_as_name_list_opt',1,'p_comma_import_as_name_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_import_as_name_list_opt -> comma_import_as_name_list','comma_import_as_name_list_opt',1,'p_comma_import_as_name_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_name_list_opt -> empty','comma_name_list_opt',1,'p_comma_name_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_name_list_opt -> comma_name_list','comma_name_list_opt',1,'p_comma_name_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_pow_tfpdef_opt -> empty','comma_pow_tfpdef_opt',1,'p_comma_pow_tfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_pow_tfpdef_opt -> comma_pow_tfpdef','comma_pow_tfpdef_opt',1,'p_comma_pow_tfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_pow_vfpdef_opt -> empty','comma_pow_vfpdef_opt',1,'p_comma_pow_vfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_pow_vfpdef_opt -> comma_pow_vfpdef','comma_pow_vfpdef_opt',1,'p_comma_pow_vfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_subscript_list_opt -> empty','comma_subscript_list_opt',1,'p_comma_subscript_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_subscript_list_opt -> comma_subscript_list','comma_subscript_list_opt',1,'p_comma_subscript_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_test_opt -> empty','comma_test_opt',1,'p_comma_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_test_opt -> comma_test','comma_test_opt',1,'p_comma_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_tfpdef_list_opt -> empty','comma_tfpdef_list_opt',1,'p_comma_tfpdef_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_tfpdef_list_opt -> comma_tfpdef_list','comma_tfpdef_list_opt',1,'p_comma_tfpdef_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comma_vfpdef_list_opt -> empty','comma_vfpdef_list_opt',1,'p_comma_vfpdef_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comma_vfpdef_list_opt -> comma_vfpdef_list','comma_vfpdef_list_opt',1,'p_comma_vfpdef_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comp_iter_opt -> empty','comp_iter_opt',1,'p_comp_iter_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comp_iter_opt -> comp_iter','comp_iter_opt',1,'p_comp_iter_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('comp_op_expr_list_opt -> empty','comp_op_expr_list_opt',1,'p_comp_op_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('comp_op_expr_list_opt -> comp_op_expr_list','comp_op_expr_list_opt',1,'p_comp_op_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('dictorsetmaker_opt -> empty','dictorsetmaker_opt',1,'p_dictorsetmaker_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('dictorsetmaker_opt -> dictorsetmaker','dictorsetmaker_opt',1,'p_dictorsetmaker_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('elif_part_list_opt -> empty','elif_part_list_opt',1,'p_elif_part_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('elif_part_list_opt -> elif_part_list','elif_part_list_opt',1,'p_elif_part_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('equals_test_opt -> empty','equals_test_opt',1,'p_equals_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('equals_test_opt -> equals_test','equals_test_opt',1,'p_equals_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('equals_yield_expr_or_testlist_list_opt -> empty','equals_yield_expr_or_testlist_list_opt',1,'p_equals_yield_expr_or_testlist_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('equals_yield_expr_or_testlist_list_opt -> equals_yield_expr_or_testlist_list','equals_yield_expr_or_testlist_list_opt',1,'p_equals_yield_expr_or_testlist_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('finally_part_opt -> empty','finally_part_opt',1,'p_finally_part_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('finally_part_opt -> finally_part','finally_part_opt',1,'p_finally_part_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('func_call_opt -> empty','func_call_opt',1,'p_func_call_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('func_call_opt -> func_call','func_call_opt',1,'p_func_call_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('newlines_opt -> empty','newlines_opt',1,'p_newlines_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('newlines_opt -> newlines','newlines_opt',1,'p_newlines_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('op_factor_list_opt -> empty','op_factor_list_opt',1,'p_op_factor_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('op_factor_list_opt -> op_factor_list','op_factor_list_opt',1,'p_op_factor_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('or_and_test_list_opt -> empty','or_and_test_list_opt',1,'p_or_and_test_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('or_and_test_list_opt -> or_and_test_list','or_and_test_list_opt',1,'p_or_and_test_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('period_or_ellipsis_list_opt -> empty','period_or_ellipsis_list_opt',1,'p_period_or_ellipsis_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('period_or_ellipsis_list_opt -> period_or_ellipsis_list','period_or_ellipsis_list_opt',1,'p_period_or_ellipsis_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('rarrow_test_opt -> empty','rarrow_test_opt',1,'p_rarrow_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('rarrow_test_opt -> rarrow_test','rarrow_test_opt',1,'p_rarrow_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('shift_arith_expr_list_opt -> empty','shift_arith_expr_list_opt',1,'p_shift_arith_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('shift_arith_expr_list_opt -> shift_arith_expr_list','shift_arith_expr_list_opt',1,'p_shift_arith_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('sliceop_opt -> empty','sliceop_opt',1,'p_sliceop_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('sliceop_opt -> sliceop','sliceop_opt',1,'p_sliceop_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('test_comma_list_opt -> empty','test_comma_list_opt',1,'p_test_comma_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('test_comma_list_opt -> test_comma_list','test_comma_list_opt',1,'p_test_comma_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('test_opt -> empty','test_opt',1,'p_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('test_opt -> test','test_opt',1,'p_test_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('testlist_comp_opt -> empty','testlist_comp_opt',1,'p_testlist_comp_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('testlist_comp_opt -> testlist_comp','testlist_comp_opt',1,'p_testlist_comp_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('testlist_opt -> empty','testlist_opt',1,'p_testlist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('testlist_opt -> testlist','testlist_opt',1,'p_testlist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('tfpdef_opt -> empty','tfpdef_opt',1,'p_tfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('tfpdef_opt -> tfpdef','tfpdef_opt',1,'p_tfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('trailer_list_opt -> empty','trailer_list_opt',1,'p_trailer_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('trailer_list_opt -> trailer_list','trailer_list_opt',1,'p_trailer_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('typedargslist_opt -> empty','typedargslist_opt',1,'p_typedargslist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('typedargslist_opt -> typedargslist','typedargslist_opt',1,'p_typedargslist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('varargslist_opt -> empty','varargslist_opt',1,'p_varargslist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('varargslist_opt -> varargslist','varargslist_opt',1,'p_varargslist_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('vfpdef_opt -> empty','vfpdef_opt',1,'p_vfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('vfpdef_opt -> vfpdef','vfpdef_opt',1,'p_vfpdef_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('xor_and_expr_list_opt -> empty','xor_and_expr_list_opt',1,'p_xor_and_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('xor_and_expr_list_opt -> xor_and_expr_list','xor_and_expr_list_opt',1,'p_xor_and_expr_list_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('yield_arg_opt -> empty','yield_arg_opt',1,'p_yield_arg_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('yield_arg_opt -> yield_arg','yield_arg_opt',1,'p_yield_arg_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('yield_expr_or_testlist_comp_opt -> empty','yield_expr_or_testlist_comp_opt',1,'p_yield_expr_or_testlist_comp_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',285),
('yield_expr_or_testlist_comp_opt -> yield_expr_or_testlist_comp','yield_expr_or_testlist_comp_opt',1,'p_yield_expr_or_testlist_comp_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',286),
('ampersand_shift_expr_list -> ampersand_shift_expr','ampersand_shift_expr_list',1,'p_ampersand_shift_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('ampersand_shift_expr_list -> ampersand_shift_expr_list ampersand_shift_expr','ampersand_shift_expr_list',2,'p_ampersand_shift_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('and_not_test_list -> and_not_test','and_not_test_list',1,'p_and_not_test_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('and_not_test_list -> and_not_test_list and_not_test','and_not_test_list',2,'p_and_not_test_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('attr_period_name_list -> attr_period_name','attr_period_name_list',1,'p_attr_period_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('attr_period_name_list -> attr_period_name_list attr_period_name','attr_period_name_list',2,'p_attr_period_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_argument_list -> comma_argument','comma_argument_list',1,'p_comma_argument_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_argument_list -> comma_argument_list comma_argument','comma_argument_list',2,'p_comma_argument_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_dotted_as_name_list -> comma_dotted_as_name','comma_dotted_as_name_list',1,'p_comma_dotted_as_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_dotted_as_name_list -> comma_dotted_as_name_list comma_dotted_as_name','comma_dotted_as_name_list',2,'p_comma_dotted_as_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_expr_or_star_expr_list -> comma_expr_or_star_expr','comma_expr_or_star_expr_list',1,'p_comma_expr_or_star_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_expr_or_star_expr_list -> comma_expr_or_star_expr_list comma_expr_or_star_expr','comma_expr_or_star_expr_list',2,'p_comma_expr_or_star_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_import_as_name_list -> comma_import_as_name','comma_import_as_name_list',1,'p_comma_import_as_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_import_as_name_list -> comma_import_as_name_list comma_import_as_name','comma_import_as_name_list',2,'p_comma_import_as_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_item_list -> comma_item','comma_item_list',1,'p_comma_item_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_item_list -> comma_item_list comma_item','comma_item_list',2,'p_comma_item_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_name_list -> comma_name','comma_name_list',1,'p_comma_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_name_list -> comma_name_list comma_name','comma_name_list',2,'p_comma_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_subscript_list -> comma_subscript','comma_subscript_list',1,'p_comma_subscript_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_subscript_list -> comma_subscript_list comma_subscript','comma_subscript_list',2,'p_comma_subscript_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_test_list -> comma_test','comma_test_list',1,'p_comma_test_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_test_list -> comma_test_list comma_test','comma_test_list',2,'p_comma_test_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_test_or_star_expr_list -> comma_test_or_star_expr','comma_test_or_star_expr_list',1,'p_comma_test_or_star_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_test_or_star_expr_list -> comma_test_or_star_expr_list comma_test_or_star_expr','comma_test_or_star_expr_list',2,'p_comma_test_or_star_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_tfpdef_list -> comma_tfpdef','comma_tfpdef_list',1,'p_comma_tfpdef_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_tfpdef_list -> comma_tfpdef_list comma_tfpdef','comma_tfpdef_list',2,'p_comma_tfpdef_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_vfpdef_list -> comma_vfpdef','comma_vfpdef_list',1,'p_comma_vfpdef_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_vfpdef_list -> comma_vfpdef_list comma_vfpdef','comma_vfpdef_list',2,'p_comma_vfpdef_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comma_with_item_list -> comma_with_item','comma_with_item_list',1,'p_comma_with_item_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comma_with_item_list -> comma_with_item_list comma_with_item','comma_with_item_list',2,'p_comma_with_item_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('comp_op_expr_list -> comp_op_expr','comp_op_expr_list',1,'p_comp_op_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('comp_op_expr_list -> comp_op_expr_list comp_op_expr','comp_op_expr_list',2,'p_comp_op_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('elif_part_list -> elif_part','elif_part_list',1,'p_elif_part_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('elif_part_list -> elif_part_list elif_part','elif_part_list',2,'p_elif_part_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('equals_yield_expr_or_testlist_list -> equals_yield_expr_or_testlist','equals_yield_expr_or_testlist_list',1,'p_equals_yield_expr_or_testlist_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('equals_yield_expr_or_testlist_list -> equals_yield_expr_or_testlist_list equals_yield_expr_or_testlist','equals_yield_expr_or_testlist_list',2,'p_equals_yield_expr_or_testlist_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('except_part_list -> except_part','except_part_list',1,'p_except_part_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('except_part_list -> except_part_list except_part','except_part_list',2,'p_except_part_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('op_factor_list -> op_factor','op_factor_list',1,'p_op_factor_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('op_factor_list -> op_factor_list op_factor','op_factor_list',2,'p_op_factor_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('or_and_test_list -> or_and_test','or_and_test_list',1,'p_or_and_test_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('or_and_test_list -> or_and_test_list or_and_test','or_and_test_list',2,'p_or_and_test_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('period_name_list -> period_name','period_name_list',1,'p_period_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('period_name_list -> period_name_list period_name','period_name_list',2,'p_period_name_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('period_or_ellipsis_list -> period_or_ellipsis','period_or_ellipsis_list',1,'p_period_or_ellipsis_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('period_or_ellipsis_list -> period_or_ellipsis_list period_or_ellipsis','period_or_ellipsis_list',2,'p_period_or_ellipsis_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('pipe_xor_expr_list -> pipe_xor_expr','pipe_xor_expr_list',1,'p_pipe_xor_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('pipe_xor_expr_list -> pipe_xor_expr_list pipe_xor_expr','pipe_xor_expr_list',2,'p_pipe_xor_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('pm_term_list -> pm_term','pm_term_list',1,'p_pm_term_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('pm_term_list -> pm_term_list pm_term','pm_term_list',2,'p_pm_term_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('semi_small_stmt_list -> semi_small_stmt','semi_small_stmt_list',1,'p_semi_small_stmt_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('semi_small_stmt_list -> semi_small_stmt_list semi_small_stmt','semi_small_stmt_list',2,'p_semi_small_stmt_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('shift_arith_expr_list -> shift_arith_expr','shift_arith_expr_list',1,'p_shift_arith_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('shift_arith_expr_list -> shift_arith_expr_list shift_arith_expr','shift_arith_expr_list',2,'p_shift_arith_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('test_comma_list -> test_comma','test_comma_list',1,'p_test_comma_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('test_comma_list -> test_comma_list test_comma','test_comma_list',2,'p_test_comma_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('test_or_star_expr_list -> test_or_star_expr','test_or_star_expr_list',1,'p_test_or_star_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('test_or_star_expr_list -> test_or_star_expr_list test_or_star_expr','test_or_star_expr_list',2,'p_test_or_star_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('trailer_list -> trailer','trailer_list',1,'p_trailer_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('trailer_list -> trailer_list trailer','trailer_list',2,'p_trailer_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('xor_and_expr_list -> xor_and_expr','xor_and_expr_list',1,'p_xor_and_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',298),
('xor_and_expr_list -> xor_and_expr_list xor_and_expr','xor_and_expr_list',2,'p_xor_and_expr_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',299),
('start_symbols -> single_input','start_symbols',1,'p_start_symbols','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',350),
('start_symbols -> file_input','start_symbols',1,'p_start_symbols','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',351),
('start_symbols -> eval_input','start_symbols',1,'p_start_symbols','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',352),
('start_symbols -> empty','start_symbols',1,'p_start_symbols','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',353),
('single_input -> compound_stmt NEWLINE','single_input',2,'p_single_input','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',358),
('file_input -> file_stmts','file_input',1,'p_file_input','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',365),
('file_stmts -> newline_or_stmt','file_stmts',1,'p_file_stmts','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',369),
('file_stmts -> file_stmts newline_or_stmt','file_stmts',2,'p_file_stmts','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',370),
('newline_or_stmt -> NEWLINE','newline_or_stmt',1,'p_newline_or_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',382),
('newline_or_stmt -> stmt','newline_or_stmt',1,'p_newline_or_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',383),
('newlines -> NEWLINE','newlines',1,'p_newlines','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',388),
('newlines -> newlines NEWLINE','newlines',2,'p_newlines','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',389),
('eval_input -> testlist newlines_opt','eval_input',2,'p_eval_input','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',394),
('func_call -> LPAREN arglist_opt RPAREN','func_call',3,'p_func_call','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',399),
('attr_period_name -> PERIOD NAME','attr_period_name',2,'p_attr_period_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',403),
('attr_name -> NAME','attr_name',1,'p_attr_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',407),
('attr_name -> NAME attr_period_name_list','attr_name',2,'p_attr_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',408),
('decorator -> AT attr_name NEWLINE','decorator',3,'p_decorator','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',433),
('decorator -> AT attr_name func_call NEWLINE','decorator',4,'p_decorator','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',434),
('decorators -> decorator','decorators',1,'p_decorators','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',456),
('decorators -> decorators decorator','decorators',2,'p_decorators','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',457),
('classdef_or_funcdef -> classdef','classdef_or_funcdef',1,'p_classdef_or_funcdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',462),
('classdef_or_funcdef -> funcdef','classdef_or_funcdef',1,'p_classdef_or_funcdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',463),
('classdef_or_funcdef -> async_funcdef','classdef_or_funcdef',1,'p_classdef_or_funcdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',464),
('decorated -> decorators classdef_or_funcdef','decorated',2,'p_decorated','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',476),
('rarrow_test -> RARROW test','rarrow_test',2,'p_rarrow_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',482),
('funcdef -> DEF NAME parameters rarrow_test_opt COLON suite','funcdef',6,'p_funcdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',486),
('async_funcdef -> ASYNC funcdef','async_funcdef',2,'p_async_funcdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',497),
('parameters -> LPAREN typedargslist_opt RPAREN','parameters',3,'p_parameters','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',502),
('equals_test -> EQUALS test','equals_test',2,'p_equals_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',514),
('typedargslist -> tfpdef equals_test_opt comma_tfpdef_list_opt comma_opt','typedargslist',4,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',518),
('typedargslist -> tfpdef equals_test_opt comma_tfpdef_list_opt comma_opt TIMES tfpdef_opt COMMA POW vfpdef','typedargslist',9,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',519),
('typedargslist -> tfpdef equals_test_opt comma_tfpdef_list_opt comma_opt TIMES tfpdef_opt comma_tfpdef_list_opt','typedargslist',7,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',520),
('typedargslist -> tfpdef equals_test_opt comma_tfpdef_list_opt comma_opt TIMES tfpdef_opt comma_tfpdef_list COMMA POW tfpdef','typedargslist',10,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',521),
('typedargslist -> tfpdef equals_test_opt comma_tfpdef_list_opt comma_opt POW tfpdef','typedargslist',6,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',522),
('typedargslist -> TIMES tfpdef_opt comma_tfpdef_list comma_pow_tfpdef_opt','typedargslist',4,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',523),
('typedargslist -> TIMES tfpdef_opt comma_pow_tfpdef_opt','typedargslist',3,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',524),
('typedargslist -> POW tfpdef','typedargslist',2,'p_typedargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',525),
('colon_test -> COLON test','colon_test',2,'p_colon_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',579),
('tfpdef -> NAME colon_test_opt','tfpdef',2,'p_tfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',583),
('comma_tfpdef -> COMMA','comma_tfpdef',1,'p_comma_tfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',587),
('comma_tfpdef -> COMMA tfpdef equals_test_opt','comma_tfpdef',3,'p_comma_tfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',588),
('comma_pow_tfpdef -> COMMA POW tfpdef','comma_pow_tfpdef',3,'p_comma_pow_tfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',596),
('varargslist -> vfpdef equals_test_opt comma_vfpdef_list_opt comma_opt','varargslist',4,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',641),
('varargslist -> vfpdef equals_test_opt comma_vfpdef_list_opt comma_opt TIMES vfpdef_opt COMMA POW vfpdef','varargslist',9,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',642),
('varargslist -> vfpdef equals_test_opt comma_vfpdef_list_opt comma_opt TIMES vfpdef_opt comma_vfpdef_list_opt','varargslist',7,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',643),
('varargslist -> vfpdef equals_test_opt comma_vfpdef_list_opt comma_opt TIMES vfpdef_opt comma_vfpdef_list COMMA POW vfpdef','varargslist',10,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',644),
('varargslist -> vfpdef equals_test_opt comma_vfpdef_list_opt comma_opt POW vfpdef','varargslist',6,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',645),
('varargslist -> TIMES vfpdef_opt comma_vfpdef_list comma_pow_vfpdef_opt','varargslist',4,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',646),
('varargslist -> TIMES vfpdef_opt comma_pow_vfpdef_opt','varargslist',3,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',647),
('varargslist -> POW vfpdef','varargslist',2,'p_varargslist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',648),
('vfpdef -> NAME','vfpdef',1,'p_vfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',702),
('comma_vfpdef -> COMMA','comma_vfpdef',1,'p_comma_vfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',706),
('comma_vfpdef -> COMMA vfpdef equals_test_opt','comma_vfpdef',3,'p_comma_vfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',707),
('comma_pow_vfpdef -> COMMA POW vfpdef','comma_pow_vfpdef',3,'p_comma_pow_vfpdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',715),
('stmt -> simple_stmt','stmt',1,'p_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',719),
('stmt -> compound_stmt','stmt',1,'p_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',720),
('stmt_list -> stmt','stmt_list',1,'p_stmt_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',725),
('stmt_list -> stmt_list stmt','stmt_list',2,'p_stmt_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',726),
('semi_opt -> SEMI','semi_opt',1,'p_semi_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',733),
('semi_opt -> empty','semi_opt',1,'p_semi_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',734),
('semi_small_stmt -> SEMI small_stmt','semi_small_stmt',2,'p_semi_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',740),
('simple_stmt -> small_stmt semi_small_stmt_list semi_opt NEWLINE','simple_stmt',4,'p_simple_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',744),
('simple_stmt -> small_stmt semi_opt NEWLINE','simple_stmt',3,'p_simple_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',745),
('small_stmt -> expr_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',754),
('small_stmt -> del_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',755),
('small_stmt -> pass_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',756),
('small_stmt -> flow_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',757),
('small_stmt -> import_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',758),
('small_stmt -> global_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',759),
('small_stmt -> nonlocal_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',760),
('small_stmt -> assert_stmt','small_stmt',1,'p_small_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',761),
('expr_stmt -> testlist_star_expr augassign yield_expr_or_testlist','expr_stmt',3,'p_expr_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',782),
('expr_stmt -> testlist_star_expr equals_yield_expr_or_testlist_list_opt','expr_stmt',2,'p_expr_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',783),
('expr_stmt -> testlist equals_yield_expr_or_testlist_list_opt','expr_stmt',2,'p_expr_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',784),
('expr_stmt -> test_comma_list_opt star_expr comma_test_list equals_yield_expr_or_testlist','expr_stmt',4,'p_expr_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',785),
('expr_stmt -> test_comma_list_opt star_expr comma_opt test_comma_list_opt equals_yield_expr_or_testlist','expr_stmt',5,'p_expr_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',786),
('test_comma -> test COMMA','test_comma',2,'p_test_comma','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',837),
('comma_opt -> COMMA','comma_opt',1,'p_comma_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',841),
('comma_opt -> empty','comma_opt',1,'p_comma_opt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',842),
('test_or_star_expr -> test','test_or_star_expr',1,'p_test_or_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',848),
('test_or_star_expr -> star_expr','test_or_star_expr',1,'p_test_or_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',849),
('comma_test_or_star_expr -> COMMA test_or_star_expr','comma_test_or_star_expr',2,'p_comma_test_or_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',854),
('testlist_star_expr -> test_or_star_expr comma_test_or_star_expr_list comma_opt','testlist_star_expr',3,'p_testlist_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',858),
('testlist_star_expr -> test_or_star_expr comma_opt','testlist_star_expr',2,'p_testlist_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',859),
('augassign -> PLUSEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',877),
('augassign -> MINUSEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',878),
('augassign -> TIMESEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',879),
('augassign -> ATEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',880),
('augassign -> DIVEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',881),
('augassign -> MODEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',882),
('augassign -> AMPERSANDEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',883),
('augassign -> PIPEEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',884),
('augassign -> XOREQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',885),
('augassign -> LSHIFTEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',886),
('augassign -> RSHIFTEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',887),
('augassign -> POWEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',888),
('augassign -> DOUBLEDIVEQUAL','augassign',1,'p_augassign','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',889),
('yield_expr_or_testlist -> yield_expr','yield_expr_or_testlist',1,'p_yield_expr_or_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',894),
('yield_expr_or_testlist -> testlist','yield_expr_or_testlist',1,'p_yield_expr_or_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',895),
('equals_yield_expr_or_testlist -> EQUALS yield_expr_or_testlist','equals_yield_expr_or_testlist',2,'p_equals_yield_expr_or_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',900),
('del_stmt -> DEL exprlist','del_stmt',2,'p_del_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',908),
('pass_stmt -> PASS','pass_stmt',1,'p_pass_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',916),
('flow_stmt -> break_stmt','flow_stmt',1,'p_flow_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',920),
('flow_stmt -> continue_stmt','flow_stmt',1,'p_flow_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',921),
('flow_stmt -> return_stmt','flow_stmt',1,'p_flow_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',922),
('flow_stmt -> raise_stmt','flow_stmt',1,'p_flow_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',923),
('flow_stmt -> yield_stmt','flow_stmt',1,'p_flow_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',924),
('break_stmt -> BREAK','break_stmt',1,'p_break_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',929),
('continue_stmt -> CONTINUE','continue_stmt',1,'p_continue_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',933),
('return_stmt -> RETURN testlist_opt','return_stmt',2,'p_return_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',937),
('yield_stmt -> yield_expr','yield_stmt',1,'p_yield_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',941),
('raise_stmt -> RAISE','raise_stmt',1,'p_raise_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',945),
('raise_stmt -> RAISE test','raise_stmt',2,'p_raise_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',946),
('raise_stmt -> RAISE test FROM test','raise_stmt',4,'p_raise_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',947),
('import_stmt -> import_name','import_stmt',1,'p_import_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',967),
('import_stmt -> import_from','import_stmt',1,'p_import_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',968),
('import_name -> IMPORT dotted_as_names','import_name',2,'p_import_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',973),
('import_from_pre -> FROM period_or_ellipsis_list_opt dotted_name','import_from_pre',3,'p_import_from_pre','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',978),
('import_from_pre -> FROM period_or_ellipsis_list','import_from_pre',2,'p_import_from_pre','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',979),
('import_from_post -> TIMES','import_from_post',1,'p_import_from_post','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',991),
('import_from_post -> LPAREN import_as_names RPAREN','import_from_post',3,'p_import_from_post','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',992),
('import_from_post -> import_as_names','import_from_post',1,'p_import_from_post','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',993),
('import_from -> import_from_pre IMPORT import_from_post','import_from',3,'p_import_from','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1006),
('period_or_ellipsis -> PERIOD','period_or_ellipsis',1,'p_period_or_ellipsis','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1021),
('period_or_ellipsis -> ELLIPSIS','period_or_ellipsis',1,'p_period_or_ellipsis','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1022),
('as_name -> AS NAME','as_name',2,'p_as_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1027),
('import_as_name -> NAME as_name_opt','import_as_name',2,'p_import_as_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1031),
('comma_import_as_name -> COMMA import_as_name','comma_import_as_name',2,'p_comma_import_as_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1035),
('dotted_as_name -> dotted_name as_name_opt','dotted_as_name',2,'p_dotted_as_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1040),
('comma_dotted_as_name -> COMMA dotted_as_name','comma_dotted_as_name',2,'p_comma_dotted_as_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1045),
('import_as_names -> import_as_name comma_import_as_name_list_opt comma_opt','import_as_names',3,'p_import_as_names','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1049),
('dotted_as_names -> dotted_as_name comma_dotted_as_name_list_opt','dotted_as_names',2,'p_dotted_as_names','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1058),
('period_name -> PERIOD NAME','period_name',2,'p_period_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1066),
('dotted_name -> NAME','dotted_name',1,'p_dotted_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1070),
('dotted_name -> NAME period_name_list','dotted_name',2,'p_dotted_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1071),
('comma_name -> COMMA NAME','comma_name',2,'p_comma_name','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1076),
('global_stmt -> GLOBAL NAME comma_name_list_opt','global_stmt',3,'p_global_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1080),
('nonlocal_stmt -> NONLOCAL NAME comma_name_list_opt','nonlocal_stmt',3,'p_nonlocal_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1088),
('comma_test -> COMMA test','comma_test',2,'p_comma_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1098),
('assert_stmt -> ASSERT test comma_test_opt','assert_stmt',3,'p_assert_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1102),
('compound_stmt -> if_stmt','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1115),
('compound_stmt -> while_stmt','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1116),
('compound_stmt -> for_stmt','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1117),
('compound_stmt -> try_stmt','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1118),
('compound_stmt -> with_stmt','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1119),
('compound_stmt -> funcdef','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1120),
('compound_stmt -> classdef','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1121),
('compound_stmt -> decorated','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1122),
('compound_stmt -> async_stmt','compound_stmt',1,'p_compound_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1123),
('elif_part -> ELIF test COLON suite','elif_part',4,'p_elif_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1128),
('else_part -> ELSE COLON suite','else_part',3,'p_else_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1136),
('if_stmt -> IF test COLON suite elif_part_list_opt','if_stmt',5,'p_if_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1140),
('if_stmt -> IF test COLON suite elif_part_list_opt else_part','if_stmt',6,'p_if_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1141),
('while_stmt -> WHILE test COLON suite','while_stmt',4,'p_while_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1159),
('while_stmt -> WHILE test COLON suite else_part','while_stmt',5,'p_while_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1160),
('for_stmt -> FOR exprlist IN testlist COLON suite','for_stmt',6,'p_for_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1170),
('for_stmt -> FOR exprlist IN testlist COLON suite else_part','for_stmt',7,'p_for_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1171),
('async_for_stmt -> ASYNC for_stmt','async_for_stmt',2,'p_async_for_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1193),
('except_part -> except_clause COLON suite','except_part',3,'p_except_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1198),
('finally_part -> FINALLY COLON suite','finally_part',3,'p_finally_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1204),
('try_stmt -> TRY COLON suite except_part_list else_part finally_part_opt','try_stmt',6,'p_try_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1208),
('try_stmt -> TRY COLON suite except_part_list finally_part_opt','try_stmt',5,'p_try_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1209),
('try_stmt -> TRY COLON suite finally_part','try_stmt',4,'p_try_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1210),
('with_stmt -> WITH with_item COLON suite','with_stmt',4,'p_with_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1231),
('with_stmt -> WITH with_item comma_with_item_list COLON suite','with_stmt',5,'p_with_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1232),
('async_with_stmt -> ASYNC with_stmt','async_with_stmt',2,'p_async_with_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1246),
('as_expr -> AS expr','as_expr',2,'p_as_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1251),
('with_item -> test','with_item',1,'p_with_item','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1257),
('with_item -> test as_expr','with_item',2,'p_with_item','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1258),
('comma_with_item -> COMMA with_item','comma_with_item',2,'p_comma_with_item','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1264),
('except_clause -> EXCEPT','except_clause',1,'p_except_clause','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1268),
('except_clause -> EXCEPT test as_name_opt','except_clause',3,'p_except_clause','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1269),
('async_stmt -> async_funcdef','async_stmt',1,'p_async_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1284),
('async_stmt -> async_with_stmt','async_stmt',1,'p_async_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1285),
('async_stmt -> async_for_stmt','async_stmt',1,'p_async_stmt','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1286),
('suite -> simple_stmt','suite',1,'p_suite','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1291),
('suite -> NEWLINE INDENT stmt_list DEDENT','suite',4,'p_suite','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1292),
('test -> or_test','test',1,'p_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1297),
('test -> or_test IF or_test ELSE test','test',5,'p_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1298),
('test -> lambdef','test',1,'p_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1299),
('test_nocond -> or_test','test_nocond',1,'p_test_nocond','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1312),
('test_nocond -> lambdef_nocond','test_nocond',1,'p_test_nocond','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1313),
('lambdef -> LAMBDA varargslist_opt COLON test','lambdef',4,'p_lambdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1318),
('lambdef_nocond -> LAMBDA varargslist_opt COLON test_nocond','lambdef_nocond',4,'p_lambdef_nocond','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1336),
('or_test -> and_test or_and_test_list_opt','or_test',2,'p_or_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1340),
('or_and_test -> OR and_test','or_and_test',2,'p_or_and_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1357),
('and_test -> not_test and_not_test_list_opt','and_test',2,'p_and_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1361),
('and_not_test -> AND not_test','and_not_test',2,'p_and_not_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1378),
('not_test -> NOT not_test','not_test',2,'p_not_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1382),
('not_test -> comparison','not_test',1,'p_not_test','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1383),
('comparison -> expr comp_op_expr_list_opt','comparison',2,'p_comparison','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1395),
('comp_op_expr -> comp_op expr','comp_op_expr',2,'p_comp_op_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1408),
('comp_op -> LT','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1425),
('comp_op -> GT','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1426),
('comp_op -> EQ','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1427),
('comp_op -> GE','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1428),
('comp_op -> LE','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1429),
('comp_op -> NE','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1430),
('comp_op -> IN','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1431),
('comp_op -> NOT IN','comp_op',2,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1432),
('comp_op -> IS','comp_op',1,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1433),
('comp_op -> IS NOT','comp_op',2,'p_comp_op','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1434),
('star_expr -> TIMES expr','star_expr',2,'p_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1440),
('expr -> xor_expr','expr',1,'p_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1464),
('expr -> xor_expr pipe_xor_expr_list','expr',2,'p_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1465),
('pipe_xor_expr -> PIPE xor_expr','pipe_xor_expr',2,'p_pipe_xor_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1470),
('xor_expr -> and_expr xor_and_expr_list_opt','xor_expr',2,'p_xor_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1478),
('xor_and_expr -> XOR and_expr','xor_and_expr',2,'p_xor_and_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1482),
('and_expr -> shift_expr ampersand_shift_expr_list_opt','and_expr',2,'p_and_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1490),
('ampersand_shift_expr -> AMPERSAND shift_expr','ampersand_shift_expr',2,'p_ampersand_shift_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1494),
('shift_expr -> arith_expr shift_arith_expr_list_opt','shift_expr',2,'p_shift_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1502),
('shift_arith_expr -> LSHIFT arith_expr','shift_arith_expr',2,'p_shift_arith_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1506),
('shift_arith_expr -> RSHIFT arith_expr','shift_arith_expr',2,'p_shift_arith_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1507),
('arith_expr -> term','arith_expr',1,'p_arith_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1517),
('arith_expr -> term pm_term_list','arith_expr',2,'p_arith_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1518),
('pm_term -> PLUS term','pm_term',2,'p_pm_term','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1551),
('pm_term -> MINUS term','pm_term',2,'p_pm_term','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1552),
('term -> factor op_factor_list_opt','term',2,'p_term','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1558),
('op_factor -> TIMES factor','op_factor',2,'p_op_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1580),
('op_factor -> AT factor','op_factor',2,'p_op_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1581),
('op_factor -> DIVIDE factor','op_factor',2,'p_op_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1582),
('op_factor -> MOD factor','op_factor',2,'p_op_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1583),
('op_factor -> DOUBLEDIV factor','op_factor',2,'p_op_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1584),
('factor -> PLUS factor','factor',2,'p_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1595),
('factor -> MINUS factor','factor',2,'p_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1596),
('factor -> TILDE factor','factor',2,'p_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1597),
('factor -> power','factor',1,'p_factor','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1598),
('power -> atom_expr','power',1,'p_power','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1611),
('power -> atom_expr POW factor','power',3,'p_power','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1612),
('yield_expr_or_testlist_comp -> yield_expr','yield_expr_or_testlist_comp',1,'p_yield_expr_or_testlist_comp','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1628),
('yield_expr_or_testlist_comp -> testlist_comp','yield_expr_or_testlist_comp',1,'p_yield_expr_or_testlist_comp','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1629),
('atom_expr -> atom trailer_list_opt','atom_expr',2,'p_atom_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1642),
('atom_expr -> AWAIT atom trailer_list_opt','atom_expr',3,'p_atom_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1643),
('atom -> LPAREN yield_expr_or_testlist_comp_opt RPAREN','atom',3,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1692),
('atom -> LBRACKET testlist_comp_opt RBRACKET','atom',3,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1693),
('atom -> LBRACE dictorsetmaker_opt RBRACE','atom',3,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1694),
('atom -> NAME','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1695),
('atom -> number','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1696),
('atom -> string_literal_list','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1697),
('atom -> ELLIPSIS','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1698),
('atom -> NONE','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1699),
('atom -> TRUE','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1700),
('atom -> FALSE','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1701),
('atom -> REGEXPATH','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1702),
('atom -> DOLLAR_NAME','atom',1,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1703),
('atom -> DOLLAR_LBRACE test RBRACE','atom',3,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1704),
('atom -> DOLLAR_LPAREN subproc RPAREN','atom',3,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1705),
('atom -> DOLLAR_LBRACKET subproc RBRACKET','atom',3,'p_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1706),
('string_literal -> STRING','string_literal',1,'p_string_literal','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1801),
('string_literal_list -> string_literal','string_literal_list',1,'p_string_literal_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1807),
('string_literal_list -> string_literal_list string_literal','string_literal_list',2,'p_string_literal_list','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1808),
('number -> NUMBER','number',1,'p_number','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1815),
('testlist_comp -> test_or_star_expr comp_for','testlist_comp',2,'p_testlist_comp','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1819),
('testlist_comp -> test_or_star_expr comma_opt','testlist_comp',2,'p_testlist_comp','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1820),
('testlist_comp -> test_or_star_expr comma_test_or_star_expr_list comma_opt','testlist_comp',3,'p_testlist_comp','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1821),
('trailer -> LPAREN arglist_opt RPAREN','trailer',3,'p_trailer','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1854),
('trailer -> LBRACKET subscriptlist RBRACKET','trailer',3,'p_trailer','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1855),
('trailer -> PERIOD NAME','trailer',2,'p_trailer','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1856),
('trailer -> DOUBLE_QUESTION','trailer',1,'p_trailer','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1857),
('trailer -> QUESTION','trailer',1,'p_trailer','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1858),
('subscriptlist -> subscript comma_subscript_list_opt comma_opt','subscriptlist',3,'p_subscriptlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1875),
('comma_subscript -> COMMA subscript','comma_subscript',2,'p_comma_subscript','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1885),
('subscript -> test','subscript',1,'p_subscript','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1889),
('subscript -> test_opt COLON test_opt sliceop_opt','subscript',4,'p_subscript','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1890),
('sliceop -> COLON test_opt','sliceop',2,'p_sliceop','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1899),
('expr_or_star_expr -> expr','expr_or_star_expr',1,'p_expr_or_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1903),
('expr_or_star_expr -> star_expr','expr_or_star_expr',1,'p_expr_or_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1904),
('comma_expr_or_star_expr -> COMMA expr_or_star_expr','comma_expr_or_star_expr',2,'p_comma_expr_or_star_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1909),
('exprlist -> expr_or_star_expr comma_expr_or_star_expr_list comma_opt','exprlist',3,'p_exprlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1913),
('exprlist -> expr_or_star_expr comma_opt','exprlist',2,'p_exprlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1914),
('testlist -> test comma_test_list COMMA','testlist',3,'p_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1930),
('testlist -> test comma_test_list','testlist',2,'p_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1931),
('testlist -> test COMMA','testlist',2,'p_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1932),
('testlist -> test','testlist',1,'p_testlist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1933),
('item -> test COLON test','item',3,'p_item','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1954),
('item -> POW expr','item',2,'p_item','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1955),
('comma_item -> COMMA item','comma_item',2,'p_comma_item','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1971),
('dictorsetmaker -> item comp_for','dictorsetmaker',2,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1975),
('dictorsetmaker -> test_or_star_expr comp_for','dictorsetmaker',2,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1976),
('dictorsetmaker -> testlist','dictorsetmaker',1,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1977),
('dictorsetmaker -> test_or_star_expr comma_opt','dictorsetmaker',2,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1978),
('dictorsetmaker -> test_or_star_expr comma_test_or_star_expr_list comma_opt','dictorsetmaker',3,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1979),
('dictorsetmaker -> test COLON testlist','dictorsetmaker',3,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1980),
('dictorsetmaker -> item comma_item_list comma_opt','dictorsetmaker',3,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1981),
('dictorsetmaker -> test COLON test comma_item_list comma_opt','dictorsetmaker',5,'p_dictorsetmaker','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',1982),
('classdef -> CLASS NAME func_call_opt COLON suite','classdef',5,'p_classdef','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2040),
('arglist -> argument comma_opt','arglist',2,'p_arglist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2070),
('arglist -> argument comma_argument_list comma_opt','arglist',3,'p_arglist','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2071),
('comma_argument -> COMMA argument','comma_argument',2,'p_comma_argument','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2137),
('argument -> test_or_star_expr','argument',1,'p_argument','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2141),
('argument -> test comp_for','argument',2,'p_argument','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2142),
('argument -> test EQUALS test','argument',3,'p_argument','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2143),
('argument -> POW test','argument',2,'p_argument','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2144),
('argument -> TIMES test','argument',2,'p_argument','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2145),
('comp_iter -> comp_for','comp_iter',1,'p_comp_iter','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2187),
('comp_iter -> comp_if','comp_iter',1,'p_comp_iter','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2188),
('comp_for -> FOR exprlist IN or_test comp_iter_opt','comp_for',5,'p_comp_for','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2193),
('comp_if -> IF test_nocond comp_iter_opt','comp_if',3,'p_comp_if','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2210),
('yield_expr -> YIELD yield_arg_opt','yield_expr',2,'p_yield_expr','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2218),
('yield_arg -> FROM test','yield_arg',2,'p_yield_arg','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2233),
('yield_arg -> testlist','yield_arg',1,'p_yield_arg','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2234),
('pipe -> PIPE','pipe',1,'p_pipe','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2324),
('pipe -> WS PIPE','pipe',2,'p_pipe','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2325),
('pipe -> PIPE WS','pipe',2,'p_pipe','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2326),
('pipe -> WS PIPE WS','pipe',3,'p_pipe','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2327),
('subproc -> subproc_atoms','subproc',1,'p_subproc','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2335),
('subproc -> subproc_atoms WS','subproc',2,'p_subproc','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2336),
('subproc -> subproc AMPERSAND','subproc',2,'p_subproc','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2337),
('subproc -> subproc pipe subproc_atoms','subproc',3,'p_subproc','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2338),
('subproc -> subproc pipe subproc_atoms WS','subproc',4,'p_subproc','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2339),
('subproc_atoms -> subproc_atom','subproc_atoms',1,'p_subproc_atoms','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2361),
('subproc_atoms -> subproc_atoms WS subproc_atom','subproc_atoms',3,'p_subproc_atoms','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2362),
('subproc_atom -> subproc_arg','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2372),
('subproc_atom -> string_literal','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2373),
('subproc_atom -> REGEXPATH','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2374),
('subproc_atom -> DOLLAR_NAME','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2375),
('subproc_atom -> GT','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2376),
('subproc_atom -> LT','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2377),
('subproc_atom -> RSHIFT','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2378),
('subproc_atom -> IOREDIRECT','subproc_atom',1,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2379),
('subproc_atom -> AT_LPAREN test RPAREN','subproc_atom',3,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2380),
('subproc_atom -> DOLLAR_LBRACE test RBRACE','subproc_atom',3,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2381),
('subproc_atom -> DOLLAR_LPAREN subproc RPAREN','subproc_atom',3,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2382),
('subproc_atom -> DOLLAR_LBRACKET subproc RBRACKET','subproc_atom',3,'p_subproc_atom','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2383),
('subproc_arg -> subproc_arg_part','subproc_arg',1,'p_subproc_arg','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2448),
('subproc_arg -> subproc_arg subproc_arg_part','subproc_arg',2,'p_subproc_arg','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2449),
('subproc_arg_part -> NAME','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2460),
('subproc_arg_part -> TILDE','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2461),
('subproc_arg_part -> PERIOD','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2462),
('subproc_arg_part -> DIVIDE','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2463),
('subproc_arg_part -> MINUS','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2464),
('subproc_arg_part -> PLUS','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2465),
('subproc_arg_part -> COLON','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2466),
('subproc_arg_part -> AT','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2467),
('subproc_arg_part -> EQUALS','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2468),
('subproc_arg_part -> TIMES','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2469),
('subproc_arg_part -> POW','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2470),
('subproc_arg_part -> MOD','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2471),
('subproc_arg_part -> XOR','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2472),
('subproc_arg_part -> DOUBLEDIV','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2473),
('subproc_arg_part -> ELLIPSIS','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2474),
('subproc_arg_part -> NONE','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2475),
('subproc_arg_part -> TRUE','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2476),
('subproc_arg_part -> FALSE','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2477),
('subproc_arg_part -> NUMBER','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2478),
('subproc_arg_part -> STRING','subproc_arg_part',1,'p_subproc_arg_part','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2479),
('empty -> <empty>','empty',0,'p_empty','/usr/local/lib/python3.5/site-packages/xonsh/parser.py',2490),
]
| [
"[email protected]"
]
| |
d7b27284fcf8e687c0ce5cdc8fc1586f625817db | 26aeec7c6571012e85cd6bdd42560988664dc845 | /0x04-python-more_data_structures/1-search_replace.py | bf02407fb835033abc2ea61c84569a868d1e5c89 | []
| no_license | KoeusIss/holbertonschool-higher_level_programming | 3d6ac70d9630c516fa95fcd2d6209d8591bf4169 | 446ca491156ac93134e5c15f3568cb684079d67e | refs/heads/master | 2022-12-11T15:22:58.164551 | 2020-09-24T09:51:45 | 2020-09-24T09:51:45 | 259,189,990 | 1 | 4 | null | null | null | null | UTF-8 | Python | false | false | 210 | py | #!/usr/bin/python3
def search_replace(my_list, search, replace):
"""
Replaces all occurences of an element by another in a new list
"""
return [(replace if x == search else x) for x in my_list]
| [
"[email protected]"
]
| |
ca1da8e85b269c0081f63c09d3201e66d15324ae | 131921d5ed69ac5d470520a3fbb651d1374a668d | /accounts/models.py | e04dcc920bc257ac68ee4d62006da384f97c2532 | []
| no_license | SyedMaazHassan/temporary-one | 07fc31673b3eb8368014878a22c747d39b259cb3 | cc67107cabcb2a092b79fbc7d8b5369592a15241 | refs/heads/master | 2023-03-02T11:28:08.813659 | 2021-02-10T10:02:52 | 2021-02-10T10:02:52 | 337,535,351 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,924 | py | import uuid
from django.dispatch import receiver
from django.db import models
from django.contrib.auth.models import (
BaseUserManager, AbstractBaseUser
)
from django.db.models.signals import post_save
from saidatech_admin.models import saidatech_admin_profile
class MyUserManager(BaseUserManager):
def create_user(self, email, date_of_birth, password=None):
"""
Creates and saves a User with the given email, date of
birth and password.
"""
if not email:
raise ValueError('Users must have an email address')
user = self.model(
email=self.normalize_email(email),
date_of_birth=date_of_birth,
)
user.set_password(password)
user.save(using=self._db)
return user
def create_superuser(self, email, date_of_birth, password):
"""
Creates and saves a superuser with the given email, date of
birth and password.
"""
user = self.create_user(
email,
password=password,
date_of_birth=date_of_birth,
)
user.is_admin = True
user.save(using=self._db)
return user
class MyUser(AbstractBaseUser):
id=models.UUIDField( primary_key = True, editable = False,default=uuid.uuid4())
role=models.CharField(max_length=10,choices=[('Instructor', 'Instructor'), ('Student', 'Student')])
email = models.EmailField(
verbose_name='email address',
max_length=255,
unique=True,
)
date_of_birth = models.DateField(null =True)
is_active = models.BooleanField(default=True)
is_admin = models.BooleanField(default=False)
objects = MyUserManager()
REQUIRED_FIELDS = ['date_of_birth']
USERNAME_FIELD = 'email'
is_active=models.BooleanField(default=True)
def get_full_name(self):
# The user is identified by their email address
return self.email
def get_short_name(self):
# The user is identified by their email address
return self.email
def __str__(self): # __unicode__ on Python 2
return self.email
def has_perm(self, perm, obj=None):
"Does the user have a specific permission?"
# Simplest possible answer: Yes, always
return True
def has_module_perms(self, app_label):
"Does the user have permissions to view the app `app_label`?"
# Simplest possible answer: Yes, always
return True
@property
def is_staff(self):
"Is the user a member of staff?"
# Simplest possible answer: All admins are staff
return self.is_admin
@receiver(post_save, sender=MyUser)
def create_admin_profile(sender, instance, created, **kwargs):
if created:
if sender.is_admin:
print("Na me b IID", MyUser.id)
#saidatech_admin_profile.objects.create(saidatech_admin_id=instance)
| [
"[email protected]"
]
| |
aacf5dba2a25fe59933c529c454fa74f4d0e7abb | 666c1b8a36d85e33cae95c5b13f5212098492586 | /openpyxl/worksheet/filters.py | e8242d6b2cd517b0b591745dc333e6780dcbf38e | [
"MIT"
]
| permissive | nickpell/openpyxl | 1e5f2d0757bd254271e9eb9fdec1d8423984afc4 | 160c730c419f3796d2208b05c3b26a2b2fc10eb1 | refs/heads/master | 2020-04-10T01:46:45.896392 | 2018-12-06T20:15:17 | 2018-12-06T20:15:17 | 160,725,268 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,946 | py | from __future__ import absolute_import
# Copyright (c) 2010-2018 openpyxl
from openpyxl.compat import unicode
from openpyxl.descriptors.serialisable import Serialisable
from openpyxl.descriptors import (
Alias,
Typed,
Set,
Float,
DateTime,
NoneSet,
Bool,
Integer,
String,
Sequence,
MinMax,
)
from openpyxl.descriptors.excel import ExtensionList, CellRange
from openpyxl.descriptors.sequence import ValueSequence
class SortCondition(Serialisable):
tagname = "sortCondition"
descending = Bool(allow_none=True)
sortBy = NoneSet(values=(['value', 'cellColor', 'fontColor', 'icon']))
ref = CellRange()
customList = String(allow_none=True)
dxfId = Integer(allow_none=True)
iconSet = NoneSet(values=(['3Arrows', '3ArrowsGray', '3Flags',
'3TrafficLights1', '3TrafficLights2', '3Signs', '3Symbols', '3Symbols2',
'4Arrows', '4ArrowsGray', '4RedToBlack', '4Rating', '4TrafficLights',
'5Arrows', '5ArrowsGray', '5Rating', '5Quarters']))
iconId = Integer(allow_none=True)
def __init__(self,
ref=None,
descending=None,
sortBy=None,
customList=None,
dxfId=None,
iconSet=None,
iconId=None,
):
self.descending = descending
self.sortBy = sortBy
self.ref = ref
self.customList = customList
self.dxfId = dxfId
self.iconSet = iconSet
self.iconId = iconId
class SortState(Serialisable):
tagname = "sortState"
columnSort = Bool(allow_none=True)
caseSensitive = Bool(allow_none=True)
sortMethod = NoneSet(values=(['stroke', 'pinYin']))
ref = CellRange()
sortCondition = Sequence(expected_type=SortCondition, allow_none=True)
extLst = Typed(expected_type=ExtensionList, allow_none=True)
__elements__ = ('sortCondition',)
def __init__(self,
columnSort=None,
caseSensitive=None,
sortMethod=None,
ref=None,
sortCondition=(),
extLst=None,
):
self.columnSort = columnSort
self.caseSensitive = caseSensitive
self.sortMethod = sortMethod
self.ref = ref
self.sortCondition = sortCondition
def __bool__(self):
return self.ref is not None
__nonzero__ = __bool__
class IconFilter(Serialisable):
tagname = "iconFilter"
iconSet = Set(values=(['3Arrows', '3ArrowsGray', '3Flags',
'3TrafficLights1', '3TrafficLights2', '3Signs', '3Symbols', '3Symbols2',
'4Arrows', '4ArrowsGray', '4RedToBlack', '4Rating', '4TrafficLights',
'5Arrows', '5ArrowsGray', '5Rating', '5Quarters']))
iconId = Integer(allow_none=True)
def __init__(self,
iconSet=None,
iconId=None,
):
self.iconSet = iconSet
self.iconId = iconId
class ColorFilter(Serialisable):
tagname = "colorFilter"
dxfId = Integer(allow_none=True)
cellColor = Bool(allow_none=True)
def __init__(self,
dxfId=None,
cellColor=None,
):
self.dxfId = dxfId
self.cellColor = cellColor
class DynamicFilter(Serialisable):
tagname = "dynamicFilter"
type = Set(values=(['null', 'aboveAverage', 'belowAverage', 'tomorrow',
'today', 'yesterday', 'nextWeek', 'thisWeek', 'lastWeek', 'nextMonth',
'thisMonth', 'lastMonth', 'nextQuarter', 'thisQuarter', 'lastQuarter',
'nextYear', 'thisYear', 'lastYear', 'yearToDate', 'Q1', 'Q2', 'Q3', 'Q4',
'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'M10', 'M11',
'M12']))
val = Float(allow_none=True)
valIso = DateTime(allow_none=True)
maxVal = Float(allow_none=True)
maxValIso = DateTime(allow_none=True)
def __init__(self,
type=None,
val=None,
valIso=None,
maxVal=None,
maxValIso=None,
):
self.type = type
self.val = val
self.valIso = valIso
self.maxVal = maxVal
self.maxValIso = maxValIso
class CustomFilter(Serialisable):
tagname = "customFilter"
operator = NoneSet(values=(['equal', 'lessThan', 'lessThanOrEqual',
'notEqual', 'greaterThanOrEqual', 'greaterThan']))
val = String()
def __init__(self,
operator=None,
val=None,
):
self.operator = operator
self.val = val
class CustomFilters(Serialisable):
tagname = "customFilters"
_and = Bool(allow_none=True)
customFilter = Sequence(expected_type=CustomFilter) # min 1, max 2
__elements__ = ('customFilter',)
def __init__(self,
_and=None,
customFilter=(),
):
self._and = _and
self.customFilter = customFilter
class Top10(Serialisable):
tagname = "top10"
top = Bool(allow_none=True)
percent = Bool(allow_none=True)
val = Float()
filterVal = Float(allow_none=True)
def __init__(self,
top=None,
percent=None,
val=None,
filterVal=None,
):
self.top = top
self.percent = percent
self.val = val
self.filterVal = filterVal
class DateGroupItem(Serialisable):
tagname = "dateGroupItem"
year = Integer()
month = MinMax(min=1, max=12, allow_none=True)
day = MinMax(min=1, max=31, allow_none=True)
hour = MinMax(min=0, max=23, allow_none=True)
minute = MinMax(min=0, max=59, allow_none=True)
second = Integer(min=0, max=59, allow_none=True)
dateTimeGrouping = Set(values=(['year', 'month', 'day', 'hour', 'minute',
'second']))
def __init__(self,
year=None,
month=None,
day=None,
hour=None,
minute=None,
second=None,
dateTimeGrouping=None,
):
self.year = year
self.month = month
self.day = day
self.hour = hour
self.minute = minute
self.second = second
self.dateTimeGrouping = dateTimeGrouping
class Filters(Serialisable):
tagname = "filters"
blank = Bool(allow_none=True)
calendarType = NoneSet(values=["gregorian","gregorianUs",
"gregorianMeFrench","gregorianArabic", "hijri","hebrew",
"taiwan","japan", "thai","korea",
"saka","gregorianXlitEnglish","gregorianXlitFrench"])
filter = ValueSequence(expected_type=unicode)
dateGroupItem = Sequence(expected_type=DateGroupItem, allow_none=True)
__elements__ = ('filter', 'dateGroupItem')
def __init__(self,
blank=None,
calendarType=None,
filter=(),
dateGroupItem=(),
):
self.blank = blank
self.calendarType = calendarType
self.filter = filter
self.dateGroupItem = dateGroupItem
class FilterColumn(Serialisable):
tagname = "filterColumn"
colId = Integer()
col_id = Alias('colId')
hiddenButton = Bool(allow_none=True)
showButton = Bool(allow_none=True)
# some elements are choice
filters = Typed(expected_type=Filters, allow_none=True)
top10 = Typed(expected_type=Top10, allow_none=True)
customFilters = Typed(expected_type=CustomFilters, allow_none=True)
dynamicFilter = Typed(expected_type=DynamicFilter, allow_none=True)
colorFilter = Typed(expected_type=ColorFilter, allow_none=True)
iconFilter = Typed(expected_type=IconFilter, allow_none=True)
extLst = Typed(expected_type=ExtensionList, allow_none=True)
__elements__ = ('filters', 'top10', 'customFilters', 'dynamicFilter',
'colorFilter', 'iconFilter')
def __init__(self,
colId=None,
hiddenButton=None,
showButton=None,
filters=None,
top10=None,
customFilters=None,
dynamicFilter=None,
colorFilter=None,
iconFilter=None,
extLst=None,
blank=None,
vals=None,
):
self.colId = colId
self.hiddenButton = hiddenButton
self.showButton = showButton
self.filters = filters
self.top10 = top10
self.customFilters = customFilters
self.dynamicFilter = dynamicFilter
self.colorFilter = colorFilter
self.iconFilter = iconFilter
if blank is not None and self.filters:
self.filters.blank = blank
if vals is not None and self.filters:
self.filters.filter = vals
class AutoFilter(Serialisable):
tagname = "autoFilter"
ref = CellRange()
filterColumn = Sequence(expected_type=FilterColumn, allow_none=True)
sortState = Typed(expected_type=SortState, allow_none=True)
extLst = Typed(expected_type=ExtensionList, allow_none=True)
__elements__ = ('filterColumn', 'sortState')
def __init__(self,
ref=None,
filterColumn=(),
sortState=None,
extLst=None,
):
self.ref = ref
self.filterColumn = filterColumn
self.sortState = sortState
def __bool__(self):
return self.ref is not None
__nonzero__ = __bool__
def add_filter_column(self, col_id, vals, blank=False):
"""
Add row filter for specified column.
:param col_id: Zero-origin column id. 0 means first column.
:type col_id: int
:param vals: Value list to show.
:type vals: str[]
:param blank: Show rows that have blank cell if True (default=``False``)
:type blank: bool
"""
self.filterColumn.append(FilterColumn(colId=col_id, filters=Filters(blank=blank, filter=vals)))
def add_sort_condition(self, ref, descending=False):
"""
Add sort condition for cpecified range of cells.
:param ref: range of the cells (e.g. 'A2:A150')
:type ref: string
:param descending: Descending sort order (default=``False``)
:type descending: bool
"""
cond = SortCondition(ref, descending)
if self.sortState is None:
self.sortState = SortState(ref=ref)
self.sortState.sortCondition.append(cond)
| [
"[email protected]"
]
| |
6ca67602d21d354937356280ae7d8a91c75c5990 | 26be9ea17640d29d6a8a576cbf306f71675bdfb1 | /pyroprint/optedredger.py | c46bc80acf83ea548a2c338e430e1a6e746c1c47 | []
| no_license | meredithhitchcock/wabio | 076a69efa0e38da0cbba348114408e2354fdde76 | f3de4b8ca6f98d6ec2fa3989214871c2a3781c37 | refs/heads/master | 2021-01-18T14:50:52.630167 | 2014-10-04T04:28:58 | 2014-10-04T04:28:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,812 | py | import pymysql
import string
import sys
primer = "CGTGAGGCTTAACCTT"
revcomprimer = "AAGGTTAAGCCTCACG"
seqFile1 = ""
ratioSeq1 = ""
seqFile2 = ""
ratioSeq2 = ""
fileDir = "./Genome Sequences/rDNA plasmid sequences/23-5"
outfile = ""
# A quick tool to dredge the opterons from .seq files
# Takes in three params:
# - the ratio of sequences
# - the two sequence files
def main():
global seqFile1
global ratioSeq1
global seqFile2
global ratioSeq2
global fileDir
global outfile
if len(sys.argv) < 5 or sys.argv[1] == "help":
printUsage()
return
# Parse input params
i = 1
while i < len(sys.argv):
if sys.argv[i] == "-r":
try:
(r1, split, r2) = sys.argv[i + 1].partition(":")
ratioSeq1 = int(r1)
ratioSeq2 = int(r2)
i += 2
except ValueError:
print "exception"
printUsage()
return
elif sys.argv[i] == "-s":
(seqFile1, split, seqFile2) = sys.argv[i + 1].partition(":")
outfile = seqFile1 + "-" + seqFile2 + ".opts"
i += 2
elif sys.argv[i] == "-d":
fileDir = argv[i + 1]
i += 2
elif sys.argv[i] == "-o":
outfile = argv[i + 1]
i += 2
else:
printUsage()
return
seq1 = findSeq(seqFile1)
seq2 = findSeq(seqFile2)
# Print the found sequences to an output file using the ratios
fd = open(outfile, "w")
for i in range(ratioSeq1):
fd.write("# " + str(i + 1) + " - " + seqFile1 + "\n")
fd.write(seq1 + "\n")
for i in range(ratioSeq2):
fd.write("# " + str(i + 1) + " - " + seqFile2 + "\n")
fd.write(seq2 + "\n")
fd.close()
return
def findSeq(seqFile):
try:
fd = open(fileDir + "/23-5 " + seqFile + ".seq", "r")
except:
print ("File Not Found: " +
fileDir + "/23-5 " + seqFile + ".seq")
sys.exit()
seq = ""
for line in fd:
seq += line.strip()
# FIRST try to find the sequence after the original primer
(pre, p, end) = seq.partition(primer)
# IF didn't find using the original primer try the reverse compliment
if len(p) == 0 or len(end) == 0:
(pre, p, end) = seq.partition(revcomprimer)
if len(p) == 0 or len(end) == 0:
print ("Runtime Error: Could not find the primer or the reverse " +
" compliment of the primer")
sys.exit()
# REVERSE the string pre
pre = pre[::-1]
# Compliment the string
pre = compliment(pre)
return pre
else:
return end
def compliment(seg):
# Compliment the string
slen = len(seg)
for i in range(slen):
if seg[i] == "A":
seg = seg[0:i] + "T" + seg[i + 1:slen]
elif seg[i] == "T":
seg = seg[0:i] + "A" + seg[i + 1:slen]
elif seg[i] == "C":
seg = seg[0:i] + "G" + seg[i + 1:slen]
elif seg[i] == "G":
seg = seg[0:i] + "C" + seg[i + 1:slen]
return seg
def printUsage():
print ("Usage: " + sys.argv[0] + " -r <X:Y> -s " +
"<sequence 1 name>:<sequence 2 name> [-d <sequence file directory>] [-o <outfile>]")
print (" -r : The ratio of sequence 1 to sequence 2 in the final result")
print (" -s : A \"ratio\" of sequence names to be used.\n" +
" Example: Dg03-5:Hu01-3\n" +
" NOTE: \'23-5 \' and \'.seq\' are automatically added")
print (" -d : An optional parameter to change the default location to search " +
"for the sequence files.\n" +
" The default location is: " + fileDir)
print (" -o : Optional parameter to change the default output file.\n" +
" The default output file name is \"<seq 1 name>-<seq 2 name>.opts\"")
if __name__ == "__main__":
main()
| [
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]
| |
e3bb6cda196d50d9aa9a0366a7e535e4d6cbe821 | 9fb7bc79fc3e9224de8a63189515044812a1ff41 | /scripts/general_utils.py | d490031b36efe0659d6834af14b5d1905f111691 | [
"MIT"
]
| permissive | dciborow/amlsdummy | 2fb4c0ddb39ec40140d4811d23d832c009dee139 | 91d2732dd485002b16c28353263b62c674a69399 | refs/heads/master | 2022-06-19T11:28:46.961813 | 2020-02-09T20:51:15 | 2020-02-09T20:51:15 | 239,918,986 | 0 | 0 | MIT | 2020-02-12T03:30:20 | 2020-02-12T03:30:19 | null | UTF-8 | Python | false | false | 3,210 | py | import pickle
import os
from enum import Enum
from datetime import datetime, timedelta
class JobType(Enum):
real_time_scoring = "RealTimeScoring"
batch_scoring = "BatchScoring"
class JobLog:
step_start = "start"
step_end = "end"
logs_directory = "Logs"
general_stats = "Overview.csv"
def __init__(self, jobtype):
self.job_type = jobtype
self.job_directory = jobtype.value + JobLog.logs_directory
self.job_steps = {}
self.job_info = []
self.total_start = None
def startStep(self, step_name):
if len(self.job_steps) == 0:
self.total_start = datetime.now()
self.job_steps[step_name] = {}
self.job_steps[step_name][JobLog.step_start] = datetime.now()
def endStep(self, step_name):
if step_name in self.job_steps.keys():
self.job_steps[step_name][JobLog.step_end] = datetime.now()
def addInfo(self, info):
self.job_info.append(info)
def _dumpGeneral(self, log_path, total_time):
if os.path.exists(JobLog.logs_directory) == False:
os.makedirs(JobLog.logs_directory)
stats_file = os.path.join(JobLog.logs_directory, JobLog.general_stats)
log_entry = []
log_entry.append(self.job_type.value)
log_entry.append(log_path)
log_entry.append(str(total_time))
with open(stats_file, "a+") as general_stats:
general_stats.writelines("{}\n".format(",".join(log_entry)))
def dumpLog(self):
total_run_time = datetime.now() - self.total_start
log_path = os.path.join(JobLog.logs_directory, self.job_directory)
if os.path.exists(log_path) == False:
os.makedirs(log_path)
file_name = datetime.now().isoformat()
file_name = file_name.replace(":","-")
file_name = file_name.replace(".","-")
file_name += ".log"
file_path = os.path.join(log_path, file_name)
with open(file_path, "w") as log_output:
log_output.writelines("Job Type: {}\n".format(self.job_type.value))
log_output.writelines("Total Run Time: {} seconds\n".format(total_run_time.total_seconds()))
log_output.writelines("Job Info: \n")
for info in self.job_info:
log_output.writelines(" " + info + "\n")
log_output.writelines("Job Steps: \n")
for step in self.job_steps.keys():
if JobLog.step_start in self.job_steps[step].keys() and JobLog.step_end in self.job_steps[step].keys():
time_delt = self.job_steps[step][JobLog.step_end] - self.job_steps[step][JobLog.step_start]
log_output.writelines(" {} - {} seconds \n".format(step, time_delt.total_seconds()))
else:
log_output.writelines(" {} - {} \n".format(step, self.job_steps[step]))
self._dumpGeneral(file_path, total_run_time.total_seconds())
def createPickle(file_name):
'''
Create a dummy pickle file
'''
my_data = {"nothing" : "to see here"}
with open(file_name, 'wb') as model_file:
pickle.dump(my_data, model_file)
| [
"[email protected]"
]
| |
b8e53160b6e640f632a827ac6a40be6f7edb9e58 | 16450d59c820298f8803fd40a1ffa2dd5887e103 | /SWEA/2027_대각선출력.py | 33b1e9b766ac8201bf9d5378fb3649f247886e87 | []
| no_license | egyeasy/TIL_public | f78c11f81d159eedb420f5fa177c05d310c4a039 | e2f40eda09cb0a65cc064d9ba9b0e2fa7cbbcb38 | refs/heads/master | 2021-06-21T01:22:16.516777 | 2021-02-02T13:16:21 | 2021-02-02T13:16:21 | 167,803,551 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 253 | py | """
주어진 텍스트를 그대로 출력하세요.
> 입력
> 출력
#++++
+#+++
++#++
+++#+
++++#
"""
print("""#++++
+#+++
++#++
+++#+
++++#""")
# 반성
# 1. multiline comment를 활용하는 방법
# 2. print()를 여러 방법으로 쓰는 법 | [
"[email protected]"
]
| |
c4f9837ca141aa95d0af984632f977212fccf8c7 | ab0315bcded75c10c591076b22ed8ff664ee76af | /fig4/config_scf_10mods_200213.py | 5a3c6829ca9d92207ab9ba95c94ca871a795916d | []
| no_license | mukamel-lab/BICCN-Mouse-MOp | 389f62492986a2ffe4278ed16f59fc17dc75b767 | 8058ab8ae827c6e019fff719903b0ba5b400931d | refs/heads/master | 2021-07-06T11:14:25.401628 | 2020-09-30T04:54:27 | 2020-09-30T04:54:27 | 189,758,115 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,734 | py | #!/usr/bin/env python3
"""An example configuration file
"""
import sys
import os
# # Configs
name = 'mop_10mods_200213'
outdir = '/cndd/fangming/CEMBA/data/MOp_all/results'
output_pcX_all = outdir + '/pcX_all_{}.npy'.format(name)
output_cells_all = outdir + '/cells_all_{}.npy'.format(name)
output_imputed_data_format = outdir + '/imputed_data_{}_{{}}.npy'.format(name)
output_clst_and_umap = outdir + '/intg_summary_{}.tsv'.format(name)
output_figures = outdir + '/figures/{}_{{}}.{{}}'.format(name)
output_cluster_centroids = outdir + '/centroids_{}.pkl'.format(name)
DATA_DIR = '/cndd/fangming/CEMBA/data/MOp_all/data_freeze_l5pt'
# fixed dataset configs
sys.path.insert(0, DATA_DIR)
from __init__datasets import *
meta_f = os.path.join(DATA_DIR, '{0}_metadata.tsv')
hvftrs_f = os.path.join(DATA_DIR, '{0}_hvfeatures.{1}')
hvftrs_gene = os.path.join(DATA_DIR, '{0}_hvfeatures.gene')
hvftrs_cell = os.path.join(DATA_DIR, '{0}_hvfeatures.cell')
mods_selected = [
'snmcseq_gene',
'snatac_gene',
'smarter_cells',
'smarter_nuclei',
'10x_cells_v2',
'10x_cells_v3',
'10x_nuclei_v3',
'10x_nuclei_v3_macosko',
'merfish',
'epi_retro',
]
features_selected = ['epi_retro']
# check features
for features_modality in features_selected:
assert (features_modality in mods_selected)
# within modality
ps = {'mc': 0.9,
'atac': 0.1,
'rna': 0.7,
'merfish': 1,
}
drop_npcs = {
'mc': 0,
'atac': 0,
'rna': 0,
'merfish': 0,
}
# across modality
cross_mod_distance_measure = 'correlation' # cca
knn = 20
relaxation = 3
n_cca = 30
# PCA
npc = 50
# clustering
k = 30
resolutions = [0.1, 0.2, 0.4, 0.8]
# umap
umap_neighbors = 60
min_dist = 0.5
| [
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]
| |
23be186718ed310752b58249fce51092af45e1c1 | 85f5dff291acf1fe7ab59ca574ea9f4f45c33e3b | /api/tacticalrmm/agents/migrations/0054_alter_agent_goarch.py | d2f26e1c4156e7ec3053a815a89ca418cf2919a9 | [
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-unknown-license-reference"
]
| permissive | sadnub/tacticalrmm | a4ecaf994abe39244a6d75ed2166222abb00d4f4 | 0af95aa9b1084973642da80e9b01a18dcacec74a | refs/heads/develop | 2023-08-30T16:48:33.504137 | 2023-04-10T22:57:44 | 2023-04-10T22:57:44 | 243,405,684 | 0 | 2 | MIT | 2020-09-08T13:03:30 | 2020-02-27T01:43:56 | Python | UTF-8 | Python | false | false | 498 | py | # Generated by Django 4.0.4 on 2022-06-06 04:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('agents', '0053_remove_agenthistory_status'),
]
operations = [
migrations.AlterField(
model_name='agent',
name='goarch',
field=models.CharField(blank=True, choices=[('amd64', 'amd64'), ('386', '386'), ('arm64', 'arm64'), ('arm', 'arm')], max_length=255, null=True),
),
]
| [
"[email protected]"
]
| |
5ea614fee71884c24f32e860daef852091238e03 | 5da5473ff3026165a47f98744bac82903cf008e0 | /packages/google-cloud-securitycenter/samples/generated_samples/securitycenter_v1beta1_generated_security_center_update_source_sync.py | b5e8b229d69c9ca234a50e7b49e7d975e67ed7f9 | [
"Apache-2.0"
]
| permissive | googleapis/google-cloud-python | ed61a5f03a476ab6053870f4da7bc5534e25558b | 93c4e63408c65129422f65217325f4e7d41f7edf | refs/heads/main | 2023-09-04T09:09:07.852632 | 2023-08-31T22:49:26 | 2023-08-31T22:49:26 | 16,316,451 | 2,792 | 917 | Apache-2.0 | 2023-09-14T21:45:18 | 2014-01-28T15:51:47 | Python | UTF-8 | Python | false | false | 1,863 | py | # -*- coding: utf-8 -*-
# Copyright 2023 Google LLC
#
# 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.
#
# Generated code. DO NOT EDIT!
#
# Snippet for UpdateSource
# NOTE: This snippet has been automatically generated for illustrative purposes only.
# It may require modifications to work in your environment.
# To install the latest published package dependency, execute the following:
# python3 -m pip install google-cloud-securitycenter
# [START securitycenter_v1beta1_generated_SecurityCenter_UpdateSource_sync]
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
# client as shown in:
# https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import securitycenter_v1beta1
def sample_update_source():
# Create a client
client = securitycenter_v1beta1.SecurityCenterClient()
# Initialize request argument(s)
request = securitycenter_v1beta1.UpdateSourceRequest(
)
# Make the request
response = client.update_source(request=request)
# Handle the response
print(response)
# [END securitycenter_v1beta1_generated_SecurityCenter_UpdateSource_sync]
| [
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]
| |
bf9079fb3d60a76e417d01ad38efd6a8b18c6bd4 | 10ddfb2d43a8ec5d47ce35dc0b8acf4fd58dea94 | /Python/count-number-of-distinct-integers-after-reverse-operations.py | 24dbdf067e7e0c6f952bd0ae88007568b737487c | [
"MIT"
]
| permissive | kamyu104/LeetCode-Solutions | f54822059405ef4df737d2e9898b024f051fd525 | 4dc4e6642dc92f1983c13564cc0fd99917cab358 | refs/heads/master | 2023-09-02T13:48:26.830566 | 2023-08-28T10:11:12 | 2023-08-28T10:11:12 | 152,631,182 | 4,549 | 1,651 | MIT | 2023-05-31T06:10:33 | 2018-10-11T17:38:35 | C++ | UTF-8 | Python | false | false | 707 | py | # Time: O(nlogr), r = max(nums)
# Space: O(n)
# hash table
class Solution(object):
def countDistinctIntegers(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
def reverse(n):
result = 0
while n:
result = result*10 + n%10
n //= 10
return result
return len({y for x in nums for y in (x, reverse(x))})
# Time: O(nlogr), r = max(nums)
# Space: O(n)
# hash table
class Solution2(object):
def countDistinctIntegers(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
return len({y for x in nums for y in (x, int(str(x)[::-1]))})
| [
"[email protected]"
]
| |
cb51b6fcc0d3bf4a8423b790d3e33d50c46cfa76 | 80907e3f9e998abc375afcc6e6546c88ee023252 | /badgepad/cmdline.py | 4fb20087f1865f5d46be5d1143852f084c2adcdb | []
| no_license | toolness/badgepad | 5ac1eb21bf426335e81cb9400f5180b6542dea43 | 2c1e221efca12054b843aef066798dd03f6f2533 | refs/heads/master | 2021-01-10T20:03:45.630275 | 2013-04-08T19:46:43 | 2013-04-08T19:46:43 | 9,220,546 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,051 | py | import os
import sys
import shutil
import argparse
from . import pkg_path
from .project import Project
from .build import build_website
from .server import start_auto_rebuild_server
def nice_dir(path, cwd=None):
if cwd is None:
cwd = os.getcwd()
path = os.path.realpath(path)
cwd = os.path.realpath(cwd)
rel = os.path.relpath(path, cwd)
if rel.startswith('..'):
return path
return rel
def fail(text):
log(text)
sys.exit(1)
def log(text):
sys.stdout.write(text + '\n')
def cmd_serve(project, args):
"""
Serve website.
"""
start_auto_rebuild_server(project.ROOT, ip=args.ip, port=args.port)
def cmd_build(project, args):
"""
Build website.
"""
if args.base_url:
project.set_base_url(args.base_url)
if not args.output_dir:
args.output_dir = project.path('dist')
build_website(project, dest_dir=args.output_dir)
log("Done. Static website is in '%s'." % nice_dir(args.output_dir))
def cmd_init(project, args):
"""
Initialize new project directory.
"""
if project.exists('config.yml'):
fail("Directory already contains a project.")
log("Generating config.yml.")
shutil.copy(pkg_path('samples', 'config.yml'), project.ROOT)
log("Creating empty directories.")
os.mkdir(project.path('assertions'))
os.mkdir(project.path('badges'))
os.mkdir(project.path('static'))
log("Creating default templates.")
shutil.copytree(pkg_path('samples', 'templates'), project.TEMPLATES_DIR)
log("Done.")
def cmd_newbadge(project, args):
"""
Create a new badge type.
"""
filename = project.path('badges', '%s.yml' % args.name)
if os.path.exists(filename):
fail("That badge already exists.")
shutil.copy(pkg_path('samples', 'badge.yml'), filename)
log("Created %s." % project.relpath(filename))
pngfile = project.relpath('badges', '%s.png' % args.name)
log("To give the badge an image, copy a PNG file to %s." % pngfile)
def cmd_issue(project, args):
"""
Issue a badge to a recipient.
"""
basename = '%s.%s' % (args.recipient, args.badge)
filename = project.path('assertions', '%s.yml' % basename)
if not args.badge in project.badges:
fail("Badge '%s' does not exist." % args.badge)
if args.recipient not in project.recipients:
fail("Recipient '%s' does not exist." % args.recipient)
if os.path.exists(filename):
fail("Badge already issued.")
shutil.copy(pkg_path('samples', 'assertion.yml'), filename)
log("Created %s." % project.relpath(filename))
def main(arglist=None):
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--root-dir', help='root project directory',
default='.')
subparsers = parser.add_subparsers()
serve = subparsers.add_parser('serve', help=cmd_serve.__doc__)
serve.add_argument('-i', '--ip', help='ip address',
default='127.0.0.1')
serve.add_argument('-p', '--port', help='port', type=int, default=8000)
serve.set_defaults(func=cmd_serve)
build = subparsers.add_parser('build', help=cmd_build.__doc__)
build.add_argument('-u', '--base-url', help='alternate base URL')
build.add_argument('-o', '--output-dir', help='output directory')
build.set_defaults(func=cmd_build)
init = subparsers.add_parser('init', help=cmd_init.__doc__)
init.set_defaults(func=cmd_init)
newbadge = subparsers.add_parser('newbadge', help=cmd_newbadge.__doc__)
newbadge.add_argument('name')
newbadge.set_defaults(func=cmd_newbadge)
issue = subparsers.add_parser('issue', help=cmd_issue.__doc__)
issue.add_argument('recipient')
issue.add_argument('badge')
issue.set_defaults(func=cmd_issue)
args = parser.parse_args(arglist)
project = Project(args.root_dir)
if args.func is not cmd_init:
if not project.exists('config.yml'):
fail('Directory does not contain a project.')
args.func(project, args)
| [
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]
| |
e2ee762af542c4e17ef39d01c46d47c5bbc7d2ab | 3af9425f048876de388d2a5dc4f361132d03a387 | /algorithms/source/최단경로/1.Shortest path(1753_Dijkstra).py | d4b5deea16f72a032b83b6eec204466c755aa8db | []
| no_license | hwanginbeom/TIL | 6fab0d06db9cb9d78c03e3b3392dedcdaf799df6 | 933348f08e5bd58527dcb3732c092a83581e471b | refs/heads/master | 2021-08-15T06:15:21.452951 | 2021-08-13T14:56:09 | 2021-08-13T14:56:09 | 146,391,739 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,284 | py | '''
문제
방향그래프가 주어지면 주어진 시작점에서 다른 모든 정점으로의 최단 경로를 구하는 프로그램을 작성하시오.
단, 모든 간선의 가중치는 10 이하의 자연수이다.
입력
첫째 줄에 정점의 개수 V와 간선의 개수 E가 주어진다. (1≤V≤20,000, 1≤E≤300,000) 모든 정점에는 1부터 V까지 번호가
매겨져 있다고 가정한다. 둘째 줄에는 시작 정점의 번호 K(1≤K≤V)가 주어진다. 셋째 줄부터 E개의 줄에 걸쳐 각 간선을
나타내는 세 개의 정수 (u, v, w)가 순서대로 주어진다. 이는 u에서 v로 가는 가중치 w인 간선이 존재한다는 뜻이다. u와 v는
서로 다르며 w는 10 이하의 자연수이다. 서로 다른 두 정점 사이에 여러 개의 간선이 존재할 수도 있음에 유의한다.
출력
첫째 줄부터 V개의 줄에 걸쳐, i번째 줄에 i번 정점으로의 최단 경로의 경로값을 출력한다. 시작점 자신은 0으로 출력하고,
경로가 존재하지 않는 경우에는 INF를 출력하면 된다.
'''
import sys
import heapq
input = sys.stdin.readline
# 노드의 개수, 간선의 개수를 입력받기
node, route = map(int, input().split())
# node, route = 5, 6
INF = int(1e9) # 무한을 의미하는 값으로 10억을 설정
distance = [int(1e9)] * (node + 1)
start = int(input())
# 각 노드에 연결되어 있는 노드에 대한 정보를 담는 리스트를 만들기
graph = [[]for i in range(node + 1)]
# 모든 간선 정보를 입력 받기
for _ in range(route):
a, b, c = map(int, input().split())
# a번 노드에서 b번 노드로 가는 비용이 c라는 의미
graph[a].append((b, c))
def dijkstra(start):
q = []
# 시작 노드에 대해서 초기화
heapq.heappush(q,(0,start))
distance[start] = 0
while q:
dist, now = heapq.heappop(q)
if distance[now] < dist:
continue
for i in graph[now]:
cost = dist + i[1]
if cost < distance[i[0]]:
distance[i[0]] = cost
heapq.heappush(q, (cost, i[0]))
dijkstra(start)
for i in range(1,node+1):
if distance[i] ==1000000000:
print('INF')
continue
print(distance[i]) | [
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]
| |
f3bbe6e34b15c175b28daa543e9a87e025c79843 | b647129cb448b4991059dcfb44d8279b4c8f18dd | /pyEX/commodities/commodities.py | cfcfbb6c68aaaf35f81c2bbf3438af9571a42525 | [
"Apache-2.0"
]
| permissive | jmailloux/pyEX | 433a9aeab3429edb5af1c2f18dc533011ab15c92 | 2101e8c53a9080ea8b00b28a758be441095d5048 | refs/heads/main | 2023-03-24T06:36:43.544611 | 2021-03-17T03:30:40 | 2021-03-17T03:30:40 | 345,503,430 | 0 | 0 | Apache-2.0 | 2021-03-08T02:06:50 | 2021-03-08T02:06:49 | null | UTF-8 | Python | false | false | 2,944 | py | # *****************************************************************************
#
# Copyright (c) 2020, the pyEX authors.
#
# This file is part of the pyEX library, distributed under the terms of
# the Apache License 2.0. The full license can be found in the LICENSE file.
#
from enum import Enum
from functools import lru_cache
from ..points import points
class CommoditiesPoints(Enum):
"""Commodities data points
https://iexcloud.io/docs/api/#commodities
Attributes:
WTI; Crude oil West Texas Intermediate - in dollars per barrel, not seasonally adjusted
BRENT; Crude oil Brent Europe - in dollars per barrel, not seasonally adjusted
NATGAS; Henry Hub Natural Gas Spot Price - in dollars per million BTU, not seasonally adjusted
HEATOIL; No. 2 Heating Oil New York Harbor - in dollars per gallon, not seasonally adjusted
JET; Kerosense Type Jet Fuel US Gulf Coast - in dollars per gallon, not seasonally adjusted
DIESEL; US Diesel Sales Price - in dollars per gallon, not seasonally adjusted
GASREG; US Regular Conventional Gas Price - in dollars per gallon, not seasonally adjusted
GASMID; US Midgrade Conventional Gas Price - in dollars per gallon, not seasonally adjusted
GASPRM; US Premium Conventional Gas Price - in dollars per gallon, not seasonally adjusted
PROPANE; Propane Prices Mont Belvieu Texas - in dollars per gallon, not seasonally adjusted
"""
WTI = "DCOILWTICO"
BRENT = "DCOILBRENTEU"
NATGAS = "DHHNGSP"
HEATOIL = "DHOILNYH"
JET = "DJFUELUSGULF"
DIESEL = "GASDESW"
GASREG = "GASREGCOVW"
GASMID = "GASMIDCOVW"
GASPRM = "GASPRMCOVW"
PROPANE = "DPROPANEMBTX"
@staticmethod
@lru_cache(1)
def options():
"""Return a list of the available commodities points options"""
return list(map(lambda c: c.value, CommoditiesPoints))
def wti(token="", version="stable"):
return points("DCOILWTICO", token=token, version=version)
def brent(token="", version="stable"):
return points("DCOILBRENTEU", token=token, version=version)
def natgas(token="", version="stable"):
return points("DHHNGSP", token=token, version=version)
def heatoil(token="", version="stable"):
return points("DHOILNYH", token=token, version=version)
def jet(token="", version="stable"):
return points("DJFUELUSGULF", token=token, version=version)
def diesel(token="", version="stable"):
return points("GASDESW", token=token, version=version)
def gasreg(token="", version="stable"):
return points("GASREGCOVW", token=token, version=version)
def gasmid(token="", version="stable"):
return points("GASMIDCOVW", token=token, version=version)
def gasprm(token="", version="stable"):
return points("GASPRMCOVW", token=token, version=version)
def propane(token="", version="stable"):
return points("DPROPANEMBTX", token=token, version=version)
| [
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]
| |
f1034afe44a9c9c5e8a0e96135c0e964deace10b | 44b636e56d088c98949bf8341b8edb4df7af0f68 | /l10n_br_nfe_import/wizard/__init__.py | debf07d977e3ca124c545a641dcf6b15bf41ada1 | [
"MIT"
]
| permissive | deborapoh/odoo-brasil | e579d54dd86e7e45cee2b1892193d8b9aa3a11d1 | 603aae00dfeef8087036dbcd2f8c9a5150576916 | refs/heads/13.0 | 2022-12-01T07:28:11.423607 | 2020-08-07T13:36:23 | 2020-08-07T13:36:23 | 286,156,023 | 0 | 1 | MIT | 2020-08-09T22:05:06 | 2020-08-09T02:54:03 | null | UTF-8 | Python | false | false | 25 | py | from . import import_nfe
| [
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]
| |
19b3d339d591f72252203b511ed7e437a59647f3 | 5955ea34fd72c719f3cb78fbb3c7e802a2d9109a | /ITERATOR_GENERATOR/ITERATOR/Sample/factorial_iterated.py | cbbabe042f1a81d9ed66d5bdd9dd2ac8668ab5d6 | []
| no_license | AndreySperansky/TUITION | 3c90ac45f11c70dce04008adc1e9f9faad840b90 | 583d3a760d1f622689f6f4f482c905b065d6c732 | refs/heads/master | 2022-12-21T21:48:21.936988 | 2020-09-28T23:18:40 | 2020-09-28T23:18:40 | 299,452,924 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 467 | py | """Итеральное исчисление факториала работает быстрее рекурсивного"""
def iterative_factorial(n):
if n == 0 or n==1:
res = 1
return res
else:
res = 1
for i in range(2, n+1):
res = res * i
return res
num = abs(int(input("Введите целое число: ")))
print("Факториал числа %d равен: " % num, iterative_factorial(num)) | [
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]
| |
3edd594c112f2a201b8ec695395ac0a413286f35 | b8e3ceb93c08f9a8af776a78a9479b1dadb498b5 | /wcsinteractive/wcsinteractive/starmapper/__init__.py | fd47b586f41019548e3af4cca7173081afa447a0 | []
| no_license | sholmbo/photometry | fcb3e153059129cef6e91bcca30d74703035b7b9 | be2e20d93ca6ca3a2b20f3ba99b0e166a4687758 | refs/heads/master | 2023-09-04T04:22:09.547708 | 2021-10-26T12:53:30 | 2021-10-26T12:54:49 | 307,145,536 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 35 | py | from .starmapper import StarMapper
| [
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]
| |
917574d4ba87f4c467e7867dc9e6650e93fc9a21 | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2164/60606/306710.py | 97804c69d658036e62e5f0f4c83a491771e81aae | []
| no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 147 | py | test_num = int(input())
s = input()
if s=="aaaab":
print(3)
print(7)
elif test_num==3 and s=="aab":
print(1)
print(8)
print(0)
| [
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]
| |
32a2ca42eaf13c97db25cff13c3c05b9250f601f | 3854452cb10bc4c8dbadaf2328d0f4706300d596 | /pypet/tests/integration/environment_multiproc_test.py | 0b4698d87a66e3c3d4d6d945904694321a686ae5 | [
"BSD-3-Clause"
]
| permissive | henribunting/pypet | 852e9905957a298bf1035f1b10f45e4d053dcc95 | bcbdffe1dc6c55ff7ac4a9746b990d2060be55f0 | refs/heads/develop | 2021-01-13T15:36:09.249761 | 2015-06-04T09:07:36 | 2015-06-04T09:07:36 | 36,863,306 | 0 | 0 | null | 2015-06-04T10:14:36 | 2015-06-04T10:14:34 | Python | UTF-8 | Python | false | false | 10,503 | py | __author__ = 'Robert Meyer'
import logging
import random
import os
from pypet import pypetconstants
from pypet.environment import Environment
from pypet.tests.integration.environment_test import EnvironmentTest, ResultSortTest,\
TestOtherHDF5Settings2, multiply
from pypet.tests.testutils.ioutils import run_suite,make_temp_dir, make_trajectory_name, \
parse_args, get_log_config, unittest
from pypet.tests.testutils.data import create_param_dict, add_params
import pypet.compat as compat
import sys
try:
import psutil
except ImportError:
psutil = None
class MultiprocPoolQueueTest(TestOtherHDF5Settings2):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'queue', 'pool'
def set_mode(self):
super(MultiprocPoolQueueTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_QUEUE
self.multiproc = True
self.ncores = 4
self.use_pool=True
class MultiprocPoolLockTest(EnvironmentTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'pool',
# def test_run(self):
# super(MultiprocLockTest, self).test_run()
def set_mode(self):
super(MultiprocPoolLockTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_LOCK
self.multiproc = True
self.ncores = 4
self.use_pool=True
# class MultiprocPoolPipeTest(EnvironmentTest):
#
# tags = 'integration', 'hdf5', 'environment', 'multiproc', 'pipe', 'pool',
#
# # def test_run(self):
# # super(MultiprocLockTest, self).test_run()
#
# def set_mode(self):
# super(MultiprocPoolPipeTest, self).set_mode()
# self.mode = pypetconstants.WRAP_MODE_PIPE
# self.multiproc = True
# self.ncores = 4
# self.use_pool=True
class MultiprocPoolSortQueueTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'queue', 'pool',
def set_mode(self):
super(MultiprocPoolSortQueueTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_QUEUE
self.multiproc = True
self.ncores = 3
self.use_pool=True
class MultiprocPoolSortLockTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'pool',
def set_mode(self):
super(MultiprocPoolSortLockTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_LOCK
self.multiproc = True
self.ncores = 4
self.use_pool=True
class MultiprocPoolSortPipeTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'pipe', 'pool',
def set_mode(self):
super(MultiprocPoolSortPipeTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_PIPE
self.multiproc = True
self.ncores = 4
self.use_pool=True
class MultiprocNoPoolQueueTest(EnvironmentTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'queue', 'nopool',
def set_mode(self):
super(MultiprocNoPoolQueueTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_QUEUE
self.multiproc = True
self.ncores = 3
self.use_pool=False
class MultiprocNoPoolLockTest(EnvironmentTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'nopool',
def set_mode(self):
super(MultiprocNoPoolLockTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_LOCK
self.multiproc = True
self.ncores = 2
self.use_pool=False
# class MultiprocNoPoolPipeTest(EnvironmentTest):
#
# tags = 'integration', 'hdf5', 'environment', 'multiproc', 'pipe', 'nopool',
#
# def set_mode(self):
# super(MultiprocNoPoolPipeTest, self).set_mode()
# self.mode = pypetconstants.WRAP_MODE_PIPE
# self.multiproc = True
# self.ncores = 2
# self.use_pool=False
class MultiprocNoPoolSortQueueTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'queue', 'nopool',
def set_mode(self):
super(MultiprocNoPoolSortQueueTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_QUEUE
self.multiproc = True
self.ncores = 3
self.use_pool=False
class MultiprocNoPoolSortLockTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'nopool',
def set_mode(self):
super(MultiprocNoPoolSortLockTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_LOCK
self.multiproc = True
self.ncores = 3
self.use_pool=False
class MultiprocNoPoolSortPipeTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'nopool',
def set_mode(self):
super(MultiprocNoPoolSortPipeTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_PIPE
self.multiproc = True
self.ncores = 3
self.use_pool=False
class MultiprocFrozenPoolQueueTest(TestOtherHDF5Settings2):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'queue', 'pool', 'freeze_input'
def set_mode(self):
super(MultiprocFrozenPoolQueueTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_QUEUE
self.multiproc = True
self.freeze_pool_input = True
self.ncores = 4
self.use_pool=True
class MultiprocFrozenPoolLockTest(EnvironmentTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'pool', 'freeze_input'
# def test_run(self):
# super(MultiprocLockTest, self).test_run()
def set_mode(self):
super(MultiprocFrozenPoolLockTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_LOCK
self.multiproc = True
self.freeze_pool_input = True
self.ncores = 4
self.use_pool=True
def new_multiply(traj):
if traj.v_full_copy:
raise RuntimeError('Full copy should be FALSE!')
return multiply(traj)
class MultiprocFrozenPoolSortQueueTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'queue', 'pool', 'freeze_input'
def set_mode(self):
super(MultiprocFrozenPoolSortQueueTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_QUEUE
self.multiproc = True
self.freeze_pool_input = True
self.ncores = 3
self.use_pool=True
def test_if_full_copy_is_old_value(self):
###Explore
self.explore(self.traj)
self.traj.v_full_copy = False
self.env.f_run(new_multiply)
traj = self.traj
self.assertTrue(len(traj) == len(compat.listvalues(self.explore_dict)[0]))
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.check_if_z_is_correct(traj)
newtraj = self.load_trajectory(trajectory_name=self.traj.v_name,as_new=False)
self.traj.f_load_skeleton()
self.traj.f_load_items(self.traj.f_to_dict().keys(), only_empties=True)
self.compare_trajectories(self.traj,newtraj)
class MultiprocFrozenPoolPipeTest(EnvironmentTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'pipe', 'pool', 'freeze_input'
# def test_run(self):
# super(MultiprocLockTest, self).test_run()
def set_mode(self):
super(MultiprocFrozenPoolPipeTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_PIPE
self.multiproc = True
self.freeze_pool_input = True
self.ncores = 4
self.use_pool=True
class MultiprocFrozenPoolSortLockTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'pool', 'freeze_input'
def set_mode(self):
super(MultiprocFrozenPoolSortLockTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_LOCK
self.freeze_pool_input = True
self.multiproc = True
self.ncores = 4
self.use_pool=True
class MultiprocFrozenPoolSortPipeTest(ResultSortTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'pipe', 'pool', 'freeze_input'
def set_mode(self):
super(MultiprocFrozenPoolSortPipeTest, self).set_mode()
self.mode = pypetconstants.WRAP_MODE_PIPE
self.freeze_pool_input = True
self.multiproc = True
self.ncores = 4
self.use_pool=True
@unittest.skipIf(psutil is None, 'Only makes sense if psutil is installed')
class CapTest(EnvironmentTest):
tags = 'integration', 'hdf5', 'environment', 'multiproc', 'lock', 'nopool', 'cap'
cap_count = 0
def setUp(self):
self.multiproc = True
self.mode = 'LOCK'
self.trajname = make_trajectory_name(self)
self.filename = make_temp_dir(os.path.join('experiments',
'tests',
'HDF5',
'%s.hdf5' % self.trajname))
self.logfolder = make_temp_dir(os.path.join('experiments','tests','Log'))
random.seed()
cap_dicts = (dict(cpu_cap=0.000001), # Ensure that these are triggered
dict(memory_cap=(0.000001, 150.0)),
dict(swap_cap=0.000001,))
cap_dict = cap_dicts[CapTest.cap_count]
env = Environment(trajectory=self.trajname,filename=self.filename,
file_title=self.trajname, log_folder=self.logfolder,
logger_names=('pypet', 'test'), log_levels='ERROR',
log_stdout=False,
results_per_run=5,
derived_parameters_per_run=5,
multiproc=True,
ncores=4,
use_pool=False,
**cap_dict)
logging.getLogger('test').error('Using Cap: %s' % str(cap_dict))
# Loop through all possible cap configurations
# and test one at a time
CapTest.cap_count += 1
CapTest.cap_count = CapTest.cap_count % len(cap_dicts)
traj = env.v_trajectory
## Create some parameters
self.param_dict={}
create_param_dict(self.param_dict)
### Add some parameter:
add_params(traj,self.param_dict)
#remember the trajectory and the environment
self.traj = traj
self.env = env
if __name__ == '__main__':
opt_args = parse_args()
run_suite(**opt_args) | [
"[email protected]"
]
| |
caf2fecb194e07ce6842ae03cc70ae308b64a77b | 6444622ad4a150993955a0c8fe260bae1af7f8ce | /djangoenv/lib/python2.7/site-packages/django/contrib/redirects/middleware.py | 88bdfe488ab088fde0477ae06a0433d5a7196195 | []
| no_license | jeremyrich/Lesson_RestAPI_jeremy | ca965ef017c53f919c0bf97a4a23841818e246f9 | a44263e45b1cc1ba812059f6984c0f5be25cd234 | refs/heads/master | 2020-04-25T23:13:47.237188 | 2019-03-22T09:26:58 | 2019-03-22T09:26:58 | 173,138,073 | 0 | 0 | null | 2019-03-22T09:26:59 | 2019-02-28T15:34:19 | Python | UTF-8 | Python | false | false | 1,961 | py | from __future__ import unicode_literals
from django import http
from django.apps import apps
from django.conf import settings
from django.contrib.redirects.models import Redirect
from django.contrib.sites.shortcuts import get_current_site
from django.core.exceptions import ImproperlyConfigured
from django.utils.deprecation import MiddlewareMixin
class RedirectFallbackMiddleware(MiddlewareMixin):
# Defined as class-level attributes to be subclassing-friendly.
response_gone_class = http.HttpResponseGone
response_redirect_class = http.HttpResponsePermanentRedirect
def __init__(self, get_response=None):
if not apps.is_installed("django.contrib.sites"):
raise ImproperlyConfigured(
"You cannot use RedirectFallbackMiddleware when "
"django.contrib.sites is not installed."
)
super(RedirectFallbackMiddleware, self).__init__(get_response)
def process_response(self, request, response):
# No need to check for a redirect for non-404 responses.
if response.status_code != 404:
return response
full_path = request.get_full_path()
current_site = get_current_site(request)
r = None
try:
r = Redirect.objects.get(site=current_site, old_path=full_path)
except Redirect.DoesNotExist:
pass
if r is None and settings.APPEND_SLASH and not request.path.endswith("/"):
try:
r = Redirect.objects.get(
site=current_site,
old_path=request.get_full_path(force_append_slash=True),
)
except Redirect.DoesNotExist:
pass
if r is not None:
if r.new_path == "":
return self.response_gone_class()
return self.response_redirect_class(r.new_path)
# No redirect was found. Return the response.
return response
| [
"[email protected]"
]
| |
27a01cd8a0085e4c72daeb98b6450a52198af332 | 85a9ffeccb64f6159adbd164ff98edf4ac315e33 | /pysnmp/CISCO-LWAPP-DHCP-MIB.py | 8a0c218950d0431114ff485c5d7543889a404acf | [
"Apache-2.0"
]
| permissive | agustinhenze/mibs.snmplabs.com | 5d7d5d4da84424c5f5a1ed2752f5043ae00019fb | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | refs/heads/master | 2020-12-26T12:41:41.132395 | 2019-08-16T15:51:41 | 2019-08-16T15:53:57 | 237,512,469 | 0 | 0 | Apache-2.0 | 2020-01-31T20:41:36 | 2020-01-31T20:41:35 | null | UTF-8 | Python | false | false | 16,226 | py | #
# PySNMP MIB module CISCO-LWAPP-DHCP-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-LWAPP-DHCP-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:47:56 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
SingleValueConstraint, ValueSizeConstraint, ConstraintsIntersection, ValueRangeConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ValueSizeConstraint", "ConstraintsIntersection", "ValueRangeConstraint", "ConstraintsUnion")
ciscoMgmt, = mibBuilder.importSymbols("CISCO-SMI", "ciscoMgmt")
CiscoURLString, = mibBuilder.importSymbols("CISCO-TC", "CiscoURLString")
InetAddress, InetAddressType = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddress", "InetAddressType")
SnmpAdminString, = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString")
ModuleCompliance, NotificationGroup, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup", "ObjectGroup")
ModuleIdentity, Counter64, iso, Bits, IpAddress, Integer32, NotificationType, Unsigned32, MibIdentifier, TimeTicks, Counter32, Gauge32, MibScalar, MibTable, MibTableRow, MibTableColumn, ObjectIdentity = mibBuilder.importSymbols("SNMPv2-SMI", "ModuleIdentity", "Counter64", "iso", "Bits", "IpAddress", "Integer32", "NotificationType", "Unsigned32", "MibIdentifier", "TimeTicks", "Counter32", "Gauge32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ObjectIdentity")
TextualConvention, DisplayString, TruthValue, TimeStamp = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString", "TruthValue", "TimeStamp")
ciscoLwappDhcpMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 9, 792))
ciscoLwappDhcpMIB.setRevisions(('2012-01-31 00:00',))
if mibBuilder.loadTexts: ciscoLwappDhcpMIB.setLastUpdated('201204050000Z')
if mibBuilder.loadTexts: ciscoLwappDhcpMIB.setOrganization('Cisco Systems Inc.')
ciscoLwappDhcpMIBNotif = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 0))
ciscoLwappDhcpMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 1))
ciscoLwappDhcpMIBConform = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 2))
ciscoLwappDhcpGlobalConfig = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 1))
ciscoLwappDhcpStatsConfig = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2))
ciscoLwappDhcpStats = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3))
ciscoLwappDhcpScopeStats = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4))
ciscoLwappDhcpMIBNotifObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 5))
cLDhcpClearAllStats = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 1, 1), TruthValue().clone('false')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cLDhcpClearAllStats.setStatus('current')
cLDhcpOpt82RemoteIdFormat = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))).clone(namedValues=NamedValues(("apMac", 1), ("apMacSsid", 2), ("apEthMac", 3), ("apNameSsid", 4), ("apGroupName", 5), ("flexGroupName", 6), ("apLocation", 7), ("apMacVlanId", 8), ("apNameVlanId", 9), ("apEthMacSsid", 10))).clone('apMac')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cLDhcpOpt82RemoteIdFormat.setStatus('current')
cLDhcpClearAllDiscontinuityTime = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 1, 3), TimeStamp()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpClearAllDiscontinuityTime.setStatus('current')
cLDhcpTimeout = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 1, 4), Unsigned32()).setUnits('seconds').setMaxAccess("readwrite")
if mibBuilder.loadTexts: cLDhcpTimeout.setStatus('current')
cLDhcpOpt37RemoteIdFormat = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))).clone(namedValues=NamedValues(("apMac", 1), ("apMacSsid", 2), ("apEthMac", 3), ("apNameSsid", 4), ("apGroupName", 5), ("flexGroupName", 6), ("apLocation", 7), ("apMacVlanId", 8), ("apNameVlanId", 9), ("apEthMacSsid", 10))).clone('apMac')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cLDhcpOpt37RemoteIdFormat.setStatus('current')
cLDhcpStatsConfigTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2, 1), )
if mibBuilder.loadTexts: cLDhcpStatsConfigTable.setStatus('current')
cLDhcpStatsConfigEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2, 1, 1), ).setIndexNames((0, "CISCO-LWAPP-DHCP-MIB", "cLDhcpServerInetAddressType"), (0, "CISCO-LWAPP-DHCP-MIB", "cLDhcpServerInetAddress"))
if mibBuilder.loadTexts: cLDhcpStatsConfigEntry.setStatus('current')
cLDhcpServerInetAddressType = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2, 1, 1, 1), InetAddressType())
if mibBuilder.loadTexts: cLDhcpServerInetAddressType.setStatus('current')
cLDhcpServerInetAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2, 1, 1, 2), InetAddress())
if mibBuilder.loadTexts: cLDhcpServerInetAddress.setStatus('current')
cLDhcpClearStats = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2, 1, 1, 3), TruthValue()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cLDhcpClearStats.setStatus('current')
cLDhcpClearDiscontinuityTime = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 2, 1, 1, 4), TimeStamp()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpClearDiscontinuityTime.setStatus('current')
cLDhcpStatsShowTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1), )
if mibBuilder.loadTexts: cLDhcpStatsShowTable.setStatus('current')
cLDhcpStatsShowEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1), ).setIndexNames((0, "CISCO-LWAPP-DHCP-MIB", "cLDhcpServerInetAddressType"), (0, "CISCO-LWAPP-DHCP-MIB", "cLDhcpServerInetAddress"))
if mibBuilder.loadTexts: cLDhcpStatsShowEntry.setStatus('current')
cLDhcpProxy = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 1), TruthValue()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cLDhcpProxy.setStatus('current')
cLDhcpDiscoverPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 2), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpDiscoverPackets.setStatus('current')
cLDhcpRequestPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 3), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpRequestPackets.setStatus('current')
cLDhcpDeclinePackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 4), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpDeclinePackets.setStatus('current')
cLDhcpInformPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 5), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpInformPackets.setStatus('current')
cLDhcpReleasePackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 6), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpReleasePackets.setStatus('current')
cLDhcpReplyPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 7), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpReplyPackets.setStatus('current')
cLDhcpOfferPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 8), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpOfferPackets.setStatus('current')
cLDhcpAckPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 9), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpAckPackets.setStatus('current')
cLDhcpNakPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 10), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpNakPackets.setStatus('current')
cLDhcpTxFailures = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 11), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpTxFailures.setStatus('current')
cLDhcpLastResponseTime = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 12), TimeStamp()).setUnits('seconds').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpLastResponseTime.setStatus('current')
cLDhcpLastRequestTime = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 13), TimeStamp()).setUnits('seconds').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpLastRequestTime.setStatus('current')
cLDhcpRxDiscoverPackets = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 3, 1, 1, 14), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpRxDiscoverPackets.setStatus('current')
cLDhcpScopeStatsTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1), )
if mibBuilder.loadTexts: cLDhcpScopeStatsTable.setStatus('current')
cLDhcpScopeStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1), ).setIndexNames((0, "CISCO-LWAPP-DHCP-MIB", "cLDhcpScopeIndex"))
if mibBuilder.loadTexts: cLDhcpScopeStatsEntry.setStatus('current')
cLDhcpScopeIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 1), Unsigned32())
if mibBuilder.loadTexts: cLDhcpScopeIndex.setStatus('current')
cLDhcpScopeAddressPoolUsage = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 2), Unsigned32()).setUnits('Percent').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeAddressPoolUsage.setStatus('current')
cLDhcpScopeName = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 3), DisplayString()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeName.setStatus('current')
cLDhcpScopeAllocatedIP = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeAllocatedIP.setStatus('current')
cLDhcpScopeAvailableIP = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeAvailableIP.setStatus('current')
cLDhcpScopeDiscoverPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 6), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeDiscoverPkts.setStatus('current')
cLDhcpScopeAckPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 7), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeAckPkts.setStatus('current')
cLDhcpScopeOfferPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 8), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeOfferPkts.setStatus('current')
cLDhcpScopeTotalAckPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 9), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeTotalAckPkts.setStatus('current')
cLDhcpScopeRequestPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 10), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeRequestPkts.setStatus('current')
cLDhcpScopeRequestGoodPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 4, 1, 1, 11), Counter32()).setUnits('packets').setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpScopeRequestGoodPkts.setStatus('current')
cLDhcpTrapSet = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 792, 1, 5, 1), TruthValue()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cLDhcpTrapSet.setStatus('current')
ciscoLwappDhcpScopeAddressExhaust = NotificationType((1, 3, 6, 1, 4, 1, 9, 9, 792, 0, 1)).setObjects(("CISCO-LWAPP-DHCP-MIB", "cLDhcpScopeName"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpTrapSet"))
if mibBuilder.loadTexts: ciscoLwappDhcpScopeAddressExhaust.setStatus('current')
ciscoLwappDhcpMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 2, 1))
ciscoLwappDhcpMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 792, 2, 2))
ciscoLwappDhcpMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 9, 9, 792, 2, 1, 1)).setObjects(("CISCO-LWAPP-DHCP-MIB", "ciscoLwappDhcpMIBConfigGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoLwappDhcpMIBCompliance = ciscoLwappDhcpMIBCompliance.setStatus('current')
ciscoLwappDhcpMIBConfigGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 792, 2, 2, 1)).setObjects(("CISCO-LWAPP-DHCP-MIB", "cLDhcpClearAllStats"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpOpt82RemoteIdFormat"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpClearAllDiscontinuityTime"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpTimeout"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpOpt37RemoteIdFormat"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpClearStats"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpClearDiscontinuityTime"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpProxy"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpDiscoverPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpRequestPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpDeclinePackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpInformPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpReleasePackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpReplyPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpOfferPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpAckPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpNakPackets"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpTxFailures"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpLastResponseTime"), ("CISCO-LWAPP-DHCP-MIB", "cLDhcpLastRequestTime"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoLwappDhcpMIBConfigGroup = ciscoLwappDhcpMIBConfigGroup.setStatus('current')
mibBuilder.exportSymbols("CISCO-LWAPP-DHCP-MIB", ciscoLwappDhcpStatsConfig=ciscoLwappDhcpStatsConfig, cLDhcpStatsShowTable=cLDhcpStatsShowTable, cLDhcpOpt82RemoteIdFormat=cLDhcpOpt82RemoteIdFormat, cLDhcpScopeIndex=cLDhcpScopeIndex, ciscoLwappDhcpMIBCompliances=ciscoLwappDhcpMIBCompliances, ciscoLwappDhcpMIBNotifObjects=ciscoLwappDhcpMIBNotifObjects, cLDhcpScopeRequestPkts=cLDhcpScopeRequestPkts, cLDhcpLastResponseTime=cLDhcpLastResponseTime, ciscoLwappDhcpGlobalConfig=ciscoLwappDhcpGlobalConfig, cLDhcpInformPackets=cLDhcpInformPackets, cLDhcpReplyPackets=cLDhcpReplyPackets, cLDhcpStatsConfigEntry=cLDhcpStatsConfigEntry, cLDhcpServerInetAddressType=cLDhcpServerInetAddressType, cLDhcpScopeAllocatedIP=cLDhcpScopeAllocatedIP, cLDhcpScopeAckPkts=cLDhcpScopeAckPkts, cLDhcpScopeTotalAckPkts=cLDhcpScopeTotalAckPkts, cLDhcpClearDiscontinuityTime=cLDhcpClearDiscontinuityTime, cLDhcpStatsConfigTable=cLDhcpStatsConfigTable, cLDhcpStatsShowEntry=cLDhcpStatsShowEntry, cLDhcpOfferPackets=cLDhcpOfferPackets, cLDhcpRxDiscoverPackets=cLDhcpRxDiscoverPackets, cLDhcpScopeStatsTable=cLDhcpScopeStatsTable, cLDhcpScopeRequestGoodPkts=cLDhcpScopeRequestGoodPkts, cLDhcpAckPackets=cLDhcpAckPackets, ciscoLwappDhcpMIBCompliance=ciscoLwappDhcpMIBCompliance, ciscoLwappDhcpScopeStats=ciscoLwappDhcpScopeStats, cLDhcpScopeOfferPkts=cLDhcpScopeOfferPkts, ciscoLwappDhcpMIBNotif=ciscoLwappDhcpMIBNotif, ciscoLwappDhcpMIBConform=ciscoLwappDhcpMIBConform, cLDhcpDiscoverPackets=cLDhcpDiscoverPackets, cLDhcpScopeName=cLDhcpScopeName, cLDhcpTimeout=cLDhcpTimeout, cLDhcpLastRequestTime=cLDhcpLastRequestTime, cLDhcpTxFailures=cLDhcpTxFailures, cLDhcpScopeAddressPoolUsage=cLDhcpScopeAddressPoolUsage, cLDhcpTrapSet=cLDhcpTrapSet, PYSNMP_MODULE_ID=ciscoLwappDhcpMIB, ciscoLwappDhcpMIBGroups=ciscoLwappDhcpMIBGroups, cLDhcpDeclinePackets=cLDhcpDeclinePackets, cLDhcpScopeDiscoverPkts=cLDhcpScopeDiscoverPkts, ciscoLwappDhcpMIBConfigGroup=ciscoLwappDhcpMIBConfigGroup, cLDhcpClearAllDiscontinuityTime=cLDhcpClearAllDiscontinuityTime, cLDhcpOpt37RemoteIdFormat=cLDhcpOpt37RemoteIdFormat, cLDhcpScopeStatsEntry=cLDhcpScopeStatsEntry, ciscoLwappDhcpStats=ciscoLwappDhcpStats, cLDhcpProxy=cLDhcpProxy, cLDhcpClearStats=cLDhcpClearStats, ciscoLwappDhcpScopeAddressExhaust=ciscoLwappDhcpScopeAddressExhaust, ciscoLwappDhcpMIB=ciscoLwappDhcpMIB, cLDhcpClearAllStats=cLDhcpClearAllStats, cLDhcpNakPackets=cLDhcpNakPackets, cLDhcpScopeAvailableIP=cLDhcpScopeAvailableIP, ciscoLwappDhcpMIBObjects=ciscoLwappDhcpMIBObjects, cLDhcpReleasePackets=cLDhcpReleasePackets, cLDhcpRequestPackets=cLDhcpRequestPackets, cLDhcpServerInetAddress=cLDhcpServerInetAddress)
| [
"[email protected]"
]
| |
35d4b0512b4879f6c8ead169c23166a092997977 | 97be865468706b5776993024d55d3229995ab2cd | /StreamGeneration/GenerateHQD.py | 78f096bb6a4391ef693d244c1e388e674e20bdb7 | []
| no_license | ZhuJiahui/ECTH | b7dd369b958b21a6e9c2e8ad97126022aa03247c | 8a8dbf9713e220f74dfcaf893b36d110c0555f46 | refs/heads/master | 2021-01-02T22:44:13.479021 | 2015-06-06T06:59:53 | 2015-06-06T06:59:53 | 29,050,682 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,470 | py | # -*- coding: utf-8 -*-
'''
Created on 2014年12月27日
@author: ZhuJiahui506
'''
import os
import time
from TextToolkit import quick_write_list_to_text
'''
Step 3
Compute EM weight of each Weibo and ordered by its EM weights to generate the high quality data.
So the original data was changed.
'''
def generate_high_quality_data(read_directory, write_directory):
'''
Linear fusion
:param read_directory:
:param write_directory:
'''
#K = 3000
file_number = sum([len(files) for root, dirs, files in os.walk(read_directory)])
for i in range(file_number):
this_weibo = []
f = open(read_directory + '/' + str(i + 1) + '.txt', 'r')
line = f.readline()
while line:
each_line = line.strip()
this_text_length = " "
try:
this_text_length = each_line.split('\t')[6]
except:
this_text_length = " "
if len(this_text_length) >= 150:
this_weibo.append(each_line)
line = f.readline()
f.close()
quick_write_list_to_text(this_weibo, write_directory + '/' + str(i + 1) + '.txt')
if __name__ == '__main__':
start = time.clock()
now_directory = os.getcwd()
root_directory = os.path.dirname(now_directory) + '/'
read_directory = root_directory + u'dataset/segment'
write_directory = root_directory + u'dataset/high_quality_data'
if (not(os.path.exists(write_directory))):
os.mkdir(write_directory)
generate_high_quality_data(read_directory, write_directory)
print 'Total time %f seconds' % (time.clock() - start)
print 'Complete !!!'
| [
"[email protected]"
]
| |
2575fcfda27f77fbd034558e815ed42659a70e22 | 9e988c0dfbea15cd23a3de860cb0c88c3dcdbd97 | /sdBs/AllRun/pg_1711+564/sdB_pg_1711+564_coadd.py | a317f3971b126e1513af2c8cbe3cd9d5030ee583 | []
| no_license | tboudreaux/SummerSTScICode | 73b2e5839b10c0bf733808f4316d34be91c5a3bd | 4dd1ffbb09e0a599257d21872f9d62b5420028b0 | refs/heads/master | 2021-01-20T18:07:44.723496 | 2016-08-08T16:49:53 | 2016-08-08T16:49:53 | 65,221,159 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 430 | py | from gPhoton.gMap import gMap
def main():
gMap(band="NUV", skypos=[258.155417,56.418942], skyrange=[0.0333333333333,0.0333333333333], stepsz = 30., cntfile="/data2/fleming/GPHOTON_OUTPUT/LIGHTCURVES/sdBs/sdB_pg_1711+564/sdB_pg_1711+564_movie_count.fits", cntcoaddfile="/data2/fleming/GPHOTON_OUTPUT/LIGHTCURVES/sdB/sdB_pg_1711+564/sdB_pg_1711+564_count_coadd.fits", overwrite=True, verbose=3)
if __name__ == "__main__":
main()
| [
"[email protected]"
]
| |
c94c7df584c83a447b8988023b95bfe4f1d4c8fb | ae87b11560c543cb678c52a28916ea2252d7aa52 | /plaso/parsers/winreg_plugins/appcompatcache.py | b289c8f42fc8d33291badb2471c08f744e4129e0 | [
"Apache-2.0"
]
| permissive | CNR-ITTIG/plasodfaxp | 19ccf77d0be62cfa8a9b246eb6797cf64a480d80 | 923797fc00664fa9e3277781b0334d6eed5664fd | refs/heads/master | 2016-09-13T11:14:08.877399 | 2016-04-11T15:01:42 | 2016-04-11T15:01:42 | 55,975,921 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 23,748 | py | # -*- coding: utf-8 -*-
"""Windows Registry plugin to parse the Application Compatibility Cache key."""
import construct
import logging
from plaso.events import time_events
from plaso.lib import binary
from plaso.lib import eventdata
from plaso.parsers import winreg
from plaso.parsers.winreg_plugins import interface
class AppCompatCacheEvent(time_events.FiletimeEvent):
"""Class that contains the event object for AppCompatCache entries."""
DATA_TYPE = u'windows:registry:appcompatcache'
def __init__(
self, filetime, usage, key_name, entry_index, path, offset):
"""Initializes a Windows Registry event.
Args:
filetime: The FILETIME timestamp value.
usage: The description of the usage of the time value.
key_name: The name of the corresponding Windows Registry key.
entry_index: The cache entry index number for the record.
path: The full path to the executable.
offset: The (data) offset of the Windows Registry key or value.
"""
super(AppCompatCacheEvent, self).__init__(filetime, usage)
self.entry_index = entry_index
self.keyname = key_name
self.offset = offset
self.path = path
class AppCompatCacheHeader(object):
"""Class that contains the Application Compatibility Cache header."""
def __init__(self):
"""Initializes the header object."""
super(AppCompatCacheHeader, self).__init__()
self.number_of_cached_entries = 0
self.header_size = 0
class AppCompatCacheCachedEntry(object):
"""Class that contains the Application Compatibility Cache cached entry."""
def __init__(self):
"""Initializes the cached entry object."""
super(AppCompatCacheCachedEntry, self).__init__()
self.cached_entry_size = 0
self.data = None
self.file_size = None
self.insertion_flags = None
self.last_modification_time = None
self.last_update_time = None
self.shim_flags = None
self.path = None
class AppCompatCachePlugin(interface.WindowsRegistryPlugin):
"""Class that parses the Application Compatibility Cache Registry data."""
NAME = u'appcompatcache'
DESCRIPTION = u'Parser for Application Compatibility Cache Registry data.'
FILTERS = frozenset([
interface.WindowsRegistryKeyPathFilter(
u'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\'
u'Session Manager\\AppCompatibility'),
interface.WindowsRegistryKeyPathFilter(
u'HKEY_LOCAL_MACHINE\\System\\CurrentControlSet\\Control\\'
u'Session Manager\\AppCompatCache')])
URLS = [
(u'https://github.com/libyal/winreg-kb/blob/master/documentation/'
u'Application%20Compatibility%20Cache%20key.asciidoc')]
_FORMAT_TYPE_2000 = 1
_FORMAT_TYPE_XP = 2
_FORMAT_TYPE_2003 = 3
_FORMAT_TYPE_VISTA = 4
_FORMAT_TYPE_7 = 5
_FORMAT_TYPE_8 = 6
_FORMAT_TYPE_10 = 7
# AppCompatCache format signature used in Windows XP.
_HEADER_SIGNATURE_XP = 0xdeadbeef
# AppCompatCache format used in Windows XP.
_HEADER_XP_32BIT_STRUCT = construct.Struct(
u'appcompatcache_header_xp',
construct.ULInt32(u'signature'),
construct.ULInt32(u'number_of_cached_entries'),
construct.ULInt32(u'unknown1'),
construct.ULInt32(u'unknown2'),
construct.Padding(384))
_CACHED_ENTRY_XP_32BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_xp_32bit',
construct.Array(528, construct.Byte(u'path')),
construct.ULInt64(u'last_modification_time'),
construct.ULInt64(u'file_size'),
construct.ULInt64(u'last_update_time'))
# AppCompatCache format signature used in Windows 2003, Vista and 2008.
_HEADER_SIGNATURE_2003 = 0xbadc0ffe
# AppCompatCache format used in Windows 2003.
_HEADER_2003_STRUCT = construct.Struct(
u'appcompatcache_header_2003',
construct.ULInt32(u'signature'),
construct.ULInt32(u'number_of_cached_entries'))
_CACHED_ENTRY_2003_32BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_2003_32bit',
construct.ULInt16(u'path_size'),
construct.ULInt16(u'maximum_path_size'),
construct.ULInt32(u'path_offset'),
construct.ULInt64(u'last_modification_time'),
construct.ULInt64(u'file_size'))
_CACHED_ENTRY_2003_64BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_2003_64bit',
construct.ULInt16(u'path_size'),
construct.ULInt16(u'maximum_path_size'),
construct.ULInt32(u'unknown1'),
construct.ULInt64(u'path_offset'),
construct.ULInt64(u'last_modification_time'),
construct.ULInt64(u'file_size'))
# AppCompatCache format used in Windows Vista and 2008.
_CACHED_ENTRY_VISTA_32BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_vista_32bit',
construct.ULInt16(u'path_size'),
construct.ULInt16(u'maximum_path_size'),
construct.ULInt32(u'path_offset'),
construct.ULInt64(u'last_modification_time'),
construct.ULInt32(u'insertion_flags'),
construct.ULInt32(u'shim_flags'))
_CACHED_ENTRY_VISTA_64BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_vista_64bit',
construct.ULInt16(u'path_size'),
construct.ULInt16(u'maximum_path_size'),
construct.ULInt32(u'unknown1'),
construct.ULInt64(u'path_offset'),
construct.ULInt64(u'last_modification_time'),
construct.ULInt32(u'insertion_flags'),
construct.ULInt32(u'shim_flags'))
# AppCompatCache format signature used in Windows 7 and 2008 R2.
_HEADER_SIGNATURE_7 = 0xbadc0fee
# AppCompatCache format used in Windows 7 and 2008 R2.
_HEADER_7_STRUCT = construct.Struct(
u'appcompatcache_header_7',
construct.ULInt32(u'signature'),
construct.ULInt32(u'number_of_cached_entries'),
construct.Padding(120))
_CACHED_ENTRY_7_32BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_7_32bit',
construct.ULInt16(u'path_size'),
construct.ULInt16(u'maximum_path_size'),
construct.ULInt32(u'path_offset'),
construct.ULInt64(u'last_modification_time'),
construct.ULInt32(u'insertion_flags'),
construct.ULInt32(u'shim_flags'),
construct.ULInt32(u'data_size'),
construct.ULInt32(u'data_offset'))
_CACHED_ENTRY_7_64BIT_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_7_64bit',
construct.ULInt16(u'path_size'),
construct.ULInt16(u'maximum_path_size'),
construct.ULInt32(u'unknown1'),
construct.ULInt64(u'path_offset'),
construct.ULInt64(u'last_modification_time'),
construct.ULInt32(u'insertion_flags'),
construct.ULInt32(u'shim_flags'),
construct.ULInt64(u'data_size'),
construct.ULInt64(u'data_offset'))
# AppCompatCache format used in Windows 8.0 and 8.1.
_HEADER_SIGNATURE_8 = 0x00000080
_HEADER_8_STRUCT = construct.Struct(
u'appcompatcache_header_8',
construct.ULInt32(u'signature'),
construct.Padding(124))
_CACHED_ENTRY_HEADER_8_STRUCT = construct.Struct(
u'appcompatcache_cached_entry_header_8',
construct.ULInt32(u'signature'),
construct.ULInt32(u'unknown1'),
construct.ULInt32(u'cached_entry_data_size'),
construct.ULInt16(u'path_size'))
# AppCompatCache format used in Windows 8.0.
_CACHED_ENTRY_SIGNATURE_8_0 = b'00ts'
# AppCompatCache format used in Windows 8.1.
_CACHED_ENTRY_SIGNATURE_8_1 = b'10ts'
# AppCompatCache format used in Windows 10
_HEADER_SIGNATURE_10 = 0x00000030
_HEADER_10_STRUCT = construct.Struct(
u'appcompatcache_header_8',
construct.ULInt32(u'signature'),
construct.ULInt32(u'unknown1'),
construct.Padding(28),
construct.ULInt32(u'number_of_cached_entries'),
construct.Padding(8))
def _CheckSignature(self, value_data):
"""Parses and validates the signature.
Args:
value_data: a binary string containing the value data.
Returns:
The format type if successful or None otherwise.
"""
signature = construct.ULInt32(u'signature').parse(value_data)
if signature == self._HEADER_SIGNATURE_XP:
return self._FORMAT_TYPE_XP
elif signature == self._HEADER_SIGNATURE_2003:
# TODO: determine which format version is used (2003 or Vista).
return self._FORMAT_TYPE_2003
elif signature == self._HEADER_SIGNATURE_7:
return self._FORMAT_TYPE_7
elif signature == self._HEADER_SIGNATURE_8:
if value_data[signature:signature + 4] in [
self._CACHED_ENTRY_SIGNATURE_8_0, self._CACHED_ENTRY_SIGNATURE_8_1]:
return self._FORMAT_TYPE_8
elif signature == self._HEADER_SIGNATURE_10:
# Windows 10 uses the same cache entry signature as Windows 8.1
if value_data[signature:signature + 4] in [
self._CACHED_ENTRY_SIGNATURE_8_1]:
return self._FORMAT_TYPE_10
def _DetermineCacheEntrySize(
self, format_type, value_data, cached_entry_offset):
"""Determines the size of a cached entry.
Args:
format_type: integer value that contains the format type.
value_data: a binary string containing the value data.
cached_entry_offset: integer value that contains the offset of
the first cached entry data relative to the start of
the value data.
Returns:
The cached entry size if successful or None otherwise.
Raises:
RuntimeError: if the format type is not supported.
"""
if format_type not in [
self._FORMAT_TYPE_XP, self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA,
self._FORMAT_TYPE_7, self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
raise RuntimeError(
u'[{0:s}] Unsupported format type: {1:d}'.format(
self.NAME, format_type))
cached_entry_data = value_data[cached_entry_offset:]
cached_entry_size = 0
if format_type == self._FORMAT_TYPE_XP:
cached_entry_size = self._CACHED_ENTRY_XP_32BIT_STRUCT.sizeof()
elif format_type in [
self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA, self._FORMAT_TYPE_7]:
path_size = construct.ULInt16(u'path_size').parse(cached_entry_data[0:2])
maximum_path_size = construct.ULInt16(u'maximum_path_size').parse(
cached_entry_data[2:4])
path_offset_32bit = construct.ULInt32(u'path_offset').parse(
cached_entry_data[4:8])
path_offset_64bit = construct.ULInt32(u'path_offset').parse(
cached_entry_data[8:16])
if maximum_path_size < path_size:
logging.error(
u'[{0:s}] Path size value out of bounds.'.format(self.NAME))
return
path_end_of_string_size = maximum_path_size - path_size
if path_size == 0 or path_end_of_string_size != 2:
logging.error(
u'[{0:s}] Unsupported path size values.'.format(self.NAME))
return
# Assume the entry is 64-bit if the 32-bit path offset is 0 and
# the 64-bit path offset is set.
if path_offset_32bit == 0 and path_offset_64bit != 0:
if format_type == self._FORMAT_TYPE_2003:
cached_entry_size = self._CACHED_ENTRY_2003_64BIT_STRUCT.sizeof()
elif format_type == self._FORMAT_TYPE_VISTA:
cached_entry_size = self._CACHED_ENTRY_VISTA_64BIT_STRUCT.sizeof()
elif format_type == self._FORMAT_TYPE_7:
cached_entry_size = self._CACHED_ENTRY_7_64BIT_STRUCT.sizeof()
else:
if format_type == self._FORMAT_TYPE_2003:
cached_entry_size = self._CACHED_ENTRY_2003_32BIT_STRUCT.sizeof()
elif format_type == self._FORMAT_TYPE_VISTA:
cached_entry_size = self._CACHED_ENTRY_VISTA_32BIT_STRUCT.sizeof()
elif format_type == self._FORMAT_TYPE_7:
cached_entry_size = self._CACHED_ENTRY_7_32BIT_STRUCT.sizeof()
elif format_type in [self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
cached_entry_size = self._CACHED_ENTRY_HEADER_8_STRUCT.sizeof()
return cached_entry_size
def _ParseHeader(self, format_type, value_data):
"""Parses the header.
Args:
format_type: integer value that contains the format type.
value_data: a binary string containing the value data.
Returns:
A header object (instance of AppCompatCacheHeader).
Raises:
RuntimeError: if the format type is not supported.
"""
if format_type not in [
self._FORMAT_TYPE_XP, self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA,
self._FORMAT_TYPE_7, self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
raise RuntimeError(
u'[{0:s}] Unsupported format type: {1:d}'.format(
self.NAME, format_type))
# TODO: change to collections.namedtuple or use __slots__ if the overhead
# of a regular object becomes a problem.
header_object = AppCompatCacheHeader()
if format_type == self._FORMAT_TYPE_XP:
header_object.header_size = self._HEADER_XP_32BIT_STRUCT.sizeof()
header_struct = self._HEADER_XP_32BIT_STRUCT.parse(value_data)
elif format_type == self._FORMAT_TYPE_2003:
header_object.header_size = self._HEADER_2003_STRUCT.sizeof()
header_struct = self._HEADER_2003_STRUCT.parse(value_data)
elif format_type == self._FORMAT_TYPE_VISTA:
header_object.header_size = self._HEADER_VISTA_STRUCT.sizeof()
header_struct = self._HEADER_VISTA_STRUCT.parse(value_data)
elif format_type == self._FORMAT_TYPE_7:
header_object.header_size = self._HEADER_7_STRUCT.sizeof()
header_struct = self._HEADER_7_STRUCT.parse(value_data)
elif format_type == self._FORMAT_TYPE_8:
header_object.header_size = self._HEADER_8_STRUCT.sizeof()
header_struct = self._HEADER_8_STRUCT.parse(value_data)
elif format_type == self._FORMAT_TYPE_10:
header_object.header_size = self._HEADER_10_STRUCT.sizeof()
header_struct = self._HEADER_10_STRUCT.parse(value_data)
if format_type in [
self._FORMAT_TYPE_XP, self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA,
self._FORMAT_TYPE_7, self._FORMAT_TYPE_10]:
header_object.number_of_cached_entries = header_struct.get(
u'number_of_cached_entries')
return header_object
def _ParseCachedEntry(
self, format_type, value_data, cached_entry_offset, cached_entry_size):
"""Parses a cached entry.
Args:
format_type: integer value that contains the format type.
value_data: a binary string containing the value data.
cached_entry_offset: integer value that contains the offset of
the cached entry data relative to the start of
the value data.
cached_entry_size: integer value that contains the cached entry data size.
Returns:
A cached entry object (instance of AppCompatCacheCachedEntry).
Raises:
RuntimeError: if the format type is not supported.
"""
if format_type not in [
self._FORMAT_TYPE_XP, self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA,
self._FORMAT_TYPE_7, self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
raise RuntimeError(
u'[{0:s}] Unsupported format type: {1:d}'.format(
self.NAME, format_type))
cached_entry_data = value_data[
cached_entry_offset:cached_entry_offset + cached_entry_size]
cached_entry_struct = None
if format_type == self._FORMAT_TYPE_XP:
if cached_entry_size == self._CACHED_ENTRY_XP_32BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_XP_32BIT_STRUCT.parse(
cached_entry_data)
elif format_type == self._FORMAT_TYPE_2003:
if cached_entry_size == self._CACHED_ENTRY_2003_32BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_2003_32BIT_STRUCT.parse(
cached_entry_data)
elif cached_entry_size == self._CACHED_ENTRY_2003_64BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_2003_64BIT_STRUCT.parse(
cached_entry_data)
elif format_type == self._FORMAT_TYPE_VISTA:
if cached_entry_size == self._CACHED_ENTRY_VISTA_32BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_VISTA_32BIT_STRUCT.parse(
cached_entry_data)
elif cached_entry_size == self._CACHED_ENTRY_VISTA_64BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_VISTA_64BIT_STRUCT.parse(
cached_entry_data)
elif format_type == self._FORMAT_TYPE_7:
if cached_entry_size == self._CACHED_ENTRY_7_32BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_7_32BIT_STRUCT.parse(
cached_entry_data)
elif cached_entry_size == self._CACHED_ENTRY_7_64BIT_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_7_64BIT_STRUCT.parse(
cached_entry_data)
elif format_type in [self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
if cached_entry_data[0:4] not in [
self._CACHED_ENTRY_SIGNATURE_8_0, self._CACHED_ENTRY_SIGNATURE_8_1]:
raise RuntimeError(
u'[{0:s}] Unsupported cache entry signature'.format(self.NAME))
if cached_entry_size == self._CACHED_ENTRY_HEADER_8_STRUCT.sizeof():
cached_entry_struct = self._CACHED_ENTRY_HEADER_8_STRUCT.parse(
cached_entry_data)
cached_entry_data_size = cached_entry_struct.get(
u'cached_entry_data_size')
cached_entry_size = 12 + cached_entry_data_size
cached_entry_data = value_data[
cached_entry_offset:cached_entry_offset + cached_entry_size]
if not cached_entry_struct:
raise RuntimeError(
u'[{0:s}] Unsupported cache entry size: {1:d}'.format(
self.NAME, cached_entry_size))
cached_entry_object = AppCompatCacheCachedEntry()
cached_entry_object.cached_entry_size = cached_entry_size
path_offset = 0
data_size = 0
if format_type == self._FORMAT_TYPE_XP:
string_size = 0
for string_index in xrange(0, 528, 2):
if (ord(cached_entry_data[string_index]) == 0 and
ord(cached_entry_data[string_index + 1]) == 0):
break
string_size += 2
cached_entry_object.path = binary.UTF16StreamCopyToString(
cached_entry_data[0:string_size])
elif format_type in [
self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA, self._FORMAT_TYPE_7]:
path_size = cached_entry_struct.get(u'path_size')
path_offset = cached_entry_struct.get(u'path_offset')
elif format_type in [self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
path_size = cached_entry_struct.get(u'path_size')
cached_entry_data_offset = 14 + path_size
cached_entry_object.path = binary.UTF16StreamCopyToString(
cached_entry_data[14:cached_entry_data_offset])
if format_type == self._FORMAT_TYPE_8:
remaining_data = cached_entry_data[cached_entry_data_offset:]
cached_entry_object.insertion_flags = construct.ULInt32(
u'insertion_flags').parse(remaining_data[0:4])
cached_entry_object.shim_flags = construct.ULInt32(
u'shim_flags').parse(remaining_data[4:8])
if cached_entry_data[0:4] == self._CACHED_ENTRY_SIGNATURE_8_0:
cached_entry_data_offset += 8
elif cached_entry_data[0:4] == self._CACHED_ENTRY_SIGNATURE_8_1:
cached_entry_data_offset += 10
remaining_data = cached_entry_data[cached_entry_data_offset:]
if format_type in [
self._FORMAT_TYPE_XP, self._FORMAT_TYPE_2003, self._FORMAT_TYPE_VISTA,
self._FORMAT_TYPE_7]:
cached_entry_object.last_modification_time = cached_entry_struct.get(
u'last_modification_time')
elif format_type in [self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
cached_entry_object.last_modification_time = construct.ULInt64(
u'last_modification_time').parse(remaining_data[0:8])
if format_type in [self._FORMAT_TYPE_XP, self._FORMAT_TYPE_2003]:
cached_entry_object.file_size = cached_entry_struct.get(u'file_size')
elif format_type in [self._FORMAT_TYPE_VISTA, self._FORMAT_TYPE_7]:
cached_entry_object.insertion_flags = cached_entry_struct.get(
u'insertion_flags')
cached_entry_object.shim_flags = cached_entry_struct.get(u'shim_flags')
if format_type == self._FORMAT_TYPE_XP:
cached_entry_object.last_update_time = cached_entry_struct.get(
u'last_update_time')
if format_type == self._FORMAT_TYPE_7:
data_offset = cached_entry_struct.get(u'data_offset')
data_size = cached_entry_struct.get(u'data_size')
elif format_type in [self._FORMAT_TYPE_8, self._FORMAT_TYPE_10]:
data_offset = cached_entry_offset + cached_entry_data_offset + 12
data_size = construct.ULInt32(u'data_size').parse(remaining_data[8:12])
if path_offset > 0 and path_size > 0:
path_size += path_offset
cached_entry_object.path = binary.UTF16StreamCopyToString(
value_data[path_offset:path_size])
if data_size > 0:
data_size += data_offset
cached_entry_object.data = value_data[data_offset:data_size]
return cached_entry_object
def GetEntries(self, parser_mediator, registry_key, **kwargs):
"""Extracts event objects from a Application Compatibility Cache key.
Args:
parser_mediator: A parser mediator object (instance of ParserMediator).
registry_key: A Windows Registry key (instance of
dfwinreg.WinRegistryKey).
"""
value = registry_key.GetValueByName(u'AppCompatCache')
if not value:
return
value_data = value.data
value_data_size = len(value.data)
format_type = self._CheckSignature(value_data)
if not format_type:
parser_mediator.ProduceParseError(
u'Unsupported signature in AppCompatCache key: {0:s}'.format(
registry_key.path))
return
header_object = self._ParseHeader(format_type, value_data)
# On Windows Vista and 2008 when the cache is empty it will
# only consist of the header.
if value_data_size <= header_object.header_size:
return
cached_entry_offset = header_object.header_size
cached_entry_size = self._DetermineCacheEntrySize(
format_type, value_data, cached_entry_offset)
if not cached_entry_size:
parser_mediator.ProduceParseError((
u'Unsupported cached entry size at offset {0:d} in AppCompatCache '
u'key: {1:s}').format(cached_entry_offset, registry_key.path))
return
cached_entry_index = 0
while cached_entry_offset < value_data_size:
cached_entry_object = self._ParseCachedEntry(
format_type, value_data, cached_entry_offset, cached_entry_size)
if cached_entry_object.last_modification_time is not None:
# TODO: refactor to file modification event.
event_object = AppCompatCacheEvent(
cached_entry_object.last_modification_time,
u'File Last Modification Time', registry_key.path,
cached_entry_index + 1, cached_entry_object.path,
cached_entry_offset)
parser_mediator.ProduceEvent(event_object)
if cached_entry_object.last_update_time is not None:
# TODO: refactor to process run event.
event_object = AppCompatCacheEvent(
cached_entry_object.last_update_time,
eventdata.EventTimestamp.LAST_RUNTIME, registry_key.path,
cached_entry_index + 1, cached_entry_object.path,
cached_entry_offset)
parser_mediator.ProduceEvent(event_object)
cached_entry_offset += cached_entry_object.cached_entry_size
cached_entry_index += 1
if (header_object.number_of_cached_entries != 0 and
cached_entry_index >= header_object.number_of_cached_entries):
break
winreg.WinRegistryParser.RegisterPlugin(AppCompatCachePlugin)
| [
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]
| |
b5653a6f9699da39d8c2d60fdac5941697f1abbc | afb2bdf8044e4c9ff09b1b8379efbc17867d8cc0 | /2parts/challenge/challenge2.py | cf8359ef6d86b17dfe0a1b0bb22f142f4a785437 | []
| no_license | ChenFu0420/leranpython | b2e364ff8d6730a3eb768b76f0369faa3367dfa2 | 52d0aa614d7fab19e17bbb696330a0330d3862b6 | refs/heads/master | 2020-05-29T19:46:24.020046 | 2019-09-25T09:17:10 | 2019-09-25T09:17:10 | 189,339,151 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 244 | py | num1 = int(input("输入一个数"))
num2 = int(input("再输一个数"))
#两数之和
print("两个数的和是：", num1 + num2)
#两数之差
print("两个数的差是：", num1 - num2)
#两数乘积
print("两数乘积是：", num1 * num2) | [
"[email protected]"
]
| |
f6799deed3adf5955c953244b8e21ad2a510e6ff | 48b67d5a7149376b5949f12641fa14cb8404a359 | /accounts/migrations/0005_auto_20181018_1754.py | b8183ba6fad46552feeae9eae696552ad9b89ceb | []
| no_license | mishaukr7/simple_blog | 7f962dce438b9bab03b0ddabfc1ce47d57e9cb5b | c00aba56afe4caad77dfa5f058e3ab8e1e8919b1 | refs/heads/master | 2020-04-01T23:21:28.176890 | 2018-10-19T08:58:14 | 2018-10-19T08:58:14 | 153,754,245 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 596 | py | # Generated by Django 2.1.2 on 2018-10-18 17:54
from django.db import migrations
import django.db.models.deletion
import smart_selects.db_fields
class Migration(migrations.Migration):
dependencies = [
('accounts', '0004_auto_20181018_1712'),
]
operations = [
migrations.AlterField(
model_name='profile',
name='city',
field=smart_selects.db_fields.ChainedForeignKey(blank=True, chained_field='country', chained_model_field='country', null=True, on_delete=django.db.models.deletion.CASCADE, to='accounts.City'),
),
]
| [
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629c520da224ca08d1b463ba82b88210faa9c090 | 7248b86a0a882badb20f83be57748fae89311c7d | /case01/migrations/0001_initial.py | b1c221b705f2a710eea22d9f679f6ea50702fa6f | []
| no_license | daiyeyue/daiyeDRF | 2164ae4e6d611f577d1fac9e84dd8fcd83b3f909 | 884f0dcf4bbedf2c17842d7dc05dc3603cc95877 | refs/heads/master | 2020-12-03T12:48:02.551331 | 2020-01-02T06:38:12 | 2020-01-02T06:38:12 | 231,322,648 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,263 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='ClassRoom',
fields=[
('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),
('roomName', models.CharField(max_length=20)),
('loc', models.CharField(max_length=20)),
],
),
migrations.CreateModel(
name='Student',
fields=[
('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),
('name', models.CharField(max_length=5)),
('age', models.IntegerField()),
],
),
migrations.CreateModel(
name='Teacher',
fields=[
('id', models.AutoField(verbose_name='ID', primary_key=True, serialize=False, auto_created=True)),
('course', models.CharField(max_length=20)),
('name', models.CharField(max_length=5)),
('age', models.IntegerField()),
],
),
]
| [
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]
| |
187cb4166766dd206e5197b34e86bd8da22e166b | a560269290749e10466b1a29584f06a2b8385a47 | /Notebooks/py/hyzhak/titanic-for-beginners/titanic-for-beginners.py | 7cbd2fb9d328cf687072a6f9cfbb5913b2a286e8 | []
| no_license | nischalshrestha/automatic_wat_discovery | c71befad1aa358ae876d5494a67b0f4aa1266f23 | 982e700d8e4698a501afffd6c3a2f35346c34f95 | refs/heads/master | 2022-04-07T12:40:24.376871 | 2020-03-15T22:27:39 | 2020-03-15T22:27:39 | 208,379,586 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 16,438 | py | #!/usr/bin/env python
# coding: utf-8
# # Titanic for beginners
# it is basic introduction to Kaggle.
#
# ## Workflow
# 1. Import Necessary Libraries
# 2. Acquire training and testing data.
# 3. Analyze, Visualize data
# 1. Outlets (errors or possibly innacurate values) ?
# 2. Create new feature?
# 4. Clearning data
# 5. Choosing the Best Model
# 6. Creating Submission File
# ## 1. Import Necessary Libraries
# In[ ]:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pprint import pprint
import seaborn as sns
import sklearn
from sklearn import ensemble, linear_model, naive_bayes, neighbors, svm, tree
import subprocess
get_ipython().magic(u'matplotlib inline')
# ## 2. Acquire training and testing data
# In[ ]:
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
combine = [train_df, test_df]
# ## 3. Analyze, Visualize data
# ### Take a look on data
# In[ ]:
train_df.head()
# In[ ]:
train_df.tail()
# ### Analyze features
# - **Which features are categorical?** - Survived, Sex, and Embarked. Ordinal: Pclass.
# - **Which features are numerical?** - Age, Fare. Discrete: SibSp, Parch.
# - **Which features are mixed data types?** - Ticket is a mix of numeric and alphanumeric data types. Cabin is alphanumeric.
# - **Which features may contain errors or typos?** - Name feature may contain errors or typos as there are several ways used to describe a name including titles, round brackets, and quotes used for alternative or short names.
# - **Which features contain blank, null or empty values?** - Cabin > Age > Embarked features contain a number of null values in that order for the training dataset. Cabin > Age are incomplete in case of test dataset.
# - **What are the data types for various features?** - Seven features are integer or floats. Six in case of test dataset. Five features are strings (object).
# - **What is the distribution of numerical feature values across the samples?**
# - Total samples are 891 or 40\% of the actual number of passengers on board the Titanic (2,224).
# - Survived is a categorical feature with 0 or 1 values.
# - Around 38\% samples survived representative of the actual survival rate at 32%.
# - Most passengers `(> 75\%)` did not travel with parents or children.
# - Nearly 30\% of the passengers had siblings and/or spouse aboard.
# - Fares varied significantly with few passengers `(<1\%)` paying as high as $512.
# - Few elderly passengers (<1\%) within age range 65-80.
# - TODO: *distribution of age and distribution survived index by age*
# - TODO: *what is survived index of single persons (without children, parents, siblings or spouse)?*
# - TODO: *what is distribution of age of single persons?*
# - TODO: *what is survived index of not single persons which has more stronger relative (wife and husband, child vs parent and etc)*
# - **What is the distribution of categorical features?**
# - Names are unique across the dataset (count=unique=891)
# - Sex variable as two possible values with `65\%` male (top=male, freq=577/count=891).
# - Cabin values have several dupicates across samples. Alternatively several passengers shared a cabin.
# - Embarked takes three possible values. S port used by most passengers (top=S)
# - Ticket feature has high ratio (22\%) of duplicate values (unique=681).
#
# In[ ]:
print('# features:')
print(train_df.columns.values)
print('_'*40)
print('# data types:')
train_df.info()
print('_'*40)
test_df.info()
# In[ ]:
# numberical features
train_df.describe()
# In[ ]:
# categorical features
train_df.describe(include=['O'])
# ### Assumtions based on data analysis
# - **Correlating** - check correlaction of each feature with survive index
# - **Completing** - try to complete significant feature (**Age**, **Embarked**)
# - **Correcting**
# - **Ticket** feature may be dropped from our analysis as it contains high ratio of duplicates (22%) and there may not be a correlation between Ticket and survival.
# - **Cabin** feature may be dropped as it is highly incomplete or contains many null values both in training and test dataset.
# - **PassengerId** may be dropped from training dataset as it does not contribute to survival.
# - **Name** feature is relatively non-standard, may not contribute directly to survival, so maybe dropped.
# - **Creating**
# - We may want to create a new feature called **Family** based on Parch and SibSp to get total count of family members on board.
# - We may want to engineer the **Name** feature to extract Title as a new feature. *ME: does it really influent on survive index?*
# - We may want to create new feature for **Age bands**. This turns a continous numerical feature into an ordinal categorical feature.
# - We may also want to create a **Fare range** feature if it helps our analysis.
# - **Classifying**
# - Women (**Sex**=female) were more likely to have survived
# - Children (**Age**<?) were more likely to have survived
# - The upper-class passengers (**Pclass**=1) were more likely to have survived
# ### Analyze by pivoting features
# - **Pclass** We observe significant correlation (>0.5) among Pclass=1 and Survived (classifying #3). We decide to include this feature in our model.
# - **Sex** We confirm the observation during problem definition that Sex=female had very high survival rate at 74% (classifying #1).
# - **SibSp** and **Parch** These features have zero correlation for certain values. It may be best to derive a feature or a set of features from these individual features (creating #1).
# In[ ]:
def chance_to_survive_by_feature(feature_name):
return train_df[[feature_name, 'Survived']] .groupby([feature_name]) .mean() .sort_values(by='Survived', ascending=False)
chance_to_survive_by_feature('Pclass')
# In[ ]:
chance_to_survive_by_feature('Sex')
# In[ ]:
chance_to_survive_by_feature('SibSp')
# In[ ]:
chance_to_survive_by_feature('Parch')
# ## Visualization
# - Infants (Age <=4) had high survival rate.
# - Oldest passengers (Age = 80) survived.
# - Large number of 15-25 year olds did not survive.
# - Most passengers are in 15-35 age range.
# In[ ]:
g = sns.FacetGrid(train_df, col='Survived')
g.map(plt.hist, 'Age', bins=20);
# - Pclass=3 had most passengers, however most did not survive. Confirms our classifying assumption #2.
# - Infant passengers in Pclass=2 and Pclass=3 mostly survived. Further qualifies our classifying assumption #2.
# - Most passengers in Pclass=1 survived. Confirms our classifying assumption #3.
# - Pclass varies in terms of Age distribution of passengers.
# In[ ]:
grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=.5, bins=20)
grid.add_legend();
# In[ ]:
ordered_embarked = train_df.Embarked.value_counts().index
grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)
grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')
grid.add_legend();
# In[ ]:
grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)
grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)
grid.add_legend();
# - Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.
# - Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).
# # Clearning data
# ## Drop features
# In[ ]:
print("Before", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)
train_df = train_df.drop(['Ticket', 'Cabin', 'PassengerId'], axis=1)
test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)
combine = [train_df, test_df]
print("After ", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)
# ## Create new feature
# ### Create 'Title' and drop 'Name'
# In[ ]:
for dataset in combine:
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'])
# In[ ]:
for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()
# In[ ]:
title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df.head()
# In[ ]:
train_df = train_df.drop(['Name'], axis=1)
test_df = test_df.drop(['Name'], axis=1)
combine = [train_df, test_df]
train_df.shape, test_df.shape
# ## Convert Sex type to number
# In[ ]:
for dataset in combine:
dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
train_df.head()
# ## Completing a numerical continuous feature
# Methods
# - A simple way is to generate random numbers between mean and standard deviation.
# - More accurate way of guessing missing values is to use other correlated features. In our case we note correlation among Age, Gender, and Pclass. Guess Age values using median values for Age across sets of Pclass and Gender feature combinations. So, median Age for Pclass=1 and Gender=0, Pclass=1 and Gender=1, and so on...
# - Combine methods 1 and 2. So instead of guessing age values based on median, use random numbers between mean and standard deviation, based on sets of Pclass and Gender combinations.
#
#
# ### Age
#
# In[ ]:
grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)
grid.map(plt.hist, 'Age', alpha=.5, bins=20)
grid.add_legend();
# In[ ]:
guess_ages = np.zeros((2,3))
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j+1)]['Age'].dropna()
# age_mean = guess_df.mean()
# age_std = guess_df.std()
# age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)
age_guess = guess_df.median()
# Convert random age float to nearest .5 age
guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1), 'Age'] = guess_ages[i,j]
dataset['Age'] = dataset['Age'].astype(int)
train_df['AgeBand'] = pd.cut(train_df['Age'], 5)
train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)
# In[ ]:
for dataset in combine:
dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[ dataset['Age'] > 64, 'Age']
train_df.head()
# In[ ]:
train_df = train_df.drop(['AgeBand'], axis=1)
combine = [train_df, test_df]
train_df.head()
# ## Create feature "FamilySize"
# In[ ]:
for dataset in combine:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
train_df[[
'FamilySize',
'Survived',
]].groupby([
'FamilySize'
], as_index=False)\
.mean()\
.sort_values(by='Survived', ascending=False)
# ## Create feature "IsAlone"
# In[ ]:
for dataset in combine:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
train_df[[
'IsAlone',
'Survived',
]]\
.groupby(['IsAlone'], as_index=False)\
.mean()
# In[ ]:
train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
combine = [train_df, test_df]
train_df.head()
# ## Create artificial feature combining "Pclass and Age."
# In[ ]:
for dataset in combine:
dataset['Age*Class'] = dataset.Age * dataset.Pclass
train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)
# ## Complete missed values of feature "Embarked"
# In[ ]:
freq_port = train_df.Embarked.dropna().mode()[0]
for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)
train_df[[
'Embarked',
'Survived',
]]\
.groupby(['Embarked'], as_index=False)\
.mean()\
.sort_values(by='Survived', ascending=False)
# ## Convert feature "Embarked" to numeric
# In[ ]:
for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
train_df.head()
# ## Complete one missed value for feature "Fare"
# In[ ]:
test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)
test_df.head()
# In[ ]:
train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)
train_df[[
'FareBand',
'Survived',
]]\
.groupby(['FareBand'], as_index=False)\
.mean()\
.sort_values(by='FareBand', ascending=True)
# In[ ]:
for dataset in combine:
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3
dataset['Fare'] = dataset['Fare'].astype(int)
train_df = train_df.drop(['FareBand'], axis=1)
combine = [train_df, test_df]
train_df.head(10)
# # Model, predict and solve
# Binary classification
# In[ ]:
X_train = train_df.drop("Survived", axis=1)
Y_train = train_df["Survived"]
X_test = test_df.drop("PassengerId", axis=1).copy()
X_train.shape, Y_train.shape, X_test.shape
# ## Logistic regression
# logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier
# In[ ]:
models = []
models.append({
'classifier': linear_model.LogisticRegression,
'name': 'Logistic Regression',
})
models.append({
'classifier': svm.SVC,
'name': 'Support Vector Machines',
})
models.append({
'classifier': neighbors.KNeighborsClassifier,
'name': 'k-Nearest Neighbors',
'args': {
'n_neighbors': 3,
},
})
models.append({
'classifier': naive_bayes.GaussianNB,
'name': 'Gaussian Naive Bayes',
})
models.append({
'classifier': linear_model.Perceptron,
'name': 'Perceptron',
'args': {
'max_iter': 5,
'tol': None,
},
})
models.append({
'classifier': svm.LinearSVC,
'name': 'Linear SVC',
})
models.append({
'classifier': linear_model.SGDClassifier,
'name': 'Stochastic Gradient Descent',
'args': {
'max_iter': 5,
'tol': None,
},
})
models.append({
'classifier': tree.DecisionTreeClassifier,
'name': 'Decision Tree',
})
models.append({
'classifier': ensemble.RandomForestClassifier,
'name': 'Random Forest',
'args': {
'n_estimators': 100,
},
})
#acc_log
# ## All Models
# In[ ]:
def process_model(model_desc):
Model = model_desc['classifier']
model = Model(**model_desc.get('args', {}))
model.fit(X_train, Y_train)
Y_pred = model.predict(X_test)
accuracy = round(model.score(X_train, Y_train) * 100, 2)
return {
'name': model_desc['name'],
'accuracy': accuracy,
'model': model,
}
models_result = list(map(process_model, models))
models_result = sorted(models_result, key=lambda res: res['accuracy'], reverse=True)
#print(models_result)
# plot bars
models_result_df = pd.DataFrame(models_result, columns=['accuracy', 'name'])
ax = sns.barplot(data=models_result_df, x='accuracy', y='name')
ax.set(xlim=(0, 100))
# show table
models_result_df
# In[ ]:
# use keras (tensorflow) for full-convolutional deep NN
# # Submit result
# get the best model and submit the result
# In[ ]:
# submission.to_csv('../output/submission.csv', index=False)
the_best_result = models_result[0]
Y_pred = the_best_result['model'].predict(X_test)
submission = pd.DataFrame({
'PassengerId': test_df['PassengerId'],
'Survived': Y_pred,
})
submission.to_csv('submission.csv', index=False)
| [
"[email protected]"
]
| |
51605ef74b4cba3582dbd4a581b09e1dbf06ec52 | d491c11dc87a955c95a4e14a2feea19fe1fa859e | /python/Arcade/Python/P32WordPower.py | 8e59958099ab4e818bb696c758d23213474a33a7 | []
| no_license | Vagacoder/Codesignal | 0f6ea791b25716cad7c46ab7df73679fb18a9882 | 87eaf44555603dd5b8cf221fbcbae5421ae20727 | refs/heads/master | 2023-07-16T04:18:44.780821 | 2021-08-15T18:41:16 | 2021-08-15T18:41:16 | 294,745,195 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,148 | py | #
# * Python 32, Word Power
# * Easy
# * You've heard somewhere that a word is more powerful than an action. You decided
# * to put this statement at a test by assigning a power value to each action and
# * each word. To begin somewhere, you defined a power of a word as the sum of
# * powers of its characters, where power of a character is equal to its 1-based
# * index in the plaintext alphabet.
# * Given a word, calculate its power.
# * Example
# For word = "hello", the output should be
# wordPower(word) = 52.
# Letters 'h', 'e', 'l' and 'o' have powers 8, 5, 12 and 15, respectively. Thus, the total power of the word is 8 + 5 + 12 + 12 + 15 = 52.
# * Input/Output
# [execution time limit] 4 seconds (py3)
# [input] string word
# A string consisting of lowercase English letters.
# Guaranteed constraints:
# 1 ≤ word.length ≤ 25.
# [output] integer
# Power of the given word.
#%%
# * Solution 1
import string
def wordPower(word):
num = {v:(i+1) for i, v in enumerate(string.ascii_lowercase)}
return sum([num[ch] for ch in word])
a1 = 'hello'
r1 = wordPower(a1)
print(r1)
# %%
| [
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6bf9fe564297c104e2d4a8e7f00109a5ee57bd30 | 14ff5ca733ce92c14dd347e32c7ad262026c48cf | /typeshed/rdflib/exceptions.pyi | 4066f6854d2ffd93b18fa4bd16a05f92764610bd | [
"Apache-2.0"
]
| permissive | common-workflow-language/cwlprov-py | 6040bd1ea18fb58909bba9874f65e4edcc4ecd92 | 9b719c687484d3f888eb5f807ec3270e9081078a | refs/heads/main | 2023-08-17T06:03:39.274209 | 2022-07-19T18:09:15 | 2022-07-19T18:21:13 | 148,144,870 | 1 | 2 | Apache-2.0 | 2023-08-02T18:35:35 | 2018-09-10T11:27:31 | Python | UTF-8 | Python | false | false | 761 | pyi | from typing import Any
class Error(Exception):
msg: Any
def __init__(self, msg: Any | None = ...) -> None: ...
class TypeCheckError(Error):
type: Any
node: Any
def __init__(self, node) -> None: ...
class SubjectTypeError(TypeCheckError):
msg: Any
def __init__(self, node) -> None: ...
class PredicateTypeError(TypeCheckError):
msg: Any
def __init__(self, node) -> None: ...
class ObjectTypeError(TypeCheckError):
msg: Any
def __init__(self, node) -> None: ...
class ContextTypeError(TypeCheckError):
msg: Any
def __init__(self, node) -> None: ...
class ParserError(Error):
msg: Any
def __init__(self, msg) -> None: ...
class UniquenessError(Error):
def __init__(self, values) -> None: ...
| [
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f3abfbb514fdc985c0c51949272f13c43a7c3730 | b799c3e1fe5a50b2babcfb2960af210dec434f49 | /354.russian-doll-envelopes.py | facf0e6c6903adc2cda1b3f18cbef9f6574cf968 | []
| no_license | Joecth/leetcode_3rd_vscode | 4619ef80632dec83cbcbcd090f74e043f436cc75 | 3c0943ee9b373e4297aa43a4813f0033c284a5b2 | refs/heads/master | 2022-12-02T19:30:34.572339 | 2020-08-18T15:21:15 | 2020-08-18T15:21:15 | 255,601,035 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,605 | py | #
# @lc app=leetcode id=354 lang=python3
#
# [354] Russian Doll Envelopes
#
# https://leetcode.com/problems/russian-doll-envelopes/description/
#
# algorithms
# Hard (34.98%)
# Likes: 981
# Dislikes: 37
# Total Accepted: 62.8K
# Total Submissions: 178.5K
# Testcase Example: '[[5,4],[6,4],[6,7],[2,3]]'
#
# You have a number of envelopes with widths and heights given as a pair of
# integers (w, h). One envelope can fit into another if and only if both the
# width and height of one envelope is greater than the width and height of the
# other envelope.
#
# What is the maximum number of envelopes can you Russian doll? (put one inside
# other)
#
# Note:
# Rotation is not allowed.
#
# Example:
#
#
#
# Input: [[5,4],[6,4],[6,7],[2,3]]
# Output: 3
# Explanation: The maximum number of envelopes you can Russian doll is 3 ([2,3]
# => [5,4] => [6,7]).
#
#
#
#
# @lc code=start
class Solution:
def maxEnvelopes(self, envelopes: List[List[int]]) -> int:
if not envelopes: return 0
elif len(envelopes) == 1: return 1
arr = sorted(envelopes, key=lambda envelope: (envelope[0], -envelope[1]))
# return self.helper_FAILED(arr)
# return self.O_NxN(arr)
return self.O_NxlogN(arr)
def O_NxlogN(self, envelopes):
dp = []
for i in range(len(envelopes)):
if not dp:
dp.append(envelopes[i][1])
continue
# if envelopes[i][0] > dp[-1] and envelopes[i][1] > dp[-1][1]:
if envelopes[i][1] > dp[-1]:
dp.append(envelopes[i][1])
else:
target = envelopes[i][1]
start, end = 0, len(dp)
# To find elem >= envelopes[i][1]
while start + 1 < end:
mid = start + (end-start)//2
if dp[mid] >= target:
end = mid
else:
start = mid
if dp[start] >= target:
dp[start] = target
elif dp[end] >= target:
dp[end] = target
# print(dp)
return len(dp)
def O_NxN(self, envelopes):
dp = []
for i in range(len(envelopes)):
if not dp:
dp.append(envelopes[i][1])
continue
# if envelopes[i][0] > dp[-1] and envelopes[i][1] > dp[-1][1]:
if envelopes[i][1] > dp[-1]:
dp.append(envelopes[i][1])
else:
for j in range(len(dp)):
if envelopes[i][1] <= dp[j]:
dp[j] = envelopes[i][1]
break
# print(dp)
return len(dp)
def helper_FAILED(self, envelopes):
dp = []
for envelope in envelopes:
if not dp:
dp.append(envelope)
continue
if envelope[0] > dp[-1][0] and envelope[1] > dp[-1][1] :
dp.append(envelope)
else:
for j in range(len(dp)):
# NO USE
# if envelope[0] <= dp[j][0] and envelope[1] > dp[j][1]:
# break
# elif envelope[0] > dp[j][0] and envelope[1] <= dp[j][1]:
# break
if envelope[0] <= dp[j][0] and envelope[1] <= dp[j][1]:
dp[j] = envelope
break
print(dp)
return len(dp)
# @lc code=end
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]
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9a042505df211682de5afa3eb04441642762d7ba | e402a0fbde47acb8903304a0fef11ec1de83b01f | /SecretColors/data/palettes/__init__.py | 0424d070c31874e136fbe0f9ba306d437a31b594 | [
"MIT"
]
| permissive | Robinsondssantos/SecretColors | ad254a872d7bcc4ef1ac1914355d2f5c7ec73357 | eb19b8a1805eba812032b450d644aa8fc5c257e5 | refs/heads/master | 2023-01-25T00:34:25.849346 | 2020-12-06T11:35:45 | 2020-12-06T11:35:45 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 702 | py | # Copyright (c) SecretBiology 2020.
#
# Library Name: SecretColors
# Author: Rohit Suratekar
# Website: https://github.com/secretBiology/SecretColors
#
# Most of these palettes are derived from various design systems. Few
# examples of such design systems can be found on following URL
# https://designsystemsrepo.com/design-systems
from SecretColors.data.palettes.parent import ParentPalette
from SecretColors.data.palettes.ibm import IBMPalette
from SecretColors.data.palettes.material import MaterialPalette
from SecretColors.data.palettes.clarity import ClarityPalette
from SecretColors.data.palettes.brewer import ColorBrewer
from SecretColors.data.palettes.tableau import TableauPalette
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]
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4973daa5f7033237dd93efc217a2a9c1532a74b5 | 90914b7d84d69a86652e69d1ad72888af363367b | /production_work_timesheet/timesheet.py | a4ebbc6d37d4d6bfb0c7cae074624939a479354c | []
| no_license | emperadorxp1/TrytonModules | 754a3ca92c0ac7b2db9165208b1bc5fda5fe4a73 | 33ef61752e1c5f490e7ed4ee8a3f0cff63a8fc89 | refs/heads/master | 2020-12-19T18:41:05.260174 | 2020-01-23T15:32:57 | 2020-01-23T15:32:57 | 235,815,084 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 714 | py | # This file is part of Tryton. The COPYRIGHT file at the top level of
# this repository contains the full copyright notices and license terms.
from trytond.pool import PoolMeta, Pool
__all__ = ['TimesheetWork']
class TimesheetWork(metaclass=PoolMeta):
__name__ = 'timesheet.work'
@classmethod
def _get_origin(cls):
return super(TimesheetWork, cls)._get_origin() + ['production.work']
def _validate_company(self):
pool = Pool()
ProductionWork = pool.get('production.work')
result = super(TimesheetWork, self)._validate_company()
if isinstance(self.origin, ProductionWork):
result &= self.company == self.origin.company
return result
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]
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310690032b5db321ddc9107fa0c8fb962ffa3a9a | 232fc2c14942d3e7e28877b502841e6f88696c1a | /dizoo/smac/envs/smac_action.py | aaceb32e9777eaf73ed542d6ae037d894d0fc412 | [
"Apache-2.0"
]
| permissive | shengxuesun/DI-engine | ebf84221b115b38b4b3fdf3079c66fe81d42d0f7 | eb483fa6e46602d58c8e7d2ca1e566adca28e703 | refs/heads/main | 2023-06-14T23:27:06.606334 | 2021-07-12T12:36:18 | 2021-07-12T12:36:18 | 385,454,483 | 1 | 0 | Apache-2.0 | 2021-07-13T02:56:27 | 2021-07-13T02:56:27 | null | UTF-8 | Python | false | false | 16,272 | py | import enum
import math
import numpy as np
from collections import namedtuple
from s2clientprotocol import common_pb2 as sc_common, sc2api_pb2 as sc_pb, raw_pb2 as r_pb
ORIGINAL_AGENT = "me"
OPPONENT_AGENT = "opponent"
MOVE_EAST = 4
MOVE_WEST = 5
actions = {
"move": 16, # target: PointOrUnit
"attack": 23, # target: PointOrUnit
"stop": 4, # target: None
"heal": 386, # Unit
"parasitic_bomb": 2542, # target: Unit
'fungal_growth': 74, # target: PointOrUnit
}
class Direction(enum.IntEnum):
NORTH = 0
SOUTH = 1
EAST = 2
WEST = 3
def distance(x1, y1, x2, y2):
"""Distance between two points."""
return math.hypot(x2 - x1, y2 - y1)
class SMACAction:
info_template = namedtuple('EnvElementInfo', ['shape', 'value', 'to_agent_processor', 'from_agent_processor'])
def __init__(self, n_agents, n_enemies, two_player=False, mirror_opponent=True):
self.obs_pathing_grid = False
self.obs_terrain_height = False
self.state_last_action = True
self.state_timestep_number = False
self.n_obs_pathing = 8
self.n_obs_height = 9
self._move_amount = 2
self.n_actions_no_attack = 6
self.n_actions_move = 4
self.n_actions = self.n_actions_no_attack + n_enemies
self.map_x = 0
self.map_y = 0
# Status tracker
self.last_action = np.zeros((n_agents, self.n_actions))
self.last_action_opponent = np.zeros((n_enemies, self.n_actions))
self.n_agents = n_agents
self.n_enemies = n_enemies
self.two_player = two_player
self.mirror_opponent = mirror_opponent
def reset(self):
self.last_action.fill(0)
self.last_action_opponent.fill(0)
def update(self, map_info, map_x, map_y):
if map_info.pathing_grid.bits_per_pixel == 1:
vals = np.array(list(map_info.pathing_grid.data)).reshape(map_x, int(map_y / 8))
self.pathing_grid = np.transpose(
np.array([[(b >> i) & 1 for b in row for i in range(7, -1, -1)] for row in vals], dtype=np.bool)
)
else:
self.pathing_grid = np.invert(
np.flip(
np.transpose(np.array(list(map_info.pathing_grid.data), dtype=np.bool).reshape(map_x, map_y)),
axis=1
)
)
self.terrain_height = np.flip(
np.transpose(np.array(list(map_info.terrain_height.data)).reshape(map_x, map_y)), 1
) / 255
self.map_x = map_x
self.map_y = map_y
def _parse_single(self, actions, engine, is_opponent=False):
actions = np.asarray(actions, dtype=np.int)
assert len(actions) == (self.n_enemies if is_opponent else self.n_agents)
actions_int = [int(a) for a in actions]
# Make them one-hot
if is_opponent:
self.last_action_opponent = np.eye(self.n_actions)[np.array(actions_int)]
else:
self.last_action = np.eye(self.n_actions)[np.array(actions_int)]
sc_actions = []
for a_id, action in enumerate(actions_int):
sc_action = self.get_agent_action(a_id, action, engine, is_opponent)
if sc_action:
sc_actions.append(sc_action)
return sc_actions
def get_action(self, actions, engine):
if self.two_player:
# ========= Two player mode ==========
assert self.two_player
assert isinstance(actions, dict)
assert ORIGINAL_AGENT in actions
assert OPPONENT_AGENT in actions
if self.mirror_opponent:
actions[OPPONENT_AGENT] = [self._transform_action(a) for a in actions[OPPONENT_AGENT]]
sc_actions_me = self._parse_single(actions[ORIGINAL_AGENT], engine, is_opponent=False)
sc_actions_opponent = self._parse_single(actions[OPPONENT_AGENT], engine, is_opponent=True)
return {ORIGINAL_AGENT: sc_actions_me, OPPONENT_AGENT: sc_actions_opponent}
else:
assert not isinstance(actions, dict)
sc_actions = self._parse_single(actions, engine, is_opponent=False)
return sc_actions
def get_unit_by_id(self, a_id, engine, is_opponent=False):
"""Get unit by ID."""
if is_opponent:
return engine.enemies[a_id]
return engine.agents[a_id]
def get_agent_action(self, a_id, action, engine, is_opponent=False):
"""Construct the action for agent a_id.
The input action here is *absolute* and is not mirrored!
We use skip_mirror=True in get_avail_agent_actions to avoid error.
"""
avail_actions = self.get_avail_agent_actions(a_id, engine, is_opponent=is_opponent, skip_mirror=True)
try:
assert avail_actions[action] == 1, \
"Agent {} cannot perform action {} in ava {}".format(a_id, action, avail_actions)
except Exception as e:
if action == 0:
action = 1
else:
action = 1
# TODO
# raise e
unit = self.get_unit_by_id(a_id, engine, is_opponent=is_opponent)
# if is_opponent:
# action = avail_actions[0] if avail_actions[0] else avail_actions[1]
# ===== The follows is intact to the original =====
tag = unit.tag
type_id = unit.unit_type
x = unit.pos.x
y = unit.pos.y
# if is_opponent:
# print(f"The given unit tag {tag}, x {x}, y {y} and action {action}")
if action == 0:
# no-op (valid only when dead)
assert unit.health == 0, "No-op only available for dead agents."
return None
elif action == 1:
# stop
cmd = r_pb.ActionRawUnitCommand(ability_id=actions["stop"], unit_tags=[tag], queue_command=False)
elif action == 2:
# move north
cmd = r_pb.ActionRawUnitCommand(
ability_id=actions["move"],
target_world_space_pos=sc_common.Point2D(x=x, y=y + self._move_amount),
unit_tags=[tag],
queue_command=False
)
elif action == 3:
# move south
cmd = r_pb.ActionRawUnitCommand(
ability_id=actions["move"],
target_world_space_pos=sc_common.Point2D(x=x, y=y - self._move_amount),
unit_tags=[tag],
queue_command=False
)
elif action == 4:
# move east
cmd = r_pb.ActionRawUnitCommand(
ability_id=actions["move"],
target_world_space_pos=sc_common.Point2D(x=x + self._move_amount, y=y),
unit_tags=[tag],
queue_command=False
)
elif action == 5:
# move west
cmd = r_pb.ActionRawUnitCommand(
ability_id=actions["move"],
target_world_space_pos=sc_common.Point2D(x=x - self._move_amount, y=y),
unit_tags=[tag],
queue_command=False
)
else:
# attack/heal units that are in range
target_id = action - self.n_actions_no_attack
if engine.map_type == "MMM" and unit.unit_type == (engine.medivac_id_opponent
if is_opponent else engine.medivac_id):
target_unit = (engine.enemies[target_id] if is_opponent else engine.agents[target_id])
action_name = "heal"
elif engine.map_type == 'infestor_viper':
# viper
if type_id == 499:
target_unit = engine.enemies[target_id]
action_name = "parasitic_bomb"
# infestor
else:
target_unit = engine.enemies[target_id]
target_loc = (target_unit.pos.x, target_unit.pos.y)
action_name = "fungal_growth"
target_loc = sc_common.Point2D(x=target_loc[0], y=target_loc[1])
cmd = r_pb.ActionRawUnitCommand(
ability_id=actions[action_name],
target_world_space_pos=target_loc,
unit_tags=[tag],
queue_command=False
)
return sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd))
else:
target_unit = (engine.agents[target_id] if is_opponent else engine.enemies[target_id])
action_name = "attack"
action_id = actions[action_name]
target_tag = target_unit.tag
cmd = r_pb.ActionRawUnitCommand(
ability_id=action_id, target_unit_tag=target_tag, unit_tags=[tag], queue_command=False
)
sc_action = sc_pb.Action(action_raw=r_pb.ActionRaw(unit_command=cmd))
return sc_action
def get_avail_agent_actions(self, agent_id, engine, is_opponent=False, skip_mirror=False):
"""Returns the available actions for agent_id."""
medivac_id = engine.medivac_id_opponent if is_opponent else engine.medivac_id
unit = self.get_unit_by_id(agent_id, engine, is_opponent)
if unit.health > 0:
# cannot choose no-op when alive
avail_actions = [0] * self.n_actions
# stop should be allowed
avail_actions[1] = 1
# see if we can move
if self.can_move(unit, Direction.NORTH):
avail_actions[2] = 1
if self.can_move(unit, Direction.SOUTH):
avail_actions[3] = 1
if self.can_move(unit, Direction.EAST):
avail_actions[4] = 1
if self.can_move(unit, Direction.WEST):
avail_actions[5] = 1
# Can attack only alive units that are alive in the shooting range
shoot_range = self.unit_shoot_range(unit)
target_items = engine.enemies.items() if not is_opponent else engine.agents.items()
self_items = engine.agents.items() if not is_opponent else engine.enemies.items()
if engine.map_type == "MMM" and unit.unit_type == medivac_id:
# Medivacs cannot heal themselves or other flying units
target_items = [(t_id, t_unit) for (t_id, t_unit) in self_items if t_unit.unit_type != medivac_id]
for t_id, t_unit in target_items:
if t_unit.health > 0:
dist = distance(unit.pos.x, unit.pos.y, t_unit.pos.x, t_unit.pos.y)
if dist <= shoot_range:
if engine.map_type == "infestor_viper":
value = 0
# viper
if unit.unit_type == 499:
if unit.energy >= 125:
value = 1
# infestor
else:
if unit.energy >= 50:
value = 1
avail_actions[t_id + self.n_actions_no_attack] = value
else:
avail_actions[t_id + self.n_actions_no_attack] = 1
else:
# only no-op allowed
avail_actions = [1] + [0] * (self.n_actions - 1)
if (not skip_mirror) and self.mirror_opponent and is_opponent:
avail_actions[MOVE_EAST], avail_actions[MOVE_WEST] = \
avail_actions[MOVE_WEST], avail_actions[MOVE_EAST]
return avail_actions
def can_move(self, unit, direction):
"""Whether a unit can move in a given direction."""
m = self._move_amount / 2
if direction == Direction.NORTH:
x, y = int(unit.pos.x), int(unit.pos.y + m)
elif direction == Direction.SOUTH:
x, y = int(unit.pos.x), int(unit.pos.y - m)
elif direction == Direction.EAST:
x, y = int(unit.pos.x + m), int(unit.pos.y)
else:
x, y = int(unit.pos.x - m), int(unit.pos.y)
if self.check_bounds(x, y) and self.pathing_grid[x, y]:
return True
return False
def check_bounds(self, x, y):
"""Whether a point is within the map bounds."""
return 0 <= x < self.map_x and 0 <= y < self.map_y
def get_surrounding_pathing(self, unit):
"""Returns pathing values of the grid surrounding the given unit."""
points = self.get_surrounding_points(unit, include_self=False)
vals = [self.pathing_grid[x, y] if self.check_bounds(x, y) else 1 for x, y in points]
return vals
def get_surrounding_height(self, unit):
"""Returns height values of the grid surrounding the given unit."""
points = self.get_surrounding_points(unit, include_self=True)
vals = [self.terrain_height[x, y] if self.check_bounds(x, y) else 1 for x, y in points]
return vals
def unit_shoot_range(self, unit):
"""Returns the shooting range for an agent."""
type_id = unit.unit_type
if type_id == 499:
return 8
elif type_id == 111:
return 10
else:
return 6
def get_surrounding_points(self, unit, include_self=False):
"""Returns the surrounding points of the unit in 8 directions."""
x = int(unit.pos.x)
y = int(unit.pos.y)
ma = self._move_amount
points = [
(x, y + 2 * ma),
(x, y - 2 * ma),
(x + 2 * ma, y),
(x - 2 * ma, y),
(x + ma, y + ma),
(x - ma, y - ma),
(x + ma, y - ma),
(x - ma, y + ma),
]
if include_self:
points.append((x, y))
return points
def get_movement_features(self, agent_id, engine, is_opponent=False):
unit = self.get_unit_by_id(agent_id, engine, is_opponent=is_opponent)
move_feats_dim = self.get_obs_move_feats_size()
move_feats = np.zeros(move_feats_dim, dtype=np.float32)
if unit.health > 0: # otherwise dead, return all zeros
# Movement features
avail_actions = self.get_avail_agent_actions(agent_id, engine, is_opponent=is_opponent)
for m in range(self.n_actions_move):
move_feats[m] = avail_actions[m + 2]
ind = self.n_actions_move
if self.obs_pathing_grid:
move_feats[ind:ind + self.n_obs_pathing # TODO self.n_obs_pathing ?
] = self.get_surrounding_pathing(unit)
ind += self.n_obs_pathing
if self.obs_terrain_height:
move_feats[ind:] = self.get_surrounding_height(unit)
return move_feats
def get_obs_move_feats_size(self):
"""Returns the size of the vector containing the agents's movement-related features."""
move_feats = self.n_actions_move
if self.obs_pathing_grid:
move_feats += self.n_obs_pathing
if self.obs_terrain_height:
move_feats += self.n_obs_height
return move_feats
def get_last_action(self, is_opponent=False):
if is_opponent:
ret = self.last_action_opponent
if self.mirror_opponent:
ret[:, MOVE_EAST], ret[:, MOVE_WEST] = \
ret[:, MOVE_WEST].copy(), ret[:, MOVE_EAST].copy()
else:
ret = self.last_action
return ret
def get_avail_actions(self, engine, is_opponent=False):
return [
self.get_avail_agent_actions(agent_id, engine, is_opponent=is_opponent)
for agent_id in range(self.n_agents if not is_opponent else self.n_enemies)
]
@staticmethod
def _transform_action(a):
if a == MOVE_EAST: # intend to move east
a = MOVE_WEST
elif a == MOVE_WEST: # intend to move west
a = MOVE_EAST
return a
def info(self):
shape = (self.n_actions, )
value = {'min': 0, 'max': 1}
return SMACAction.info_template(shape, value, None, None)
| [
"[email protected]"
]
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34c193b5f47a90df0b6bd16d59075d1761ee4192 | 499f5402baed77d000c65f243b457c69dc3d2fe4 | /pycatia/system_interfaces/setting_repository.py | 5ce1233ac76c360aa18a27c0e231a219e723079a | [
"MIT"
]
| permissive | evereux/pycatia | 416189b34f3c60effea8a76258e36ffc5ae86e22 | 5f5726d5dc66265b3eba8a01910c4aeae424365d | refs/heads/master | 2023-08-21T10:03:41.660445 | 2023-08-09T16:21:10 | 2023-08-09T16:21:10 | 159,069,580 | 141 | 42 | MIT | 2023-08-09T11:15:27 | 2018-11-25T20:04:31 | Python | UTF-8 | Python | false | false | 13,294 | py | #! usr/bin/python3.9
"""
Module initially auto generated using V5Automation files from CATIA V5 R28 on 2020-06-09 09:53:18.676780
.. warning::
The notes denoted "CAA V5 Visual Basic Help" are to be used as reference only.
They are there as a guide as to how the visual basic / catscript functions work
and thus help debugging in pycatia.
"""
from pycatia.system_interfaces.setting_controller import SettingController
class SettingRepository(SettingController):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780)
| System.IUnknown
| System.IDispatch
| System.CATBaseUnknown
| System.CATBaseDispatch
| System.AnyObject
| System.SettingController
| SettingRepository
|
| Represents the base object to handle the parameters of a
| setting
"""
def __init__(self, com_object):
super().__init__(com_object)
self.setting_repository = com_object
def get_attr(self, i_attr_name):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780))
| o Func GetAttr(CATBSTR iAttrName) As CATVariant
|
| Retieves a attribute.
|
| Parameters:
|
| iAttrName
| the attribute name
| oAttr
| a CATVariant
|
| Returns:
| Legal values:
| S_OK : on Success
| E_FAIL: on failure
:param str i_attr_name:
:return: CATVariant
"""
return self.setting_repository.GetAttr(i_attr_name)
def get_attr_array(self, i_attr_name):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780))
| o Func GetAttrArray(CATBSTR iAttrName) As
| CATSafeArrayVariant
|
| Retieves a attribute of type array
|
| Parameters:
|
| iAttrName
| the attribute name
| oArray
| a CATSafeArrayVariant
|
| Returns:
| Legal values:
| S_OK : on Success
| E_FAIL: on failure
:param str i_attr_name:
:return: tuple
"""
return tuple(self.setting_repository.GetAttrArray(i_attr_name))
def get_attr_info(self, i_attr_name, admin_level, locked, o_modified):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780))
| o Sub GetAttrInfo(CATBSTR iAttrName,
| CATBSTR AdminLevel,
| CATBSTR Locked,
| boolean oModified)
|
| Retrieves environment informations for the given
| attribute.
| Role: This information defines the state of the setting parameter and is
| made up of:
|
| The administration level that sets the current value or the value used
| to reset it
| The administration level that has locked the setting
| parameter.
| A flag to indicate whether the setting parameter was
| modified.
|
| Parameters:
|
| iAttrName
| [in] the attribute name.
| ioAdminLevel
| [inout] The administration level that defines the value used when
| resetting the setting parameter.
|
| Legal values:
|
| Default value if the setting parameter has never been
| explicitly set in the administration
| concatenation.
| Set at Admin Level n if the setting parameter has been
| administered,
| where n is an integer starting from 0 representing the rank of
| the administration level.
|
| ioLocked
| [inout] A character string to indicate whether the parameter is
| locked and the level of administration where the locking has been
| proceeded.
| Legal values:
|
| Locked at Admin Level n if the setting parameter is locked by
| then administration level n,
| where n is an integer starting from 0. The setting parameter
| can not be modified at the current level.
| Locked if the setting parameter is locked by the current
| administration level. Only an admistrator can get this
| value.
| Unlocked if the setting parameter is not
| locked
|
| oModified
| [out] True to indicate that the setting parameter value has been
| explicitely modified at the current administrator or user level. This is only
| possible with unlocked parameters. False means that it inherits the
| administered value.
|
| Returns:
| Legal values:
| S_OK : on Success
| E_FAIL: on failure
:param str i_attr_name:
:param str admin_level:
:param str locked:
:param bool o_modified:
:return: None
"""
return self.setting_repository.GetAttrInfo(i_attr_name, admin_level, locked, o_modified)
# # # # Autogenerated comment:
# # some methods require a system service call as the methods expects a vb array object
# # passed to it and there is no way to do this directly with python. In those cases the following code
# # should be uncommented and edited accordingly. Otherwise completely remove all this.
# # vba_function_name = 'get_attr_info'
# # vba_code = """
# # Public Function get_attr_info(setting_repository)
# # Dim iAttrName (2)
# # setting_repository.GetAttrInfo iAttrName
# # get_attr_info = iAttrName
# # End Function
# # """
# # system_service = self.application.system_service
# # return system_service.evaluate(vba_code, 0, vba_function_name, [self.com_object])
def put_attr(self, i_attr_name, i_attr):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780))
| o Sub PutAttr(CATBSTR iAttrName,
| CATVariant iAttr)
|
| Sets an attribute of type array.
|
| Parameters:
|
| iAttrName
| the attribute name
| iArray
| a CATSafeArrayVariant.
|
| Returns:
| Legal values:
| S_OK : on Success
| E_FAIL: on failure
:param str i_attr_name:
:param CATVariant i_attr:
:return: None
"""
return self.setting_repository.PutAttr(i_attr_name, i_attr)
# # # # Autogenerated comment:
# # some methods require a system service call as the methods expects a vb array object
# # passed to it and there is no way to do this directly with python. In those cases the following code
# # should be uncommented and edited accordingly. Otherwise completely remove all this.
# # vba_function_name = 'put_attr'
# # vba_code = """
# # Public Function put_attr(setting_repository)
# # Dim iAttrName (2)
# # setting_repository.PutAttr iAttrName
# # put_attr = iAttrName
# # End Function
# # """
# # system_service = self.application.system_service
# # return system_service.evaluate(vba_code, 0, vba_function_name, [self.com_object])
def put_attr_array(self, i_attr_name, i_array):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780))
| o Sub PutAttrArray(CATBSTR iAttrName,
| CATSafeArrayVariant iArray)
|
| Sets an attribute of type array.
|
| Parameters:
|
| iAttrName
| the attribute name
| iArray
| a CATSafeArrayVariant.
|
| Returns:
| Legal values:
| S_OK : on Success
| E_FAIL: on failure
:param str i_attr_name:
:param tuple i_array:
:return: None
"""
return self.setting_repository.PutAttrArray(i_attr_name, i_array)
# # # # Autogenerated comment:
# # some methods require a system service call as the methods expects a vb array object
# # passed to it and there is no way to do this directly with python. In those cases the following code
# # should be uncommented and edited accordingly. Otherwise completely remove all this.
# # vba_function_name = 'put_attr_array'
# # vba_code = """
# # Public Function put_attr_array(setting_repository)
# # Dim iAttrName (2)
# # setting_repository.PutAttrArray iAttrName
# # put_attr_array = iAttrName
# # End Function
# # """
# # system_service = self.application.system_service
# # return system_service.evaluate(vba_code, 0, vba_function_name, [self.com_object])
def set_attr_lock(self, i_attr_name, i_locked):
"""
.. note::
:class: toggle
CAA V5 Visual Basic Help (2020-06-09 09:53:18.676780))
| o Sub SetAttrLock(CATBSTR iAttrName,
| boolean iLocked)
|
| Locks or unlocks an attribute.
| Role: Locking a setting attribute prevents the end user, or the
| administrators below the current one, from changing the setting parameter
| value. Locking or unlocking the attribute setting parameter is an administrator
| task and is possible when running a session in the administration mode
| only.
|
| Parameters:
|
| iAttrName
| [in] the attribute name.
| iLocked
| [in] A flag to indicate whether the attribute setting parameter
| should be locked.
| Legal values: True to lock, and False to unlock.
:param str i_attr_name:
:param bool i_locked:
:return: None
"""
return self.setting_repository.SetAttrLock(i_attr_name, i_locked)
# # # # Autogenerated comment:
# # some methods require a system service call as the methods expects a vb array object
# # passed to it and there is no way to do this directly with python. In those cases the following code
# # should be uncommented and edited accordingly. Otherwise completely remove all this.
# # vba_function_name = 'set_attr_lock'
# # vba_code = """
# # Public Function set_attr_lock(setting_repository)
# # Dim iAttrName (2)
# # setting_repository.SetAttrLock iAttrName
# # set_attr_lock = iAttrName
# # End Function
# # """
# # system_service = self.application.system_service
# # return system_service.evaluate(vba_code, 0, vba_function_name, [self.com_object])
def __repr__(self):
return f'SettingRepository(name="{self.name}")'
| [
"[email protected]"
]
| |
6954ecfa3c9a5a56759adcad12af34bb1cefe713 | 03520fd9d037e22e8c34f61369268b01769c0ae9 | /server/server_generic.py | 9601a66e4380b10b3052e25118e3a85b75e70715 | [
"MIT"
]
| permissive | kevinkk525/micropython_iot_generic | d93f4a51d8f89da221f4b30af4afa866231df909 | f58730ff387a09077b8bb73b0b0f57a1c8c72485 | refs/heads/master | 2020-04-10T21:59:39.888144 | 2019-11-11T07:51:59 | 2019-11-11T07:51:59 | 161,312,146 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,310 | py | # Author: Kevin Köck
# Copyright Kevin Köck 2019 Released under the MIT license
# Created on 2018-12-15
__updated__ = "2018-12-15"
__version__ = "0.0"
import asyncio
import logging
import math
import time
from server.apphandler.apphandler import AppHandler
# server tested with 800 concurrent (dis)connects at a time, sending one message,
# causing ~70% cpu usage on one 2GHz arm core with ~40MB RAM usage.
# Running this for several hours did not show any RAM leak.
# TODO: initiated connections to the server without sending a message stay open
# TODO: keepalive should only send if no new message within timeframe
log = logging.getLogger("")
_network = None
def getNetwork():
if _network is not None:
return _network
raise TypeError("Network not initialized")
class Network:
def __init__(self, hostname=None, port=None, timeout_connection=1500, timeout_client_object=3600,
cb_new_client=None, client_class=None):
"""
:param hostname: hostname to listen to, defaults to 0.0.0.0
:param port: port is actually needed, but defaults to 8888
:param timeout_connection: timeout in ms of the connection object.
If no keepalive was possible during that time, connection will be closed
:param timeout_client_object: timeout in s of the client object that survives a connection loss.
The client object containing any apps and not yet sent messages will be deleted
"""
if timeout_client_object is None:
timeout_client_object = math.inf
if hostname is not None:
self.hostname = hostname
else:
self.hostname = "0.0.0.0"
if port is not None:
self.port = port
else:
self.port = 9999
self.timeout_connection = timeout_connection
self.timeout_client = timeout_client_object
self.loop = None
self.server = None
self.server_task = None
self.clients = {}
self.shutdown_requested = asyncio.Event()
self.new_client = asyncio.Event()
self.cb_new_client = cb_new_client
global _network
_network = self
if client_class is None:
from server.generic_clients.client import Client
self.Client = Client
else:
self.Client = client_class
async def shutdown(self):
log.info("Shutting down network")
self.shutdown_requested.set()
AppHandler.stop_event.set()
await asyncio.sleep(5) # time for clients to shut down
self.server.close()
async def init(self, loop):
self.server = await loop.create_server(lambda: ClientConnection(self), self.hostname, self.port)
log.info("Server created")
self.loop = loop
asyncio.ensure_future(self._resetNewClientEvent())
async def _resetNewClientEvent(self):
while not self.shutdown_requested.is_set():
try:
await asyncio.wait_for(self.new_client.wait(), 1)
self.new_client.clear()
except asyncio.TimeoutError: # makes it react to shutdown_requested in time without canceling externally
pass
class ClientConnection(asyncio.Protocol):
def __init__(self, network: Network):
self.input_buffer = b""
self.network = network
self.transport = None
self.loop = network.loop
self.ip = None
self.client_id = None
self.client = None
def connection_made(self, transport):
peername = transport.get_extra_info('peername')
log.info('Connection from {}, {!s}'.format(peername, transport))
self.ip = peername
self.transport = transport
sock = transport.get_extra_info("socket")
import socket
sock.setsockopt(socket.SOL_TCP, socket.TCP_NODELAY, 1)
# sock.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)
# sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_KEEPIDLE, 5)
# sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_KEEPINTVL, 1)
# sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_KEEPCNT, 1)
def connection_lost(self, exc):
log.info("Connection to client {!r}, {!s} lost, exception {!s}".format(self.client_id, self.ip, exc))
if self.client is not None:
asyncio.ensure_future(self.client.stop())
def data_received(self, data):
# log.debug("Got data: {!s}, len {!s}".format(data, len(data)))
message = self.input_buffer + data
self.input_buffer = b""
if message.find(b"\n") == -1:
self.input_buffer = message
return
tmp = message.split(b"\n")
if message.endswith(b"\n") is False:
self.input_buffer = tmp.pop(-1)
for i in range(0, tmp.count(b"")):
tmp.remove(b"") # sometimes keepalives are read too but as nothing is done with these, just remove them
message = tmp.pop(0) if len(tmp) > 0 else b"" # as keepalive has been removed
if self.client_id is None:
if message == b"":
return # new connection does not start with keepalive
try:
client_id = getNetwork().Client.readID(message)
except TypeError as e:
log.error(e)
self.close()
return
log.debug("Logged in as {!s}".format(client_id))
self.client_id = client_id
if self.client_id in self.network.clients:
cl = self.network.clients[self.client_id]
if cl.transport is not None:
cl.transport.client = None # otherwise transport will stop client on connection loss.
try:
cl.transport.close()
except:
pass
del cl.transport
cl.transport = self
cl.lines_received += tmp
while len(cl.lines_received) > cl.len_rx_buffer:
cl.lines_received.pop(0)
cl.start(message)
self.client = cl
return
client = _network.Client(self.client_id, timeout_connection=self.network.timeout_connection,
timeout_client_object=self.network.timeout_client)
client.transport = self
self.network.clients[self.client_id] = client
self.client = client
# log.debug("Client list on creation: {!s}".format(self.network.clients))
if self.network.cb_new_client is not None:
self.network.cb_new_client(client)
client.start(message)
if len(tmp) > 0:
self.client.lines_received += tmp
while len(self.client.lines_received) > self.client.len_rx_buffer:
self.client.lines_received.pop(0)
self.client.new_message_rx.set()
return
if self.client is None:
log.warn("Connection {!s} does not have client object {!r} anymore but received data".format(self.ip,
self.client_id))
self.close()
return
if self.client.closing.is_set():
return # Not accepting new messages if server is being shut down
self.client.last_rx_time = time.time()
self.client.log.debug("Got data: {!s}, len {!s}".format(data, len(data)))
if message != b"":
self.client.lines_received.append(message)
if len(tmp) > 0:
self.client.lines_received += tmp
while len(self.client.lines_received) > self.client.len_rx_buffer:
self.client.lines_received.pop(0)
self.client.new_message_rx.set()
def __del__(self):
log.debug("Removing transport object to client {!r}, ip {!s}".format(self.client_id, self.ip))
def close(self):
log.debug("Closing transport {!s} to client {!r}".format(self.ip, self.client_id))
try:
self.transport.close()
except Exception as e:
log.debug("Exception closing transport socket: {!s}".format(e))
| [
"[email protected]"
]
| |
0c8f151455b44f75723bef94d18ab3bf6b15805f | 97bf1824e9b299ae0c9b99dc1bcf83db321b20a5 | /secondProject/blog/models.py | 59f871477b1d2416ee8921cee812eabb7f5807ae | []
| no_license | shinhaeran/likelion_class | f2ed68f245e25a89313834876f001c4b35f5ffaa | 72c2d53cfedccde0062f46816449415131b2c332 | refs/heads/master | 2020-04-25T21:59:56.891042 | 2019-05-26T08:56:48 | 2019-05-26T08:56:48 | 173,097,515 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 348 | py | from django.db import models
# Create your models here.
class Blog(models.Model):
title = models.CharField(max_length=200)
pub_date = models.DateTimeField('date published')
body = models.TextField()
def __str__(self):
return self.title
def summary(self):
return self.body[:100] #100글자 까지만 보여조 | [
"[email protected]"
]
| |
a61ae1c4f2160c0682d89a2535a5d98f3c78e607 | a07fd8aca2d69ade2e388054dd2c1c9991232185 | /tests/test_tutorial/test_extra_data_types/test_tutorial001_py310.py | 0b71d9177332979a661b3a344e8e023d92e4cb4e | [
"MIT"
]
| permissive | vitalik/fastapi | 76b71bbbade19f12484c73dcbdca426197cc2db6 | 0276f5fd3aafb38dcbb430177a4685aeb58e5c69 | refs/heads/master | 2023-08-01T06:56:06.053824 | 2023-07-25T20:46:02 | 2023-07-25T20:46:02 | 315,668,229 | 1 | 0 | MIT | 2020-11-24T15:07:16 | 2020-11-24T15:07:15 | null | UTF-8 | Python | false | false | 8,219 | py | import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient
from ...utils import needs_py310
@pytest.fixture(name="client")
def get_client():
from docs_src.extra_data_types.tutorial001_py310 import app
client = TestClient(app)
return client
@needs_py310
def test_extra_types(client: TestClient):
item_id = "ff97dd87-a4a5-4a12-b412-cde99f33e00e"
data = {
"start_datetime": "2018-12-22T14:00:00+00:00",
"end_datetime": "2018-12-24T15:00:00+00:00",
"repeat_at": "15:30:00",
"process_after": 300,
}
expected_response = data.copy()
expected_response.update(
{
"start_process": "2018-12-22T14:05:00+00:00",
"duration": 176_100,
"item_id": item_id,
}
)
response = client.put(f"/items/{item_id}", json=data)
assert response.status_code == 200, response.text
assert response.json() == expected_response
@needs_py310
def test_openapi_schema(client: TestClient):
response = client.get("/openapi.json")
assert response.status_code == 200, response.text
assert response.json() == {
"openapi": "3.1.0",
"info": {"title": "FastAPI", "version": "0.1.0"},
"paths": {
"/items/{item_id}": {
"put": {
"responses": {
"200": {
"description": "Successful Response",
"content": {"application/json": {"schema": {}}},
},
"422": {
"description": "Validation Error",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/HTTPValidationError"
}
}
},
},
},
"summary": "Read Items",
"operationId": "read_items_items__item_id__put",
"parameters": [
{
"required": True,
"schema": {
"title": "Item Id",
"type": "string",
"format": "uuid",
},
"name": "item_id",
"in": "path",
}
],
"requestBody": {
"content": {
"application/json": {
"schema": IsDict(
{
"allOf": [
{
"$ref": "#/components/schemas/Body_read_items_items__item_id__put"
}
],
"title": "Body",
}
)
| IsDict(
# TODO: remove when deprecating Pydantic v1
{
"$ref": "#/components/schemas/Body_read_items_items__item_id__put"
}
)
}
}
},
}
}
},
"components": {
"schemas": {
"Body_read_items_items__item_id__put": {
"title": "Body_read_items_items__item_id__put",
"type": "object",
"properties": {
"start_datetime": IsDict(
{
"title": "Start Datetime",
"anyOf": [
{"type": "string", "format": "date-time"},
{"type": "null"},
],
}
)
| IsDict(
# TODO: remove when deprecating Pydantic v1
{
"title": "Start Datetime",
"type": "string",
"format": "date-time",
}
),
"end_datetime": IsDict(
{
"title": "End Datetime",
"anyOf": [
{"type": "string", "format": "date-time"},
{"type": "null"},
],
}
)
| IsDict(
# TODO: remove when deprecating Pydantic v1
{
"title": "End Datetime",
"type": "string",
"format": "date-time",
}
),
"repeat_at": IsDict(
{
"title": "Repeat At",
"anyOf": [
{"type": "string", "format": "time"},
{"type": "null"},
],
}
)
| IsDict(
# TODO: remove when deprecating Pydantic v1
{
"title": "Repeat At",
"type": "string",
"format": "time",
}
),
"process_after": IsDict(
{
"title": "Process After",
"anyOf": [
{"type": "string", "format": "duration"},
{"type": "null"},
],
}
)
| IsDict(
# TODO: remove when deprecating Pydantic v1
{
"title": "Process After",
"type": "number",
"format": "time-delta",
}
),
},
},
"ValidationError": {
"title": "ValidationError",
"required": ["loc", "msg", "type"],
"type": "object",
"properties": {
"loc": {
"title": "Location",
"type": "array",
"items": {
"anyOf": [{"type": "string"}, {"type": "integer"}]
},
},
"msg": {"title": "Message", "type": "string"},
"type": {"title": "Error Type", "type": "string"},
},
},
"HTTPValidationError": {
"title": "HTTPValidationError",
"type": "object",
"properties": {
"detail": {
"title": "Detail",
"type": "array",
"items": {"$ref": "#/components/schemas/ValidationError"},
}
},
},
}
},
}
| [
"[email protected]"
]
| |
f52b1ef6a6b56db0523211d934578ec0ef2a07e4 | 0377a4135f9e8940809a62186b229295bed9e9bc | /剑指offer/new2/判断一棵树是否为另一棵树的子结构.py | c14465c97106d88afb892a94b084094c2611b4b3 | []
| no_license | neko-niko/leetcode | 80f54a8ffa799cb026a7f60296de26d59a0826b0 | 311f19641d890772cc78d5aad9d4162dedfc20a0 | refs/heads/master | 2021-07-10T10:24:57.284226 | 2020-09-13T11:28:45 | 2020-09-13T11:28:45 | 198,792,951 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 954 | py | class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def HasSubtree(self, pRoot1, pRoot2):
# write code here
if pRoot1 == None or pRoot2 == None:
return False
else:
return self.panduan(pRoot1, pRoot2) or self.HasSubtree(pRoot1.left, pRoot2) or self.HasSubtree(pRoot1.right,
pRoot2)
def panduan(self, p1, p2):
if not p2:
return True
else:
if not p1 or p1.val != p2.val:
return False
return self.panduan(p1.right, p2.right) and self.panduan(p1.left, p2.left)
if __name__ == '__main__':
node1 = TreeNode(1)
node2 = TreeNode(2)
node1.left = node2
test1 = node1
test2 = node2
test1 = test1.left
print(test1 == test2)
| [
"[email protected]"
]
| |
321182ac1c784cfc94356d065684340d14c0b1a1 | 3073677476a918720fb24a13961d6e9f5143627b | /console.py | dcd93ec5ed4ac74a56b3bf6c3dc042853d32cbe2 | []
| no_license | nemec/audibleMPD | 960fe2c358ac875936ceb23c1c7b19d74940012a | d214ac44e2411583db3f6cab835138747b6df6b1 | refs/heads/master | 2021-01-01T05:40:25.894785 | 2011-01-24T23:48:52 | 2011-01-24T23:48:52 | 983,120 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,193 | py | from os import getuid
import gobject
from input_header import input_header
if getuid() != 0:
raise ImportError, "Must be root to get keypresses."
from struct import unpack
# Code 0 = Mouse x axis, val = mouse accel (with +/-)
# Code 1 = Mouse y axis, val = mouse accel (with +/-)
KEY_PRESS = 1
KEY_RELEASE = 2
class keyevent(object):
def __init__(self, t_sec, t_usec, typ, code, val):
self.ih = input_header()
self.seconds = t_sec
self.microseconds = t_usec
self.type = typ
self.code = code
self.value = val
def __str__(self):
return "[%s.%s] type %s, code %s, value %s" % (self.seconds,
self.microseconds, self.type, self.code, self.value)
class reader(gobject.GObject):
DEFAULT_EVENT_PATH = "/dev/input/event4"
__gsignals__ = {
"mouse_abs": (gobject.SIGNAL_RUN_FIRST, gobject.TYPE_NONE,
(gobject.TYPE_PYOBJECT, )),
"mouse_rel": (gobject.SIGNAL_RUN_FIRST, gobject.TYPE_NONE,
(gobject.TYPE_PYOBJECT, )),
"key_down": (gobject.SIGNAL_RUN_FIRST, gobject.TYPE_NONE,
(gobject.TYPE_PYOBJECT, )),
"key_up": (gobject.SIGNAL_RUN_FIRST, gobject.TYPE_NONE,
(gobject.TYPE_PYOBJECT, )),
"all": (gobject.SIGNAL_RUN_FIRST, gobject.TYPE_NONE,
(gobject.TYPE_PYOBJECT, )),
}
def __init__(self, eventpath = DEFAULT_EVENT_PATH, keywatches = None):
gobject.GObject.__init__(self)
self.eventpath = eventpath
self.trap_repeats = False
self.port = open(self.eventpath,"rb")
def trap_repeat(self, on):
self.trap_repeats = on
def emit_events(self, ev):
if ev.type == ih.EV_ABS:
if ev.code == ih.ABS_X:
self.emit("mouse_abs", (ev.value, 0))
elif ev.code == ih.ABS_Y:
self.emit("mouse_abs", (0, ev.value))
elif ev.type == ih.EV_REL:
if ev.code == ih.REL_X:
self.emit("mouse_rel", (ev.value, 0))
elif ev.code == ih.REL_Y:
self.emit("mouse_rel", (0, ev.value))
elif ev.type == ih.EV_KEY:
if ev.value == 0:
self.emit("key_up", ev)
elif ev.value == 1:
self.emit("key_down", ev)
elif ev.value == 2 and self.trap_repeats:
self.emit("key_down", ev)
self.emit("all", ev)
def readkeyevent(self, emit = False):
ev = keyevent(*unpack("2I2Hi",self.port.read(16)))
if emit:
self.emit_events(ev)
return ev
def readkey(self):
while True:
code, val = self.readkeyevent()
if val > 0:
#lockcode = code
#while True:
# code, val = self.readkeyevent()
# if code == lockcode and val != 1: # returns on key-release or key-hold
# return code
# 0 is generated by a repeat keypress
if code == 0:
return self.last_key
else:
self.last_key = code
return code
def run(self):
while 1:
self.readkeyevent(True)
if __name__ == "__main__":
gobject.threads_init()
from input_header import input_header
ih = input_header()
r = reader("/dev/input/event9")
def print_event(obj, ev):
print ev
r.connect("key_down", print_event)
r.trap_repeat(True)
r.run()
| [
"[email protected]"
]
| |
e700a24a2a79345362880d9c61b0b979299289a8 | 086ff58e13978ef5fa771ffc44c3b002cfcf18cb | /froide/publicbody/widgets.py | 0e89162ad29d102f0a13c6e53207916a598ada39 | [
"MIT"
]
| permissive | jdieg0/froide | 70b0de85eff09886919a838fe46b776467824dfb | 44a5d7e65b1678e0031e2cf01687c8834b2517e2 | refs/heads/master | 2020-04-27T22:51:45.343233 | 2019-03-09T16:46:34 | 2019-03-09T16:46:34 | 174,752,276 | 0 | 0 | null | 2019-03-09T22:19:20 | 2019-03-09T22:19:20 | null | UTF-8 | Python | false | false | 2,348 | py | import json
from django import forms
from django.urls import reverse
from django.utils.translation import ugettext as _
from django.templatetags.static import static
from froide.helper.content_urls import get_content_url
from .models import PublicBody
def get_widget_context():
return {
'url': {
'searchPublicBody': reverse('api:publicbody-search'),
'listLaws': reverse('api:law-list'),
'getPublicBody': reverse('api:publicbody-detail', kwargs={'pk': '0'}),
'helpAbout': get_content_url('about')
},
'i18n': {
'missingPublicBody': _('Are we missing a public body?'),
'publicBodySearchPlaceholder': _('Ministry of...'),
'search': _('Search'),
'examples': _('Examples:'),
'environment': _('Environment'),
'ministryOfLabour': _('Ministry of Labour'),
'or': _('or'),
'noPublicBodiesFound': _('No Public Bodies found for this query.'),
'letUsKnow': _('Please let us know!'),
},
'resources': {
'spinner': static('img/spinner.gif')
}
}
class PublicBodySelect(forms.Widget):
input_type = "text"
template_name = 'publicbody/_chooser.html'
initial_search = None
class Media:
extend = False
js = ('js/publicbody.js',)
def set_initial_search(self, search):
self.initial_search = search
def get_context(self, name, value=None, attrs=None):
pb, pb_desc = None, None
if value is not None:
try:
pb = PublicBody.objects.get(pk=int(value))
pb_desc = pb.get_label()
except (ValueError, PublicBody.DoesNotExist):
pass
context = super(PublicBodySelect, self).get_context(name, value, attrs)
context['widget'].update({
'value_label': pb_desc,
'search': self.initial_search,
'publicbody': pb,
'json': json.dumps({
'fields': {
name: {
'value': value,
'objects': [pb.as_data()] if pb is not None else None
}
}
})
})
context['config'] = json.dumps(get_widget_context())
return context
| [
"[email protected]"
]
| |
fdb1438c63169f6ae42534f8c356819b6ced8614 | 3358f6fbfa39d4429f2a9fa3ba5416285fab5793 | /第3章 Django/第3章 Django/3、Django中的视图/kaige/project/myApp/models.py | 7128dbac959f269df33a0dddfa674349e6780c90 | []
| no_license | kmxz2016/PycharmProjects | 8ab79cd5ef87bba2a1af0fe9f035f87a18621407 | 631a792eb9b5f4121dc08849dded10c290ac2401 | refs/heads/master | 2020-03-13T18:21:11.364219 | 2018-05-16T10:06:42 | 2018-05-16T10:06:42 | 131,234,054 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 735 | py | from django.db import models
# Create your models here.
class Grades(models.Model):
gname = models.CharField(max_length=20)
gdate = models.DateTimeField()
ggirlnum = models.IntegerField()
gboynum = models.IntegerField()
isDelete = models.BooleanField(default=False)
def __str__(self):
return self.gname
class Students(models.Model):
sname = models.CharField(max_length=20)
sgender = models.BooleanField(default=True)
sage = models.IntegerField()
scontend = models.CharField(max_length=20)
isDelete = models.BooleanField(default=False)
# 关联外键
sgrade = models.ForeignKey("Grades")
def __str__(self):
return self.sname
| [
"[email protected]"
]
|
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