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7489a928ee4ce01e0514e27f5339bfa54dd0436b | 745 | py | Python | idManager/view/group_view.py | lgarciasbr/idm-api | 3517d29d55eb2a06fb5b4b21359b6cf6d11529a0 | [
"Apache-2.0"
] | 2 | 2018-01-14T22:43:43.000Z | 2018-01-14T22:43:48.000Z | idManager/view/group_view.py | lgarciasbr/idm-api | 3517d29d55eb2a06fb5b4b21359b6cf6d11529a0 | [
"Apache-2.0"
] | null | null | null | idManager/view/group_view.py | lgarciasbr/idm-api | 3517d29d55eb2a06fb5b4b21359b6cf6d11529a0 | [
"Apache-2.0"
] | null | null | null | from flask import jsonify
| 24.833333 | 94 | 0.660403 | from flask import jsonify
def register_group(http_status_code, group, message):
view = jsonify({'status_code': http_status_code, 'message': message, 'group': group.data})
return view
def get_groups(groups, total, pages, http_status_code):
view = jsonify({'groups': groups.data,
'total': total,
'pages': pages,
'status_code': http_status_code})
return view
def get_group_by_id(http_status_code, group):
view = jsonify({'status_code': http_status_code, 'group': group.data})
return view
def delete_group(http_status_code, group, message):
view = jsonify({'status_code': http_status_code, 'message': message, 'group': group.data})
return view
| 623 | 0 | 92 |
1f752fceba17b43584813efd37c1d1e243571d98 | 69,340 | py | Python | Lib/subprocess.py | sireliah/polish-python | 605df4944c2d3bc25f8bf6964b274c0a0d297cc3 | [
"PSF-2.0"
] | 1 | 2018-06-21T18:21:24.000Z | 2018-06-21T18:21:24.000Z | Lib/subprocess.py | sireliah/polish-python | 605df4944c2d3bc25f8bf6964b274c0a0d297cc3 | [
"PSF-2.0"
] | null | null | null | Lib/subprocess.py | sireliah/polish-python | 605df4944c2d3bc25f8bf6964b274c0a0d297cc3 | [
"PSF-2.0"
] | null | null | null | # subprocess - Subprocesses przy accessible I/O streams
#
# For more information about this module, see PEP 324.
#
# Copyright (c) 2003-2005 by Peter Astrand <astrand@lysator.liu.se>
#
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license dla licensing details.
r"""subprocess - Subprocesses przy accessible I/O streams
This module allows you to spawn processes, connect to their
input/output/error pipes, oraz obtain their zwróć codes. This module
intends to replace several older modules oraz functions:
os.system
os.spawn*
Information about how the subprocess module can be used to replace these
modules oraz functions can be found below.
Using the subprocess module
===========================
This module defines one klasa called Popen:
klasa Popen(args, bufsize=-1, executable=Nic,
stdin=Nic, stdout=Nic, stderr=Nic,
preexec_fn=Nic, close_fds=Prawda, shell=Nieprawda,
cwd=Nic, env=Nic, universal_newlines=Nieprawda,
startupinfo=Nic, creationflags=0,
restore_signals=Prawda, start_new_session=Nieprawda, dalej_fds=()):
Arguments are:
args should be a string, albo a sequence of program arguments. The
program to execute jest normally the first item w the args sequence albo
string, but can be explicitly set by using the executable argument.
On POSIX, przy shell=Nieprawda (default): In this case, the Popen class
uses os.execvp() to execute the child program. args should normally
be a sequence. A string will be treated jako a sequence przy the string
as the only item (the program to execute).
On POSIX, przy shell=Prawda: If args jest a string, it specifies the
command string to execute through the shell. If args jest a sequence,
the first item specifies the command string, oraz any additional items
will be treated jako additional shell arguments.
On Windows: the Popen klasa uses CreateProcess() to execute the child
program, which operates on strings. If args jest a sequence, it will be
converted to a string using the list2cmdline method. Please note that
not all MS Windows applications interpret the command line the same
way: The list2cmdline jest designed dla applications using the same
rules jako the MS C runtime.
bufsize will be supplied jako the corresponding argument to the io.open()
function when creating the stdin/stdout/stderr pipe file objects:
0 means unbuffered (read & write are one system call oraz can zwróć short),
1 means line buffered, any other positive value means use a buffer of
approximately that size. A negative bufsize, the default, means the system
default of io.DEFAULT_BUFFER_SIZE will be used.
stdin, stdout oraz stderr specify the executed programs' standard
input, standard output oraz standard error file handles, respectively.
Valid values are PIPE, an existing file descriptor (a positive
integer), an existing file object, oraz Nic. PIPE indicates that a
new pipe to the child should be created. With Nic, no redirection
will occur; the child's file handles will be inherited z the
parent. Additionally, stderr can be STDOUT, which indicates that the
stderr data z the applications should be captured into the same
file handle jako dla stdout.
On POSIX, jeżeli preexec_fn jest set to a callable object, this object will be
called w the child process just before the child jest executed. The use
of preexec_fn jest nie thread safe, using it w the presence of threads
could lead to a deadlock w the child process before the new executable
is executed.
If close_fds jest true, all file descriptors wyjąwszy 0, 1 oraz 2 will be
closed before the child process jest executed. The default dla close_fds
varies by platform: Always true on POSIX. Prawda when stdin/stdout/stderr
are Nic on Windows, false otherwise.
pass_fds jest an optional sequence of file descriptors to keep open between the
parent oraz child. Providing any dalej_fds implicitly sets close_fds to true.
jeżeli shell jest true, the specified command will be executed through the
shell.
If cwd jest nie Nic, the current directory will be changed to cwd
before the child jest executed.
On POSIX, jeżeli restore_signals jest Prawda all signals that Python sets to
SIG_IGN are restored to SIG_DFL w the child process before the exec.
Currently this includes the SIGPIPE, SIGXFZ oraz SIGXFSZ signals. This
parameter does nothing on Windows.
On POSIX, jeżeli start_new_session jest Prawda, the setsid() system call will be made
in the child process prior to executing the command.
If env jest nie Nic, it defines the environment variables dla the new
process.
If universal_newlines jest Nieprawda, the file objects stdin, stdout oraz stderr
are opened jako binary files, oraz no line ending conversion jest done.
If universal_newlines jest Prawda, the file objects stdout oraz stderr are
opened jako a text file, but lines may be terminated by any of '\n',
the Unix end-of-line convention, '\r', the old Macintosh convention albo
'\r\n', the Windows convention. All of these external representations
are seen jako '\n' by the Python program. Also, the newlines attribute
of the file objects stdout, stdin oraz stderr are nie updated by the
communicate() method.
In either case, the process being communicated przy should start up
expecting to receive bytes on its standard input oraz decode them with
the same encoding they are sent in.
The startupinfo oraz creationflags, jeżeli given, will be dalejed to the
underlying CreateProcess() function. They can specify things such as
appearance of the main window oraz priority dla the new process.
(Windows only)
This module also defines some shortcut functions:
call(*popenargs, **kwargs):
Run command przy arguments. Wait dla command to complete, then
zwróć the returncode attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> retcode = subprocess.call(["ls", "-l"])
check_call(*popenargs, **kwargs):
Run command przy arguments. Wait dla command to complete. If the
exit code was zero then return, otherwise podnieś
CalledProcessError. The CalledProcessError object will have the
zwróć code w the returncode attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> subprocess.check_call(["ls", "-l"])
0
getstatusoutput(cmd):
Return (status, output) of executing cmd w a shell.
Execute the string 'cmd' w a shell przy 'check_output' oraz
zwróć a 2-tuple (status, output). Universal newlines mode jest used,
meaning that the result przy be decoded to a string.
A trailing newline jest stripped z the output.
The exit status dla the command can be interpreted
according to the rules dla the function 'wait'. Example:
>>> subprocess.getstatusoutput('ls /bin/ls')
(0, '/bin/ls')
>>> subprocess.getstatusoutput('cat /bin/junk')
(256, 'cat: /bin/junk: No such file albo directory')
>>> subprocess.getstatusoutput('/bin/junk')
(256, 'sh: /bin/junk: nie found')
getoutput(cmd):
Return output (stdout albo stderr) of executing cmd w a shell.
Like getstatusoutput(), wyjąwszy the exit status jest ignored oraz the zwróć
value jest a string containing the command's output. Example:
>>> subprocess.getoutput('ls /bin/ls')
'/bin/ls'
check_output(*popenargs, **kwargs):
Run command przy arguments oraz zwróć its output.
If the exit code was non-zero it podnieśs a CalledProcessError. The
CalledProcessError object will have the zwróć code w the returncode
attribute oraz output w the output attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> output = subprocess.check_output(["ls", "-l", "/dev/null"])
There jest an additional optional argument, "input", allowing you to
dalej a string to the subprocess's stdin. If you use this argument
you may nie also use the Popen constructor's "stdin" argument.
If universal_newlines jest set to Prawda, the "input" argument must
be a string rather than bytes, oraz the zwróć value will be a string.
Exceptions
----------
Exceptions podnieśd w the child process, before the new program has
started to execute, will be re-raised w the parent. Additionally,
the exception object will have one extra attribute called
'child_traceback', which jest a string containing traceback information
z the child's point of view.
The most common exception podnieśd jest OSError. This occurs, for
example, when trying to execute a non-existent file. Applications
should prepare dla OSErrors.
A ValueError will be podnieśd jeżeli Popen jest called przy invalid arguments.
Exceptions defined within this module inherit z SubprocessError.
check_call() oraz check_output() will podnieś CalledProcessError jeżeli the
called process returns a non-zero zwróć code. TimeoutExpired
be podnieśd jeżeli a timeout was specified oraz expired.
Security
--------
Unlike some other popen functions, this implementation will never call
/bin/sh implicitly. This means that all characters, including shell
metacharacters, can safely be dalejed to child processes.
Popen objects
=============
Instances of the Popen klasa have the following methods:
poll()
Check jeżeli child process has terminated. Returns returncode
attribute.
wait()
Wait dla child process to terminate. Returns returncode attribute.
communicate(input=Nic)
Interact przy process: Send data to stdin. Read data z stdout
oraz stderr, until end-of-file jest reached. Wait dla process to
terminate. The optional input argument should be data to be
sent to the child process, albo Nic, jeżeli no data should be sent to
the child. If the Popen instance was constructed przy universal_newlines
set to Prawda, the input argument should be a string oraz will be encoded
using the preferred system encoding (see locale.getpreferredencoding);
jeżeli universal_newlines jest Nieprawda, the input argument should be a
byte string.
communicate() returns a tuple (stdout, stderr).
Note: The data read jest buffered w memory, so do nie use this
method jeżeli the data size jest large albo unlimited.
The following attributes are also available:
stdin
If the stdin argument jest PIPE, this attribute jest a file object
that provides input to the child process. Otherwise, it jest Nic.
stdout
If the stdout argument jest PIPE, this attribute jest a file object
that provides output z the child process. Otherwise, it jest
Nic.
stderr
If the stderr argument jest PIPE, this attribute jest file object that
provides error output z the child process. Otherwise, it jest
Nic.
pid
The process ID of the child process.
returncode
The child zwróć code. A Nic value indicates that the process
hasn't terminated yet. A negative value -N indicates that the
child was terminated by signal N (POSIX only).
Replacing older functions przy the subprocess module
====================================================
In this section, "a ==> b" means that b can be used jako a replacement
dla a.
Note: All functions w this section fail (more albo less) silently if
the executed program cannot be found; this module podnieśs an OSError
exception.
In the following examples, we assume that the subprocess module jest
imported przy "z subprocess zaimportuj *".
Replacing /bin/sh shell backquote
---------------------------------
output=`mycmd myarg`
==>
output = Popen(["mycmd", "myarg"], stdout=PIPE).communicate()[0]
Replacing shell pipe line
-------------------------
output=`dmesg | grep hda`
==>
p1 = Popen(["dmesg"], stdout=PIPE)
p2 = Popen(["grep", "hda"], stdin=p1.stdout, stdout=PIPE)
output = p2.communicate()[0]
Replacing os.system()
---------------------
sts = os.system("mycmd" + " myarg")
==>
p = Popen("mycmd" + " myarg", shell=Prawda)
pid, sts = os.waitpid(p.pid, 0)
Note:
* Calling the program through the shell jest usually nie required.
* It's easier to look at the returncode attribute than the
exitstatus.
A more real-world example would look like this:
spróbuj:
retcode = call("mycmd" + " myarg", shell=Prawda)
jeżeli retcode < 0:
print("Child was terminated by signal", -retcode, file=sys.stderr)
inaczej:
print("Child returned", retcode, file=sys.stderr)
wyjąwszy OSError jako e:
print("Execution failed:", e, file=sys.stderr)
Replacing os.spawn*
-------------------
P_NOWAIT example:
pid = os.spawnlp(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg")
==>
pid = Popen(["/bin/mycmd", "myarg"]).pid
P_WAIT example:
retcode = os.spawnlp(os.P_WAIT, "/bin/mycmd", "mycmd", "myarg")
==>
retcode = call(["/bin/mycmd", "myarg"])
Vector example:
os.spawnvp(os.P_NOWAIT, path, args)
==>
Popen([path] + args[1:])
Environment example:
os.spawnlpe(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg", env)
==>
Popen(["/bin/mycmd", "myarg"], env={"PATH": "/usr/bin"})
"""
zaimportuj sys
_mswindows = (sys.platform == "win32")
zaimportuj io
zaimportuj os
zaimportuj time
zaimportuj signal
zaimportuj builtins
zaimportuj warnings
zaimportuj errno
z time zaimportuj monotonic jako _time
# Exception classes used by this module.
klasa SubprocessError(Exception): dalej
klasa CalledProcessError(SubprocessError):
"""This exception jest podnieśd when a process run by check_call() albo
check_output() returns a non-zero exit status.
The exit status will be stored w the returncode attribute;
check_output() will also store the output w the output attribute.
"""
@property
def stdout(self):
"""Alias dla output attribute, to match stderr"""
zwróć self.output
@stdout.setter
klasa TimeoutExpired(SubprocessError):
"""This exception jest podnieśd when the timeout expires dopóki waiting dla a
child process.
"""
@property
@stdout.setter
jeżeli _mswindows:
zaimportuj threading
zaimportuj msvcrt
zaimportuj _winapi
klasa STARTUPINFO:
dwFlags = 0
hStdInput = Nic
hStdOutput = Nic
hStdError = Nic
wShowWindow = 0
inaczej:
zaimportuj _posixsubprocess
zaimportuj select
zaimportuj selectors
spróbuj:
zaimportuj threading
wyjąwszy ImportError:
zaimportuj dummy_threading jako threading
# When select albo poll has indicated that the file jest writable,
# we can write up to _PIPE_BUF bytes without risk of blocking.
# POSIX defines PIPE_BUF jako >= 512.
_PIPE_BUF = getattr(select, 'PIPE_BUF', 512)
# poll/select have the advantage of nie requiring any extra file
# descriptor, contrarily to epoll/kqueue (also, they require a single
# syscall).
jeżeli hasattr(selectors, 'PollSelector'):
_PopenSelector = selectors.PollSelector
inaczej:
_PopenSelector = selectors.SelectSelector
__all__ = ["Popen", "PIPE", "STDOUT", "call", "check_call", "getstatusoutput",
"getoutput", "check_output", "run", "CalledProcessError", "DEVNULL",
"SubprocessError", "TimeoutExpired", "CompletedProcess"]
# NOTE: We intentionally exclude list2cmdline jako it jest
# considered an internal implementation detail. issue10838.
jeżeli _mswindows:
z _winapi zaimportuj (CREATE_NEW_CONSOLE, CREATE_NEW_PROCESS_GROUP,
STD_INPUT_HANDLE, STD_OUTPUT_HANDLE,
STD_ERROR_HANDLE, SW_HIDE,
STARTF_USESTDHANDLES, STARTF_USESHOWWINDOW)
__all__.extend(["CREATE_NEW_CONSOLE", "CREATE_NEW_PROCESS_GROUP",
"STD_INPUT_HANDLE", "STD_OUTPUT_HANDLE",
"STD_ERROR_HANDLE", "SW_HIDE",
"STARTF_USESTDHANDLES", "STARTF_USESHOWWINDOW"])
klasa Handle(int):
closed = Nieprawda
__del__ = Close
__str__ = __repr__
# This lists holds Popen instances dla which the underlying process had nie
# exited at the time its __del__ method got called: those processes are wait()ed
# dla synchronously z _cleanup() when a new Popen object jest created, to avoid
# zombie processes.
_active = []
PIPE = -1
STDOUT = -2
DEVNULL = -3
# XXX This function jest only used by multiprocessing oraz the test suite,
# but it's here so that it can be imported when Python jest compiled without
# threads.
args.append('-' + opt * v)
dla opt w sys.warnoptions:
args.append('-W' + opt)
zwróć args
def call(*popenargs, timeout=Nic, **kwargs):
"""Run command przy arguments. Wait dla command to complete albo
timeout, then zwróć the returncode attribute.
The arguments are the same jako dla the Popen constructor. Example:
retcode = call(["ls", "-l"])
"""
przy Popen(*popenargs, **kwargs) jako p:
spróbuj:
zwróć p.wait(timeout=timeout)
wyjąwszy:
p.kill()
p.wait()
podnieś
def check_call(*popenargs, **kwargs):
"""Run command przy arguments. Wait dla command to complete. If
the exit code was zero then return, otherwise podnieś
CalledProcessError. The CalledProcessError object will have the
zwróć code w the returncode attribute.
The arguments are the same jako dla the call function. Example:
check_call(["ls", "-l"])
"""
retcode = call(*popenargs, **kwargs)
jeżeli retcode:
cmd = kwargs.get("args")
jeżeli cmd jest Nic:
cmd = popenargs[0]
podnieś CalledProcessError(retcode, cmd)
zwróć 0
def check_output(*popenargs, timeout=Nic, **kwargs):
r"""Run command przy arguments oraz zwróć its output.
If the exit code was non-zero it podnieśs a CalledProcessError. The
CalledProcessError object will have the zwróć code w the returncode
attribute oraz output w the output attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> check_output(["ls", "-l", "/dev/null"])
b'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n'
The stdout argument jest nie allowed jako it jest used internally.
To capture standard error w the result, use stderr=STDOUT.
>>> check_output(["/bin/sh", "-c",
... "ls -l non_existent_file ; exit 0"],
... stderr=STDOUT)
b'ls: non_existent_file: No such file albo directory\n'
There jest an additional optional argument, "input", allowing you to
dalej a string to the subprocess's stdin. If you use this argument
you may nie also use the Popen constructor's "stdin" argument, as
it too will be used internally. Example:
>>> check_output(["sed", "-e", "s/foo/bar/"],
... input=b"when w the course of fooman events\n")
b'when w the course of barman events\n'
If universal_newlines=Prawda jest dalejed, the "input" argument must be a
string oraz the zwróć value will be a string rather than bytes.
"""
jeżeli 'stdout' w kwargs:
podnieś ValueError('stdout argument nie allowed, it will be overridden.')
jeżeli 'input' w kwargs oraz kwargs['input'] jest Nic:
# Explicitly dalejing input=Nic was previously equivalent to dalejing an
# empty string. That jest maintained here dla backwards compatibility.
kwargs['input'] = '' jeżeli kwargs.get('universal_newlines', Nieprawda) inaczej b''
zwróć run(*popenargs, stdout=PIPE, timeout=timeout, check=Prawda,
**kwargs).stdout
klasa CompletedProcess(object):
"""A process that has finished running.
This jest returned by run().
Attributes:
args: The list albo str args dalejed to run().
returncode: The exit code of the process, negative dla signals.
stdout: The standard output (Nic jeżeli nie captured).
stderr: The standard error (Nic jeżeli nie captured).
"""
def check_returncode(self):
"""Raise CalledProcessError jeżeli the exit code jest non-zero."""
jeżeli self.returncode:
podnieś CalledProcessError(self.returncode, self.args, self.stdout,
self.stderr)
def run(*popenargs, input=Nic, timeout=Nic, check=Nieprawda, **kwargs):
"""Run command przy arguments oraz zwróć a CompletedProcess instance.
The returned instance will have attributes args, returncode, stdout oraz
stderr. By default, stdout oraz stderr are nie captured, oraz those attributes
will be Nic. Pass stdout=PIPE and/or stderr=PIPE w order to capture them.
If check jest Prawda oraz the exit code was non-zero, it podnieśs a
CalledProcessError. The CalledProcessError object will have the zwróć code
w the returncode attribute, oraz output & stderr attributes jeżeli those streams
were captured.
If timeout jest given, oraz the process takes too long, a TimeoutExpired
exception will be podnieśd.
There jest an optional argument "input", allowing you to
dalej a string to the subprocess's stdin. If you use this argument
you may nie also use the Popen constructor's "stdin" argument, as
it will be used internally.
The other arguments are the same jako dla the Popen constructor.
If universal_newlines=Prawda jest dalejed, the "input" argument must be a
string oraz stdout/stderr w the returned object will be strings rather than
bytes.
"""
jeżeli input jest nie Nic:
jeżeli 'stdin' w kwargs:
podnieś ValueError('stdin oraz input arguments may nie both be used.')
kwargs['stdin'] = PIPE
przy Popen(*popenargs, **kwargs) jako process:
spróbuj:
stdout, stderr = process.communicate(input, timeout=timeout)
wyjąwszy TimeoutExpired:
process.kill()
stdout, stderr = process.communicate()
podnieś TimeoutExpired(process.args, timeout, output=stdout,
stderr=stderr)
wyjąwszy:
process.kill()
process.wait()
podnieś
retcode = process.poll()
jeżeli check oraz retcode:
podnieś CalledProcessError(retcode, process.args,
output=stdout, stderr=stderr)
zwróć CompletedProcess(process.args, retcode, stdout, stderr)
def list2cmdline(seq):
"""
Translate a sequence of arguments into a command line
string, using the same rules jako the MS C runtime:
1) Arguments are delimited by white space, which jest either a
space albo a tab.
2) A string surrounded by double quotation marks jest
interpreted jako a single argument, regardless of white space
contained within. A quoted string can be embedded w an
argument.
3) A double quotation mark preceded by a backslash jest
interpreted jako a literal double quotation mark.
4) Backslashes are interpreted literally, unless they
immediately precede a double quotation mark.
5) If backslashes immediately precede a double quotation mark,
every pair of backslashes jest interpreted jako a literal
backslash. If the number of backslashes jest odd, the last
backslash escapes the next double quotation mark as
described w rule 3.
"""
# See
# http://msdn.microsoft.com/en-us/library/17w5ykft.aspx
# albo search http://msdn.microsoft.com for
# "Parsing C++ Command-Line Arguments"
result = []
needquote = Nieprawda
dla arg w seq:
bs_buf = []
# Add a space to separate this argument z the others
jeżeli result:
result.append(' ')
needquote = (" " w arg) albo ("\t" w arg) albo nie arg
jeżeli needquote:
result.append('"')
dla c w arg:
jeżeli c == '\\':
# Don't know jeżeli we need to double yet.
bs_buf.append(c)
albo_inaczej c == '"':
# Double backslashes.
result.append('\\' * len(bs_buf)*2)
bs_buf = []
result.append('\\"')
inaczej:
# Normal char
jeżeli bs_buf:
result.extend(bs_buf)
bs_buf = []
result.append(c)
# Add remaining backslashes, jeżeli any.
jeżeli bs_buf:
result.extend(bs_buf)
jeżeli needquote:
result.extend(bs_buf)
result.append('"')
zwróć ''.join(result)
# Various tools dla executing commands oraz looking at their output oraz status.
#
zwróć status, data
def getoutput(cmd):
"""Return output (stdout albo stderr) of executing cmd w a shell.
Like getstatusoutput(), wyjąwszy the exit status jest ignored oraz the zwróć
value jest a string containing the command's output. Example:
>>> zaimportuj subprocess
>>> subprocess.getoutput('ls /bin/ls')
'/bin/ls'
"""
zwróć getstatusoutput(cmd)[1]
_PLATFORM_DEFAULT_CLOSE_FDS = object()
klasa Popen(object):
_child_created = Nieprawda # Set here since __del__ checks it
jeżeli _mswindows:
jeżeli preexec_fn jest nie Nic:
podnieś ValueError("preexec_fn jest nie supported on Windows "
"platforms")
any_stdio_set = (stdin jest nie Nic albo stdout jest nie Nic albo
stderr jest nie Nic)
jeżeli close_fds jest _PLATFORM_DEFAULT_CLOSE_FDS:
jeżeli any_stdio_set:
close_fds = Nieprawda
inaczej:
close_fds = Prawda
albo_inaczej close_fds oraz any_stdio_set:
podnieś ValueError(
"close_fds jest nie supported on Windows platforms"
" jeżeli you redirect stdin/stdout/stderr")
inaczej:
# POSIX
jeżeli close_fds jest _PLATFORM_DEFAULT_CLOSE_FDS:
close_fds = Prawda
jeżeli dalej_fds oraz nie close_fds:
warnings.warn("pass_fds overriding close_fds.", RuntimeWarning)
close_fds = Prawda
jeżeli startupinfo jest nie Nic:
podnieś ValueError("startupinfo jest only supported on Windows "
"platforms")
jeżeli creationflags != 0:
podnieś ValueError("creationflags jest only supported on Windows "
"platforms")
self.args = args
self.stdin = Nic
self.stdout = Nic
self.stderr = Nic
self.pid = Nic
self.returncode = Nic
self.universal_newlines = universal_newlines
# Input oraz output objects. The general principle jest like
# this:
#
# Parent Child
# ------ -----
# p2cwrite ---stdin---> p2cread
# c2pread <--stdout--- c2pwrite
# errread <--stderr--- errwrite
#
# On POSIX, the child objects are file descriptors. On
# Windows, these are Windows file handles. The parent objects
# are file descriptors on both platforms. The parent objects
# are -1 when nie using PIPEs. The child objects are -1
# when nie redirecting.
(p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite) = self._get_handles(stdin, stdout, stderr)
# We wrap OS handles *before* launching the child, otherwise a
# quickly terminating child could make our fds unwrappable
# (see #8458).
jeżeli _mswindows:
jeżeli p2cwrite != -1:
p2cwrite = msvcrt.open_osfhandle(p2cwrite.Detach(), 0)
jeżeli c2pread != -1:
c2pread = msvcrt.open_osfhandle(c2pread.Detach(), 0)
jeżeli errread != -1:
errread = msvcrt.open_osfhandle(errread.Detach(), 0)
jeżeli p2cwrite != -1:
self.stdin = io.open(p2cwrite, 'wb', bufsize)
jeżeli universal_newlines:
self.stdin = io.TextIOWrapper(self.stdin, write_through=Prawda,
line_buffering=(bufsize == 1))
jeżeli c2pread != -1:
self.stdout = io.open(c2pread, 'rb', bufsize)
jeżeli universal_newlines:
self.stdout = io.TextIOWrapper(self.stdout)
jeżeli errread != -1:
self.stderr = io.open(errread, 'rb', bufsize)
jeżeli universal_newlines:
self.stderr = io.TextIOWrapper(self.stderr)
self._closed_child_pipe_fds = Nieprawda
spróbuj:
self._execute_child(args, executable, preexec_fn, close_fds,
dalej_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
restore_signals, start_new_session)
wyjąwszy:
# Cleanup jeżeli the child failed starting.
dla f w filter(Nic, (self.stdin, self.stdout, self.stderr)):
spróbuj:
f.close()
wyjąwszy OSError:
dalej # Ignore EBADF albo other errors.
jeżeli nie self._closed_child_pipe_fds:
to_close = []
jeżeli stdin == PIPE:
to_close.append(p2cread)
jeżeli stdout == PIPE:
to_close.append(c2pwrite)
jeżeli stderr == PIPE:
to_close.append(errwrite)
jeżeli hasattr(self, '_devnull'):
to_close.append(self._devnull)
dla fd w to_close:
spróbuj:
os.close(fd)
wyjąwszy OSError:
dalej
podnieś
zwróć self._devnull
def _stdin_write(self, input):
jeżeli input:
spróbuj:
self.stdin.write(input)
wyjąwszy BrokenPipeError:
# communicate() must ignore broken pipe error
dalej
wyjąwszy OSError jako e:
jeżeli e.errno == errno.EINVAL oraz self.poll() jest nie Nic:
# Issue #19612: On Windows, stdin.write() fails przy EINVAL
# jeżeli the process already exited before the write
dalej
inaczej:
podnieś
self.stdin.close()
def communicate(self, input=Nic, timeout=Nic):
"""Interact przy process: Send data to stdin. Read data from
stdout oraz stderr, until end-of-file jest reached. Wait for
process to terminate.
The optional "input" argument should be data to be sent to the
child process (jeżeli self.universal_newlines jest Prawda, this should
be a string; jeżeli it jest Nieprawda, "input" should be bytes), albo
Nic, jeżeli no data should be sent to the child.
communicate() returns a tuple (stdout, stderr). These will be
bytes or, jeżeli self.universal_newlines was Prawda, a string.
"""
jeżeli self._communication_started oraz input:
podnieś ValueError("Cannot send input after starting communication")
# Optimization: If we are nie worried about timeouts, we haven't
# started communicating, oraz we have one albo zero pipes, using select()
# albo threads jest unnecessary.
jeżeli (timeout jest Nic oraz nie self._communication_started oraz
[self.stdin, self.stdout, self.stderr].count(Nic) >= 2):
stdout = Nic
stderr = Nic
jeżeli self.stdin:
self._stdin_write(input)
albo_inaczej self.stdout:
stdout = self.stdout.read()
self.stdout.close()
albo_inaczej self.stderr:
stderr = self.stderr.read()
self.stderr.close()
self.wait()
inaczej:
jeżeli timeout jest nie Nic:
endtime = _time() + timeout
inaczej:
endtime = Nic
spróbuj:
stdout, stderr = self._communicate(input, endtime, timeout)
w_końcu:
self._communication_started = Prawda
sts = self.wait(timeout=self._remaining_time(endtime))
zwróć (stdout, stderr)
def poll(self):
zwróć self._internal_poll()
def _remaining_time(self, endtime):
"""Convenience dla _communicate when computing timeouts."""
jeżeli endtime jest Nic:
zwróć Nic
inaczej:
zwróć endtime - _time()
def _check_timeout(self, endtime, orig_timeout):
"""Convenience dla checking jeżeli a timeout has expired."""
jeżeli endtime jest Nic:
zwróć
jeżeli _time() > endtime:
podnieś TimeoutExpired(self.args, orig_timeout)
jeżeli _mswindows:
#
# Windows methods
#
def _get_handles(self, stdin, stdout, stderr):
"""Construct oraz zwróć tuple przy IO objects:
p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite
"""
jeżeli stdin jest Nic oraz stdout jest Nic oraz stderr jest Nic:
zwróć (-1, -1, -1, -1, -1, -1)
p2cread, p2cwrite = -1, -1
c2pread, c2pwrite = -1, -1
errread, errwrite = -1, -1
jeżeli stdin jest Nic:
p2cread = _winapi.GetStdHandle(_winapi.STD_INPUT_HANDLE)
jeżeli p2cread jest Nic:
p2cread, _ = _winapi.CreatePipe(Nic, 0)
p2cread = Handle(p2cread)
_winapi.CloseHandle(_)
albo_inaczej stdin == PIPE:
p2cread, p2cwrite = _winapi.CreatePipe(Nic, 0)
p2cread, p2cwrite = Handle(p2cread), Handle(p2cwrite)
albo_inaczej stdin == DEVNULL:
p2cread = msvcrt.get_osfhandle(self._get_devnull())
albo_inaczej isinstance(stdin, int):
p2cread = msvcrt.get_osfhandle(stdin)
inaczej:
# Assuming file-like object
p2cread = msvcrt.get_osfhandle(stdin.fileno())
p2cread = self._make_inheritable(p2cread)
jeżeli stdout jest Nic:
c2pwrite = _winapi.GetStdHandle(_winapi.STD_OUTPUT_HANDLE)
jeżeli c2pwrite jest Nic:
_, c2pwrite = _winapi.CreatePipe(Nic, 0)
c2pwrite = Handle(c2pwrite)
_winapi.CloseHandle(_)
albo_inaczej stdout == PIPE:
c2pread, c2pwrite = _winapi.CreatePipe(Nic, 0)
c2pread, c2pwrite = Handle(c2pread), Handle(c2pwrite)
albo_inaczej stdout == DEVNULL:
c2pwrite = msvcrt.get_osfhandle(self._get_devnull())
albo_inaczej isinstance(stdout, int):
c2pwrite = msvcrt.get_osfhandle(stdout)
inaczej:
# Assuming file-like object
c2pwrite = msvcrt.get_osfhandle(stdout.fileno())
c2pwrite = self._make_inheritable(c2pwrite)
jeżeli stderr jest Nic:
errwrite = _winapi.GetStdHandle(_winapi.STD_ERROR_HANDLE)
jeżeli errwrite jest Nic:
_, errwrite = _winapi.CreatePipe(Nic, 0)
errwrite = Handle(errwrite)
_winapi.CloseHandle(_)
albo_inaczej stderr == PIPE:
errread, errwrite = _winapi.CreatePipe(Nic, 0)
errread, errwrite = Handle(errread), Handle(errwrite)
albo_inaczej stderr == STDOUT:
errwrite = c2pwrite
albo_inaczej stderr == DEVNULL:
errwrite = msvcrt.get_osfhandle(self._get_devnull())
albo_inaczej isinstance(stderr, int):
errwrite = msvcrt.get_osfhandle(stderr)
inaczej:
# Assuming file-like object
errwrite = msvcrt.get_osfhandle(stderr.fileno())
errwrite = self._make_inheritable(errwrite)
zwróć (p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite)
def _make_inheritable(self, handle):
"""Return a duplicate of handle, which jest inheritable"""
h = _winapi.DuplicateHandle(
_winapi.GetCurrentProcess(), handle,
_winapi.GetCurrentProcess(), 0, 1,
_winapi.DUPLICATE_SAME_ACCESS)
zwróć Handle(h)
def _execute_child(self, args, executable, preexec_fn, close_fds,
dalej_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
unused_restore_signals, unused_start_new_session):
"""Execute program (MS Windows version)"""
assert nie dalej_fds, "pass_fds nie supported on Windows."
jeżeli nie isinstance(args, str):
args = list2cmdline(args)
# Process startup details
jeżeli startupinfo jest Nic:
startupinfo = STARTUPINFO()
jeżeli -1 nie w (p2cread, c2pwrite, errwrite):
startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES
startupinfo.hStdInput = p2cread
startupinfo.hStdOutput = c2pwrite
startupinfo.hStdError = errwrite
jeżeli shell:
startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW
startupinfo.wShowWindow = _winapi.SW_HIDE
comspec = os.environ.get("COMSPEC", "cmd.exe")
args = '{} /c "{}"'.format (comspec, args)
# Start the process
spróbuj:
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
# no special security
Nic, Nic,
int(nie close_fds),
creationflags,
env,
cwd,
startupinfo)
w_końcu:
# Child jest launched. Close the parent's copy of those pipe
# handles that only the child should have open. You need
# to make sure that no handles to the write end of the
# output pipe are maintained w this process albo inaczej the
# pipe will nie close when the child process exits oraz the
# ReadFile will hang.
jeżeli p2cread != -1:
p2cread.Close()
jeżeli c2pwrite != -1:
c2pwrite.Close()
jeżeli errwrite != -1:
errwrite.Close()
jeżeli hasattr(self, '_devnull'):
os.close(self._devnull)
# Retain the process handle, but close the thread handle
self._child_created = Prawda
self._handle = Handle(hp)
self.pid = pid
_winapi.CloseHandle(ht)
def _internal_poll(self, _deadstate=Nic,
_WaitForSingleObject=_winapi.WaitForSingleObject,
_WAIT_OBJECT_0=_winapi.WAIT_OBJECT_0,
_GetExitCodeProcess=_winapi.GetExitCodeProcess):
"""Check jeżeli child process has terminated. Returns returncode
attribute.
This method jest called by __del__, so it can only refer to objects
w its local scope.
"""
jeżeli self.returncode jest Nic:
jeżeli _WaitForSingleObject(self._handle, 0) == _WAIT_OBJECT_0:
self.returncode = _GetExitCodeProcess(self._handle)
zwróć self.returncode
def wait(self, timeout=Nic, endtime=Nic):
"""Wait dla child process to terminate. Returns returncode
attribute."""
jeżeli endtime jest nie Nic:
timeout = self._remaining_time(endtime)
jeżeli timeout jest Nic:
timeout_millis = _winapi.INFINITE
inaczej:
timeout_millis = int(timeout * 1000)
jeżeli self.returncode jest Nic:
result = _winapi.WaitForSingleObject(self._handle,
timeout_millis)
jeżeli result == _winapi.WAIT_TIMEOUT:
podnieś TimeoutExpired(self.args, timeout)
self.returncode = _winapi.GetExitCodeProcess(self._handle)
zwróć self.returncode
def _readerthread(self, fh, buffer):
buffer.append(fh.read())
fh.close()
def _communicate(self, input, endtime, orig_timeout):
# Start reader threads feeding into a list hanging off of this
# object, unless they've already been started.
jeżeli self.stdout oraz nie hasattr(self, "_stdout_buff"):
self._stdout_buff = []
self.stdout_thread = \
threading.Thread(target=self._readerthread,
args=(self.stdout, self._stdout_buff))
self.stdout_thread.daemon = Prawda
self.stdout_thread.start()
jeżeli self.stderr oraz nie hasattr(self, "_stderr_buff"):
self._stderr_buff = []
self.stderr_thread = \
threading.Thread(target=self._readerthread,
args=(self.stderr, self._stderr_buff))
self.stderr_thread.daemon = Prawda
self.stderr_thread.start()
jeżeli self.stdin:
self._stdin_write(input)
# Wait dla the reader threads, albo time out. If we time out, the
# threads remain reading oraz the fds left open w case the user
# calls communicate again.
jeżeli self.stdout jest nie Nic:
self.stdout_thread.join(self._remaining_time(endtime))
jeżeli self.stdout_thread.is_alive():
podnieś TimeoutExpired(self.args, orig_timeout)
jeżeli self.stderr jest nie Nic:
self.stderr_thread.join(self._remaining_time(endtime))
jeżeli self.stderr_thread.is_alive():
podnieś TimeoutExpired(self.args, orig_timeout)
# Collect the output z oraz close both pipes, now that we know
# both have been read successfully.
stdout = Nic
stderr = Nic
jeżeli self.stdout:
stdout = self._stdout_buff
self.stdout.close()
jeżeli self.stderr:
stderr = self._stderr_buff
self.stderr.close()
# All data exchanged. Translate lists into strings.
jeżeli stdout jest nie Nic:
stdout = stdout[0]
jeżeli stderr jest nie Nic:
stderr = stderr[0]
zwróć (stdout, stderr)
def send_signal(self, sig):
"""Send a signal to the process
"""
jeżeli sig == signal.SIGTERM:
self.terminate()
albo_inaczej sig == signal.CTRL_C_EVENT:
os.kill(self.pid, signal.CTRL_C_EVENT)
albo_inaczej sig == signal.CTRL_BREAK_EVENT:
os.kill(self.pid, signal.CTRL_BREAK_EVENT)
inaczej:
podnieś ValueError("Unsupported signal: {}".format(sig))
def terminate(self):
"""Terminates the process
"""
spróbuj:
_winapi.TerminateProcess(self._handle, 1)
wyjąwszy PermissionError:
# ERROR_ACCESS_DENIED (winerror 5) jest received when the
# process already died.
rc = _winapi.GetExitCodeProcess(self._handle)
jeżeli rc == _winapi.STILL_ACTIVE:
podnieś
self.returncode = rc
kill = terminate
inaczej:
#
# POSIX methods
#
albo_inaczej stdin == DEVNULL:
p2cread = self._get_devnull()
albo_inaczej isinstance(stdin, int):
p2cread = stdin
inaczej:
# Assuming file-like object
p2cread = stdin.fileno()
jeżeli stdout jest Nic:
dalej
albo_inaczej stdout == PIPE:
c2pread, c2pwrite = os.pipe()
albo_inaczej stdout == DEVNULL:
c2pwrite = self._get_devnull()
albo_inaczej isinstance(stdout, int):
c2pwrite = stdout
inaczej:
# Assuming file-like object
c2pwrite = stdout.fileno()
jeżeli stderr jest Nic:
dalej
albo_inaczej stderr == PIPE:
errread, errwrite = os.pipe()
albo_inaczej stderr == STDOUT:
errwrite = c2pwrite
albo_inaczej stderr == DEVNULL:
errwrite = self._get_devnull()
albo_inaczej isinstance(stderr, int):
errwrite = stderr
inaczej:
# Assuming file-like object
errwrite = stderr.fileno()
zwróć (p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite)
def _execute_child(self, args, executable, preexec_fn, close_fds,
dalej_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
restore_signals, start_new_session):
"""Execute program (POSIX version)"""
jeżeli isinstance(args, (str, bytes)):
args = [args]
inaczej:
args = list(args)
jeżeli shell:
args = ["/bin/sh", "-c"] + args
jeżeli executable:
args[0] = executable
jeżeli executable jest Nic:
executable = args[0]
orig_executable = executable
# For transferring possible exec failure z child to parent.
# Data format: "exception name:hex errno:description"
# Pickle jest nie used; it jest complex oraz involves memory allocation.
errpipe_read, errpipe_write = os.pipe()
# errpipe_write must nie be w the standard io 0, 1, albo 2 fd range.
low_fds_to_close = []
dopóki errpipe_write < 3:
low_fds_to_close.append(errpipe_write)
errpipe_write = os.dup(errpipe_write)
dla low_fd w low_fds_to_close:
os.close(low_fd)
spróbuj:
spróbuj:
# We must avoid complex work that could involve
# malloc albo free w the child process to avoid
# potential deadlocks, thus we do all this here.
# oraz dalej it to fork_exec()
jeżeli env jest nie Nic:
env_list = [os.fsencode(k) + b'=' + os.fsencode(v)
dla k, v w env.items()]
inaczej:
env_list = Nic # Use execv instead of execve.
executable = os.fsencode(executable)
jeżeli os.path.dirname(executable):
executable_list = (executable,)
inaczej:
# This matches the behavior of os._execvpe().
executable_list = tuple(
os.path.join(os.fsencode(dir), executable)
dla dir w os.get_exec_path(env))
fds_to_keep = set(pass_fds)
fds_to_keep.add(errpipe_write)
self.pid = _posixsubprocess.fork_exec(
args, executable_list,
close_fds, sorted(fds_to_keep), cwd, env_list,
p2cread, p2cwrite, c2pread, c2pwrite,
errread, errwrite,
errpipe_read, errpipe_write,
restore_signals, start_new_session, preexec_fn)
self._child_created = Prawda
w_końcu:
# be sure the FD jest closed no matter what
os.close(errpipe_write)
# self._devnull jest nie always defined.
devnull_fd = getattr(self, '_devnull', Nic)
jeżeli p2cread != -1 oraz p2cwrite != -1 oraz p2cread != devnull_fd:
os.close(p2cread)
jeżeli c2pwrite != -1 oraz c2pread != -1 oraz c2pwrite != devnull_fd:
os.close(c2pwrite)
jeżeli errwrite != -1 oraz errread != -1 oraz errwrite != devnull_fd:
os.close(errwrite)
jeżeli devnull_fd jest nie Nic:
os.close(devnull_fd)
# Prevent a double close of these fds z __init__ on error.
self._closed_child_pipe_fds = Prawda
# Wait dla exec to fail albo succeed; possibly raising an
# exception (limited w size)
errpipe_data = bytearray()
dopóki Prawda:
part = os.read(errpipe_read, 50000)
errpipe_data += part
jeżeli nie part albo len(errpipe_data) > 50000:
przerwij
w_końcu:
# be sure the FD jest closed no matter what
os.close(errpipe_read)
jeżeli errpipe_data:
spróbuj:
os.waitpid(self.pid, 0)
wyjąwszy ChildProcessError:
dalej
spróbuj:
exception_name, hex_errno, err_msg = (
errpipe_data.split(b':', 2))
wyjąwszy ValueError:
exception_name = b'SubprocessError'
hex_errno = b'0'
err_msg = (b'Bad exception data z child: ' +
repr(errpipe_data))
child_exception_type = getattr(
builtins, exception_name.decode('ascii'),
SubprocessError)
err_msg = err_msg.decode(errors="surrogatepass")
jeżeli issubclass(child_exception_type, OSError) oraz hex_errno:
errno_num = int(hex_errno, 16)
child_exec_never_called = (err_msg == "noexec")
jeżeli child_exec_never_called:
err_msg = ""
jeżeli errno_num != 0:
err_msg = os.strerror(errno_num)
jeżeli errno_num == errno.ENOENT:
jeżeli child_exec_never_called:
# The error must be z chdir(cwd).
err_msg += ': ' + repr(cwd)
inaczej:
err_msg += ': ' + repr(orig_executable)
podnieś child_exception_type(errno_num, err_msg)
podnieś child_exception_type(err_msg)
def _handle_exitstatus(self, sts, _WIFSIGNALED=os.WIFSIGNALED,
_WTERMSIG=os.WTERMSIG, _WIFEXITED=os.WIFEXITED,
_WEXITSTATUS=os.WEXITSTATUS):
"""All callers to this function MUST hold self._waitpid_lock."""
# This method jest called (indirectly) by __del__, so it cannot
# refer to anything outside of its local scope.
jeżeli _WIFSIGNALED(sts):
self.returncode = -_WTERMSIG(sts)
albo_inaczej _WIFEXITED(sts):
self.returncode = _WEXITSTATUS(sts)
inaczej:
# Should never happen
podnieś SubprocessError("Unknown child exit status!")
def _internal_poll(self, _deadstate=Nic, _waitpid=os.waitpid,
_WNOHANG=os.WNOHANG, _ECHILD=errno.ECHILD):
"""Check jeżeli child process has terminated. Returns returncode
attribute.
This method jest called by __del__, so it cannot reference anything
outside of the local scope (nor can any methods it calls).
"""
jeżeli self.returncode jest Nic:
jeżeli nie self._waitpid_lock.acquire(Nieprawda):
# Something inaczej jest busy calling waitpid. Don't allow two
# at once. We know nothing yet.
zwróć Nic
spróbuj:
jeżeli self.returncode jest nie Nic:
zwróć self.returncode # Another thread waited.
pid, sts = _waitpid(self.pid, _WNOHANG)
jeżeli pid == self.pid:
self._handle_exitstatus(sts)
wyjąwszy OSError jako e:
jeżeli _deadstate jest nie Nic:
self.returncode = _deadstate
albo_inaczej e.errno == _ECHILD:
# This happens jeżeli SIGCLD jest set to be ignored albo
# waiting dla child processes has otherwise been
# disabled dla our process. This child jest dead, we
# can't get the status.
# http://bugs.python.org/issue15756
self.returncode = 0
w_końcu:
self._waitpid_lock.release()
zwróć self.returncode
def _try_wait(self, wait_flags):
"""All callers to this function MUST hold self._waitpid_lock."""
spróbuj:
(pid, sts) = os.waitpid(self.pid, wait_flags)
wyjąwszy ChildProcessError:
# This happens jeżeli SIGCLD jest set to be ignored albo waiting
# dla child processes has otherwise been disabled dla our
# process. This child jest dead, we can't get the status.
pid = self.pid
sts = 0
zwróć (pid, sts)
def wait(self, timeout=Nic, endtime=Nic):
"""Wait dla child process to terminate. Returns returncode
attribute."""
jeżeli self.returncode jest nie Nic:
zwróć self.returncode
# endtime jest preferred to timeout. timeout jest only used for
# printing.
jeżeli endtime jest nie Nic albo timeout jest nie Nic:
jeżeli endtime jest Nic:
endtime = _time() + timeout
albo_inaczej timeout jest Nic:
timeout = self._remaining_time(endtime)
jeżeli endtime jest nie Nic:
# Enter a busy loop jeżeli we have a timeout. This busy loop was
# cribbed z Lib/threading.py w Thread.wait() at r71065.
delay = 0.0005 # 500 us -> initial delay of 1 ms
dopóki Prawda:
jeżeli self._waitpid_lock.acquire(Nieprawda):
spróbuj:
jeżeli self.returncode jest nie Nic:
przerwij # Another thread waited.
(pid, sts) = self._try_wait(os.WNOHANG)
assert pid == self.pid albo pid == 0
jeżeli pid == self.pid:
self._handle_exitstatus(sts)
przerwij
w_końcu:
self._waitpid_lock.release()
remaining = self._remaining_time(endtime)
jeżeli remaining <= 0:
podnieś TimeoutExpired(self.args, timeout)
delay = min(delay * 2, remaining, .05)
time.sleep(delay)
inaczej:
dopóki self.returncode jest Nic:
przy self._waitpid_lock:
jeżeli self.returncode jest nie Nic:
przerwij # Another thread waited.
(pid, sts) = self._try_wait(0)
# Check the pid oraz loop jako waitpid has been known to
# zwróć 0 even without WNOHANG w odd situations.
# http://bugs.python.org/issue14396.
jeżeli pid == self.pid:
self._handle_exitstatus(sts)
zwróć self.returncode
def _communicate(self, input, endtime, orig_timeout):
jeżeli self.stdin oraz nie self._communication_started:
# Flush stdio buffer. This might block, jeżeli the user has
# been writing to .stdin w an uncontrolled fashion.
self.stdin.flush()
jeżeli nie input:
self.stdin.close()
stdout = Nic
stderr = Nic
# Only create this mapping jeżeli we haven't already.
jeżeli nie self._communication_started:
self._fileobj2output = {}
jeżeli self.stdout:
self._fileobj2output[self.stdout] = []
jeżeli self.stderr:
self._fileobj2output[self.stderr] = []
jeżeli self.stdout:
stdout = self._fileobj2output[self.stdout]
jeżeli self.stderr:
stderr = self._fileobj2output[self.stderr]
self._save_input(input)
jeżeli self._input:
input_view = memoryview(self._input)
przy _PopenSelector() jako selector:
jeżeli self.stdin oraz input:
selector.register(self.stdin, selectors.EVENT_WRITE)
jeżeli self.stdout:
selector.register(self.stdout, selectors.EVENT_READ)
jeżeli self.stderr:
selector.register(self.stderr, selectors.EVENT_READ)
dopóki selector.get_map():
timeout = self._remaining_time(endtime)
jeżeli timeout jest nie Nic oraz timeout < 0:
podnieś TimeoutExpired(self.args, orig_timeout)
ready = selector.select(timeout)
self._check_timeout(endtime, orig_timeout)
# XXX Rewrite these to use non-blocking I/O on the file
# objects; they are no longer using C stdio!
dla key, events w ready:
jeżeli key.fileobj jest self.stdin:
chunk = input_view[self._input_offset :
self._input_offset + _PIPE_BUF]
spróbuj:
self._input_offset += os.write(key.fd, chunk)
wyjąwszy BrokenPipeError:
selector.unregister(key.fileobj)
key.fileobj.close()
inaczej:
jeżeli self._input_offset >= len(self._input):
selector.unregister(key.fileobj)
key.fileobj.close()
albo_inaczej key.fileobj w (self.stdout, self.stderr):
data = os.read(key.fd, 32768)
jeżeli nie data:
selector.unregister(key.fileobj)
key.fileobj.close()
self._fileobj2output[key.fileobj].append(data)
self.wait(timeout=self._remaining_time(endtime))
# All data exchanged. Translate lists into strings.
jeżeli stdout jest nie Nic:
stdout = b''.join(stdout)
jeżeli stderr jest nie Nic:
stderr = b''.join(stderr)
# Translate newlines, jeżeli requested.
# This also turns bytes into strings.
jeżeli self.universal_newlines:
jeżeli stdout jest nie Nic:
stdout = self._translate_newlines(stdout,
self.stdout.encoding)
jeżeli stderr jest nie Nic:
stderr = self._translate_newlines(stderr,
self.stderr.encoding)
zwróć (stdout, stderr)
def _save_input(self, input):
# This method jest called z the _communicate_with_*() methods
# so that jeżeli we time out dopóki communicating, we can kontynuuj
# sending input jeżeli we retry.
jeżeli self.stdin oraz self._input jest Nic:
self._input_offset = 0
self._input = input
jeżeli self.universal_newlines oraz input jest nie Nic:
self._input = self._input.encode(self.stdin.encoding)
def send_signal(self, sig):
"""Send a signal to the process
"""
os.kill(self.pid, sig)
def terminate(self):
"""Terminate the process przy SIGTERM
"""
self.send_signal(signal.SIGTERM)
def kill(self):
"""Kill the process przy SIGKILL
"""
self.send_signal(signal.SIGKILL)
| 39.175141 | 91 | 0.5909 | # subprocess - Subprocesses przy accessible I/O streams
#
# For more information about this module, see PEP 324.
#
# Copyright (c) 2003-2005 by Peter Astrand <astrand@lysator.liu.se>
#
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/2.4/license dla licensing details.
r"""subprocess - Subprocesses przy accessible I/O streams
This module allows you to spawn processes, connect to their
input/output/error pipes, oraz obtain their zwróć codes. This module
intends to replace several older modules oraz functions:
os.system
os.spawn*
Information about how the subprocess module can be used to replace these
modules oraz functions can be found below.
Using the subprocess module
===========================
This module defines one klasa called Popen:
klasa Popen(args, bufsize=-1, executable=Nic,
stdin=Nic, stdout=Nic, stderr=Nic,
preexec_fn=Nic, close_fds=Prawda, shell=Nieprawda,
cwd=Nic, env=Nic, universal_newlines=Nieprawda,
startupinfo=Nic, creationflags=0,
restore_signals=Prawda, start_new_session=Nieprawda, dalej_fds=()):
Arguments are:
args should be a string, albo a sequence of program arguments. The
program to execute jest normally the first item w the args sequence albo
string, but can be explicitly set by using the executable argument.
On POSIX, przy shell=Nieprawda (default): In this case, the Popen class
uses os.execvp() to execute the child program. args should normally
be a sequence. A string will be treated jako a sequence przy the string
as the only item (the program to execute).
On POSIX, przy shell=Prawda: If args jest a string, it specifies the
command string to execute through the shell. If args jest a sequence,
the first item specifies the command string, oraz any additional items
will be treated jako additional shell arguments.
On Windows: the Popen klasa uses CreateProcess() to execute the child
program, which operates on strings. If args jest a sequence, it will be
converted to a string using the list2cmdline method. Please note that
not all MS Windows applications interpret the command line the same
way: The list2cmdline jest designed dla applications using the same
rules jako the MS C runtime.
bufsize will be supplied jako the corresponding argument to the io.open()
function when creating the stdin/stdout/stderr pipe file objects:
0 means unbuffered (read & write are one system call oraz can zwróć short),
1 means line buffered, any other positive value means use a buffer of
approximately that size. A negative bufsize, the default, means the system
default of io.DEFAULT_BUFFER_SIZE will be used.
stdin, stdout oraz stderr specify the executed programs' standard
input, standard output oraz standard error file handles, respectively.
Valid values are PIPE, an existing file descriptor (a positive
integer), an existing file object, oraz Nic. PIPE indicates that a
new pipe to the child should be created. With Nic, no redirection
will occur; the child's file handles will be inherited z the
parent. Additionally, stderr can be STDOUT, which indicates that the
stderr data z the applications should be captured into the same
file handle jako dla stdout.
On POSIX, jeżeli preexec_fn jest set to a callable object, this object will be
called w the child process just before the child jest executed. The use
of preexec_fn jest nie thread safe, using it w the presence of threads
could lead to a deadlock w the child process before the new executable
is executed.
If close_fds jest true, all file descriptors wyjąwszy 0, 1 oraz 2 will be
closed before the child process jest executed. The default dla close_fds
varies by platform: Always true on POSIX. Prawda when stdin/stdout/stderr
are Nic on Windows, false otherwise.
pass_fds jest an optional sequence of file descriptors to keep open between the
parent oraz child. Providing any dalej_fds implicitly sets close_fds to true.
jeżeli shell jest true, the specified command will be executed through the
shell.
If cwd jest nie Nic, the current directory will be changed to cwd
before the child jest executed.
On POSIX, jeżeli restore_signals jest Prawda all signals that Python sets to
SIG_IGN are restored to SIG_DFL w the child process before the exec.
Currently this includes the SIGPIPE, SIGXFZ oraz SIGXFSZ signals. This
parameter does nothing on Windows.
On POSIX, jeżeli start_new_session jest Prawda, the setsid() system call will be made
in the child process prior to executing the command.
If env jest nie Nic, it defines the environment variables dla the new
process.
If universal_newlines jest Nieprawda, the file objects stdin, stdout oraz stderr
are opened jako binary files, oraz no line ending conversion jest done.
If universal_newlines jest Prawda, the file objects stdout oraz stderr are
opened jako a text file, but lines may be terminated by any of '\n',
the Unix end-of-line convention, '\r', the old Macintosh convention albo
'\r\n', the Windows convention. All of these external representations
are seen jako '\n' by the Python program. Also, the newlines attribute
of the file objects stdout, stdin oraz stderr are nie updated by the
communicate() method.
In either case, the process being communicated przy should start up
expecting to receive bytes on its standard input oraz decode them with
the same encoding they are sent in.
The startupinfo oraz creationflags, jeżeli given, will be dalejed to the
underlying CreateProcess() function. They can specify things such as
appearance of the main window oraz priority dla the new process.
(Windows only)
This module also defines some shortcut functions:
call(*popenargs, **kwargs):
Run command przy arguments. Wait dla command to complete, then
zwróć the returncode attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> retcode = subprocess.call(["ls", "-l"])
check_call(*popenargs, **kwargs):
Run command przy arguments. Wait dla command to complete. If the
exit code was zero then return, otherwise podnieś
CalledProcessError. The CalledProcessError object will have the
zwróć code w the returncode attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> subprocess.check_call(["ls", "-l"])
0
getstatusoutput(cmd):
Return (status, output) of executing cmd w a shell.
Execute the string 'cmd' w a shell przy 'check_output' oraz
zwróć a 2-tuple (status, output). Universal newlines mode jest used,
meaning that the result przy be decoded to a string.
A trailing newline jest stripped z the output.
The exit status dla the command can be interpreted
according to the rules dla the function 'wait'. Example:
>>> subprocess.getstatusoutput('ls /bin/ls')
(0, '/bin/ls')
>>> subprocess.getstatusoutput('cat /bin/junk')
(256, 'cat: /bin/junk: No such file albo directory')
>>> subprocess.getstatusoutput('/bin/junk')
(256, 'sh: /bin/junk: nie found')
getoutput(cmd):
Return output (stdout albo stderr) of executing cmd w a shell.
Like getstatusoutput(), wyjąwszy the exit status jest ignored oraz the zwróć
value jest a string containing the command's output. Example:
>>> subprocess.getoutput('ls /bin/ls')
'/bin/ls'
check_output(*popenargs, **kwargs):
Run command przy arguments oraz zwróć its output.
If the exit code was non-zero it podnieśs a CalledProcessError. The
CalledProcessError object will have the zwróć code w the returncode
attribute oraz output w the output attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> output = subprocess.check_output(["ls", "-l", "/dev/null"])
There jest an additional optional argument, "input", allowing you to
dalej a string to the subprocess's stdin. If you use this argument
you may nie also use the Popen constructor's "stdin" argument.
If universal_newlines jest set to Prawda, the "input" argument must
be a string rather than bytes, oraz the zwróć value will be a string.
Exceptions
----------
Exceptions podnieśd w the child process, before the new program has
started to execute, will be re-raised w the parent. Additionally,
the exception object will have one extra attribute called
'child_traceback', which jest a string containing traceback information
z the child's point of view.
The most common exception podnieśd jest OSError. This occurs, for
example, when trying to execute a non-existent file. Applications
should prepare dla OSErrors.
A ValueError will be podnieśd jeżeli Popen jest called przy invalid arguments.
Exceptions defined within this module inherit z SubprocessError.
check_call() oraz check_output() will podnieś CalledProcessError jeżeli the
called process returns a non-zero zwróć code. TimeoutExpired
be podnieśd jeżeli a timeout was specified oraz expired.
Security
--------
Unlike some other popen functions, this implementation will never call
/bin/sh implicitly. This means that all characters, including shell
metacharacters, can safely be dalejed to child processes.
Popen objects
=============
Instances of the Popen klasa have the following methods:
poll()
Check jeżeli child process has terminated. Returns returncode
attribute.
wait()
Wait dla child process to terminate. Returns returncode attribute.
communicate(input=Nic)
Interact przy process: Send data to stdin. Read data z stdout
oraz stderr, until end-of-file jest reached. Wait dla process to
terminate. The optional input argument should be data to be
sent to the child process, albo Nic, jeżeli no data should be sent to
the child. If the Popen instance was constructed przy universal_newlines
set to Prawda, the input argument should be a string oraz will be encoded
using the preferred system encoding (see locale.getpreferredencoding);
jeżeli universal_newlines jest Nieprawda, the input argument should be a
byte string.
communicate() returns a tuple (stdout, stderr).
Note: The data read jest buffered w memory, so do nie use this
method jeżeli the data size jest large albo unlimited.
The following attributes are also available:
stdin
If the stdin argument jest PIPE, this attribute jest a file object
that provides input to the child process. Otherwise, it jest Nic.
stdout
If the stdout argument jest PIPE, this attribute jest a file object
that provides output z the child process. Otherwise, it jest
Nic.
stderr
If the stderr argument jest PIPE, this attribute jest file object that
provides error output z the child process. Otherwise, it jest
Nic.
pid
The process ID of the child process.
returncode
The child zwróć code. A Nic value indicates that the process
hasn't terminated yet. A negative value -N indicates that the
child was terminated by signal N (POSIX only).
Replacing older functions przy the subprocess module
====================================================
In this section, "a ==> b" means that b can be used jako a replacement
dla a.
Note: All functions w this section fail (more albo less) silently if
the executed program cannot be found; this module podnieśs an OSError
exception.
In the following examples, we assume that the subprocess module jest
imported przy "z subprocess zaimportuj *".
Replacing /bin/sh shell backquote
---------------------------------
output=`mycmd myarg`
==>
output = Popen(["mycmd", "myarg"], stdout=PIPE).communicate()[0]
Replacing shell pipe line
-------------------------
output=`dmesg | grep hda`
==>
p1 = Popen(["dmesg"], stdout=PIPE)
p2 = Popen(["grep", "hda"], stdin=p1.stdout, stdout=PIPE)
output = p2.communicate()[0]
Replacing os.system()
---------------------
sts = os.system("mycmd" + " myarg")
==>
p = Popen("mycmd" + " myarg", shell=Prawda)
pid, sts = os.waitpid(p.pid, 0)
Note:
* Calling the program through the shell jest usually nie required.
* It's easier to look at the returncode attribute than the
exitstatus.
A more real-world example would look like this:
spróbuj:
retcode = call("mycmd" + " myarg", shell=Prawda)
jeżeli retcode < 0:
print("Child was terminated by signal", -retcode, file=sys.stderr)
inaczej:
print("Child returned", retcode, file=sys.stderr)
wyjąwszy OSError jako e:
print("Execution failed:", e, file=sys.stderr)
Replacing os.spawn*
-------------------
P_NOWAIT example:
pid = os.spawnlp(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg")
==>
pid = Popen(["/bin/mycmd", "myarg"]).pid
P_WAIT example:
retcode = os.spawnlp(os.P_WAIT, "/bin/mycmd", "mycmd", "myarg")
==>
retcode = call(["/bin/mycmd", "myarg"])
Vector example:
os.spawnvp(os.P_NOWAIT, path, args)
==>
Popen([path] + args[1:])
Environment example:
os.spawnlpe(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg", env)
==>
Popen(["/bin/mycmd", "myarg"], env={"PATH": "/usr/bin"})
"""
zaimportuj sys
_mswindows = (sys.platform == "win32")
zaimportuj io
zaimportuj os
zaimportuj time
zaimportuj signal
zaimportuj builtins
zaimportuj warnings
zaimportuj errno
z time zaimportuj monotonic jako _time
# Exception classes used by this module.
klasa SubprocessError(Exception): dalej
klasa CalledProcessError(SubprocessError):
"""This exception jest podnieśd when a process run by check_call() albo
check_output() returns a non-zero exit status.
The exit status will be stored w the returncode attribute;
check_output() will also store the output w the output attribute.
"""
def __init__(self, returncode, cmd, output=Nic, stderr=Nic):
self.returncode = returncode
self.cmd = cmd
self.output = output
self.stderr = stderr
def __str__(self):
zwróć "Command '%s' returned non-zero exit status %d" % (self.cmd, self.returncode)
@property
def stdout(self):
"""Alias dla output attribute, to match stderr"""
zwróć self.output
@stdout.setter
def stdout(self, value):
# There's no obvious reason to set this, but allow it anyway so
# .stdout jest a transparent alias dla .output
self.output = value
klasa TimeoutExpired(SubprocessError):
"""This exception jest podnieśd when the timeout expires dopóki waiting dla a
child process.
"""
def __init__(self, cmd, timeout, output=Nic, stderr=Nic):
self.cmd = cmd
self.timeout = timeout
self.output = output
self.stderr = stderr
def __str__(self):
zwróć ("Command '%s' timed out after %s seconds" %
(self.cmd, self.timeout))
@property
def stdout(self):
zwróć self.output
@stdout.setter
def stdout(self, value):
# There's no obvious reason to set this, but allow it anyway so
# .stdout jest a transparent alias dla .output
self.output = value
jeżeli _mswindows:
zaimportuj threading
zaimportuj msvcrt
zaimportuj _winapi
klasa STARTUPINFO:
dwFlags = 0
hStdInput = Nic
hStdOutput = Nic
hStdError = Nic
wShowWindow = 0
inaczej:
zaimportuj _posixsubprocess
zaimportuj select
zaimportuj selectors
spróbuj:
zaimportuj threading
wyjąwszy ImportError:
zaimportuj dummy_threading jako threading
# When select albo poll has indicated that the file jest writable,
# we can write up to _PIPE_BUF bytes without risk of blocking.
# POSIX defines PIPE_BUF jako >= 512.
_PIPE_BUF = getattr(select, 'PIPE_BUF', 512)
# poll/select have the advantage of nie requiring any extra file
# descriptor, contrarily to epoll/kqueue (also, they require a single
# syscall).
jeżeli hasattr(selectors, 'PollSelector'):
_PopenSelector = selectors.PollSelector
inaczej:
_PopenSelector = selectors.SelectSelector
__all__ = ["Popen", "PIPE", "STDOUT", "call", "check_call", "getstatusoutput",
"getoutput", "check_output", "run", "CalledProcessError", "DEVNULL",
"SubprocessError", "TimeoutExpired", "CompletedProcess"]
# NOTE: We intentionally exclude list2cmdline jako it jest
# considered an internal implementation detail. issue10838.
jeżeli _mswindows:
z _winapi zaimportuj (CREATE_NEW_CONSOLE, CREATE_NEW_PROCESS_GROUP,
STD_INPUT_HANDLE, STD_OUTPUT_HANDLE,
STD_ERROR_HANDLE, SW_HIDE,
STARTF_USESTDHANDLES, STARTF_USESHOWWINDOW)
__all__.extend(["CREATE_NEW_CONSOLE", "CREATE_NEW_PROCESS_GROUP",
"STD_INPUT_HANDLE", "STD_OUTPUT_HANDLE",
"STD_ERROR_HANDLE", "SW_HIDE",
"STARTF_USESTDHANDLES", "STARTF_USESHOWWINDOW"])
klasa Handle(int):
closed = Nieprawda
def Close(self, CloseHandle=_winapi.CloseHandle):
jeżeli nie self.closed:
self.closed = Prawda
CloseHandle(self)
def Detach(self):
jeżeli nie self.closed:
self.closed = Prawda
zwróć int(self)
podnieś ValueError("already closed")
def __repr__(self):
zwróć "%s(%d)" % (self.__class__.__name__, int(self))
__del__ = Close
__str__ = __repr__
# This lists holds Popen instances dla which the underlying process had nie
# exited at the time its __del__ method got called: those processes are wait()ed
# dla synchronously z _cleanup() when a new Popen object jest created, to avoid
# zombie processes.
_active = []
def _cleanup():
dla inst w _active[:]:
res = inst._internal_poll(_deadstate=sys.maxsize)
jeżeli res jest nie Nic:
spróbuj:
_active.remove(inst)
wyjąwszy ValueError:
# This can happen jeżeli two threads create a new Popen instance.
# It's harmless that it was already removed, so ignore.
dalej
PIPE = -1
STDOUT = -2
DEVNULL = -3
# XXX This function jest only used by multiprocessing oraz the test suite,
# but it's here so that it can be imported when Python jest compiled without
# threads.
def _args_from_interpreter_flags():
"""Return a list of command-line arguments reproducing the current
settings w sys.flags oraz sys.warnoptions."""
flag_opt_map = {
'debug': 'd',
# 'inspect': 'i',
# 'interactive': 'i',
'optimize': 'O',
'dont_write_bytecode': 'B',
'no_user_site': 's',
'no_site': 'S',
'ignore_environment': 'E',
'verbose': 'v',
'bytes_warning': 'b',
'quiet': 'q',
'hash_randomization': 'R',
}
args = []
dla flag, opt w flag_opt_map.items():
v = getattr(sys.flags, flag)
jeżeli v > 0:
jeżeli flag == 'hash_randomization':
v = 1 # Handle specification of an exact seed
args.append('-' + opt * v)
dla opt w sys.warnoptions:
args.append('-W' + opt)
zwróć args
def call(*popenargs, timeout=Nic, **kwargs):
"""Run command przy arguments. Wait dla command to complete albo
timeout, then zwróć the returncode attribute.
The arguments are the same jako dla the Popen constructor. Example:
retcode = call(["ls", "-l"])
"""
przy Popen(*popenargs, **kwargs) jako p:
spróbuj:
zwróć p.wait(timeout=timeout)
wyjąwszy:
p.kill()
p.wait()
podnieś
def check_call(*popenargs, **kwargs):
"""Run command przy arguments. Wait dla command to complete. If
the exit code was zero then return, otherwise podnieś
CalledProcessError. The CalledProcessError object will have the
zwróć code w the returncode attribute.
The arguments are the same jako dla the call function. Example:
check_call(["ls", "-l"])
"""
retcode = call(*popenargs, **kwargs)
jeżeli retcode:
cmd = kwargs.get("args")
jeżeli cmd jest Nic:
cmd = popenargs[0]
podnieś CalledProcessError(retcode, cmd)
zwróć 0
def check_output(*popenargs, timeout=Nic, **kwargs):
r"""Run command przy arguments oraz zwróć its output.
If the exit code was non-zero it podnieśs a CalledProcessError. The
CalledProcessError object will have the zwróć code w the returncode
attribute oraz output w the output attribute.
The arguments are the same jako dla the Popen constructor. Example:
>>> check_output(["ls", "-l", "/dev/null"])
b'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n'
The stdout argument jest nie allowed jako it jest used internally.
To capture standard error w the result, use stderr=STDOUT.
>>> check_output(["/bin/sh", "-c",
... "ls -l non_existent_file ; exit 0"],
... stderr=STDOUT)
b'ls: non_existent_file: No such file albo directory\n'
There jest an additional optional argument, "input", allowing you to
dalej a string to the subprocess's stdin. If you use this argument
you may nie also use the Popen constructor's "stdin" argument, as
it too will be used internally. Example:
>>> check_output(["sed", "-e", "s/foo/bar/"],
... input=b"when w the course of fooman events\n")
b'when w the course of barman events\n'
If universal_newlines=Prawda jest dalejed, the "input" argument must be a
string oraz the zwróć value will be a string rather than bytes.
"""
jeżeli 'stdout' w kwargs:
podnieś ValueError('stdout argument nie allowed, it will be overridden.')
jeżeli 'input' w kwargs oraz kwargs['input'] jest Nic:
# Explicitly dalejing input=Nic was previously equivalent to dalejing an
# empty string. That jest maintained here dla backwards compatibility.
kwargs['input'] = '' jeżeli kwargs.get('universal_newlines', Nieprawda) inaczej b''
zwróć run(*popenargs, stdout=PIPE, timeout=timeout, check=Prawda,
**kwargs).stdout
klasa CompletedProcess(object):
"""A process that has finished running.
This jest returned by run().
Attributes:
args: The list albo str args dalejed to run().
returncode: The exit code of the process, negative dla signals.
stdout: The standard output (Nic jeżeli nie captured).
stderr: The standard error (Nic jeżeli nie captured).
"""
def __init__(self, args, returncode, stdout=Nic, stderr=Nic):
self.args = args
self.returncode = returncode
self.stdout = stdout
self.stderr = stderr
def __repr__(self):
args = ['args={!r}'.format(self.args),
'returncode={!r}'.format(self.returncode)]
jeżeli self.stdout jest nie Nic:
args.append('stdout={!r}'.format(self.stdout))
jeżeli self.stderr jest nie Nic:
args.append('stderr={!r}'.format(self.stderr))
zwróć "{}({})".format(type(self).__name__, ', '.join(args))
def check_returncode(self):
"""Raise CalledProcessError jeżeli the exit code jest non-zero."""
jeżeli self.returncode:
podnieś CalledProcessError(self.returncode, self.args, self.stdout,
self.stderr)
def run(*popenargs, input=Nic, timeout=Nic, check=Nieprawda, **kwargs):
"""Run command przy arguments oraz zwróć a CompletedProcess instance.
The returned instance will have attributes args, returncode, stdout oraz
stderr. By default, stdout oraz stderr are nie captured, oraz those attributes
will be Nic. Pass stdout=PIPE and/or stderr=PIPE w order to capture them.
If check jest Prawda oraz the exit code was non-zero, it podnieśs a
CalledProcessError. The CalledProcessError object will have the zwróć code
w the returncode attribute, oraz output & stderr attributes jeżeli those streams
were captured.
If timeout jest given, oraz the process takes too long, a TimeoutExpired
exception will be podnieśd.
There jest an optional argument "input", allowing you to
dalej a string to the subprocess's stdin. If you use this argument
you may nie also use the Popen constructor's "stdin" argument, as
it will be used internally.
The other arguments are the same jako dla the Popen constructor.
If universal_newlines=Prawda jest dalejed, the "input" argument must be a
string oraz stdout/stderr w the returned object will be strings rather than
bytes.
"""
jeżeli input jest nie Nic:
jeżeli 'stdin' w kwargs:
podnieś ValueError('stdin oraz input arguments may nie both be used.')
kwargs['stdin'] = PIPE
przy Popen(*popenargs, **kwargs) jako process:
spróbuj:
stdout, stderr = process.communicate(input, timeout=timeout)
wyjąwszy TimeoutExpired:
process.kill()
stdout, stderr = process.communicate()
podnieś TimeoutExpired(process.args, timeout, output=stdout,
stderr=stderr)
wyjąwszy:
process.kill()
process.wait()
podnieś
retcode = process.poll()
jeżeli check oraz retcode:
podnieś CalledProcessError(retcode, process.args,
output=stdout, stderr=stderr)
zwróć CompletedProcess(process.args, retcode, stdout, stderr)
def list2cmdline(seq):
"""
Translate a sequence of arguments into a command line
string, using the same rules jako the MS C runtime:
1) Arguments are delimited by white space, which jest either a
space albo a tab.
2) A string surrounded by double quotation marks jest
interpreted jako a single argument, regardless of white space
contained within. A quoted string can be embedded w an
argument.
3) A double quotation mark preceded by a backslash jest
interpreted jako a literal double quotation mark.
4) Backslashes are interpreted literally, unless they
immediately precede a double quotation mark.
5) If backslashes immediately precede a double quotation mark,
every pair of backslashes jest interpreted jako a literal
backslash. If the number of backslashes jest odd, the last
backslash escapes the next double quotation mark as
described w rule 3.
"""
# See
# http://msdn.microsoft.com/en-us/library/17w5ykft.aspx
# albo search http://msdn.microsoft.com for
# "Parsing C++ Command-Line Arguments"
result = []
needquote = Nieprawda
dla arg w seq:
bs_buf = []
# Add a space to separate this argument z the others
jeżeli result:
result.append(' ')
needquote = (" " w arg) albo ("\t" w arg) albo nie arg
jeżeli needquote:
result.append('"')
dla c w arg:
jeżeli c == '\\':
# Don't know jeżeli we need to double yet.
bs_buf.append(c)
albo_inaczej c == '"':
# Double backslashes.
result.append('\\' * len(bs_buf)*2)
bs_buf = []
result.append('\\"')
inaczej:
# Normal char
jeżeli bs_buf:
result.extend(bs_buf)
bs_buf = []
result.append(c)
# Add remaining backslashes, jeżeli any.
jeżeli bs_buf:
result.extend(bs_buf)
jeżeli needquote:
result.extend(bs_buf)
result.append('"')
zwróć ''.join(result)
# Various tools dla executing commands oraz looking at their output oraz status.
#
def getstatusoutput(cmd):
""" Return (status, output) of executing cmd w a shell.
Execute the string 'cmd' w a shell przy 'check_output' oraz
zwróć a 2-tuple (status, output). Universal newlines mode jest used,
meaning that the result przy be decoded to a string.
A trailing newline jest stripped z the output.
The exit status dla the command can be interpreted
according to the rules dla the function 'wait'. Example:
>>> zaimportuj subprocess
>>> subprocess.getstatusoutput('ls /bin/ls')
(0, '/bin/ls')
>>> subprocess.getstatusoutput('cat /bin/junk')
(256, 'cat: /bin/junk: No such file albo directory')
>>> subprocess.getstatusoutput('/bin/junk')
(256, 'sh: /bin/junk: nie found')
"""
spróbuj:
data = check_output(cmd, shell=Prawda, universal_newlines=Prawda, stderr=STDOUT)
status = 0
wyjąwszy CalledProcessError jako ex:
data = ex.output
status = ex.returncode
jeżeli data[-1:] == '\n':
data = data[:-1]
zwróć status, data
def getoutput(cmd):
"""Return output (stdout albo stderr) of executing cmd w a shell.
Like getstatusoutput(), wyjąwszy the exit status jest ignored oraz the zwróć
value jest a string containing the command's output. Example:
>>> zaimportuj subprocess
>>> subprocess.getoutput('ls /bin/ls')
'/bin/ls'
"""
zwróć getstatusoutput(cmd)[1]
_PLATFORM_DEFAULT_CLOSE_FDS = object()
klasa Popen(object):
_child_created = Nieprawda # Set here since __del__ checks it
def __init__(self, args, bufsize=-1, executable=Nic,
stdin=Nic, stdout=Nic, stderr=Nic,
preexec_fn=Nic, close_fds=_PLATFORM_DEFAULT_CLOSE_FDS,
shell=Nieprawda, cwd=Nic, env=Nic, universal_newlines=Nieprawda,
startupinfo=Nic, creationflags=0,
restore_signals=Prawda, start_new_session=Nieprawda,
dalej_fds=()):
"""Create new Popen instance."""
_cleanup()
# Held dopóki anything jest calling waitpid before returncode has been
# updated to prevent clobbering returncode jeżeli wait() albo poll() are
# called z multiple threads at once. After acquiring the lock,
# code must re-check self.returncode to see jeżeli another thread just
# finished a waitpid() call.
self._waitpid_lock = threading.Lock()
self._input = Nic
self._communication_started = Nieprawda
jeżeli bufsize jest Nic:
bufsize = -1 # Restore default
jeżeli nie isinstance(bufsize, int):
podnieś TypeError("bufsize must be an integer")
jeżeli _mswindows:
jeżeli preexec_fn jest nie Nic:
podnieś ValueError("preexec_fn jest nie supported on Windows "
"platforms")
any_stdio_set = (stdin jest nie Nic albo stdout jest nie Nic albo
stderr jest nie Nic)
jeżeli close_fds jest _PLATFORM_DEFAULT_CLOSE_FDS:
jeżeli any_stdio_set:
close_fds = Nieprawda
inaczej:
close_fds = Prawda
albo_inaczej close_fds oraz any_stdio_set:
podnieś ValueError(
"close_fds jest nie supported on Windows platforms"
" jeżeli you redirect stdin/stdout/stderr")
inaczej:
# POSIX
jeżeli close_fds jest _PLATFORM_DEFAULT_CLOSE_FDS:
close_fds = Prawda
jeżeli dalej_fds oraz nie close_fds:
warnings.warn("pass_fds overriding close_fds.", RuntimeWarning)
close_fds = Prawda
jeżeli startupinfo jest nie Nic:
podnieś ValueError("startupinfo jest only supported on Windows "
"platforms")
jeżeli creationflags != 0:
podnieś ValueError("creationflags jest only supported on Windows "
"platforms")
self.args = args
self.stdin = Nic
self.stdout = Nic
self.stderr = Nic
self.pid = Nic
self.returncode = Nic
self.universal_newlines = universal_newlines
# Input oraz output objects. The general principle jest like
# this:
#
# Parent Child
# ------ -----
# p2cwrite ---stdin---> p2cread
# c2pread <--stdout--- c2pwrite
# errread <--stderr--- errwrite
#
# On POSIX, the child objects are file descriptors. On
# Windows, these are Windows file handles. The parent objects
# are file descriptors on both platforms. The parent objects
# are -1 when nie using PIPEs. The child objects are -1
# when nie redirecting.
(p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite) = self._get_handles(stdin, stdout, stderr)
# We wrap OS handles *before* launching the child, otherwise a
# quickly terminating child could make our fds unwrappable
# (see #8458).
jeżeli _mswindows:
jeżeli p2cwrite != -1:
p2cwrite = msvcrt.open_osfhandle(p2cwrite.Detach(), 0)
jeżeli c2pread != -1:
c2pread = msvcrt.open_osfhandle(c2pread.Detach(), 0)
jeżeli errread != -1:
errread = msvcrt.open_osfhandle(errread.Detach(), 0)
jeżeli p2cwrite != -1:
self.stdin = io.open(p2cwrite, 'wb', bufsize)
jeżeli universal_newlines:
self.stdin = io.TextIOWrapper(self.stdin, write_through=Prawda,
line_buffering=(bufsize == 1))
jeżeli c2pread != -1:
self.stdout = io.open(c2pread, 'rb', bufsize)
jeżeli universal_newlines:
self.stdout = io.TextIOWrapper(self.stdout)
jeżeli errread != -1:
self.stderr = io.open(errread, 'rb', bufsize)
jeżeli universal_newlines:
self.stderr = io.TextIOWrapper(self.stderr)
self._closed_child_pipe_fds = Nieprawda
spróbuj:
self._execute_child(args, executable, preexec_fn, close_fds,
dalej_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
restore_signals, start_new_session)
wyjąwszy:
# Cleanup jeżeli the child failed starting.
dla f w filter(Nic, (self.stdin, self.stdout, self.stderr)):
spróbuj:
f.close()
wyjąwszy OSError:
dalej # Ignore EBADF albo other errors.
jeżeli nie self._closed_child_pipe_fds:
to_close = []
jeżeli stdin == PIPE:
to_close.append(p2cread)
jeżeli stdout == PIPE:
to_close.append(c2pwrite)
jeżeli stderr == PIPE:
to_close.append(errwrite)
jeżeli hasattr(self, '_devnull'):
to_close.append(self._devnull)
dla fd w to_close:
spróbuj:
os.close(fd)
wyjąwszy OSError:
dalej
podnieś
def _translate_newlines(self, data, encoding):
data = data.decode(encoding)
zwróć data.replace("\r\n", "\n").replace("\r", "\n")
def __enter__(self):
zwróć self
def __exit__(self, type, value, traceback):
jeżeli self.stdout:
self.stdout.close()
jeżeli self.stderr:
self.stderr.close()
spróbuj: # Flushing a BufferedWriter may podnieś an error
jeżeli self.stdin:
self.stdin.close()
w_końcu:
# Wait dla the process to terminate, to avoid zombies.
self.wait()
def __del__(self, _maxsize=sys.maxsize):
jeżeli nie self._child_created:
# We didn't get to successfully create a child process.
zwróć
# In case the child hasn't been waited on, check jeżeli it's done.
self._internal_poll(_deadstate=_maxsize)
jeżeli self.returncode jest Nic oraz _active jest nie Nic:
# Child jest still running, keep us alive until we can wait on it.
_active.append(self)
def _get_devnull(self):
jeżeli nie hasattr(self, '_devnull'):
self._devnull = os.open(os.devnull, os.O_RDWR)
zwróć self._devnull
def _stdin_write(self, input):
jeżeli input:
spróbuj:
self.stdin.write(input)
wyjąwszy BrokenPipeError:
# communicate() must ignore broken pipe error
dalej
wyjąwszy OSError jako e:
jeżeli e.errno == errno.EINVAL oraz self.poll() jest nie Nic:
# Issue #19612: On Windows, stdin.write() fails przy EINVAL
# jeżeli the process already exited before the write
dalej
inaczej:
podnieś
self.stdin.close()
def communicate(self, input=Nic, timeout=Nic):
"""Interact przy process: Send data to stdin. Read data from
stdout oraz stderr, until end-of-file jest reached. Wait for
process to terminate.
The optional "input" argument should be data to be sent to the
child process (jeżeli self.universal_newlines jest Prawda, this should
be a string; jeżeli it jest Nieprawda, "input" should be bytes), albo
Nic, jeżeli no data should be sent to the child.
communicate() returns a tuple (stdout, stderr). These will be
bytes or, jeżeli self.universal_newlines was Prawda, a string.
"""
jeżeli self._communication_started oraz input:
podnieś ValueError("Cannot send input after starting communication")
# Optimization: If we are nie worried about timeouts, we haven't
# started communicating, oraz we have one albo zero pipes, using select()
# albo threads jest unnecessary.
jeżeli (timeout jest Nic oraz nie self._communication_started oraz
[self.stdin, self.stdout, self.stderr].count(Nic) >= 2):
stdout = Nic
stderr = Nic
jeżeli self.stdin:
self._stdin_write(input)
albo_inaczej self.stdout:
stdout = self.stdout.read()
self.stdout.close()
albo_inaczej self.stderr:
stderr = self.stderr.read()
self.stderr.close()
self.wait()
inaczej:
jeżeli timeout jest nie Nic:
endtime = _time() + timeout
inaczej:
endtime = Nic
spróbuj:
stdout, stderr = self._communicate(input, endtime, timeout)
w_końcu:
self._communication_started = Prawda
sts = self.wait(timeout=self._remaining_time(endtime))
zwróć (stdout, stderr)
def poll(self):
zwróć self._internal_poll()
def _remaining_time(self, endtime):
"""Convenience dla _communicate when computing timeouts."""
jeżeli endtime jest Nic:
zwróć Nic
inaczej:
zwróć endtime - _time()
def _check_timeout(self, endtime, orig_timeout):
"""Convenience dla checking jeżeli a timeout has expired."""
jeżeli endtime jest Nic:
zwróć
jeżeli _time() > endtime:
podnieś TimeoutExpired(self.args, orig_timeout)
jeżeli _mswindows:
#
# Windows methods
#
def _get_handles(self, stdin, stdout, stderr):
"""Construct oraz zwróć tuple przy IO objects:
p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite
"""
jeżeli stdin jest Nic oraz stdout jest Nic oraz stderr jest Nic:
zwróć (-1, -1, -1, -1, -1, -1)
p2cread, p2cwrite = -1, -1
c2pread, c2pwrite = -1, -1
errread, errwrite = -1, -1
jeżeli stdin jest Nic:
p2cread = _winapi.GetStdHandle(_winapi.STD_INPUT_HANDLE)
jeżeli p2cread jest Nic:
p2cread, _ = _winapi.CreatePipe(Nic, 0)
p2cread = Handle(p2cread)
_winapi.CloseHandle(_)
albo_inaczej stdin == PIPE:
p2cread, p2cwrite = _winapi.CreatePipe(Nic, 0)
p2cread, p2cwrite = Handle(p2cread), Handle(p2cwrite)
albo_inaczej stdin == DEVNULL:
p2cread = msvcrt.get_osfhandle(self._get_devnull())
albo_inaczej isinstance(stdin, int):
p2cread = msvcrt.get_osfhandle(stdin)
inaczej:
# Assuming file-like object
p2cread = msvcrt.get_osfhandle(stdin.fileno())
p2cread = self._make_inheritable(p2cread)
jeżeli stdout jest Nic:
c2pwrite = _winapi.GetStdHandle(_winapi.STD_OUTPUT_HANDLE)
jeżeli c2pwrite jest Nic:
_, c2pwrite = _winapi.CreatePipe(Nic, 0)
c2pwrite = Handle(c2pwrite)
_winapi.CloseHandle(_)
albo_inaczej stdout == PIPE:
c2pread, c2pwrite = _winapi.CreatePipe(Nic, 0)
c2pread, c2pwrite = Handle(c2pread), Handle(c2pwrite)
albo_inaczej stdout == DEVNULL:
c2pwrite = msvcrt.get_osfhandle(self._get_devnull())
albo_inaczej isinstance(stdout, int):
c2pwrite = msvcrt.get_osfhandle(stdout)
inaczej:
# Assuming file-like object
c2pwrite = msvcrt.get_osfhandle(stdout.fileno())
c2pwrite = self._make_inheritable(c2pwrite)
jeżeli stderr jest Nic:
errwrite = _winapi.GetStdHandle(_winapi.STD_ERROR_HANDLE)
jeżeli errwrite jest Nic:
_, errwrite = _winapi.CreatePipe(Nic, 0)
errwrite = Handle(errwrite)
_winapi.CloseHandle(_)
albo_inaczej stderr == PIPE:
errread, errwrite = _winapi.CreatePipe(Nic, 0)
errread, errwrite = Handle(errread), Handle(errwrite)
albo_inaczej stderr == STDOUT:
errwrite = c2pwrite
albo_inaczej stderr == DEVNULL:
errwrite = msvcrt.get_osfhandle(self._get_devnull())
albo_inaczej isinstance(stderr, int):
errwrite = msvcrt.get_osfhandle(stderr)
inaczej:
# Assuming file-like object
errwrite = msvcrt.get_osfhandle(stderr.fileno())
errwrite = self._make_inheritable(errwrite)
zwróć (p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite)
def _make_inheritable(self, handle):
"""Return a duplicate of handle, which jest inheritable"""
h = _winapi.DuplicateHandle(
_winapi.GetCurrentProcess(), handle,
_winapi.GetCurrentProcess(), 0, 1,
_winapi.DUPLICATE_SAME_ACCESS)
zwróć Handle(h)
def _execute_child(self, args, executable, preexec_fn, close_fds,
dalej_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
unused_restore_signals, unused_start_new_session):
"""Execute program (MS Windows version)"""
assert nie dalej_fds, "pass_fds nie supported on Windows."
jeżeli nie isinstance(args, str):
args = list2cmdline(args)
# Process startup details
jeżeli startupinfo jest Nic:
startupinfo = STARTUPINFO()
jeżeli -1 nie w (p2cread, c2pwrite, errwrite):
startupinfo.dwFlags |= _winapi.STARTF_USESTDHANDLES
startupinfo.hStdInput = p2cread
startupinfo.hStdOutput = c2pwrite
startupinfo.hStdError = errwrite
jeżeli shell:
startupinfo.dwFlags |= _winapi.STARTF_USESHOWWINDOW
startupinfo.wShowWindow = _winapi.SW_HIDE
comspec = os.environ.get("COMSPEC", "cmd.exe")
args = '{} /c "{}"'.format (comspec, args)
# Start the process
spróbuj:
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
# no special security
Nic, Nic,
int(nie close_fds),
creationflags,
env,
cwd,
startupinfo)
w_końcu:
# Child jest launched. Close the parent's copy of those pipe
# handles that only the child should have open. You need
# to make sure that no handles to the write end of the
# output pipe are maintained w this process albo inaczej the
# pipe will nie close when the child process exits oraz the
# ReadFile will hang.
jeżeli p2cread != -1:
p2cread.Close()
jeżeli c2pwrite != -1:
c2pwrite.Close()
jeżeli errwrite != -1:
errwrite.Close()
jeżeli hasattr(self, '_devnull'):
os.close(self._devnull)
# Retain the process handle, but close the thread handle
self._child_created = Prawda
self._handle = Handle(hp)
self.pid = pid
_winapi.CloseHandle(ht)
def _internal_poll(self, _deadstate=Nic,
_WaitForSingleObject=_winapi.WaitForSingleObject,
_WAIT_OBJECT_0=_winapi.WAIT_OBJECT_0,
_GetExitCodeProcess=_winapi.GetExitCodeProcess):
"""Check jeżeli child process has terminated. Returns returncode
attribute.
This method jest called by __del__, so it can only refer to objects
w its local scope.
"""
jeżeli self.returncode jest Nic:
jeżeli _WaitForSingleObject(self._handle, 0) == _WAIT_OBJECT_0:
self.returncode = _GetExitCodeProcess(self._handle)
zwróć self.returncode
def wait(self, timeout=Nic, endtime=Nic):
"""Wait dla child process to terminate. Returns returncode
attribute."""
jeżeli endtime jest nie Nic:
timeout = self._remaining_time(endtime)
jeżeli timeout jest Nic:
timeout_millis = _winapi.INFINITE
inaczej:
timeout_millis = int(timeout * 1000)
jeżeli self.returncode jest Nic:
result = _winapi.WaitForSingleObject(self._handle,
timeout_millis)
jeżeli result == _winapi.WAIT_TIMEOUT:
podnieś TimeoutExpired(self.args, timeout)
self.returncode = _winapi.GetExitCodeProcess(self._handle)
zwróć self.returncode
def _readerthread(self, fh, buffer):
buffer.append(fh.read())
fh.close()
def _communicate(self, input, endtime, orig_timeout):
# Start reader threads feeding into a list hanging off of this
# object, unless they've already been started.
jeżeli self.stdout oraz nie hasattr(self, "_stdout_buff"):
self._stdout_buff = []
self.stdout_thread = \
threading.Thread(target=self._readerthread,
args=(self.stdout, self._stdout_buff))
self.stdout_thread.daemon = Prawda
self.stdout_thread.start()
jeżeli self.stderr oraz nie hasattr(self, "_stderr_buff"):
self._stderr_buff = []
self.stderr_thread = \
threading.Thread(target=self._readerthread,
args=(self.stderr, self._stderr_buff))
self.stderr_thread.daemon = Prawda
self.stderr_thread.start()
jeżeli self.stdin:
self._stdin_write(input)
# Wait dla the reader threads, albo time out. If we time out, the
# threads remain reading oraz the fds left open w case the user
# calls communicate again.
jeżeli self.stdout jest nie Nic:
self.stdout_thread.join(self._remaining_time(endtime))
jeżeli self.stdout_thread.is_alive():
podnieś TimeoutExpired(self.args, orig_timeout)
jeżeli self.stderr jest nie Nic:
self.stderr_thread.join(self._remaining_time(endtime))
jeżeli self.stderr_thread.is_alive():
podnieś TimeoutExpired(self.args, orig_timeout)
# Collect the output z oraz close both pipes, now that we know
# both have been read successfully.
stdout = Nic
stderr = Nic
jeżeli self.stdout:
stdout = self._stdout_buff
self.stdout.close()
jeżeli self.stderr:
stderr = self._stderr_buff
self.stderr.close()
# All data exchanged. Translate lists into strings.
jeżeli stdout jest nie Nic:
stdout = stdout[0]
jeżeli stderr jest nie Nic:
stderr = stderr[0]
zwróć (stdout, stderr)
def send_signal(self, sig):
"""Send a signal to the process
"""
jeżeli sig == signal.SIGTERM:
self.terminate()
albo_inaczej sig == signal.CTRL_C_EVENT:
os.kill(self.pid, signal.CTRL_C_EVENT)
albo_inaczej sig == signal.CTRL_BREAK_EVENT:
os.kill(self.pid, signal.CTRL_BREAK_EVENT)
inaczej:
podnieś ValueError("Unsupported signal: {}".format(sig))
def terminate(self):
"""Terminates the process
"""
spróbuj:
_winapi.TerminateProcess(self._handle, 1)
wyjąwszy PermissionError:
# ERROR_ACCESS_DENIED (winerror 5) jest received when the
# process already died.
rc = _winapi.GetExitCodeProcess(self._handle)
jeżeli rc == _winapi.STILL_ACTIVE:
podnieś
self.returncode = rc
kill = terminate
inaczej:
#
# POSIX methods
#
def _get_handles(self, stdin, stdout, stderr):
"""Construct oraz zwróć tuple przy IO objects:
p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite
"""
p2cread, p2cwrite = -1, -1
c2pread, c2pwrite = -1, -1
errread, errwrite = -1, -1
jeżeli stdin jest Nic:
dalej
albo_inaczej stdin == PIPE:
p2cread, p2cwrite = os.pipe()
albo_inaczej stdin == DEVNULL:
p2cread = self._get_devnull()
albo_inaczej isinstance(stdin, int):
p2cread = stdin
inaczej:
# Assuming file-like object
p2cread = stdin.fileno()
jeżeli stdout jest Nic:
dalej
albo_inaczej stdout == PIPE:
c2pread, c2pwrite = os.pipe()
albo_inaczej stdout == DEVNULL:
c2pwrite = self._get_devnull()
albo_inaczej isinstance(stdout, int):
c2pwrite = stdout
inaczej:
# Assuming file-like object
c2pwrite = stdout.fileno()
jeżeli stderr jest Nic:
dalej
albo_inaczej stderr == PIPE:
errread, errwrite = os.pipe()
albo_inaczej stderr == STDOUT:
errwrite = c2pwrite
albo_inaczej stderr == DEVNULL:
errwrite = self._get_devnull()
albo_inaczej isinstance(stderr, int):
errwrite = stderr
inaczej:
# Assuming file-like object
errwrite = stderr.fileno()
zwróć (p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite)
def _execute_child(self, args, executable, preexec_fn, close_fds,
dalej_fds, cwd, env,
startupinfo, creationflags, shell,
p2cread, p2cwrite,
c2pread, c2pwrite,
errread, errwrite,
restore_signals, start_new_session):
"""Execute program (POSIX version)"""
jeżeli isinstance(args, (str, bytes)):
args = [args]
inaczej:
args = list(args)
jeżeli shell:
args = ["/bin/sh", "-c"] + args
jeżeli executable:
args[0] = executable
jeżeli executable jest Nic:
executable = args[0]
orig_executable = executable
# For transferring possible exec failure z child to parent.
# Data format: "exception name:hex errno:description"
# Pickle jest nie used; it jest complex oraz involves memory allocation.
errpipe_read, errpipe_write = os.pipe()
# errpipe_write must nie be w the standard io 0, 1, albo 2 fd range.
low_fds_to_close = []
dopóki errpipe_write < 3:
low_fds_to_close.append(errpipe_write)
errpipe_write = os.dup(errpipe_write)
dla low_fd w low_fds_to_close:
os.close(low_fd)
spróbuj:
spróbuj:
# We must avoid complex work that could involve
# malloc albo free w the child process to avoid
# potential deadlocks, thus we do all this here.
# oraz dalej it to fork_exec()
jeżeli env jest nie Nic:
env_list = [os.fsencode(k) + b'=' + os.fsencode(v)
dla k, v w env.items()]
inaczej:
env_list = Nic # Use execv instead of execve.
executable = os.fsencode(executable)
jeżeli os.path.dirname(executable):
executable_list = (executable,)
inaczej:
# This matches the behavior of os._execvpe().
executable_list = tuple(
os.path.join(os.fsencode(dir), executable)
dla dir w os.get_exec_path(env))
fds_to_keep = set(pass_fds)
fds_to_keep.add(errpipe_write)
self.pid = _posixsubprocess.fork_exec(
args, executable_list,
close_fds, sorted(fds_to_keep), cwd, env_list,
p2cread, p2cwrite, c2pread, c2pwrite,
errread, errwrite,
errpipe_read, errpipe_write,
restore_signals, start_new_session, preexec_fn)
self._child_created = Prawda
w_końcu:
# be sure the FD jest closed no matter what
os.close(errpipe_write)
# self._devnull jest nie always defined.
devnull_fd = getattr(self, '_devnull', Nic)
jeżeli p2cread != -1 oraz p2cwrite != -1 oraz p2cread != devnull_fd:
os.close(p2cread)
jeżeli c2pwrite != -1 oraz c2pread != -1 oraz c2pwrite != devnull_fd:
os.close(c2pwrite)
jeżeli errwrite != -1 oraz errread != -1 oraz errwrite != devnull_fd:
os.close(errwrite)
jeżeli devnull_fd jest nie Nic:
os.close(devnull_fd)
# Prevent a double close of these fds z __init__ on error.
self._closed_child_pipe_fds = Prawda
# Wait dla exec to fail albo succeed; possibly raising an
# exception (limited w size)
errpipe_data = bytearray()
dopóki Prawda:
part = os.read(errpipe_read, 50000)
errpipe_data += part
jeżeli nie part albo len(errpipe_data) > 50000:
przerwij
w_końcu:
# be sure the FD jest closed no matter what
os.close(errpipe_read)
jeżeli errpipe_data:
spróbuj:
os.waitpid(self.pid, 0)
wyjąwszy ChildProcessError:
dalej
spróbuj:
exception_name, hex_errno, err_msg = (
errpipe_data.split(b':', 2))
wyjąwszy ValueError:
exception_name = b'SubprocessError'
hex_errno = b'0'
err_msg = (b'Bad exception data z child: ' +
repr(errpipe_data))
child_exception_type = getattr(
builtins, exception_name.decode('ascii'),
SubprocessError)
err_msg = err_msg.decode(errors="surrogatepass")
jeżeli issubclass(child_exception_type, OSError) oraz hex_errno:
errno_num = int(hex_errno, 16)
child_exec_never_called = (err_msg == "noexec")
jeżeli child_exec_never_called:
err_msg = ""
jeżeli errno_num != 0:
err_msg = os.strerror(errno_num)
jeżeli errno_num == errno.ENOENT:
jeżeli child_exec_never_called:
# The error must be z chdir(cwd).
err_msg += ': ' + repr(cwd)
inaczej:
err_msg += ': ' + repr(orig_executable)
podnieś child_exception_type(errno_num, err_msg)
podnieś child_exception_type(err_msg)
def _handle_exitstatus(self, sts, _WIFSIGNALED=os.WIFSIGNALED,
_WTERMSIG=os.WTERMSIG, _WIFEXITED=os.WIFEXITED,
_WEXITSTATUS=os.WEXITSTATUS):
"""All callers to this function MUST hold self._waitpid_lock."""
# This method jest called (indirectly) by __del__, so it cannot
# refer to anything outside of its local scope.
jeżeli _WIFSIGNALED(sts):
self.returncode = -_WTERMSIG(sts)
albo_inaczej _WIFEXITED(sts):
self.returncode = _WEXITSTATUS(sts)
inaczej:
# Should never happen
podnieś SubprocessError("Unknown child exit status!")
def _internal_poll(self, _deadstate=Nic, _waitpid=os.waitpid,
_WNOHANG=os.WNOHANG, _ECHILD=errno.ECHILD):
"""Check jeżeli child process has terminated. Returns returncode
attribute.
This method jest called by __del__, so it cannot reference anything
outside of the local scope (nor can any methods it calls).
"""
jeżeli self.returncode jest Nic:
jeżeli nie self._waitpid_lock.acquire(Nieprawda):
# Something inaczej jest busy calling waitpid. Don't allow two
# at once. We know nothing yet.
zwróć Nic
spróbuj:
jeżeli self.returncode jest nie Nic:
zwróć self.returncode # Another thread waited.
pid, sts = _waitpid(self.pid, _WNOHANG)
jeżeli pid == self.pid:
self._handle_exitstatus(sts)
wyjąwszy OSError jako e:
jeżeli _deadstate jest nie Nic:
self.returncode = _deadstate
albo_inaczej e.errno == _ECHILD:
# This happens jeżeli SIGCLD jest set to be ignored albo
# waiting dla child processes has otherwise been
# disabled dla our process. This child jest dead, we
# can't get the status.
# http://bugs.python.org/issue15756
self.returncode = 0
w_końcu:
self._waitpid_lock.release()
zwróć self.returncode
def _try_wait(self, wait_flags):
"""All callers to this function MUST hold self._waitpid_lock."""
spróbuj:
(pid, sts) = os.waitpid(self.pid, wait_flags)
wyjąwszy ChildProcessError:
# This happens jeżeli SIGCLD jest set to be ignored albo waiting
# dla child processes has otherwise been disabled dla our
# process. This child jest dead, we can't get the status.
pid = self.pid
sts = 0
zwróć (pid, sts)
def wait(self, timeout=Nic, endtime=Nic):
"""Wait dla child process to terminate. Returns returncode
attribute."""
jeżeli self.returncode jest nie Nic:
zwróć self.returncode
# endtime jest preferred to timeout. timeout jest only used for
# printing.
jeżeli endtime jest nie Nic albo timeout jest nie Nic:
jeżeli endtime jest Nic:
endtime = _time() + timeout
albo_inaczej timeout jest Nic:
timeout = self._remaining_time(endtime)
jeżeli endtime jest nie Nic:
# Enter a busy loop jeżeli we have a timeout. This busy loop was
# cribbed z Lib/threading.py w Thread.wait() at r71065.
delay = 0.0005 # 500 us -> initial delay of 1 ms
dopóki Prawda:
jeżeli self._waitpid_lock.acquire(Nieprawda):
spróbuj:
jeżeli self.returncode jest nie Nic:
przerwij # Another thread waited.
(pid, sts) = self._try_wait(os.WNOHANG)
assert pid == self.pid albo pid == 0
jeżeli pid == self.pid:
self._handle_exitstatus(sts)
przerwij
w_końcu:
self._waitpid_lock.release()
remaining = self._remaining_time(endtime)
jeżeli remaining <= 0:
podnieś TimeoutExpired(self.args, timeout)
delay = min(delay * 2, remaining, .05)
time.sleep(delay)
inaczej:
dopóki self.returncode jest Nic:
przy self._waitpid_lock:
jeżeli self.returncode jest nie Nic:
przerwij # Another thread waited.
(pid, sts) = self._try_wait(0)
# Check the pid oraz loop jako waitpid has been known to
# zwróć 0 even without WNOHANG w odd situations.
# http://bugs.python.org/issue14396.
jeżeli pid == self.pid:
self._handle_exitstatus(sts)
zwróć self.returncode
def _communicate(self, input, endtime, orig_timeout):
jeżeli self.stdin oraz nie self._communication_started:
# Flush stdio buffer. This might block, jeżeli the user has
# been writing to .stdin w an uncontrolled fashion.
self.stdin.flush()
jeżeli nie input:
self.stdin.close()
stdout = Nic
stderr = Nic
# Only create this mapping jeżeli we haven't already.
jeżeli nie self._communication_started:
self._fileobj2output = {}
jeżeli self.stdout:
self._fileobj2output[self.stdout] = []
jeżeli self.stderr:
self._fileobj2output[self.stderr] = []
jeżeli self.stdout:
stdout = self._fileobj2output[self.stdout]
jeżeli self.stderr:
stderr = self._fileobj2output[self.stderr]
self._save_input(input)
jeżeli self._input:
input_view = memoryview(self._input)
przy _PopenSelector() jako selector:
jeżeli self.stdin oraz input:
selector.register(self.stdin, selectors.EVENT_WRITE)
jeżeli self.stdout:
selector.register(self.stdout, selectors.EVENT_READ)
jeżeli self.stderr:
selector.register(self.stderr, selectors.EVENT_READ)
dopóki selector.get_map():
timeout = self._remaining_time(endtime)
jeżeli timeout jest nie Nic oraz timeout < 0:
podnieś TimeoutExpired(self.args, orig_timeout)
ready = selector.select(timeout)
self._check_timeout(endtime, orig_timeout)
# XXX Rewrite these to use non-blocking I/O on the file
# objects; they are no longer using C stdio!
dla key, events w ready:
jeżeli key.fileobj jest self.stdin:
chunk = input_view[self._input_offset :
self._input_offset + _PIPE_BUF]
spróbuj:
self._input_offset += os.write(key.fd, chunk)
wyjąwszy BrokenPipeError:
selector.unregister(key.fileobj)
key.fileobj.close()
inaczej:
jeżeli self._input_offset >= len(self._input):
selector.unregister(key.fileobj)
key.fileobj.close()
albo_inaczej key.fileobj w (self.stdout, self.stderr):
data = os.read(key.fd, 32768)
jeżeli nie data:
selector.unregister(key.fileobj)
key.fileobj.close()
self._fileobj2output[key.fileobj].append(data)
self.wait(timeout=self._remaining_time(endtime))
# All data exchanged. Translate lists into strings.
jeżeli stdout jest nie Nic:
stdout = b''.join(stdout)
jeżeli stderr jest nie Nic:
stderr = b''.join(stderr)
# Translate newlines, jeżeli requested.
# This also turns bytes into strings.
jeżeli self.universal_newlines:
jeżeli stdout jest nie Nic:
stdout = self._translate_newlines(stdout,
self.stdout.encoding)
jeżeli stderr jest nie Nic:
stderr = self._translate_newlines(stderr,
self.stderr.encoding)
zwróć (stdout, stderr)
def _save_input(self, input):
# This method jest called z the _communicate_with_*() methods
# so that jeżeli we time out dopóki communicating, we can kontynuuj
# sending input jeżeli we retry.
jeżeli self.stdin oraz self._input jest Nic:
self._input_offset = 0
self._input = input
jeżeli self.universal_newlines oraz input jest nie Nic:
self._input = self._input.encode(self.stdin.encoding)
def send_signal(self, sig):
"""Send a signal to the process
"""
os.kill(self.pid, sig)
def terminate(self):
"""Terminate the process przy SIGTERM
"""
self.send_signal(signal.SIGTERM)
def kill(self):
"""Kill the process przy SIGKILL
"""
self.send_signal(signal.SIGKILL)
| 6,483 | 0 | 591 |
c55bcbcf6e523260b0959341789ad27b8dd43606 | 1,279 | py | Python | examples/party.py | hulu316/zoonado | cb06102f95e7da9c0e418bb9e327045e012a1497 | [
"Apache-2.0"
] | 12 | 2016-04-14T09:55:38.000Z | 2018-01-07T13:12:47.000Z | examples/party.py | hulu316/zoonado | cb06102f95e7da9c0e418bb9e327045e012a1497 | [
"Apache-2.0"
] | 16 | 2016-07-21T09:45:38.000Z | 2017-09-22T19:06:14.000Z | examples/party.py | hulu316/zoonado | cb06102f95e7da9c0e418bb9e327045e012a1497 | [
"Apache-2.0"
] | 8 | 2016-07-21T09:06:37.000Z | 2019-07-26T05:48:00.000Z | import logging
import random
from tornado import gen
log = logging.getLogger()
@gen.coroutine
@gen.coroutine
| 23.685185 | 74 | 0.605942 | import logging
import random
from tornado import gen
log = logging.getLogger()
def arguments(parser):
parser.add_argument(
"--workers", "-w", type=int, default=5,
help="Number of workers to launch."
)
parser.add_argument(
"--znode-path", "-p", type=str, default="examplelock",
help="ZNode path to use for the election."
)
@gen.coroutine
def run(client, args):
log.info("Launching %d workers.", args.workers)
yield client.start()
yield [
worker(i, client, args)
for i in range(args.workers)
]
yield client.close()
@gen.coroutine
def worker(number, client, args):
party = client.recipes.Party(args.znode_path, "worker_%d" % number)
log.info("[WORKER #%d] Joining the party", number)
yield party.join()
for _ in range(10):
log.info("[WORKER #%d] Members I see: %s", number, party.members)
yield gen.sleep(.5)
should_leave = random.choice([False, False, True])
if should_leave:
log.info("[WORKER #%d] Leaving the party temporarily", number)
yield party.leave()
yield gen.sleep(1)
log.info("[WORKER #%d] Rejoining the party", number)
yield party.join()
yield party.leave()
| 1,095 | 0 | 67 |
15674cff063c40fbe52c1f8ea5a648cc8e393e80 | 20,501 | py | Python | custom-actions/actions/actions.py | moreymat/conciergerie-open-data | 5d7575af93dd9d7f5e2c4bd35c2f6c816d3c3d33 | [
"MIT"
] | null | null | null | custom-actions/actions/actions.py | moreymat/conciergerie-open-data | 5d7575af93dd9d7f5e2c4bd35c2f6c816d3c3d33 | [
"MIT"
] | 3 | 2021-09-10T15:36:06.000Z | 2021-10-06T08:27:57.000Z | custom-actions/actions/actions.py | moreymat/conciergerie-open-data | 5d7575af93dd9d7f5e2c4bd35c2f6c816d3c3d33 | [
"MIT"
] | null | null | null | # This files contains your custom actions which can be used to run
# custom Python code.
#
# See this guide on how to implement these action:
# https://rasa.com/docs/rasa/custom-actions
import json
import re
import numpy as np
from typing import Any, Text, Dict, List
from datetime import datetime
from actions import api_call
from rasa_sdk import Action, Tracker
from rasa_sdk.executor import CollectingDispatcher
from rasa_sdk.events import SlotSet, EventType, FollowupAction
keywords_delimitor = " |,|;|_|\|"
def levenshtein_distance(string1, string2):
"""compute the levenshtein distance between two words"""
distances = np.zeros((len(string1) + 1, len(string2) + 1))
for s1 in range(len(string1) + 1):
distances[s1][0] = s1
for s2 in range(len(string2) + 1):
distances[0][s2] = s2
a = 0
b = 0
c = 0
for s1 in range(1, len(string1) + 1):
for s2 in range(1, len(string2) + 1):
if string1[s1 - 1] == string2[s2 - 1]:
distances[s1][s2] = distances[s1 - 1][s2 - 1]
else:
a = distances[s1][s2 - 1]
b = distances[s1 - 1][s2]
c = distances[s1 - 1][s2 - 1]
if a <= b and a <= c:
distances[s1][s2] = a + 1
elif b <= a and b <= c:
distances[s1][s2] = b + 1
else:
distances[s1][s2] = c + 1
return distances[len(string1)][len(string2)]
def is_keyword_already_in_search(
keyword, user_search_list, exp_terms, minimal_distance=1
):
"""
Input: keyword: string
user_search_list: list of string
exp_terms: list of string
minimal_distance: int (default to 1)
Return True if there is a word in user_search_list or exp_terms that has a levenshtein distance of less or equal than minimal_distance with keyword
Return False if not
"""
flag_already_suggested = False
# Check the current word isn't already proposed (or a very similar word)
for suggested_keyword in exp_terms:
if levenshtein_distance(keyword.lower(), suggested_keyword) <= minimal_distance:
flag_already_suggested = True
# Check the current word isn't already entered by the user (or a very similar word)
for user_keyword in user_search_list:
if (
levenshtein_distance(keyword.lower(), user_keyword.lower())
<= minimal_distance
):
flag_already_suggested = True
return flag_already_suggested
def get_keywords_expanded_list(keywords_expanded, user_search):
"""
Input: keywords_expanded: Output of the expansion API
user_search: search entered by the user
Output: A list of keywords to display to the user
Order example: [barrage éolien]
- take the first keyword for barrage from the referentiel
- take the first keyword for barrage from the embeddings
- take the first keyword for éolien from the referentiel
- take the first keyword for éolien from the embeddings
- take the second keyword for barrage from the referentiel
[...]
Ignore words that have a levenshtein distance of less than 1 to another word already in the list: mainly to prevent plural version
"""
user_search_list = re.split(keywords_delimitor, user_search)
for i in range(len(user_search_list)):
user_search_list[i] = user_search_list[i].lower()
exp_terms = []
done = False
index = 0
while not done and index < 25:
done = True
for og_key in keywords_expanded: # loop on original keywords
# Take the n°index of the referentiel
og_word = og_key["referentiel"]["tags"]
if og_word is not None and len(og_word) > index: # if it exist
done = False
for ref_tag_word in re.split(keywords_delimitor, og_word[index]):
if not is_keyword_already_in_search(
ref_tag_word, user_search_list, exp_terms, 1
):
exp_terms.append(ref_tag_word.lower())
# Take the n°index of the embeddings
og_word = og_key["tree"][0]["similar_senses"]
if og_word is not None and len(og_word) > index: # if it exist
done = False
for ref_tag_word in re.split(
keywords_delimitor, og_word[index][0]["sense"]
):
if not is_keyword_already_in_search(
ref_tag_word, user_search_list, exp_terms, 1
):
exp_terms.append(ref_tag_word.lower())
index += 1
return "|".join(exp_terms)
def keyword_originating_from_og_key(keyword, og_key_tree):
"""
Input: keyword: A keyword proposed by the expansion API
og_key_tree: A tree resulting from a keyword entered by the user
Output: True if keyword was proposed because of its similarity with og_key_tree["original_keyword], False if not
"""
if og_key_tree["referentiel"]["tags"] is not None:
for keyw in og_key_tree["referentiel"]["tags"]:
if keyw == keyword:
return True
for sense in og_key_tree["tree"]:
for similar_sense in sense["similar_senses"]:
if similar_sense[0]["sense"] == keyword:
return True
return False
def process_keyword_feedback(keyword_proposed, keywords_expanded, keywords_feedback):
"""
Input: keyword_proposed: A keyword proposed to the user
keywords_expanded: Output of the API expansion (see API doc)
keywords_feedback: List of keywords chosen by the user
Output: original_keywords: list of keywords that resulted in the proposition of keyword by the API
feedback: wether or not keyword_proposed was chosen by the user
"""
original_keywords = []
for og_key_tree in keywords_expanded:
if keyword_originating_from_og_key(keyword_proposed, og_key_tree):
original_keywords.append(og_key_tree["original_keyword"])
if keyword_proposed in keywords_feedback:
feedback = 1
else:
feedback = -1
return original_keywords, feedback
class ResetKeywordsSlot(Action):
"""
Reset all the slots of the rasa chatbot
"""
class AskForKeywordsFeedbackSlotAction(Action):
"""
Ask the user to choose which keywords are useful to him during the search_form
"""
class SearchKeywordsInDatabase(Action):
"""
Search DataSud API with keywords provided by the users and display results
"""
class SendSearchInfo(Action):
"""
Send search information to the database
"""
class SendKeywordsFeedback(Action):
"""
Send keywords proposed to the user to the database
"""
class FeedbackProposition(Action):
"""
If we found results, ask the user if he wants to give feedback
"""
class AskForResultsFeedbackSlotAction(Action):
"""
Ask the user to choose which results were useful to him
"""
class RecapResultsFeedback(Action):
"""
Feedback format example: 0 2 1
Summarize the feedback to the user
"""
class InitialMessage(Action):
"""
First message sent to user by chatbot
"""
class SetDatasudFlag(Action):
"""
Set has_asked_datasud_flag to True
"""
class SetCUFlag(Action):
"""
Set has_asked_cu_flag to True
"""
class AnythingElse(Action):
"""
Message sent to user to ask if he needs anything else
"""
class SendResultsFeedback(Action):
"""
Feedback format example: 0 3 4
Send Search results feedback to the database
"""
class ProposeAlternativeSearch(Action):
"""
Let the user know what he can do if he didn't what he needed via the chatbot.
"""
| 30.875 | 174 | 0.588801 | # This files contains your custom actions which can be used to run
# custom Python code.
#
# See this guide on how to implement these action:
# https://rasa.com/docs/rasa/custom-actions
import json
import re
import numpy as np
from typing import Any, Text, Dict, List
from datetime import datetime
from actions import api_call
from rasa_sdk import Action, Tracker
from rasa_sdk.executor import CollectingDispatcher
from rasa_sdk.events import SlotSet, EventType, FollowupAction
keywords_delimitor = " |,|;|_|\|"
def levenshtein_distance(string1, string2):
"""compute the levenshtein distance between two words"""
distances = np.zeros((len(string1) + 1, len(string2) + 1))
for s1 in range(len(string1) + 1):
distances[s1][0] = s1
for s2 in range(len(string2) + 1):
distances[0][s2] = s2
a = 0
b = 0
c = 0
for s1 in range(1, len(string1) + 1):
for s2 in range(1, len(string2) + 1):
if string1[s1 - 1] == string2[s2 - 1]:
distances[s1][s2] = distances[s1 - 1][s2 - 1]
else:
a = distances[s1][s2 - 1]
b = distances[s1 - 1][s2]
c = distances[s1 - 1][s2 - 1]
if a <= b and a <= c:
distances[s1][s2] = a + 1
elif b <= a and b <= c:
distances[s1][s2] = b + 1
else:
distances[s1][s2] = c + 1
return distances[len(string1)][len(string2)]
def is_keyword_already_in_search(
keyword, user_search_list, exp_terms, minimal_distance=1
):
"""
Input: keyword: string
user_search_list: list of string
exp_terms: list of string
minimal_distance: int (default to 1)
Return True if there is a word in user_search_list or exp_terms that has a levenshtein distance of less or equal than minimal_distance with keyword
Return False if not
"""
flag_already_suggested = False
# Check the current word isn't already proposed (or a very similar word)
for suggested_keyword in exp_terms:
if levenshtein_distance(keyword.lower(), suggested_keyword) <= minimal_distance:
flag_already_suggested = True
# Check the current word isn't already entered by the user (or a very similar word)
for user_keyword in user_search_list:
if (
levenshtein_distance(keyword.lower(), user_keyword.lower())
<= minimal_distance
):
flag_already_suggested = True
return flag_already_suggested
def get_keywords_expanded_list(keywords_expanded, user_search):
"""
Input: keywords_expanded: Output of the expansion API
user_search: search entered by the user
Output: A list of keywords to display to the user
Order example: [barrage éolien]
- take the first keyword for barrage from the referentiel
- take the first keyword for barrage from the embeddings
- take the first keyword for éolien from the referentiel
- take the first keyword for éolien from the embeddings
- take the second keyword for barrage from the referentiel
[...]
Ignore words that have a levenshtein distance of less than 1 to another word already in the list: mainly to prevent plural version
"""
user_search_list = re.split(keywords_delimitor, user_search)
for i in range(len(user_search_list)):
user_search_list[i] = user_search_list[i].lower()
exp_terms = []
done = False
index = 0
while not done and index < 25:
done = True
for og_key in keywords_expanded: # loop on original keywords
# Take the n°index of the referentiel
og_word = og_key["referentiel"]["tags"]
if og_word is not None and len(og_word) > index: # if it exist
done = False
for ref_tag_word in re.split(keywords_delimitor, og_word[index]):
if not is_keyword_already_in_search(
ref_tag_word, user_search_list, exp_terms, 1
):
exp_terms.append(ref_tag_word.lower())
# Take the n°index of the embeddings
og_word = og_key["tree"][0]["similar_senses"]
if og_word is not None and len(og_word) > index: # if it exist
done = False
for ref_tag_word in re.split(
keywords_delimitor, og_word[index][0]["sense"]
):
if not is_keyword_already_in_search(
ref_tag_word, user_search_list, exp_terms, 1
):
exp_terms.append(ref_tag_word.lower())
index += 1
return "|".join(exp_terms)
def keyword_originating_from_og_key(keyword, og_key_tree):
"""
Input: keyword: A keyword proposed by the expansion API
og_key_tree: A tree resulting from a keyword entered by the user
Output: True if keyword was proposed because of its similarity with og_key_tree["original_keyword], False if not
"""
if og_key_tree["referentiel"]["tags"] is not None:
for keyw in og_key_tree["referentiel"]["tags"]:
if keyw == keyword:
return True
for sense in og_key_tree["tree"]:
for similar_sense in sense["similar_senses"]:
if similar_sense[0]["sense"] == keyword:
return True
return False
def process_keyword_feedback(keyword_proposed, keywords_expanded, keywords_feedback):
"""
Input: keyword_proposed: A keyword proposed to the user
keywords_expanded: Output of the API expansion (see API doc)
keywords_feedback: List of keywords chosen by the user
Output: original_keywords: list of keywords that resulted in the proposition of keyword by the API
feedback: wether or not keyword_proposed was chosen by the user
"""
original_keywords = []
for og_key_tree in keywords_expanded:
if keyword_originating_from_og_key(keyword_proposed, og_key_tree):
original_keywords.append(og_key_tree["original_keyword"])
if keyword_proposed in keywords_feedback:
feedback = 1
else:
feedback = -1
return original_keywords, feedback
class ResetKeywordsSlot(Action):
"""
Reset all the slots of the rasa chatbot
"""
def name(self):
return "action_reset_all_slots"
def run(self, dispatcher, tracker, domain):
return [
SlotSet("keywords", None),
SlotSet("keywords_expanded", None),
SlotSet("keywords_feedback", None),
SlotSet("results", None),
SlotSet("results_feedback", None),
SlotSet("search_target_feedback", None),
]
class AskForKeywordsFeedbackSlotAction(Action):
"""
Ask the user to choose which keywords are useful to him during the search_form
"""
def name(self) -> Text:
return "action_ask_keywords_feedback"
def run(
self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict
) -> List[EventType]:
user_search = tracker.get_slot("keywords")
keywords_expanded = api_call.get_keywords_expansion_query(
user_search, [{"name": "datasud", "type": "tags"}]
)
keywords_expanded_list = get_keywords_expanded_list(
keywords_expanded, user_search
)
if len(keywords_expanded_list) > 0:
keywords = [
{"content_type": "text", "title": x, "payload": "k" + str(i)}
for i, x in enumerate(
re.split(keywords_delimitor, keywords_expanded_list)
)
]
# Le custom payload est détecté comme un texte, donc j'ajoute un type qui permet facilement de détecter que c'est un un custom payload au niveau du widget
payload = {
"type": "custom_payload_keywords",
"text": "Voici quelques mots-clés supplémentaires. Cliquez sur ceux qui vous semblent pertinents, puis appuyez sur entrée, pour approfondir votre recherche.",
"nb_max_keywords": 8,
"keywords": keywords,
}
dispatcher.utter_message(json.dumps(payload))
return [
SlotSet("keywords_expanded", keywords_expanded),
SlotSet("keywords_proposed", keywords_expanded_list),
]
else:
return [
SlotSet("keywords_expanded", ""),
SlotSet("keywords_proposed", ""),
SlotSet("keywords_feedback", ""),
SlotSet("requested_slot", None),
]
class SearchKeywordsInDatabase(Action):
"""
Search DataSud API with keywords provided by the users and display results
"""
def name(self):
return "action_search_database"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
keywords = tracker.get_slot("keywords")
conversation_id = tracker.sender_id
keywords_feedback = tracker.get_slot("keywords_feedback")
results = api_call.get_results_from_keywords(keywords, keywords_feedback, 5)
reranking_data = []
reranking_data.append({"api_hostname": "datasud", "results_list": results})
catalog_url = "https://trouver.datasud.fr/dataset/"
if len(results) > 0:
results = api_call.get_search_reranking_query(
conversation_id, keywords, reranking_data
)
results_payload = []
for i, result in enumerate(results[0:5]):
results_payload.append(
{
"title": result["title"],
"author": result["owner_org"],
"url": catalog_url + result["url"],
"description": result["description"],
}
)
dispatcher.utter_message(text="Voici les résultats que j'ai pu trouver :")
payload = {
"type": "custom_payload_results_display",
"nb_max_results": 5,
"results": results_payload,
}
dispatcher.utter_message(json.dumps(payload))
return [
SlotSet("results", results),
]
else:
dispatcher.utter_message(
text="Je suis désolé, je n'ai trouvé aucun résultat pour votre recherche."
)
return [
SlotSet("results", None),
]
class SendSearchInfo(Action):
"""
Send search information to the database
"""
def name(self):
return "action_send_search_information_to_API"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
api_call.add_expansion_search_query(
tracker.sender_id, tracker.get_slot("keywords"), date
)
api_call.add_reranking_search_query(
tracker.sender_id, tracker.get_slot("keywords"), date
)
class SendKeywordsFeedback(Action):
"""
Send keywords proposed to the user to the database
"""
def name(self):
return "action_send_keywords_feedback_to_expansion_API"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
keywords_expanded = tracker.get_slot("keywords_expanded")
keywords_proposed = tracker.get_slot("keywords_proposed")
keywords_feedback = tracker.get_slot("keywords_feedback")
feedbacks_list = []
if keywords_proposed is not None and keywords_feedback is not None:
keywords_proposed = re.split(keywords_delimitor, keywords_proposed)
keywords_feedback = re.split(keywords_delimitor, keywords_feedback)
for keyword in keywords_proposed:
original_keywords, feedback = process_keyword_feedback(
keyword, keywords_expanded, keywords_feedback
)
for og_keyword in original_keywords:
feedbacks_list.append(
{
"original_keyword": og_keyword,
"proposed_keyword": keyword,
"feedback": feedback,
}
)
api_call.add_expansion_feedback_query(
tracker.sender_id, tracker.get_slot("keywords"), feedbacks_list
)
class FeedbackProposition(Action):
"""
If we found results, ask the user if he wants to give feedback
"""
def name(self):
return "action_feedback_proposition"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
yesnoButtons = [
{"payload": "/affirm", "title": "Oui !"},
{"payload": "/deny", "title": "Non merci",},
]
results = tracker.get_slot("results")
if results != None and len(results) > 0:
dispatcher.utter_message(
text="Seriez-vous d'accord de prendre quelques secondes de votre temps pour m'aider à m'améliorer ?",
buttons=yesnoButtons,
)
else:
# return {"event": "followup", "name": "action_recap_feedback_to_user"}
return [FollowupAction("utter_submit_feedback_form")]
class AskForResultsFeedbackSlotAction(Action):
"""
Ask the user to choose which results were useful to him
"""
def name(self) -> Text:
return "action_ask_results_feedback"
def run(
self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict
) -> List[EventType]:
results = tracker.get_slot("results")
feedback_payload = []
catalog_url = "https://trouver.datasud.fr/dataset/"
for i, result in enumerate(results[0:5]):
feedback_payload.append(
{
"title": result["title"],
"author": result["owner_org"],
"url": catalog_url + result["url"],
"description": result["description"],
}
)
dispatcher.utter_message(
text="Veuillez cocher les résultats qui vous ont étés utiles :"
)
payload = {
"type": "custom_payload_feedbacks_display",
"nb_max_feedbacks": 5,
"feedbacks": feedback_payload,
}
dispatcher.utter_message(json.dumps(payload))
class RecapResultsFeedback(Action):
"""
Feedback format example: 0 2 1
Summarize the feedback to the user
"""
def name(self):
return "action_recap_feedback_to_user"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
dispatcher.utter_message(text="Merci beaucoup pour votre retour !")
class InitialMessage(Action):
"""
First message sent to user by chatbot
"""
def name(self):
return "action_initial_message"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
propositions = [
{"payload": "/request_search", "title": "Je recherche des données."},
{
"payload": "/ask_datasud",
"title": "J'aimerais en savoir plus sur Datasud.",
},
{
"payload": "/ask_cu",
"title": "Je voudrais connaître les conditions d'utilisation.",
},
{"payload": "/goodbye", "title": "Je n'ai besoin de rien."},
]
dispatcher.utter_message(
text="Bonjour, comment puis-je vous aider ?", buttons=propositions
)
class SetDatasudFlag(Action):
"""
Set has_asked_datasud_flag to True
"""
def name(self):
return "action_set_datasud_flag"
def run(self, dispatcher, tracker, domain):
return [
SlotSet("has_asked_datasud_flag", True),
]
class SetCUFlag(Action):
"""
Set has_asked_cu_flag to True
"""
def name(self):
return "action_set_cu_flag"
def run(self, dispatcher, tracker, domain):
return [
SlotSet("has_asked_cu_flag", True),
]
class AnythingElse(Action):
"""
Message sent to user to ask if he needs anything else
"""
def name(self):
return "action_anything_else"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
has_asked_datasud_flag = tracker.get_slot("has_asked_datasud_flag")
has_asked_cu_flag = tracker.get_slot("has_asked_cu_flag")
propositions = []
propositions.append(
{"payload": "/request_search", "title": "Je recherche des données."}
)
if not has_asked_datasud_flag:
propositions.append(
{
"payload": "/ask_datasud",
"title": "J'aimerais en savoir plus sur Datasud.",
}
)
if not has_asked_cu_flag:
propositions.append(
{
"payload": "/ask_cu",
"title": "Je voudrais connaître les conditions d'utilisation.",
}
)
propositions.append({"payload": "/goodbye", "title": "Je n'ai besoin de rien."})
dispatcher.utter_message(
text="Avez-vous besoin d'autre chose ?", buttons=propositions
)
class SendResultsFeedback(Action):
"""
Feedback format example: 0 3 4
Send Search results feedback to the database
"""
def name(self):
return "action_send_results_feedback_to_reranking_API"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
conversation_id = tracker.sender_id
user_search = tracker.get_slot("keywords")
results = tracker.get_slot("results")
search_target_feedback = tracker.get_slot("search_target_feedback")
results_feedback = tracker.get_slot("results_feedback")
if results_feedback is not None:
results_feedback = results_feedback.split(" ")
else:
results_feedback = []
feedbacks_list = []
if results is not None and len(results) > 0:
if len(results_feedback) > 0:
for i, result in enumerate(results):
if str(i) in results_feedback:
feedbacks_list.append({"result": result, "feedback": 1})
else:
feedbacks_list.append({"result": result, "feedback": -1})
else:
for i, result in enumerate(results):
feedbacks_list.append({"result": result, "feedback": 0})
api_call.add_reranking_feedback_query(
conversation_id, user_search, search_target_feedback, feedbacks_list,
)
class ProposeAlternativeSearch(Action):
"""
Let the user know what he can do if he didn't what he needed via the chatbot.
"""
def name(self):
return "action_data_not_found"
def run(
self,
dispatcher: CollectingDispatcher,
tracker: Tracker,
domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
user_search = tracker.get_slot("keywords")
print(keywords_delimitor, user_search)
classic_search_link = "https://trouver.datasud.fr/dataset?q=" + "&&".join(
re.split(keywords_delimitor, user_search)
)
dispatcher.utter_message(
response="utter_data_not_found", classic_search_link=classic_search_link
)
dispatcher.utter_message(response="utter_contact_datasud")
| 11,868 | 0 | 756 |
8b90a4f43bd5093ec4d3568f8c57eccff8dc5136 | 1,580 | py | Python | ea_makers_be/ea_makers_app/urls.py | kimtuan1102/ea_makers_be | 509b21bc2b444bfe5be252acbbc4340be2439104 | [
"MIT"
] | null | null | null | ea_makers_be/ea_makers_app/urls.py | kimtuan1102/ea_makers_be | 509b21bc2b444bfe5be252acbbc4340be2439104 | [
"MIT"
] | 1 | 2021-06-10T23:12:09.000Z | 2021-06-10T23:12:09.000Z | ea_makers_be/ea_makers_app/urls.py | kimtuan1102/ea_makers_be | 509b21bc2b444bfe5be252acbbc4340be2439104 | [
"MIT"
] | null | null | null | from django.urls import path, include
from rest_framework.routers import DefaultRouter
from .views import TransactionViewSet, AccountMT4ViewSet, AccountHistoryViewSet, \
AccountConfigViewSet, transaction_approve, transaction_reject, user_info, ea_license, PackageViewSet, \
ServerInfoViewSet, account_config_admin_approve, create_order, \
account_config_superadmin_approve, OfficeViewSet, extension_order, license_time, \
guarantee_time, ChangePasswordView
router = DefaultRouter()
router.register(r'transaction', TransactionViewSet)
router.register(r'account-mt4', AccountMT4ViewSet)
router.register(r'account-history', AccountHistoryViewSet)
router.register(r'account-config', AccountConfigViewSet)
router.register(r'package', PackageViewSet)
router.register(r'server-info', ServerInfoViewSet)
router.register(r'office', OfficeViewSet)
urlpatterns = [
path('api/', include(router.urls)),
path(r'api/transaction/approve/<int:id>', transaction_approve),
path(r'api/transaction/reject/<int:id>', transaction_reject),
path(r'api/user-info', user_info),
path(r'api/ea-license/<int:id>', ea_license),
path(r'api/account-config/admin-approve/<int:id>', account_config_admin_approve),
path(r'api/account-config/superadmin-approve/<int:id>', account_config_superadmin_approve),
path(r'api/create-order', create_order),
path(r'api/extension-order/<int:id>', extension_order),
path(r'api/license-time', license_time),
path(r'api/guarantee-time', guarantee_time),
path(r'api/change-password', ChangePasswordView.as_view())
]
| 50.967742 | 107 | 0.779114 | from django.urls import path, include
from rest_framework.routers import DefaultRouter
from .views import TransactionViewSet, AccountMT4ViewSet, AccountHistoryViewSet, \
AccountConfigViewSet, transaction_approve, transaction_reject, user_info, ea_license, PackageViewSet, \
ServerInfoViewSet, account_config_admin_approve, create_order, \
account_config_superadmin_approve, OfficeViewSet, extension_order, license_time, \
guarantee_time, ChangePasswordView
router = DefaultRouter()
router.register(r'transaction', TransactionViewSet)
router.register(r'account-mt4', AccountMT4ViewSet)
router.register(r'account-history', AccountHistoryViewSet)
router.register(r'account-config', AccountConfigViewSet)
router.register(r'package', PackageViewSet)
router.register(r'server-info', ServerInfoViewSet)
router.register(r'office', OfficeViewSet)
urlpatterns = [
path('api/', include(router.urls)),
path(r'api/transaction/approve/<int:id>', transaction_approve),
path(r'api/transaction/reject/<int:id>', transaction_reject),
path(r'api/user-info', user_info),
path(r'api/ea-license/<int:id>', ea_license),
path(r'api/account-config/admin-approve/<int:id>', account_config_admin_approve),
path(r'api/account-config/superadmin-approve/<int:id>', account_config_superadmin_approve),
path(r'api/create-order', create_order),
path(r'api/extension-order/<int:id>', extension_order),
path(r'api/license-time', license_time),
path(r'api/guarantee-time', guarantee_time),
path(r'api/change-password', ChangePasswordView.as_view())
]
| 0 | 0 | 0 |
c84be1fd2203937f1b01a3ed6d022fb5c41f653d | 14,418 | py | Python | osmdigest/pythonify.py | MatthewDaws/OSMDigest | aba65c4bf28b14e123711f760bf0baf1be2e82c5 | [
"MIT"
] | null | null | null | osmdigest/pythonify.py | MatthewDaws/OSMDigest | aba65c4bf28b14e123711f760bf0baf1be2e82c5 | [
"MIT"
] | null | null | null | osmdigest/pythonify.py | MatthewDaws/OSMDigest | aba65c4bf28b14e123711f760bf0baf1be2e82c5 | [
"MIT"
] | null | null | null | """
pythonify
~~~~~~~~
Converts an OSM XML file into a number of Python dictionaries. This process
is slow, and (very) memory intensive, so can be split into a number of parts.
We also support saving and loading the results from compressed pickle'd files
(these should be viewed a temporary files, which are unlikely to be compatible
across different Python runtime systems.)
"""
from . import digest as _digest
import pickle as _pickle
import lzma as _lzma
import array as _array
import bisect as _bisect
from collections import defaultdict as _defaultdict
def unpickle(filename):
"""Load an object from a .xz compressed pickle file."""
with _lzma.open(filename, "rb") as file:
return _pickle.load(file)
def pickle(object, filename):
"""Save an object to a .xz compressed pickle file."""
with _lzma.open(filename, "wb") as file:
return _pickle.dump(object, file)
class Nodes():
"""Extracts the coordinate data (only) for the nodes. Stores data in a
dictionary.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
class NodesPacked():
"""A more efficient storage method than the :class:`Nodes` implements, but
at the cost of slower querying.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.untitled0.py
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
@staticmethod
@staticmethod
@staticmethod
def from_Nodes(nodes):
"""Construct a new instance from an instance of :class:`Nodes`."""
new = NodesPacked(None)
interim_list = [ (osm_id, lon, lat) for osm_id, (lon, lat) in nodes._nodes.items() ]
new._osm_ids, new._longitude, new._latitude = NodesPacked._arrays_from_unordered_list(interim_list)
return new
class Ways():
"""Extracts (only) the list of nodes for each way.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.untitled0.py
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
class Relations():
"""Extracts (only) the list of members of each relation.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.untitled0.py
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
class Tags():
"""Stores all the tags in a lookup optimised for finding the objects
(nodes, ways and relations) which have a given tag.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.
"""
@staticmethod
@staticmethod
@property
def all_node_tags(self):
"""Set of all `(key, value)` pairs of tags of nodes."""
return set(self.from_nodes)
@property
def all_node_tag_keys(self):
"""Set of all keys which occur on tags of nodes."""
return set(k for (k,v) in self.from_nodes)
@property
def all_way_tags(self):
"""Set of all `(key, value)` pairs of tags of ways."""
return set(self.from_ways)
@property
def all_way_tag_keys(self):
"""Set of all keys which occur on tags of ways."""
return set(k for (k,v) in self.from_ways)
@property
def all_relation_tags(self):
"""Set of all `(key, value)` pairs of tags of relations."""
return set(self.from_relations)
@property
def all_relation_tag_keys(self):
"""Set of all keys which occur on tags of relations."""
return set(k for (k,v) in self.from_relations)
def from_key_value(self, key, value):
"""Return a list of all element which have the tag `{key: value}`.
:param key: The key to search for tags with.
:param value: The value to search for tags with.
:return: A list of pairs `(typename, osm_id)` where `typename` is one
of "node", "way" or "relation" and `osm_id` is the id of the element.
"""
out = []
for osm_id in self.nodes((key, value)):
out.append(("node", osm_id))
for osm_id in self.ways((key, value)):
out.append(("way", osm_id))
for osm_id in self.relations((key, value)):
out.append(("relation", osm_id))
return out
def from_key(self, key):
"""Return a list of all element which have the tag `key`.
:param key: The key to search for tags with.
:return: A list of triple `(typename, value, osm_id)` where `typename`
is one of "node", "way" or "relation", `value` is the value of the
tag, and `osm_id` is the id of the element.
"""
out = []
for value, osm_id in self.nodes_from_key(key):
out.append(("node", value, osm_id))
for value, osm_id in self.ways_from_key(key):
out.append(("way", value, osm_id))
for value, osm_id in self.relations_from_key(key):
out.append(("relation", value, osm_id))
return out
def nodes(self, key_pair):
"""Returns a list of all the nodes which have the tag `{key: value}`.
:param key_pair: `(key, value)` of tag to search for.
:return: list, maybe empty, of ids of nodes with this tag.
"""
return self._by_key_pair(self.from_nodes, key_pair)
def nodes_from_key(self, key):
"""Returns a list of all the nodes which have the tag key.
:param key: The key of tags to search for.
:return: list, maybe empty, of pairs `(value, id)` where `value` is the
value from the tag, and `id` is osm id of the node.
"""
return self._by_key(self.from_nodes, key)
def ways(self, key_pair):
"""Returns a list of all the ways which have the tag `{key: value}`.
:param key_pair: `(key, value)` of tag to search for.
:return: list, maybe empty, of ids of ways with this tag.
"""
return self._by_key_pair(self.from_ways, key_pair)
def ways_from_key(self, key):
"""Returns a list of all the ways which have the tag key.
:param key: The key of tags to search for.
:return: list, maybe empty, of pairs `(value, id)` where `value` is the
value from the tag, and `id` is osm id of the way.
"""
return self._by_key(self.from_ways, key)
def relations(self, key_pair):
"""Returns a list of all the relations which have the tag
`{key: value}`.
:param key_pair: `(key, value)` of tag to search for.
:return: list, maybe empty, of ids of relations with this tag.
"""
return self._by_key_pair(self.from_relations, key_pair)
def relations_from_key(self, key):
"""Returns a list of all the relations which have the tag key.
:param key: The key of tags to search for.
:return: list, maybe empty, of pairs `(value, id)` where `value` is the
value from the tag, and `id` is osm id of the relation.
"""
return self._by_key(self.from_relations, key)
class TagsById():
"""A lookup from osm id number to tags.
:param tags: An instance of :class:`Tags`.
"""
@staticmethod
def node(self, osm_id):
"""Return a (possibly empty) dictionary of tags for the node with
this id.
"""
if osm_id in self._nodes:
return self._nodes[osm_id]
return dict()
def way(self, osm_id):
"""Return a (possibly empty) dictionary of tags for the node with
this id.
"""
if osm_id in self._ways:
return self._ways[osm_id]
return dict()
def relation(self, osm_id):
"""Return a (possibly empty) dictionary of tags for the node with
this id.
"""
if osm_id in self._relations:
return self._relations[osm_id]
return dict()
def pythonify_and_pickle(file, out_filename):
"""Convert all the data in the XML file and save as pickled files for
nodes, ways, relations and tags separately.
:param file: Filename (the file will be opened 4 times, so passing a file
object will not work). Can be anything which :module:`digest` can parse.
:param out_filename: If is `test` then writes files `test_nodes.pic.xz`
through `test_tags.pic.xz`
:return: A tuple of the 4 output filenames for nodes, ways, relations
and tags.
"""
obj = NodesPacked(file)
out = [out_filename + "_nodes.pic.xz"]
pickle(obj, out[0])
for typpe, name in [(Ways, "ways"), (Relations, "relations"),
(Tags, "tags")]:
obj = None
obj = typpe(file)
name = "{}_{}.pic.xz".format(out_filename, name)
pickle(obj, name)
out.append(name)
return out
| 36.593909 | 107 | 0.608337 | """
pythonify
~~~~~~~~
Converts an OSM XML file into a number of Python dictionaries. This process
is slow, and (very) memory intensive, so can be split into a number of parts.
We also support saving and loading the results from compressed pickle'd files
(these should be viewed a temporary files, which are unlikely to be compatible
across different Python runtime systems.)
"""
from . import digest as _digest
import pickle as _pickle
import lzma as _lzma
import array as _array
import bisect as _bisect
from collections import defaultdict as _defaultdict
def unpickle(filename):
"""Load an object from a .xz compressed pickle file."""
with _lzma.open(filename, "rb") as file:
return _pickle.load(file)
def pickle(object, filename):
"""Save an object to a .xz compressed pickle file."""
with _lzma.open(filename, "wb") as file:
return _pickle.dump(object, file)
def _all_elements(file):
gen = _digest.parse(file)
osm, bounds = next(gen), next(gen)
if not isinstance(osm, _digest.OSM) or not isinstance(bounds, _digest.Bounds):
raise Exception("Unexpected initial two elements. Has the XML file format changed?")
yield from gen
def _all_elements_of_type(file, typename, fast=True):
if not fast:
yield from (element for element in _all_elements(file)
if element.name == typename )
else:
for element in _all_elements(file):
if element.name == typename:
yield element
else:
if typename == "node":
return
elif typename == "way" and element.name == "relation":
return
class Nodes():
"""Extracts the coordinate data (only) for the nodes. Stores data in a
dictionary.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
def __init__(self, file, fast=True):
self._nodes = dict()
for node in _all_elements_of_type(file, "node", fast):
self._nodes[node.osm_id] = (node.longitude, node.latitude)
def __getitem__(self, index):
if index in self._nodes:
return self._nodes[index]
raise KeyError()
def __iter__(self):
yield from self._nodes.items()
class NodesPacked():
"""A more efficient storage method than the :class:`Nodes` implements, but
at the cost of slower querying.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.untitled0.py
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
def __init__(self, file, fast=True):
if file is not None:
interim_list = []
for node in _all_elements_of_type(file, "node", fast):
interim_list.append( (node.osm_id, node.longitude, node.latitude) )
self._osm_ids, self._longitude, self._latitude = self._arrays_from_unordered_list(interim_list)
else:
self._osm_ids, self._longitude, self._latitude = None, None, None
def __getitem__(self, index):
i = _bisect.bisect_left(self._osm_ids, index)
if i == len(self._osm_ids) or self._osm_ids[i] != index:
raise KeyError()
lon, lat = self._longitude[i], self._latitude[i]
return lon / 1e7, lat / 1e7
def __iter__(self):
for osm_id, lon, lat in zip(self._osm_ids, self._longitude, self._latitude):
yield (osm_id, (lon / 1e7, lat / 1e7))
@staticmethod
def _from_float(fl):
if fl >= 0:
return int(fl * 1e7 + 0.5)
return int(fl * 1e7 - 0.5)
@staticmethod
def _arrays_from_unordered_list(input):
input.sort(key = lambda tri : tri[0])
osm_ids = _array.array("Q")
lons, lats = _array.array("l"), _array.array("l")
for (osm_id, lon, lat) in input:
osm_ids.append(osm_id)
lons.append(NodesPacked._from_float(lon))
lats.append(NodesPacked._from_float(lat))
return osm_ids, lons, lats
@staticmethod
def from_Nodes(nodes):
"""Construct a new instance from an instance of :class:`Nodes`."""
new = NodesPacked(None)
interim_list = [ (osm_id, lon, lat) for osm_id, (lon, lat) in nodes._nodes.items() ]
new._osm_ids, new._longitude, new._latitude = NodesPacked._arrays_from_unordered_list(interim_list)
return new
class Ways():
"""Extracts (only) the list of nodes for each way.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.untitled0.py
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
def __init__(self, file, fast=True):
self._ways = dict()
for way in _all_elements_of_type(file, "way", fast):
self._ways[way.osm_id] = way.nodes
def __getitem__(self, index):
if index in self._ways:
return self._ways[index]
raise KeyError()
def __iter__(self):
yield from self._ways.items()
class Relations():
"""Extracts (only) the list of members of each relation.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.untitled0.py
:param fast: If True (default) then assume the input XML file is organised
so that all nodes occur first, then all ways, and then all relations.
"""
def __init__(self, file, fast=True):
self._rels = dict()
for rel in _all_elements_of_type(file, "relation", fast):
self._rels[rel.osm_id] = rel.members
def __getitem__(self, index):
if index in self._rels:
return self._rels[index]
raise KeyError()
def __iter__(self):
yield from self._rels.items()
class Tags():
"""Stores all the tags in a lookup optimised for finding the objects
(nodes, ways and relations) which have a given tag.
:param file: Construct from the filename or file-like object; can be
anything which :module:`digest` can parse.
"""
def __init__(self, file):
self.from_nodes = _defaultdict(list)
self.from_ways = _defaultdict(list)
self.from_relations = _defaultdict(list)
lookup = {"node" : self.from_nodes, "way": self.from_ways,
"relation": self.from_relations }
for element in _all_elements(file):
d = lookup[element.name]
for key_pair in element.tags.items():
d[key_pair].append(element.osm_id)
@staticmethod
def _by_key_pair(dictionary, key_pair):
if key_pair in dictionary:
return dictionary[key_pair]
return []
@staticmethod
def _by_key(dictionary, key):
out = []
for key_pair in dictionary:
if key_pair[0] == key:
for osm_id in dictionary[key_pair]:
out.append( (key_pair[1], osm_id) )
return out
@property
def all_node_tags(self):
"""Set of all `(key, value)` pairs of tags of nodes."""
return set(self.from_nodes)
@property
def all_node_tag_keys(self):
"""Set of all keys which occur on tags of nodes."""
return set(k for (k,v) in self.from_nodes)
@property
def all_way_tags(self):
"""Set of all `(key, value)` pairs of tags of ways."""
return set(self.from_ways)
@property
def all_way_tag_keys(self):
"""Set of all keys which occur on tags of ways."""
return set(k for (k,v) in self.from_ways)
@property
def all_relation_tags(self):
"""Set of all `(key, value)` pairs of tags of relations."""
return set(self.from_relations)
@property
def all_relation_tag_keys(self):
"""Set of all keys which occur on tags of relations."""
return set(k for (k,v) in self.from_relations)
def from_key_value(self, key, value):
"""Return a list of all element which have the tag `{key: value}`.
:param key: The key to search for tags with.
:param value: The value to search for tags with.
:return: A list of pairs `(typename, osm_id)` where `typename` is one
of "node", "way" or "relation" and `osm_id` is the id of the element.
"""
out = []
for osm_id in self.nodes((key, value)):
out.append(("node", osm_id))
for osm_id in self.ways((key, value)):
out.append(("way", osm_id))
for osm_id in self.relations((key, value)):
out.append(("relation", osm_id))
return out
def from_key(self, key):
"""Return a list of all element which have the tag `key`.
:param key: The key to search for tags with.
:return: A list of triple `(typename, value, osm_id)` where `typename`
is one of "node", "way" or "relation", `value` is the value of the
tag, and `osm_id` is the id of the element.
"""
out = []
for value, osm_id in self.nodes_from_key(key):
out.append(("node", value, osm_id))
for value, osm_id in self.ways_from_key(key):
out.append(("way", value, osm_id))
for value, osm_id in self.relations_from_key(key):
out.append(("relation", value, osm_id))
return out
def nodes(self, key_pair):
"""Returns a list of all the nodes which have the tag `{key: value}`.
:param key_pair: `(key, value)` of tag to search for.
:return: list, maybe empty, of ids of nodes with this tag.
"""
return self._by_key_pair(self.from_nodes, key_pair)
def nodes_from_key(self, key):
"""Returns a list of all the nodes which have the tag key.
:param key: The key of tags to search for.
:return: list, maybe empty, of pairs `(value, id)` where `value` is the
value from the tag, and `id` is osm id of the node.
"""
return self._by_key(self.from_nodes, key)
def ways(self, key_pair):
"""Returns a list of all the ways which have the tag `{key: value}`.
:param key_pair: `(key, value)` of tag to search for.
:return: list, maybe empty, of ids of ways with this tag.
"""
return self._by_key_pair(self.from_ways, key_pair)
def ways_from_key(self, key):
"""Returns a list of all the ways which have the tag key.
:param key: The key of tags to search for.
:return: list, maybe empty, of pairs `(value, id)` where `value` is the
value from the tag, and `id` is osm id of the way.
"""
return self._by_key(self.from_ways, key)
def relations(self, key_pair):
"""Returns a list of all the relations which have the tag
`{key: value}`.
:param key_pair: `(key, value)` of tag to search for.
:return: list, maybe empty, of ids of relations with this tag.
"""
return self._by_key_pair(self.from_relations, key_pair)
def relations_from_key(self, key):
"""Returns a list of all the relations which have the tag key.
:param key: The key of tags to search for.
:return: list, maybe empty, of pairs `(value, id)` where `value` is the
value from the tag, and `id` is osm id of the relation.
"""
return self._by_key(self.from_relations, key)
class TagsById():
"""A lookup from osm id number to tags.
:param tags: An instance of :class:`Tags`.
"""
def __init__(self, tags):
if not isinstance(tags, Tags):
raise ValueError("Need an instance of Tags for construction.")
self._nodes = _defaultdict(dict)
self._ways = _defaultdict(dict)
self._relations = _defaultdict(dict)
self._populate(tags.from_nodes, self._nodes)
self._populate(tags.from_ways, self._ways)
self._populate(tags.from_relations, self._relations)
@staticmethod
def _populate(input, output):
for (key, value), ids in input.items():
for osm_id in ids:
output[osm_id][key] = value
def node(self, osm_id):
"""Return a (possibly empty) dictionary of tags for the node with
this id.
"""
if osm_id in self._nodes:
return self._nodes[osm_id]
return dict()
def way(self, osm_id):
"""Return a (possibly empty) dictionary of tags for the node with
this id.
"""
if osm_id in self._ways:
return self._ways[osm_id]
return dict()
def relation(self, osm_id):
"""Return a (possibly empty) dictionary of tags for the node with
this id.
"""
if osm_id in self._relations:
return self._relations[osm_id]
return dict()
def pythonify_and_pickle(file, out_filename):
"""Convert all the data in the XML file and save as pickled files for
nodes, ways, relations and tags separately.
:param file: Filename (the file will be opened 4 times, so passing a file
object will not work). Can be anything which :module:`digest` can parse.
:param out_filename: If is `test` then writes files `test_nodes.pic.xz`
through `test_tags.pic.xz`
:return: A tuple of the 4 output filenames for nodes, ways, relations
and tags.
"""
obj = NodesPacked(file)
out = [out_filename + "_nodes.pic.xz"]
pickle(obj, out[0])
for typpe, name in [(Ways, "ways"), (Relations, "relations"),
(Tags, "tags")]:
obj = None
obj = typpe(file)
name = "{}_{}.pic.xz".format(out_filename, name)
pickle(obj, name)
out.append(name)
return out
| 4,228 | 0 | 608 |
27998331b309e55cd8a388ea6423e56a7a5e56ce | 3,081 | py | Python | odoo/custom/src/private/rml_reports/stock/picking.py | ecosoft-odoo/mh-doodba | 093f14850aaff337951b4829b24bf32eee6e6d40 | [
"BSL-1.0"
] | 1 | 2021-10-03T08:11:18.000Z | 2021-10-03T08:11:18.000Z | odoo/custom/src/private/rml_reports/stock/picking.py | ecosoft-odoo/mh-doodba | 093f14850aaff337951b4829b24bf32eee6e6d40 | [
"BSL-1.0"
] | null | null | null | odoo/custom/src/private/rml_reports/stock/picking.py | ecosoft-odoo/mh-doodba | 093f14850aaff337951b4829b24bf32eee6e6d40 | [
"BSL-1.0"
] | null | null | null | # -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2004-2010 Tiny SPRL (<http://tiny.be>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import time
from report import report_sxw
from osv import osv
import pooler
report_sxw.report_sxw('report.stock.picking.list.mh','stock.picking.out','',parser=picking, header="internal")
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
| 45.308824 | 123 | 0.61863 | # -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2004-2010 Tiny SPRL (<http://tiny.be>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import time
from report import report_sxw
from osv import osv
import pooler
class picking(report_sxw.rml_parse):
def __init__(self, cr, uid, name, context):
super(picking, self).__init__(cr, uid, name, context=context)
self.localcontext.update({
'time': time,
'get_product_desc':self.get_product_desc,
'display_invoice_address':self.display_invoice_address
})
def get_product_desc(self,move_line):
desc = move_line.name.replace(move_line.product_id.default_code,'').replace('[]','')
if move_line.product_id.partner_barcode:
desc = desc + ' ' + '(' + move_line.product_id.partner_barcode + ')'
desc = desc + ' ' + '[' + move_line.product_id.product_main_code + ']'
return desc
def display_invoice_address(self,picking):
# Only for this report, update printed = True
self.pool.get('stock.picking.out').write(self.cr, self.uid, picking.id, {'printed':True})
# Get the SO
order_obj = self.pool.get('sale.order')
order_ids = order_obj.search(self.cr, self.uid, [('name','=',picking.origin)], limit=1)
if len(order_ids) == 0: # Sale Order not found.
return False
order = order_obj.browse(self.cr, self.uid, order_ids[0])
# Get Invoice Address object
if not (order.partner_invoice_id and order.partner_invoice_id.id):
return False
address_string = (order.partner_invoice_id.title.name or '') + ' ' + order.partner_invoice_id.name + '\n'
address_string += self.pool.get('res.partner')._display_address(self.cr, self.uid, order.partner_invoice_id) + '\n'
address_string += order.partner_invoice_id.phone or order.partner_invoice_id.email or ''
return address_string
report_sxw.report_sxw('report.stock.picking.list.mh','stock.picking.out','',parser=picking, header="internal")
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
| 1,703 | 15 | 126 |
d4c29d051bf16489715a8cedb73ff6c590a2f1fb | 7,846 | py | Python | CatVsDogs(TestLoop,DogsOverfitting97%,Cats(veryLess)).py | shyamgupta196/LearningPyTorch | a585b708039357207ad4d3b29a3fc7bd2d1cc7e8 | [
"Apache-2.0"
] | 1 | 2021-04-06T11:26:39.000Z | 2021-04-06T11:26:39.000Z | CatVsDogs(TestLoop,DogsOverfitting97%,Cats(veryLess)).py | shyamgupta196/LearningPyTorch | a585b708039357207ad4d3b29a3fc7bd2d1cc7e8 | [
"Apache-2.0"
] | null | null | null | CatVsDogs(TestLoop,DogsOverfitting97%,Cats(veryLess)).py | shyamgupta196/LearningPyTorch | a585b708039357207ad4d3b29a3fc7bd2d1cc7e8 | [
"Apache-2.0"
] | null | null | null | """
In this TorchDaily we will TRAIN
A MODEL USING TRANSFER LEARNING
Cats Vs Dogs Dataset
EARLIER ACC==14% OR LESS
NOW ITS 70% AND MORE (BUT ONLY FOR DOGS )
### Cats are very poorly classified (IDK WHY but soon i will figure that out)
THE POWER OF ALEXNET (PRETRAINED MODELS IS VISIBLE)
DATE ==> 10-05-21
"""
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from torchvision import transforms, datasets, models
import torchvision
from tqdm import tqdm
import os
import PIL.Image as Image
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# prepare data
convert = transforms.Compose(
[
transforms.Resize((128, 128)),
transforms.RandomHorizontalFlip(0.2),
transforms.ToTensor(),
]
)
# dataloader
data = datasets.ImageFolder(root="PetImages/", transform=convert)
Loader = DataLoader(data, batch_size=64, shuffle=True)
MAP = {0: "Cat", 1: "Dog"}
##UNCOMMENT FOR SEEING THE DATA IMAGES
# fig, ax = plt.subplots(8, 8, figsize=(20, 20))
# fig.suptitle("Dogs And Cats IMages")
# for i, (img, lab) in zip(range(0, 8 * 8), Loader):
# x = i // 8
# y = i % 8
# print(f"{x},{y}")
# ax[x, y].imshow(img[i].squeeze().permute(1,2,0))
# ax[x, y].set_title(f"{lab[i]}")
# ax[x, y].axis("off")
# plt.show()
# # Add on classifier
# # HOW TO CHANGE THE INPUT LAYER WHICH ACCEPTS THE 224*224 INPUT
# # I WANNA CHANGE THAT TO 128*128 THIS SIZE WILL SUFFICE
# We Use AlexNet for transfer learning
##answers below
alexnet = torchvision.models.alexnet(pretrained=True)
for param in alexnet.parameters():
param.requires_grad = False
# Add a avgpool here
avgpool = nn.AdaptiveAvgPool2d((7, 7))
# Replace the classifier layer
# to customise it according to our output
alexnet.classifier = nn.Sequential(
nn.Linear(256 * 7 * 7, 1024),
nn.Linear(1024, 256),
nn.Linear(256, 2),
)
# putting model in a training mode
alexnet.train()
print(alexnet)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(alexnet.parameters(), lr=0.001)
EPOCHS = 1
TRAIN = False
losses = []
if TRAIN:
training_loop(alexnet, optimizer, EPOCHS)
plt.plot(losses)
plt.show()
TEST = False
history = []
if TEST:
test()
print("Validation Complete")
with open("ModelHistory.txt", "w") as f:
for i in history:
f.writelines(f"{i}")
print("Validation Complete")
## This model reported a accuracy of 97%(on DOGS ONLY) using AlexNet
## the Pros of using a pretrained model is clearly seen here
## date -- 13th May 2021 (thursday)
####ACCURACY AND OTHER THINGS TOO TO BE APPENDED SOON ######
PREDICT = True
def predict(model, test_image_name):
"""
Function to predict the class of a single test image
Parameters
:param model: Model to test
:param test_image_name: Test image
"""
transform = transforms.Compose(
[transforms.Resize((128, 128)), transforms.ToTensor()]
)
test_image = Image.open(test_image_name)
test_image_tensor = transform(test_image).to(device)
plt.imshow(test_image)
plt.imshow(test_image_tensor.squeeze().permute(1, 2, 0))
plt.show()
with torch.no_grad():
model.eval()
# Model outputs log probabilities
test_image_tensor = test_image_tensor.unsqueeze(0)
print(test_image_tensor.shape)
x = alexnet.features(test_image_tensor)
x = avgpool(x)
x = x.view(-1, 256 * 7 * 7)
out = alexnet.classifier(x)
ps = torch.exp(out)
topk, topclass = ps.topk(2, dim=1)
for i in range(2):
print(
"Predcition",
i + 1,
":",
f"topclass {topclass.numpy()}",
MAP[topclass.numpy()[0][i]],
", Score: ",
f"topk {topk.numpy()}",
topk.numpy()[0][i],
)
print(f"out: {out}")
if PREDICT:
checkpoint = torch.load("catsvdogs.pth")
alexnet.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
for params in alexnet.parameters():
params.requires_grad == False
predict(alexnet, "PetTest/CatTest.jpg")
| 29.8327 | 114 | 0.582207 | """
In this TorchDaily we will TRAIN
A MODEL USING TRANSFER LEARNING
Cats Vs Dogs Dataset
EARLIER ACC==14% OR LESS
NOW ITS 70% AND MORE (BUT ONLY FOR DOGS )
### Cats are very poorly classified (IDK WHY but soon i will figure that out)
THE POWER OF ALEXNET (PRETRAINED MODELS IS VISIBLE)
DATE ==> 10-05-21
"""
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from torchvision import transforms, datasets, models
import torchvision
from tqdm import tqdm
import os
import PIL.Image as Image
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
# prepare data
convert = transforms.Compose(
[
transforms.Resize((128, 128)),
transforms.RandomHorizontalFlip(0.2),
transforms.ToTensor(),
]
)
# dataloader
data = datasets.ImageFolder(root="PetImages/", transform=convert)
Loader = DataLoader(data, batch_size=64, shuffle=True)
MAP = {0: "Cat", 1: "Dog"}
##UNCOMMENT FOR SEEING THE DATA IMAGES
# fig, ax = plt.subplots(8, 8, figsize=(20, 20))
# fig.suptitle("Dogs And Cats IMages")
# for i, (img, lab) in zip(range(0, 8 * 8), Loader):
# x = i // 8
# y = i % 8
# print(f"{x},{y}")
# ax[x, y].imshow(img[i].squeeze().permute(1,2,0))
# ax[x, y].set_title(f"{lab[i]}")
# ax[x, y].axis("off")
# plt.show()
# # Add on classifier
# # HOW TO CHANGE THE INPUT LAYER WHICH ACCEPTS THE 224*224 INPUT
# # I WANNA CHANGE THAT TO 128*128 THIS SIZE WILL SUFFICE
# We Use AlexNet for transfer learning
##answers below
alexnet = torchvision.models.alexnet(pretrained=True)
for param in alexnet.parameters():
param.requires_grad = False
# Add a avgpool here
avgpool = nn.AdaptiveAvgPool2d((7, 7))
# Replace the classifier layer
# to customise it according to our output
alexnet.classifier = nn.Sequential(
nn.Linear(256 * 7 * 7, 1024),
nn.Linear(1024, 256),
nn.Linear(256, 2),
)
# putting model in a training mode
alexnet.train()
print(alexnet)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(alexnet.parameters(), lr=0.001)
EPOCHS = 1
TRAIN = False
losses = []
def training_loop(model, optimizer, epochs):
for epoch in range(epochs):
try:
for img, lab in tqdm(Loader):
img = img.to(device)
lab = lab.to(device)
x = alexnet.features(img)
x = avgpool(x)
x = x.view(-1, 256 * 7 * 7)
predictions = alexnet.classifier(x)
loss = criterion(predictions, lab)
optimizer.step()
optimizer.zero_grad()
losses.append(loss.item())
print(f"loss: {loss.item():.4f}")
except Exception as e:
print(str(e))
state = {
"epoch": epoch,
"state_dict": alexnet.state_dict(),
"optimizer": optimizer.state_dict(),
}
torch.save(state, "catsvdogs.pth")
if TRAIN:
training_loop(alexnet, optimizer, EPOCHS)
plt.plot(losses)
plt.show()
TEST = False
history = []
def test():
test = datasets.ImageFolder(root="PetTest/", transform=convert)
testLoader = DataLoader(test, batch_size=16, shuffle=False)
checkpoint = torch.load("catsvdogs.pth")
alexnet.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
for params in alexnet.parameters():
params.requires_grad == False
print(alexnet)
with torch.no_grad():
# Set to evaluation mode
alexnet.eval()
train_data_size = 101
valid_data_size = 101
# Validation loop
# Loss and Accuracy within the epoch
valid_loss = 0.0
valid_acc = 0.0
for j, (inputs, labels) in enumerate(testLoader):
inputs = inputs.to(device)
labels = labels.to(device)
# Forward pass - compute outputs on input data using the model
x = alexnet.features(inputs)
x = avgpool(x)
x = x.view(-1, 256 * 7 * 7)
outputs = alexnet.classifier(x)
# Compute loss
loss = criterion(outputs, labels)
# Compute the total loss for the batch and add it to valid_loss
valid_loss += loss.item() * inputs.size(0)
# Calculate validation accuracy
ret, predictions = torch.max(outputs.data, 1)
correct_counts = predictions.eq(labels.data.view_as(predictions))
# Convert correct_counts to float and then compute the mean
acc = torch.mean(correct_counts.type(torch.FloatTensor))
# Compute total accuracy in the whole batch and add to valid_acc
valid_acc += acc.item() * inputs.size(0)
print(
"""Validation Batch number: {:03d},
Validation: Loss: {:.4f},
Accuracy: {:.4f}""".format(j,
loss.item(),
acc.item()
)
)
# Find average training loss and training accuracy
avg_valid_loss = valid_loss / valid_data_size
avg_valid_acc = valid_acc / valid_data_size
history.append([avg_valid_loss, avg_valid_acc])
print(
" Training: Loss: {:.4f}, Accuracy: {:.4f}%, \n\t\tValidation : Loss : {:.4f}, Accuracy: {:.4f}%".format(
avg_train_loss,
avg_train_acc * 100,
avg_valid_loss,
avg_valid_acc * 100,
)
)
plt.plot(valid_acc)
plt.plot(valid_loss)
plt.show()
if TEST:
test()
print("Validation Complete")
with open("ModelHistory.txt", "w") as f:
for i in history:
f.writelines(f"{i}")
print("Validation Complete")
## This model reported a accuracy of 97%(on DOGS ONLY) using AlexNet
## the Pros of using a pretrained model is clearly seen here
## date -- 13th May 2021 (thursday)
####ACCURACY AND OTHER THINGS TOO TO BE APPENDED SOON ######
PREDICT = True
def predict(model, test_image_name):
"""
Function to predict the class of a single test image
Parameters
:param model: Model to test
:param test_image_name: Test image
"""
transform = transforms.Compose(
[transforms.Resize((128, 128)), transforms.ToTensor()]
)
test_image = Image.open(test_image_name)
test_image_tensor = transform(test_image).to(device)
plt.imshow(test_image)
plt.imshow(test_image_tensor.squeeze().permute(1, 2, 0))
plt.show()
with torch.no_grad():
model.eval()
# Model outputs log probabilities
test_image_tensor = test_image_tensor.unsqueeze(0)
print(test_image_tensor.shape)
x = alexnet.features(test_image_tensor)
x = avgpool(x)
x = x.view(-1, 256 * 7 * 7)
out = alexnet.classifier(x)
ps = torch.exp(out)
topk, topclass = ps.topk(2, dim=1)
for i in range(2):
print(
"Predcition",
i + 1,
":",
f"topclass {topclass.numpy()}",
MAP[topclass.numpy()[0][i]],
", Score: ",
f"topk {topk.numpy()}",
topk.numpy()[0][i],
)
print(f"out: {out}")
if PREDICT:
checkpoint = torch.load("catsvdogs.pth")
alexnet.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
for params in alexnet.parameters():
params.requires_grad == False
predict(alexnet, "PetTest/CatTest.jpg")
| 3,367 | 0 | 50 |
10a37586581b383194d5be72752acbb49c929082 | 40,380 | py | Python | score_based_pruning.py | Joey61Liuyi/Early-Bird-Tickets | 7bb23243bc4343519d58d828f0d3254e80c5fedd | [
"MIT"
] | null | null | null | score_based_pruning.py | Joey61Liuyi/Early-Bird-Tickets | 7bb23243bc4343519d58d828f0d3254e80c5fedd | [
"MIT"
] | null | null | null | score_based_pruning.py | Joey61Liuyi/Early-Bird-Tickets | 7bb23243bc4343519d58d828f0d3254e80c5fedd | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# @Time : 2021/12/27 16:34
# @Author : LIU YI
import argparse
import copy
import random
import pandas as pd
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
import wandb
# from models import *
import models
import wandb
# os.environ['CUDA_VISIBLE_DEVICES'] = '4'
from model_complexity import get_model_infos
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=str, default='cifar100',
help='training dataset (default: cifar10)')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',
help='input batch size for testing (default: 256)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--depth', type=int, default=16,
help='depth of the vgg')
parser.add_argument('--percent', type=float, default=0.5,
help='scale sparse rate (default: 0.5)')
parser.add_argument('--model', default='', type=str, metavar='PATH',
help='path to the model (default: none)')
parser.add_argument('--save', default='./baseline/vgg16-cifar100', type=str, metavar='PATH',
help='path to save pruned model (default: none)')
parser.add_argument('--save_1', default='./baseline/vgg16-cifar100', type=str, metavar='PATH',
help='path to save pruned model (default: none)')
parser.add_argument('--start_epoch', default=1, type=int, metavar='N', help='manual start epoch number')
parser.add_argument('--end_epoch', default=160, type=int, metavar='N', help='manual end epoch number')
# quantized parameters
parser.add_argument('--bits_A', default=8, type=int, help='input quantization bits')
parser.add_argument('--bits_W', default=8, type=int, help='weight quantization bits')
parser.add_argument('--bits_G', default=8, type=int, help='gradient quantization bits')
parser.add_argument('--bits_E', default=8, type=int, help='error quantization bits')
parser.add_argument('--bits_R', default=16, type=int, help='rand number quantization bits')
parser.add_argument('--arch', default='vgg', type=str,
help='architecture to use')
# multi-gpus
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
seed = 1
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if not os.path.exists(args.save):
os.makedirs(args.save)
gpu = args.gpu_ids
gpu_ids = args.gpu_ids.split(',')
args.gpu_ids = []
for gpu_id in gpu_ids:
id = int(gpu_id)
if id > 0:
args.gpu_ids.append(id)
if len(args.gpu_ids) > 0:
torch.cuda.set_device(args.gpu_ids[0])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.arch.endswith('lp'):
# model = models.__dict__[args.arch](bits_A=args.bits_A, bits_E=args.bits_E, bits_W=args.bits_W, dataset=args.dataset, depth=args.depth)
model = models.__dict__[args.arch](8, 8, 32, dataset=args.dataset, depth=args.depth)
elif args.dataset == 'imagenet':
model = models.__dict__[args.arch](pretrained=False)
if len(args.gpu_ids) > 1:
model = torch.nn.DataParallel(model, device_ids=args.gpu_ids)
else:
model = models.__dict__[args.arch](dataset=args.dataset, depth=args.depth)
if args.dataset == 'cifar10':
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
else:
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
if args.cuda:
model.cuda()
wandb_project = 'pruning_score'
name = 'with_out_normalization_60'
# wandb.init(project=wandb_project, name=name)
#
# random_search(cfg_mask_all, args.percent)
# channel_score_search(model, args.percent, train_loader)
# greedy_search(model, args.percent, train_loader)
# layer_remove_check(model, train_loader)
# rate_check(model, args.percent, train_loader)
channel_remove_check(model, train_loader, 0.61, 0.2)
# layer_wise_pruning(model, train_loader, 0.8)
#
# data = np.load('1633.66.npy', allow_pickle=True)
# data = data.item()
# cfg = data['cfg']
# cfg_mask = data['mask_cfg']
# for i in range(len(cfg_mask)):
# cfg_mask[i] = np.asarray(cfg_mask[i].cpu().numpy())
# score = check_score(model, cfg, cfg_mask)
# print(score)
| 37.879925 | 146 | 0.58316 | # -*- coding: utf-8 -*-
# @Time : 2021/12/27 16:34
# @Author : LIU YI
import argparse
import copy
import random
import pandas as pd
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
import wandb
# from models import *
import models
import wandb
# os.environ['CUDA_VISIBLE_DEVICES'] = '4'
from model_complexity import get_model_infos
def create_model(model, cfg, cfg_mask):
while 0 in cfg:
cfg.remove(0)
# if args.arch.endswith('lp'):
# # model = models.__dict__[args.arch](bits_A=args.bits_A, bits_E=args.bits_E, bits_W=args.bits_W, dataset=args.dataset, depth=args.depth)
# newmodel = models.__dict__[args.arch](8, 8, 32, dataset=args.dataset, depth=args.depth)
# elif args.dataset == 'imagenet':
# newmodel = models.__dict__[args.arch](pretrained=False)
# if len(args.gpu_ids) > 1:
# model = torch.nn.DataParallel(model, device_ids=args.gpu_ids)
# else:
newmodel = models.__dict__['vgg'](dataset='cifar100', cfg = cfg)
# for [m0, m1] in zip(model.modules(), newmodel.modules()):
# if isinstance(m0, nn.BatchNorm2d):
# if np.sum(end_mask) == 0:
# continue
# idx1 = np.squeeze(np.argwhere(end_mask))
# if idx1.size == 1:
# idx1 = np.resize(idx1, (1,))
# m1.weight.data = m0.weight.data[idx1.tolist()].clone()
# m1.bias.data = m0.bias.data[idx1.tolist()].clone()
# m1.running_mean = m0.running_mean[idx1.tolist()].clone()
# m1.running_var = m0.running_var[idx1.tolist()].clone()
# layer_id_in_cfg += 1
# start_mask = copy.copy(end_mask)
# if layer_id_in_cfg < len(cfg_mask): # do not change in Final FC
# end_mask = cfg_mask[layer_id_in_cfg]
# elif isinstance(m0, nn.Conv2d):
# if np.sum(end_mask) == 0:
# continue
# idx0 = np.squeeze(np.argwhere(start_mask))
# idx1 = np.squeeze(np.argwhere(end_mask))
# # random set for test
# # new_end_mask = np.asarray(end_mask.cpu().numpy())
# # new_end_mask = np.append(new_end_mask[int(len(new_end_mask)/2):], new_end_mask[:int(len(new_end_mask)/2)])
# # idx1 = np.squeeze(np.argwhere(new_end_mask))
#
# # print('In shape: {:d}, Out shape {:d}.'.format(idx0.size, idx1.size))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# if idx1.size == 1:
# idx1 = np.resize(idx1, (1,))
# w1 = m0.weight.data[:, idx0.tolist(), :, :].clone()
# w1 = w1[idx1.tolist(), :, :, :].clone()
# m1.weight.data = w1.clone()
# elif isinstance(m0, nn.Linear):
# idx0 = np.squeeze(np.argwhere(start_mask))
# if idx0.size == 1:
# idx0 = np.resize(idx0, (1,))
# m1.weight.data = m0.weight.data[:, idx0].clone()
# m1.bias.data = m0.bias.data.clone()
layer_id_in_cfg = 0
start_mask = np.ones(3)
end_mask = cfg_mask[layer_id_in_cfg]
parameter_buffer = {}
for m0 in model.modules():
if isinstance(m0, nn.BatchNorm2d):
key = str(layer_id_in_cfg) + 'BatchNorm'
value = []
if np.sum(end_mask) == 0:
pass
else:
idx1 = np.squeeze(np.argwhere(end_mask))
if idx1.size == 1:
idx1 = np.resize(idx1, (1,))
value.append(m0.weight.data[idx1.tolist()].clone())
value.append(m0.bias.data[idx1.tolist()].clone())
value.append(m0.running_mean[idx1.tolist()].clone())
value.append(m0.running_var[idx1.tolist()].clone())
start_mask = copy.copy(end_mask)
parameter_buffer[key] = value
layer_id_in_cfg += 1
if layer_id_in_cfg < len(cfg_mask): # do not change in Final FC
end_mask = cfg_mask[layer_id_in_cfg]
elif isinstance(m0, nn.Conv2d):
key = str(layer_id_in_cfg) + 'Conv'
value = []
if np.sum(end_mask) == 0:
pass
else:
idx0 = np.squeeze(np.argwhere(start_mask))
idx1 = np.squeeze(np.argwhere(end_mask))
if idx0.size == 1:
idx0 = np.resize(idx0, (1,))
if idx1.size == 1:
idx1 = np.resize(idx1, (1,))
w1 = m0.weight.data[:, idx0.tolist(), :, :].clone()
w1 = w1[idx1.tolist(), :, :, :].clone()
value.append(w1.clone())
parameter_buffer[key] = value
elif isinstance(m0, nn.Linear):
key = str(layer_id_in_cfg) + 'Linear'
value = []
idx0 = np.squeeze(np.argwhere(start_mask))
if idx0.size == 1:
idx0 = np.resize(idx0, (1,))
value.append(m0.weight.data[:, idx0].clone())
value.append(m0.bias.data.clone())
parameter_buffer[key] = value
layer_id_in_cfg = 0
for m1 in newmodel.modules():
if isinstance(m1, nn.BatchNorm2d):
key = str(layer_id_in_cfg) + 'BatchNorm'
while len(parameter_buffer[key]) == 0:
layer_id_in_cfg += 1
key = str(layer_id_in_cfg) + 'BatchNorm'
m1.weight.data = parameter_buffer[key][0]
m1.bias.data = parameter_buffer[key][1]
m1.running_mean = parameter_buffer[key][2]
m1.running_var = parameter_buffer[key][3]
layer_id_in_cfg += 1
elif isinstance(m1, nn.Conv2d):
key = str(layer_id_in_cfg) + 'Conv'
while len(parameter_buffer[key]) == 0:
layer_id_in_cfg += 1
key = str(layer_id_in_cfg) + 'Conv'
m1.weight.data = parameter_buffer[key][0]
elif isinstance(m1, nn.Linear):
key = str(layer_id_in_cfg) + 'Linear'
m1.weight.data = parameter_buffer[key][0]
m1.bias.data = parameter_buffer[key][1]
pass
return newmodel
def get_batch_jacobian(net, x, target, device):
net.zero_grad()
x.requires_grad_(True)
y = net(x)
y.backward(torch.ones_like(y))
jacob = x.grad.detach()
return jacob, target.detach(), y.detach()
def check_score(model, train_loader, sanity_check=False):
newmodel = copy.deepcopy(model)
reset_seed()
newmodel.K = np.zeros((args.test_batch_size, args.test_batch_size))
def counting_forward_hook(module, inp, out):
try:
if not module.visited_backwards:
return
if isinstance(inp, tuple):
inp = inp[0]
inp = inp.view(inp.size(0), -1)
x = (inp > 0).float()
K = x @ x.t()
K2 = (1. - x) @ (1. - x.t())
newmodel.K = newmodel.K + K.cpu().numpy() + K2.cpu().numpy()
except:
pass
def counting_backward_hook(module, inp, out):
module.visited_backwards = True
for name, module in newmodel.named_modules():
if 'ReLU' in str(type(module)):
# hooks[name] = module.register_forward_hook(counting_hook)
module.register_forward_hook(counting_forward_hook)
module.register_backward_hook(counting_backward_hook)
newmodel = newmodel.to(device)
s = []
for j in range(5):
data_iterator = iter(train_loader)
x, target = next(data_iterator)
if sanity_check:
x = shuffle_data(x)
x2 = torch.clone(x)
x2 = x2.to(device)
x, target = x.to(device), target.to(device)
jacobs, labels, y = get_batch_jacobian(newmodel, x, target, device)
newmodel(x2.to(device))
s_, ld = np.linalg.slogdet(newmodel.K)
s.append(ld)
score = np.mean(s)
return score
def check_channel_score(model, train_loader, sanity_check=False):
newmodel = copy.deepcopy(model)
reset_seed()
def counting_forward_hook(module, inp, out):
try:
# if not module.visited_backwards:
# return
if isinstance(inp, tuple):
inp = inp[0]
K_layer = np.zeros((args.test_batch_size, args.test_batch_size))
inp = inp.permute(1, 0, 2, 3)
inp = inp.view(inp.size(0), inp.size(1), -1)
inp = (inp > 0).float()
score_list = []
for i in range(inp.size(0)):
x = inp[i]
K1 = x @ x.t()
K2 = (1. - x) @ (1. - x.t())
K = K1.cpu().numpy() + K2.cpu().numpy()
K_layer += K
# s_, ld = np.linalg.slogdet(K)
s_, ld = np.linalg.slogdet(K/inp.size(2))
score_list.append(ld)
s_, ld = np.linalg.slogdet(K_layer/(inp.size(0)*inp.size(2)))
# s_, ld = np.linalg.slogdet(K_layer)
newmodel.layer_score.append(ld)
newmodel.channel_score.append(score_list)
except Exception as e:
print(e)
def counting_backward_hook(module, inp, out):
module.visited_backwards = True
def counting_backward_hook_ini(module, inp, out):
newmodel.layer_score = []
newmodel.channel_score = []
for name, module in newmodel.named_modules():
if 'ReLU' in str(type(module)):
# hooks[name] = module.register_forward_hook(counting_hook)
module.register_forward_hook(counting_forward_hook)
if name == 'feature.0':
module.register_forward_hook(counting_backward_hook_ini)
newmodel = newmodel.to(device)
s = []
layer_s = []
for j in range(5):
data_iterator = iter(train_loader)
x, target = next(data_iterator)
if sanity_check:
x = shuffle_data(x)
x2 = torch.clone(x)
x2 = x2.to(device)
x, target = x.to(device), target.to(device)
# jacobs, labels, y = get_batch_jacobian(newmodel, x, target, device)
newmodel(x2.to(device))
s.append(copy.deepcopy(newmodel.channel_score))
layer_s.append(copy.deepcopy(newmodel.layer_score))
layer_s = np.array(layer_s)
layer_s = np.mean(layer_s, axis=0)
channel_score = []
for channel in range(len(s[0])):
tep = []
for j in range(len(s)):
tep.append(s[j][channel])
tep = np.array(tep)
tep = np.mean(tep, axis=0)
channel_score.append(tep)
# s = np.array(s).astype(float)
# # s = np.mean(s, axis=0)
# s = s.transpose()
# tep = np.array(s[0])
return layer_s, channel_score
def pruning(model):
total = 0
cfg = []
cfg_mask = []
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
total += m.weight.data.shape[0]
bn = torch.zeros(total)
index = 0
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
size = m.weight.data.shape[0]
bn[index:(index+size)] = m.weight.data.abs().clone()
index += size
y, i = torch.sort(bn)
thre_index = int(total * args.percent)
thre = y[thre_index]
# print('Pruning threshold: {}'.format(thre))
mask = torch.zeros(total)
index = 0
for k, m in enumerate(model.modules()):
if isinstance(m, nn.BatchNorm2d):
size = m.weight.data.numel()
weight_copy = m.weight.data.abs().clone()
_mask = weight_copy.gt(thre.cuda()).float().cuda()
cfg_mask.append(_mask.clone())
if int(torch.sum(_mask)) > 0:
cfg.append(int(torch.sum(_mask)))
mask[index:(index+size)] = _mask.view(-1)
# print('layer index: {:d} \t total channel: {:d} \t remaining channel: {:d}'.format(k, _mask.shape[0], int(torch.sum(_mask))))
index += size
elif isinstance(m, nn.MaxPool2d):
cfg.append('M')
# print('Pre-processing Successful!')
return mask, cfg, cfg_mask
def create_cfg(cfg_mask_all, indicator):
form = copy.deepcopy(cfg_mask_all)
while 'M' in form:
form.remove('M')
# np.random.shuffle(mask_all)
cfg_mask = []
end = 0
for i in form:
cfg_mask.append(indicator[end:end + i])
end += i
cfg = []
index = 0
for i in range(len(cfg_mask_all)):
if cfg_mask_all[i] != 'M':
if np.sum(cfg_mask[index]) != 0:
cfg.append(int(np.sum(cfg_mask[index])))
index += 1
else:
cfg.append('M')
return cfg, cfg_mask
def random_search(cfg_mask_all, percent):
form = copy.deepcopy(cfg_mask_all)
while 'M' in form:
form.remove('M')
total = np.sum(form)
choose_num = int(total * percent)
mask_all = np.append(np.ones(choose_num), np.zeros(total - choose_num))
record_dict = {}
for i in range(len(mask_all)):
record_dict[i] = []
score_test = 0
trail_index = 0
while score_test < 1450:
for i in range(100):
np.random.shuffle(mask_all)
cfg, cfg_mask = create_cfg(cfg_mask_all, mask_all)
model_new = create_model(model, cfg, cfg_mask)
score = check_score(model_new, train_loader)
for i in range(len(mask_all)):
if not mask_all[i]:
record_dict[i].append(score)
average_score = pd.DataFrame([], columns=["position", "score"])
for i in range(len(mask_all)):
info_dict = {
'position':i,
'score':np.max(record_dict[i])
}
average_score = average_score.append(info_dict, ignore_index=True)
average_score = average_score.sort_values(by=['score'], ascending=False)
indexes = average_score['position'][0: int(len(average_score) * percent)]
indexes = indexes.astype(int)
indicator = np.ones(total)
indicator[indexes] = 0
cfg, cfg_mask = create_cfg(cfg_mask_all, indicator)
model_new = create_model(model, cfg, cfg_mask)
score = check_score(model_new, train_loader)
info_dict = {
'index': trail_index,
'cfg': cfg,
'cfg_mask': cfg_mask,
'score': score
}
wandb.log(info_dict)
print('The trial of {}, the score is {:.2f}'.format(trail_index, score))
trail_index += 1
if score > score_test:
score_test = score
np.save('{:.2f}.npy'.format(score_test), info_dict)
def reset_seed(seed=1):
np.random.seed(seed)
torch.manual_seed(seed)
random.seed(seed)
def count_channel(model):
cfg_mask_all = []
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
cfg_mask_all.append(m.weight.data.shape[0])
elif isinstance(m, nn.MaxPool2d):
cfg_mask_all.append('M')
form = copy.deepcopy(cfg_mask_all)
while 'M' in cfg_mask_all:
cfg_mask_all.remove('M')
total = np.sum(cfg_mask_all)
return total, form
def shuffle_data(xs):
Size = xs.size()
# e.g. for CIFAR100, is 50000 * 32 * 32 * 3
xs = xs.reshape(Size[0], -1)
for i in range(Size[0]):
xs[i] = xs[i][torch.randperm(xs[i].nelement())]
xs = xs.reshape(Size)
return xs
def greedy_search_new(model, percent, train_loader):
buffer = 33
total, form = count_channel(model)
channel_num = total
progress_index = 0
indicator = np.ones(total)
while channel_num > total * percent:
# indicator = np.ones(total)
score_dict = pd.DataFrame([], columns=['index', 'score'])
for position in range(total):
if indicator[position]:
indicator_tep = copy.deepcopy(indicator)
indicator_tep[position] = 0
cfg, cfg_mask = create_cfg(form, indicator_tep)
model_new = create_model(model, cfg, cfg_mask)
score = check_score(model_new, train_loader)
info_dict = {
'index': position,
'score': score
}
score_dict = score_dict.append(info_dict, ignore_index=True)
print('{}----{}/{}: score {:.2f}'.format(channel_num, position, total, score))
else:
score = -1
info_dict = {
'index': position,
'score': -1,
}
score_dict = score_dict.append(info_dict, ignore_index=True)
print('{}----{}/{}: score {:.2f}'.format(channel_num, position, total, score))
score_dict = score_dict.sort_values(by=['score'], ascending=False)
indexes = score_dict['index'][0:buffer]
indexes = indexes.astype(int)
indicator[indexes] = 0
cfg, cfg_mask = create_cfg(form, indicator)
channel_num = count_cfg_channel(cfg)
newmodel = create_model(model, cfg, cfg_mask)
score = check_score(newmodel, train_loader)
info_dict = {
'index': progress_index*buffer,
'score': score
}
wandb.log(info_dict)
progress_index += 1
# for i in range(len(cfg_mask)):
# cfg = copy.copy(cfg_mask)
# cfg[i] -= 1
# newmodel = models.__dict__[args.arch](dataset=args.dataset, cfg=cfg)
# score = check_score(newmodel, train_loader)
# score_dict[i] = score
# print(score_dict)
save_dict = {
'state_dict': newmodel.state_dict(),
'cfg': cfg,
'cfg_mask': cfg_mask,
'score': score
}
torch.save(save_dict, '{:.2f}.pth'.format(score))
# np.save('{:.2f}.npy'.format(score), save_dict)
def channel_score_search(model, percent, train_loader):
total, form = count_channel(model)
indicator = np.ones(total)
cfg, cfg_mask = create_cfg(form, indicator)
new_model = copy.copy(model)
for i in range(0, len(cfg_mask)):
# score = check_score(new_model, train_loader)
channel_score = check_channel_score(new_model, train_loader)
channel_score = channel_score[i]
channel_score_rank = copy.deepcopy(channel_score)
channel_score_rank.sort()
thre_index = int(len(channel_score)*percent)
thre_score = channel_score_rank[thre_index-1]
mask = [0 if j <= thre_score else 1 for j in channel_score]
cfg_mask[i] = mask
indicator_tep = []
for j in cfg_mask:
indicator_tep += list(j)
cfg_new, cfg_mask_new = create_cfg(form, indicator_tep)
new_model = create_model(model, cfg_new, cfg_mask_new)
score = check_score(new_model, train_loader)
print(score)
def rate_check(model, percent, train_loader):
xshape = (1, 3, 32, 32)
flops_original, param_original = get_model_infos(model, xshape)
total, form = count_channel(model)
indicator = np.ones(total)
cfg, cfg_mask = create_cfg(form, indicator)
f_list = []
p_list = []
for i in range(len(cfg_mask)):
cfg_mask_new = copy.deepcopy(cfg_mask)
cfg_mask_new[i][0:30] = 0
indicator_new = []
for one in cfg_mask_new:
indicator_new += list(one)
cfg_new, cfg_mask_new = create_cfg(form, indicator_new)
model_new = create_model(model, cfg_new, cfg_mask_new)
flops, param = get_model_infos(model_new, xshape)
flops_rate = (flops_original-flops)/flops_original
param_rate = (param_original-param)/param_original
f_list.append(flops_rate)
p_list.append(param_rate)
print("")
def channel_remove_check(model, train_loader, goal_f_rate, goal_p_rate):
baseline_f_rate = 260292527 / 313772032
baseline_p_rate = 8300726 / 15299748
xshape = (1, 3, 32, 32)
flops_original, param_original = get_model_infos(model, xshape)
score = check_score(model, train_loader, True)
score_layer_original, score_channel_original = check_channel_score(model, train_loader, True)
total, form = count_channel(model)
indicator = np.ones(total)
cfg_original, cfg_mask_original = create_cfg(form, indicator)
cfg_mask = copy.deepcopy(cfg_mask_original)
model_new = copy.deepcopy(model)
for i in range(len(cfg_mask_original)-1):
score_layer, score_channel = check_channel_score(model_new, train_loader, True)
cfg_mask[i] = score_channel[i] != -np.inf
indicator = []
for one in cfg_mask:
indicator += list(one)
cfg, cfg_mask = create_cfg(form, indicator)
model_new = create_model(model, cfg, cfg_mask)
flops, param = get_model_infos(model_new, xshape)
f_rate = flops / flops_original
p_rate = param / param_original
print(f_rate)
index = 0
while f_rate > goal_f_rate:
score_layer, score_channel = check_channel_score(model_new, train_loader)
score_channel[-1] = np.ones(score_channel[-1].shape)*5000
indicator = []
for one in score_channel:
indicator += list(one)
min_index = indicator.index(min(indicator))
indicator = np.ones(len(indicator))
indicator[min_index] = 0
cfg, cfg_mask = create_cfg(cfg, indicator)
model_new = create_model(model_new, cfg, cfg_mask)
flops, param = get_model_infos(model_new, xshape)
f_rate = flops / flops_original
p_rate = param / param_original
print(f_rate)
info_dict = {
"f_rate": f_rate,
"p_rate": p_rate,
"cfg": cfg,
"index": index,
}
# wandb.log(info_dict)
index += 1
score_prune = check_score(model_new, train_loader)
torch.save(model_new, 'shuffle_channel_remove_n_f{:.4f}_p{:.4f}_{:.2f}.pth'.format(f_rate, p_rate, score_prune))
def ratio_cfg(form, ratio):
output = []
for i in range(len(form)-1):
if form[i] != 'M':
form[i] = round(form[i]*ratio)
return form
def layer_wise_pruning(model, train_loader, pruning_rate):
xshape = (1, 3, 32, 32)
flops_original, param_original = get_model_infos(model, xshape)
score = check_score(model, train_loader)
score_layer_original, score_channel_original = check_channel_score(model, train_loader)
total, form = count_channel(model)
cfg_goal = ratio_cfg(copy.deepcopy(form), pruning_rate)
# model_new = models.__dict__['vgg'](dataset='cifar100', cfg=cfg_goal)
# flops, param = get_model_infos(model_new, xshape)
# f_rate = flops / flops_original
# p_rate = param / param_original
# score = check_score(model_new, train_loader)
while 'M' in cfg_goal:
cfg_goal.remove('M')
indicator = np.ones(total)
cfg_original, cfg_mask_original = create_cfg(form, indicator)
cfg_mask = copy.deepcopy(cfg_mask_original)
model_new = copy.deepcopy(model)
for i in range(len(cfg_mask_original)-1):
score_layer, score_channel = check_channel_score(model_new, train_loader)
pruning_num = np.sum(cfg_mask[i])-cfg_goal[i]
ranked_score = copy.deepcopy(score_channel[i])
ranked_score.sort()
thre_score = ranked_score[int(pruning_num)-1]
if thre_score != -np.inf:
cfg_mask[i] = score_channel[i] > thre_score
else:
tep = score_channel[i] == -np.inf
index = np.where(tep == 1)
index = np.random.choice(index[0], int(pruning_num), replace= False)
tep = np.array(cfg_mask[i])
tep[index] = 0
cfg_mask[i] = list(tep)
indicator = []
for one in cfg_mask:
indicator += list(one)
cfg, cfg_mask = create_cfg(form, indicator)
model_new = create_model(model, cfg, cfg_mask)
flops, param = get_model_infos(model_new, xshape)
f_rate = flops / flops_original
p_rate = param / param_original
print(f_rate)
#
# index = 0
# while f_rate > goal_f_rate:
# score_layer, score_channel = check_channel_score(model_new, train_loader)
# score_channel[-1] = np.ones(score_channel[-1].shape)*5000
# indicator = []
# for one in score_channel:
# indicator += list(one)
# min_index = indicator.index(min(indicator))
# indicator = np.ones(len(indicator))
# indicator[min_index] = 0
# cfg, cfg_mask = create_cfg(cfg, indicator)
# model_new = create_model(model_new, cfg, cfg_mask)
# flops, param = get_model_infos(model_new, xshape)
# f_rate = flops / flops_original
# p_rate = param / param_original
# print(f_rate)
# info_dict = {
# "f_rate": f_rate,
# "p_rate": p_rate,
# "cfg": cfg,
# "index": index,
# }
# wandb.log(info_dict)
# index += 1
score_prune = check_score(model_new, train_loader)
torch.save(model_new, 'layer_wise_pruning_rate{}_f{:.4f}_p{:.4f}_{:.2f}.pth'.format(pruning_rate, f_rate, p_rate, score_prune))
def layer_remove_check(model, train_loader):
baseline_f_rate = 260292527/313772032
baseline_p_rate = 8300726/15299748
xshape = (1, 3, 32, 32)
flops_original, param_original = get_model_infos(model, xshape)
score = check_score(model, train_loader)
score_layer_original, score_channel_original = check_channel_score(model, train_loader)
total, form = count_channel(model)
indicator = np.ones(total)
cfg, cfg_mask = create_cfg(form, indicator)
# cut_list = [11,10,8,7,4,5,2,0]
cut_list_all = [10, 0, 11, 7, 8, 2, 4, 5]
cut_list = cut_list_all[:1]
for one in cut_list:
cfg_mask[one] = np.zeros(len(cfg_mask[one]))
# cfg_mask[4] = np.zeros(len(cfg_mask[4]))
indicator_new = []
for one in cfg_mask:
indicator_new += list(one)
cfg_new, cfg_mask_new = create_cfg(form, indicator_new)
model_new = create_model(model, cfg_new, cfg_mask_new)
flops, param = get_model_infos(model_new, xshape)
score_prune = check_score(model_new, train_loader)
f_rate = flops/flops_original
p_rate = param/param_original
score_layer, score_channel = check_channel_score(model_new, train_loader)
# for one in score_channel:
# print(np.sum(one == -np.inf), len(one))
#
# save_dict = {
# 'state_dict': model_new.state_dict(),
# 'cfg': cfg_new,
# 'cfg_mask': cfg_mask_new,
# 'score': score_prune
# }
# torch.save(model_new, '{:.2f}.pth'.format(score_prune))
# model_new = models.__dict__[args.arch](dataset=args.dataset, cfg = cfg_new)
# score_prune = check_score(model_new, train_loader)
# score_layer, score_channel = check_channel_score(model_new, train_loader)
torch.save(model_new, '{}-{:.2f}.pth'.format(len(cut_list),score_prune))
print("")
# f_list = []
# p_list = []
#
# for i in range(len(cfg_mask)):
# cfg_mask_new = copy.deepcopy(cfg_mask)
# cfg_mask_new[i][0:30] = 0
# indicator_new = []
# for one in cfg_mask_new:
# indicator_new += list(one)
# cfg_new, cfg_mask_new = create_cfg(form, indicator_new)
# model_new = create_model(model, cfg_new, cfg_mask_new)
# flops, param = get_model_infos(model_new, xshape)
# flops_rate = (flops_original-flops)/flops_original
# param_rate = (param_original-param)/param_original
# f_list.append(flops_rate)
# p_list.append(param_rate)
def count_cfg_channel(cfg):
form = copy.deepcopy(cfg)
while 'M' in form:
form.remove('M')
channel = np.sum(form)
return channel
def create_base(model, train_loader):
total, form = count_channel(model)
indicator = []
for one in form:
if one != 'M':
tep = np.zeros(one)
tep[0] = 1
indicator.append(tep)
for i in range(len(indicator)):
record = pd.DataFrame([], columns=["index", "score"])
for j in range(len(indicator[i])):
tep = np.zeros(len(indicator[i]))
tep[j] = 1
indicator_tep = copy.deepcopy(indicator)
indicator_tep[i] = tep
indicator_list = []
for k in indicator_tep:
indicator_list += list(k)
indicator_tep = np.array(indicator_list).astype(int)
cfg, cfg_mask = create_cfg(form, indicator_tep)
new_model = create_model(model, cfg, cfg_mask)
score = check_score(new_model, train_loader)
info_dict = {
"index": j,
"score": score
}
record = record.append(info_dict, ignore_index=True)
# print("for the {}-th module, tried {}/{}, the score is {:.2f}".format(i, j, len(indicator[i]), score))
record = record.sort_values(by=['score'], ascending=False)
indexes = record['index'][0]
tep = np.zeros(len(indicator[i]))
tep[int(indexes)] = 1
indicator[i] = tep
print("for the {}-th module, tried {}/{}, the score is {:.2f}".format(i, j, len(indicator[i]), record['score'][0]))
indicator_list = []
for i in indicator:
indicator_list += (list(i))
indicator = np.array(indicator_list)
return indicator
def greedy_search_increase(model, percent, train_loader):
buffer = 11
total, form = count_channel(model)
indicator = create_base(model, train_loader)
left_channels = int(total*percent - np.sum(indicator))
while left_channels > 0:
record = pd.DataFrame([], columns=["index", "score"])
for i in range(len(indicator)):
if indicator[i]:
print("already chosen")
else:
indicator_tep = copy.deepcopy(indicator)
cfg, cfg_mask = create_cfg(form, indicator_tep)
new_model = create_model(model, cfg, cfg_mask)
score = check_score(new_model, train_loader)
info_dict = {
"index": i,
"score": score
}
record = record.append(info_dict, ignore_index=True)
record = record.sort_values(by=['score'], ascending=False)
if left_channels < buffer:
buffer = left_channels
indexes = record['index'][0:buffer]
indexes = indexes.astype(int)
indicator[indexes] = 1
cfg, cfg_mask = create_cfg(form, indicator)
new_model = create_model(model, cfg, cfg_mask)
score = check_score(new_model, train_loader)
left_channels = int(total * percent - np.sum(indicator))
print("Still have {} channels to prune, now the highest score is {:.2f}".format(left_channels, score))
info_dict = {
"channels": np.sum(indicator),
"score": score
}
wandb.log(info_dict)
save_dict = {
'state_dict': new_model.state_dict(),
'cfg': cfg,
'cfg_mask': cfg_mask,
'score': score
}
torch.save(save_dict, '{:.2f}.pth'.format(score))
def greedy_search(model, percent, train_loader):
total, form = count_channel(model)
index_trial = 0
while count_cfg_channel(form) > total*percent:
score_list = []
for i in range(len(form)):
if form[i] != 'M':
cfg = copy.deepcopy(form)
cfg[i] -= 1
newmodel = models.__dict__[args.arch](dataset=args.dataset, cfg = cfg)
score = check_score(newmodel, train_loader)
score_list.append(score)
else:
score_list.append(-1)
max_score = max(score_list)
index = score_list.index(max_score)
info_dict = {
'index': index_trial,
'cfg': form,
'score': max_score
}
index_trial +=1
wandb.log(info_dict)
form[index] -= 1
print(max_score)
info_dict = {
'cfg': form,
'score': max_score
}
np.save('{:.2f}.npy'.format(score), info_dict)
# cfg_mask_all = []
# for m in model.modules():
# if isinstance(m, nn.BatchNorm2d):
# cfg_mask_all.append(m.weight.data.shape[0])
# elif isinstance(m, nn.MaxPool2d):
# cfg_mask_all.append('M')
#
# form = copy.deepcopy(cfg_mask_all)
# while 'M' in form:
# form.remove('M')
# total = np.sum(form)
# indicator = np.ones(total)
# cfg_mask = []
# end = 0
# for i in form:
# cfg_mask.append(indicator[end:end + i])
# end += i
# bn = torch.zeros(total)
# cfg = []
# index = 0
# for i in range(len(cfg_mask_all)):
# if cfg_mask_all[i] != 'M':
# cfg.append(int(np.sum(cfg_mask[index])))
# index += 1
# else:
# cfg.append('M')
# model_new = create_model(model, cfg, cfg_mask)
# score = check_score(model_new, train_loader)
# score_dict = pd.DataFrame([], columns=['index', 'score'])
# score_dict.to_csv('score_indicator.csv')
# score_dict = score_dict.sort_values(by=['score'], ascending=False)
# indexes = score_dict['index'][0: int(len(score_dict)*percent)]
# indexes = indexes.astype(int)
# indicator_tep = copy.deepcopy(indicator)
# indicator_tep[indexes] = 0
# cfg_mask = []
# end = 0
# for i in form:
# cfg_mask.append(indicator_tep[end:end + i])
# end += i
# cfg = []
# index = 0
# for i in range(len(cfg_mask_all)):
# if cfg_mask_all[i] != 'M':
# cfg.append(int(np.sum(cfg_mask[index])))
# index += 1
# else:
# cfg.append('M')
# model_new = create_model(model, cfg, cfg_mask)
# score = check_score(model_new, train_loader)
# info_dict = {
# 'cfg': cfg,
# 'cfg_mask': cfg_mask,
# 'score': score
# }
# np.save('{:.2f}.npy'.format(score), info_dict)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='PyTorch Slimming CIFAR prune')
parser.add_argument('--dataset', type=str, default='cifar100',
help='training dataset (default: cifar10)')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',
help='input batch size for testing (default: 256)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--depth', type=int, default=16,
help='depth of the vgg')
parser.add_argument('--percent', type=float, default=0.5,
help='scale sparse rate (default: 0.5)')
parser.add_argument('--model', default='', type=str, metavar='PATH',
help='path to the model (default: none)')
parser.add_argument('--save', default='./baseline/vgg16-cifar100', type=str, metavar='PATH',
help='path to save pruned model (default: none)')
parser.add_argument('--save_1', default='./baseline/vgg16-cifar100', type=str, metavar='PATH',
help='path to save pruned model (default: none)')
parser.add_argument('--start_epoch', default=1, type=int, metavar='N', help='manual start epoch number')
parser.add_argument('--end_epoch', default=160, type=int, metavar='N', help='manual end epoch number')
# quantized parameters
parser.add_argument('--bits_A', default=8, type=int, help='input quantization bits')
parser.add_argument('--bits_W', default=8, type=int, help='weight quantization bits')
parser.add_argument('--bits_G', default=8, type=int, help='gradient quantization bits')
parser.add_argument('--bits_E', default=8, type=int, help='error quantization bits')
parser.add_argument('--bits_R', default=16, type=int, help='rand number quantization bits')
parser.add_argument('--arch', default='vgg', type=str,
help='architecture to use')
# multi-gpus
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
seed = 1
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if not os.path.exists(args.save):
os.makedirs(args.save)
gpu = args.gpu_ids
gpu_ids = args.gpu_ids.split(',')
args.gpu_ids = []
for gpu_id in gpu_ids:
id = int(gpu_id)
if id > 0:
args.gpu_ids.append(id)
if len(args.gpu_ids) > 0:
torch.cuda.set_device(args.gpu_ids[0])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.arch.endswith('lp'):
# model = models.__dict__[args.arch](bits_A=args.bits_A, bits_E=args.bits_E, bits_W=args.bits_W, dataset=args.dataset, depth=args.depth)
model = models.__dict__[args.arch](8, 8, 32, dataset=args.dataset, depth=args.depth)
elif args.dataset == 'imagenet':
model = models.__dict__[args.arch](pretrained=False)
if len(args.gpu_ids) > 1:
model = torch.nn.DataParallel(model, device_ids=args.gpu_ids)
else:
model = models.__dict__[args.arch](dataset=args.dataset, depth=args.depth)
if args.dataset == 'cifar10':
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
else:
train_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=True, download=True,
transform=transforms.Compose([
transforms.Pad(4),
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])),
batch_size=args.test_batch_size, shuffle=True)
if args.cuda:
model.cuda()
wandb_project = 'pruning_score'
name = 'with_out_normalization_60'
# wandb.init(project=wandb_project, name=name)
#
# random_search(cfg_mask_all, args.percent)
# channel_score_search(model, args.percent, train_loader)
# greedy_search(model, args.percent, train_loader)
# layer_remove_check(model, train_loader)
# rate_check(model, args.percent, train_loader)
channel_remove_check(model, train_loader, 0.61, 0.2)
# layer_wise_pruning(model, train_loader, 0.8)
#
# data = np.load('1633.66.npy', allow_pickle=True)
# data = data.item()
# cfg = data['cfg']
# cfg_mask = data['mask_cfg']
# for i in range(len(cfg_mask)):
# cfg_mask[i] = np.asarray(cfg_mask[i].cpu().numpy())
# score = check_score(model, cfg, cfg_mask)
# print(score)
| 33,255 | 0 | 483 |
6716a726078f16e1423a1e22103925e9d6218fcc | 234 | py | Python | ui/config.py | fakeNetflix/pinterest-repo-soundwave | 189b30708502cb6b76cd7a895bbd4389b0142e2f | [
"Apache-2.0"
] | 104 | 2017-05-14T23:28:10.000Z | 2021-04-25T11:20:19.000Z | ui/config.py | fakeNetflix/pinterest-repo-soundwave | 189b30708502cb6b76cd7a895bbd4389b0142e2f | [
"Apache-2.0"
] | 17 | 2020-03-04T23:22:31.000Z | 2021-12-09T21:31:06.000Z | ui/config.py | fakeNetflix/pinterest-repo-soundwave | 189b30708502cb6b76cd7a895bbd4389b0142e2f | [
"Apache-2.0"
] | 18 | 2017-05-16T22:40:24.000Z | 2022-02-21T15:35:20.000Z | """Config data for cmdb UI."""
SOUNDWAVE_API = "http://soundwave-api/v2/"
SOUNDWAVE_HOST = "0.0.0.0"
SOUNDWAVE_PORT = 80
SOUNDWAVE_LOG_PATH = "/var/log/soundwave_ui/"
SOUNDWAVE_ACCESS_LOG = "access.log"
SOUNDWAVE_APP_LOG = "info.log"
| 29.25 | 45 | 0.739316 | """Config data for cmdb UI."""
SOUNDWAVE_API = "http://soundwave-api/v2/"
SOUNDWAVE_HOST = "0.0.0.0"
SOUNDWAVE_PORT = 80
SOUNDWAVE_LOG_PATH = "/var/log/soundwave_ui/"
SOUNDWAVE_ACCESS_LOG = "access.log"
SOUNDWAVE_APP_LOG = "info.log"
| 0 | 0 | 0 |
f6ca7e10f361d5fba876d39453a170a545ad8aea | 786 | py | Python | config/env_variables.py | Calpax-aaS/web-app | 155a40a51b27f9741c137dcf144daabcb09d39ed | [
"BSD-3-Clause"
] | null | null | null | config/env_variables.py | Calpax-aaS/web-app | 155a40a51b27f9741c137dcf144daabcb09d39ed | [
"BSD-3-Clause"
] | 2 | 2021-06-09T18:48:49.000Z | 2021-09-22T19:55:17.000Z | config/env_variables.py | Calpax-aaS/web-app | 155a40a51b27f9741c137dcf144daabcb09d39ed | [
"BSD-3-Clause"
] | 1 | 2022-03-02T20:55:50.000Z | 2022-03-02T20:55:50.000Z | import os
django_secret = os.getenv('DJANGO_SECRET_KEY', 'l0cAl-t3st*')
django_is_debug_activated = os.getenv('DJANGO_DEBUG', 'False').lower() == 'true'
django_relative_path_for_static_file = os.getenv('DJANGO_STATIC_PATH', './public/static')
auth0_client_id = os.getenv('AUTH0_CLIENT_ID')
auth0_client_secret = os.getenv('AUTH0_CLIENT_SECRET')
# POSTGRESQL_ADDON -> env variables in CleverCloud
database = {
'name': os.getenv('POSTGRESQL_ADDON_DB', os.getenv('PG_DB', '')),
'user': os.getenv('POSTGRESQL_ADDON_USER', os.getenv('PG_USER', '')),
'password': os.getenv('POSTGRESQL_ADDON_PASSWORD', os.getenv('PG_PWD', '')),
'host': os.getenv('POSTGRESQL_ADDON_HOST', os.getenv('PG_HOST', '')),
'port': os.getenv('POSTGRESQL_ADDON_PORT', os.getenv('PG_PORT', '')),
} | 46.235294 | 89 | 0.717557 | import os
django_secret = os.getenv('DJANGO_SECRET_KEY', 'l0cAl-t3st*')
django_is_debug_activated = os.getenv('DJANGO_DEBUG', 'False').lower() == 'true'
django_relative_path_for_static_file = os.getenv('DJANGO_STATIC_PATH', './public/static')
auth0_client_id = os.getenv('AUTH0_CLIENT_ID')
auth0_client_secret = os.getenv('AUTH0_CLIENT_SECRET')
# POSTGRESQL_ADDON -> env variables in CleverCloud
database = {
'name': os.getenv('POSTGRESQL_ADDON_DB', os.getenv('PG_DB', '')),
'user': os.getenv('POSTGRESQL_ADDON_USER', os.getenv('PG_USER', '')),
'password': os.getenv('POSTGRESQL_ADDON_PASSWORD', os.getenv('PG_PWD', '')),
'host': os.getenv('POSTGRESQL_ADDON_HOST', os.getenv('PG_HOST', '')),
'port': os.getenv('POSTGRESQL_ADDON_PORT', os.getenv('PG_PORT', '')),
} | 0 | 0 | 0 |
2c6268758b8e73bd29f7948fb2074d858df22206 | 10,073 | py | Python | cpdb/twitterbot/tests/test_response_builders.py | invinst/CPDBv2_backend | b4e96d620ff7a437500f525f7e911651e4a18ef9 | [
"Apache-2.0"
] | 25 | 2018-07-20T22:31:40.000Z | 2021-07-15T16:58:41.000Z | cpdb/twitterbot/tests/test_response_builders.py | invinst/CPDBv2_backend | b4e96d620ff7a437500f525f7e911651e4a18ef9 | [
"Apache-2.0"
] | 13 | 2018-06-18T23:08:47.000Z | 2022-02-10T07:38:25.000Z | cpdb/twitterbot/tests/test_response_builders.py | invinst/CPDBv2_backend | b4e96d620ff7a437500f525f7e911651e4a18ef9 | [
"Apache-2.0"
] | 6 | 2018-05-17T21:59:43.000Z | 2020-11-17T00:30:26.000Z | from django.test import TestCase
from django.test.utils import override_settings
from mock.mock import mock_open
from robber import expect
from mock import patch, Mock
from twitterbot.response_builders import (
SingleOfficerResponseBuilder, CoaccusedPairResponseBuilder, BaseResponseBuilder, NotFoundResponseBuilder)
from twitterbot.factories import ResponseTemplateFactory
from twitterbot.models import ResponseTemplate
from data.factories import OfficerFactory, OfficerAllegationFactory, AllegationFactory
| 37.868421 | 115 | 0.586022 | from django.test import TestCase
from django.test.utils import override_settings
from mock.mock import mock_open
from robber import expect
from mock import patch, Mock
from twitterbot.response_builders import (
SingleOfficerResponseBuilder, CoaccusedPairResponseBuilder, BaseResponseBuilder, NotFoundResponseBuilder)
from twitterbot.factories import ResponseTemplateFactory
from twitterbot.models import ResponseTemplate
from data.factories import OfficerFactory, OfficerAllegationFactory, AllegationFactory
class BaseResponseBuilderTestCase(TestCase):
def setUp(self):
ResponseTemplate.objects.all().delete()
class DummyResponseBuilder(BaseResponseBuilder):
response_type = 'single_officer'
def get_variables_sets(self, entities, context):
yield dict()
self.builder_class = DummyResponseBuilder
def test_build_with_round_robined_syntax(self):
builder = self.builder_class()
ResponseTemplateFactory(id=20, response_type='single_officer', syntax='temp1')
ResponseTemplateFactory(id=21, response_type='single_officer', syntax='temp2')
expect(list(builder.build(extra_variables={'user_name': 'abc'}))).to.eq([{
'source': (),
'tweet_content': 'temp1',
'url': '',
'type': 'single_officer',
'entity': None,
'coaccused': 0,
'officer1': None,
'officer2': None
}])
expect(list(builder.build(extra_variables={'user_name': 'def'}))).to.eq([{
'source': (),
'tweet_content': 'temp1',
'url': '',
'type': 'single_officer',
'entity': None,
'coaccused': 0,
'officer1': None,
'officer2': None
}])
expect(list(builder.build(extra_variables={'user_name': 'abc'}))).to.eq([{
'source': (),
'tweet_content': 'temp2',
'url': '',
'type': 'single_officer',
'entity': None,
'coaccused': 0,
'officer1': None,
'officer2': None
}])
expect(list(builder.build(extra_variables={'user_name': 'abc'}))).to.eq([{
'source': (),
'tweet_content': 'temp1',
'url': '',
'type': 'single_officer',
'entity': None,
'coaccused': 0,
'officer1': None,
'officer2': None
}])
def test_build_with_syntax_depend_on_right_response_type(self):
builder = self.builder_class()
ResponseTemplateFactory(response_type='single_officer', syntax='b')
ResponseTemplateFactory(response_type='test', syntax='c')
context = dict()
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context=context))).to.eq([{
'source': (),
'tweet_content': 'b',
'url': '',
'type': 'single_officer',
'entity': None,
'coaccused': 0,
'officer1': None,
'officer2': None
}])
expect(context['responses_count']).to.eq(1)
def test_build_with_truncating_user_name_if_tweet_content_longer_than_140_characters(self):
builder = self.builder_class()
ResponseTemplateFactory(response_type='single_officer', syntax='@{{user_name}} anything else')
with patch('twitterbot.response_builders.len', return_value=150):
first_built = list(builder.build(extra_variables={'user_name': 'abc'}))[0]
tweet_content = first_built['tweet_content']
expect(tweet_content).to.eq('anything else')
class SingleOfficerResponseBuilderTestCase(TestCase):
def setUp(self):
ResponseTemplate.objects.all().delete()
@override_settings(DOMAIN='http://foo.co')
def test_build(self):
_mock_open = mock_open()
with patch('twitterbot.handlers.open', _mock_open, create=True):
officer1 = OfficerFactory(id=1, first_name='Jerome', last_name='Finnigan', allegation_count=3)
officer1_doc = {
'id': officer1.id, 'full_name': officer1.full_name
}
officer2 = OfficerFactory(id=2, first_name='Raymond', last_name='Piwnicki')
officer2_doc = {
'id': officer2.id, 'full_name': officer2.full_name
}
ResponseTemplateFactory(
response_type='single_officer',
syntax='@{{user_name}} {{officer.full_name}} has {{officer.allegation_count}} complaints')
builder = SingleOfficerResponseBuilder()
officers = [('source1', officer1_doc), ('source2', officer2_doc)]
expect(list(builder.build(officers, {'user_name': 'abc'}))).to.eq([{
'source': ('source1',),
'tweet_content': '@abc Jerome Finnigan has 3 complaints',
'url': 'http://foo.co/officer/1/',
'type': 'single_officer',
'entity': officer1_doc,
'officer1': None,
'officer2': None,
'coaccused': 0,
}, {
'source': ('source2',),
'tweet_content': '@abc Raymond Piwnicki has 0 complaints',
'url': 'http://foo.co/officer/2/',
'type': 'single_officer',
'entity': officer2_doc,
'officer1': None,
'officer2': None,
'coaccused': 0,
}])
class CoaccusedPairResponseBuilderTestCase(TestCase):
def setUp(self):
ResponseTemplate.objects.all().delete()
def test_build(self):
officer1 = OfficerFactory(first_name='Jerome', last_name='Finnigan')
allegation = AllegationFactory()
OfficerAllegationFactory(officer=officer1, allegation=allegation)
officer1_doc = {
'id': officer1.id, 'full_name': officer1.full_name, 'complaints': 3
}
officer2 = OfficerFactory(first_name='Raymond', last_name='Piwnicki')
OfficerAllegationFactory(officer=officer2, allegation=allegation)
officer2_doc = {
'id': officer2.id, 'full_name': officer2.full_name, 'complaints': 3
}
officer3 = OfficerFactory(first_name='Jesse', last_name='Acosta')
OfficerAllegationFactory(officer=officer3)
officer3_doc = {
'id': officer3.id, 'full_name': officer3.full_name, 'complaints': 3
}
ResponseTemplateFactory(
response_type='coaccused_pair',
syntax=(
'@{{user_name}} {{officer1.full_name}} and {{officer2.full_name}} '
'were co-accused in {{coaccused}} case'
)
)
builder = CoaccusedPairResponseBuilder()
expect(list(builder.build(
[('source1', officer1_doc), ('source2', officer2_doc), ('source3', officer3_doc)],
{'user_name': 'abc'}))
).to.eq([{
'source': ('source1', 'source2'),
'tweet_content': '@abc Jerome Finnigan and Raymond Piwnicki were co-accused in 1 case',
'url': '',
'type': 'coaccused_pair',
'entity': None,
'officer1': officer1,
'officer2': officer2,
'coaccused': 1,
}])
class NotFoundResponseBuilderTestCase(TestCase):
def setUp(self):
ResponseTemplate.objects.all().delete()
ResponseTemplateFactory(
response_type='not_found',
syntax='Sorry, @{{user_name}}, the bot find nothing')
def test_build_with_0_response(self):
builder = NotFoundResponseBuilder()
tweet = Mock(
is_tweet_from_followed_accounts=False,
is_retweet_of_twitterbot=False,
is_quoted_tweet_of_twitterbot=False)
context = {
'response_count': 0,
'incoming_tweet': tweet
}
with self.settings(DOMAIN='http://foo.co'):
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context=context))).to.eq([{
'source': (),
'tweet_content': 'Sorry, @abc, the bot find nothing',
'url': 'http://foo.co',
'type': 'not_found',
'entity': None,
'officer1': None,
'officer2': None,
'coaccused': 0
}])
def test_build_with_response(self):
builder = NotFoundResponseBuilder()
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context={'responses_count': 1}))).to.eq([])
def test_do_nothing_if_retweet_of_twitterbot(self):
builder = NotFoundResponseBuilder()
tweet = Mock(
is_tweet_from_followed_accounts=False,
is_retweet_of_twitterbot=True,
is_quoted_tweet_of_twitterbot=False)
context = {
'responses_count': 0,
'incoming_tweet': tweet
}
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context=context))).to.eq([])
def test_do_nothing_if_quoted_tweet_of_twitterbot(self):
builder = NotFoundResponseBuilder()
tweet = Mock(
is_tweet_from_followed_accounts=False,
is_retweet_of_twitterbot=False,
is_quoted_tweet_of_twitterbot=True)
context = {
'responses_count': 0,
'incoming_tweet': tweet
}
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context=context))).to.eq([])
def test_do_nothing_if_there_is_no_incoming_tweet(self):
builder = NotFoundResponseBuilder()
context = {
'responses_count': 0,
'incoming_tweet': None
}
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context=context))).to.eq([])
def test_do_nothing_if_there_is_no_context(self):
builder = NotFoundResponseBuilder()
expect(list(builder.build(extra_variables={'user_name': 'abc'}, context=None))).to.eq([])
| 8,901 | 214 | 440 |
7f8b17757fa5f2876737685c8360f39e1e1cef47 | 1,357 | py | Python | soundrts/tests/test_world.py | fcnjd/soundrts | 3492503d0f4712a31c662d57434ddf8a852d0816 | [
"BSD-3-Clause"
] | 2 | 2019-12-29T16:18:10.000Z | 2019-12-29T16:18:39.000Z | soundrts/tests/test_world.py | fcnjd/soundrts | 3492503d0f4712a31c662d57434ddf8a852d0816 | [
"BSD-3-Clause"
] | 1 | 2018-02-17T10:41:18.000Z | 2018-02-17T10:41:18.000Z | soundrts/tests/test_world.py | fcnjd/soundrts | 3492503d0f4712a31c662d57434ddf8a852d0816 | [
"BSD-3-Clause"
] | null | null | null | import unittest
from soundrts.world import World
from soundrts.mapfile import Map
# print self.w.get_objects_string()[-160:]
if __name__ == "__main__":
unittest.main()
| 32.309524 | 82 | 0.608696 | import unittest
from soundrts.world import World
from soundrts.mapfile import Map
class WorldTestCase(unittest.TestCase):
def setUp(self):
self.w = World([])
self.w.load_and_build_map(Map("multi/m2.txt"))
self.w2 = World([])
self.w2.load_and_build_map(Map("multi/jl2.txt"))
def tearDown(self):
pass
def testShortestPath(self):
g = self.w.grid
self.assertEqual(g["a1"].shortest_path_distance_to(g["a2"]),
g["a1"].shortest_path_distance_to(g["b1"]))
self.assertIn(
g["a1"].shortest_path_to(g["e5"]).other_side.place.name, ("a2", "b1"))
self.assertEqual(
g["b1"].shortest_path_to(g["e5"]).other_side.place.name, "b2")
self.assertEqual(
g["b1"].shortest_path_to(g["d2"]).other_side.place.name, "c1")
g2 = self.w2.grid
self.assertEqual(g2["a1"].shortest_path_to(g2["c1"]), None)
self.assertEqual(g2["c1"].shortest_path_to(g2["a1"]), None)
def testCheckString(self):
World([]).get_digest()
self.assertEqual(self.w.get_digest(), self.w.get_digest())
self.assertNotEqual(self.w.get_digest(), self.w2.get_digest())
self.w.get_objects_string()
# print self.w.get_objects_string()[-160:]
if __name__ == "__main__":
unittest.main()
| 1,021 | 18 | 135 |
178c0c20dcc9abe86434ad86ea0996c5a40260ae | 58 | py | Python | frontend/gunicorn.confi.py | laughinging/yaes | 0893f7848ee0530fa6c3bd553f89aa430f9b2f02 | [
"MIT"
] | null | null | null | frontend/gunicorn.confi.py | laughinging/yaes | 0893f7848ee0530fa6c3bd553f89aa430f9b2f02 | [
"MIT"
] | null | null | null | frontend/gunicorn.confi.py | laughinging/yaes | 0893f7848ee0530fa6c3bd553f89aa430f9b2f02 | [
"MIT"
] | null | null | null | workers = 2
worker_class = 'gevent'
bind = "0.0.0.0:8888"
| 14.5 | 23 | 0.655172 | workers = 2
worker_class = 'gevent'
bind = "0.0.0.0:8888"
| 0 | 0 | 0 |
e8e0a0444c4be29eeeb8e912e2a2e9fbfd16637f | 5,641 | py | Python | src/OTLMOW/OTLModel/Classes/Overstortrand.py | davidvlaminck/OTLClassPython | 71330afeb37c3ea6d9981f521ff8f4a3f8b946fc | [
"MIT"
] | 2 | 2022-02-01T08:58:11.000Z | 2022-02-08T13:35:17.000Z | src/OTLMOW/OTLModel/Classes/Overstortrand.py | davidvlaminck/OTLMOW | 71330afeb37c3ea6d9981f521ff8f4a3f8b946fc | [
"MIT"
] | null | null | null | src/OTLMOW/OTLModel/Classes/Overstortrand.py | davidvlaminck/OTLMOW | 71330afeb37c3ea6d9981f521ff8f4a3f8b946fc | [
"MIT"
] | null | null | null | # coding=utf-8
from OTLMOW.OTLModel.BaseClasses.OTLAttribuut import OTLAttribuut
from OTLMOW.OTLModel.Classes.AIMObject import AIMObject
from OTLMOW.OTLModel.Datatypes.DtcDocument import DtcDocument
from OTLMOW.OTLModel.Datatypes.KlOverstortrandMateriaal import KlOverstortrandMateriaal
from OTLMOW.OTLModel.Datatypes.KlVariabelDeelType import KlVariabelDeelType
from OTLMOW.OTLModel.Datatypes.KwantWrdInMillimeter import KwantWrdInMillimeter
from OTLMOW.GeometrieArtefact.VlakGeometrie import VlakGeometrie
# Generated with OTLClassCreator. To modify: extend, do not edit
class Overstortrand(AIMObject, VlakGeometrie):
"""Een overstortrand heeft als doel het afvoeren van (pieken in) overtollig rioolwater vanuit de gemengde riolering naar het oppervlaktewater."""
typeURI = 'https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand'
"""De URI van het object volgens https://www.w3.org/2001/XMLSchema#anyURI."""
@property
def breedte(self):
"""De afstand tussen de uiterste zijden van de overstortrand in millimeter."""
return self._breedte.get_waarde()
@breedte.setter
@property
def hoogte(self):
"""De afstand tussen de vaste drempel en het hoogste punt van de overstortrand in millimeter."""
return self._hoogte.get_waarde()
@hoogte.setter
@property
def materiaal(self):
"""Het materiaal waaruit de overstortrand vervaardigd is."""
return self._materiaal.get_waarde()
@materiaal.setter
@property
def technischeFiche(self):
"""De technische fiche van de de overstortrand."""
return self._technischeFiche.get_waarde()
@technischeFiche.setter
@property
def variabelDeelType(self):
"""Bepaalt het type van het variabel deel van de overstortrand."""
return self._variabelDeelType.get_waarde()
@variabelDeelType.setter
@property
def wanddikte(self):
"""De wanddikte van de overstortrand in millimeter."""
return self._wanddikte.get_waarde()
@wanddikte.setter
| 48.213675 | 149 | 0.593157 | # coding=utf-8
from OTLMOW.OTLModel.BaseClasses.OTLAttribuut import OTLAttribuut
from OTLMOW.OTLModel.Classes.AIMObject import AIMObject
from OTLMOW.OTLModel.Datatypes.DtcDocument import DtcDocument
from OTLMOW.OTLModel.Datatypes.KlOverstortrandMateriaal import KlOverstortrandMateriaal
from OTLMOW.OTLModel.Datatypes.KlVariabelDeelType import KlVariabelDeelType
from OTLMOW.OTLModel.Datatypes.KwantWrdInMillimeter import KwantWrdInMillimeter
from OTLMOW.GeometrieArtefact.VlakGeometrie import VlakGeometrie
# Generated with OTLClassCreator. To modify: extend, do not edit
class Overstortrand(AIMObject, VlakGeometrie):
"""Een overstortrand heeft als doel het afvoeren van (pieken in) overtollig rioolwater vanuit de gemengde riolering naar het oppervlaktewater."""
typeURI = 'https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand'
"""De URI van het object volgens https://www.w3.org/2001/XMLSchema#anyURI."""
def __init__(self):
AIMObject.__init__(self)
VlakGeometrie.__init__(self)
self._breedte = OTLAttribuut(field=KwantWrdInMillimeter,
naam='breedte',
label='breedte',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand.breedte',
definition='De afstand tussen de uiterste zijden van de overstortrand in millimeter.',
owner=self)
self._hoogte = OTLAttribuut(field=KwantWrdInMillimeter,
naam='hoogte',
label='hoogte',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand.hoogte',
definition='De afstand tussen de vaste drempel en het hoogste punt van de overstortrand in millimeter.',
owner=self)
self._materiaal = OTLAttribuut(field=KlOverstortrandMateriaal,
naam='materiaal',
label='materiaal',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand.materiaal',
definition='Het materiaal waaruit de overstortrand vervaardigd is.',
owner=self)
self._technischeFiche = OTLAttribuut(field=DtcDocument,
naam='technischeFiche',
label='technische fiche',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand.technischeFiche',
definition='De technische fiche van de de overstortrand.',
owner=self)
self._variabelDeelType = OTLAttribuut(field=KlVariabelDeelType,
naam='variabelDeelType',
label='variabel deel type',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand.variabelDeelType',
definition='Bepaalt het type van het variabel deel van de overstortrand.',
owner=self)
self._wanddikte = OTLAttribuut(field=KwantWrdInMillimeter,
naam='wanddikte',
label='wanddikte',
objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#Overstortrand.wanddikte',
definition='De wanddikte van de overstortrand in millimeter.',
owner=self)
@property
def breedte(self):
"""De afstand tussen de uiterste zijden van de overstortrand in millimeter."""
return self._breedte.get_waarde()
@breedte.setter
def breedte(self, value):
self._breedte.set_waarde(value, owner=self)
@property
def hoogte(self):
"""De afstand tussen de vaste drempel en het hoogste punt van de overstortrand in millimeter."""
return self._hoogte.get_waarde()
@hoogte.setter
def hoogte(self, value):
self._hoogte.set_waarde(value, owner=self)
@property
def materiaal(self):
"""Het materiaal waaruit de overstortrand vervaardigd is."""
return self._materiaal.get_waarde()
@materiaal.setter
def materiaal(self, value):
self._materiaal.set_waarde(value, owner=self)
@property
def technischeFiche(self):
"""De technische fiche van de de overstortrand."""
return self._technischeFiche.get_waarde()
@technischeFiche.setter
def technischeFiche(self, value):
self._technischeFiche.set_waarde(value, owner=self)
@property
def variabelDeelType(self):
"""Bepaalt het type van het variabel deel van de overstortrand."""
return self._variabelDeelType.get_waarde()
@variabelDeelType.setter
def variabelDeelType(self, value):
self._variabelDeelType.set_waarde(value, owner=self)
@property
def wanddikte(self):
"""De wanddikte van de overstortrand in millimeter."""
return self._wanddikte.get_waarde()
@wanddikte.setter
def wanddikte(self, value):
self._wanddikte.set_waarde(value, owner=self)
| 3,394 | 0 | 183 |
76fcb2603b88ccac0913fe789652456f73c7fa0b | 209 | py | Python | synth.py | codyoss/sloth | a3e67e5dd5f776db8b5df33b593f1a20707e9511 | [
"Apache-2.0"
] | 2 | 2019-02-19T08:42:32.000Z | 2019-02-19T08:42:41.000Z | synth.py | codyoss/sloth | a3e67e5dd5f776db8b5df33b593f1a20707e9511 | [
"Apache-2.0"
] | 534 | 2019-11-15T02:30:56.000Z | 2021-11-25T16:00:20.000Z | synth.py | codyoss/sloth | a3e67e5dd5f776db8b5df33b593f1a20707e9511 | [
"Apache-2.0"
] | 1 | 2019-01-14T16:18:36.000Z | 2019-01-14T16:18:36.000Z | import synthtool as s
import synthtool.gcp as gcp
import logging
logging.basicConfig(level=logging.DEBUG)
common_templates = gcp.CommonTemplates()
templates = common_templates.node_library()
s.copy(templates)
| 26.125 | 43 | 0.832536 | import synthtool as s
import synthtool.gcp as gcp
import logging
logging.basicConfig(level=logging.DEBUG)
common_templates = gcp.CommonTemplates()
templates = common_templates.node_library()
s.copy(templates)
| 0 | 0 | 0 |
5acb38c9155130c3e42b9f2df9274104a0876546 | 275 | py | Python | server.py | LordGhostX/graphql-profanity-filter | 71465693cb961066dc7a05bfc78260471cde82bc | [
"MIT"
] | 2 | 2021-07-19T21:32:00.000Z | 2021-07-28T20:47:44.000Z | server.py | LordGhostX/graphql-profanity-filter | 71465693cb961066dc7a05bfc78260471cde82bc | [
"MIT"
] | null | null | null | server.py | LordGhostX/graphql-profanity-filter | 71465693cb961066dc7a05bfc78260471cde82bc | [
"MIT"
] | null | null | null | from flask import Flask
from flask_graphql import GraphQLView
from schema import schema
app = Flask(__name__)
app.add_url_rule("/", view_func=GraphQLView.as_view("graphql",
schema=schema, graphiql=True))
if __name__ == "__main__":
app.run(debug=True)
| 22.916667 | 62 | 0.72 | from flask import Flask
from flask_graphql import GraphQLView
from schema import schema
app = Flask(__name__)
app.add_url_rule("/", view_func=GraphQLView.as_view("graphql",
schema=schema, graphiql=True))
if __name__ == "__main__":
app.run(debug=True)
| 0 | 0 | 0 |
ac8b81faa9ffcb252fdc732dd62f427ed94c8fa0 | 10,156 | py | Python | core/aws_ddk_core/resources/_lambda.py | vemel/aws-ddk | d34bd1d98f5a170026a1b65f9629e909ca839930 | [
"Apache-2.0"
] | 37 | 2022-02-21T19:06:35.000Z | 2022-03-31T17:12:26.000Z | core/aws_ddk_core/resources/_lambda.py | vemel/aws-ddk | d34bd1d98f5a170026a1b65f9629e909ca839930 | [
"Apache-2.0"
] | 25 | 2022-02-22T15:13:31.000Z | 2022-03-29T20:22:43.000Z | core/aws_ddk_core/resources/_lambda.py | vemel/aws-ddk | d34bd1d98f5a170026a1b65f9629e909ca839930 | [
"Apache-2.0"
] | 6 | 2022-02-21T19:07:01.000Z | 2022-03-08T05:20:59.000Z | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Any, Dict, List, Optional
import aws_cdk as cdk
import aws_cdk.aws_ec2 as ec2
import aws_cdk.aws_lambda as lmb
from aws_cdk.aws_iam import IRole, PolicyStatement
from aws_cdk.aws_sqs import IQueue
from aws_ddk_core.config import Config
from aws_ddk_core.resources.commons import BaseSchema, Duration, SubnetType
from constructs import Construct
from marshmallow import ValidationError, fields
_logger: logging.Logger = logging.getLogger(__name__)
class FunctionSchema(BaseSchema):
"""DDK Lambda function Marshmallow schema."""
# Lambda function CDK construct fields
dead_letter_queue_enabled = fields.Bool()
memory_size = fields.Int(load_default=256)
environment = fields.Dict(keys=fields.Str, values=fields.Str)
profiling = fields.Bool()
reserved_concurrent_executions = fields.Int()
timeout = Duration(load_default=cdk.Duration.seconds(120))
tracing = fields.Method(deserialize="load_tracing")
max_event_age = Duration()
retry_attempts = fields.Int()
allow_all_outbound = fields.Bool()
allow_public_subnet = fields.Bool()
# Config specific fields
vpc_id = fields.Str(metadata={"config_only": True})
vpc_subnet_type = SubnetType(metadata={"config_only": True})
vpc_subnet_group_name = fields.Str(metadata={"config_only": True})
vpc_subnet_ids = fields.List(fields.Str(), metadata={"config_only": True})
security_group_ids = fields.List(fields.Str(), metadata={"config_only": True})
class LambdaFactory:
"""
Class factory to create and configure Lambda DDK resources, including Functions.
"""
@staticmethod
def function(
scope: Construct,
id: str,
environment_id: str,
code: lmb.Code,
handler: str,
runtime: lmb.Runtime = lmb.Runtime.PYTHON_3_9,
function_name: Optional[str] = None,
description: Optional[str] = None,
role: Optional[IRole] = None,
dead_letter_queue_enabled: Optional[bool] = None,
dead_letter_queue: Optional[IQueue] = None,
memory_size: Optional[int] = None,
timeout: Optional[cdk.Duration] = None,
**function_props: Any,
) -> lmb.IFunction:
"""
Create and configure Lambda function.
This construct allows to configure parameters of the function using ddk.json configuration file
depending on the `environment_id` in which the function is used. Supported parameters are:
`dead_letter_queue_enabled`,`memory_size`, `environment`, `profiling`,
`reserved_concurrent_executions`, `timeout`, `tracing`, `max_event_age`, `retry_attempts`,
`allow_all_outbound`, and `allow_public_subnet`.
The parameters are respected in the following order:
1 - Explicit arguments are always preferred
2 - Values from configuration file
3 - Defaults are used otherwise
Parameters
----------
scope : Construct
Scope within which this construct is defined
id : str
Identifier of the Lambda function
environment_id : str
Identifier of the environment
code : Code
The source code of the Lambda function
handler : str
The name of the method within the code that Lambda calls to execute the function
runtime : Runtime
The runtime environment for the Lambda function
function_name : Optional[str]
The name of the Lambda function
description : Optional[str]
The description of the Lambda function
role : Optional[IRole]
Lambda execution role
dead_letter_queue_enabled : Optional[bool]
Determines if DLQ is enabled. `False` by default.
dead_letter_queue : Optional[IQueue]
The SQS queue to use if DLQ is enabled
memory_size : Optional[int]
The amount of memory, in MB, that is allocated to the Lambda function. `256` by default.
timeout : Optional[Duration]
The function execution time (in seconds) after which Lambda terminates the function.
`aws_cdk.Duration.seconds(120)` by default.
function_props : Any
Additional function properties. For complete list of properties refer to CDK Documentation -
Lambda Function: https://docs.aws.amazon.com/cdk/api/v2/python/aws_cdk.aws_lambda/Function.html
Returns
-------
fn : aws_cdk.aws_lambda.Function
Lambda function
"""
# Load function configuration from ddk.json based on environment id and resource id
function_config_dict: Dict[str, Any] = Config().get_resource_config(
environment_id=environment_id,
id=id,
)
declared_fields = FunctionSchema().declared_fields.items()
# Load CDK-only fields from schema
function_construct_props: Dict[str, Any] = FunctionSchema(
only=[name for name, field in declared_fields if not field.metadata]
).load(function_config_dict, partial=["removal_policy"])
# Load config-only fields from schema
function_config_only_props: Dict[str, Any] = FunctionSchema(
only=[name for name, field in declared_fields if field.metadata == {"config_only": True}]
).load(function_config_dict, partial=["removal_policy"])
# Set up networking
function_construct_props["vpc"] = LambdaFactory._get_vpc(
scope, id, vpc_id=function_config_only_props.get("vpc_id")
)
function_construct_props["vpc_subnets"] = LambdaFactory._get_vpc_subnets(
scope,
id,
vpc_subnet_ids=function_config_only_props.get("vpc_subnet_ids"),
vpc_subnet_type=function_config_only_props.get("vpc_subnet_type"),
vpc_subnet_group_name=function_config_only_props.get("vpc_subnet_group_name"),
)
function_construct_props["security_groups"] = LambdaFactory._get_security_groups(
scope, id, security_group_ids=function_config_only_props.get("security_group_ids")
)
# Collect all explicit function arguments
function_props = {
"code": code,
"handler": handler,
"runtime": runtime,
"role": role,
"function_name": function_name,
"description": description,
"dead_letter_queue_enabled": dead_letter_queue_enabled,
"dead_letter_queue": dead_letter_queue,
"memory_size": memory_size,
"timeout": timeout,
**function_props,
}
# Explicit ("hardcoded") props should always take precedence over config
for key, value in function_props.items():
if value is not None:
function_construct_props[key] = value
_logger.debug(f"function_construct_props: {function_construct_props}")
# Create the function
fn: lmb.IFunction = lmb.Function(scope, id, **function_construct_props)
# Add IAM permissions
if "vpc" in function_construct_props:
LambdaFactory.add_vpc_permissions(fn)
return fn
@staticmethod
@staticmethod
@staticmethod
@staticmethod
| 40.951613 | 114 | 0.652422 | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
from typing import Any, Dict, List, Optional
import aws_cdk as cdk
import aws_cdk.aws_ec2 as ec2
import aws_cdk.aws_lambda as lmb
from aws_cdk.aws_iam import IRole, PolicyStatement
from aws_cdk.aws_sqs import IQueue
from aws_ddk_core.config import Config
from aws_ddk_core.resources.commons import BaseSchema, Duration, SubnetType
from constructs import Construct
from marshmallow import ValidationError, fields
_logger: logging.Logger = logging.getLogger(__name__)
class FunctionSchema(BaseSchema):
"""DDK Lambda function Marshmallow schema."""
# Lambda function CDK construct fields
dead_letter_queue_enabled = fields.Bool()
memory_size = fields.Int(load_default=256)
environment = fields.Dict(keys=fields.Str, values=fields.Str)
profiling = fields.Bool()
reserved_concurrent_executions = fields.Int()
timeout = Duration(load_default=cdk.Duration.seconds(120))
tracing = fields.Method(deserialize="load_tracing")
max_event_age = Duration()
retry_attempts = fields.Int()
allow_all_outbound = fields.Bool()
allow_public_subnet = fields.Bool()
def load_tracing(self, value: str) -> lmb.Tracing:
tracings: Dict[str, lmb.Tracing] = lmb.Tracing._member_map_
try:
return tracings[value.upper()]
except KeyError as error:
raise ValidationError(f"`tracing` value must be one of {tracings.values()}.") from error
# Config specific fields
vpc_id = fields.Str(metadata={"config_only": True})
vpc_subnet_type = SubnetType(metadata={"config_only": True})
vpc_subnet_group_name = fields.Str(metadata={"config_only": True})
vpc_subnet_ids = fields.List(fields.Str(), metadata={"config_only": True})
security_group_ids = fields.List(fields.Str(), metadata={"config_only": True})
class LambdaFactory:
"""
Class factory to create and configure Lambda DDK resources, including Functions.
"""
@staticmethod
def function(
scope: Construct,
id: str,
environment_id: str,
code: lmb.Code,
handler: str,
runtime: lmb.Runtime = lmb.Runtime.PYTHON_3_9,
function_name: Optional[str] = None,
description: Optional[str] = None,
role: Optional[IRole] = None,
dead_letter_queue_enabled: Optional[bool] = None,
dead_letter_queue: Optional[IQueue] = None,
memory_size: Optional[int] = None,
timeout: Optional[cdk.Duration] = None,
**function_props: Any,
) -> lmb.IFunction:
"""
Create and configure Lambda function.
This construct allows to configure parameters of the function using ddk.json configuration file
depending on the `environment_id` in which the function is used. Supported parameters are:
`dead_letter_queue_enabled`,`memory_size`, `environment`, `profiling`,
`reserved_concurrent_executions`, `timeout`, `tracing`, `max_event_age`, `retry_attempts`,
`allow_all_outbound`, and `allow_public_subnet`.
The parameters are respected in the following order:
1 - Explicit arguments are always preferred
2 - Values from configuration file
3 - Defaults are used otherwise
Parameters
----------
scope : Construct
Scope within which this construct is defined
id : str
Identifier of the Lambda function
environment_id : str
Identifier of the environment
code : Code
The source code of the Lambda function
handler : str
The name of the method within the code that Lambda calls to execute the function
runtime : Runtime
The runtime environment for the Lambda function
function_name : Optional[str]
The name of the Lambda function
description : Optional[str]
The description of the Lambda function
role : Optional[IRole]
Lambda execution role
dead_letter_queue_enabled : Optional[bool]
Determines if DLQ is enabled. `False` by default.
dead_letter_queue : Optional[IQueue]
The SQS queue to use if DLQ is enabled
memory_size : Optional[int]
The amount of memory, in MB, that is allocated to the Lambda function. `256` by default.
timeout : Optional[Duration]
The function execution time (in seconds) after which Lambda terminates the function.
`aws_cdk.Duration.seconds(120)` by default.
function_props : Any
Additional function properties. For complete list of properties refer to CDK Documentation -
Lambda Function: https://docs.aws.amazon.com/cdk/api/v2/python/aws_cdk.aws_lambda/Function.html
Returns
-------
fn : aws_cdk.aws_lambda.Function
Lambda function
"""
# Load function configuration from ddk.json based on environment id and resource id
function_config_dict: Dict[str, Any] = Config().get_resource_config(
environment_id=environment_id,
id=id,
)
declared_fields = FunctionSchema().declared_fields.items()
# Load CDK-only fields from schema
function_construct_props: Dict[str, Any] = FunctionSchema(
only=[name for name, field in declared_fields if not field.metadata]
).load(function_config_dict, partial=["removal_policy"])
# Load config-only fields from schema
function_config_only_props: Dict[str, Any] = FunctionSchema(
only=[name for name, field in declared_fields if field.metadata == {"config_only": True}]
).load(function_config_dict, partial=["removal_policy"])
# Set up networking
function_construct_props["vpc"] = LambdaFactory._get_vpc(
scope, id, vpc_id=function_config_only_props.get("vpc_id")
)
function_construct_props["vpc_subnets"] = LambdaFactory._get_vpc_subnets(
scope,
id,
vpc_subnet_ids=function_config_only_props.get("vpc_subnet_ids"),
vpc_subnet_type=function_config_only_props.get("vpc_subnet_type"),
vpc_subnet_group_name=function_config_only_props.get("vpc_subnet_group_name"),
)
function_construct_props["security_groups"] = LambdaFactory._get_security_groups(
scope, id, security_group_ids=function_config_only_props.get("security_group_ids")
)
# Collect all explicit function arguments
function_props = {
"code": code,
"handler": handler,
"runtime": runtime,
"role": role,
"function_name": function_name,
"description": description,
"dead_letter_queue_enabled": dead_letter_queue_enabled,
"dead_letter_queue": dead_letter_queue,
"memory_size": memory_size,
"timeout": timeout,
**function_props,
}
# Explicit ("hardcoded") props should always take precedence over config
for key, value in function_props.items():
if value is not None:
function_construct_props[key] = value
_logger.debug(f"function_construct_props: {function_construct_props}")
# Create the function
fn: lmb.IFunction = lmb.Function(scope, id, **function_construct_props)
# Add IAM permissions
if "vpc" in function_construct_props:
LambdaFactory.add_vpc_permissions(fn)
return fn
@staticmethod
def _get_vpc(scope: Construct, id: str, vpc_id: Optional[str] = None) -> Optional[ec2.IVpc]:
return ec2.Vpc.from_lookup(scope, f"{id}-{vpc_id}-vpc", vpc_id=vpc_id) if vpc_id else None
@staticmethod
def _get_vpc_subnets(
scope: Construct,
id: str,
vpc_subnet_ids: Optional[List[str]] = None,
vpc_subnet_type: Optional[ec2.SubnetType] = None,
vpc_subnet_group_name: Optional[str] = None,
) -> Optional[ec2.SubnetSelection]:
subnets: Optional[List[ec2.ISubnet]] = (
[ec2.Subnet.from_subnet_id(scope, f"{id}-{subnet_id}-sb", subnet_id) for subnet_id in vpc_subnet_ids]
if vpc_subnet_ids
else None
)
return (
ec2.SubnetSelection(
subnet_type=vpc_subnet_type,
subnet_group_name=vpc_subnet_group_name,
subnets=subnets,
)
if vpc_subnet_type or vpc_subnet_group_name or subnets
else None
)
@staticmethod
def _get_security_groups(
scope: Construct,
id: str,
security_group_ids: Optional[List[str]] = None,
) -> Optional[List[ec2.ISecurityGroup]]:
return (
[
ec2.SecurityGroup.from_security_group_id(scope, f"{id}-{security_group_id}-sg", security_group_id)
for security_group_id in security_group_ids
]
if security_group_ids
else None
)
@staticmethod
def add_vpc_permissions(fn: lmb.IFunction) -> None:
fn.add_to_role_policy(
PolicyStatement(
actions=[
"ec2:AssignPrivateIpAddresses",
"ec2:CreateNetworkInterface",
"ec2:DeleteNetworkInterface",
"ec2:DescribeNetworkInterfaces",
"ec2:UnassignPrivateIpAddresses",
],
resources=["*"],
)
)
| 2,112 | 0 | 131 |
f414007c79614bcaf6561ed9cfe13b9f48716da0 | 8,240 | py | Python | hanlp/components/mtl/tasks/dep.py | antfootAlex/HanLP | e8044b27ae1de54b9070db08549853d3ca8271e2 | [
"Apache-2.0"
] | 3 | 2022-03-07T08:33:16.000Z | 2022-03-07T08:38:08.000Z | hanlp/components/mtl/tasks/dep.py | hushaoyun/HanLP | 967b52404c9d0adbc0cff2699690c127ecfca36e | [
"Apache-2.0"
] | null | null | null | hanlp/components/mtl/tasks/dep.py | hushaoyun/HanLP | 967b52404c9d0adbc0cff2699690c127ecfca36e | [
"Apache-2.0"
] | null | null | null | # -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2020-08-13 21:39
import logging
from typing import Dict, Any, Union, Iterable, List
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
from hanlp.common.dataset import SamplerBuilder, PadSequenceDataLoader
from hanlp.common.transform import VocabDict, TransformList
from hanlp.components.mtl.tasks import Task
from hanlp.components.parsers.biaffine.biaffine_dep import BiaffineDependencyParser
from hanlp.components.parsers.biaffine.biaffine_model import BiaffineDecoder
from hanlp.datasets.parsing.loaders.conll_dataset import append_bos
from hanlp.layers.scalar_mix import ScalarMixWithDropoutBuilder
from hanlp.metrics.metric import Metric
from hanlp.metrics.mtl import MetricDict
from hanlp.utils.time_util import CountdownTimer
from hanlp_common.constant import EOS
from hanlp_common.util import merge_locals_kwargs
| 49.047619 | 119 | 0.617597 | # -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2020-08-13 21:39
import logging
from typing import Dict, Any, Union, Iterable, List
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
from hanlp.common.dataset import SamplerBuilder, PadSequenceDataLoader
from hanlp.common.transform import VocabDict, TransformList
from hanlp.components.mtl.tasks import Task
from hanlp.components.parsers.biaffine.biaffine_dep import BiaffineDependencyParser
from hanlp.components.parsers.biaffine.biaffine_model import BiaffineDecoder
from hanlp.datasets.parsing.loaders.conll_dataset import append_bos
from hanlp.layers.scalar_mix import ScalarMixWithDropoutBuilder
from hanlp.metrics.metric import Metric
from hanlp.metrics.mtl import MetricDict
from hanlp.utils.time_util import CountdownTimer
from hanlp_common.constant import EOS
from hanlp_common.util import merge_locals_kwargs
class BiaffineDependencyParsing(Task, BiaffineDependencyParser):
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=2e-3, separate_optimizer=False,
cls_is_bos=True,
sep_is_eos=False,
punct=False,
tree=False,
proj=False,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
mu=.9,
nu=.9,
epsilon=1e-12,
decay=.75,
decay_steps=5000,
use_pos=False,
max_seq_len=None,
**kwargs) -> None:
"""Biaffine dependency parsing (:cite:`dozat:17a`).
Args:
trn: Path to training set.
dev: Path to dev set.
tst: Path to test set.
sampler_builder: A builder which builds a sampler.
dependencies: Its dependencies on other tasks.
scalar_mix: A builder which builds a `ScalarMixWithDropout` object.
use_raw_hidden_states: Whether to use raw hidden states from transformer without any pooling.
lr: Learning rate for this task.
separate_optimizer: Use customized separate optimizer for this task.
cls_is_bos: ``True`` to treat the first token as ``BOS``.
sep_is_eos: ``True`` to treat the last token as ``EOS``.
punct: ``True`` to include punctuations in evaluation.
tree: ``True`` to enforce tree constraint.
proj: ``True`` for projective parsing.
n_mlp_arc: Number of features for arc representation.
n_mlp_rel: Number of features for rel representation.
mlp_dropout: Dropout applied to MLPs.
mu: First coefficient used for computing running averages of gradient and its square in Adam.
nu: Second coefficient used for computing running averages of gradient and its square in Adam.
epsilon: Term added to the denominator to improve numerical stability
decay: Decay rate for exceptional lr scheduler.
decay_steps: Decay every ``decay_steps`` steps.
use_pos: Use pos feature.
max_seq_len: Prune samples longer than this length.
**kwargs: Not used.
"""
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
def update_metrics(self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
prediction: Dict[str, Any], metric: Union[MetricDict, Metric]):
BiaffineDependencyParser.update_metric(self, *prediction, batch['arc'], batch['rel_id'], output[1],
batch.get('punct_mask', None), metric, batch)
def decode_output(self,
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
mask: torch.BoolTensor,
batch: Dict[str, Any],
decoder, **kwargs) -> Union[Dict[str, Any], Any]:
(arc_scores, rel_scores), mask = output
return BiaffineDependencyParser.decode(self, arc_scores, rel_scores, mask, batch)
def compute_loss(self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any], criterion) -> \
Union[torch.FloatTensor, Dict[str, torch.FloatTensor]]:
(arc_scores, rel_scores), mask = output
return BiaffineDependencyParser.compute_loss(self, arc_scores, rel_scores, batch['arc'], batch['rel_id'], mask,
criterion,
batch)
def build_model(self, encoder_size, training=True, **kwargs) -> torch.nn.Module:
return BiaffineDecoder(encoder_size, self.config.n_mlp_arc, self.config.n_mlp_rel, self.config.mlp_dropout,
len(self.vocabs.rel))
def build_metric(self, **kwargs):
return BiaffineDependencyParser.build_metric(self, **kwargs)
def build_dataloader(self, data, transform: TransformList = None, training=False, device=None,
logger: logging.Logger = None, gradient_accumulation=1, **kwargs) -> DataLoader:
transform.insert(0, append_bos)
dataset = BiaffineDependencyParser.build_dataset(self, data, transform)
if isinstance(data, str):
dataset.purge_cache()
if self.vocabs.mutable:
BiaffineDependencyParser.build_vocabs(self, dataset, logger, transformer=True)
if dataset.cache:
timer = CountdownTimer(len(dataset))
BiaffineDependencyParser.cache_dataset(self, dataset, timer, training, logger)
max_seq_len = self.config.get('max_seq_len', None)
if max_seq_len and isinstance(data, str):
dataset.prune(lambda x: len(x['token_input_ids']) > 510, logger)
return PadSequenceDataLoader(
batch_sampler=self.sampler_builder.build(self.compute_lens(data, dataset, length_field='FORM'),
shuffle=training, gradient_accumulation=gradient_accumulation),
device=device,
dataset=dataset,
pad=self.get_pad_dict())
def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tensor], mask: torch.BoolTensor,
decoder: torch.nn.Module):
logits = super().feed_batch(h, batch, mask, decoder)
mask = mask.clone()
mask[:, 0] = 0
return logits, mask
def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
config = self.config
optimizer = Adam(decoder.parameters(),
config.lr,
(config.mu, config.nu),
config.epsilon)
scheduler = ExponentialLR(optimizer, config.decay ** (1 / config.decay_steps))
return optimizer, scheduler
def input_is_flat(self, data) -> bool:
return BiaffineDependencyParser.input_is_flat(self, data, self.config.use_pos)
def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict[str, Any]) -> List:
arcs, rels = prediction
arcs = arcs[:, 1:] # Skip the ROOT
rels = rels[:, 1:]
arcs = arcs.tolist()
rels = rels.tolist()
vocab = self.vocabs['rel'].idx_to_token
for arcs_per_sent, rels_per_sent, tokens in zip(arcs, rels, batch['token']):
tokens = tokens[1:]
sent_len = len(tokens)
result = list(zip(arcs_per_sent[:sent_len], [vocab[r] for r in rels_per_sent[:sent_len]]))
yield result
def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
return [{'FORM': token + ([EOS] if sep_is_eos else [])} for token in inputs]
| 4,262 | 3,001 | 23 |
cf676ab96dacbd16b460fdb6d7e34436bac6a565 | 419 | py | Python | pySOT/__init__.py | WY-Wang/pySOT | a2de59a0ab00da0465b964e070199fe4773918cd | [
"BSD-3-Clause"
] | 180 | 2015-11-17T03:36:31.000Z | 2022-03-16T10:09:42.000Z | pySOT/__init__.py | WY-Wang/pySOT | a2de59a0ab00da0465b964e070199fe4773918cd | [
"BSD-3-Clause"
] | 41 | 2015-06-30T15:43:10.000Z | 2021-02-04T06:03:02.000Z | pySOT/__init__.py | WY-Wang/pySOT | a2de59a0ab00da0465b964e070199fe4773918cd | [
"BSD-3-Clause"
] | 45 | 2015-10-29T16:40:11.000Z | 2021-11-14T01:44:39.000Z | #!/usr/bin/env python3
from . import auxiliary_problems, controller, experimental_design, optimization_problems, strategy, surrogate
__version__ = "0.3.3"
__author__ = "David Eriksson, David Bindel, Christine Shoemaker"
__all__ = [
"auxiliary_problems",
"controller",
"experimental_design",
"optimization_problems",
"strategy",
"surrogate",
# Other
"__author__",
"__version__",
]
| 22.052632 | 109 | 0.704057 | #!/usr/bin/env python3
from . import auxiliary_problems, controller, experimental_design, optimization_problems, strategy, surrogate
__version__ = "0.3.3"
__author__ = "David Eriksson, David Bindel, Christine Shoemaker"
__all__ = [
"auxiliary_problems",
"controller",
"experimental_design",
"optimization_problems",
"strategy",
"surrogate",
# Other
"__author__",
"__version__",
]
| 0 | 0 | 0 |
6d2cdfac699b5c92a28578e4397e7d6a16af2a88 | 2,799 | py | Python | message_body.py | juhyun0/python_network | fe730a6b405d80b910fe18e082b5bf1d85cba0dd | [
"Unlicense"
] | null | null | null | message_body.py | juhyun0/python_network | fe730a6b405d80b910fe18e082b5bf1d85cba0dd | [
"Unlicense"
] | null | null | null | message_body.py | juhyun0/python_network | fe730a6b405d80b910fe18e082b5bf1d85cba0dd | [
"Unlicense"
] | null | null | null | from message import ISerializable
import message
import struct
| 23.923077 | 84 | 0.540907 | from message import ISerializable
import message
import struct
class BodyRequest(ISerializable):
def __init__(self, buffer):
if buffer != None:
slen = len(buffer)
self.struct_fmt = str.format('=Q{0}s', slen - 8)
self.struct_len = struct.calcsize(self.struct_fmt)
if slen > 4:
slen = slen - 4
else:
slen = 0
unpacked = struct.unpack(self.struct_fmt, buffer)
self.FILESIZE = unpacked[0]
self.FILENAME = unpacked[1].decode(encoding='utf-8').replace('\x00', '')
else:
self.struct_fmt = str.format('=Q{0}s', 0)
self.struct_len = struct.calcsize(self.struct_fmt)
self.FILESIZE = 0
self.FILENAME = ''
def GetBytes(self):
buffer = self.FILENAME.encode(encoding='utf-8')
self.struct_fmt = str.format('=Q{0}s', len(buffer))
return struct.pack(
self.struct_fmt,
*(
self.FILESIZE,
buffer
))
def GetSize(self):
buffer = self.FILENAME.encode(encoding='utf-8')
self.struct_fmt = str.format('=Q{0}s', len(buffer))
self.struct_len = struct.calcsize(self.struct_fmt)
return self.struct_len
class BodyResponse(ISerializable):
def __init__(self, buffer):
self.struct_fmt = 'IB'
self.struct_len = struct.calcsize(self.struct_fmt)
if buffer != None:
unpacked = struct.unpack(self.struct_fmt, buffer)
self.MSGID = unpacked[0]
self.RESPONSE = unpacked[1]
else:
self.MSGID = 0
self.RESPONSE = message.DENIED
def GetBytes(self):
return struct.pack(
self.struct_fmt,
*(
self.MSGID,
self.RESPONSE
))
def GetSize(self):
return self.struct_len
class BodyData(ISerializable):
def __init__(self, buffer):
if buffer != None:
self.DATA = buffer
def GetBytes(self):
return self.DATA
def GetSize(self):
return len(self.DATA)
class BodyResult(ISerializable):
def __init__(self, buffer):
self.struct_fmt = '=IB'
self.struct_len = struct.calcsize(self.struct_fmt)
if buffer:
unpacked = struct.unpack(self.struct_fmt, buffer)
self.MSGID = unpacked[0]
self.RESULT = unpacked[1]
else:
self.MSGID = 0
self.RESULT = message.FAIL
def GetBytes(self):
return struct.pack(
self.struct_fmt,
*(
self.MSGID,
self.RESULT
))
def GetSize(self):
return self.struct_len | 2,272 | 45 | 414 |
0ae4e1aacab37222a89735b99e10a43c842bb161 | 17,586 | py | Python | moabb/tests/paradigms.py | plcrodrigues/moabb | aa4274fe7905631864e854c121c92e1927061f29 | [
"BSD-3-Clause"
] | 321 | 2017-06-03T16:14:45.000Z | 2022-03-28T17:43:59.000Z | moabb/tests/paradigms.py | plcrodrigues/moabb | aa4274fe7905631864e854c121c92e1927061f29 | [
"BSD-3-Clause"
] | 223 | 2017-06-03T17:41:57.000Z | 2022-03-29T09:07:44.000Z | moabb/tests/paradigms.py | girafe-ai/moabb | 78bbb48a2a0058b0725ebeba1ba1e3203f0eacd5 | [
"BSD-3-Clause"
] | 118 | 2017-06-03T18:36:35.000Z | 2022-03-16T06:22:02.000Z | import logging
import unittest
import numpy as np
from mne import BaseEpochs
from moabb.datasets.fake import FakeDataset
from moabb.paradigms import (
P300,
SSVEP,
BaseMotorImagery,
BaseP300,
BaseSSVEP,
FilterBankLeftRightImagery,
FilterBankMotorImagery,
FilterBankSSVEP,
LeftRightImagery,
)
log = logging.getLogger(__name__)
log.setLevel(logging.ERROR)
| 43.315271 | 89 | 0.65501 | import logging
import unittest
import numpy as np
from mne import BaseEpochs
from moabb.datasets.fake import FakeDataset
from moabb.paradigms import (
P300,
SSVEP,
BaseMotorImagery,
BaseP300,
BaseSSVEP,
FilterBankLeftRightImagery,
FilterBankMotorImagery,
FilterBankSSVEP,
LeftRightImagery,
)
log = logging.getLogger(__name__)
log.setLevel(logging.ERROR)
class SimpleMotorImagery(BaseMotorImagery): # Needed to assess BaseImagery
def used_events(self, dataset):
return dataset.event_id
class Test_MotorImagery(unittest.TestCase):
def test_BaseImagery_paradigm(self):
paradigm = SimpleMotorImagery()
dataset = FakeDataset(paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# we should have all the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D Array
self.assertEqual(len(X.shape), 3)
# labels must contain 3 values
self.assertEqual(len(np.unique(labels)), 3)
# metadata must have subjets, sessions, runs
self.assertTrue("subject" in metadata.columns)
self.assertTrue("session" in metadata.columns)
self.assertTrue("run" in metadata.columns)
# we should have only one subject in the metadata
self.assertEqual(np.unique(metadata.subject), 1)
# we should have two sessions in the metadata
self.assertEqual(len(np.unique(metadata.session)), 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_BaseImagery_channel_order(self):
"""test if paradigm return correct channel order, see issue #227"""
datasetA = FakeDataset(paradigm="imagery", channels=["C3", "Cz", "C4"])
datasetB = FakeDataset(paradigm="imagery", channels=["Cz", "C4", "C3"])
paradigm = SimpleMotorImagery(channels=["C4", "C3", "Cz"])
ep1, _, _ = paradigm.get_data(datasetA, subjects=[1], return_epochs=True)
ep2, _, _ = paradigm.get_data(datasetB, subjects=[1], return_epochs=True)
self.assertEqual(ep1.info["ch_names"], ep2.info["ch_names"])
def test_BaseImagery_tmintmax(self):
self.assertRaises(ValueError, SimpleMotorImagery, tmin=1, tmax=0)
def test_BaseImagery_filters(self):
# can work with filter bank
paradigm = SimpleMotorImagery(filters=[[7, 12], [12, 24]])
dataset = FakeDataset(paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D Array
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_baseImagery_wrongevent(self):
# test process_raw return empty list if raw does not contain any
# selected event. cetain runs in dataset are event specific.
paradigm = SimpleMotorImagery(filters=[[7, 12], [12, 24]])
dataset = FakeDataset(paradigm="imagery")
raw = dataset.get_data([1])[1]["session_0"]["run_0"]
# add something on the event channel
raw._data[-1] *= 10
self.assertIsNone(paradigm.process_raw(raw, dataset))
# zeros it out
raw._data[-1] *= 0
self.assertIsNone(paradigm.process_raw(raw, dataset))
def test_BaseImagery_noevent(self):
# Assert error if events from paradigm and dataset dont overlap
paradigm = SimpleMotorImagery(events=["left_hand", "right_hand"])
dataset = FakeDataset(paradigm="imagery")
self.assertRaises(AssertionError, paradigm.get_data, dataset)
def test_LeftRightImagery_paradigm(self):
# with a good dataset
paradigm = LeftRightImagery()
dataset = FakeDataset(event_list=["left_hand", "right_hand"], paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
self.assertEqual(len(np.unique(labels)), 2)
self.assertEqual(list(np.unique(labels)), ["left_hand", "right_hand"])
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_LeftRightImagery_noevent(self):
# we cant pass event to this class
self.assertRaises(ValueError, LeftRightImagery, events=["a"])
def test_LeftRightImagery_badevents(self):
paradigm = LeftRightImagery()
# does not accept dataset with bad event
dataset = FakeDataset(paradigm="imagery")
self.assertRaises(AssertionError, paradigm.get_data, dataset)
def test_FilterBankMotorImagery_paradigm(self):
# can work with filter bank
paradigm = FilterBankMotorImagery()
dataset = FakeDataset(paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D Array
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 6)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_FilterBankMotorImagery_moreclassesthanevent(self):
self.assertRaises(
AssertionError, FilterBankMotorImagery, n_classes=3, events=["hands", "feet"]
)
def test_FilterBankLeftRightImagery_paradigm(self):
# can work with filter bank
paradigm = FilterBankLeftRightImagery()
dataset = FakeDataset(event_list=["left_hand", "right_hand"], paradigm="imagery")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D Array
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 6)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
class SimpleP300(BaseP300): # Needed to assess BaseP300
def used_events(self, dataset):
return dataset.event_id
class Test_P300(unittest.TestCase):
def test_BaseP300_paradigm(self):
paradigm = SimpleP300()
dataset = FakeDataset(paradigm="p300", event_list=["Target", "NonTarget"])
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# we should have all the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D Array
self.assertEqual(len(X.shape), 3)
# labels must contain 2 values (Target/NonTarget)
self.assertEqual(len(np.unique(labels)), 2)
# metadata must have subjets, sessions, runs
self.assertTrue("subject" in metadata.columns)
self.assertTrue("session" in metadata.columns)
self.assertTrue("run" in metadata.columns)
# we should have only one subject in the metadata
self.assertEqual(np.unique(metadata.subject), 1)
# we should have two sessions in the metadata
self.assertEqual(len(np.unique(metadata.session)), 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_BaseP300_channel_order(self):
"""test if paradigm return correct channel order, see issue #227"""
datasetA = FakeDataset(
paradigm="p300",
channels=["C3", "Cz", "C4"],
event_list=["Target", "NonTarget"],
)
datasetB = FakeDataset(
paradigm="p300",
channels=["Cz", "C4", "C3"],
event_list=["Target", "NonTarget"],
)
paradigm = SimpleP300(channels=["C4", "C3", "Cz"])
ep1, _, _ = paradigm.get_data(datasetA, subjects=[1], return_epochs=True)
ep2, _, _ = paradigm.get_data(datasetB, subjects=[1], return_epochs=True)
self.assertEqual(ep1.info["ch_names"], ep2.info["ch_names"])
def test_BaseP300_tmintmax(self):
self.assertRaises(ValueError, SimpleP300, tmin=1, tmax=0)
def test_BaseP300_filters(self):
# can work with filter bank
paradigm = SimpleP300(filters=[[1, 12], [12, 24]])
dataset = FakeDataset(paradigm="p300", event_list=["Target", "NonTarget"])
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D Array
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_BaseP300_wrongevent(self):
# test process_raw return empty list if raw does not contain any
# selected event. cetain runs in dataset are event specific.
paradigm = SimpleP300(filters=[[1, 12], [12, 24]])
dataset = FakeDataset(paradigm="p300", event_list=["Target", "NonTarget"])
raw = dataset.get_data([1])[1]["session_0"]["run_0"]
# add something on the event channel
raw._data[-1] *= 10
self.assertIsNone(paradigm.process_raw(raw, dataset))
# zeros it out
raw._data[-1] *= 0
self.assertIsNone(paradigm.process_raw(raw, dataset))
def test_P300_specifyevent(self):
# we cant pass event to this class
self.assertRaises(ValueError, P300, events=["a"])
def test_P300_wrongevent(self):
# does not accept dataset with bad event
paradigm = P300()
dataset = FakeDataset(paradigm="p300")
self.assertRaises(AssertionError, paradigm.get_data, dataset)
def test_P300_paradigm(self):
# with a good dataset
paradigm = P300()
dataset = FakeDataset(event_list=["Target", "NonTarget"], paradigm="p300")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
self.assertEqual(len(np.unique(labels)), 2)
self.assertEqual(list(np.unique(labels)), sorted(["Target", "NonTarget"]))
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
class Test_SSVEP(unittest.TestCase):
def test_BaseSSVEP_paradigm(self):
paradigm = BaseSSVEP(n_classes=None)
dataset = FakeDataset(paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain 3 values
self.assertEqual(len(np.unique(labels)), 3)
# metadata must have subjets, sessions, runs
self.assertTrue("subject" in metadata.columns)
self.assertTrue("session" in metadata.columns)
self.assertTrue("run" in metadata.columns)
# Only one subject in the metadata
self.assertEqual(np.unique(metadata.subject), 1)
# we should have two sessions in the metadata, n_classes = 2 as default
self.assertEqual(len(np.unique(metadata.session)), 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_BaseSSVEP_channel_order(self):
"""test if paradigm return correct channel order, see issue #227"""
datasetA = FakeDataset(paradigm="ssvep", channels=["C3", "Cz", "C4"])
datasetB = FakeDataset(paradigm="ssvep", channels=["Cz", "C4", "C3"])
paradigm = BaseSSVEP(channels=["C4", "C3", "Cz"])
ep1, _, _ = paradigm.get_data(datasetA, subjects=[1], return_epochs=True)
ep2, _, _ = paradigm.get_data(datasetB, subjects=[1], return_epochs=True)
self.assertEqual(ep1.info["ch_names"], ep2.info["ch_names"])
def test_baseSSVEP_tmintmax(self):
# Verify that tmin < tmax
self.assertRaises(ValueError, BaseSSVEP, tmin=1, tmax=0)
def test_BaseSSVEP_filters(self):
# Accept filters
paradigm = BaseSSVEP(filters=[(10.5, 11.5), (12.5, 13.5)])
dataset = FakeDataset(paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array
self.assertEqual(len(X.shape), 4)
# Last dim should be 2 as the number of filters
self.assertEqual(X.shape[-1], 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_BaseSSVEP_nclasses_default(self):
# Default is with 3 classes
paradigm = BaseSSVEP()
dataset = FakeDataset(paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# labels must contain all 3 classes of dataset,
# as n_classes is "None" by default (taking all classes)
self.assertEqual(len(np.unique(labels)), 3)
def test_BaseSSVEP_specified_nclasses(self):
# Set the number of classes
paradigm = BaseSSVEP(n_classes=3)
dataset = FakeDataset(event_list=["13", "15", "17", "19"], paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# labels must contain 3 values
self.assertEqual(len(np.unique(labels)), 3)
def test_BaseSSVEP_toomany_nclasses(self):
paradigm = BaseSSVEP(n_classes=4)
dataset = FakeDataset(event_list=["13", "15"], paradigm="ssvep")
self.assertRaises(ValueError, paradigm.get_data, dataset)
def test_BaseSSVEP_moreclassesthanevent(self):
self.assertRaises(AssertionError, BaseSSVEP, n_classes=3, events=["13.", "14."])
def test_SSVEP_noevent(self):
# Assert error if events from paradigm and dataset dont overlap
paradigm = SSVEP(events=["11", "12"], n_classes=2)
dataset = FakeDataset(event_list=["13", "14"], paradigm="ssvep")
self.assertRaises(AssertionError, paradigm.get_data, dataset)
def test_SSVEP_paradigm(self):
paradigm = SSVEP(n_classes=None)
dataset = FakeDataset(event_list=["13", "15", "17", "19"], paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain 4 values, defined in the dataset
self.assertEqual(len(np.unique(labels)), 4)
# metadata must have subjets, sessions, runs
self.assertTrue("subject" in metadata.columns)
self.assertTrue("session" in metadata.columns)
self.assertTrue("run" in metadata.columns)
# Only one subject in the metadata
self.assertEqual(np.unique(metadata.subject), 1)
# We should have two sessions in the metadata
self.assertEqual(len(np.unique(metadata.session)), 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_SSVEP_singlepass(self):
# Accept only single pass filter
paradigm = SSVEP(fmin=2, fmax=25)
dataset = FakeDataset(paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# Verify that they have the same length
self.assertEqual(len(X), len(labels), len(metadata))
# X must be a 3D array
self.assertEqual(len(X.shape), 3)
# labels must contain all 3 classes of dataset,
# as n_classes is "None" by default (taking all classes)
self.assertEqual(len(np.unique(labels)), 3)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_SSVEP_filter(self):
# Do not accept multiple filters
self.assertRaises(ValueError, SSVEP, filters=[(10.5, 11.5), (12.5, 13.5)])
def test_FilterBankSSVEP_paradigm(self):
# FilterBankSSVEP with all events
paradigm = FilterBankSSVEP(n_classes=None)
dataset = FakeDataset(event_list=["13", "15", "17", "19"], paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array
self.assertEqual(len(X.shape), 4)
# X must be a 4D array with d=4 as last dimension for the 4 events
self.assertEqual(X.shape[-1], 4)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
def test_FilterBankSSVEP_filters(self):
# can work with filter bank
paradigm = FilterBankSSVEP(filters=[(10.5, 11.5), (12.5, 13.5)])
dataset = FakeDataset(event_list=["13", "15", "17"], paradigm="ssvep")
X, labels, metadata = paradigm.get_data(dataset, subjects=[1])
# X must be a 4D array with d=2 as last dimension for the 2 filters
self.assertEqual(len(X.shape), 4)
self.assertEqual(X.shape[-1], 2)
# should return epochs
epochs, _, _ = paradigm.get_data(dataset, subjects=[1], return_epochs=True)
self.assertIsInstance(epochs, BaseEpochs)
| 14,160 | 2,860 | 167 |
6c54ad6eb0c9dcb90c8b78eed507fc7172e160ae | 5,067 | py | Python | lib_photo_scanner/api.py | Techainer/lib_photo_scanner | 58f7efa5e5b9630bc063d614bfbfde904bfa4f00 | [
"MIT"
] | null | null | null | lib_photo_scanner/api.py | Techainer/lib_photo_scanner | 58f7efa5e5b9630bc063d614bfbfde904bfa4f00 | [
"MIT"
] | null | null | null | lib_photo_scanner/api.py | Techainer/lib_photo_scanner | 58f7efa5e5b9630bc063d614bfbfde904bfa4f00 | [
"MIT"
] | null | null | null | from __future__ import absolute_import
from fastapi import FastAPI, HTTPException, Depends, Request, status
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import time
import os
from lib import Scanner, logger
import traceback
from typing import Optional
import sys
app = FastAPI()
ls, scanners, scanners_set, scanner_name = get_scanner()
print(ls, scanners, scanners_set, scanner_name)
lock = False
last_lock = time.time()
LOCK_THRESHOLD = int(os.environ.get('LOCK_THRESHOLD', None)) if os.environ.get('LOCK_THRESHOLD', None) is not None else 3
@app.route('/')
@app.route('/list')
@app.route('/scan') | 31.867925 | 121 | 0.586738 | from __future__ import absolute_import
from fastapi import FastAPI, HTTPException, Depends, Request, status
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import time
import os
from lib import Scanner, logger
import traceback
from typing import Optional
import sys
app = FastAPI()
def get_scanner(scanner_name: str = None):
ls = Scanner()
scanners = ls.getScanners()
if scanner_name is None:
if scanners is None or len(scanners) == 0:
return None, [], {}, None
scanner_name = scanners[0]
if isinstance(scanner_name, tuple) or isinstance(scanner_name, list):
scanner_name = scanner_name[0]
try:
ls.setScanner(scanner_name)
ls.setDPI(300)
ls.setPixelType("color")
if sys.platform == "win32":
ls.close()
except Exception as ex:
logger.error('Can not set Scanner: {0}'.format(ex))
print('Can not set Scanner: {0}'.format(ex))
return None, scanners, set(scanners), None
return ls, scanners, set(scanners), scanner_name
ls, scanners, scanners_set, scanner_name = get_scanner()
print(ls, scanners, scanners_set, scanner_name)
lock = False
last_lock = time.time()
LOCK_THRESHOLD = int(os.environ.get('LOCK_THRESHOLD', None)) if os.environ.get('LOCK_THRESHOLD', None) is not None else 3
@app.route('/')
def ping(request):
start_time = time.time()
output = {
"output": "pong",
"code": "SUCCESS",
"msg": "SUCCESS",
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_200_OK, content=output)
@app.route('/list')
def list_scanners(request):
start_time = time.time()
global scanners
output = {
"output": scanners,
"code": "SUCCESS",
"msg": "SUCCESS",
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_200_OK, content=output)
@app.route('/scan')
def scanning(request, request_id: Optional[str] = None, scanner_name: Optional[str] = None):
start_time = time.time()
global ls
global lock
global scanners
global scanners_set
global last_lock
if lock:
if last_lock + LOCK_THRESHOLD < time.time():
lock = False
else:
output = {
"request_id": request_id,
"output": False,
"code": "LOCK",
"msg": "The scanner is locking, please wait",
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_423_LOCKED, content=output)
if scanner_name is not None:
if scanner_name not in scanners_set:
ls, scanners, scanners_set, scanner_name = get_scanner()
if scanner_name not in scanners_set:
output = {
"request_id": request_id,
"output": scanners,
"code": "NOT_FOUND",
"msg": "Not found scanner {0}".format(scanner_name),
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_409_CONFLICT, content=output)
else:
if len(scanners) == 0:
ls, scanners, scanners_set, scanner_name = get_scanner()
if len(scanners) == 0:
output = {
"request_id": request_id,
"output": False,
"code": "NO_SCANNER",
"msg": "There's no plugin scanners, check your cable",
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, content=output)
scanner_name = scanners[0]
lock = True
last_lock = time.time()
try:
image = ls.scan(scanner_name=scanner_name, return_type="based64")
except Exception as ex:
lock = False
output = {
"request_id": request_id,
"error": "Scan Error",
"code": "ERROR",
"msg": "There's some error when scanner: {0}".format(traceback.format_exc()),
"time": time.time() - start_time
}
ls, scanners, scanners_set, scanner_name = get_scanner()
return JSONResponse(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, content=output)
lock = False
if image is False or image is None:
output = {
"request_id": request_id,
"error": "Scan Error",
"code": "ERROR",
"msg": "There's some error when scanner, I doesn't know.",
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, content=output)
output = {
"request_id": request_id,
"output": image,
"code": "SUCCESS",
"msg": "SUCCESS",
"time": time.time() - start_time
}
return JSONResponse(status_code=status.HTTP_200_OK, content=output) | 4,343 | 0 | 89 |
575d58b5390a86bb5b50713c8330c07cdc1503d8 | 1,841 | py | Python | TextClient.py | michaelxiao16/HackGT6 | 8ea6a16196fa8226792f0ec9c6e2d3e33513ce9e | [
"MIT"
] | null | null | null | TextClient.py | michaelxiao16/HackGT6 | 8ea6a16196fa8226792f0ec9c6e2d3e33513ce9e | [
"MIT"
] | null | null | null | TextClient.py | michaelxiao16/HackGT6 | 8ea6a16196fa8226792f0ec9c6e2d3e33513ce9e | [
"MIT"
] | null | null | null | import json
import boto3 as boto3
DESTINATION_NUMBER = "4702637816"
FIRST_NAME = "Austin"
LAST_NAME = "Miles"
if __name__ == '__main__':
send_grade_change_message()
| 21.406977 | 38 | 0.611624 | import json
import boto3 as boto3
DESTINATION_NUMBER = "4702637816"
FIRST_NAME = "Austin"
LAST_NAME = "Miles"
def send_grade_change_message():
payload3 = b"""{
"destinationNumber": "4702637816",
"firstName": "Austin",
"lastName": "Miles",
"source": "Grade Change"
}"""
client = get_boto3_client()
client.invoke(
FunctionName="CanvasText",
InvocationType="Event",
Payload=payload3
)
def send_announcement_message():
payload3 = b"""{
"destinationNumber": "4702637816",
"firstName": "Austin",
"lastName": "Miles",
"source": "New Announcement"
}"""
client = get_boto3_client()
client.invoke(
FunctionName="CanvasText",
InvocationType="Event",
Payload=payload3
)
def send_quiz_message():
payload3 = b"""{
"destinationNumber": "4702637816",
"firstName": "Austin",
"lastName": "Miles",
"source": "New Quiz"
}"""
client = get_boto3_client()
client.invoke(
FunctionName="CanvasText",
InvocationType="Event",
Payload=payload3
)
def send_assignment_message():
payload3 = b"""{
"destinationNumber": "4702637816",
"firstName": "Austin",
"lastName": "Miles",
"source": "New Assignment"
}"""
client = get_boto3_client()
client.invoke(
FunctionName="CanvasText",
InvocationType="Event",
Payload=payload3
)
def get_boto3_client():
with open('aws-creds.json') as f:
creds = json.load(f)
access_key = creds['access_key']
secret = creds['secret_key']
client = boto3.client(
'lambda',
aws_access_key_id=access_key,
aws_secret_access_key=secret,
region_name='us-east-1'
)
return client
if __name__ == '__main__':
send_grade_change_message()
| 1,548 | 0 | 115 |
01632dfbdb66922b0869d002c262c48492266f05 | 3,226 | py | Python | src/stack_exchange_graph_data/coroutines/links.py | Peilonrayz/stack_exchange_graph_data | d2d8d9f80dba727509f98b31d25e20e1e3824bb3 | [
"MIT"
] | null | null | null | src/stack_exchange_graph_data/coroutines/links.py | Peilonrayz/stack_exchange_graph_data | d2d8d9f80dba727509f98b31d25e20e1e3824bb3 | [
"MIT"
] | null | null | null | src/stack_exchange_graph_data/coroutines/links.py | Peilonrayz/stack_exchange_graph_data | d2d8d9f80dba727509f98b31d25e20e1e3824bb3 | [
"MIT"
] | null | null | null | """Link coroutine control flow functions."""
import argparse
import collections
import urllib.parse
from typing import DefaultDict, Generator, List, Set
from ..helpers.coroutines import coroutine
from ..segd import graph
@coroutine
def handle_links(filter_: Generator, good: Generator) -> Generator:
"""Send http and id links to correct target."""
while True:
orig_id, link, link_type = yield
target = filter_ if isinstance(link, str) else good
target.send((orig_id, link, link_type))
@coroutine
def filter_links(domains: Set[str], target: Generator) -> Generator:
"""Filter links to links to posts on the provided site."""
while True:
orig_id, link, link_type = yield
url = urllib.parse.urlparse(link)
if url.netloc not in domains:
continue
segments: List[str] = url.path.split("/")
list_ = segments[1] if len(segments) >= 2 else None
post_id = segments[2] if len(segments) >= 3 else ""
if list_ not in {"questions", "a", "q"}:
continue
if url.query:
try:
_, post_id = url.query.split("_", 1)
int(post_id)
except ValueError:
pass
try:
int(post_id)
except (ValueError, TypeError):
continue
target.send((orig_id, post_id, link_type))
@coroutine
def filter_duplicates(target: Generator) -> Generator:
"""Remove duplicate links from the output."""
link_lookup: DefaultDict[int, DefaultDict[int, Set[graph.LinkType]]]
link_lookup = collections.defaultdict(lambda: collections.defaultdict(set))
try:
while True:
from_node, to_node, link_type = yield
link_lookup[from_node][to_node].add(link_type)
finally:
for from_node, links_to in link_lookup.items():
for to_node, types in links_to.items():
target.send(
(from_node, to_node, max(types, key=lambda i: i.value.weight),)
)
@coroutine
def filter_network_size(arguments: argparse.Namespace, target: Generator,) -> Generator:
"""
Filter networks that aren't the wanted size.
:param arguments: CLI parser arguments that dictate the min and max size.
"""
graph_ = graph.Graph()
try:
while True:
item = yield
graph_.add(*item)
finally:
for network in graph_.get_networks():
if not arguments.min <= len(network) <= arguments.max:
continue
for node in network:
for link in node.links:
edge_type = link.type.value
target.send(
(
node.value,
link.target.value,
edge_type.weight,
edge_type.type,
)
)
@coroutine
def sheet_prep(target: Generator) -> Generator:
"""Convert into the format required to be sent to disk."""
target.send("Source;Target;Weight;Type\n")
while True:
edge = yield
target.send(";".join(map(str, edge)) + "\n")
| 31.019231 | 88 | 0.574706 | """Link coroutine control flow functions."""
import argparse
import collections
import urllib.parse
from typing import DefaultDict, Generator, List, Set
from ..helpers.coroutines import coroutine
from ..segd import graph
@coroutine
def handle_links(filter_: Generator, good: Generator) -> Generator:
"""Send http and id links to correct target."""
while True:
orig_id, link, link_type = yield
target = filter_ if isinstance(link, str) else good
target.send((orig_id, link, link_type))
@coroutine
def filter_links(domains: Set[str], target: Generator) -> Generator:
"""Filter links to links to posts on the provided site."""
while True:
orig_id, link, link_type = yield
url = urllib.parse.urlparse(link)
if url.netloc not in domains:
continue
segments: List[str] = url.path.split("/")
list_ = segments[1] if len(segments) >= 2 else None
post_id = segments[2] if len(segments) >= 3 else ""
if list_ not in {"questions", "a", "q"}:
continue
if url.query:
try:
_, post_id = url.query.split("_", 1)
int(post_id)
except ValueError:
pass
try:
int(post_id)
except (ValueError, TypeError):
continue
target.send((orig_id, post_id, link_type))
@coroutine
def filter_duplicates(target: Generator) -> Generator:
"""Remove duplicate links from the output."""
link_lookup: DefaultDict[int, DefaultDict[int, Set[graph.LinkType]]]
link_lookup = collections.defaultdict(lambda: collections.defaultdict(set))
try:
while True:
from_node, to_node, link_type = yield
link_lookup[from_node][to_node].add(link_type)
finally:
for from_node, links_to in link_lookup.items():
for to_node, types in links_to.items():
target.send(
(from_node, to_node, max(types, key=lambda i: i.value.weight),)
)
@coroutine
def filter_network_size(arguments: argparse.Namespace, target: Generator,) -> Generator:
"""
Filter networks that aren't the wanted size.
:param arguments: CLI parser arguments that dictate the min and max size.
"""
graph_ = graph.Graph()
try:
while True:
item = yield
graph_.add(*item)
finally:
for network in graph_.get_networks():
if not arguments.min <= len(network) <= arguments.max:
continue
for node in network:
for link in node.links:
edge_type = link.type.value
target.send(
(
node.value,
link.target.value,
edge_type.weight,
edge_type.type,
)
)
@coroutine
def sheet_prep(target: Generator) -> Generator:
"""Convert into the format required to be sent to disk."""
target.send("Source;Target;Weight;Type\n")
while True:
edge = yield
target.send(";".join(map(str, edge)) + "\n")
| 0 | 0 | 0 |
68b34ecd7f5ec5322690ac437666c06998f402ae | 11,621 | py | Python | Project01/Final/profile_data_extraction.py | nayeem990/Korean-website-scraping | fd26c913bec3a2ba27ca9e8413a9e92bfc618f85 | [
"Apache-2.0"
] | null | null | null | Project01/Final/profile_data_extraction.py | nayeem990/Korean-website-scraping | fd26c913bec3a2ba27ca9e8413a9e92bfc618f85 | [
"Apache-2.0"
] | null | null | null | Project01/Final/profile_data_extraction.py | nayeem990/Korean-website-scraping | fd26c913bec3a2ba27ca9e8413a9e92bfc618f85 | [
"Apache-2.0"
] | null | null | null | # importing the required modules
import requests
from bs4 import BeautifulSoup
import pandas as pd
from selenium import webdriver
import time
pd.options.display.max_columns = 999
pd.options.display.max_rows = 999
df = pd.read_csv('new_profile_links.csv')
df = df.drop(['Unnamed: 0'], axis = 1)
df["0"]
pl = list(df["0"])
all_user = []
for i in range(len(pl)):
try:
one_user = []
print("Loop :",i)
url = pl[i]
print(url)
driver = webdriver.Chrome(r'C:\Users\David\chromedriver_win32\chromedriver.exe')
driver.get(url)
time.sleep(3)
# basic data extraction
basic = []
source_code = driver.page_source
soup = BeautifulSoup(source_code, 'html.parser')
name = soup.find_all('strong', class_ = 'userName--1ZA07')
for n in name:
basic.append(n.text)
sp = soup.find_all('div', class_ = 'categoryName--1zWtA')
category = soup.find_all('strong', class_ = 'introCategory--F81Ky')
for e in category:
basic.append(e.text)
sp = soup.find_all('div', class_ = 'categoryName--1zWtA')
for m in sp:
basic.append(m.text)
rating = soup.find_all('div', class_ = 'itemRating--360UA itemRating--2-rFv typeLarge--1cEMN')
for k in rating:
a = k.text
if "평균 평점" in a:
a = a.replace("평균 평점", "")
basic.append(a)
### Project data for one user
maininfo = []
infos = soup.find_all('ul', class_ = 'productInfoList--1-H-D')
for f in infos:
li = f.find_all('li')
for ll in li:
uh = ["대표자","상호명","사업자등록번호","통신판매업번호-", "사업장 주소", "고객센터",'메일']
for u in range(len(uh)):
if uh[u] in ll.text:
b = uh[u]
la = ll.text
maininfo.append(la.replace(b , ""))
#count product and review section
products = []
tt = soup.find_all('div', class_ = "list--e6w5E")
for t in tt:
cc = t.find_all('div', class_='count--2w5o6')
for cd in cc:
cd.find_all('div', class_ = "count--2w5o6")
ce = cd.text
products.append(ce)
#profile informations
profile = []
heading = soup.find_all('strong', class_ = 'introduceMainTitle--2MZc-')
for h in heading:
profile.append(h.text)
text = soup.find_all('p', class_ = 'introduceText--2R5pY')
for e in text:
profile.append(e.text)
#new section
recom = soup.find_all('ul', class_ = 'listDisc--1Cc80')
for rc in recom:
profile.append(rc.text)
rest = soup.find_all('div', class_ = ['profileCareer--3_uFh','isExpert--2GkDA'])
# for mm in rest:
# mm.find_all('div', class_ = 'profileBox--1jlog')
for m in rest:
m.find_all('div', class_ = "careerJob--2-hX4")
for i in m:
profile.append(i.text)
### Project data for one user
projects = soup.find_all('div', class_ = 'listArea--peDdh')
#projects and consultations
all_project = []
for y in projects:
one = []
yy = y.find_all('div', class_ = 'item--1ZJSx')
for t in yy:
project_item = []
tdiv = t.find_all('div', class_ =['itemTitle--2vWBq','elip2--nFWXY'])
for td in tdiv:
project_title = td.text
project_item.append(project_title)
ratdiv = t.find_all('div', class_ =['itemGroup--2RnIL','ItemGroup_itemGroup--1f-on'])
for rd in ratdiv:
ratscore = rd.find_all("div", class_ = "itemRating--360UA")
for r in ratscore:
b = r.text
if "평균 평점" in b:
b = b.replace("평균 평점", " ")
project_item.append(b)
ratreview = rd.find_all("div", class_ = "itemCount--2HsJv")
for rr in ratreview:
c = rr.text
if "후기" in c:
c = c.replace("후기", " ")
project_item.append(c)
feediv = t.find_all('span', class_ =['priceInner--1HE2v'])
for fd in feediv:
fee = fd.find_all("span", class_=["priceNum--1rXJI","ItemPrice_priceNum--2OFHI"])
for f in fee:
project_item.append(f.text)
discount = fd.find_all("em", class_="discountPercent--3n0bl")
for dis in discount:
project_item.append(dis.text)
actualPrize = fd.find_all("span", class_="beforeDiscount--W1C4G")
for fp in actualPrize:
project_item.append(fp.text)
one.append([*project_item])
all_project.append([*one])
proj = []
for i in range(len(all_project)):
data = all_project[i]
for j in range(len(data)):
dj = data[j]
for k in range(len(dj)):
bb = dj[k]
proj.append(bb)
lis = ["평균 평점","후기","판매가","원할인률","할인 전 가격", "할인률"]
for i in range(len(proj)):
for j in range(len(lis)):
if lis[j] in proj[i]:
proj[i] = proj[i].replace(lis[j], " ")
rdiv = soup.find_all('div', class_ = "listSection--kViCl")
reviews_user = []
reviews_rating = []
reviews_heading = []
reviews_text = []
for eachr in rdiv:
ee = eachr.find_all('div', class_ = "reviewItem--1OwNO")
for each in ee:
name = each.find_all('span', class_ = ["item--3sQA9 ","nickname--2OOe6"])
for nm in name:
reviews_user.append(nm.text)
rating = each.find_all('div', class_ = ["expertPoint--2Zrvr","expertPoint--13H3V"])
for r in rating:
b = r.text
if "평점" in b:
b = b.replace("평점", "")
reviews_rating.append(b)
head = each.find_all('div', class_ = "reviewTitle--qv3Pk")
for r in head:
reviews_heading.append(r.text)
commentdiv = each.find_all('p', class_ = "reviewText--28mzN")
for ecom in commentdiv:
reviews_text.append(ecom.text)
review_obj = []
for i in range(len(reviews_user)):
review_obj.append(reviews_user[i])
review_obj.append(reviews_heading[i])
review_obj.append(reviews_rating[i])
review_obj.append(reviews_text[i])
#final works
all_user.append([url,*basic, *maininfo, *profile, *products, *review_obj ,*proj])
driver.quit()
except :
driver.quit()
print("Error")
try:
df = pd.DataFrame(all_user)
main = []
for index, row in df.iterrows():
if row[3] in ['법률','노동/노무','지식재산/특허',"등기/공탁/법무",'민원/행정']:
main.append("법률")
elif row[3] in ['세금/세무','회계/감사','통관/관세','온라인 마케팅','온라인쇼핑몰','엑스퍼트 사업','경영/기술컨설팅','유통관리','가맹점창업','건축','번역/통역','날씨컨설팅','원가 분석']:
main.append('비즈니스',)
elif row[3] in ['자산컨설팅','부동산 상담','손해사정','신용상담','감정평가']:
main.append("금융/재테크")
elif row[3] in ['심리상담','영양/다이어트','MBTI ']:
main.append("건강")
elif row[3] in ['운세/사주','타로카드','작명','꿈해몽','관상','풍수']:
main.append("운세")
elif row[3] in ['펫 관리','연애','육아','명상','패션/스타일','뷰티','요리/홈쿠킹','커피/주류','인테리어','청소/세탁','교통사고 분석','자동차수리']:
main.append("생활")
elif row[3] in ['음악/악기','미술/디자인','공예/공방','무용/ 발레','사진','실용/방송댄스','뮤지컬/공연','낚시','원예/홈가드닝','여행','글쓰기/논술']:
main.append("취미")
elif row[3] in ['외국어학습','입시/진학','해외유학','대학교학습','고등학교학습','중학교학습','초등학교학습']:
main.append( '교육/학습')
elif row[3] in ['피트니스','골프','필라테스','요가','생활스포츠','자전거','수상 스포츠','동계 스포츠','유아체육']:
main.append('운동/스포츠')
elif row[3] in ['게임하우투','IT노하우','코딩','오피스문서','동영상 제작']:
main.append('IT/컨텐츠')
elif row[3] in ['라이프 코칭','취업','자기PR','공무원시험 ','자격증시험']:
main.append('자기계발')
else:
main.append('네이버고객센터')
df.insert(3, "main_category", main)
df.to_csv("DATA.csv")
except:
print("Error occured during data load to pd df")
# ######################################### Project data section ########################################
# ## Project data for one user
# projects = soup.find_all('div', class_ = 'listArea--peDdh')
# #projects and consultations
# one = []
# for y in projects:
# yy = y.find_all('div', class_ = 'item--1ZJSx')
# for t in yy:
# project_item = []
# adiv = t.find_all('a', class_ =['itemCard--2Whvq','ItemSmallThumbnail_itemCard--196nY','productCard--1WjeX'])
# for div in adiv:
# eachdiv = div.find_all('div', class_ =['itemLink--2ljnw'])
# for e in eachdiv:
# ed = e.find_all('div', class_ =['itemBox--3y657'])
# for each in ed:
# ee = each.find_all('div', class_ =['itemInfo--24tcX'])
# for eee in ee:
# title = eee.find_all('div', class_ =['itemTitle--2vWBq','elip2--nFWXY'])
# for t in title:
# project_item.append([t.text])
# title = project_item[0][0]
# print(title)
# project_item.append(project_title)
# ratdiv = t.find_all('div', class_ =['itemGroup--2RnIL','ItemGroup_itemGroup--1f-on'])
# for rd in ratdiv:
# each = rd.find_all('div')
# for e in each:
# project_item.append(e)
# feediv = t.find_all('div', class_ = ['itemPrice--cscFn','ItemPrice_itemPrice--1dTfW','alignCenter--2NQfI'])
# for fd in feediv:
# fee = fd.find_all("span", class_ = ['priceNum--1rXJI','ItemPrice_priceNum--2OFHI'])
# for f in fee:
# project_item.append(f.text)
# one.append([*project_item])
# all_project.append(one)
# p = []
# for i in range(len(all_project)):
# data = all_project[i]
# for j in range(len(data)):
# dj = data[j]
# for k in range(len(dj)):
# bb = dj[k]
# p.append(bb)
| 33.586705 | 133 | 0.45435 | # importing the required modules
import requests
from bs4 import BeautifulSoup
import pandas as pd
from selenium import webdriver
import time
pd.options.display.max_columns = 999
pd.options.display.max_rows = 999
df = pd.read_csv('new_profile_links.csv')
df = df.drop(['Unnamed: 0'], axis = 1)
df["0"]
pl = list(df["0"])
all_user = []
for i in range(len(pl)):
try:
one_user = []
print("Loop :",i)
url = pl[i]
print(url)
driver = webdriver.Chrome(r'C:\Users\David\chromedriver_win32\chromedriver.exe')
driver.get(url)
time.sleep(3)
# basic data extraction
basic = []
source_code = driver.page_source
soup = BeautifulSoup(source_code, 'html.parser')
name = soup.find_all('strong', class_ = 'userName--1ZA07')
for n in name:
basic.append(n.text)
sp = soup.find_all('div', class_ = 'categoryName--1zWtA')
category = soup.find_all('strong', class_ = 'introCategory--F81Ky')
for e in category:
basic.append(e.text)
sp = soup.find_all('div', class_ = 'categoryName--1zWtA')
for m in sp:
basic.append(m.text)
rating = soup.find_all('div', class_ = 'itemRating--360UA itemRating--2-rFv typeLarge--1cEMN')
for k in rating:
a = k.text
if "평균 평점" in a:
a = a.replace("평균 평점", "")
basic.append(a)
### Project data for one user
maininfo = []
infos = soup.find_all('ul', class_ = 'productInfoList--1-H-D')
for f in infos:
li = f.find_all('li')
for ll in li:
uh = ["대표자","상호명","사업자등록번호","통신판매업번호-", "사업장 주소", "고객센터",'메일']
for u in range(len(uh)):
if uh[u] in ll.text:
b = uh[u]
la = ll.text
maininfo.append(la.replace(b , ""))
#count product and review section
products = []
tt = soup.find_all('div', class_ = "list--e6w5E")
for t in tt:
cc = t.find_all('div', class_='count--2w5o6')
for cd in cc:
cd.find_all('div', class_ = "count--2w5o6")
ce = cd.text
products.append(ce)
#profile informations
profile = []
heading = soup.find_all('strong', class_ = 'introduceMainTitle--2MZc-')
for h in heading:
profile.append(h.text)
text = soup.find_all('p', class_ = 'introduceText--2R5pY')
for e in text:
profile.append(e.text)
#new section
recom = soup.find_all('ul', class_ = 'listDisc--1Cc80')
for rc in recom:
profile.append(rc.text)
rest = soup.find_all('div', class_ = ['profileCareer--3_uFh','isExpert--2GkDA'])
# for mm in rest:
# mm.find_all('div', class_ = 'profileBox--1jlog')
for m in rest:
m.find_all('div', class_ = "careerJob--2-hX4")
for i in m:
profile.append(i.text)
### Project data for one user
projects = soup.find_all('div', class_ = 'listArea--peDdh')
#projects and consultations
all_project = []
for y in projects:
one = []
yy = y.find_all('div', class_ = 'item--1ZJSx')
for t in yy:
project_item = []
tdiv = t.find_all('div', class_ =['itemTitle--2vWBq','elip2--nFWXY'])
for td in tdiv:
project_title = td.text
project_item.append(project_title)
ratdiv = t.find_all('div', class_ =['itemGroup--2RnIL','ItemGroup_itemGroup--1f-on'])
for rd in ratdiv:
ratscore = rd.find_all("div", class_ = "itemRating--360UA")
for r in ratscore:
b = r.text
if "평균 평점" in b:
b = b.replace("평균 평점", " ")
project_item.append(b)
ratreview = rd.find_all("div", class_ = "itemCount--2HsJv")
for rr in ratreview:
c = rr.text
if "후기" in c:
c = c.replace("후기", " ")
project_item.append(c)
feediv = t.find_all('span', class_ =['priceInner--1HE2v'])
for fd in feediv:
fee = fd.find_all("span", class_=["priceNum--1rXJI","ItemPrice_priceNum--2OFHI"])
for f in fee:
project_item.append(f.text)
discount = fd.find_all("em", class_="discountPercent--3n0bl")
for dis in discount:
project_item.append(dis.text)
actualPrize = fd.find_all("span", class_="beforeDiscount--W1C4G")
for fp in actualPrize:
project_item.append(fp.text)
one.append([*project_item])
all_project.append([*one])
proj = []
for i in range(len(all_project)):
data = all_project[i]
for j in range(len(data)):
dj = data[j]
for k in range(len(dj)):
bb = dj[k]
proj.append(bb)
lis = ["평균 평점","후기","판매가","원할인률","할인 전 가격", "할인률"]
for i in range(len(proj)):
for j in range(len(lis)):
if lis[j] in proj[i]:
proj[i] = proj[i].replace(lis[j], " ")
rdiv = soup.find_all('div', class_ = "listSection--kViCl")
reviews_user = []
reviews_rating = []
reviews_heading = []
reviews_text = []
for eachr in rdiv:
ee = eachr.find_all('div', class_ = "reviewItem--1OwNO")
for each in ee:
name = each.find_all('span', class_ = ["item--3sQA9 ","nickname--2OOe6"])
for nm in name:
reviews_user.append(nm.text)
rating = each.find_all('div', class_ = ["expertPoint--2Zrvr","expertPoint--13H3V"])
for r in rating:
b = r.text
if "평점" in b:
b = b.replace("평점", "")
reviews_rating.append(b)
head = each.find_all('div', class_ = "reviewTitle--qv3Pk")
for r in head:
reviews_heading.append(r.text)
commentdiv = each.find_all('p', class_ = "reviewText--28mzN")
for ecom in commentdiv:
reviews_text.append(ecom.text)
review_obj = []
for i in range(len(reviews_user)):
review_obj.append(reviews_user[i])
review_obj.append(reviews_heading[i])
review_obj.append(reviews_rating[i])
review_obj.append(reviews_text[i])
#final works
all_user.append([url,*basic, *maininfo, *profile, *products, *review_obj ,*proj])
driver.quit()
except :
driver.quit()
print("Error")
try:
df = pd.DataFrame(all_user)
main = []
for index, row in df.iterrows():
if row[3] in ['법률','노동/노무','지식재산/특허',"등기/공탁/법무",'민원/행정']:
main.append("법률")
elif row[3] in ['세금/세무','회계/감사','통관/관세','온라인 마케팅','온라인쇼핑몰','엑스퍼트 사업','경영/기술컨설팅','유통관리','가맹점창업','건축','번역/통역','날씨컨설팅','원가 분석']:
main.append('비즈니스',)
elif row[3] in ['자산컨설팅','부동산 상담','손해사정','신용상담','감정평가']:
main.append("금융/재테크")
elif row[3] in ['심리상담','영양/다이어트','MBTI ']:
main.append("건강")
elif row[3] in ['운세/사주','타로카드','작명','꿈해몽','관상','풍수']:
main.append("운세")
elif row[3] in ['펫 관리','연애','육아','명상','패션/스타일','뷰티','요리/홈쿠킹','커피/주류','인테리어','청소/세탁','교통사고 분석','자동차수리']:
main.append("생활")
elif row[3] in ['음악/악기','미술/디자인','공예/공방','무용/ 발레','사진','실용/방송댄스','뮤지컬/공연','낚시','원예/홈가드닝','여행','글쓰기/논술']:
main.append("취미")
elif row[3] in ['외국어학습','입시/진학','해외유학','대학교학습','고등학교학습','중학교학습','초등학교학습']:
main.append( '교육/학습')
elif row[3] in ['피트니스','골프','필라테스','요가','생활스포츠','자전거','수상 스포츠','동계 스포츠','유아체육']:
main.append('운동/스포츠')
elif row[3] in ['게임하우투','IT노하우','코딩','오피스문서','동영상 제작']:
main.append('IT/컨텐츠')
elif row[3] in ['라이프 코칭','취업','자기PR','공무원시험 ','자격증시험']:
main.append('자기계발')
else:
main.append('네이버고객센터')
df.insert(3, "main_category", main)
df.to_csv("DATA.csv")
except:
print("Error occured during data load to pd df")
# ######################################### Project data section ########################################
# ## Project data for one user
# projects = soup.find_all('div', class_ = 'listArea--peDdh')
# #projects and consultations
# one = []
# for y in projects:
# yy = y.find_all('div', class_ = 'item--1ZJSx')
# for t in yy:
# project_item = []
# adiv = t.find_all('a', class_ =['itemCard--2Whvq','ItemSmallThumbnail_itemCard--196nY','productCard--1WjeX'])
# for div in adiv:
# eachdiv = div.find_all('div', class_ =['itemLink--2ljnw'])
# for e in eachdiv:
# ed = e.find_all('div', class_ =['itemBox--3y657'])
# for each in ed:
# ee = each.find_all('div', class_ =['itemInfo--24tcX'])
# for eee in ee:
# title = eee.find_all('div', class_ =['itemTitle--2vWBq','elip2--nFWXY'])
# for t in title:
# project_item.append([t.text])
# title = project_item[0][0]
# print(title)
# project_item.append(project_title)
# ratdiv = t.find_all('div', class_ =['itemGroup--2RnIL','ItemGroup_itemGroup--1f-on'])
# for rd in ratdiv:
# each = rd.find_all('div')
# for e in each:
# project_item.append(e)
# feediv = t.find_all('div', class_ = ['itemPrice--cscFn','ItemPrice_itemPrice--1dTfW','alignCenter--2NQfI'])
# for fd in feediv:
# fee = fd.find_all("span", class_ = ['priceNum--1rXJI','ItemPrice_priceNum--2OFHI'])
# for f in fee:
# project_item.append(f.text)
# one.append([*project_item])
# all_project.append(one)
# p = []
# for i in range(len(all_project)):
# data = all_project[i]
# for j in range(len(data)):
# dj = data[j]
# for k in range(len(dj)):
# bb = dj[k]
# p.append(bb)
| 0 | 0 | 0 |
19fc44400262a2a3e22b403b44260ab7e740721d | 6,183 | py | Python | augmentation/edge_detection/canny_edge.py | mnguyen0226/image-augmentation-dnn-performance | 793b0e513a6984d13e5ae33e1dd74fb8c45556fd | [
"MIT"
] | null | null | null | augmentation/edge_detection/canny_edge.py | mnguyen0226/image-augmentation-dnn-performance | 793b0e513a6984d13e5ae33e1dd74fb8c45556fd | [
"MIT"
] | null | null | null | augmentation/edge_detection/canny_edge.py | mnguyen0226/image-augmentation-dnn-performance | 793b0e513a6984d13e5ae33e1dd74fb8c45556fd | [
"MIT"
] | null | null | null | import math
import cv2 as cv
import numpy as np
from scipy.ndimage.filters import convolve
from scipy import ndimage
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
def get_sobel_kernel(im):
"""Takes in smoothed images then do matrix multiplication with Sobel kernel to get the gradient magnitude and angle
Parameters
----------
im
input image
Returns
-------
gradient
gradient magnitude of sobel filter
theta
the angle of sobel filter
"""
kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], np.float32)
kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], np.float32)
image_x = ndimage.filters.convolve(im, kernel_x)
image_y = ndimage.filters.convolve(im, kernel_y)
gradient = np.hypot(image_x, image_y)
gradient = gradient / gradient.max() * 255
theta = np.arctan2(image_y, image_x)
return (gradient, theta)
def non_max_supression(grad_im, theta):
"""Goes thru all points on the gradient intensity matrix to finds the pixels with the max value in the edge directions
Parameters
----------
grad_im
gradient magnitude from sobel filter
theta
angle from sobel filter
Returns
-------
out_im
output image
"""
row, col = grad_im.shape # M, N
out_im = np.zeros((row, col), dtype=np.int32)
angle = theta*180. / np.pi
angle[angle < 0] += 180
for i in range(1,row-1):
for j in range(1,col-1):
q = 255
r = 255
# for angle 0
if (0 <= angle[i,j] < 22.5) or (157.5 <= angle[i,j] <= 180):
q = grad_im[i, j+1]
r = grad_im[i, j-1]
# for angle 45
elif (22.5 <= angle[i,j] < 67.5):
q = grad_im[i+1, j-1]
r = grad_im[i-1, j+1]
# for angle 90
elif (67.5 <= angle[i,j] < 112.5):
q = grad_im[i+1, j]
r = grad_im[i-1, j]
# for angle 135
elif (112.5 <= angle[i,j] < 157.5):
q = grad_im[i-1, j-1]
r = grad_im[i+1, j+1]
if (grad_im[i,j] >= q) and (grad_im[i,j] >= r):
out_im[i,j] = grad_im[i,j]
else:
out_im[i,j] = 0
return out_im
def double_threshold(im, ltr, htr):
"""Double threshold aim to identify strong, weak, and non-relevant pixels
Parameters
----------
im
input image
ltr
low threshold ratio
htr
high threshold ratio
Returns
-------
weak
weak pixel that has intensity value that is not enough to be considered strong ones, but not non-relevant
strong
pixel with very high intensity
res
non-relevant pixel, not high, not low
"""
high_thres = im.max() * htr
low_thres = high_thres * ltr
row, col = im.shape
res = np.zeros((row,col), dtype=np.int32)
weak = np.int32(25)
strong = np.int32(255)
strong_i, strong_j = np.where(im >= high_thres)
zeros_i, zeros_j = np.where(im < low_thres)
weak_i, weak_j = np.where((im <= high_thres) & (im >= low_thres))
res[strong_i, strong_j] = strong
res[weak_i, weak_j] = weak
return (res, weak, strong)
def hysteresis(im, weak, strong=255):
"""Transforms weak pixel into strong ones if at least 1 pixel around the one being processed is a strong one
Parameters
----------
im
input image
weak
weak pixel that has intensity value that is not enough to be considered strong ones, but not non-relevant
strong
pixel with very high intensity
Returns
-------
im
output result image
"""
row, col = im.shape
for i in range(1, row-1):
for j in range(1, col-1):
if (im[i,j] == weak):
if ((im[i+1, j-1] == strong) or (im[i+1, j] == strong) or (im[i+1, j+1] == strong)
or (im[i, j-1] == strong) or (im[i, j+1] == strong)
or (im[i-1, j-1] == strong) or (im[i-1, j] == strong) or (im[i-1, j+1] == strong)):
im[i, j] = strong
else:
im[i, j] = 0
return im
def rgb2gray(rgb):
"""Transform 3 colors channels image to grayscale
Parameters
----------
rgb
input image rgb
Returns
-------
np.dot
grayscale image
"""
return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])
def canny(origin_img):
"""Applies Canny Edge Detection Steps
Parameters
----------
origin_img
input original image
Returns
-------
im_final
final output edge-detected image
"""
origin_img = rgb2gray(origin_img)
origin_img = origin_img/255.0
# blur image with Gaussian filter with sigma = 2,8,16
filtered_im1 = cv.GaussianBlur(origin_img, (0,0), 2)
filtered_im2 = cv.GaussianBlur(origin_img, (0,0), 8)
filtered_im3 = cv.GaussianBlur(origin_img, (0,0), 16)
# apply Canny edge detectors with the scales you choose to detect edges
grad_mag1, angle1 = get_sobel_kernel(filtered_im1)
grad_mag2, angle2 = get_sobel_kernel(filtered_im2)
grad_mag3, angle3 = get_sobel_kernel(filtered_im3)
# apply non-max-suppression
im_nms1 = non_max_supression(grad_mag1, angle1)
im_nms2 = non_max_supression(grad_mag2, angle2)
im_nms3 = non_max_supression(grad_mag3, angle3)
im_thres1, weak1, strong1 = double_threshold(im_nms1, ltr=0.07, htr=0.19)
im_thres2, weak2, strong2 = double_threshold(im_nms2, ltr=0.07, htr=0.19)
im_thres3, weak3, strong3 = double_threshold(im_nms3, ltr=0.07, htr=0.19)
im_hys1 = hysteresis(im_thres1, weak1, strong1)
im_hys2 = hysteresis(im_thres2, weak2, strong2)
im_hys3 = hysteresis(im_thres3, weak3, strong3)
im_final = im_hys1 + im_hys2 + im_hys3
im_final = im_final/3.0
# convert two 3 dim
if (im_final.ndim == 2):
im_final = np.expand_dims(im_final, axis = 2)
return im_final
| 27.602679 | 122 | 0.569303 | import math
import cv2 as cv
import numpy as np
from scipy.ndimage.filters import convolve
from scipy import ndimage
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
def get_sobel_kernel(im):
"""Takes in smoothed images then do matrix multiplication with Sobel kernel to get the gradient magnitude and angle
Parameters
----------
im
input image
Returns
-------
gradient
gradient magnitude of sobel filter
theta
the angle of sobel filter
"""
kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], np.float32)
kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], np.float32)
image_x = ndimage.filters.convolve(im, kernel_x)
image_y = ndimage.filters.convolve(im, kernel_y)
gradient = np.hypot(image_x, image_y)
gradient = gradient / gradient.max() * 255
theta = np.arctan2(image_y, image_x)
return (gradient, theta)
def non_max_supression(grad_im, theta):
"""Goes thru all points on the gradient intensity matrix to finds the pixels with the max value in the edge directions
Parameters
----------
grad_im
gradient magnitude from sobel filter
theta
angle from sobel filter
Returns
-------
out_im
output image
"""
row, col = grad_im.shape # M, N
out_im = np.zeros((row, col), dtype=np.int32)
angle = theta*180. / np.pi
angle[angle < 0] += 180
for i in range(1,row-1):
for j in range(1,col-1):
q = 255
r = 255
# for angle 0
if (0 <= angle[i,j] < 22.5) or (157.5 <= angle[i,j] <= 180):
q = grad_im[i, j+1]
r = grad_im[i, j-1]
# for angle 45
elif (22.5 <= angle[i,j] < 67.5):
q = grad_im[i+1, j-1]
r = grad_im[i-1, j+1]
# for angle 90
elif (67.5 <= angle[i,j] < 112.5):
q = grad_im[i+1, j]
r = grad_im[i-1, j]
# for angle 135
elif (112.5 <= angle[i,j] < 157.5):
q = grad_im[i-1, j-1]
r = grad_im[i+1, j+1]
if (grad_im[i,j] >= q) and (grad_im[i,j] >= r):
out_im[i,j] = grad_im[i,j]
else:
out_im[i,j] = 0
return out_im
def double_threshold(im, ltr, htr):
"""Double threshold aim to identify strong, weak, and non-relevant pixels
Parameters
----------
im
input image
ltr
low threshold ratio
htr
high threshold ratio
Returns
-------
weak
weak pixel that has intensity value that is not enough to be considered strong ones, but not non-relevant
strong
pixel with very high intensity
res
non-relevant pixel, not high, not low
"""
high_thres = im.max() * htr
low_thres = high_thres * ltr
row, col = im.shape
res = np.zeros((row,col), dtype=np.int32)
weak = np.int32(25)
strong = np.int32(255)
strong_i, strong_j = np.where(im >= high_thres)
zeros_i, zeros_j = np.where(im < low_thres)
weak_i, weak_j = np.where((im <= high_thres) & (im >= low_thres))
res[strong_i, strong_j] = strong
res[weak_i, weak_j] = weak
return (res, weak, strong)
def hysteresis(im, weak, strong=255):
"""Transforms weak pixel into strong ones if at least 1 pixel around the one being processed is a strong one
Parameters
----------
im
input image
weak
weak pixel that has intensity value that is not enough to be considered strong ones, but not non-relevant
strong
pixel with very high intensity
Returns
-------
im
output result image
"""
row, col = im.shape
for i in range(1, row-1):
for j in range(1, col-1):
if (im[i,j] == weak):
if ((im[i+1, j-1] == strong) or (im[i+1, j] == strong) or (im[i+1, j+1] == strong)
or (im[i, j-1] == strong) or (im[i, j+1] == strong)
or (im[i-1, j-1] == strong) or (im[i-1, j] == strong) or (im[i-1, j+1] == strong)):
im[i, j] = strong
else:
im[i, j] = 0
return im
def rgb2gray(rgb):
"""Transform 3 colors channels image to grayscale
Parameters
----------
rgb
input image rgb
Returns
-------
np.dot
grayscale image
"""
return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])
def canny(origin_img):
"""Applies Canny Edge Detection Steps
Parameters
----------
origin_img
input original image
Returns
-------
im_final
final output edge-detected image
"""
origin_img = rgb2gray(origin_img)
origin_img = origin_img/255.0
# blur image with Gaussian filter with sigma = 2,8,16
filtered_im1 = cv.GaussianBlur(origin_img, (0,0), 2)
filtered_im2 = cv.GaussianBlur(origin_img, (0,0), 8)
filtered_im3 = cv.GaussianBlur(origin_img, (0,0), 16)
# apply Canny edge detectors with the scales you choose to detect edges
grad_mag1, angle1 = get_sobel_kernel(filtered_im1)
grad_mag2, angle2 = get_sobel_kernel(filtered_im2)
grad_mag3, angle3 = get_sobel_kernel(filtered_im3)
# apply non-max-suppression
im_nms1 = non_max_supression(grad_mag1, angle1)
im_nms2 = non_max_supression(grad_mag2, angle2)
im_nms3 = non_max_supression(grad_mag3, angle3)
im_thres1, weak1, strong1 = double_threshold(im_nms1, ltr=0.07, htr=0.19)
im_thres2, weak2, strong2 = double_threshold(im_nms2, ltr=0.07, htr=0.19)
im_thres3, weak3, strong3 = double_threshold(im_nms3, ltr=0.07, htr=0.19)
im_hys1 = hysteresis(im_thres1, weak1, strong1)
im_hys2 = hysteresis(im_thres2, weak2, strong2)
im_hys3 = hysteresis(im_thres3, weak3, strong3)
im_final = im_hys1 + im_hys2 + im_hys3
im_final = im_final/3.0
# convert two 3 dim
if (im_final.ndim == 2):
im_final = np.expand_dims(im_final, axis = 2)
return im_final
| 0 | 0 | 0 |
45440d5a96a0fad81f3cedc890db5f5adc5e505e | 3,586 | py | Python | Python/221.maximal-square.py | Dxyk/LeetCode | e64b405f40b4e3c0f14c617897b775699dd46872 | [
"MIT"
] | null | null | null | Python/221.maximal-square.py | Dxyk/LeetCode | e64b405f40b4e3c0f14c617897b775699dd46872 | [
"MIT"
] | null | null | null | Python/221.maximal-square.py | Dxyk/LeetCode | e64b405f40b4e3c0f14c617897b775699dd46872 | [
"MIT"
] | null | null | null | #
# @lc app=leetcode id=221 lang=python3
#
# [221] Maximal Square
#
# https://leetcode.com/problems/maximal-square/description/
#
# algorithms
# Medium (34.82%)
# Likes: 1997
# Dislikes: 49
# Total Accepted: 175K
# Total Submissions: 500.4K
# Testcase Example:
# '[["1","0","1","0","0"],
# ["1","0","1","1","1"],
# ["1","1","1","1","1"],
# ["1","0","0","1","0"]]'
#
# Given a 2D binary matrix filled with 0's and 1's, find the largest square
# containing only 1's and return its area.
#
# Example:
#
#
# Input:
#
# 1 0 1 0 0
# 1 0 1 1 1
# 1 1 1 1 1
# 1 0 0 1 0
#
# Output: 4
#
#
# @lc code=start
from typing import List
# @lc code=end
if __name__ == "__main__":
m = [["1", "0", "1", "0", "0"],
["1", "0", "1", "1", "1"],
["1", "1", "1", "1", "1"],
["1", "0", "0", "1", "0"]]
print(Solution().maximalSquare(m), 4)
| 27.374046 | 75 | 0.444228 | #
# @lc app=leetcode id=221 lang=python3
#
# [221] Maximal Square
#
# https://leetcode.com/problems/maximal-square/description/
#
# algorithms
# Medium (34.82%)
# Likes: 1997
# Dislikes: 49
# Total Accepted: 175K
# Total Submissions: 500.4K
# Testcase Example:
# '[["1","0","1","0","0"],
# ["1","0","1","1","1"],
# ["1","1","1","1","1"],
# ["1","0","0","1","0"]]'
#
# Given a 2D binary matrix filled with 0's and 1's, find the largest square
# containing only 1's and return its area.
#
# Example:
#
#
# Input:
#
# 1 0 1 0 0
# 1 0 1 1 1
# 1 1 1 1 1
# 1 0 0 1 0
#
# Output: 4
#
#
# @lc code=start
from typing import List
class Solution:
def maximalSquare(self, matrix: List[List[str]]) -> int:
return self.dp_improved_bottom_up(matrix)
def dp_improved_bottom_up(self, matrix: List[List[str]]) -> int:
"""
DP Bottom up solution with improved space efficiency
Runtime: O(mn)
Space: O(n)
"""
if len(matrix) == 0 or len(matrix[0]) == 0:
return 0
res_len = 0
prev = 0
memo = [0] * (len(matrix[0]) + 1)
for i in range(1, len(matrix) + 1):
for j in range(1, len(matrix[0]) + 1):
temp = memo[j]
if matrix[i - 1][j - 1] == "1":
memo[j] = min(memo[j - 1], prev, memo[j]) + 1
res_len = max(memo[j], res_len)
else:
memo[j] = 0
prev = temp
return res_len ** 2
def dp_bottom_up(self, matrix: List[List[str]]) -> int:
"""
DP Bottom up solution
Runtime: O(mn)
Space: O(mn)
"""
if len(matrix) == 0 or len(matrix[0]) == 0:
return 0
res_len = 0
memo = [[0] * (len(matrix[0]) + 1) for _ in range(len(matrix) + 1)]
for i in range(1, len(matrix) + 1):
for j in range(1, len(matrix[0]) + 1):
if matrix[i - 1][j - 1] == "1":
memo[i][j] = min(memo[i - 1][j],
memo[i][j - 1],
memo[i - 1][j - 1]) + 1
res_len = max(memo[i][j], res_len)
return res_len ** 2
def dp_recursive(self, matrix: List[List[str]]) -> int:
"""
DP Recursive solution
Subproblem Structure:
Let m = num rows, n = num cols
P(i, j) := max area's length using the element at matrix[i][j]
P(i, j) =
- 0 if matrix[i][j] == "0"
- min(P(i - 1, j), P(i, j - 1), P(i - 1, j - 1)) + 1
if matrix[i][j] == "1"
"""
if len(matrix) == 0 or len(matrix[0]) == 0:
return 0
res_len = [0]
def helper(i: int, j: int) -> int:
if i < 0 or j < 0:
return 0
if i == 0 or j == 0:
return 1 if matrix[i][j] == "1" else 0
top_res = helper(i, j - 1)
left_res = helper(i - 1, j)
top_left_res = helper(i - 1, j - 1)
if matrix[i][j] == "1":
res = min(top_res, left_res, top_left_res) + 1
else:
res = 0
res_len[0] = max(res, res_len[0])
return res
helper(len(matrix) - 1, len(matrix[0]) - 1)
return res_len[0] ** 2
# @lc code=end
if __name__ == "__main__":
m = [["1", "0", "1", "0", "0"],
["1", "0", "1", "1", "1"],
["1", "1", "1", "1", "1"],
["1", "0", "0", "1", "0"]]
print(Solution().maximalSquare(m), 4)
| 579 | 2,115 | 23 |
5db5e96c68daebdf9c023c5d9ea8446b92163a47 | 17,891 | py | Python | statsmodels/graphics/dotplots.py | nikhase/statsmodels | e1822d4513f442002816bb898ca5794785f35c32 | [
"BSD-3-Clause"
] | 15 | 2015-03-03T09:47:42.000Z | 2022-01-05T18:28:31.000Z | statsmodels/graphics/dotplots.py | nikhase/statsmodels | e1822d4513f442002816bb898ca5794785f35c32 | [
"BSD-3-Clause"
] | 7 | 2015-11-20T08:33:04.000Z | 2020-07-24T19:34:39.000Z | statsmodels/graphics/dotplots.py | nikhase/statsmodels | e1822d4513f442002816bb898ca5794785f35c32 | [
"BSD-3-Clause"
] | 14 | 2015-01-06T22:08:34.000Z | 2021-01-01T16:33:23.000Z | import numpy as np
from statsmodels.compat import range
from . import utils
def dot_plot(points, intervals=None, lines=None, sections=None,
styles=None, marker_props=None, line_props=None,
split_names=None, section_order=None, line_order=None,
stacked=False, styles_order=None, striped=False,
horizontal=True, show_names="both",
fmt_left_name=None, fmt_right_name=None,
show_section_titles=None, ax=None):
"""
Produce a dotplot similar in style to those in Cleveland's
"Visualizing Data" book. These are also known as "forest plots".
Parameters
----------
points : array_like
The quantitative values to be plotted as markers.
intervals : array_like
The intervals to be plotted around the points. The elements
of `intervals` are either scalars or sequences of length 2. A
scalar indicates the half width of a symmetric interval. A
sequence of length 2 contains the left and right half-widths
(respectively) of a nonsymmetric interval. If None, no
intervals are drawn.
lines : array_like
A grouping variable indicating which points/intervals are
drawn on a common line. If None, each point/interval appears
on its own line.
sections : array_like
A grouping variable indicating which lines are grouped into
sections. If None, everything is drawn in a single section.
styles : array_like
A grouping label defining the plotting style of the markers
and intervals.
marker_props : dict
A dictionary mapping style codes (the values in `styles`) to
dictionaries defining key/value pairs to be passed as keyword
arguments to `plot` when plotting markers. Useful keyword
arguments are "color", "marker", and "ms" (marker size).
line_props : dict
A dictionary mapping style codes (the values in `styles`) to
dictionaries defining key/value pairs to be passed as keyword
arguments to `plot` when plotting interval lines. Useful
keyword arguments are "color", "linestyle", "solid_capstyle",
and "linewidth".
split_names : string
If not None, this is used to split the values of `lines` into
substrings that are drawn in the left and right margins,
respectively. If None, the values of `lines` are drawn in the
left margin.
section_order : array_like
The section labels in the order in which they appear in the
dotplot.
line_order : array_like
The line labels in the order in which they appear in the
dotplot.
stacked : boolean
If True, when multiple points or intervals are drawn on the
same line, they are offset from each other.
styles_order : array_like
If stacked=True, this is the order in which the point styles
on a given line are drawn from top to bottom (if horizontal
is True) or from left to right (if horiontal is False). If
None (default), the order is lexical.
striped : boolean
If True, every other line is enclosed in a shaded box.
horizontal : boolean
If True (default), the lines are drawn horizontally, otherwise
they are drawn vertically.
show_names : string
Determines whether labels (names) are shown in the left and/or
right margins (top/bottom margins if `horizontal` is True).
If `both`, labels are drawn in both margins, if 'left', labels
are drawn in the left or top margin. If `right`, labels are
drawn in the right or bottom margin.
fmt_left_name : function
The left/top margin names are passed through this function
before drawing on the plot.
fmt_right_name : function
The right/bottom marginnames are passed through this function
before drawing on the plot.
show_section_titles : bool or None
If None, section titles are drawn only if there is more than
one section. If False/True, section titles are never/always
drawn, respectively.
ax : matplotlib.axes
The axes on which the dotplot is drawn. If None, a new axes
is created.
Returns
-------
fig : Figure
The figure given by `ax.figure` or a new instance.
Notes
-----
`points`, `intervals`, `lines`, `sections`, `styles` must all have
the same length whenever present.
Examples
--------
This is a simple dotplot with one point per line:
>>> dot_plot(points=point_values)
This dotplot has labels on the lines (if elements in
`label_values` are repeated, the corresponding points appear on
the same line):
>>> dot_plot(points=point_values, lines=label_values)
References
----------
* Cleveland, William S. (1993). "Visualizing Data". Hobart
Press.
* Jacoby, William G. (2006) "The Dot Plot: A Graphical Display
for Labeled Quantitative Values." The Political Methodologist
14(1): 6-14.
"""
import matplotlib.transforms as transforms
fig, ax = utils.create_mpl_ax(ax)
# Convert to numpy arrays if that is not what we are given.
points = np.asarray(points)
asarray_or_none = lambda x : None if x is None else np.asarray(x)
intervals = asarray_or_none(intervals)
lines = asarray_or_none(lines)
sections = asarray_or_none(sections)
styles = asarray_or_none(styles)
# Total number of points
npoint = len(points)
# Set default line values if needed
if lines is None:
lines = np.arange(npoint)
# Set default section values if needed
if sections is None:
sections = np.zeros(npoint)
# Set default style values if needed
if styles is None:
styles = np.zeros(npoint)
# The vertical space (in inches) for a section title
section_title_space = 0.5
# The number of sections
nsect = len(set(sections))
if section_order is not None:
nsect = len(set(section_order))
# The number of section titles
if show_section_titles == False:
draw_section_titles = False
nsect_title = 0
elif show_section_titles == True:
draw_section_titles = True
nsect_title = nsect
else:
draw_section_titles = nsect > 1
nsect_title = nsect if nsect > 1 else 0
# The total vertical space devoted to section titles.
section_space_total = section_title_space * nsect_title
# Add a bit of room so that points that fall at the axis limits
# are not cut in half.
ax.set_xmargin(0.02)
ax.set_ymargin(0.02)
if section_order is None:
lines0 = list(set(sections))
lines0.sort()
else:
lines0 = section_order
if line_order is None:
lines1 = list(set(lines))
lines1.sort()
else:
lines1 = line_order
# A map from (section,line) codes to index positions.
lines_map = {}
for i in range(npoint):
if section_order is not None and sections[i] not in section_order:
continue
if line_order is not None and lines[i] not in line_order:
continue
ky = (sections[i], lines[i])
if ky not in lines_map:
lines_map[ky] = []
lines_map[ky].append(i)
# Get the size of the axes on the parent figure in inches
bbox = ax.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
awidth, aheight = bbox.width, bbox.height
# The number of lines in the plot.
nrows = len(lines_map)
# The positions of the lowest and highest guideline in axes
# coordinates (for horizontal dotplots), or the leftmost and
# rightmost guidelines (for vertical dotplots).
bottom, top = 0, 1
if horizontal:
# x coordinate is data, y coordinate is axes
trans = transforms.blended_transform_factory(ax.transData,
ax.transAxes)
else:
# x coordinate is axes, y coordinate is data
trans = transforms.blended_transform_factory(ax.transAxes,
ax.transData)
# Space used for a section title, in axes coordinates
title_space_axes = section_title_space / aheight
# Space between lines
if horizontal:
dpos = (top - bottom - nsect_title*title_space_axes) /\
float(nrows)
else:
dpos = (top - bottom) / float(nrows)
# Determine the spacing for stacked points
if styles_order is not None:
style_codes = styles_order
else:
style_codes = list(set(styles))
style_codes.sort()
# Order is top to bottom for horizontal plots, so need to
# flip.
if horizontal:
style_codes = style_codes[::-1]
# nval is the maximum number of points on one line.
nval = len(style_codes)
if nval > 1:
stackd = dpos / (2.5*(float(nval)-1))
else:
stackd = 0.
# Map from style code to its integer position
style_codes_map = {x: style_codes.index(x) for x in style_codes}
# Setup default marker styles
colors = ["r", "g", "b", "y", "k", "purple", "orange"]
if marker_props is None:
marker_props = {x: {} for x in style_codes}
for j in range(nval):
sc = style_codes[j]
if "color" not in marker_props[sc]:
marker_props[sc]["color"] = colors[j % len(colors)]
if "marker" not in marker_props[sc]:
marker_props[sc]["marker"] = "o"
if "ms" not in marker_props[sc]:
marker_props[sc]["ms"] = 10 if stackd == 0 else 6
# Setup default line styles
if line_props is None:
line_props = {x: {} for x in style_codes}
for j in range(nval):
sc = style_codes[j]
if "color" not in line_props[sc]:
line_props[sc]["color"] = "grey"
if "linewidth" not in line_props[sc]:
line_props[sc]["linewidth"] = 2 if stackd > 0 else 8
if horizontal:
# The vertical position of the first line.
pos = top - dpos/2 if nsect == 1 else top
else:
# The horizontal position of the first line.
pos = bottom + dpos/2
# Points that have already been labeled
labeled = set()
# Positions of the y axis grid lines
ticks = []
# Loop through the sections
for k0 in lines0:
# Draw a section title
if draw_section_titles:
if horizontal:
y0 = pos + dpos/2 if k0 == lines0[0] else pos
ax.fill_between((0, 1), (y0,y0),
(pos-0.7*title_space_axes,
pos-0.7*title_space_axes),
color='darkgrey',
transform=ax.transAxes,
zorder=1)
txt = ax.text(0.5, pos - 0.35*title_space_axes, k0,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
txt.set_fontweight("bold")
pos -= title_space_axes
else:
m = len([k for k in lines_map if k[0] == k0])
ax.fill_between((pos-dpos/2+0.01,
pos+(m-1)*dpos+dpos/2-0.01),
(1.01,1.01), (1.06,1.06),
color='darkgrey',
transform=ax.transAxes,
zorder=1, clip_on=False)
txt = ax.text(pos + (m-1)*dpos/2, 1.02, k0,
horizontalalignment='center',
verticalalignment='bottom',
transform=ax.transAxes)
txt.set_fontweight("bold")
jrow = 0
for k1 in lines1:
# No data to plot
if (k0, k1) not in lines_map:
continue
# Draw the guideline
if horizontal:
ax.axhline(pos, color='grey')
else:
ax.axvline(pos, color='grey')
# Set up the labels
if split_names is not None:
us = k1.split(split_names)
if len(us) >= 2:
left_label, right_label = us[0], us[1]
else:
left_label, right_label = k1, None
else:
left_label, right_label = k1, None
if fmt_left_name is not None:
left_label = fmt_left_name(left_label)
if fmt_right_name is not None:
right_label = fmt_right_name(right_label)
# Draw the stripe
if striped and jrow % 2 == 0:
if horizontal:
ax.fill_between((0, 1), (pos-dpos/2, pos-dpos/2),
(pos+dpos/2, pos+dpos/2),
color='lightgrey',
transform=ax.transAxes,
zorder=0)
else:
ax.fill_between((pos-dpos/2, pos+dpos/2),
(0, 0), (1, 1),
color='lightgrey',
transform=ax.transAxes,
zorder=0)
jrow += 1
# Draw the left margin label
if show_names.lower() in ("left", "both"):
if horizontal:
ax.text(-0.1/awidth, pos, left_label,
horizontalalignment="right",
verticalalignment='center',
transform=ax.transAxes,
family='monospace')
else:
ax.text(pos, -0.1/aheight, left_label,
horizontalalignment="center",
verticalalignment='top',
transform=ax.transAxes,
family='monospace')
# Draw the right margin label
if show_names.lower() in ("right", "both"):
if right_label is not None:
if horizontal:
ax.text(1 + 0.1/awidth, pos, right_label,
horizontalalignment="left",
verticalalignment='center',
transform=ax.transAxes,
family='monospace')
else:
ax.text(pos, 1 + 0.1/aheight, right_label,
horizontalalignment="center",
verticalalignment='bottom',
transform=ax.transAxes,
family='monospace')
# Save the vertical position so that we can place the
# tick marks
ticks.append(pos)
# Loop over the points in one line
for ji,jp in enumerate(lines_map[(k0,k1)]):
# Calculate the vertical offset
yo = 0
if stacked:
yo = -dpos/5 + style_codes_map[styles[jp]]*stackd
pt = points[jp]
# Plot the interval
if intervals is not None:
# Symmetric interval
if np.isscalar(intervals[jp]):
lcb, ucb = pt - intervals[jp],\
pt + intervals[jp]
# Nonsymmetric interval
else:
lcb, ucb = pt - intervals[jp][0],\
pt + intervals[jp][1]
# Draw the interval
if horizontal:
ax.plot([lcb, ucb], [pos+yo, pos+yo], '-',
transform=trans,
**line_props[styles[jp]])
else:
ax.plot([pos+yo, pos+yo], [lcb, ucb], '-',
transform=trans,
**line_props[styles[jp]])
# Plot the point
sl = styles[jp]
sll = sl if sl not in labeled else None
labeled.add(sl)
if horizontal:
ax.plot([pt,], [pos+yo,], ls='None',
transform=trans, label=sll,
**marker_props[sl])
else:
ax.plot([pos+yo,], [pt,], ls='None',
transform=trans, label=sll,
**marker_props[sl])
if horizontal:
pos -= dpos
else:
pos += dpos
# Set up the axis
if horizontal:
ax.xaxis.set_ticks_position("bottom")
ax.yaxis.set_ticks_position("none")
ax.set_yticklabels([])
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_position(('axes', -0.1/aheight))
ax.set_ylim(0, 1)
ax.yaxis.set_ticks(ticks)
ax.autoscale_view(scaley=False, tight=True)
else:
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("none")
ax.set_xticklabels([])
ax.spines['bottom'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('axes', -0.1/awidth))
ax.set_xlim(0, 1)
ax.xaxis.set_ticks(ticks)
ax.autoscale_view(scalex=False, tight=True)
return fig
| 36.737166 | 74 | 0.54519 | import numpy as np
from statsmodels.compat import range
from . import utils
def dot_plot(points, intervals=None, lines=None, sections=None,
styles=None, marker_props=None, line_props=None,
split_names=None, section_order=None, line_order=None,
stacked=False, styles_order=None, striped=False,
horizontal=True, show_names="both",
fmt_left_name=None, fmt_right_name=None,
show_section_titles=None, ax=None):
"""
Produce a dotplot similar in style to those in Cleveland's
"Visualizing Data" book. These are also known as "forest plots".
Parameters
----------
points : array_like
The quantitative values to be plotted as markers.
intervals : array_like
The intervals to be plotted around the points. The elements
of `intervals` are either scalars or sequences of length 2. A
scalar indicates the half width of a symmetric interval. A
sequence of length 2 contains the left and right half-widths
(respectively) of a nonsymmetric interval. If None, no
intervals are drawn.
lines : array_like
A grouping variable indicating which points/intervals are
drawn on a common line. If None, each point/interval appears
on its own line.
sections : array_like
A grouping variable indicating which lines are grouped into
sections. If None, everything is drawn in a single section.
styles : array_like
A grouping label defining the plotting style of the markers
and intervals.
marker_props : dict
A dictionary mapping style codes (the values in `styles`) to
dictionaries defining key/value pairs to be passed as keyword
arguments to `plot` when plotting markers. Useful keyword
arguments are "color", "marker", and "ms" (marker size).
line_props : dict
A dictionary mapping style codes (the values in `styles`) to
dictionaries defining key/value pairs to be passed as keyword
arguments to `plot` when plotting interval lines. Useful
keyword arguments are "color", "linestyle", "solid_capstyle",
and "linewidth".
split_names : string
If not None, this is used to split the values of `lines` into
substrings that are drawn in the left and right margins,
respectively. If None, the values of `lines` are drawn in the
left margin.
section_order : array_like
The section labels in the order in which they appear in the
dotplot.
line_order : array_like
The line labels in the order in which they appear in the
dotplot.
stacked : boolean
If True, when multiple points or intervals are drawn on the
same line, they are offset from each other.
styles_order : array_like
If stacked=True, this is the order in which the point styles
on a given line are drawn from top to bottom (if horizontal
is True) or from left to right (if horiontal is False). If
None (default), the order is lexical.
striped : boolean
If True, every other line is enclosed in a shaded box.
horizontal : boolean
If True (default), the lines are drawn horizontally, otherwise
they are drawn vertically.
show_names : string
Determines whether labels (names) are shown in the left and/or
right margins (top/bottom margins if `horizontal` is True).
If `both`, labels are drawn in both margins, if 'left', labels
are drawn in the left or top margin. If `right`, labels are
drawn in the right or bottom margin.
fmt_left_name : function
The left/top margin names are passed through this function
before drawing on the plot.
fmt_right_name : function
The right/bottom marginnames are passed through this function
before drawing on the plot.
show_section_titles : bool or None
If None, section titles are drawn only if there is more than
one section. If False/True, section titles are never/always
drawn, respectively.
ax : matplotlib.axes
The axes on which the dotplot is drawn. If None, a new axes
is created.
Returns
-------
fig : Figure
The figure given by `ax.figure` or a new instance.
Notes
-----
`points`, `intervals`, `lines`, `sections`, `styles` must all have
the same length whenever present.
Examples
--------
This is a simple dotplot with one point per line:
>>> dot_plot(points=point_values)
This dotplot has labels on the lines (if elements in
`label_values` are repeated, the corresponding points appear on
the same line):
>>> dot_plot(points=point_values, lines=label_values)
References
----------
* Cleveland, William S. (1993). "Visualizing Data". Hobart
Press.
* Jacoby, William G. (2006) "The Dot Plot: A Graphical Display
for Labeled Quantitative Values." The Political Methodologist
14(1): 6-14.
"""
import matplotlib.transforms as transforms
fig, ax = utils.create_mpl_ax(ax)
# Convert to numpy arrays if that is not what we are given.
points = np.asarray(points)
asarray_or_none = lambda x : None if x is None else np.asarray(x)
intervals = asarray_or_none(intervals)
lines = asarray_or_none(lines)
sections = asarray_or_none(sections)
styles = asarray_or_none(styles)
# Total number of points
npoint = len(points)
# Set default line values if needed
if lines is None:
lines = np.arange(npoint)
# Set default section values if needed
if sections is None:
sections = np.zeros(npoint)
# Set default style values if needed
if styles is None:
styles = np.zeros(npoint)
# The vertical space (in inches) for a section title
section_title_space = 0.5
# The number of sections
nsect = len(set(sections))
if section_order is not None:
nsect = len(set(section_order))
# The number of section titles
if show_section_titles == False:
draw_section_titles = False
nsect_title = 0
elif show_section_titles == True:
draw_section_titles = True
nsect_title = nsect
else:
draw_section_titles = nsect > 1
nsect_title = nsect if nsect > 1 else 0
# The total vertical space devoted to section titles.
section_space_total = section_title_space * nsect_title
# Add a bit of room so that points that fall at the axis limits
# are not cut in half.
ax.set_xmargin(0.02)
ax.set_ymargin(0.02)
if section_order is None:
lines0 = list(set(sections))
lines0.sort()
else:
lines0 = section_order
if line_order is None:
lines1 = list(set(lines))
lines1.sort()
else:
lines1 = line_order
# A map from (section,line) codes to index positions.
lines_map = {}
for i in range(npoint):
if section_order is not None and sections[i] not in section_order:
continue
if line_order is not None and lines[i] not in line_order:
continue
ky = (sections[i], lines[i])
if ky not in lines_map:
lines_map[ky] = []
lines_map[ky].append(i)
# Get the size of the axes on the parent figure in inches
bbox = ax.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
awidth, aheight = bbox.width, bbox.height
# The number of lines in the plot.
nrows = len(lines_map)
# The positions of the lowest and highest guideline in axes
# coordinates (for horizontal dotplots), or the leftmost and
# rightmost guidelines (for vertical dotplots).
bottom, top = 0, 1
if horizontal:
# x coordinate is data, y coordinate is axes
trans = transforms.blended_transform_factory(ax.transData,
ax.transAxes)
else:
# x coordinate is axes, y coordinate is data
trans = transforms.blended_transform_factory(ax.transAxes,
ax.transData)
# Space used for a section title, in axes coordinates
title_space_axes = section_title_space / aheight
# Space between lines
if horizontal:
dpos = (top - bottom - nsect_title*title_space_axes) /\
float(nrows)
else:
dpos = (top - bottom) / float(nrows)
# Determine the spacing for stacked points
if styles_order is not None:
style_codes = styles_order
else:
style_codes = list(set(styles))
style_codes.sort()
# Order is top to bottom for horizontal plots, so need to
# flip.
if horizontal:
style_codes = style_codes[::-1]
# nval is the maximum number of points on one line.
nval = len(style_codes)
if nval > 1:
stackd = dpos / (2.5*(float(nval)-1))
else:
stackd = 0.
# Map from style code to its integer position
style_codes_map = {x: style_codes.index(x) for x in style_codes}
# Setup default marker styles
colors = ["r", "g", "b", "y", "k", "purple", "orange"]
if marker_props is None:
marker_props = {x: {} for x in style_codes}
for j in range(nval):
sc = style_codes[j]
if "color" not in marker_props[sc]:
marker_props[sc]["color"] = colors[j % len(colors)]
if "marker" not in marker_props[sc]:
marker_props[sc]["marker"] = "o"
if "ms" not in marker_props[sc]:
marker_props[sc]["ms"] = 10 if stackd == 0 else 6
# Setup default line styles
if line_props is None:
line_props = {x: {} for x in style_codes}
for j in range(nval):
sc = style_codes[j]
if "color" not in line_props[sc]:
line_props[sc]["color"] = "grey"
if "linewidth" not in line_props[sc]:
line_props[sc]["linewidth"] = 2 if stackd > 0 else 8
if horizontal:
# The vertical position of the first line.
pos = top - dpos/2 if nsect == 1 else top
else:
# The horizontal position of the first line.
pos = bottom + dpos/2
# Points that have already been labeled
labeled = set()
# Positions of the y axis grid lines
ticks = []
# Loop through the sections
for k0 in lines0:
# Draw a section title
if draw_section_titles:
if horizontal:
y0 = pos + dpos/2 if k0 == lines0[0] else pos
ax.fill_between((0, 1), (y0,y0),
(pos-0.7*title_space_axes,
pos-0.7*title_space_axes),
color='darkgrey',
transform=ax.transAxes,
zorder=1)
txt = ax.text(0.5, pos - 0.35*title_space_axes, k0,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes)
txt.set_fontweight("bold")
pos -= title_space_axes
else:
m = len([k for k in lines_map if k[0] == k0])
ax.fill_between((pos-dpos/2+0.01,
pos+(m-1)*dpos+dpos/2-0.01),
(1.01,1.01), (1.06,1.06),
color='darkgrey',
transform=ax.transAxes,
zorder=1, clip_on=False)
txt = ax.text(pos + (m-1)*dpos/2, 1.02, k0,
horizontalalignment='center',
verticalalignment='bottom',
transform=ax.transAxes)
txt.set_fontweight("bold")
jrow = 0
for k1 in lines1:
# No data to plot
if (k0, k1) not in lines_map:
continue
# Draw the guideline
if horizontal:
ax.axhline(pos, color='grey')
else:
ax.axvline(pos, color='grey')
# Set up the labels
if split_names is not None:
us = k1.split(split_names)
if len(us) >= 2:
left_label, right_label = us[0], us[1]
else:
left_label, right_label = k1, None
else:
left_label, right_label = k1, None
if fmt_left_name is not None:
left_label = fmt_left_name(left_label)
if fmt_right_name is not None:
right_label = fmt_right_name(right_label)
# Draw the stripe
if striped and jrow % 2 == 0:
if horizontal:
ax.fill_between((0, 1), (pos-dpos/2, pos-dpos/2),
(pos+dpos/2, pos+dpos/2),
color='lightgrey',
transform=ax.transAxes,
zorder=0)
else:
ax.fill_between((pos-dpos/2, pos+dpos/2),
(0, 0), (1, 1),
color='lightgrey',
transform=ax.transAxes,
zorder=0)
jrow += 1
# Draw the left margin label
if show_names.lower() in ("left", "both"):
if horizontal:
ax.text(-0.1/awidth, pos, left_label,
horizontalalignment="right",
verticalalignment='center',
transform=ax.transAxes,
family='monospace')
else:
ax.text(pos, -0.1/aheight, left_label,
horizontalalignment="center",
verticalalignment='top',
transform=ax.transAxes,
family='monospace')
# Draw the right margin label
if show_names.lower() in ("right", "both"):
if right_label is not None:
if horizontal:
ax.text(1 + 0.1/awidth, pos, right_label,
horizontalalignment="left",
verticalalignment='center',
transform=ax.transAxes,
family='monospace')
else:
ax.text(pos, 1 + 0.1/aheight, right_label,
horizontalalignment="center",
verticalalignment='bottom',
transform=ax.transAxes,
family='monospace')
# Save the vertical position so that we can place the
# tick marks
ticks.append(pos)
# Loop over the points in one line
for ji,jp in enumerate(lines_map[(k0,k1)]):
# Calculate the vertical offset
yo = 0
if stacked:
yo = -dpos/5 + style_codes_map[styles[jp]]*stackd
pt = points[jp]
# Plot the interval
if intervals is not None:
# Symmetric interval
if np.isscalar(intervals[jp]):
lcb, ucb = pt - intervals[jp],\
pt + intervals[jp]
# Nonsymmetric interval
else:
lcb, ucb = pt - intervals[jp][0],\
pt + intervals[jp][1]
# Draw the interval
if horizontal:
ax.plot([lcb, ucb], [pos+yo, pos+yo], '-',
transform=trans,
**line_props[styles[jp]])
else:
ax.plot([pos+yo, pos+yo], [lcb, ucb], '-',
transform=trans,
**line_props[styles[jp]])
# Plot the point
sl = styles[jp]
sll = sl if sl not in labeled else None
labeled.add(sl)
if horizontal:
ax.plot([pt,], [pos+yo,], ls='None',
transform=trans, label=sll,
**marker_props[sl])
else:
ax.plot([pos+yo,], [pt,], ls='None',
transform=trans, label=sll,
**marker_props[sl])
if horizontal:
pos -= dpos
else:
pos += dpos
# Set up the axis
if horizontal:
ax.xaxis.set_ticks_position("bottom")
ax.yaxis.set_ticks_position("none")
ax.set_yticklabels([])
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_position(('axes', -0.1/aheight))
ax.set_ylim(0, 1)
ax.yaxis.set_ticks(ticks)
ax.autoscale_view(scaley=False, tight=True)
else:
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("none")
ax.set_xticklabels([])
ax.spines['bottom'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('axes', -0.1/awidth))
ax.set_xlim(0, 1)
ax.xaxis.set_ticks(ticks)
ax.autoscale_view(scalex=False, tight=True)
return fig
| 0 | 0 | 0 |
70fe63fd252ad0c12abc04de082c33c848a23ee4 | 633 | py | Python | agent/alembic/versions/746f77fcf519_create_pipeline_offset_table.py | eacherkan-aternity/daria | 7c77a2f52c09c852017b16949a848fa51f0fb579 | [
"Apache-2.0"
] | 16 | 2019-04-03T08:31:54.000Z | 2021-01-24T17:12:04.000Z | agent/alembic/versions/746f77fcf519_create_pipeline_offset_table.py | eacherkan-aternity/daria | 7c77a2f52c09c852017b16949a848fa51f0fb579 | [
"Apache-2.0"
] | 10 | 2020-01-20T14:59:06.000Z | 2022-01-21T10:19:16.000Z | agent/alembic/versions/746f77fcf519_create_pipeline_offset_table.py | eacherkan-aternity/daria | 7c77a2f52c09c852017b16949a848fa51f0fb579 | [
"Apache-2.0"
] | 5 | 2021-01-08T19:23:03.000Z | 2021-11-09T14:15:49.000Z | """create_pipeline_offset_table
Revision ID: 746f77fcf519
Revises: 63c90017aa5d
Create Date: 2020-10-20 13:32:59.329693
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '746f77fcf519'
down_revision = '63c90017aa5d'
branch_labels = None
depends_on = None
| 21.1 | 74 | 0.71564 | """create_pipeline_offset_table
Revision ID: 746f77fcf519
Revises: 63c90017aa5d
Create Date: 2020-10-20 13:32:59.329693
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '746f77fcf519'
down_revision = '63c90017aa5d'
branch_labels = None
depends_on = None
def upgrade():
op.create_table(
'pipeline_offsets',
sa.Column('id', sa.Integer, autoincrement=True, primary_key=True),
sa.Column('pipeline_id', sa.Integer, nullable=False),
sa.Column('offset', sa.String, nullable=False)
)
def downgrade():
op.drop_table('pipeline_offsets')
| 273 | 0 | 46 |
86ff824699222c69c36860f3f9d1fe253fde8c59 | 2,791 | py | Python | python/src/main/python/pygw/geotools/simple_feature_type_builder.py | jhickman-prominent/geowave | fb421588e22a7c68ef13be1e57bc2c674dd1b090 | [
"Apache-2.0"
] | null | null | null | python/src/main/python/pygw/geotools/simple_feature_type_builder.py | jhickman-prominent/geowave | fb421588e22a7c68ef13be1e57bc2c674dd1b090 | [
"Apache-2.0"
] | 2 | 2019-09-20T15:39:27.000Z | 2019-12-03T14:07:43.000Z | python/src/main/python/pygw/geotools/simple_feature_type_builder.py | jhickman-prominent/geowave | fb421588e22a7c68ef13be1e57bc2c674dd1b090 | [
"Apache-2.0"
] | null | null | null | #
# Copyright (c) 2013-2019 Contributors to the Eclipse Foundation
#
# See the NOTICE file distributed with this work for additional information regarding copyright
# ownership. All rights reserved. This program and the accompanying materials are made available
# under the terms of the Apache License, Version 2.0 which accompanies this distribution and is
# available at http://www.apache.org/licenses/LICENSE-2.0.txt
#===============================================================================================
from pygw.config import java_pkg
from pygw.base import GeoWaveObject
from .simple_feature_type import SimpleFeatureType
from .attribute_descriptor import AttributeDescriptor
class SimpleFeatureTypeBuilder(GeoWaveObject):
"""
Builds `pygw.geotools.simple_feature_type.SimpleFeatureType` instances.
"""
def set_name(self, name):
"""
Sets the name of the feature type.
Args:
name (str): The name to use.
Returns:
This feature type builder.
"""
self._java_ref.setName(name)
return self
def set_namespace_uri(self, namespace_uri):
"""
Sets the namespace URI of the feature type.
Args:
namespace_uri (str): The namespace URI to use.
Returns:
This feature type builder.
"""
self._java_ref.setNamespaceURI(namespace_uri)
return self
def set_srs(self, srs):
"""
Sets the spatial reference system of the feature type.
Args:
srs (str): The spatial reference system to use.
Returns:
This feature type builder.
"""
self._java_ref.setSRS(srs)
return self
def add(self, attribute_descriptor):
"""
Adds an attribute to the feature type.
Args:
attribute_descriptor (pygw.geotools.attribute_descriptor.AttributeDescriptor): The attribute to add.
Returns:
This feature type builder.
"""
if isinstance(attribute_descriptor, AttributeDescriptor):
self.attributes.append(attribute_descriptor)
self._java_ref.add(attribute_descriptor._java_ref)
return self
else:
raise ValueError("attribute_descriptor should be of type AttributeDescriptor")
def build_feature_type(self):
"""
Builds the configured feature type.
Returns:
A `pygw.geotools.simple_feature_type.SimpleFeatureType` with the given configuration.
"""
return SimpleFeatureType(self._java_ref.buildFeatureType(), self.attributes)
| 32.835294 | 112 | 0.638839 | #
# Copyright (c) 2013-2019 Contributors to the Eclipse Foundation
#
# See the NOTICE file distributed with this work for additional information regarding copyright
# ownership. All rights reserved. This program and the accompanying materials are made available
# under the terms of the Apache License, Version 2.0 which accompanies this distribution and is
# available at http://www.apache.org/licenses/LICENSE-2.0.txt
#===============================================================================================
from pygw.config import java_pkg
from pygw.base import GeoWaveObject
from .simple_feature_type import SimpleFeatureType
from .attribute_descriptor import AttributeDescriptor
class SimpleFeatureTypeBuilder(GeoWaveObject):
"""
Builds `pygw.geotools.simple_feature_type.SimpleFeatureType` instances.
"""
def __init__(self):
self.attributes = []
super().__init__(java_pkg.org.geotools.feature.simple.SimpleFeatureTypeBuilder())
def set_name(self, name):
"""
Sets the name of the feature type.
Args:
name (str): The name to use.
Returns:
This feature type builder.
"""
self._java_ref.setName(name)
return self
def set_namespace_uri(self, namespace_uri):
"""
Sets the namespace URI of the feature type.
Args:
namespace_uri (str): The namespace URI to use.
Returns:
This feature type builder.
"""
self._java_ref.setNamespaceURI(namespace_uri)
return self
def set_srs(self, srs):
"""
Sets the spatial reference system of the feature type.
Args:
srs (str): The spatial reference system to use.
Returns:
This feature type builder.
"""
self._java_ref.setSRS(srs)
return self
def add(self, attribute_descriptor):
"""
Adds an attribute to the feature type.
Args:
attribute_descriptor (pygw.geotools.attribute_descriptor.AttributeDescriptor): The attribute to add.
Returns:
This feature type builder.
"""
if isinstance(attribute_descriptor, AttributeDescriptor):
self.attributes.append(attribute_descriptor)
self._java_ref.add(attribute_descriptor._java_ref)
return self
else:
raise ValueError("attribute_descriptor should be of type AttributeDescriptor")
def build_feature_type(self):
"""
Builds the configured feature type.
Returns:
A `pygw.geotools.simple_feature_type.SimpleFeatureType` with the given configuration.
"""
return SimpleFeatureType(self._java_ref.buildFeatureType(), self.attributes)
| 117 | 0 | 27 |
263312b88cc0b035cd76732103e670965aa5d5b7 | 3,099 | py | Python | robots/sc-3dof/src/sc_3dof_manipulate.py | frdedynamics/ik_solver_test | e162080838dca9cdba2c576836acd4a06563db3c | [
"RSA-MD"
] | null | null | null | robots/sc-3dof/src/sc_3dof_manipulate.py | frdedynamics/ik_solver_test | e162080838dca9cdba2c576836acd4a06563db3c | [
"RSA-MD"
] | null | null | null | robots/sc-3dof/src/sc_3dof_manipulate.py | frdedynamics/ik_solver_test | e162080838dca9cdba2c576836acd4a06563db3c | [
"RSA-MD"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from openravepy import *
import sys
import numpy as np
# initialize environment
env = Environment()
env.StopSimulation()
# openrave_root = './'
# env.Load(openrave_root + "baxter_ik.xml")
env.Load("sc_3dof.xml")
env.SetViewer('qtcoin') # start the viewer (conflicts with matplotlib)
robot = env.GetRobots()[0] # get the first robot
manip = robot.SetActiveManipulator('manipulator') # set the manipulator to right_arm
dummy_joints = [1.57, 0.0, 0.0]
robot.SetDOFValues(dummy_joints, manip.GetArmIndices()) # set the current solution
current_pose = manip.GetEndEffectorTransform()
print current_pose
# # try IK for similar pose
goal_pose = current_pose
# # goal_pose[2,3] -= 0.05
ikmodel = databases.inversekinematics.InverseKinematicsModel(robot,iktype=IkParameterization.Type.Translation3D)
print ikmodel.load()
# sol = manip.FindIKSolution(goal_pose, 18) # get collision-free solution
# print "IK solution:", sol
# ikmodel = databases.inversekinematics.InverseKinematicsModel(robot,iktype=IkParameterization.Type.Translation3D)
# if not ikmodel.load():
# ikmodel.autogenerate()
index = 0
with env:
# move the robot in a random collision-free position and call the IK
while True:
target=ikmodel.manip.GetTransform()[0:3,3]+(np.random.rand(3)-0.5)
solutions = ikmodel.manip.FindIKSolutions(IkParameterization(target,IkParameterization.Type.Lookat3D),IkFilterOptions.CheckEnvCollisions)
print "index:", index
index += 1
if len(solutions) > 0:
break
raw_input('Hit ENTER to continue.')
env.Destroy()
with robot: # lock environment and save robot state
robot.SetDOFValues([0.0, 0.0, 0.0],[0,1,2]) # set the first 3 dof values
Tee = manip.GetEndEffectorTransform() # get end effector
print Tee
ikparam = IkParameterization(Tee[0:3,3],ikmodel.iktype) # build up the translation3d ik query
print ikparam
print ikparam.GetType()
print ikparam.GetNumberOfValues()
print ikparam.GetValues()
print ikparam.GetDOF()
print ikparam.GetRotation3D()
print ikparam.GetTranslation3D()
# joint_conf = databases.inversekinematics.RaveCreateIkSolver(env, 'sc_3dof')
# print "joint_conf", joint_conf
ikmodel = databases.inversekinematics.InverseKinematicsModel(robot, iktype=IkParameterization.Type.Translation3D)
ikname = ikmodel.getikname()
iktest = ikmodel.testik()
print "ikname", ikname
sys.exit("Done")
sols = manip.FindIKSolutions(ikparam, IkFilterOptions.CheckEnvCollisions) # get all solutions
sol = manip.FindIKSolution(goal_pose, 18) # get collision-free solution
print sols
sys.exit("Done")
h = env.plot3(Tee[0:3,3],10) # plot one point
with robot: # save robot state
for sol in sols[::10]: # go through every 10th solution
robot.SetDOFValues(sol,manip.GetArmIndices()) # set the current solution
env.UpdatePublishedBodies() # allow viewer to update new robot
raw_input('press any key')
raveLogInfo('restored dof values: '+repr(robot.GetDOFValues())) # robot state is restored to original
| 34.054945 | 141 | 0.734108 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from openravepy import *
import sys
import numpy as np
# initialize environment
env = Environment()
env.StopSimulation()
# openrave_root = './'
# env.Load(openrave_root + "baxter_ik.xml")
env.Load("sc_3dof.xml")
env.SetViewer('qtcoin') # start the viewer (conflicts with matplotlib)
robot = env.GetRobots()[0] # get the first robot
manip = robot.SetActiveManipulator('manipulator') # set the manipulator to right_arm
dummy_joints = [1.57, 0.0, 0.0]
robot.SetDOFValues(dummy_joints, manip.GetArmIndices()) # set the current solution
current_pose = manip.GetEndEffectorTransform()
print current_pose
# # try IK for similar pose
goal_pose = current_pose
# # goal_pose[2,3] -= 0.05
ikmodel = databases.inversekinematics.InverseKinematicsModel(robot,iktype=IkParameterization.Type.Translation3D)
print ikmodel.load()
# sol = manip.FindIKSolution(goal_pose, 18) # get collision-free solution
# print "IK solution:", sol
# ikmodel = databases.inversekinematics.InverseKinematicsModel(robot,iktype=IkParameterization.Type.Translation3D)
# if not ikmodel.load():
# ikmodel.autogenerate()
index = 0
with env:
# move the robot in a random collision-free position and call the IK
while True:
target=ikmodel.manip.GetTransform()[0:3,3]+(np.random.rand(3)-0.5)
solutions = ikmodel.manip.FindIKSolutions(IkParameterization(target,IkParameterization.Type.Lookat3D),IkFilterOptions.CheckEnvCollisions)
print "index:", index
index += 1
if len(solutions) > 0:
break
raw_input('Hit ENTER to continue.')
env.Destroy()
with robot: # lock environment and save robot state
robot.SetDOFValues([0.0, 0.0, 0.0],[0,1,2]) # set the first 3 dof values
Tee = manip.GetEndEffectorTransform() # get end effector
print Tee
ikparam = IkParameterization(Tee[0:3,3],ikmodel.iktype) # build up the translation3d ik query
print ikparam
print ikparam.GetType()
print ikparam.GetNumberOfValues()
print ikparam.GetValues()
print ikparam.GetDOF()
print ikparam.GetRotation3D()
print ikparam.GetTranslation3D()
# joint_conf = databases.inversekinematics.RaveCreateIkSolver(env, 'sc_3dof')
# print "joint_conf", joint_conf
ikmodel = databases.inversekinematics.InverseKinematicsModel(robot, iktype=IkParameterization.Type.Translation3D)
ikname = ikmodel.getikname()
iktest = ikmodel.testik()
print "ikname", ikname
sys.exit("Done")
sols = manip.FindIKSolutions(ikparam, IkFilterOptions.CheckEnvCollisions) # get all solutions
sol = manip.FindIKSolution(goal_pose, 18) # get collision-free solution
print sols
sys.exit("Done")
h = env.plot3(Tee[0:3,3],10) # plot one point
with robot: # save robot state
for sol in sols[::10]: # go through every 10th solution
robot.SetDOFValues(sol,manip.GetArmIndices()) # set the current solution
env.UpdatePublishedBodies() # allow viewer to update new robot
raw_input('press any key')
raveLogInfo('restored dof values: '+repr(robot.GetDOFValues())) # robot state is restored to original
| 0 | 0 | 0 |
e7d0c8fc9a6524561313e97fa779be7c5c6960c4 | 5,826 | py | Python | bot/player_commands/missing.py | UP929312/CommunityBot | c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a | [
"Apache-2.0"
] | 1 | 2021-06-15T07:31:13.000Z | 2021-06-15T07:31:13.000Z | bot/player_commands/missing.py | UP929312/CommunityBot | c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a | [
"Apache-2.0"
] | 1 | 2021-06-01T10:14:32.000Z | 2021-06-02T10:54:12.000Z | bot/player_commands/missing.py | UP929312/CommunityBot | c16294e8ff4f47d9a1e8c18c9cd4011e7ebbd67a | [
"Apache-2.0"
] | 2 | 2021-06-01T10:59:15.000Z | 2021-06-03T18:29:36.000Z | import discord # type: ignore
from discord.ext import commands # type: ignore
from discord.commands import Option # type: ignore
from typing import Optional
import json
import requests
from utils import error, PROFILE_NAMES, hf, guild_ids
from menus import generate_static_preset_menu
from emojis import ITEM_RARITY
from parse_profile import get_profile_data
from extract_ids import extract_internal_names
RARITY_LIST = list(ITEM_RARITY.keys())
# Create the master list!
from text_files.accessory_list import talisman_upgrades
# Get a list of all accessories
ACCESSORIES: list[dict] = []
with open("text_files/MASTER_ITEM_DICT.json", "r", encoding="utf-8") as file:
item_dict = json.load(file)
for item in item_dict:
if item_dict[item].get("rarity", False) and item_dict[item]["rarity"] != "UNKNOWN":
ACCESSORIES.append(item_dict[item])
# Now remove all the low tier ones
MASTER_ACCESSORIES = []
for accessory in ACCESSORIES:
if accessory["internal_name"] not in talisman_upgrades.keys():
MASTER_ACCESSORIES.append(accessory)
EMOJI_LIST = ["<:alphabetically:905066318720544779>", "<:recombobulator:854750106376339477>", "<:by_price:900069290143797299>"]
| 50.66087 | 166 | 0.650704 | import discord # type: ignore
from discord.ext import commands # type: ignore
from discord.commands import Option # type: ignore
from typing import Optional
import json
import requests
from utils import error, PROFILE_NAMES, hf, guild_ids
from menus import generate_static_preset_menu
from emojis import ITEM_RARITY
from parse_profile import get_profile_data
from extract_ids import extract_internal_names
RARITY_LIST = list(ITEM_RARITY.keys())
# Create the master list!
from text_files.accessory_list import talisman_upgrades
# Get a list of all accessories
ACCESSORIES: list[dict] = []
with open("text_files/MASTER_ITEM_DICT.json", "r", encoding="utf-8") as file:
item_dict = json.load(file)
for item in item_dict:
if item_dict[item].get("rarity", False) and item_dict[item]["rarity"] != "UNKNOWN":
ACCESSORIES.append(item_dict[item])
# Now remove all the low tier ones
MASTER_ACCESSORIES = []
for accessory in ACCESSORIES:
if accessory["internal_name"] not in talisman_upgrades.keys():
MASTER_ACCESSORIES.append(accessory)
EMOJI_LIST = ["<:alphabetically:905066318720544779>", "<:recombobulator:854750106376339477>", "<:by_price:900069290143797299>"]
class missing_cog(commands.Cog):
def __init__(self, bot) -> None:
self.client = bot
@commands.command(name="missing", aliases=['missing_accessories', 'accessories', 'miss', 'm'])
async def missing_command(self, ctx, provided_username: Optional[str] = None, provided_profile: Optional[str] = None) -> None:
await self.get_missing(ctx, provided_username, provided_profile, is_response=False)
@commands.slash_command(name="missing", description="Gets someone's missing accessories", guild_ids=guild_ids)
async def missing_slash(self, ctx, username: Option(str, "username:", required=False),
profile: Option(str, "profile", choices=PROFILE_NAMES, required=False)):
if not (ctx.channel.permissions_for(ctx.guild.me)).send_messages:
return await ctx.respond("You're not allowed to do that here.", ephemeral=True)
await self.get_missing(ctx, username, profile, is_response=True)
#=========================================================================================================================================
async def get_missing(self, ctx, provided_username: Optional[str] = None, provided_profile_name: Optional[str] = None, is_response: bool = False) -> None:
player_data: Optional[dict] = await get_profile_data(ctx, provided_username, provided_profile_name, is_response=is_response)
if player_data is None:
return
username = player_data["username"]
accessory_bag = player_data.get("talisman_bag", None)
inv_content = player_data.get("inv_contents", {"data": []})
if not accessory_bag:
return await error(ctx, "Error, could not find this person's accessory bag", "Do they have their API disabled for this command?", is_response=is_response)
accessory_bag = extract_internal_names(accessory_bag["data"])
inventory = extract_internal_names(inv_content["data"])
missing = [x for x in MASTER_ACCESSORIES if x["internal_name"] not in accessory_bag+inventory]
if not missing:
return await error(ctx, f"Completion!", f"{username} already has all accessories!", is_response=is_response)
try:
lowest_bin_data = requests.get("http://moulberry.codes/lowestbin.json").json()
except:
return await error(ctx, f"Error, price API is down!", f"Please wait for it to return, and try again later!", is_response=is_response)
for accessory in missing:
accessory["price"] = lowest_bin_data.get(accessory["internal_name"], 9999999999)
list_of_embeds = []
for page, parameter in zip(["alphabetically", "by rarity", "by price"], ["name", "rarity", "price"]):
sort_func = lambda x: x[parameter] if parameter != "rarity" else RARITY_LIST.index(x["rarity"])
sorted_accessories = sorted(missing, key=sort_func)[:42]
extra = "" if len(missing) <= 36 else f", showing the first {int(len(sorted_accessories)/6)}"
embed = discord.Embed(title=f"Missing {len(missing)} accessories for {username}{extra}, sorted: {page}", colour=0x3498DB)
def make_embed(embed, acc_list, num):
text = ""
for item in acc_list:
wiki_link = "<Unknown>" if not item['wiki_link'] else f"[wiki]({item['wiki_link']})"
price = hf(item['price']) if item['price'] != 9999999999 else 'N/A'
text += f"{ITEM_RARITY[item['rarity']]} {item['name']}\n➜ For {price}, link: {wiki_link}\n"
embed_title = f"{acc_list[0]['name'][0]}-{acc_list[-1]['name'][0]}" if parameter == "name" else f"Group {num}"
embed.add_field(name=f"{embed_title}", value=text, inline=True)
if len(sorted_accessories) < 6: # For people with only a few missing
make_embed(embed, sorted_accessories, 1)
else:
list_length = int(len(sorted_accessories)/6)
for row in range(6):
row_accessories = sorted_accessories[row*list_length:(row+1)*list_length] # Get the first group out of 6
make_embed(embed, row_accessories, row+1)
embed.set_footer(text=f"Command executed by {ctx.author.display_name} | Community Bot. By the community, for the community.")
list_of_embeds.append(embed)
await generate_static_preset_menu(ctx=ctx, list_of_embeds=list_of_embeds, emoji_list=EMOJI_LIST, is_response=is_response)
| 4,116 | 484 | 23 |
8f765cb99284c504f466ca30e7deb4f59c374dad | 3,797 | py | Python | model/MyModel.py | seanzhangJM/torch_nlp_job_default | fe9126c4abbde441fcc65d14d42477e4fff9d509 | [
"MIT"
] | null | null | null | model/MyModel.py | seanzhangJM/torch_nlp_job_default | fe9126c4abbde441fcc65d14d42477e4fff9d509 | [
"MIT"
] | null | null | null | model/MyModel.py | seanzhangJM/torch_nlp_job_default | fe9126c4abbde441fcc65d14d42477e4fff9d509 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# _*_ coding: utf-8 _*_
# @Time : 2021/12/27 14:03
# @Author : zhangjianming
# @Email : YYDSPanda@163.com
# @File : MyModel.py
# @Software: PyCharm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_nlp_job_default.util.log_config import get_logger
logger = get_logger()
class RNNModel(nn.Module):
"""循环神经网络模型"""
| 37.97 | 114 | 0.628654 | #!/usr/bin/env python
# _*_ coding: utf-8 _*_
# @Time : 2021/12/27 14:03
# @Author : zhangjianming
# @Email : YYDSPanda@163.com
# @File : MyModel.py
# @Software: PyCharm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_nlp_job_default.util.log_config import get_logger
logger = get_logger()
class RNNModel(nn.Module):
"""循环神经网络模型"""
def __init__(self, rnn_layer, vocab_size, **kwargs):
super(RNNModel, self).__init__(**kwargs)
self.rnn = rnn_layer
self.vocab_size = vocab_size
self.num_hiddens = self.rnn.hidden_size
# 如果RNN是双向的（之后将介绍），num_directions应该是2，否则应该是1
if not self.rnn.bidirectional:
self.num_directions = 1
self.linear = nn.Linear(self.num_hiddens, self.vocab_size)
else:
self.num_directions = 2
self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size)
def forward(self, inputs, state):
# nn.Embedding
X = F.one_hot(inputs.T.long(), self.vocab_size)
X = X.to(torch.float32)
Y, state = self.rnn(X, state)
# 全连接层首先将Y的形状改为(时间步数*批量大小,隐藏单元数)
# 它的输出形状是(时间步数*批量大小,词表大小)。
output = self.linear(Y.reshape((-1, Y.shape[-1])))
return output, state
def begin_state(self, device, batch_size=1):
if not isinstance(self.rnn, nn.LSTM):
# nn.GRU以张量作为隐状态
return torch.zeros((self.num_directions * self.rnn.num_layers,
batch_size, self.num_hiddens),
device=device)
else:
# nn.LSTM以元组作为隐状态
return (torch.zeros((
self.num_directions * self.rnn.num_layers,
batch_size, self.num_hiddens), device=device),
torch.zeros((
self.num_directions * self.rnn.num_layers,
batch_size, self.num_hiddens), device=device))
class Seq2SeqEncoder(nn.Module):
def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
super(Seq2SeqEncoder, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embed_size = embed_size
self.num_hiddens = num_hiddens
self.embedding = nn.Embedding(self.vocab_size, self.embed_size)
self.rnn = nn.GRU(embed_size, num_hiddens, num_layers=num_layers, dropout=dropout)
def forward(self, X):
# X shape:[batch_size,time_steps,embed_size]
X = self.embedding(X)
X = X.permute(1, 0, 2)
output, state = self.rnn(X)
# output:[time_steps,batch_size,num_hiddens]
# state[0]:[2*num_layers,batch_size,num_hiddens] if bidirectional else [num_layers,batch_size,num_hiddens]
return output, state
class Seq2SeqDecoder(nn.Module):
def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
super(Seq2SeqDecoder, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embed_size = embed_size
self.num_hiddens = num_hiddens
self.embedding = nn.Embedding(self.vocab_size, self.embed_size)
self.rnn = nn.GRU(embed_size + num_hiddens, num_hiddens, num_layers=num_layers, dropout=dropout)
self.dense = nn.Linear(num_hiddens, vocab_size)
def init_state(self, enc_outputs, *args):
return enc_outputs[1]
def forward(self, X, state):
X = self.embedding(X)
X = X.permute(1, 0, 2)
context = state[-1].repeat(X.shape[0], 1, 1)
X_and_context = torch.cat((X, context), dim=2)
output, state = self.rnn(X_and_context, state)
# output shape:[time_steps,batch_size,num_hiddens]
output = self.dense(output).permute(1, 0, 2)
return output, state
| 3,302 | 22 | 260 |
7f96ffe28e1306cbb636608c457e89fc1dd8aeb0 | 1,679 | py | Python | molsysmt/_private/exceptions/not_implemented_engine_error.py | uibcdf/MolModMTs | 4f6b6f671a9fa3e73008d1e9c48686d5f20a6573 | [
"MIT"
] | null | null | null | molsysmt/_private/exceptions/not_implemented_engine_error.py | uibcdf/MolModMTs | 4f6b6f671a9fa3e73008d1e9c48686d5f20a6573 | [
"MIT"
] | null | null | null | molsysmt/_private/exceptions/not_implemented_engine_error.py | uibcdf/MolModMTs | 4f6b6f671a9fa3e73008d1e9c48686d5f20a6573 | [
"MIT"
] | null | null | null | class NotImplementedEngineError(NotImplementedError):
"""Exception raised when executing a method with a specific engine is not supported yet.
This exception is raised when a method can not be executed with the selected engine by the user.
Parameters
----------
engine : str
The engine not supported yet.
Raises
------
NotImplementedFormError
A message is printed out with the name of the engine not supported, the name of the class or
the method raising the exception, the link to the API documentation, and the link to the
issues board of Sabueso's GitHub repository.
Examples
--------
>>> from molsysmt._private.exceptions import NotImplementedEngineError
>>> def method_name(engine):
... if engine not in ['MolSysMT', 'MDTraj']:
... raise NotImplementedEngineError(engine)
... pass
.. admonition:: See Also
:class: attention
:ref:`Developer Guide \> Exceptions \> NotImplementedEngineError <developer:exceptions:NotImplementedEngineError>`
"""
| 31.092593 | 122 | 0.646814 | class NotImplementedEngineError(NotImplementedError):
"""Exception raised when executing a method with a specific engine is not supported yet.
This exception is raised when a method can not be executed with the selected engine by the user.
Parameters
----------
engine : str
The engine not supported yet.
Raises
------
NotImplementedFormError
A message is printed out with the name of the engine not supported, the name of the class or
the method raising the exception, the link to the API documentation, and the link to the
issues board of Sabueso's GitHub repository.
Examples
--------
>>> from molsysmt._private.exceptions import NotImplementedEngineError
>>> def method_name(engine):
... if engine not in ['MolSysMT', 'MDTraj']:
... raise NotImplementedEngineError(engine)
... pass
.. admonition:: See Also
:class: attention
:ref:`Developer Guide \> Exceptions \> NotImplementedEngineError <developer:exceptions:NotImplementedEngineError>`
"""
def __init__(self, syntaxis):
from molsysmt import __github_issues_web__
from inspect import stack
all_stack_frames = stack()
caller_stack_frame = all_stack_frames[1]
caller_name = caller_stack_frame[3]
api_doc = ''
message = (
f"The \"{engine}\" engine in \"{caller_name}\" has not been implemented yet. "
f"Check {api_doc} for more information. "
f"Write a new issue in {__github_issues_web__} asking for its implementation."
)
super().__init__(message)
| 567 | 0 | 27 |
b9ba88b9235ace559185b77c2db576c890cd1f1b | 1,783 | py | Python | tests/plugins/test_beautify_icon.py | enricorotundo/folium | c3d21e6a90fbb21dff0a8b0851a835515b156eb8 | [
"MIT"
] | 10 | 2019-09-09T15:53:39.000Z | 2022-01-15T19:35:41.000Z | tests/plugins/test_beautify_icon.py | enricorotundo/folium | c3d21e6a90fbb21dff0a8b0851a835515b156eb8 | [
"MIT"
] | 1 | 2018-10-26T14:07:53.000Z | 2018-10-26T14:07:53.000Z | tests/plugins/test_beautify_icon.py | enricorotundo/folium | c3d21e6a90fbb21dff0a8b0851a835515b156eb8 | [
"MIT"
] | 1 | 2020-08-03T08:46:26.000Z | 2020-08-03T08:46:26.000Z | # -*- coding: utf-8 -*-
"""
Test BeautifyIcon
---------------
"""
from __future__ import (absolute_import, division, print_function)
from jinja2 import Template
import folium
from folium import plugins
| 29.229508 | 140 | 0.588334 | # -*- coding: utf-8 -*-
"""
Test BeautifyIcon
---------------
"""
from __future__ import (absolute_import, division, print_function)
from jinja2 import Template
import folium
from folium import plugins
def test_beautify_icon():
m = folium.Map([30., 0.], zoom_start=3)
# BeautifyIcons
ic1 = plugins.BeautifyIcon(
icon='plane', border_color='#b3334f', text_color='#b3334f')
ic2 = plugins.BeautifyIcon(border_color='#00ABDC',
text_color='#00ABDC',
number=10,
inner_icon_style='margin-top:0;')
# Markers, add icons as keyword argument
bm1 = folium.Marker(location=[46, -122],
popup='Portland, OR',
icon=ic1
).add_to(m)
bm2 = folium.Marker(
location=[50, -121],
icon=ic2
).add_to(m)
m.add_child(bm1)
m.add_child(bm2)
m._repr_html_()
out = m._parent.render()
# We verify that the script import is present.
script = '<script src="https://cdn.rawgit.com/marslan390/BeautifyMarker/master/leaflet-beautify-marker-icon.js"></script>' # noqa
assert script in out
# We verify that the css import is present.
css = '<link rel="stylesheet" href="https://cdn.rawgit.com/marslan390/BeautifyMarker/master/leaflet-beautify-marker-icon.css"/>' # noqa
assert css in out
# We verify that the Beautiful Icons are rendered correctly.
tmpl = Template(u"""
var {{this.get_name()}} = new L.BeautifyIcon.icon({{ this.options }})
{{this._parent.get_name()}}.setIcon({{this.get_name()}});
""") # noqa
assert tmpl.render(this=ic1) in out
assert tmpl.render(this=ic2) in out
| 1,551 | 0 | 23 |
a8f874247ca4e35e657f742398b5bb62716b1a49 | 1,661 | py | Python | models/vgg.py | wangren09/ASK | f4b0f47341a1f2e3fd2a2c20898779fb589c29c5 | [
"MIT"
] | 54 | 2021-08-11T08:59:52.000Z | 2021-09-29T17:35:53.000Z | models/vgg.py | wangren09/ASK | f4b0f47341a1f2e3fd2a2c20898779fb589c29c5 | [
"MIT"
] | null | null | null | models/vgg.py | wangren09/ASK | f4b0f47341a1f2e3fd2a2c20898779fb589c29c5 | [
"MIT"
] | 13 | 2021-08-11T23:50:15.000Z | 2022-02-27T18:04:08.000Z | import torch.nn as nn
| 32.568627 | 86 | 0.509332 | import torch.nn as nn
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
cfg1 = [64, 64, 'M']
cfg2 = [128, 128, 'M']
cfg3 = [256, 256, 256, 'M']
cfg4 = [512, 512, 512, 'M']
cfg5 = [512, 512, 512, 'M']
self.layer1 = self._make_layers(cfg1, 3)
self.layer2 = self._make_layers(cfg2, 64)
self.layer3 = self._make_layers(cfg3, 128)
self.layer4 = self._make_layers(cfg4, 256)
self.layer5 = self._make_layers(cfg5, 512)
self.classifier = nn.Sequential(
nn.Flatten(start_dim=1),
nn.Dropout(),
nn.Linear(512, 512),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(512, 512),
nn.ReLU(True),
nn.Linear(512, 10),
)
def _make_layers(self, cfg, in_channels):
layers = []
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
nn.BatchNorm2d(x),
nn.ReLU(inplace=True)]
in_channels = x
return nn.Sequential(*layers)
def forward(self, x, extra_out=None):
out1 = self.layer1(x)
out2 = self.layer2(out1)
out3 = self.layer3(out2)
out4 = self.layer4(out3)
out5 = self.layer5(out4)
out = self.classifier(out5)
if extra_out is None:
return out
else:
return [out1, out2, out3, out4, out5][extra_out].flatten(start_dim=1), out
| 1,533 | 2 | 103 |
1509e1c5188bd7290ad8c4d58170e67abd20cdbe | 10,235 | py | Python | landsat8/core.py | paddyesch/landsat8 | 9e5736eed73ad7932c27077220362c6dac70be35 | [
"MIT"
] | 1 | 2016-11-24T10:42:57.000Z | 2016-11-24T10:42:57.000Z | landsat8/core.py | paddyesch/landsat8 | 9e5736eed73ad7932c27077220362c6dac70be35 | [
"MIT"
] | null | null | null | landsat8/core.py | paddyesch/landsat8 | 9e5736eed73ad7932c27077220362c6dac70be35 | [
"MIT"
] | null | null | null | # MIT License
#
# Copyright (c) 2016 Patrick Eschenbach
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import urllib
import httplib
import os
import gzip
import shutil
import random
import subprocess
import json
import time
import math
from landsat.downloader import Downloader
from landsat.image import Simple
from colorama import init, Fore
init(autoreset=True)
work_folder = "temp"
def calculate_latlon_midpoint(lat1, lon1, lat2, lon2):
"Calculation of geographic midpoint with method A: http://www.geomidpoint.com/calculation.html"
lat1 = math.radians(lat1)
lon1 = math.radians(lon1)
lat2 = math.radians(lat2)
lon2 = math.radians(lon2)
w1 = 1
w2 = 2
w_total = w1 + w2
x1 = math.cos(lat1) * math.cos(lon1)
y1 = math.cos(lat1) * math.sin(lon1)
z1 = math.sin(lat1)
x2 = math.cos(lat2) * math.cos(lon2)
y2 = math.cos(lat2) * math.sin(lon2)
z2 = math.sin(lat2)
x = ((x1 * w1) + (x2 * w2)) / w_total
y = ((y1 * w1) + (y2 * w2)) / w_total
z = ((z1 * w1) + (z2 * w2)) / w_total
hyp = math.sqrt(x * x + y * y)
mid_lat = math.atan2(z, hyp)
mid_lon = math.atan2(y, x)
return (math.degrees(mid_lat), math.degrees(mid_lon))
| 37.767528 | 137 | 0.669858 | # MIT License
#
# Copyright (c) 2016 Patrick Eschenbach
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import urllib
import httplib
import os
import gzip
import shutil
import random
import subprocess
import json
import time
import math
from landsat.downloader import Downloader
from landsat.image import Simple
from colorama import init, Fore
init(autoreset=True)
work_folder = "temp"
def download_scene_list():
gzip_path = work_folder + "/scene_list.gz"
unpack_path = work_folder + "/scene_list.csv"
if os.path.isfile(unpack_path):
now = time.time()
last_modified = os.path.getmtime(unpack_path)
interval_in_seconds = now - last_modified
if interval_in_seconds < (3 * 60 * 60):
print "Already downloaded list is recent enough (<3h) ..."
return unpack_path
print "Downloading ..."
urllib.urlretrieve ("http://landsat-pds.s3.amazonaws.com/scene_list.gz", gzip_path)
gzip_handle = gzip.open(gzip_path)
with open(unpack_path, "w") as out:
for line in gzip_handle:
out.write(line)
return unpack_path
def tail_from_scene_list(file_path, num_scenes):
stdin, stdout = os.popen2("tail -n " + str(num_scenes) + " " + file_path)
stdin.close()
lines = stdout.readlines(); stdout.close()
scenes = list()
for line in lines:
tokens = line.split(',')
scene = (tokens[0], tokens[10][:-11]) # Remove /index.hmtl\n
scenes.append(scene)
return scenes
def parse_mtl(mtl_path):
mtl = dict()
groupStack = [mtl];
for line in open(mtl_path, "r"):
if line.strip() == "END":
break
parts = line.split("=", 1)
key = parts[0].strip()
value = parts[1].strip()
if key == "GROUP":
group = dict()
groupStack[-1][value] = group
groupStack.append(group)
elif key == "END_GROUP":
groupStack.pop()
else:
groupStack[-1][key] = value
return mtl
def download_mtl(scene):
mtl_path = work_folder + "/" + scene[0] + "_scene.mtl"
urllib.urlretrieve (scene[1] + scene[0] + "_MTL.txt", mtl_path)
return parse_mtl(mtl_path)
def is_scene_suitable(scene, max_cloud_cover):
mtl = download_mtl(scene)
projection = mtl["L1_METADATA_FILE"]["PROJECTION_PARAMETERS"]["MAP_PROJECTION"]
cloud_cover = mtl["L1_METADATA_FILE"]["IMAGE_ATTRIBUTES"]["CLOUD_COVER"]
cloud_cover = float(cloud_cover)
return projection == '"UTM"' and cloud_cover < max_cloud_cover
def find_scene(max_cloud_cover):
print Fore.GREEN + "# Downloading scene list ..."
if not os.path.exists(work_folder):
os.makedirs(work_folder)
num_scenes = 500
file_path = download_scene_list()
print ""
print Fore.GREEN + "# Finding recent and suitable scene ..."
scenes = tail_from_scene_list(file_path, num_scenes)
for count in range(0, num_scenes):
random_index = random.randint(0, num_scenes - 1)
scene = scenes[random_index]
if is_scene_suitable(scene, max_cloud_cover):
print scene[0]
print ""
return scene[0]
print "No suitable scene found ..."
return ""
def download_scene(scene_id):
print Fore.GREEN + "# Downloading scene ..."
downloader = Downloader(verbose=True, download_dir=work_folder)
loaded = downloader.download([str(scene_id)], [4, 3, 2])
print ""
return len(loaded) == 1
def convert_scene(scene_id, quality_in_percent, resize_in_percent):
print Fore.GREEN + "# Processing scene ..."
processor = Simple(work_folder + "/" + scene_id, [4, 3, 2], work_folder)
processedTIF = processor.run()
convertedJPG = scene_id + ".jpg"
command = "convert -quality {} -resize {}% {} {}".format(str(quality_in_percent), str(resize_in_percent), processedTIF, convertedJPG)
subprocess.call(command, shell=True)
print ""
def determine_country(lat, lon):
connection = httplib.HTTPConnection("maps.googleapis.com")
connection.request("GET", "/maps/api/geocode/json?latlng={},{}".format(lat, lon))
response = json.load(connection.getresponse())
if response["status"] == "OK":
for result in response["results"]:
if "country" in result["types"]:
return result["formatted_address"]
return ""
def determine_countries(mtl):
ll_lat = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_LL_LAT_PRODUCT"]
ll_lon = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_LL_LON_PRODUCT"]
lr_lat = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_LR_LAT_PRODUCT"]
lr_lon = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_LR_LON_PRODUCT"]
ur_lat = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_UR_LAT_PRODUCT"]
ur_lon = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_UR_LON_PRODUCT"]
ul_lat = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_UL_LAT_PRODUCT"]
ul_lon = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_UL_LON_PRODUCT"]
countries = set([determine_country(ll_lat, ll_lon),
determine_country(lr_lat, lr_lon),
determine_country(ur_lat, ur_lon),
determine_country(ul_lat, ul_lon)])
if "" in countries: countries.remove("")
return countries
def calculate_latlon_midpoint(lat1, lon1, lat2, lon2):
"Calculation of geographic midpoint with method A: http://www.geomidpoint.com/calculation.html"
lat1 = math.radians(lat1)
lon1 = math.radians(lon1)
lat2 = math.radians(lat2)
lon2 = math.radians(lon2)
w1 = 1
w2 = 2
w_total = w1 + w2
x1 = math.cos(lat1) * math.cos(lon1)
y1 = math.cos(lat1) * math.sin(lon1)
z1 = math.sin(lat1)
x2 = math.cos(lat2) * math.cos(lon2)
y2 = math.cos(lat2) * math.sin(lon2)
z2 = math.sin(lat2)
x = ((x1 * w1) + (x2 * w2)) / w_total
y = ((y1 * w1) + (y2 * w2)) / w_total
z = ((z1 * w1) + (z2 * w2)) / w_total
hyp = math.sqrt(x * x + y * y)
mid_lat = math.atan2(z, hyp)
mid_lon = math.atan2(y, x)
return (math.degrees(mid_lat), math.degrees(mid_lon))
def collect_metadata(scene_id):
print Fore.GREEN + "# Collecting metadata ..."
mtl_path = work_folder + "/" + scene_id + "/" + scene_id + "_MTL.txt"
mtl = parse_mtl(mtl_path)
date = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["DATE_ACQUIRED"]
cloud_cover = mtl["L1_METADATA_FILE"]["IMAGE_ATTRIBUTES"]["CLOUD_COVER"]
ll_lat = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_LL_LAT_PRODUCT"]
ll_lon = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_LL_LON_PRODUCT"]
ur_lat = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_UR_LAT_PRODUCT"]
ur_lon = mtl["L1_METADATA_FILE"]["PRODUCT_METADATA"]["CORNER_UR_LON_PRODUCT"]
center_latlon = calculate_latlon_midpoint(float(ll_lat), float(ll_lon), float(ur_lat), float(ur_lon))
countries = list(determine_countries(mtl))
print "Scene ID: " + scene_id
print "Date acquired: " + date
print "Cloud cover: " + cloud_cover
print "Lower left latitude: " + ll_lat
print "Lower left longitude: " + ll_lon
print "Upper right latitude: " + ur_lat
print "Upper right longitude: " + ur_lon
print "Center latitude: " + str(center_latlon[0])
print "Center longitude: " + str(center_latlon[1])
print "Countries: " + str(countries)
metadata = {}
metadata["scene_id"] = scene_id
metadata["date_acquired"] = date
metadata["cloud_cover"] = float(cloud_cover)
metadata["lower_left_lat"] = float(ll_lat)
metadata["lower_left_lon"] = float(ll_lon)
metadata["upper_right_lat"] = float(ur_lat)
metadata["upper_right_lon"] = float(ur_lon)
metadata["center_lat"] = center_latlon[0]
metadata["center_lon"] = center_latlon[1]
metadata["countries"] = countries
metadataPath = scene_id + "_metadata.json"
with open(metadataPath, "w") as outfile:
json.dump(metadata, outfile)
print ""
def create_soft_links(scene_id):
print Fore.GREEN + "# Creating soft links ..."
lns_source_path = scene_id + ".jpg"
lns_target_path = "LATEST.jpg"
lns_meta_source_path = scene_id + "_metadata.json"
lns_meta_target_path = "LATEST_metadata.json"
try:
os.remove(lns_target_path)
except:
pass
try:
os.remove(lns_meta_target_path)
except:
pass
os.symlink(lns_source_path, lns_target_path)
os.symlink(lns_meta_source_path, lns_meta_target_path)
print lns_target_path
print lns_meta_target_path
print ""
def clean_up_scene(scene_id):
print Fore.GREEN + "# Cleaning up ..."
clean_path = work_folder + "/" + scene_id
shutil.rmtree(clean_path)
print clean_path
clean_mtl = work_folder + "/" + scene_id + "_scene.mtl"
try:
os.remove(clean_mtl)
print clean_mtl
except:
pass
try:
os.rmdir(work_folder)
print work_folder + "/"
except:
print work_folder + "/ is not empty: no action"
print ""
def print_scene_result(scene_id):
print Fore.GREEN + "# Scene image created ..."
print scene_id + ".JPG"
print scene_id + "_metadata.json"
print ""
| 7,701 | 0 | 322 |
a8653de9534d324f4c8a879ca13240c42270307c | 3,734 | py | Python | Shelby/driving_school.py | itinstructor/WNCCNASA | 64896f896cfaa694f4ae7f9f62948811164d10c0 | [
"CC0-1.0"
] | null | null | null | Shelby/driving_school.py | itinstructor/WNCCNASA | 64896f896cfaa694f4ae7f9f62948811164d10c0 | [
"CC0-1.0"
] | null | null | null | Shelby/driving_school.py | itinstructor/WNCCNASA | 64896f896cfaa694f4ae7f9f62948811164d10c0 | [
"CC0-1.0"
] | null | null | null | # name: driving_school.py
#--------------------------------- IMPORTS -------------------------------------#
import time # Import time library for sleep function
import easygopigo3 as easy # Import the GoPiGo3 library
gpg = easy.EasyGoPiGo3() # Create a EasyGoPiGo3 object
# Initialize a distance sensor object
distance_sensor = gpg.init_distance_sensor()
# Initialize a servo object on Servo Port 1
servo = gpg.init_servo("SERVO1")
# Set servo pointing straight ahead at 90 degrees
# You may have to change the degrees to adapt to your servo
# All servos line up slightly differently
# Less than 90 moves the servo to the right
# Greater than 90 moves the servo to the left
servo.rotate_servo(90)
gpg.set_speed(200) # Set initial speed
AVOIDANCE_DISTANCE = 12 # Distance in inches from obstacle where the GoPiGo should stop
# If a standalone program, call the main function
# Else, use as a module
if __name__ == '__main__':
main()
| 37.34 | 135 | 0.630691 | # name: driving_school.py
#--------------------------------- IMPORTS -------------------------------------#
import time # Import time library for sleep function
import easygopigo3 as easy # Import the GoPiGo3 library
gpg = easy.EasyGoPiGo3() # Create a EasyGoPiGo3 object
# Initialize a distance sensor object
distance_sensor = gpg.init_distance_sensor()
# Initialize a servo object on Servo Port 1
servo = gpg.init_servo("SERVO1")
# Set servo pointing straight ahead at 90 degrees
# You may have to change the degrees to adapt to your servo
# All servos line up slightly differently
# Less than 90 moves the servo to the right
# Greater than 90 moves the servo to the left
servo.rotate_servo(90)
gpg.set_speed(200) # Set initial speed
AVOIDANCE_DISTANCE = 12 # Distance in inches from obstacle where the GoPiGo should stop
def main():
#Print menu
#menu method
while True:
print_menu_choice()
menu_choice = int(input("Please pick a number from the menu: "))
if(menu_choice ==1):
right_square_turn()
elif(menu_choice ==2):
left_square_turn()
elif(menu_choice == 3):
triangle_turn()
elif(menu_choice == 4):
octagon_turn()
elif(menu_choice ==5):
five_point_star()
def print_menu_choice():
print("1. right square turn:")
print("2. left square turn: ")
print("3. triangle turn: ")
print("4. octagon turn: ")
print("5. 5 point star turn: ")
def right_square_turn(): # Menu option 1
# need a way to start it I think
for x in range(4):
gpg.drive_inches(12)
gpg.turn_degrees(90)
def left_square_turn(): #Menu option 2
for x in range(4): # loop the things so I don't have to type that shit 4 times
gpg.drive_inches(12) #go forward 12 inches
gpg.turn_degrees(-90) #-90 degrees is turning left I think
def triangle_turn(): # Menu option 3
for x in range(3):
gpg.drive_inches(12) #go forward 12 inches
gpg.turn_degrees(120) # 60 degrees is interior angle for a triangle. If this gives weird results, try the anterior angle of 120
def octagon_turn(): #Option 4
for x in range(8): #Loop it 8 times to complete the shape
gpg.drive_inches(12) #forward 12inches
gpg.turn_degrees(45) #45 degrees for each turn
def five_point_star():#Option 5 #stars have like a lot of angles.
for x in range (5):
gpg.drive_inches(12) #12 inches
gpg.turn_degrees(252) #idk if that is right maybe 72, or 108
def obstacle_avoid():
print("Press ENTER to start")
input() # Wait for input to start
gpg.forward() # Start moving forward, GoPiGo will continue moving forward until it receives another movement command
running = True # Boolean/flag to control the while loop
while running == True: # Loop while running == True
dist = distance_sensor.read_inches() # Find the distance of the object in front
print("Dist:", dist, 'inches') # Print feedback to the console
# If the object is closer than the "distance_to_stop" distance, stop the GoPiGo
if dist < AVOIDANCE_DISTANCE:
print("Stopping") # Print feedback to the console
gpg.stop() # Stop the GoPiGo
running = False # Set running to false to break out of the loop
# sleep is blocking code, nothing else can happen during sleep
time.sleep(.1) # 100 milliseconds
# If a standalone program, call the main function
# Else, use as a module
if __name__ == '__main__':
main()
| 2,524 | 0 | 184 |
b634679a67b91315266daf78a009e523cc08ee09 | 130 | py | Python | awesome_panel_extensions/developer_tools/test_apps/__init__.py | Jhsmit/awesome-panel-extensions | 41eba7cf84caa911be4ed0df2a96e16fc1e70263 | [
"CC-BY-4.0"
] | 3 | 2020-07-16T07:28:45.000Z | 2020-07-17T12:53:56.000Z | awesome_panel_extensions/developer_tools/test_apps/__init__.py | MarcSkovMadsen/panel-extensions-template | f41ad8d8fb8502f87de3a4992917cbffb6299012 | [
"CC-BY-4.0"
] | null | null | null | awesome_panel_extensions/developer_tools/test_apps/__init__.py | MarcSkovMadsen/panel-extensions-template | f41ad8d8fb8502f87de3a4992917cbffb6299012 | [
"CC-BY-4.0"
] | null | null | null | """The PanelComponentExplorer makes it easy to explore components"""
from .panel_component_explorer import PanelComponentExplorer
| 43.333333 | 68 | 0.853846 | """The PanelComponentExplorer makes it easy to explore components"""
from .panel_component_explorer import PanelComponentExplorer
| 0 | 0 | 0 |
fe026149c752b3c212f98f38d4913e3dea8346b2 | 1,967 | py | Python | Classes/ResUNetPlusPlus/infer.py | Nitin-Mane/ALL-PyTorch-Segmentation-2021 | 0f3c7b129629cc2863c502898bcfa3c45077af85 | [
"MIT"
] | null | null | null | Classes/ResUNetPlusPlus/infer.py | Nitin-Mane/ALL-PyTorch-Segmentation-2021 | 0f3c7b129629cc2863c502898bcfa3c45077af85 | [
"MIT"
] | null | null | null | Classes/ResUNetPlusPlus/infer.py | Nitin-Mane/ALL-PyTorch-Segmentation-2021 | 0f3c7b129629cc2863c502898bcfa3c45077af85 | [
"MIT"
] | null | null | null |
import os
import numpy as np
import cv2
from glob import glob
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import CustomObjectScope
from data_generator import *
from metrics import dice_coef, dice_loss
if __name__ == "__main__":
model_path = "../model/model/resunetplusplus.h5"
save_path = "../model/result"
test_path = "../model/data/"
image_size = 256
batch_size = 1
test_image_paths = glob(os.path.join(test_path, "imgs", "*"))
test_mask_paths = glob(os.path.join(test_path, "masks", "*"))
test_image_paths.sort()
test_mask_paths.sort()
## Create result folder
try:
os.mkdir(save_path)
except:
pass
## Model
with CustomObjectScope({'dice_loss': dice_loss, 'dice_coef': dice_coef}):
model = load_model(model_path)
## Test
print("Test Result: ")
test_steps = len(test_image_paths)//batch_size
test_gen = DataGen(image_size, test_image_paths, test_mask_paths, batch_size=batch_size)
model.evaluate_generator(test_gen, steps=test_steps, verbose=1)
## Generating the result
for i, path in tqdm(enumerate(test_image_paths), total=len(test_image_paths)):
image = parse_image(test_image_paths[i], image_size)
mask = parse_mask(test_mask_paths[i], image_size)
predict_mask = model.predict(np.expand_dims(image, axis=0))[0]
predict_mask = (predict_mask > 0.5) * 255.0
sep_line = np.ones((image_size, 10, 3)) * 255
mask = mask_to_3d(mask)
predict_mask = mask_to_3d(predict_mask)
all_images = [image * 255, sep_line, mask * 255, sep_line, predict_mask]
cv2.imwrite(f"{save_path}/{i}.png", np.concatenate(all_images, axis=1))
print("Test image generation complete")
| 30.261538 | 92 | 0.682766 |
import os
import numpy as np
import cv2
from glob import glob
from tqdm import tqdm
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import CustomObjectScope
from data_generator import *
from metrics import dice_coef, dice_loss
def mask_to_3d(mask):
mask = np.squeeze(mask)
mask = [mask, mask, mask]
mask = np.transpose(mask, (1, 2, 0))
return mask
if __name__ == "__main__":
model_path = "../model/model/resunetplusplus.h5"
save_path = "../model/result"
test_path = "../model/data/"
image_size = 256
batch_size = 1
test_image_paths = glob(os.path.join(test_path, "imgs", "*"))
test_mask_paths = glob(os.path.join(test_path, "masks", "*"))
test_image_paths.sort()
test_mask_paths.sort()
## Create result folder
try:
os.mkdir(save_path)
except:
pass
## Model
with CustomObjectScope({'dice_loss': dice_loss, 'dice_coef': dice_coef}):
model = load_model(model_path)
## Test
print("Test Result: ")
test_steps = len(test_image_paths)//batch_size
test_gen = DataGen(image_size, test_image_paths, test_mask_paths, batch_size=batch_size)
model.evaluate_generator(test_gen, steps=test_steps, verbose=1)
## Generating the result
for i, path in tqdm(enumerate(test_image_paths), total=len(test_image_paths)):
image = parse_image(test_image_paths[i], image_size)
mask = parse_mask(test_mask_paths[i], image_size)
predict_mask = model.predict(np.expand_dims(image, axis=0))[0]
predict_mask = (predict_mask > 0.5) * 255.0
sep_line = np.ones((image_size, 10, 3)) * 255
mask = mask_to_3d(mask)
predict_mask = mask_to_3d(predict_mask)
all_images = [image * 255, sep_line, mask * 255, sep_line, predict_mask]
cv2.imwrite(f"{save_path}/{i}.png", np.concatenate(all_images, axis=1))
print("Test image generation complete")
| 115 | 0 | 23 |
ce6f68a12848e1fc926ef7139ad157fd2c14acc3 | 1,157 | py | Python | tests/test_base.py | Czaki/tsp_spanning | 3f0e5f338f17996c5f634824ed61282d2ea2487c | [
"MIT"
] | null | null | null | tests/test_base.py | Czaki/tsp_spanning | 3f0e5f338f17996c5f634824ed61282d2ea2487c | [
"MIT"
] | null | null | null | tests/test_base.py | Czaki/tsp_spanning | 3f0e5f338f17996c5f634824ed61282d2ea2487c | [
"MIT"
] | null | null | null | import unittest
import numpy as np
from tsp_spanning import tsp, point_tsp, points_to_distance_matrix
if __name__ == "__main__":
unittest.main()
| 32.138889 | 66 | 0.587727 | import unittest
import numpy as np
from tsp_spanning import tsp, point_tsp, points_to_distance_matrix
class TestTSP(unittest.TestCase):
def test_work_point(self):
points = [(0, 0), (1, 1), (-1, -1), (1, 0)]
res = point_tsp(points)
self.assertTrue(len(res) == 4)
self.assertTrue(set(points) == set(map(tuple,res)))
def test_work_point_3d(self):
points = [(0, 0, 0), (1, 1, 1), (-1, -1, -1), (1, 0, 0)]
res = point_tsp(points)
self.assertTrue(len(res) == 4)
self.assertTrue(set(points) == set(map(tuple,res)))
def test_work_tsp(self):
points = np.array([(0, 0), (1, 1), (-1, -1), (1, 0)])
distances = points_to_distance_matrix(points)
self.assertTrue(distances.shape == (4,4))
points_order = tsp(distances)
self.assertTrue(len(points_order) == 4)
def test_work_tsp_end(self):
points = np.array([(0, 0), (1, 1), (-1, -1), (1, 0)])
distances = points_to_distance_matrix(points)
points_order = tsp(distances, 1)
self.assertTrue(points_order[-1] == 1)
if __name__ == "__main__":
unittest.main()
| 858 | 12 | 134 |
f19d2d3c111237c6c5a7402ac82046221d1da7af | 3,012 | py | Python | generation-items/items-processor.py | miomao34/data-generator | 89380929b42ba1f95f8e3e57c1f93ee7365a56d7 | [
"MIT"
] | null | null | null | generation-items/items-processor.py | miomao34/data-generator | 89380929b42ba1f95f8e3e57c1f93ee7365a56d7 | [
"MIT"
] | null | null | null | generation-items/items-processor.py | miomao34/data-generator | 89380929b42ba1f95f8e3e57c1f93ee7365a56d7 | [
"MIT"
] | null | null | null | import json
import csv
from typing import Dict, List, Union
def get_items_list(filename: str) -> List:
"""Loads items data from formatted csv files and returns
the items list
"""
primary_title_column = 0
secondary_title_column = 1
rarity_column = 2
name_column = 3
# stats are messy and aren't applicable, so not used
# stats_column = 4
description_column = 5
primary_title = ''
secondary_title = ''
output = []
with open(filename, 'r') as file:
reader = csv.reader(file)
index = 1
for row in reader:
if row[primary_title_column] != '':
primary_title = row[primary_title_column]
secondary_title = ''
continue
if row[secondary_title_column] != '':
secondary_title = row[secondary_title_column]
continue
item = {}
# passed through titles, so here's data only
# no EOF because reader iterates over not empty rows only
if row[name_column] == '':
# TODO: push error to log
print(f'No name on line {index}, under {primary_title}{" - " + secondary_title if secondary_title != "" else ""}')
continue
if row[description_column] == '':
# TODO: push error to log
print(f'No description on line {index}, under {primary_title}{" - " + secondary_title if secondary_title != "" else ""}')
continue
item['primary_title'] = primary_title
if secondary_title != '':
item['secondary_title'] = secondary_title
if row[rarity_column] != '':
item['rarity'] = int(row[rarity_column])
item['name'] = row[name_column]
item['description'] = row[description_column]
print(f'Got a {item["primary_title"]} item{"" if "secondary_title" not in item else " for " + item["secondary_title"]}: {item["name"]}. \"{item["description"]}\"')
output.append(item)
index += 1
return output
if __name__ == '__main__':
files = [
['generation-items/anno/csv/academy-items.csv', 'generation-items/anno/json/academy-items.json'],
['generation-items/anno/csv/ark-items.csv', 'generation-items/anno/json/ark-items.json'],
['generation-items/anno/csv/lab-items.csv', 'generation-items/anno/json/lab-items.json'],
['generation-items/anno/csv/vehicle-items.csv', 'generation-items/anno/json/vehicle-items.json'],
['generation-items/anno/csv/warehouse-items.csv', 'generation-items/anno/json/warehouse-items.json']
]
for file_pair in files:
save_list_to_json(
get_items_list(file_pair[0]),
file_pair[1]
)
| 34.227273 | 175 | 0.583333 | import json
import csv
from typing import Dict, List, Union
def get_items_list(filename: str) -> List:
"""Loads items data from formatted csv files and returns
the items list
"""
primary_title_column = 0
secondary_title_column = 1
rarity_column = 2
name_column = 3
# stats are messy and aren't applicable, so not used
# stats_column = 4
description_column = 5
primary_title = ''
secondary_title = ''
output = []
with open(filename, 'r') as file:
reader = csv.reader(file)
index = 1
for row in reader:
if row[primary_title_column] != '':
primary_title = row[primary_title_column]
secondary_title = ''
continue
if row[secondary_title_column] != '':
secondary_title = row[secondary_title_column]
continue
item = {}
# passed through titles, so here's data only
# no EOF because reader iterates over not empty rows only
if row[name_column] == '':
# TODO: push error to log
print(f'No name on line {index}, under {primary_title}{" - " + secondary_title if secondary_title != "" else ""}')
continue
if row[description_column] == '':
# TODO: push error to log
print(f'No description on line {index}, under {primary_title}{" - " + secondary_title if secondary_title != "" else ""}')
continue
item['primary_title'] = primary_title
if secondary_title != '':
item['secondary_title'] = secondary_title
if row[rarity_column] != '':
item['rarity'] = int(row[rarity_column])
item['name'] = row[name_column]
item['description'] = row[description_column]
print(f'Got a {item["primary_title"]} item{"" if "secondary_title" not in item else " for " + item["secondary_title"]}: {item["name"]}. \"{item["description"]}\"')
output.append(item)
index += 1
return output
def save_list_to_json(list: List, output_filename: str) -> None:
with open(output_filename, 'a+') as output:
json.dump(list, output)
if __name__ == '__main__':
files = [
['generation-items/anno/csv/academy-items.csv', 'generation-items/anno/json/academy-items.json'],
['generation-items/anno/csv/ark-items.csv', 'generation-items/anno/json/ark-items.json'],
['generation-items/anno/csv/lab-items.csv', 'generation-items/anno/json/lab-items.json'],
['generation-items/anno/csv/vehicle-items.csv', 'generation-items/anno/json/vehicle-items.json'],
['generation-items/anno/csv/warehouse-items.csv', 'generation-items/anno/json/warehouse-items.json']
]
for file_pair in files:
save_list_to_json(
get_items_list(file_pair[0]),
file_pair[1]
)
| 123 | 0 | 23 |
786d33695c7f8f1b30e7a5ec291198dcf8be08ba | 2,325 | py | Python | tests/opytimizer/optimizers/science/test_efo.py | anukaal/opytimizer | 5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9 | [
"Apache-2.0"
] | 528 | 2018-10-01T20:00:09.000Z | 2022-03-27T11:15:31.000Z | tests/opytimizer/optimizers/science/test_efo.py | anukaal/opytimizer | 5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9 | [
"Apache-2.0"
] | 17 | 2019-10-30T00:47:03.000Z | 2022-03-21T11:39:28.000Z | tests/opytimizer/optimizers/science/test_efo.py | anukaal/opytimizer | 5f1ccc0da80e6a4cabd99578fa24cf4f6466f9b9 | [
"Apache-2.0"
] | 35 | 2018-10-01T20:03:23.000Z | 2022-03-20T03:54:15.000Z | import numpy as np
from opytimizer.optimizers.science import efo
from opytimizer.spaces import search
np.random.seed(0)
| 18.164063 | 79 | 0.569462 | import numpy as np
from opytimizer.optimizers.science import efo
from opytimizer.spaces import search
np.random.seed(0)
def test_efo_params():
params = {
'positive_field': 0.1,
'negative_field': 0.5,
'ps_ratio': 0.1,
'r_ratio': 0.4
}
new_efo = efo.EFO(params=params)
assert new_efo.positive_field == 0.1
assert new_efo.negative_field == 0.5
assert new_efo.ps_ratio == 0.1
assert new_efo.r_ratio == 0.4
assert new_efo.phi == (1 + np.sqrt(5)) / 2
assert new_efo.RI == 0
def test_efo_params_setter():
new_efo = efo.EFO()
try:
new_efo.positive_field = 'a'
except:
new_efo.positive_field = 0.5
try:
new_efo.positive_field = -1
except:
new_efo.positive_field = 0.5
assert new_efo.positive_field == 0.5
try:
new_efo.negative_field = 'b'
except:
new_efo.negative_field = 0.2
try:
new_efo.negative_field = 0.99
except:
new_efo.negative_field = 0.2
try:
new_efo.negative_field = -1
except:
new_efo.negative_field = 0.2
assert new_efo.negative_field == 0.2
try:
new_efo.ps_ratio = 'c'
except:
new_efo.ps_ratio = 0.25
try:
new_efo.ps_ratio = -1
except:
new_efo.ps_ratio = 0.25
assert new_efo.ps_ratio == 0.25
try:
new_efo.r_ratio = 'd'
except:
new_efo.r_ratio = 0.25
try:
new_efo.r_ratio = -1
except:
new_efo.r_ratio = 0.25
assert new_efo.r_ratio == 0.25
try:
new_efo.phi = 'e'
except:
new_efo.phi = (1 + np.sqrt(5)) / 2
assert new_efo.phi == (1 + np.sqrt(5)) / 2
try:
new_efo.RI = 'f'
except:
new_efo.RI = 0
try:
new_efo.RI = -1
except:
new_efo.RI = 0
assert new_efo.RI == 0
def test_efo_calculate_indexes():
new_efo = efo.EFO()
a, b, c = new_efo._calculate_indexes(30)
assert a >= 0
assert b >= 0
assert c >= 0
def test_efo_update():
def square(x):
return np.sum(x**2)
new_efo = efo.EFO()
search_space = search.SearchSpace(n_agents=2, n_variables=2,
lower_bound=[1, 1], upper_bound=[10, 10])
new_efo.update(search_space, square)
| 2,107 | 0 | 92 |
22af80f58a32481092f7cbbec89e46075559aa22 | 783 | py | Python | src/jams/task.py | saromanov/jams | 76b95b373cc41466c0d397c8c5681a02b0345e16 | [
"MIT"
] | null | null | null | src/jams/task.py | saromanov/jams | 76b95b373cc41466c0d397c8c5681a02b0345e16 | [
"MIT"
] | 3 | 2019-01-03T19:41:01.000Z | 2019-01-31T13:14:26.000Z | src/jams/task.py | saromanov/jams | 76b95b373cc41466c0d397c8c5681a02b0345e16 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""
james.task
~~~~~~~~~~~~~
Implementation of the task. Task this is action which contains
checking. It should contains rule, weight
"""
| 22.371429 | 66 | 0.58493 | # -*- coding: utf-8 -*-
"""
james.task
~~~~~~~~~~~~~
Implementation of the task. Task this is action which contains
checking. It should contains rule, weight
"""
class TaskBase:
def __init__(self, name, *args, **kwargs):
self._name = name
def __str__(self):
return self._name
def __getattr__(self, name):
return '{0} call'.format(name)
def do(self):
raise NotImplementedError
def weight(self):
"""return weight of the task in the case
if task was completed
"""
return 0
class Task(TaskBase):
def __init__(self, name, *args, **kwargs):
self._weight = kwargs.get('weight')
self._action = kwargs.get('action')
super().__init__(name, *args, **kwargs)
| 299 | 232 | 72 |
2d15005c1f91448fffe8337592d219ad885c8d0d | 964 | py | Python | baloney/__main__.py | tonybaloney/baloney-script | 24b1158634e473d48f14d23c9f66a049bdcce6f9 | [
"MIT"
] | null | null | null | baloney/__main__.py | tonybaloney/baloney-script | 24b1158634e473d48f14d23c9f66a049bdcce6f9 | [
"MIT"
] | null | null | null | baloney/__main__.py | tonybaloney/baloney-script | 24b1158634e473d48f14d23c9f66a049bdcce6f9 | [
"MIT"
] | null | null | null | import argparse
import baloney.parser as baloney_parser
from baloney.interpreter import BaloneyInterpreter
parser = argparse.ArgumentParser()
parser.add_argument('--lex', type=bool)
parser.add_argument("file", type=str,
help="Target .bs file")
if __name__ == '__main__':
args = parser.parse_args()
main(args)
| 25.368421 | 54 | 0.61722 | import argparse
import baloney.parser as baloney_parser
from baloney.interpreter import BaloneyInterpreter
parser = argparse.ArgumentParser()
parser.add_argument('--lex', type=bool)
parser.add_argument("file", type=str,
help="Target .bs file")
def run_lexer(source):
from baloney.lexer import lexer
lexer.input(source)
while True:
tok = lexer.token()
if not tok:
break # No more input
print(tok)
def main(args):
with open(args.file, 'rb') as source:
source_str = source.read()
if args.lex:
run_lexer(source_str)
else:
program = baloney_parser.parse(source_str)
if not program:
raise SystemExit
interpreted_program = BaloneyInterpreter(prog)
try:
b.run()
raise SystemExit
except RuntimeError:
pass
if __name__ == '__main__':
args = parser.parse_args()
main(args)
| 579 | 0 | 46 |
e0cad9a632da0f19e6edc97400a819c5abc7284c | 2,088 | py | Python | app.py | totoropo/Emotion-Detection- | cbecd89a0693f44ad2abc29fd96320841f875ad9 | [
"RSA-MD"
] | 3 | 2021-06-18T10:21:02.000Z | 2021-06-21T05:54:38.000Z | app.py | tsangharry/Emotion-Detection- | cbecd89a0693f44ad2abc29fd96320841f875ad9 | [
"RSA-MD"
] | null | null | null | app.py | tsangharry/Emotion-Detection- | cbecd89a0693f44ad2abc29fd96320841f875ad9 | [
"RSA-MD"
] | null | null | null | import streamlit as st
import cv2
import os
from inference import return_annotated_images
from tensorflow.keras.models import load_model
from streamlit_webrtc import (
ClientSettings,
VideoTransformerBase,
WebRtcMode,
webrtc_streamer,
)
WEBRTC_CLIENT_SETTINGS = ClientSettings(
rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
media_stream_constraints={"video": True, "audio": False},
)
def app_object_detection():
"""Launches video with webrtc, calls annotated images.
Returns:
video : Annotated video.
"""
webrtc_ctx = webrtc_streamer(
key="object-detection",
mode=WebRtcMode.SENDRECV,
client_settings=WEBRTC_CLIENT_SETTINGS,
video_transformer_factory=NNVideoTransformer,
async_transform=True)
if webrtc_ctx.video_transformer:
webrtc_ctx.video_transformer.confidence_threshold = 0.5
def main():
"""
Streamlit interface.
"""
page_bg_img = '''
<style>
body {
background-image: url("https://www.fg-a.com/wallpapers/white-marble-1-2018.jpg");
background-size: cover;
}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)
st.header("Emotion Detection from Facial Expressions")
st.subheader('Unable to tell emotions like Sheldon Cooper? Let us help.')
st.subheader('😀😮😔😡😐')
app_object_detection()
main()
| 27.84 | 92 | 0.665709 | import streamlit as st
import cv2
import os
from inference import return_annotated_images
from tensorflow.keras.models import load_model
from streamlit_webrtc import (
ClientSettings,
VideoTransformerBase,
WebRtcMode,
webrtc_streamer,
)
WEBRTC_CLIENT_SETTINGS = ClientSettings(
rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
media_stream_constraints={"video": True, "audio": False},
)
def app_object_detection():
"""Launches video with webrtc, calls annotated images.
Returns:
video : Annotated video.
"""
class NNVideoTransformer(VideoTransformerBase):
def __init__(self):
prototxtPath = os.path.sep.join(['face_detector', "deploy.prototxt"])
weightsPath = os.path.sep.join(['face_detector',
"res10_300x300_ssd_iter_140000.caffemodel"])
self.faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
self.emotionsNet = load_model('model/emo.h5')
def transform(self, frame):
image = frame.to_ndarray(format="bgr24")
annotated_image = return_annotated_images(image, self.faceNet, self.emotionsNet)
return annotated_image
webrtc_ctx = webrtc_streamer(
key="object-detection",
mode=WebRtcMode.SENDRECV,
client_settings=WEBRTC_CLIENT_SETTINGS,
video_transformer_factory=NNVideoTransformer,
async_transform=True)
if webrtc_ctx.video_transformer:
webrtc_ctx.video_transformer.confidence_threshold = 0.5
def main():
"""
Streamlit interface.
"""
page_bg_img = '''
<style>
body {
background-image: url("https://www.fg-a.com/wallpapers/white-marble-1-2018.jpg");
background-size: cover;
}
</style>
'''
st.markdown(page_bg_img, unsafe_allow_html=True)
st.header("Emotion Detection from Facial Expressions")
st.subheader('Unable to tell emotions like Sheldon Cooper? Let us help.')
st.subheader('😀😮😔😡😐')
app_object_detection()
main()
| 546 | 26 | 88 |
92c03b96267c810090cd7e2215635b87807b5c40 | 6,913 | py | Python | fluxory/lib/packet/geneve.py | viniarck/fluxory | a00b930785039b81807290d585c6b2a31142e9d2 | [
"Apache-2.0"
] | null | null | null | fluxory/lib/packet/geneve.py | viniarck/fluxory | a00b930785039b81807290d585c6b2a31142e9d2 | [
"Apache-2.0"
] | 13 | 2019-04-03T15:08:45.000Z | 2019-05-17T23:10:16.000Z | fluxory/lib/packet/geneve.py | viniarck/fluxory | a00b930785039b81807290d585c6b2a31142e9d2 | [
"Apache-2.0"
] | null | null | null | # Copyright (C) 2016 Nippon Telegraph and Telephone 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.
"""
Geneve packet parser/serializer
"""
import struct
from fluxory.lib import stringify
from fluxory.lib import type_desc
from . import packet_base
from . import ether_types
UDP_DST_PORT = 6081
class geneve(packet_base.PacketBase):
"""Geneve (RFC draft-ietf-nvo3-geneve-03) header encoder/decoder class.
An instance has the following attributes at least.
Most of them are same to the on-wire counterparts but in host byte order.
__init__ takes the corresponding args in this order.
============== ========================================================
Attribute Description
============== ========================================================
version Version.
opt_len The length of the options fields.
flags Flag field for OAM packet and Critical options present.
protocol Protocol Type field.
The Protocol Type is defined as "ETHER TYPES".
vni Identifier for unique element of virtual network.
options List of ``Option*`` instance.
============== ========================================================
"""
_HEADER_FMT = "!BBHI"
_MIN_LEN = struct.calcsize(_HEADER_FMT)
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# |Ver| Opt Len |O|C| Rsvd. | Protocol Type |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Virtual Network Identifier (VNI) | Reserved |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Variable Length Options |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# Flags
OAM_PACKET_FLAG = 1 << 7
CRITICAL_OPTIONS_FLAG = 1 << 6
@classmethod
class Option(stringify.StringifyMixin, type_desc.TypeDisp):
"""
Tunnel Options
"""
_OPTION_PACK_STR = "!HBB"
_OPTION_LEN = struct.calcsize(_OPTION_PACK_STR)
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Option Class | Type |R|R|R| Length |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Variable Option Data |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
@classmethod
@classmethod
@Option.register_unknown_type()
class OptionDataUnknown(Option):
"""
Unknown Option Class and Type specific Option
"""
@classmethod
| 36.193717 | 77 | 0.542022 | # Copyright (C) 2016 Nippon Telegraph and Telephone 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.
"""
Geneve packet parser/serializer
"""
import struct
from fluxory.lib import stringify
from fluxory.lib import type_desc
from . import packet_base
from . import ether_types
UDP_DST_PORT = 6081
class geneve(packet_base.PacketBase):
"""Geneve (RFC draft-ietf-nvo3-geneve-03) header encoder/decoder class.
An instance has the following attributes at least.
Most of them are same to the on-wire counterparts but in host byte order.
__init__ takes the corresponding args in this order.
============== ========================================================
Attribute Description
============== ========================================================
version Version.
opt_len The length of the options fields.
flags Flag field for OAM packet and Critical options present.
protocol Protocol Type field.
The Protocol Type is defined as "ETHER TYPES".
vni Identifier for unique element of virtual network.
options List of ``Option*`` instance.
============== ========================================================
"""
_HEADER_FMT = "!BBHI"
_MIN_LEN = struct.calcsize(_HEADER_FMT)
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# |Ver| Opt Len |O|C| Rsvd. | Protocol Type |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Virtual Network Identifier (VNI) | Reserved |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Variable Length Options |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# Flags
OAM_PACKET_FLAG = 1 << 7
CRITICAL_OPTIONS_FLAG = 1 << 6
def __init__(self, version=0, opt_len=0, flags=0,
protocol=ether_types.ETH_TYPE_TEB, vni=None, options=None):
super(geneve, self).__init__()
self.version = version
self.opt_len = opt_len
assert (flags & 0x3F) == 0
self.flags = flags
self.protocol = protocol
self.vni = vni
for o in options:
assert isinstance(o, Option)
self.options = options
@classmethod
def parser(cls, buf):
(ver_opt_len, flags, protocol,
vni) = struct.unpack_from(cls._HEADER_FMT, buf)
version = ver_opt_len >> 6
# The Opt Len field expressed in four byte multiples.
opt_len = (ver_opt_len & 0x3F) * 4
opt_bin = buf[cls._MIN_LEN:cls._MIN_LEN + opt_len]
options = []
while opt_bin:
option, opt_bin = Option.parser(opt_bin)
options.append(option)
msg = cls(version, opt_len, flags, protocol, vni >> 8, options)
from . import ethernet
geneve._TYPES = ethernet.ethernet._TYPES
geneve.register_packet_type(ethernet.ethernet,
ether_types.ETH_TYPE_TEB)
return (msg, geneve.get_packet_type(protocol),
buf[cls._MIN_LEN + opt_len:])
def serialize(self, payload=None, prev=None):
tunnel_options = bytearray()
for o in self.options:
tunnel_options += o.serialize()
self.opt_len = len(tunnel_options)
# The Opt Len field expressed in four byte multiples.
opt_len = self.opt_len // 4
return (struct.pack(self._HEADER_FMT,
(self.version << 6) | opt_len,
self.flags, self.protocol, self.vni << 8)
+ tunnel_options)
class Option(stringify.StringifyMixin, type_desc.TypeDisp):
"""
Tunnel Options
"""
_OPTION_PACK_STR = "!HBB"
_OPTION_LEN = struct.calcsize(_OPTION_PACK_STR)
# 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Option Class | Type |R|R|R| Length |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
# | Variable Option Data |
# +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
def __init__(self, option_class=None, type_=None, length=0):
super(Option, self).__init__()
if option_class is None or type_ is None:
(option_class, type_) = self._rev_lookup_type(self.__class__)
self.option_class = option_class
self.type = type_
self.length = length
@classmethod
def parse_value(cls, buf):
# Sub-classes should override this method, if needed.
return {}
def serialize_value(self):
# Sub-classes should override this method, if needed.
return b''
@classmethod
def parser(cls, buf):
(option_class, type_,
length) = struct.unpack_from(cls._OPTION_PACK_STR, buf)
# The Length field expressed in four byte multiples.
length *= 4
subcls = Option._lookup_type((option_class, type_))
return (
subcls(option_class=option_class, type_=type_, length=length,
**subcls.parse_value(
buf[cls._OPTION_LEN:cls._OPTION_LEN + length])),
buf[cls._OPTION_LEN + length:])
def serialize(self, _payload=None, _prev=None):
data = self.serialize_value()
self.length = len(data)
# The Length field expressed in four byte multiples.
length = self.length // 4
return (struct.pack(self._OPTION_PACK_STR, int(self.option_class),
self.type, length) + data)
@Option.register_unknown_type()
class OptionDataUnknown(Option):
"""
Unknown Option Class and Type specific Option
"""
def __init__(self, buf, option_class=None, type_=None, length=0):
super(OptionDataUnknown, self).__init__(option_class=option_class,
type_=type_,
length=length)
self.buf = buf
@classmethod
def parse_value(cls, buf):
return {"buf": buf}
def serialize_value(self):
return self.buf
| 3,316 | 0 | 292 |
463b9880708ab93eb7c556b07a756c4f2a013afe | 4,116 | py | Python | adafruit_pn532/i2c.py | dunkmann00/Adafruit_CircuitPython_PN532 | b741c77cee4c1fce61bc478b6afa2e11528389f9 | [
"MIT"
] | null | null | null | adafruit_pn532/i2c.py | dunkmann00/Adafruit_CircuitPython_PN532 | b741c77cee4c1fce61bc478b6afa2e11528389f9 | [
"MIT"
] | null | null | null | adafruit_pn532/i2c.py | dunkmann00/Adafruit_CircuitPython_PN532 | b741c77cee4c1fce61bc478b6afa2e11528389f9 | [
"MIT"
] | null | null | null | # Adafruit PN532 NFC/RFID control library.
# Author: Tony DiCola
#
# The MIT License (MIT)
#
# Copyright (c) 2015-2018 Adafruit Industries
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""
``adafruit_pn532.i2c``
====================================================
This module will let you communicate with a PN532 RFID/NFC shield or breakout
using I2C.
* Author(s): Original Raspberry Pi code by Tony DiCola, CircuitPython by ladyada,
refactor by Carter Nelson
"""
__version__ = "0.0.0-auto.0"
__repo__ = "https://github.com/adafruit/Adafruit_CircuitPython_PN532.git"
import time
import adafruit_bus_device.i2c_device as i2c_device
from digitalio import Direction
from micropython import const
from adafruit_pn532.adafruit_pn532 import PN532, BusyError
_I2C_ADDRESS = const(0x24)
class PN532_I2C(PN532):
"""Driver for the PN532 connected over I2C."""
def __init__(self, i2c, *, irq=None, reset=None, req=None, debug=False):
"""Create an instance of the PN532 class using I2C. Note that PN532
uses clock stretching. Optional IRQ pin (not used),
reset pin and debugging output.
"""
self.debug = debug
self._req = req
self._i2c = i2c_device.I2CDevice(i2c, _I2C_ADDRESS)
super().__init__(debug=debug, irq=irq, reset=reset)
def _wakeup(self): # pylint: disable=no-self-use
"""Send any special commands/data to wake up PN532"""
if self._reset_pin:
self._reset_pin.value = True
time.sleep(0.01)
if self._req:
self._req.direction = Direction.OUTPUT
self._req.value = False
time.sleep(0.01)
self._req.value = True
time.sleep(0.01)
self.low_power = False
self.SAM_configuration() # Put the PN532 back in normal mode
def _wait_ready(self, timeout=1):
"""Poll PN532 if status byte is ready, up to `timeout` seconds"""
status = bytearray(1)
timestamp = time.monotonic()
while (time.monotonic() - timestamp) < timeout:
try:
with self._i2c:
self._i2c.readinto(status)
except OSError:
continue
if status == b"\x01":
return True # No longer busy
time.sleep(0.01) # lets ask again soon!
# Timed out!
return False
def _read_data(self, count):
"""Read a specified count of bytes from the PN532."""
# Build a read request frame.
frame = bytearray(count + 1)
with self._i2c as i2c:
i2c.readinto(frame, end=1) # read status byte!
if frame[0] != 0x01: # not ready
raise BusyError
i2c.readinto(frame) # ok get the data, plus statusbyte
if self.debug:
print("Reading: ", [hex(i) for i in frame[1:]])
return frame[1:] # don't return the status byte
def _write_data(self, framebytes):
"""Write a specified count of bytes to the PN532"""
with self._i2c as i2c:
i2c.write(framebytes)
| 37.761468 | 81 | 0.649417 | # Adafruit PN532 NFC/RFID control library.
# Author: Tony DiCola
#
# The MIT License (MIT)
#
# Copyright (c) 2015-2018 Adafruit Industries
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""
``adafruit_pn532.i2c``
====================================================
This module will let you communicate with a PN532 RFID/NFC shield or breakout
using I2C.
* Author(s): Original Raspberry Pi code by Tony DiCola, CircuitPython by ladyada,
refactor by Carter Nelson
"""
__version__ = "0.0.0-auto.0"
__repo__ = "https://github.com/adafruit/Adafruit_CircuitPython_PN532.git"
import time
import adafruit_bus_device.i2c_device as i2c_device
from digitalio import Direction
from micropython import const
from adafruit_pn532.adafruit_pn532 import PN532, BusyError
_I2C_ADDRESS = const(0x24)
class PN532_I2C(PN532):
"""Driver for the PN532 connected over I2C."""
def __init__(self, i2c, *, irq=None, reset=None, req=None, debug=False):
"""Create an instance of the PN532 class using I2C. Note that PN532
uses clock stretching. Optional IRQ pin (not used),
reset pin and debugging output.
"""
self.debug = debug
self._req = req
self._i2c = i2c_device.I2CDevice(i2c, _I2C_ADDRESS)
super().__init__(debug=debug, irq=irq, reset=reset)
def _wakeup(self): # pylint: disable=no-self-use
"""Send any special commands/data to wake up PN532"""
if self._reset_pin:
self._reset_pin.value = True
time.sleep(0.01)
if self._req:
self._req.direction = Direction.OUTPUT
self._req.value = False
time.sleep(0.01)
self._req.value = True
time.sleep(0.01)
self.low_power = False
self.SAM_configuration() # Put the PN532 back in normal mode
def _wait_ready(self, timeout=1):
"""Poll PN532 if status byte is ready, up to `timeout` seconds"""
status = bytearray(1)
timestamp = time.monotonic()
while (time.monotonic() - timestamp) < timeout:
try:
with self._i2c:
self._i2c.readinto(status)
except OSError:
continue
if status == b"\x01":
return True # No longer busy
time.sleep(0.01) # lets ask again soon!
# Timed out!
return False
def _read_data(self, count):
"""Read a specified count of bytes from the PN532."""
# Build a read request frame.
frame = bytearray(count + 1)
with self._i2c as i2c:
i2c.readinto(frame, end=1) # read status byte!
if frame[0] != 0x01: # not ready
raise BusyError
i2c.readinto(frame) # ok get the data, plus statusbyte
if self.debug:
print("Reading: ", [hex(i) for i in frame[1:]])
return frame[1:] # don't return the status byte
def _write_data(self, framebytes):
"""Write a specified count of bytes to the PN532"""
with self._i2c as i2c:
i2c.write(framebytes)
| 0 | 0 | 0 |
606edc3070bd5e542a9a768c731f9e95ef340f46 | 4,670 | py | Python | script.py | AleksMVP/mitm-proxy | 89c1ee40b78aecb0621c28515906ba635c98c7d8 | [
"MIT"
] | 2 | 2021-06-06T14:06:19.000Z | 2021-11-30T19:12:38.000Z | script.py | AleksMVP/myproxy | 89c1ee40b78aecb0621c28515906ba635c98c7d8 | [
"MIT"
] | 1 | 2021-12-11T18:30:39.000Z | 2022-02-27T15:39:34.000Z | script.py | AleksMVP/myproxy | 89c1ee40b78aecb0621c28515906ba635c98c7d8 | [
"MIT"
] | 2 | 2021-06-06T15:21:52.000Z | 2021-08-12T10:15:10.000Z | from http.server import BaseHTTPRequestHandler
import http
import argparse
from urllib.parse import urlparse
from urllib.parse import urlencode
from urllib.parse import parse_qs
from urllib.parse import unquote
import socket, ssl, pprint
from io import BytesIO
XSS = "'\"><img src=\"\" onerror=alert(\"\")>"
FILE_PATH = "/Users/aleks/Desktop/tp-thirdsem/myproxy/requests/POSTauth.mail.rufe133c21211f65cb1240f2354a24b4eac6be149529c92188d25ee97e149666db"
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Search vulnarabilities")
parser.add_argument("-f", "--filepath", metavar="", required=True, type=argparse.FileType('r'), help="path to file with request")
parser.add_argument("-b", "--body", action="store_true", help="flag to check body params")
parser.add_argument("-q", "--query", action="store_true", help="flag to check query params")
args = parser.parse_args()
request_str = "" # we don't really need this because it's python but ...
request_lines = args.filepath.readlines()
request_str = "".join(request_lines)
searcher = VulnerabilitySearcher(request_str)
if args.query:
searcher.check_query_params()
if args.body:
searcher.check_body_params()
| 34.850746 | 144 | 0.631906 | from http.server import BaseHTTPRequestHandler
import http
import argparse
from urllib.parse import urlparse
from urllib.parse import urlencode
from urllib.parse import parse_qs
from urllib.parse import unquote
import socket, ssl, pprint
from io import BytesIO
XSS = "'\"><img src=\"\" onerror=alert(\"\")>"
FILE_PATH = "/Users/aleks/Desktop/tp-thirdsem/myproxy/requests/POSTauth.mail.rufe133c21211f65cb1240f2354a24b4eac6be149529c92188d25ee97e149666db"
def make_https_request(host, data):
port = 443
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.settimeout(10)
ssl_sock = ssl.wrap_socket(s)
ssl_sock.connect((host, port))
ssl_sock.sendall(data.encode())
data = ssl_sock.recv(100000024)
return data.decode()
def make_http_request(host, data):
port = 80
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
ssl_sock = s
ssl_sock.connect((host, port))
ssl_sock.sendall(data.encode())
data = ssl_sock.recv(1000024)
return data.decode()
def prepare_query(query_array):
for key in query_array.keys():
query_array[key] = query_array[key][0]
def generate_xss_dict(query_array):
for key in query_array.keys():
old_value = query_array[key]
query_array[key] = XSS
yield dict(query_array)
query_array[key] = old_value
class HTTPRequest(BaseHTTPRequestHandler):
def __init__(self, request_text):
self.rfile = BytesIO(request_text)
self.raw_requestline = self.rfile.readline()
self.error_code = self.error_message = None
self.parse_request()
def send_error(self, code, message):
self.error_code = code
self.error_message = message
class VulnerabilitySearcher:
def __init__(self, request):
self.request = request
def check_query_params(self):
request = HTTPRequest(self.request.encode())
host = request.headers["Host"]
parsed_url = urlparse(request.path)
parsed_query = parse_qs(parsed_url.query)
if parsed_query:
prepare_query(parsed_query)
for i in generate_xss_dict(parsed_query):
print(f"New query params: {i}")
query_string = urlencode(i)
new_request = self.request.replace(parsed_url.query, unquote(query_string))
response = make_https_request(host, new_request)
if response.find(XSS) != -1:
print(f"Found: {i}")
def check_body_params(self):
request = HTTPRequest(self.request.encode())
host = request.headers["Host"]
if "Content-Type" in request.headers.keys() and \
request.headers["Content-Type"] == "application/x-www-form-urlencoded":
pattern = "\n\n"
pos = request_str.find(pattern)
request_body = request_str[pos+len(pattern):]
parsed_body_parameters = parse_qs(request_body)
prepare_query(parsed_body_parameters)
if parsed_body_parameters:
for i in generate_xss_dict(parsed_body_parameters):
print(f"New body params: {i}")
query_string = urlencode(i)
old_content_length = f"Content-Length: {request.headers['Content-Length']}"
new_content_length = f"Content-Length: {len(unquote(query_string))}"
new_request = self.request.replace(
request_body,
unquote(query_string)
).replace(
old_content_length,
new_content_length
)
response = make_https_request(host, new_request)
if response.find(XSS) != -1:
print(f"Found: {i}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Search vulnarabilities")
parser.add_argument("-f", "--filepath", metavar="", required=True, type=argparse.FileType('r'), help="path to file with request")
parser.add_argument("-b", "--body", action="store_true", help="flag to check body params")
parser.add_argument("-q", "--query", action="store_true", help="flag to check query params")
args = parser.parse_args()
request_str = "" # we don't really need this because it's python but ...
request_lines = args.filepath.readlines()
request_str = "".join(request_lines)
searcher = VulnerabilitySearcher(request_str)
if args.query:
searcher.check_query_params()
if args.body:
searcher.check_body_params()
| 3,119 | 28 | 271 |
b1892cce88651cc532dbfc88347950e86e324f77 | 8,579 | py | Python | cvm/tests/test_tensorflow.py | CortexFoundation/tvm-cvm | d8941dc60a51dd27a6d2accc1eff2eced3b3640d | [
"Apache-2.0"
] | 6 | 2019-07-04T09:42:53.000Z | 2021-12-28T13:19:48.000Z | cvm/tests/test_tensorflow.py | CortexFoundation/tvm-cvm | d8941dc60a51dd27a6d2accc1eff2eced3b3640d | [
"Apache-2.0"
] | 4 | 2019-06-27T08:05:18.000Z | 2021-09-09T18:59:11.000Z | cvm/tests/test_tensorflow.py | CortexFoundation/tvm-cvm | d8941dc60a51dd27a6d2accc1eff2eced3b3640d | [
"Apache-2.0"
] | null | null | null | import tensorflow as tf
import utils
from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model
# from keras import backend as K
# from keras.models import load_model
# from tensorflow.python.keras import backend as K
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
"""
Freezes the state of a session into a pruned computation graph.
Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
pruned so subgraphs that are not necessary to compute the requested
outputs are removed.
@param session The TensorFlow session to be frozen.
@param keep_var_names A list of variable names that should not be frozen,
or None to freeze all the variables in the graph.
@param output_names Names of the relevant graph outputs.
@param clear_devices Remove the device directives from the graph for better portability.
@return The frozen graph definition.
"""
from tensorflow.python.framework.graph_util import convert_variables_to_constants
graph = session.graph
with graph.as_default():
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
# Graph -> GraphDef ProtoBuf
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
import os
from tvm.relay.frontend import tensorflow_parser as tp
from official.vision.image_classification import resnet_model as resm
from tensorflow.python.framework.graph_util import convert_variables_to_constants
import keras_applications as kapp
import keras
from tensorflow_parser import TFParser
from tensorflow.python.framework import dtypes
import mxnet as mx
if __name__ == '__main__':
utils.log_init()
# net = keras.applications.resnet.ResNet50(weights='imagenet')
# net = tf.keras.applications.ResNet50(weights='imagenet')
# net = tf.keras.applications.InceptionV3(weights='imagenet')
# net = keras.applications.InceptionV3(weights='imagenet')
net = tf.keras.applications.MobileNet(weights='imagenet')
# dump_model(net, "/data/tfmodels/resnet50_v1_new")
# load_imagenet()
test_tf_parser()
| 38.819005 | 111 | 0.659751 | import tensorflow as tf
import utils
from tensorflow.keras import backend as K
from tensorflow.keras.models import load_model
# from keras import backend as K
# from keras.models import load_model
# from tensorflow.python.keras import backend as K
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
"""
Freezes the state of a session into a pruned computation graph.
Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
pruned so subgraphs that are not necessary to compute the requested
outputs are removed.
@param session The TensorFlow session to be frozen.
@param keep_var_names A list of variable names that should not be frozen,
or None to freeze all the variables in the graph.
@param output_names Names of the relevant graph outputs.
@param clear_devices Remove the device directives from the graph for better portability.
@return The frozen graph definition.
"""
from tensorflow.python.framework.graph_util import convert_variables_to_constants
graph = session.graph
with graph.as_default():
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
# Graph -> GraphDef ProtoBuf
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
import os
from tvm.relay.frontend import tensorflow_parser as tp
from official.vision.image_classification import resnet_model as resm
from tensorflow.python.framework.graph_util import convert_variables_to_constants
def dump_resnet50_v1():
root = "/tmp/tfmodels"
model = resm.resnet50(1000)
# model.compile(optimizer=tf.keras.optimizers.Adam(),
# loss=tf.keras.losses.sparse_categorical_crossentropy,
# metrics=['accuracy'])
os.makedirs(root, exist_ok=True)
model.save(os.path.join(root, "keras.h5"))
# tf.contrib.saved_model.save_keras_model(model, root,
# serving_only=False)
K.set_learning_phase(0)
model = load_model(os.path.join(root, "keras.h5"))
print("DDDDDDD", model.outputs)
frozen_graph = freeze_session(K.get_session(),
output_names=[out.op.name for out in model.outputs])
# with K.get_session() as sess:
# frozen_graph = convert_variables_to_constants(sess, sess.graph_def,
# [x.op.name for x in model.outputs])
tf.train.write_graph(frozen_graph, root, "resnet50_v1.pb", as_text=False)
def dump_pretrain_resnet50_v1():
root = "/tmp/tf/resnet50_v1"
os.makedirs(root, exist_ok=True)
# resnet50_v1 = tf.keras.applications.ResNet50(weights=None)
resnet50_v1 = tf.keras.applications.ResNet50(weights='imagenet')
resnet50_v1.save(os.path.join(root, "model.h5"))
K.set_learning_phase(0)
model = load_model(os.path.join(root, "model.h5"))
print ("ResNet50_V1 Model: ", model.outputs)
frozen_graph = freeze_session(K.get_session(),
output_names=[out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph, root, "model.pb", as_text=False)
import keras_applications as kapp
def preprocess_image(image):
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize_images(image, [224, 224])
# image /= 255.0 # normalize to [0,1] range
# image = kapp.resnet.preprocess_input(image)
image = tf.keras.applications.resnet50.preprocess_input(image)
print (image.numpy().min(), image.numpy().max())
return image
def load_and_preprocess_image(path):
image = tf.read_file(path)
return preprocess_image(image)
def load_and_preprocess_from_path_label(path, label):
return load_and_preprocess_image(path), label
def load_imagenet():
data_root = os.path.expanduser("~/.mxnet/datasets/imagenet/val")
print (data_root)
import random
import pathlib
data_root = pathlib.Path(data_root)
all_image_paths = list(data_root.glob('*/*'))
all_image_paths = [str(path) for path in all_image_paths]
image_count = len(all_image_paths)
print (image_count)
print (all_image_paths[:10])
label_names = sorted(item.name for item in data_root.glob('*/') if item.is_dir())
print (label_names[-10:])
label_to_index = dict((name, index) for index,name in enumerate(label_names))
all_image_labels = [label_to_index[pathlib.Path(path).parent.name]
for path in all_image_paths]
print (all_image_labels[:10])
ds = tf.data.Dataset.from_tensor_slices((all_image_paths, all_image_labels))
image_label_ds = ds.map(load_and_preprocess_from_path_label)
print (image_label_ds)
ds = image_label_ds.apply(
tf.data.experimental.shuffle_and_repeat(buffer_size=image_count))
ds = ds.batch(16)
# ds = ds.prefetch(buffer_size=AUTOTUNE)
print (ds)
import keras
def dump_model(net, root="/tmp/tfmodels/"):
os.makedirs(root, exist_ok=True)
# resnet50_v1 = tf.keras.applications.ResNet50(weights=None)
net.trainable = False
net.save(os.path.join(root, "model.h5"))
K.set_learning_phase(0)
model = load_model(os.path.join(root, "model.h5"))
print ("Model Output: ", model.outputs)
frozen_graph = freeze_session(K.get_session(),
output_names=[out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph, root, "model.pb", as_text=False)
from tensorflow_parser import TFParser
from tensorflow.python.framework import dtypes
import mxnet as mx
def test_tf_parser():
def _tf_shape_to_list(shp):
return [1 if d.size < 0 else d.size for d in shp.dim]
def _get_attr(buf):
fields = ["s", "i", "f", "b", "type", "shape", "tensor", "func"]
x = buf
ret = []
# Treat an empty oneof value as an empty list.
if not x.WhichOneof("value"):
return ret
if x.HasField("list"):
for f in fields:
if getattr(x.list, f):
if f == "type":
ret += [dtypes.as_dtype(x) for x in list(getattr(x.list, f))]
else:
ret += list(getattr(x.list, f))
else:
for f in fields:
if x.HasField(f):
if f == "type":
ret = dtypes.as_dtype(getattr(x, f))
else:
ret = getattr(x, f)
return ret
def _parse_attr(attr_proto):
"""Convert a list of AttributeProto to a dict, with names as keys."""
attrs = {}
for key, value in attr_proto.items():
attrs[key] = _get_attr(value)
print (key, value, "---", attrs[key])
return attrs
model_path = "/data/tfmodels/resnet50_v1_new/model.pb"
parser = TFParser(model_path)
graph = parser.parse()
nodes = {}
node_map = {}
input_shapes = {}
tf_ops = set()
for i, node in enumerate(graph.node):
node_map[node.name] = node.op
if node.op == 'Const':
val = node.attr['value'].tensor
input_shapes[node.name] = _tf_shape_to_list(val.tensor_shape)
# print (node.name, node.op, input_shapes[node.name])
elif node.op == 'Placeholder' or node.op == 'PlaceholderWithDefault':
input_shapes[node.name] = _tf_shape_to_list(node.attr['shape'].shape)
print (node.name, node.op, input_shapes[node.name])
nodes[node.name] = [mx.sym.var(node.name, shape=input_shapes[node.name])]
else:
# print (node.op)
tf_ops.add(node.op)
print (tf_ops)
for node in graph.node:
if __name__ == '__main__':
utils.log_init()
# net = keras.applications.resnet.ResNet50(weights='imagenet')
# net = tf.keras.applications.ResNet50(weights='imagenet')
# net = tf.keras.applications.InceptionV3(weights='imagenet')
# net = keras.applications.InceptionV3(weights='imagenet')
net = tf.keras.applications.MobileNet(weights='imagenet')
# dump_model(net, "/data/tfmodels/resnet50_v1_new")
# load_imagenet()
test_tf_parser()
| 5,730 | 0 | 180 |
de000d216bcbdb7cd4f3bca92009dd2bf900cfcc | 4,372 | py | Python | guitarfan/controlers/admin/data.py | timgates42/GuitarFan | 1a6d6bcc7708cbb214648e7f5657728f6c27c48a | [
"MIT"
] | 48 | 2015-02-02T02:25:07.000Z | 2022-03-11T12:39:39.000Z | guitarfan/controlers/admin/data.py | timgates42/GuitarFan | 1a6d6bcc7708cbb214648e7f5657728f6c27c48a | [
"MIT"
] | 2 | 2015-09-13T14:00:41.000Z | 2021-08-04T16:28:25.000Z | guitarfan/controlers/admin/data.py | timgates42/GuitarFan | 1a6d6bcc7708cbb214648e7f5657728f6c27c48a | [
"MIT"
] | 16 | 2015-01-09T08:15:13.000Z | 2020-06-20T11:07:49.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import shutil
from uuid import uuid4
from flask import render_template, request, current_app, redirect, url_for, flash, Blueprint, jsonify
from flask.ext.login import login_required
from sqlalchemy import exists
from guitarfan.extensions.flasksqlalchemy import db
from guitarfan.utilities import oshelper
from guitarfan.utilities import validator
from guitarfan.models import *
from forms.tab import *
bp_admin_data = Blueprint('bp_admin_data', __name__, template_folder="../../templates/admin/tabs")
@bp_admin_data.route('/admin/data/import', methods=['GET', 'POST'])
@login_required | 40.481481 | 127 | 0.527219 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import shutil
from uuid import uuid4
from flask import render_template, request, current_app, redirect, url_for, flash, Blueprint, jsonify
from flask.ext.login import login_required
from sqlalchemy import exists
from guitarfan.extensions.flasksqlalchemy import db
from guitarfan.utilities import oshelper
from guitarfan.utilities import validator
from guitarfan.models import *
from forms.tab import *
bp_admin_data = Blueprint('bp_admin_data', __name__, template_folder="../../templates/admin/tabs")
@bp_admin_data.route('/admin/data/import', methods=['GET', 'POST'])
@login_required
def data_import():
if request.method == 'GET':
return render_template('data_import.html')
elif request.method == 'POST':
root_path = request.form['path'] if 'path' in request.form else ''
result_info = {
'artists': 0,
'tabs': 0,
'errors': []
}
# check path
if root_path == '' or not os.path.isdir(root_path):
return jsonify(result='failed', msg='invalid path')
# valid folder names are '0-9', 'other', 'a', 'b', 'c'...'z'
valid_letter = map(chr, range(97, 123)) + ['0-9', 'other']
### Traverse first level folders - letters
for letter_dir_name in os.listdir(root_path):
letter_dir_path = os.path.join(root_path, letter_dir_name)
if not os.path.isdir(letter_dir_path) or not letter_dir_name.lower() in valid_letter:
continue
### Traverse second level folders - artists
for artist_dir_name in os.listdir(letter_dir_path):
artist_dir_path = os.path.join(letter_dir_path, artist_dir_name)
if not os.path.isdir(artist_dir_path):
continue
# create artist if not exist or just fetch it
artist = Artist.query.filter_by(name=artist_dir_name).first()
if artist is None:
artist = Artist(str(uuid4()), artist_dir_name, letter_dir_name, '', 1, 1)
db.session.add(artist)
result_info['artists'] += 1
## Traverse third level folders - tabs
for tab_dir_name in os.listdir(artist_dir_path):
tab_dir_path = os.path.join(artist_dir_path, tab_dir_name)
if not os.path.isdir(tab_dir_path):
continue
# import tab if not exists
if not db.session.query(exists().where(Tab.title == tab_dir_name and Tab.artist_id == artist.id)).scalar():
tab = Tab(str(uuid4()), tab_dir_name, 1, artist.id, 1, 1, '', None)
db.session.add(tab)
result_info['tabs'] += 1
### Traverse imgs files under tab folder
for file_name in os.listdir(tab_dir_path):
file_path = os.path.join(tab_dir_path, file_name)
if not os.path.isfile(file_path):
continue
if not oshelper.get_extension(file_name) in current_app.config['TAB_FILE_ALLOWED_EXTENSIONS']:
continue
try:
dest_path = os.path.join(oshelper.get_tabfile_upload_abspath(), tab.id)
if not os.path.isdir(dest_path):
os.mkdir(dest_path)
shutil.copy(file_path, dest_path)
tabfile = TabFile(str(uuid4()), tab.id, os.path.join(tab.id, file_name))
db.session.add(tabfile)
except:
e = sys.exc_info()[0]
result_info['errors'].append({
'artist': artist_dir_name,
'tab': tab_dir_name,
'file': file_name,
'error': e
})
continue
db.session.commit()
return jsonify(result='success', msg=result_info) | 3,692 | 0 | 22 |
1a6635e183b5e8541c98be0db7c6d9ba49118650 | 5,655 | py | Python | tests/test_rest_of_website.py | total-impact/impactstory-tester | 7a1ba294aca1ccb97d03ad5abb48505790926d27 | [
"MIT"
] | 1 | 2015-02-19T15:17:56.000Z | 2015-02-19T15:17:56.000Z | tests/test_rest_of_website.py | ourresearch/impactstory-tester | 7a1ba294aca1ccb97d03ad5abb48505790926d27 | [
"MIT"
] | null | null | null | tests/test_rest_of_website.py | ourresearch/impactstory-tester | 7a1ba294aca1ccb97d03ad5abb48505790926d27 | [
"MIT"
] | 1 | 2015-01-10T13:50:12.000Z | 2015-01-10T13:50:12.000Z | import os, requests
from selenium_test_case import SeleniumTestCase, slow, online, wd, host
import tests
from tests.pages import faq_page, about_page
from nose.tools import assert_equals, raises
| 65 | 1,876 | 0.681874 | import os, requests
from selenium_test_case import SeleniumTestCase, slow, online, wd, host
import tests
from tests.pages import faq_page, about_page
from nose.tools import assert_equals, raises
class TestFAQ(SeleniumTestCase):
def setUp(self):
self.page = faq_page.FaqPage(self.wd, self.host)
def test_title(self):
self.page.get()
assert_equals(self.page.title, "FAQ")
def test_which_products(self):
self.page.get()
assert "The number of readers who have added the article to their libraries" in self.page.html
def test_tos_anchor_link(self):
self.wd.get(self.page.url + "#tos")
self.page.wait_till_loaded()
assert_equals(self.page.title, "FAQ")
@slow
@online
def test_all_links(self):
self.page.get()
print self.page.links
#expected = [u'http://localhost:5000/faq#tos', u'http://www.wikipedia.org/', u'http://feedback.impactstory.org/', u'http://citedin.org/', u'http://www.crossref.org/', u'http://sciencecard.org/', u'http://www.delicious.com/', u'http://article-level-metrics.plos.org/', u'http://youtube.com/', u'http://github.com/', u'http://altmetric.com/', u'http://www.mendeley.com/groups/586171/altmetrics/papers/', u'http://blog.impactstory.org/2012/03/01/18535014681/', u'http://vimeo.com/', u'http://www.citeulike.org/', u'http://www.mendeley.com/', u'http://blog.impactstory.org/', u'http://www.topsy.com/', u'http://www.slideshare.net/', u'http://sloan.org/', u'http://altmetrics.org/manifesto/', u'http://gradschool.unc.edu/programs/royster', u'http://www.scienceseeker.org/', u'http://creativecommons.org/licenses/by/2.0/', u'http://beyond-impact.org/', u'http://figshare.com/', u'http://twitter.com/#!/ImpactStory', u'http://readermeter.org/', u'http://www.altmetric.com/', u'http://localhost:5000/#cool', u'http://localhost:5000/#limitations', u'http://localhost:5000/faq', u'https://www.zotero.org/groups/impact_factor_problems/items', u'http://pubmed.gov/', u'http://localhost:5000/about', u'https://github.com/mhahnel/Total-Impact/contributors', u'http://www.soros.org/', u'http://arxiv.org/', u'http://nsf.gov/', u'https://github.com/total-impact', u'http://www.info.sciverse.com/scopus/about', u'http://localhost:5000/#meaning', u'http://www.plumanalytics.com/', u'http://twitter.com/#!/ImpactStory_now', u'http://eprints.rclis.org/8605/', u'http://localhost:5000/settings/profile', u'http://asis.org/Bulletin/Apr-13/AprMay13_Piwowar_Priem.html', u'http://dataone.org/', u'http://localhost:5000/signup/name', u'http://impactstory.org/', u'http://localhost:5000/', u'http://wordpress.com/', u'http://www.datadryad.org/', u'http://twitter.com/', u'http://www.plos.org/']
#assert_equals(self.page.links, expected)
for url in self.page.links:
print url
r = requests.get(url, verify=False) # don't check SSL certificates
assert_equals(r.status_code, 200)
class TestAbout(SeleniumTestCase):
def setUp(self):
SeleniumTestCase.setUp(self)
self.page = about_page.AboutPage(self.wd, self.host)
def test_title(self):
self.page.get()
assert_equals(self.page.title, "About")
def test_team_anchor_link(self):
self.wd.get(self.page.url + "#team")
assert_equals(self.page.title, "About")
@slow
@online
def test_all_links(self):
self.page.get()
print self.page.links
#expected = [u'http://www.zotero.org/', u'http://localhost:5000/faq#tos', u'http://feedback.impactstory.org/', u'http://beyond-impact.org/', u'http://twitter.com/#!/ImpactStory', u'http://blog.impactstory.org/2012/03/01/18535014681/', u'http://blog.impactstory.org/', u'http://jasonpriem.org/blog', u'http://blog.impactstory.org/2013/06/17/sloan/', u'http://sloan.org/', u'http://altmetrics.org/manifesto/', u'http://twitter.com/researchremix', u'http://nsf.gov/', u'http://creativecommons.org/licenses/by/2.0/', u'http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000308', u'http://localhost:5000/about', u'http://blog.impactstory.org/2012/03/29/20131290500/', u'http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018657', u'https://twitter.com/jasonpriem/status/25844968813', u'http://jasonpriem.org/cv/#invited', u'http://blog.impactstory.org/2012/06/08/24638498595/', u'http://localhost:5000/faq', u'http://www.soros.org/', u'http://en.wikipedia.org/wiki/Radical_transparency', u'http://researchremix.wordpress.com/2010/10/12/journalpolicyproposal', u'http://localhost:5000/settings/profile', u'https://github.com/total-impact', u'http://localhost:5000/altmetrics.org/altmetrics12', u'http://twitter.com/#!/ImpactStory_now', u'http://researchremix.wordpress.com/', u'http://jasonpriem.org/cv/#refereed', u'http://asis.org/Bulletin/Apr-13/AprMay13_Piwowar_Priem.html', u'http://feedvis.com/', u'http://localhost:5000/signup/name', u'http://www.slideshare.net/hpiwowar', u'http://localhost:5000/', u'https://twitter.com/#!/jasonpriem', u'https://peerj.com/preprints/1/']
#assert_equals(self.page.links, expected)
for url in self.page.links:
print url
r = requests.get(url, verify=False) # don't check SSL certificates
assert_equals(r.status_code, 200)
class TestFeedback(SeleniumTestCase):
def setUp(self):
SeleniumTestCase.setUp(self)
self.url = "http://feedback.impactstory.org"
def test_title(self):
self.wd.get(self.url)
title = self.wd.find_element_by_tag_name("h1").text
expected = "General"
assert_equals(title, expected)
| 4,976 | 335 | 132 |
d34a269d477cde58129f95111e3bfe221e6fa472 | 2,275 | py | Python | USV_V1/applications/sailing_robot_control/src/sailing_robot/src/sailing_robot/heading_planning_station_keeping.py | supcon-nzic/- | 52c97759f97f3222ca5465a5745842cfeb7f26a1 | [
"Apache-2.0"
] | 2 | 2020-07-16T03:15:10.000Z | 2020-09-02T09:06:36.000Z | USV_V1/applications/sailing_robot_control/src/sailing_robot/src/sailing_robot/heading_planning_station_keeping.py | supcon-nzic/- | 52c97759f97f3222ca5465a5745842cfeb7f26a1 | [
"Apache-2.0"
] | null | null | null | USV_V1/applications/sailing_robot_control/src/sailing_robot/src/sailing_robot/heading_planning_station_keeping.py | supcon-nzic/- | 52c97759f97f3222ca5465a5745842cfeb7f26a1 | [
"Apache-2.0"
] | 1 | 2021-01-20T12:47:14.000Z | 2021-01-20T12:47:14.000Z | import heading_planning_laylines
import json
import make_ros_tasks
| 48.404255 | 115 | 0.64967 | import heading_planning_laylines
import json
import make_ros_tasks
class HeadingPlan(heading_planning_laylines.HeadingPlan):
def __init__(self, *args, **kwargs):
super(HeadingPlan, self).__init__(*args, **kwargs)
self.debug_topics.extend(
[('dbg_ball_position', 'String'), ('dbg_relative_position_list_len', 'Int16'), ('dbg_keeping', 'Bool'),
('dbg_keep_station_waypoint', 'String'), ])
self.station_keeping = None
self.taskdict = {
'target_radius': self.target_radius,
'tack_voting_radius': self.tack_voting_radius,
'tasks': [{'kind': 'keep_station_three_point'}, ]
}
def calculate_state_and_goal(self):
self.debug_pub('dbg_keeping', self.station_keeping is not None)
self.debug_pub('dbg_relative_position_list_len', len(self.nav.relative_position_list))
if len(self.nav.relative_position_list) > 0:
self.nav.calculate_ball_position()
if self.nav.ball_position.distance(self.waypoint) < 10:
self.debug_pub('dbg_ball_position', json.dumps(
[self.nav.ball_position.lat.decimal_degree, self.nav.ball_position.lon.decimal_degree]))
self.update_waypoint(self.nav.ball_position)
if self.station_keeping is not None:
self.station_keeping.update_waypoint(self.nav.ball_position)
if self.station_keeping is None:
dwp, hwp = self.nav.distance_and_heading(self.waypoint_xy)
if dwp < self.target_radius:
self.taskdict['tasks'][0].update({
'waypoint': (self.waypoint.lat.decimal_degree, self.waypoint.lon.decimal_degree)
})
self.station_keeping = \
make_ros_tasks.make_ros_tasks(self.taskdict, self.nav, self.name + '/station_keeping')[0]
self.station_keeping.debug_pub = self.debug_pub
return super(HeadingPlan, self).calculate_state_and_goal()
else:
return self.station_keeping.calculate_state_and_goal()
def check_end_condition(self):
"""Are we there yet?"""
return self.station_keeping is not None and self.station_keeping.check_end_condition()
| 1,932 | 252 | 23 |
c6524458859680d0d69f3723d59cb8ecc7c64fa7 | 205 | py | Python | src/reverseWords/rev.py | rajitbanerjee/leetcode | 720fcdd88d371e2d6592ceec8370a6760a77bb89 | [
"CC0-1.0"
] | null | null | null | src/reverseWords/rev.py | rajitbanerjee/leetcode | 720fcdd88d371e2d6592ceec8370a6760a77bb89 | [
"CC0-1.0"
] | null | null | null | src/reverseWords/rev.py | rajitbanerjee/leetcode | 720fcdd88d371e2d6592ceec8370a6760a77bb89 | [
"CC0-1.0"
] | 1 | 2021-04-28T18:17:55.000Z | 2021-04-28T18:17:55.000Z |
if __name__ == '__main__':
s = input("Input: ")
print(f"Output: {Solution().reverseWords(s)}")
| 22.777778 | 50 | 0.57561 | class Solution:
def reverseWords(self, s: str) -> str:
return " ".join(s.split()[::-1])
if __name__ == '__main__':
s = input("Input: ")
print(f"Output: {Solution().reverseWords(s)}")
| 58 | -6 | 48 |
4c691f66af872c1b99704980dafd3d5978718ed7 | 12,750 | py | Python | tests/components/tradfri/test_light.py | petewill/home-assistant | 5859dba4344f05fb8774aa1207e47ac28f627a67 | [
"Apache-2.0"
] | 3 | 2020-05-18T10:18:16.000Z | 2020-12-08T11:27:55.000Z | tests/components/tradfri/test_light.py | petewill/home-assistant | 5859dba4344f05fb8774aa1207e47ac28f627a67 | [
"Apache-2.0"
] | 19 | 2020-01-29T23:21:07.000Z | 2021-07-23T23:26:51.000Z | tests/components/tradfri/test_light.py | petewill/home-assistant | 5859dba4344f05fb8774aa1207e47ac28f627a67 | [
"Apache-2.0"
] | 6 | 2020-04-10T06:21:11.000Z | 2021-07-01T08:53:38.000Z | """Tradfri lights platform tests."""
from copy import deepcopy
from unittest.mock import Mock, MagicMock, patch, PropertyMock
import pytest
from pytradfri.device import Device
from pytradfri.device.light import Light
from pytradfri.device.light_control import LightControl
from homeassistant.components import tradfri
from tests.common import MockConfigEntry
DEFAULT_TEST_FEATURES = {
"can_set_dimmer": False,
"can_set_color": False,
"can_set_temp": False,
}
# [
# {bulb features},
# {turn_on arguments},
# {expected result}
# ]
TURN_ON_TEST_CASES = [
# Turn On
[{}, {}, {"state": "on"}],
# Brightness > 0
[{"can_set_dimmer": True}, {"brightness": 100}, {"state": "on", "brightness": 100}],
# Brightness == 1
[{"can_set_dimmer": True}, {"brightness": 1}, {"brightness": 1}],
# Brightness > 254
[{"can_set_dimmer": True}, {"brightness": 1000}, {"brightness": 254}],
# color_temp
[{"can_set_temp": True}, {"color_temp": 250}, {"color_temp": 250}],
# color_temp < 250
[{"can_set_temp": True}, {"color_temp": 1}, {"color_temp": 250}],
# color_temp > 454
[{"can_set_temp": True}, {"color_temp": 1000}, {"color_temp": 454}],
# hs color
[
{"can_set_color": True},
{"hs_color": [300, 100]},
{"state": "on", "hs_color": [300, 100]},
],
# ct + brightness
[
{"can_set_dimmer": True, "can_set_temp": True},
{"color_temp": 250, "brightness": 200},
{"state": "on", "color_temp": 250, "brightness": 200},
],
# ct + brightness (no temp support)
[
{"can_set_dimmer": True, "can_set_temp": False, "can_set_color": True},
{"color_temp": 250, "brightness": 200},
{"state": "on", "hs_color": [26.807, 34.869], "brightness": 200},
],
# ct + brightness (no temp or color support)
[
{"can_set_dimmer": True, "can_set_temp": False, "can_set_color": False},
{"color_temp": 250, "brightness": 200},
{"state": "on", "brightness": 200},
],
# hs + brightness
[
{"can_set_dimmer": True, "can_set_color": True},
{"hs_color": [300, 100], "brightness": 200},
{"state": "on", "hs_color": [300, 100], "brightness": 200},
],
]
# Result of transition is not tested, but data is passed to turn on service.
TRANSITION_CASES_FOR_TESTS = [None, 0, 1]
@pytest.fixture(autouse=True, scope="module")
def setup(request):
"""Set up patches for pytradfri methods."""
p_1 = patch(
"pytradfri.device.LightControl.raw",
new_callable=PropertyMock,
return_value=[{"mock": "mock"}],
)
p_2 = patch("pytradfri.device.LightControl.lights")
p_1.start()
p_2.start()
def teardown():
"""Remove patches for pytradfri methods."""
p_1.stop()
p_2.stop()
request.addfinalizer(teardown)
@pytest.fixture
def mock_gateway():
"""Mock a Tradfri gateway."""
def get_devices():
"""Return mock devices."""
return gateway.mock_devices
def get_groups():
"""Return mock groups."""
return gateway.mock_groups
gateway = Mock(
get_devices=get_devices,
get_groups=get_groups,
mock_devices=[],
mock_groups=[],
mock_responses=[],
)
return gateway
@pytest.fixture
def mock_api(mock_gateway):
"""Mock api."""
async def api(command):
"""Mock api function."""
# Store the data for "real" command objects.
if hasattr(command, "_data") and not isinstance(command, Mock):
mock_gateway.mock_responses.append(command._data)
return command
return api
async def generate_psk(self, code):
"""Mock psk."""
return "mock"
async def setup_gateway(hass, mock_gateway, mock_api):
"""Load the Tradfri platform with a mock gateway."""
entry = MockConfigEntry(
domain=tradfri.DOMAIN,
data={
"host": "mock-host",
"identity": "mock-identity",
"key": "mock-key",
"import_groups": True,
"gateway_id": "mock-gateway-id",
},
)
hass.data[tradfri.KEY_GATEWAY] = {entry.entry_id: mock_gateway}
hass.data[tradfri.KEY_API] = {entry.entry_id: mock_api}
await hass.config_entries.async_forward_entry_setup(entry, "light")
def mock_light(test_features={}, test_state={}, n=0):
"""Mock a tradfri light."""
mock_light_data = Mock(**test_state)
mock_light = Mock(
id="mock-light-id-{}".format(n),
reachable=True,
observe=Mock(),
device_info=MagicMock(),
)
mock_light.name = "tradfri_light_{}".format(n)
# Set supported features for the light.
features = {**DEFAULT_TEST_FEATURES, **test_features}
lc = LightControl(mock_light)
for k, v in features.items():
setattr(lc, k, v)
# Store the initial state.
setattr(lc, "lights", [mock_light_data])
mock_light.light_control = lc
return mock_light
async def test_light(hass, mock_gateway, mock_api):
"""Test that lights are correctly added."""
features = {"can_set_dimmer": True, "can_set_color": True, "can_set_temp": True}
state = {
"state": True,
"dimmer": 100,
"color_temp": 250,
"hsb_xy_color": (100, 100, 100, 100, 100),
}
mock_gateway.mock_devices.append(
mock_light(test_features=features, test_state=state)
)
await setup_gateway(hass, mock_gateway, mock_api)
lamp_1 = hass.states.get("light.tradfri_light_0")
assert lamp_1 is not None
assert lamp_1.state == "on"
assert lamp_1.attributes["brightness"] == 100
assert lamp_1.attributes["hs_color"] == (0.549, 0.153)
async def test_light_observed(hass, mock_gateway, mock_api):
"""Test that lights are correctly observed."""
light = mock_light()
mock_gateway.mock_devices.append(light)
await setup_gateway(hass, mock_gateway, mock_api)
assert len(light.observe.mock_calls) > 0
async def test_light_available(hass, mock_gateway, mock_api):
"""Test light available property."""
light = mock_light({"state": True}, n=1)
light.reachable = True
light2 = mock_light({"state": True}, n=2)
light2.reachable = False
mock_gateway.mock_devices.append(light)
mock_gateway.mock_devices.append(light2)
await setup_gateway(hass, mock_gateway, mock_api)
assert hass.states.get("light.tradfri_light_1").state == "on"
assert hass.states.get("light.tradfri_light_2").state == "unavailable"
# Combine TURN_ON_TEST_CASES and TRANSITION_CASES_FOR_TESTS
ALL_TURN_ON_TEST_CASES = [["test_features", "test_data", "expected_result", "id"], []]
idx = 1
for tc in TURN_ON_TEST_CASES:
for trans in TRANSITION_CASES_FOR_TESTS:
case = deepcopy(tc)
if trans is not None:
case[1]["transition"] = trans
case.append(idx)
idx = idx + 1
ALL_TURN_ON_TEST_CASES[1].append(case)
@pytest.mark.parametrize(*ALL_TURN_ON_TEST_CASES)
async def test_turn_on(
hass, mock_gateway, mock_api, test_features, test_data, expected_result, id
):
"""Test turning on a light."""
# Note pytradfri style, not hass. Values not really important.
initial_state = {
"state": False,
"dimmer": 0,
"color_temp": 250,
"hsb_xy_color": (100, 100, 100, 100, 100),
}
# Setup the gateway with a mock light.
light = mock_light(test_features=test_features, test_state=initial_state, n=id)
mock_gateway.mock_devices.append(light)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_on service call to change the light state.
await hass.services.async_call(
"light",
"turn_on",
{"entity_id": "light.tradfri_light_{}".format(id), **test_data},
blocking=True,
)
await hass.async_block_till_done()
# Check that the light is observed.
mock_func = light.observe
assert len(mock_func.mock_calls) > 0
_, callkwargs = mock_func.call_args
assert "callback" in callkwargs
# Callback function to refresh light state.
cb = callkwargs["callback"]
responses = mock_gateway.mock_responses
# State on command data.
data = {"3311": [{"5850": 1}]}
# Add data for all sent commands.
for r in responses:
data["3311"][0] = {**data["3311"][0], **r["3311"][0]}
# Use the callback function to update the light state.
dev = Device(data)
light_data = Light(dev, 0)
light.light_control.lights[0] = light_data
cb(light)
await hass.async_block_till_done()
# Check that the state is correct.
states = hass.states.get("light.tradfri_light_{}".format(id))
for k, v in expected_result.items():
if k == "state":
assert states.state == v
else:
# Allow some rounding error in color conversions.
assert states.attributes[k] == pytest.approx(v, abs=0.01)
async def test_turn_off(hass, mock_gateway, mock_api):
"""Test turning off a light."""
state = {"state": True, "dimmer": 100}
light = mock_light(test_state=state)
mock_gateway.mock_devices.append(light)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_off service call to change the light state.
await hass.services.async_call(
"light", "turn_off", {"entity_id": "light.tradfri_light_0"}, blocking=True
)
await hass.async_block_till_done()
# Check that the light is observed.
mock_func = light.observe
assert len(mock_func.mock_calls) > 0
_, callkwargs = mock_func.call_args
assert "callback" in callkwargs
# Callback function to refresh light state.
cb = callkwargs["callback"]
responses = mock_gateway.mock_responses
data = {"3311": [{}]}
# Add data for all sent commands.
for r in responses:
data["3311"][0] = {**data["3311"][0], **r["3311"][0]}
# Use the callback function to update the light state.
dev = Device(data)
light_data = Light(dev, 0)
light.light_control.lights[0] = light_data
cb(light)
await hass.async_block_till_done()
# Check that the state is correct.
states = hass.states.get("light.tradfri_light_0")
assert states.state == "off"
def mock_group(test_state={}, n=0):
"""Mock a Tradfri group."""
default_state = {"state": False, "dimmer": 0}
state = {**default_state, **test_state}
mock_group = Mock(member_ids=[], observe=Mock(), **state)
mock_group.name = "tradfri_group_{}".format(n)
return mock_group
async def test_group(hass, mock_gateway, mock_api):
"""Test that groups are correctly added."""
mock_gateway.mock_groups.append(mock_group())
state = {"state": True, "dimmer": 100}
mock_gateway.mock_groups.append(mock_group(state, 1))
await setup_gateway(hass, mock_gateway, mock_api)
group = hass.states.get("light.tradfri_group_0")
assert group is not None
assert group.state == "off"
group = hass.states.get("light.tradfri_group_1")
assert group is not None
assert group.state == "on"
assert group.attributes["brightness"] == 100
async def test_group_turn_on(hass, mock_gateway, mock_api):
"""Test turning on a group."""
group = mock_group()
group2 = mock_group(n=1)
group3 = mock_group(n=2)
mock_gateway.mock_groups.append(group)
mock_gateway.mock_groups.append(group2)
mock_gateway.mock_groups.append(group3)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_off service call to change the light state.
await hass.services.async_call(
"light", "turn_on", {"entity_id": "light.tradfri_group_0"}, blocking=True
)
await hass.services.async_call(
"light",
"turn_on",
{"entity_id": "light.tradfri_group_1", "brightness": 100},
blocking=True,
)
await hass.services.async_call(
"light",
"turn_on",
{"entity_id": "light.tradfri_group_2", "brightness": 100, "transition": 1},
blocking=True,
)
await hass.async_block_till_done()
group.set_state.assert_called_with(1)
group2.set_dimmer.assert_called_with(100)
group3.set_dimmer.assert_called_with(100, transition_time=10)
async def test_group_turn_off(hass, mock_gateway, mock_api):
"""Test turning off a group."""
group = mock_group({"state": True})
mock_gateway.mock_groups.append(group)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_off service call to change the light state.
await hass.services.async_call(
"light", "turn_off", {"entity_id": "light.tradfri_group_0"}, blocking=True
)
await hass.async_block_till_done()
group.set_state.assert_called_with(0)
| 30.871671 | 88 | 0.645569 | """Tradfri lights platform tests."""
from copy import deepcopy
from unittest.mock import Mock, MagicMock, patch, PropertyMock
import pytest
from pytradfri.device import Device
from pytradfri.device.light import Light
from pytradfri.device.light_control import LightControl
from homeassistant.components import tradfri
from tests.common import MockConfigEntry
DEFAULT_TEST_FEATURES = {
"can_set_dimmer": False,
"can_set_color": False,
"can_set_temp": False,
}
# [
# {bulb features},
# {turn_on arguments},
# {expected result}
# ]
TURN_ON_TEST_CASES = [
# Turn On
[{}, {}, {"state": "on"}],
# Brightness > 0
[{"can_set_dimmer": True}, {"brightness": 100}, {"state": "on", "brightness": 100}],
# Brightness == 1
[{"can_set_dimmer": True}, {"brightness": 1}, {"brightness": 1}],
# Brightness > 254
[{"can_set_dimmer": True}, {"brightness": 1000}, {"brightness": 254}],
# color_temp
[{"can_set_temp": True}, {"color_temp": 250}, {"color_temp": 250}],
# color_temp < 250
[{"can_set_temp": True}, {"color_temp": 1}, {"color_temp": 250}],
# color_temp > 454
[{"can_set_temp": True}, {"color_temp": 1000}, {"color_temp": 454}],
# hs color
[
{"can_set_color": True},
{"hs_color": [300, 100]},
{"state": "on", "hs_color": [300, 100]},
],
# ct + brightness
[
{"can_set_dimmer": True, "can_set_temp": True},
{"color_temp": 250, "brightness": 200},
{"state": "on", "color_temp": 250, "brightness": 200},
],
# ct + brightness (no temp support)
[
{"can_set_dimmer": True, "can_set_temp": False, "can_set_color": True},
{"color_temp": 250, "brightness": 200},
{"state": "on", "hs_color": [26.807, 34.869], "brightness": 200},
],
# ct + brightness (no temp or color support)
[
{"can_set_dimmer": True, "can_set_temp": False, "can_set_color": False},
{"color_temp": 250, "brightness": 200},
{"state": "on", "brightness": 200},
],
# hs + brightness
[
{"can_set_dimmer": True, "can_set_color": True},
{"hs_color": [300, 100], "brightness": 200},
{"state": "on", "hs_color": [300, 100], "brightness": 200},
],
]
# Result of transition is not tested, but data is passed to turn on service.
TRANSITION_CASES_FOR_TESTS = [None, 0, 1]
@pytest.fixture(autouse=True, scope="module")
def setup(request):
"""Set up patches for pytradfri methods."""
p_1 = patch(
"pytradfri.device.LightControl.raw",
new_callable=PropertyMock,
return_value=[{"mock": "mock"}],
)
p_2 = patch("pytradfri.device.LightControl.lights")
p_1.start()
p_2.start()
def teardown():
"""Remove patches for pytradfri methods."""
p_1.stop()
p_2.stop()
request.addfinalizer(teardown)
@pytest.fixture
def mock_gateway():
"""Mock a Tradfri gateway."""
def get_devices():
"""Return mock devices."""
return gateway.mock_devices
def get_groups():
"""Return mock groups."""
return gateway.mock_groups
gateway = Mock(
get_devices=get_devices,
get_groups=get_groups,
mock_devices=[],
mock_groups=[],
mock_responses=[],
)
return gateway
@pytest.fixture
def mock_api(mock_gateway):
"""Mock api."""
async def api(command):
"""Mock api function."""
# Store the data for "real" command objects.
if hasattr(command, "_data") and not isinstance(command, Mock):
mock_gateway.mock_responses.append(command._data)
return command
return api
async def generate_psk(self, code):
"""Mock psk."""
return "mock"
async def setup_gateway(hass, mock_gateway, mock_api):
"""Load the Tradfri platform with a mock gateway."""
entry = MockConfigEntry(
domain=tradfri.DOMAIN,
data={
"host": "mock-host",
"identity": "mock-identity",
"key": "mock-key",
"import_groups": True,
"gateway_id": "mock-gateway-id",
},
)
hass.data[tradfri.KEY_GATEWAY] = {entry.entry_id: mock_gateway}
hass.data[tradfri.KEY_API] = {entry.entry_id: mock_api}
await hass.config_entries.async_forward_entry_setup(entry, "light")
def mock_light(test_features={}, test_state={}, n=0):
"""Mock a tradfri light."""
mock_light_data = Mock(**test_state)
mock_light = Mock(
id="mock-light-id-{}".format(n),
reachable=True,
observe=Mock(),
device_info=MagicMock(),
)
mock_light.name = "tradfri_light_{}".format(n)
# Set supported features for the light.
features = {**DEFAULT_TEST_FEATURES, **test_features}
lc = LightControl(mock_light)
for k, v in features.items():
setattr(lc, k, v)
# Store the initial state.
setattr(lc, "lights", [mock_light_data])
mock_light.light_control = lc
return mock_light
async def test_light(hass, mock_gateway, mock_api):
"""Test that lights are correctly added."""
features = {"can_set_dimmer": True, "can_set_color": True, "can_set_temp": True}
state = {
"state": True,
"dimmer": 100,
"color_temp": 250,
"hsb_xy_color": (100, 100, 100, 100, 100),
}
mock_gateway.mock_devices.append(
mock_light(test_features=features, test_state=state)
)
await setup_gateway(hass, mock_gateway, mock_api)
lamp_1 = hass.states.get("light.tradfri_light_0")
assert lamp_1 is not None
assert lamp_1.state == "on"
assert lamp_1.attributes["brightness"] == 100
assert lamp_1.attributes["hs_color"] == (0.549, 0.153)
async def test_light_observed(hass, mock_gateway, mock_api):
"""Test that lights are correctly observed."""
light = mock_light()
mock_gateway.mock_devices.append(light)
await setup_gateway(hass, mock_gateway, mock_api)
assert len(light.observe.mock_calls) > 0
async def test_light_available(hass, mock_gateway, mock_api):
"""Test light available property."""
light = mock_light({"state": True}, n=1)
light.reachable = True
light2 = mock_light({"state": True}, n=2)
light2.reachable = False
mock_gateway.mock_devices.append(light)
mock_gateway.mock_devices.append(light2)
await setup_gateway(hass, mock_gateway, mock_api)
assert hass.states.get("light.tradfri_light_1").state == "on"
assert hass.states.get("light.tradfri_light_2").state == "unavailable"
# Combine TURN_ON_TEST_CASES and TRANSITION_CASES_FOR_TESTS
ALL_TURN_ON_TEST_CASES = [["test_features", "test_data", "expected_result", "id"], []]
idx = 1
for tc in TURN_ON_TEST_CASES:
for trans in TRANSITION_CASES_FOR_TESTS:
case = deepcopy(tc)
if trans is not None:
case[1]["transition"] = trans
case.append(idx)
idx = idx + 1
ALL_TURN_ON_TEST_CASES[1].append(case)
@pytest.mark.parametrize(*ALL_TURN_ON_TEST_CASES)
async def test_turn_on(
hass, mock_gateway, mock_api, test_features, test_data, expected_result, id
):
"""Test turning on a light."""
# Note pytradfri style, not hass. Values not really important.
initial_state = {
"state": False,
"dimmer": 0,
"color_temp": 250,
"hsb_xy_color": (100, 100, 100, 100, 100),
}
# Setup the gateway with a mock light.
light = mock_light(test_features=test_features, test_state=initial_state, n=id)
mock_gateway.mock_devices.append(light)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_on service call to change the light state.
await hass.services.async_call(
"light",
"turn_on",
{"entity_id": "light.tradfri_light_{}".format(id), **test_data},
blocking=True,
)
await hass.async_block_till_done()
# Check that the light is observed.
mock_func = light.observe
assert len(mock_func.mock_calls) > 0
_, callkwargs = mock_func.call_args
assert "callback" in callkwargs
# Callback function to refresh light state.
cb = callkwargs["callback"]
responses = mock_gateway.mock_responses
# State on command data.
data = {"3311": [{"5850": 1}]}
# Add data for all sent commands.
for r in responses:
data["3311"][0] = {**data["3311"][0], **r["3311"][0]}
# Use the callback function to update the light state.
dev = Device(data)
light_data = Light(dev, 0)
light.light_control.lights[0] = light_data
cb(light)
await hass.async_block_till_done()
# Check that the state is correct.
states = hass.states.get("light.tradfri_light_{}".format(id))
for k, v in expected_result.items():
if k == "state":
assert states.state == v
else:
# Allow some rounding error in color conversions.
assert states.attributes[k] == pytest.approx(v, abs=0.01)
async def test_turn_off(hass, mock_gateway, mock_api):
"""Test turning off a light."""
state = {"state": True, "dimmer": 100}
light = mock_light(test_state=state)
mock_gateway.mock_devices.append(light)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_off service call to change the light state.
await hass.services.async_call(
"light", "turn_off", {"entity_id": "light.tradfri_light_0"}, blocking=True
)
await hass.async_block_till_done()
# Check that the light is observed.
mock_func = light.observe
assert len(mock_func.mock_calls) > 0
_, callkwargs = mock_func.call_args
assert "callback" in callkwargs
# Callback function to refresh light state.
cb = callkwargs["callback"]
responses = mock_gateway.mock_responses
data = {"3311": [{}]}
# Add data for all sent commands.
for r in responses:
data["3311"][0] = {**data["3311"][0], **r["3311"][0]}
# Use the callback function to update the light state.
dev = Device(data)
light_data = Light(dev, 0)
light.light_control.lights[0] = light_data
cb(light)
await hass.async_block_till_done()
# Check that the state is correct.
states = hass.states.get("light.tradfri_light_0")
assert states.state == "off"
def mock_group(test_state={}, n=0):
"""Mock a Tradfri group."""
default_state = {"state": False, "dimmer": 0}
state = {**default_state, **test_state}
mock_group = Mock(member_ids=[], observe=Mock(), **state)
mock_group.name = "tradfri_group_{}".format(n)
return mock_group
async def test_group(hass, mock_gateway, mock_api):
"""Test that groups are correctly added."""
mock_gateway.mock_groups.append(mock_group())
state = {"state": True, "dimmer": 100}
mock_gateway.mock_groups.append(mock_group(state, 1))
await setup_gateway(hass, mock_gateway, mock_api)
group = hass.states.get("light.tradfri_group_0")
assert group is not None
assert group.state == "off"
group = hass.states.get("light.tradfri_group_1")
assert group is not None
assert group.state == "on"
assert group.attributes["brightness"] == 100
async def test_group_turn_on(hass, mock_gateway, mock_api):
"""Test turning on a group."""
group = mock_group()
group2 = mock_group(n=1)
group3 = mock_group(n=2)
mock_gateway.mock_groups.append(group)
mock_gateway.mock_groups.append(group2)
mock_gateway.mock_groups.append(group3)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_off service call to change the light state.
await hass.services.async_call(
"light", "turn_on", {"entity_id": "light.tradfri_group_0"}, blocking=True
)
await hass.services.async_call(
"light",
"turn_on",
{"entity_id": "light.tradfri_group_1", "brightness": 100},
blocking=True,
)
await hass.services.async_call(
"light",
"turn_on",
{"entity_id": "light.tradfri_group_2", "brightness": 100, "transition": 1},
blocking=True,
)
await hass.async_block_till_done()
group.set_state.assert_called_with(1)
group2.set_dimmer.assert_called_with(100)
group3.set_dimmer.assert_called_with(100, transition_time=10)
async def test_group_turn_off(hass, mock_gateway, mock_api):
"""Test turning off a group."""
group = mock_group({"state": True})
mock_gateway.mock_groups.append(group)
await setup_gateway(hass, mock_gateway, mock_api)
# Use the turn_off service call to change the light state.
await hass.services.async_call(
"light", "turn_off", {"entity_id": "light.tradfri_group_0"}, blocking=True
)
await hass.async_block_till_done()
group.set_state.assert_called_with(0)
| 0 | 0 | 0 |
6b3c1d8a07e268a18b08211e3dee8a308ba8a690 | 144 | py | Python | main/context_processors.py | Aaron1011/texting_wall | c20b421652fbdaef927e9d206fc17d8f1f40ae46 | [
"MIT"
] | null | null | null | main/context_processors.py | Aaron1011/texting_wall | c20b421652fbdaef927e9d206fc17d8f1f40ae46 | [
"MIT"
] | null | null | null | main/context_processors.py | Aaron1011/texting_wall | c20b421652fbdaef927e9d206fc17d8f1f40ae46 | [
"MIT"
] | null | null | null | from texting_wall import settings as django_settings
| 28.8 | 65 | 0.826389 | from texting_wall import settings as django_settings
def analytics(request):
return {'GOOGLE_ANALYTICS': django_settings.GOOGLE_ANALYTICS}
| 68 | 0 | 23 |
75bbbe94c8482fd95f86698a468a6f4f4f6262de | 2,620 | py | Python | utils/preprocessing.py | birds-on-mars/birdsonearth | 62921423b787ad8b81b8e60e8de42a3f6e113d88 | [
"Apache-2.0"
] | 13 | 2019-04-11T10:02:11.000Z | 2021-12-01T22:27:18.000Z | utils/preprocessing.py | birds-on-mars/birdsonearth | 62921423b787ad8b81b8e60e8de42a3f6e113d88 | [
"Apache-2.0"
] | 2 | 2019-12-17T13:31:09.000Z | 2020-05-14T09:48:10.000Z | utils/preprocessing.py | birds-on-mars/birdsonearth | 62921423b787ad8b81b8e60e8de42a3f6e113d88 | [
"Apache-2.0"
] | 2 | 2020-07-17T21:03:18.000Z | 2021-07-14T02:09:31.000Z | import random
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.sampler import SubsetRandomSampler
import imp
from librosa.core import load
from librosa.output import write_wav
from scipy.io import wavfile
from utils import vggish_input
imp.reload(vggish_input)
def process_wavs_for_training(params):
'''
iterates through data under root that must have the structure
root/{classes}/{instances}. Data must be in integer(!) .wav format.
Under save_to the same subdirectories save_to/{classes}
are generated and for every instance vggish compatible inputs are generated according
to the parameters specified in vggish_params.py
Args:
root (string): path to data root
saveto (string): path to root where processed data is stored
'''
if os.path.exists(params.mel_spec_root):
print('spectrograms seem to have been computed')
print('delete {} manually and rerun preprocessing or keep goint to use what is there.'.format(
params.mel_spec_root))
return
labels = os.listdir(params.data_root)
for label in os.listdir(params.data_root):
if not os.path.isdir(os.path.join(params.data_root, label)): continue
print('processing data for label {}'.format(label))
os.makedirs(os.path.join(params.mel_spec_root, label))
for file in os.listdir(os.path.join(params.data_root, label)):
data = vggish_input.wavfile_to_examples(os.path.join(params.data_root, label, file))
for i in range(data.shape[0]):
np.save(os.path.join(params.mel_spec_root, label, file[:-4]+str(i)+'.npy'), data[i])
| 39.104478 | 102 | 0.695038 | import random
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import torch
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.sampler import SubsetRandomSampler
import imp
from librosa.core import load
from librosa.output import write_wav
from scipy.io import wavfile
from utils import vggish_input
imp.reload(vggish_input)
def preprocess(src, dst):
y, sr = load(src)
y *= 32768
y = y.astype(np.int16)
wavfile.write(dst, rate=22050, data=y)
def process_wavs_for_training(params):
'''
iterates through data under root that must have the structure
root/{classes}/{instances}. Data must be in integer(!) .wav format.
Under save_to the same subdirectories save_to/{classes}
are generated and for every instance vggish compatible inputs are generated according
to the parameters specified in vggish_params.py
Args:
root (string): path to data root
saveto (string): path to root where processed data is stored
'''
if os.path.exists(params.mel_spec_root):
print('spectrograms seem to have been computed')
print('delete {} manually and rerun preprocessing or keep goint to use what is there.'.format(
params.mel_spec_root))
return
labels = os.listdir(params.data_root)
for label in os.listdir(params.data_root):
if not os.path.isdir(os.path.join(params.data_root, label)): continue
print('processing data for label {}'.format(label))
os.makedirs(os.path.join(params.mel_spec_root, label))
for file in os.listdir(os.path.join(params.data_root, label)):
data = vggish_input.wavfile_to_examples(os.path.join(params.data_root, label, file))
for i in range(data.shape[0]):
np.save(os.path.join(params.mel_spec_root, label, file[:-4]+str(i)+'.npy'), data[i])
def summary(params):
labels = [f for f in os.listdir(params.data_root)
if os.path.isdir(os.path.join(params.data_root, f))]
counts = [len(os.listdir(os.path.join(params.data_root, label))) for label in labels]
df = pd.DataFrame(data={'labels':labels, 'instances':counts})
print('the following classes and numer of instances are found:\n')
print(df)
processed = os.path.exists(params.mel_spec_root)
if processed:
print('spectrograms for the data seem to have been computed under {} \
and will be used for training'.format(params.mel_spec_root))
else:
print('spectrograms for the given data need to be computed by running \
preprocessing')
| 823 | 0 | 46 |
8ac93b1625af7009e5262f966de85fc5e93bd08d | 11,215 | py | Python | dicom2nifti/image_volume.py | JuanPabloMontoya271/dicom2nifti | dfea030fbc47ed9c43d7bb1c8a468c2be963a043 | [
"MIT"
] | 197 | 2016-04-05T15:24:23.000Z | 2022-03-25T17:37:10.000Z | dicom2nifti/image_volume.py | JuanPabloMontoya271/dicom2nifti | dfea030fbc47ed9c43d7bb1c8a468c2be963a043 | [
"MIT"
] | 102 | 2017-05-12T07:08:48.000Z | 2022-03-22T00:21:54.000Z | dicom2nifti/image_volume.py | JuanPabloMontoya271/dicom2nifti | dfea030fbc47ed9c43d7bb1c8a468c2be963a043 | [
"MIT"
] | 60 | 2016-12-13T22:11:56.000Z | 2022-03-30T22:55:52.000Z | # -*- coding: utf-8 -*-
"""
Created on Fri Jun 7 07:40:20 2013
@author: abrys
"""
# To ignore numpy errors:
# pylint: disable=E1101
import nibabel
import numpy
from dicom2nifti.common import get_nifti_data
class Slice(object):
"""
Class containing all data for a single slice in an image volume
"""
original_data = None
slice_orientation = None
class SliceType(object):
"""
ENUM like container for the slice types
"""
AXIAL = 1
SAGITTAL = 2
CORONAL = 3
class SliceOrientation(object):
"""
Class containing the orientation of a slice
"""
x_component = None
y_component = None
normal_component = None
x_inverted = False
y_inverted = False
class ImageVolume(object):
"""
Class representing an imagevolume.
You can provide it with a nifti and can be used to get slices in a certain direction
It will take the affine matrix into account to find the correct orientation
"""
def get_slice(self, slice_type, slice_number, time_point=0):
"""
Returns a slice of the dataset.
slice.data contains the window/levelled values, in uint8
slice.original_data contains the original data for this slice
:param time_point: in case of 4d nifti the 4th dimension
:param slice_number: the slice number
:param slice_type: tye slice type (AXIAL, SAGITTAL, CORONAL)
"""
slice_ = Slice()
slice_.slice_number = slice_number
# assert that slice_ number is withing the range
assert slice_number >= 0
assert slice_number < self._get_number_of_slices(slice_type)
slice_data = None
if slice_type == SliceType.AXIAL:
slice_data = self.__get_raw_slice__(slice_number, self.axial_orientation, time_point)
slice_.slice_orientation = self.axial_orientation
elif slice_type == SliceType.SAGITTAL:
slice_data = self.__get_raw_slice__(slice_number, self.sagittal_orientation, time_point)
slice_.slice_orientation = self.sagittal_orientation
elif slice_type == SliceType.CORONAL:
slice_data = self.__get_raw_slice__(slice_number, self.coronal_orientation, time_point)
slice_.slice_orientation = self.coronal_orientation
# make a copy of the slice_ so we do not modify the orignal
slice_.original_data = slice_data
return slice_
def _get_number_of_slices(self, slice_type):
"""
Get the number of slices in a certain direction
"""
if slice_type == SliceType.AXIAL:
return self.dimensions[self.axial_orientation.normal_component]
elif slice_type == SliceType.SAGITTAL:
return self.dimensions[self.sagittal_orientation.normal_component]
elif slice_type == SliceType.CORONAL:
return self.dimensions[self.coronal_orientation.normal_component]
def __calc_most_likely_direction__(transformed_x, transformed_y, transformed_z):
"""
Calculate which is the most likely component for a given direction
"""
# calculate the x component
tx_dot_x = numpy.abs(numpy.dot(transformed_x, [1, 0, 0, 0]))
tx_dot_y = numpy.abs(numpy.dot(transformed_x, [0, 1, 0, 0]))
tx_dot_z = numpy.abs(numpy.dot(transformed_x, [0, 0, 1, 0]))
x_dots = [tx_dot_x, tx_dot_y, tx_dot_z]
x_component = numpy.argmax(x_dots)
x_max = numpy.max(x_dots)
# calculate the y component
ty_dot_x = numpy.abs(numpy.dot(transformed_y, [1, 0, 0, 0]))
ty_dot_y = numpy.abs(numpy.dot(transformed_y, [0, 1, 0, 0]))
ty_dot_z = numpy.abs(numpy.dot(transformed_y, [0, 0, 1, 0]))
y_dots = [ty_dot_x, ty_dot_y, ty_dot_z]
y_component = numpy.argmax(y_dots)
y_max = numpy.max(y_dots)
# calculate the z component
tz_dot_x = numpy.abs(numpy.dot(transformed_z, [1, 0, 0, 0]))
tz_dot_y = numpy.abs(numpy.dot(transformed_z, [0, 1, 0, 0]))
tz_dot_z = numpy.abs(numpy.dot(transformed_z, [0, 0, 1, 0]))
z_dots = [tz_dot_x, tz_dot_y, tz_dot_z]
z_component = numpy.argmax(z_dots)
z_max = numpy.max(z_dots)
# as long as there are duplicate directions try to correct
while x_component == y_component or x_component == z_component or y_component == z_component:
if x_component == y_component:
# keep the strongest one and change the other
if x_max >= y_max: # update the y component
y_dots[y_component] = 0
y_component = numpy.argmax(y_dots)
y_max = numpy.max(y_dots)
else: # update the x component
x_dots[x_component] = 0
x_component = numpy.argmax(x_dots)
x_max = numpy.max(x_dots)
if x_component == z_component:
# keep the strongest one and change the other
if x_max >= z_max: # update the z component
z_dots[z_component] = 0
z_component = numpy.argmax(z_dots)
z_max = numpy.max(z_dots)
else: # update the x component
x_dots[x_component] = 0
x_component = numpy.argmax(x_dots)
x_max = numpy.max(x_dots)
if y_component == z_component:
# keep the strongest one and change the other
if y_max >= z_max: # update the z component
z_dots[z_component] = 0
z_component = numpy.argmax(z_dots)
z_max = numpy.max(z_dots)
else: # update the y component
y_dots[y_component] = 0
y_component = numpy.argmax(y_dots)
y_max = numpy.max(y_dots)
return x_component, y_component, z_component
| 45.221774 | 115 | 0.659028 | # -*- coding: utf-8 -*-
"""
Created on Fri Jun 7 07:40:20 2013
@author: abrys
"""
# To ignore numpy errors:
# pylint: disable=E1101
import nibabel
import numpy
from dicom2nifti.common import get_nifti_data
class Slice(object):
"""
Class containing all data for a single slice in an image volume
"""
original_data = None
slice_orientation = None
class SliceType(object):
"""
ENUM like container for the slice types
"""
AXIAL = 1
SAGITTAL = 2
CORONAL = 3
class SliceOrientation(object):
"""
Class containing the orientation of a slice
"""
x_component = None
y_component = None
normal_component = None
x_inverted = False
y_inverted = False
def load(nifti_file):
nifti_image = nibabel.load(nifti_file)
return ImageVolume(nifti_image)
class ImageVolume(object):
"""
Class representing an imagevolume.
You can provide it with a nifti and can be used to get slices in a certain direction
It will take the affine matrix into account to find the correct orientation
"""
def __init__(self, nifti_image):
self.nifti = nifti_image
# assert that it is a 3D image
self.nifti_data = get_nifti_data(self.nifti).squeeze()
if self.nifti_data.ndim == 2:
self.nifti_data = numpy.expand_dims(self.nifti_data, 2)
if self.nifti_data.ndim != 3:
assert self.nifti_data.ndim >= 3
# do some basic processing like setting dimensions and min/max values
self.dimensions = self.nifti_data.shape
self.axial_orientation = None
self.coronal_orientation = None
self.sagittal_orientation = None
self.__calculate_slice_orientation__()
def __calculate_slice_orientation__(self):
# Not all image data has the same orientation
# We use the affine matrix and multiplying it with one component
# of the slice we can find the correct orientation
affine_inverse = numpy.linalg.inv(self.nifti.affine)
transformed_x = numpy.transpose(numpy.dot(affine_inverse, [[1], [0], [0], [0]]))[0]
transformed_y = numpy.transpose(numpy.dot(affine_inverse, [[0], [1], [0], [0]]))[0]
transformed_z = numpy.transpose(numpy.dot(affine_inverse, [[0], [0], [1], [0]]))[0]
# calculate the most likely x,y,z direction
x_component, y_component, z_component = __calc_most_likely_direction__(transformed_x,
transformed_y,
transformed_z)
# Find slice orientiation for the axial size
# Find the index of the max component to know which component is the direction in the size
self.axial_orientation = SliceOrientation()
self.axial_orientation.normal_component = z_component
self.axial_orientation.x_component = x_component
self.axial_orientation.x_inverted = numpy.sign(transformed_x[self.axial_orientation.x_component]) < 0
self.axial_orientation.y_component = y_component
self.axial_orientation.y_inverted = numpy.sign(transformed_y[self.axial_orientation.y_component]) < 0
# Find slice orientiation for the coronal size
# Find the index of the max component to know which component is the direction in the size
self.coronal_orientation = SliceOrientation()
self.coronal_orientation.normal_component = y_component
self.coronal_orientation.x_component = x_component
self.coronal_orientation.x_inverted = numpy.sign(transformed_x[self.coronal_orientation.x_component]) < 0
self.coronal_orientation.y_component = z_component
self.coronal_orientation.y_inverted = numpy.sign(transformed_z[self.coronal_orientation.y_component]) < 0
# Find slice orientation for the sagittal size
# Find the index of the max component to know which component is the direction in the size
self.sagittal_orientation = SliceOrientation()
self.sagittal_orientation.normal_component = x_component
self.sagittal_orientation.x_component = y_component
self.sagittal_orientation.x_inverted = numpy.sign(transformed_y[self.sagittal_orientation.x_component]) < 0
self.sagittal_orientation.y_component = z_component
self.sagittal_orientation.y_inverted = numpy.sign(transformed_z[self.sagittal_orientation.y_component]) < 0
# Assert that the slice normals are not equal
assert self.axial_orientation.normal_component != self.coronal_orientation.normal_component
assert self.coronal_orientation.normal_component != self.sagittal_orientation.normal_component
assert self.sagittal_orientation.normal_component != self.axial_orientation.normal_component
def __get_raw_slice__(self, slice_number, slice_orientation, time_point=0):
# Take the slice out of one of the timepoints of a 4 d nifti
if len(self.nifti_data.shape) >= 4:
if slice_orientation.normal_component == 0:
slice_data = self.nifti_data[slice_number, :, :, time_point]
elif slice_orientation.normal_component == 1:
slice_data = self.nifti_data[:, slice_number, :, time_point]
else:
slice_data = self.nifti_data[:, :, slice_number, time_point]
else:
if slice_orientation.normal_component == 0:
slice_data = self.nifti_data[slice_number, :, :]
elif slice_orientation.normal_component == 1:
slice_data = self.nifti_data[:, slice_number, :]
else:
slice_data = self.nifti_data[:, :, slice_number]
# If the x_component is larger than the y_component we need to flip
# As a consequence of the retrieval of the data the y component can be first
# In this case we need to swap x and y
if slice_orientation.x_component > slice_orientation.y_component:
slice_data = numpy.swapaxes(slice_data, 0, 1)
# Flip the numpy direction to display direction (only if needed)
# Beware: the left right is actually up down in the image
if not slice_orientation.y_inverted:
slice_data = numpy.fliplr(slice_data)
if not slice_orientation.x_inverted:
slice_data = numpy.flipud(slice_data)
return slice_data
def get_slice(self, slice_type, slice_number, time_point=0):
"""
Returns a slice of the dataset.
slice.data contains the window/levelled values, in uint8
slice.original_data contains the original data for this slice
:param time_point: in case of 4d nifti the 4th dimension
:param slice_number: the slice number
:param slice_type: tye slice type (AXIAL, SAGITTAL, CORONAL)
"""
slice_ = Slice()
slice_.slice_number = slice_number
# assert that slice_ number is withing the range
assert slice_number >= 0
assert slice_number < self._get_number_of_slices(slice_type)
slice_data = None
if slice_type == SliceType.AXIAL:
slice_data = self.__get_raw_slice__(slice_number, self.axial_orientation, time_point)
slice_.slice_orientation = self.axial_orientation
elif slice_type == SliceType.SAGITTAL:
slice_data = self.__get_raw_slice__(slice_number, self.sagittal_orientation, time_point)
slice_.slice_orientation = self.sagittal_orientation
elif slice_type == SliceType.CORONAL:
slice_data = self.__get_raw_slice__(slice_number, self.coronal_orientation, time_point)
slice_.slice_orientation = self.coronal_orientation
# make a copy of the slice_ so we do not modify the orignal
slice_.original_data = slice_data
return slice_
def _get_number_of_slices(self, slice_type):
"""
Get the number of slices in a certain direction
"""
if slice_type == SliceType.AXIAL:
return self.dimensions[self.axial_orientation.normal_component]
elif slice_type == SliceType.SAGITTAL:
return self.dimensions[self.sagittal_orientation.normal_component]
elif slice_type == SliceType.CORONAL:
return self.dimensions[self.coronal_orientation.normal_component]
def __calc_most_likely_direction__(transformed_x, transformed_y, transformed_z):
"""
Calculate which is the most likely component for a given direction
"""
# calculate the x component
tx_dot_x = numpy.abs(numpy.dot(transformed_x, [1, 0, 0, 0]))
tx_dot_y = numpy.abs(numpy.dot(transformed_x, [0, 1, 0, 0]))
tx_dot_z = numpy.abs(numpy.dot(transformed_x, [0, 0, 1, 0]))
x_dots = [tx_dot_x, tx_dot_y, tx_dot_z]
x_component = numpy.argmax(x_dots)
x_max = numpy.max(x_dots)
# calculate the y component
ty_dot_x = numpy.abs(numpy.dot(transformed_y, [1, 0, 0, 0]))
ty_dot_y = numpy.abs(numpy.dot(transformed_y, [0, 1, 0, 0]))
ty_dot_z = numpy.abs(numpy.dot(transformed_y, [0, 0, 1, 0]))
y_dots = [ty_dot_x, ty_dot_y, ty_dot_z]
y_component = numpy.argmax(y_dots)
y_max = numpy.max(y_dots)
# calculate the z component
tz_dot_x = numpy.abs(numpy.dot(transformed_z, [1, 0, 0, 0]))
tz_dot_y = numpy.abs(numpy.dot(transformed_z, [0, 1, 0, 0]))
tz_dot_z = numpy.abs(numpy.dot(transformed_z, [0, 0, 1, 0]))
z_dots = [tz_dot_x, tz_dot_y, tz_dot_z]
z_component = numpy.argmax(z_dots)
z_max = numpy.max(z_dots)
# as long as there are duplicate directions try to correct
while x_component == y_component or x_component == z_component or y_component == z_component:
if x_component == y_component:
# keep the strongest one and change the other
if x_max >= y_max: # update the y component
y_dots[y_component] = 0
y_component = numpy.argmax(y_dots)
y_max = numpy.max(y_dots)
else: # update the x component
x_dots[x_component] = 0
x_component = numpy.argmax(x_dots)
x_max = numpy.max(x_dots)
if x_component == z_component:
# keep the strongest one and change the other
if x_max >= z_max: # update the z component
z_dots[z_component] = 0
z_component = numpy.argmax(z_dots)
z_max = numpy.max(z_dots)
else: # update the x component
x_dots[x_component] = 0
x_component = numpy.argmax(x_dots)
x_max = numpy.max(x_dots)
if y_component == z_component:
# keep the strongest one and change the other
if y_max >= z_max: # update the z component
z_dots[z_component] = 0
z_component = numpy.argmax(z_dots)
z_max = numpy.max(z_dots)
else: # update the y component
y_dots[y_component] = 0
y_component = numpy.argmax(y_dots)
y_max = numpy.max(y_dots)
return x_component, y_component, z_component
| 5,355 | 0 | 104 |
6ca5840b2afb8548af45325d440f2ce279a04e57 | 8,291 | py | Python | pymarlin/plugins/hf_seq2seq/module_classes.py | nifarn/PyMarlin | ea1f5f927aa85112ecebc206d53b5c3ee65704fa | [
"MIT"
] | 20 | 2021-06-09T18:46:45.000Z | 2022-02-09T01:08:13.000Z | pymarlin/plugins/hf_seq2seq/module_classes.py | nifarn/PyMarlin | ea1f5f927aa85112ecebc206d53b5c3ee65704fa | [
"MIT"
] | 50 | 2021-06-09T17:50:35.000Z | 2022-02-07T23:02:30.000Z | pymarlin/plugins/hf_seq2seq/module_classes.py | nifarn/PyMarlin | ea1f5f927aa85112ecebc206d53b5c3ee65704fa | [
"MIT"
] | 5 | 2021-06-21T22:24:30.000Z | 2021-12-21T17:08:21.000Z | from typing import List, Dict
import torch
from pymarlin.core import trainer_backend, module_interface, trainer
from torch.utils.data import DataLoader
# too long import
from pymarlin.utils.stats import global_stats
from pymarlin.utils.config_parser.custom_arg_parser import CustomArgParser
from pymarlin.utils.distributed import rank_zero_only
from pymarlin.utils.logger import getlogger
from pymarlin.plugins import PluginModuleInterface
from transformers import AutoModelForSeq2SeqLM
from torch.optim.lr_scheduler import OneCycleLR
import dataclasses
from .data_classes import HfSeq2SeqData
from .metric_utils import get_metric_func
import re
from filelock import FileLock
try:
import nltk
NLTK_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
NLTK_AVAILABLE = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
@dataclasses.dataclass
@dataclasses.dataclass
@dataclasses.dataclass
if __name__ == "__main__":
config = CustomArgParser(yaml_file_arg_key="config_path").parse()
print(config)
dm = HfSeq2SeqData()
dm.setup_datasets(root=config["data_path"])
tm = HfSeq2SeqModule(dm, **config["tm"], generate_kwargs=config["generate"])
TrainerBackendClass = eval("trainer_backend." + config["trainer_backend_class"])
tr = TrainerBackendClass()
tr = trainer_backend.DDPTrainerBackend(tr) if config["dist"] else tr
tmArgs = trainer.TrainerArguments(
**config["tmgr"],
stats_args=trainer.stats.StatInitArguments(**config["stat"]),
writer_args=trainer.WriterInitArguments(**config["wrt"]),
checkpointer_args=trainer.DefaultCheckpointerArguments(**config["chkp"]),
)
if config["dist"]:
tr = trainer_backend.DDPTrainerBackend(tr)
else:
tmArgs.distributed_training_args = trainer.DistributedTrainingArguments(
local_rank=config["cuda"]
)
trainer = trainer.Trainer(trainer_backend=tr, module=tm, args=tmArgs)
trainer.validate()
| 34.260331 | 102 | 0.644554 | from typing import List, Dict
import torch
from pymarlin.core import trainer_backend, module_interface, trainer
from torch.utils.data import DataLoader
# too long import
from pymarlin.utils.stats import global_stats
from pymarlin.utils.config_parser.custom_arg_parser import CustomArgParser
from pymarlin.utils.distributed import rank_zero_only
from pymarlin.utils.logger import getlogger
from pymarlin.plugins import PluginModuleInterface
from transformers import AutoModelForSeq2SeqLM
from torch.optim.lr_scheduler import OneCycleLR
import dataclasses
from .data_classes import HfSeq2SeqData
from .metric_utils import get_metric_func
import re
from filelock import FileLock
try:
import nltk
NLTK_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
NLTK_AVAILABLE = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
@dataclasses.dataclass
class ModelArguments:
encoder_key: str = "bert"
num_labels: int = None
hf_model: str = "facebook/bart-base"
tokenizer_path: str = None
model_config_path: str = None
model_config_file: str = "config.json"
model_path: str = None
model_file: str = "pytorch_model.bin"
get_latest_ckpt: bool = True
@dataclasses.dataclass
class GenerateArguments:
do_sample: bool = False
num_beams: int = 1
length_penalty: float = 1.0
early_stopping: bool = False
@dataclasses.dataclass
class ModuleInterfaceArguments:
max_lr: float = 2e-5 # Maximum learning rate.
output_mode: str = "s2s"
max_length_encoder: int = 128
max_length_decoder: int = 128
model_args: ModelArguments = ModelArguments()
generate_args: GenerateArguments = GenerateArguments()
metric: str = "rouge"
class HfSeq2SeqModule(PluginModuleInterface):
def __init__(self, data: HfSeq2SeqData, args: ModuleInterfaceArguments):
super().__init__()
self.args = args
self.data = data
self.auto_setup(AutoModelForSeq2SeqLM)
self.logger = getlogger(__name__, log_level="DEBUG")
self.metric_func = get_metric_func(self.args.metric)
def get_optimizers_schedulers(
self, estimated_global_steps_per_epoch: int, epochs: int
):
optimizer = torch.optim.Adam(self.model.parameters(), self.args.max_lr)
scheduler = OneCycleLR(
optimizer,
max_lr=self.args.max_lr,
steps_per_epoch=estimated_global_steps_per_epoch,
epochs=epochs,
anneal_strategy="linear",
)
self.schedulers = scheduler
return [optimizer], [scheduler]
def get_train_dataloader(self, sampler: torch.utils.data.Sampler, batch_size: int):
train_ds = self.data.get_train_dataset()
dl = DataLoader(
train_ds,
batch_size=batch_size,
collate_fn=self.collate_fun,
sampler=sampler(train_ds),
)
return dl
def get_val_dataloaders(self, sampler: torch.utils.data.Sampler, batch_size: int):
val_ds = self.data.get_val_dataset()
dl = DataLoader(
val_ds,
batch_size=batch_size,
collate_fn=self.collate_fun,
sampler=sampler(val_ds),
)
return dl
def collate_fun(self, batch):
source, target = torch.utils.data._utils.collate.default_collate(batch)
# this is probably truncating. repeat positional embeddings and increase embedding layer size.
source_tokens = self.tokenizer(
list(source),
padding=True,
truncation=True,
return_tensors="pt",
max_length=self.args.max_length_encoder,
)
target_tokens = self.tokenizer(
list(target),
padding=True,
truncation=True,
return_tensors="pt",
max_length=self.args.max_length_decoder,
)
labels = target_tokens["input_ids"]
labels[labels[:, :] == self.model.config.pad_token_id] = -100
source_tokens["labels"] = labels
return source_tokens
def train_step(self, global_step: int, batch, device):
batch = batch.to(device)
result = self.model.forward(**batch)
global_stats.update("lr", self.schedulers.get_last_lr()[0], frequent=True)
return result.loss
def val_step(self, global_step: int, batch, device):
self.logger.debug("inside val_step")
batch = batch.to(device)
# result = self.model.forward(**batch)
self.logger.debug("pre-generate")
outputs = self.model.generate(
input_ids=batch.input_ids,
attention_mask=batch.attention_mask,
max_length=self.args.max_length_decoder,
**dataclasses.asdict(self.args.generate_args),
)
self.logger.debug("post-generate")
labels = batch.labels
self.logger.debug("pre-pad")
labels = self._pad(labels, device)
outputs = self._pad(outputs, device)
self.logger.debug("post-pad")
# print('output size', outputs.size())
# print('label size', labels.size())
self.logger.debug("returning...")
return outputs, labels
def _pad(self, outputs, device, max_len=None):
padded_outputs = []
if max_len is None:
max_len = self.args.max_length_decoder
for output in outputs:
# print('unpadded size', output.size())
padding = torch.tensor(
(max_len - len(output)) * [self.tokenizer.pad_token_id]
).to(device)
padded = torch.cat([output, padding])
# print('padded.size()',padded.size())
padded_outputs.append(padded)
return torch.stack(padded_outputs)
@rank_zero_only
def on_end_val_epoch(self, global_step, *collated_output, key="default"):
# move all batch decoding to here so that we can collate tensors in DDP
self.logger.debug("inside on_end_val_epoch")
outputs, labels = collated_output
labels[labels[:, :] == -100] = self.model.config.pad_token_id
self.logger.debug("pre-decode")
preds, refs = [], []
for output, label in zip(outputs, labels):
try:
pred = self.tokenizer.decode(
output, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
ref = self.tokenizer.decode(
label, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
preds.append(pred)
refs.append(ref)
except Exception as e:
self.logger.debug("hit decoding exception")
self.logger.debug(e)
self.logger.debug("post-decode")
self.logger.debug(f"len(preds): {len(preds)}")
self.logger.debug(f"len(refs): {len(refs)}")
if len(preds) > 0:
self.logger.debug(f"preds[0]: {preds[0]}")
if len(refs) > 0:
self.logger.debug(f"refs[0]: {refs[0]}")
metrics = self.metric_func(preds, refs)
for k in metrics:
global_stats.update(key + "/val_" + k, metrics[k])
if __name__ == "__main__":
config = CustomArgParser(yaml_file_arg_key="config_path").parse()
print(config)
dm = HfSeq2SeqData()
dm.setup_datasets(root=config["data_path"])
tm = HfSeq2SeqModule(dm, **config["tm"], generate_kwargs=config["generate"])
TrainerBackendClass = eval("trainer_backend." + config["trainer_backend_class"])
tr = TrainerBackendClass()
tr = trainer_backend.DDPTrainerBackend(tr) if config["dist"] else tr
tmArgs = trainer.TrainerArguments(
**config["tmgr"],
stats_args=trainer.stats.StatInitArguments(**config["stat"]),
writer_args=trainer.WriterInitArguments(**config["wrt"]),
checkpointer_args=trainer.DefaultCheckpointerArguments(**config["chkp"]),
)
if config["dist"]:
tr = trainer_backend.DDPTrainerBackend(tr)
else:
tmArgs.distributed_training_args = trainer.DistributedTrainingArguments(
local_rank=config["cuda"]
)
trainer = trainer.Trainer(trainer_backend=tr, module=tm, args=tmArgs)
trainer.validate()
| 5,156 | 1,006 | 89 |
31e188b24e477c5a5d5c14f9c92fc0e596b84bcc | 6,565 | py | Python | src/pybind/setup.py | Booritas/slideio | fdee97747cc73f087a5538aef6a0315ec75becca | [
"BSD-3-Clause"
] | 6 | 2021-01-25T15:21:31.000Z | 2022-03-07T09:23:37.000Z | src/pybind/setup.py | Booritas/slideio | fdee97747cc73f087a5538aef6a0315ec75becca | [
"BSD-3-Clause"
] | 3 | 2020-12-30T16:21:42.000Z | 2022-03-07T09:23:18.000Z | src/pybind/setup.py | Booritas/slideio | fdee97747cc73f087a5538aef6a0315ec75becca | [
"BSD-3-Clause"
] | null | null | null | import os
import re
import sys
import platform
import subprocess
import fnmatch
import shutil
from setuptools import setup, Extension, find_packages
from setuptools.command.build_ext import build_ext
import setuptools.command.build_py
from distutils.version import LooseVersion
from ctypes.util import find_library
version = '0.5.'
vrs_sub = '0'
if os.environ.get('CI_PIPELINE_IID'):
ci_id = os.environ['CI_PIPELINE_IID']
if (isinstance(ci_id, str) and len(ci_id)>0) or isinstance(ci_id, int):
vrs_sub = ci_id
version = version + vrs_sub
source_dir= os.path.abspath('../../')
build_dir= os.path.abspath('../../build_py')
PLATFORM = get_platform()
REDISTR_LIBS = []
if PLATFORM=='Windows':
REDISTR_LIBS = [
'concrt140.dll',
'msvcp140.dll',
'msvcp140_1.dll',
'msvcp140_2.dll',
'msvcp140_codecvt_ids.dll',
'vccorlib140.dll',
'vcruntime140.dll',
'vcruntime140_1.dll']
# Get the long description from the README file
here = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
# Get requirements from requirements-dev.txt file
with open(os.path.join(here, 'requirements-dev.txt')) as f:
requirements_dev = f.read().replace('==', '>=').splitlines()
setup(
name='slideio',
version=version,
author='Stanislav Melnikov, Vadim Popov',
author_email='stanislav.melnikov@gmail.com',
description='Reading of medical images',
long_description=long_description,
ext_modules=[CMakeExtension(name = 'slideio', source_dir=source_dir, build_dir=build_dir)],
cmdclass=dict(build_ext=CMakeBuild),
packages=find_packages(),
project_urls={
'Documentation':'http://slideio.com',
"Source Code": "https://gitlab.com/bioslide/slideio.git"
},
keywords = 'images, pathology, tissue, medical, czi, svs, afi, scn',
package_data={},
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Bio-Informatics',
'Topic :: Scientific/Engineering :: Image Recognition',
'Topic :: Scientific/Engineering :: Medical Science Apps.',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
install_requires=['numpy'],
extras_require={},
data_files=[(
'.', [
'requirements-dev.txt'
]
)],
zip_safe=False,
) | 32.181373 | 95 | 0.585986 | import os
import re
import sys
import platform
import subprocess
import fnmatch
import shutil
from setuptools import setup, Extension, find_packages
from setuptools.command.build_ext import build_ext
import setuptools.command.build_py
from distutils.version import LooseVersion
from ctypes.util import find_library
version = '0.5.'
vrs_sub = '0'
if os.environ.get('CI_PIPELINE_IID'):
ci_id = os.environ['CI_PIPELINE_IID']
if (isinstance(ci_id, str) and len(ci_id)>0) or isinstance(ci_id, int):
vrs_sub = ci_id
version = version + vrs_sub
source_dir= os.path.abspath('../../')
build_dir= os.path.abspath('../../build_py')
def get_platform():
platforms = {
'linux' : 'Linux',
'linux1' : 'Linux',
'linux2' : 'Linux',
'darwin' : 'Macos',
'win32' : 'Windows'
}
if sys.platform not in platforms:
return sys.platform
return platforms[sys.platform]
PLATFORM = get_platform()
REDISTR_LIBS = []
if PLATFORM=='Windows':
REDISTR_LIBS = [
'concrt140.dll',
'msvcp140.dll',
'msvcp140_1.dll',
'msvcp140_2.dll',
'msvcp140_codecvt_ids.dll',
'vccorlib140.dll',
'vcruntime140.dll',
'vcruntime140_1.dll']
def find_shared_libs(dir, pattern):
matches = []
for root, dirnames, filenames in os.walk(dir):
for filename in fnmatch.filter(filenames, pattern):
matches.append(os.path.join(root, filename))
return matches
# Get the long description from the README file
here = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
# Get requirements from requirements-dev.txt file
with open(os.path.join(here, 'requirements-dev.txt')) as f:
requirements_dev = f.read().replace('==', '>=').splitlines()
class CMakeExtension(Extension):
def __init__(self, name, source_dir='', build_dir=None):
Extension.__init__(self, name, sources=[])
self.source_dir = os.path.abspath(source_dir)
self.build_dir = os.path.abspath(build_dir)
class CMakeBuild(build_ext):
def run(self):
try:
out = subprocess.check_output(['cmake', '--version'])
except OSError:
raise RuntimeError(
"CMake must be installed to build the following extensions: " +
", ".join(e.name for e in self.extensions)
)
if platform.system() == "Windows":
cmake_version = LooseVersion(
re.search(r'version\s*([\d.]+)', out.decode()).group(1)
)
if cmake_version < '3.1.0':
raise RuntimeError("CMake >= 3.1.0 is required on Windows")
for ext in self.extensions:
self.build_extension(ext)
def build_extension(self, ext):
if not (ext.build_dir is None):
self.build_temp = ext.build_dir
extdir = os.path.abspath(os.path.dirname(
self.get_ext_fullpath(ext.name)))
cmake_args = [
'-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir,
'-DPYTHON_EXECUTABLE=' + sys.executable
]
cfg = 'Debug' if self.debug else 'Release'
build_args = ['--config', cfg]
if platform.system() == "Windows":
cmake_args += [
'-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(
cfg.upper(), extdir
)
]
if sys.maxsize > 2**32:
cmake_args += ['-A', 'x64']
build_args += ['--', '/m']
else:
cmake_args += ['-DCMAKE_BUILD_TYPE=' + cfg]
build_args += ['--', '-j2']
env = os.environ.copy()
env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format(
env.get('CXXFLAGS', ''),
self.distribution.get_version()
)
if not os.path.exists(self.build_temp):
os.makedirs(self.build_temp)
subprocess.check_call(
['cmake', ext.source_dir] + cmake_args,
cwd=self.build_temp, env=env
)
subprocess.check_call(
['cmake', '--build', '.'] + build_args,
cwd=self.build_temp
)
patterns = ["*.so"]
if PLATFORM == "Windows":
patterns = ["*.dll"]
elif PLATFORM == "Macos":
patterns = ["*.so", "*.dylib"]
print("----Look for shared libraries int directory", self.build_temp)
extra_files = []
for pattern in patterns:
files = find_shared_libs(self.build_temp, pattern)
if len(files)>0:
extra_files.extend(files)
print("----Found libraries:", extra_files)
if not os.path.exists(extdir):
os.makedirs(extdir)
for fl in extra_files:
file_name = os.path.basename(fl)
destination = os.path.join(extdir, file_name)
print("Copy",fl,"->",destination)
shutil.copyfile(fl, destination)
for lib in REDISTR_LIBS:
shutil.copy(find_library(lib), extdir)
setup(
name='slideio',
version=version,
author='Stanislav Melnikov, Vadim Popov',
author_email='stanislav.melnikov@gmail.com',
description='Reading of medical images',
long_description=long_description,
ext_modules=[CMakeExtension(name = 'slideio', source_dir=source_dir, build_dir=build_dir)],
cmdclass=dict(build_ext=CMakeBuild),
packages=find_packages(),
project_urls={
'Documentation':'http://slideio.com',
"Source Code": "https://gitlab.com/bioslide/slideio.git"
},
keywords = 'images, pathology, tissue, medical, czi, svs, afi, scn',
package_data={},
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Bio-Informatics',
'Topic :: Scientific/Engineering :: Image Recognition',
'Topic :: Scientific/Engineering :: Medical Science Apps.',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
install_requires=['numpy'],
extras_require={},
data_files=[(
'.', [
'requirements-dev.txt'
]
)],
zip_safe=False,
) | 3,599 | 18 | 171 |
a02429054cb0cf7b20e8267bcc65eb77f6e899a8 | 1,064 | py | Python | workflow/scripts/prep_trinity.py | matinnuhamunada/rnaseq_kadal | 4e915866b460206ac499c3022b853aa1dcc65b51 | [
"MIT"
] | null | null | null | workflow/scripts/prep_trinity.py | matinnuhamunada/rnaseq_kadal | 4e915866b460206ac499c3022b853aa1dcc65b51 | [
"MIT"
] | 2 | 2022-01-14T17:43:24.000Z | 2022-01-21T11:33:44.000Z | workflow/scripts/prep_trinity.py | matinnuhamunada/rnaseq_kadal | 4e915866b460206ac499c3022b853aa1dcc65b51 | [
"MIT"
] | null | null | null | import pandas as pd
from pathlib import Path
import sys
def get_trinity_samples(run_name, condition, outfile, samples_path="config/samples.tsv", units_path="config/units.tsv"):
"""
"""
samples = (
pd.read_csv(samples_path, sep="\t", dtype={"ID": str})
.set_index("ID", drop=False)
.sort_index()
)
units = (
pd.read_csv(units_path, sep="\t", dtype={"ID": str})
.set_index("ID", drop=False)
.sort_index()
)
trinity = []
subset = samples[samples.loc[:, "Condition"] == condition]
for s in subset.index:
s_dict = {}
s_dict["Condition"] = samples.loc[s, "Condition"]
s_dict["Replicate"] = units.loc[s, "unit_name"]
s_dict["F"] = units.loc[s, "fq1"]
s_dict["R"] = units.loc[s, "fq2"]
trinity.append(s_dict)
df = pd.DataFrame(trinity)
df.to_csv(f"{outfile}", sep="\t", index=False, header=False)
return None
if __name__ == "__main__":
get_trinity_samples(sys.argv[1], sys.argv[2], sys.argv[3]) | 29.555556 | 120 | 0.577068 | import pandas as pd
from pathlib import Path
import sys
def get_trinity_samples(run_name, condition, outfile, samples_path="config/samples.tsv", units_path="config/units.tsv"):
"""
"""
samples = (
pd.read_csv(samples_path, sep="\t", dtype={"ID": str})
.set_index("ID", drop=False)
.sort_index()
)
units = (
pd.read_csv(units_path, sep="\t", dtype={"ID": str})
.set_index("ID", drop=False)
.sort_index()
)
trinity = []
subset = samples[samples.loc[:, "Condition"] == condition]
for s in subset.index:
s_dict = {}
s_dict["Condition"] = samples.loc[s, "Condition"]
s_dict["Replicate"] = units.loc[s, "unit_name"]
s_dict["F"] = units.loc[s, "fq1"]
s_dict["R"] = units.loc[s, "fq2"]
trinity.append(s_dict)
df = pd.DataFrame(trinity)
df.to_csv(f"{outfile}", sep="\t", index=False, header=False)
return None
if __name__ == "__main__":
get_trinity_samples(sys.argv[1], sys.argv[2], sys.argv[3]) | 0 | 0 | 0 |
066d4d4b7e35674f6bd507d4b6745a2269a00cea | 2,779 | py | Python | interfaces/srvr_tools/c_assign.py | krattai/AEBL | a7b12c97479e1236d5370166b15ca9f29d7d4265 | [
"BSD-2-Clause"
] | 4 | 2016-04-26T03:43:54.000Z | 2016-11-17T08:09:04.000Z | interfaces/srvr_tools/c_assign.py | krattai/AEBL | a7b12c97479e1236d5370166b15ca9f29d7d4265 | [
"BSD-2-Clause"
] | 17 | 2015-01-05T21:06:22.000Z | 2015-12-07T20:45:44.000Z | interfaces/srvr_tools/c_assign.py | krattai/AEBL | a7b12c97479e1236d5370166b15ca9f29d7d4265 | [
"BSD-2-Clause"
] | 3 | 2016-04-26T03:43:55.000Z | 2020-11-06T11:02:08.000Z | #!/usr/bin/env python
# Copyright (C) 2016 Uvea I. S., Kevin Rattai
#
# Watch for channel request and assign channel based on prior or unassigned chan
#
# This software is based on an unknown license, but is available
# with no license statement from here:
# http://stackoverflow.com/questions/31775450/publish-and-subscribe-with-paho-mqtt-client
#
# All changes will be under BSD 2 clause license. Code of unknown license
# will be removed and replaced with code that will be BSD 2 clause
#
# Portions of this code is based on the noo-ebs project:
# https://github.com/krattai/noo-ebs
#
# And portions of this code is based on the AEBL project:
# https://github.com/krattai/AEBL
#
import os
import paho.mqtt.client as mqtt
# reference to MQTT python found here: http://mosquitto.org/documentation/python/
# requires: sudo pip install paho-mqtt
# pip requires: sudo apt-get install python-pip
message = 'ON'
# mqttc.publish("response/" + str(msg.payload),msg.payload);
# if 'ACK' in message:
# mqttc.publish("alive/chan","NAK");
# if 'TEST' in message:
# os.system("/home/user/scripts/test.sh")
# mqttc.publish("test/output","NAK");
# mqttcb.publish("test/output",msg.payload);
mqttc = mqtt.Client()
# Assign event callbacks
mqttc.on_message = on_message
mqttc.on_connect = on_connect
mqttc.on_publish = on_publish
mqttc.on_subscribe = on_subscribe
# Connect
mqttc.connect("ihdn.ca", 1883,60)
# mqttc.connect("2001:dead::beef:1", 1883,60)
# mosquitto_sub -h 2001:dead::beef -t "hello/+" -t "ihdn/+" -t "test/+"
# Continue the network loop
mqttc.loop_forever()
| 29.88172 | 89 | 0.651673 | #!/usr/bin/env python
# Copyright (C) 2016 Uvea I. S., Kevin Rattai
#
# Watch for channel request and assign channel based on prior or unassigned chan
#
# This software is based on an unknown license, but is available
# with no license statement from here:
# http://stackoverflow.com/questions/31775450/publish-and-subscribe-with-paho-mqtt-client
#
# All changes will be under BSD 2 clause license. Code of unknown license
# will be removed and replaced with code that will be BSD 2 clause
#
# Portions of this code is based on the noo-ebs project:
# https://github.com/krattai/noo-ebs
#
# And portions of this code is based on the AEBL project:
# https://github.com/krattai/AEBL
#
import os
import paho.mqtt.client as mqtt
# reference to MQTT python found here: http://mosquitto.org/documentation/python/
# requires: sudo pip install paho-mqtt
# pip requires: sudo apt-get install python-pip
message = 'ON'
def on_connect(mosq, obj, rc):
mqttc.subscribe("request/chan", 0)
print("rc: " + str(rc))
def on_message(mosq, obj, msg):
global message
print(msg.topic + " " + str(msg.qos) + " " + str(msg.payload))
message = msg.payload
if os.path.isfile('chan/' + str(msg.payload)):
# get channel from file
fchan = open("chan/" + str(msg.payload))
chan = fchan.readline()
chan = chan.rstrip('\n')
fchan.close()
else:
# get next channel, create file and insert channel
fchan = open("chan/.0chan","r+")
chan = fchan.readline()
chan = chan.rstrip('\n')
nchan = int(chan) + 1
fchan.seek(0, 0)
fchan.write(repr(nchan).zfill(6) + "\n")
fchan.close()
fchan = open("chan/" + str(msg.payload),"w+")
fchan.write(chan + "\n")
fchan.close()
mqttc.publish("response/" + str(msg.payload),chan);
# mqttc.publish("response/" + str(msg.payload),msg.payload);
# if 'ACK' in message:
# mqttc.publish("alive/chan","NAK");
# if 'TEST' in message:
# os.system("/home/user/scripts/test.sh")
# mqttc.publish("test/output","NAK");
# mqttcb.publish("test/output",msg.payload);
def on_publish(mosq, obj, mid):
print("mid: " + str(mid))
def on_subscribe(mosq, obj, mid, granted_qos):
print("Subscribed: " + str(mid) + " " + str(granted_qos))
def on_log(mosq, obj, level, string):
print(string)
mqttc = mqtt.Client()
# Assign event callbacks
mqttc.on_message = on_message
mqttc.on_connect = on_connect
mqttc.on_publish = on_publish
mqttc.on_subscribe = on_subscribe
# Connect
mqttc.connect("ihdn.ca", 1883,60)
# mqttc.connect("2001:dead::beef:1", 1883,60)
# mosquitto_sub -h 2001:dead::beef -t "hello/+" -t "ihdn/+" -t "test/+"
# Continue the network loop
mqttc.loop_forever()
| 1,050 | 0 | 114 |
4388231220b15bd9c8b6a792db56c557ffa340b3 | 2,948 | py | Python | source/infrastructure/personalize/aws_lambda/functions/environment.py | turnoutnow/maintaining-personalized-experiences-with-machine-learning | b45588c094734cce70198811890a28e65b8e39e1 | [
"Apache-2.0"
] | null | null | null | source/infrastructure/personalize/aws_lambda/functions/environment.py | turnoutnow/maintaining-personalized-experiences-with-machine-learning | b45588c094734cce70198811890a28e65b8e39e1 | [
"Apache-2.0"
] | null | null | null | source/infrastructure/personalize/aws_lambda/functions/environment.py | turnoutnow/maintaining-personalized-experiences-with-machine-learning | b45588c094734cce70198811890a28e65b8e39e1 | [
"Apache-2.0"
] | null | null | null | # ######################################################################################################################
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. #
# #
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance #
# with the License. You may obtain a copy of the License at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed #
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for #
# the specific language governing permissions and limitations under the License. #
# ######################################################################################################################
from dataclasses import dataclass, field
from aws_cdk.aws_lambda import IFunction
from aws_cdk.core import Aws
from aws_solutions.cdk.aws_lambda.environment_variable import EnvironmentVariable
@dataclass
class Environment:
"""
Tracks environment variables common to AWS Lambda functions deployed by this solution
"""
scope: IFunction
solution_name: EnvironmentVariable = field(init=False, repr=False)
solution_id: EnvironmentVariable = field(init=False, repr=False)
solution_version: EnvironmentVariable = field(init=False, repr=False)
log_level: EnvironmentVariable = field(init=False, repr=False)
powertools_service_name: EnvironmentVariable = field(init=False, repr=False)
| 60.163265 | 120 | 0.542062 | # ######################################################################################################################
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. #
# #
# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance #
# with the License. You may obtain a copy of the License at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed #
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for #
# the specific language governing permissions and limitations under the License. #
# ######################################################################################################################
from dataclasses import dataclass, field
from aws_cdk.aws_lambda import IFunction
from aws_cdk.core import Aws
from aws_solutions.cdk.aws_lambda.environment_variable import EnvironmentVariable
@dataclass
class Environment:
"""
Tracks environment variables common to AWS Lambda functions deployed by this solution
"""
scope: IFunction
solution_name: EnvironmentVariable = field(init=False, repr=False)
solution_id: EnvironmentVariable = field(init=False, repr=False)
solution_version: EnvironmentVariable = field(init=False, repr=False)
log_level: EnvironmentVariable = field(init=False, repr=False)
powertools_service_name: EnvironmentVariable = field(init=False, repr=False)
def __post_init__(self):
cloudwatch_namespace_id = f"personalize_solution_{Aws.STACK_NAME}"
cloudwatch_service_id_default = f"Workflow"
self.solution_name = EnvironmentVariable(self.scope, "SOLUTION_NAME")
self.solution_id = EnvironmentVariable(self.scope, "SOLUTION_ID")
self.solution_version = EnvironmentVariable(self.scope, "SOLUTION_VERSION")
self.log_level = EnvironmentVariable(self.scope, "LOG_LEVEL", "INFO")
self.powertools_service_name = EnvironmentVariable(
self.scope, "POWERTOOLS_SERVICE_NAME", cloudwatch_service_id_default
)
self.powertools_metrics_namespace = EnvironmentVariable(
self.scope, "POWERTOOLS_METRICS_NAMESPACE", cloudwatch_namespace_id
)
| 751 | 0 | 27 |
f4b84ddd46fdf014d7210c8b0802837e20498fe3 | 356 | py | Python | 8kyu/test_swap_values.py | adun/codewars.py | 89e7d81e9ca05a432007d634892c1cba28f5b715 | [
"MIT"
] | null | null | null | 8kyu/test_swap_values.py | adun/codewars.py | 89e7d81e9ca05a432007d634892c1cba28f5b715 | [
"MIT"
] | null | null | null | 8kyu/test_swap_values.py | adun/codewars.py | 89e7d81e9ca05a432007d634892c1cba28f5b715 | [
"MIT"
] | null | null | null | # I would like to be able to pass an array with two elements to my swapValues function
# to swap the values. However it appears that the values aren't changing.
# Can you figure out what's wrong here?
| 25.428571 | 86 | 0.676966 | # I would like to be able to pass an array with two elements to my swapValues function
# to swap the values. However it appears that the values aren't changing.
# Can you figure out what's wrong here?
def swap_values(args):
args[0], args[1] = args[1], args[0]
def test_swap_values():
arr = [1, 2]
swap_values(arr)
assert arr == [2, 1]
| 106 | 0 | 46 |
bcd43fdd32c531b45f4fbb862a550f3a2becbeff | 20,304 | py | Python | infrastructure-provisioning/src/general/scripts/azure/edge_configure.py | ofuks/DLab | 460804a2559843d099936fe40373093f9bf9edcb | [
"Apache-2.0"
] | null | null | null | infrastructure-provisioning/src/general/scripts/azure/edge_configure.py | ofuks/DLab | 460804a2559843d099936fe40373093f9bf9edcb | [
"Apache-2.0"
] | null | null | null | infrastructure-provisioning/src/general/scripts/azure/edge_configure.py | ofuks/DLab | 460804a2559843d099936fe40373093f9bf9edcb | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
# *****************************************************************************
#
# Copyright (c) 2016, EPAM SYSTEMS INC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ******************************************************************************
import json
from dlab.fab import *
from dlab.meta_lib import *
import sys, time, os
from dlab.actions_lib import *
if __name__ == "__main__":
local_log_filename = "{}_{}_{}.log".format(os.environ['conf_resource'], os.environ['edge_user_name'],
os.environ['request_id'])
local_log_filepath = "/logs/edge/" + local_log_filename
logging.basicConfig(format='%(levelname)-8s [%(asctime)s] %(message)s',
level=logging.DEBUG,
filename=local_log_filepath)
try:
print('Generating infrastructure names and tags')
edge_conf = dict()
edge_conf['service_base_name'] = os.environ['conf_service_base_name']
edge_conf['resource_group_name'] = os.environ['azure_resource_group_name']
edge_conf['key_name'] = os.environ['conf_key_name']
edge_conf['vpc_name'] = os.environ['azure_vpc_name']
edge_conf['region'] = os.environ['azure_region']
edge_conf['subnet_name'] = os.environ['azure_subnet_name']
edge_conf['user_name'] = os.environ['edge_user_name'].replace('_', '-')
edge_conf['user_keyname'] = os.environ['edge_user_name']
edge_conf['private_subnet_name'] = edge_conf['service_base_name'] + '-' + edge_conf['user_name'] + '-subnet'
edge_conf['instance_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + '-edge'
edge_conf['network_interface_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + '-edge-nif'
edge_conf['static_public_ip_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + '-edge-ip'
edge_conf['primary_disk_name'] = edge_conf['instance_name'] + '-disk0'
edge_conf['instance_dns_name'] = 'host-' + edge_conf['instance_name'] + '.' + edge_conf['region'] + '.cloudapp.azure.com'
edge_conf['user_storage_account_name'] = edge_conf['service_base_name'] + '-' + edge_conf[
'user_name'] + '-storage'
edge_conf['user_container_name'] = (edge_conf['service_base_name'] + '-' + edge_conf['user_name'] + '-container').lower()
edge_conf['shared_storage_account_name'] = edge_conf['service_base_name'] + '-shared-storage'
edge_conf['shared_container_name'] = (edge_conf['service_base_name'] + '-shared-container').lower()
edge_conf['datalake_store_name'] = edge_conf['service_base_name'] + '-ssn-datalake'
edge_conf['datalake_shared_directory_name'] = edge_conf['service_base_name'] + '-shared-folder'
edge_conf['datalake_user_directory_name'] = '{0}-{1}-folder'.format(edge_conf['service_base_name'], edge_conf['user_name'])
edge_conf['edge_security_group_name'] = edge_conf['instance_name'] + '-sg'
edge_conf['notebook_security_group_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + \
'-nb-sg'
edge_conf['master_security_group_name'] = edge_conf['service_base_name'] + '-' \
+ edge_conf['user_name'] + '-dataengine-master-sg'
edge_conf['slave_security_group_name'] = edge_conf['service_base_name'] + '-' \
+ edge_conf['user_name'] + '-dataengine-slave-sg'
edge_conf['dlab_ssh_user'] = os.environ['conf_os_user']
keyfile_name = "{}{}.pem".format(os.environ['conf_key_dir'], edge_conf['key_name'])
edge_conf['private_subnet_cidr'] = AzureMeta().get_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name']).address_prefix
edge_conf['edge_public_ip'] = AzureMeta().get_instance_public_ip_address(edge_conf['resource_group_name'],
edge_conf['instance_name'])
edge_conf['edge_private_ip'] = AzureMeta().get_private_ip_address(edge_conf['resource_group_name'],
edge_conf['instance_name'])
instance_hostname = AzureMeta().get_private_ip_address(edge_conf['resource_group_name'],
edge_conf['instance_name'])
except Exception as err:
append_result("Failed to generate infrastructure names", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
if os.environ['conf_os_family'] == 'debian':
initial_user = 'ubuntu'
sudo_group = 'sudo'
if os.environ['conf_os_family'] == 'redhat':
initial_user = 'ec2-user'
sudo_group = 'wheel'
logging.info('[CREATING DLAB SSH USER]')
print('[CREATING DLAB SSH USER]')
params = "--hostname {} --keyfile {} --initial_user {} --os_user {} --sudo_group {}".format\
(instance_hostname, os.environ['conf_key_dir'] + os.environ['conf_key_name'] + ".pem", initial_user,
edge_conf['dlab_ssh_user'], sudo_group)
try:
local("~/scripts/{}.py {}".format('create_ssh_user', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed creating ssh user 'dlab'.", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
print('[INSTALLING PREREQUISITES]')
logging.info('[INSTALLING PREREQUISITES]')
params = "--hostname {} --keyfile {} --user {} --region {}".\
format(instance_hostname, keyfile_name, edge_conf['dlab_ssh_user'], os.environ['azure_region'])
try:
local("~/scripts/{}.py {}".format('install_prerequisites', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed installing apps: apt & pip.", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
print('[INSTALLING HTTP PROXY]')
logging.info('[INSTALLING HTTP PROXY]')
additional_config = {"exploratory_subnet": edge_conf['private_subnet_cidr'],
"template_file": "/root/templates/squid.conf"}
params = "--hostname {} --keyfile {} --additional_config '{}' --user {}" \
.format(instance_hostname, keyfile_name, json.dumps(additional_config), edge_conf['dlab_ssh_user'])
try:
local("~/scripts/{}.py {}".format('configure_http_proxy', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed installing http proxy.", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
print('[INSTALLING USERs KEY]')
logging.info('[INSTALLING USERs KEY]')
additional_config = {"user_keyname": edge_conf['user_keyname'],
"user_keydir": os.environ['conf_key_dir']}
params = "--hostname {} --keyfile {} --additional_config '{}' --user {}".format(
instance_hostname, keyfile_name, json.dumps(additional_config), edge_conf['dlab_ssh_user'])
try:
local("~/scripts/{}.py {}".format('install_user_key', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed installing users key. Excpeption: " + str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['shared_storage_account_name'] == storage_account.tags["Name"]:
shared_storage_account_name = storage_account.name
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
user_storage_account_name = storage_account.name
print('[SUMMARY]')
logging.info('[SUMMARY]')
print("Instance name: {}".format(edge_conf['instance_name']))
print("Hostname: {}".format(edge_conf['instance_dns_name']))
print("Public IP: {}".format(edge_conf['edge_public_ip']))
print("Private IP: {}".format(edge_conf['edge_private_ip']))
print("Key name: {}".format(edge_conf['key_name']))
print("User storage account name: {}".format(user_storage_account_name))
print("User container name: {}".format(edge_conf['user_container_name']))
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
datalake_id = datalake.name
print("Data Lake name: {}".format(datalake_id))
print("Data Lake tag name: {}".format(edge_conf['datalake_store_name']))
print("Data Lake Store user directory name: {}".format(edge_conf['datalake_user_directory_name']))
print("Notebook SG: {}".format(edge_conf['notebook_security_group_name']))
print("Edge SG: {}".format(edge_conf['edge_security_group_name']))
print("Notebook subnet: {}".format(edge_conf['private_subnet_cidr']))
with open("/root/result.json", 'w') as result:
if os.environ['azure_datalake_enable'] == 'false':
res = {"hostname": edge_conf['instance_dns_name'],
"public_ip": edge_conf['edge_public_ip'],
"ip": edge_conf['edge_private_ip'],
"key_name": edge_conf['key_name'],
"user_storage_account_name": user_storage_account_name,
"user_container_name": edge_conf['user_container_name'],
"shared_storage_account_name": shared_storage_account_name,
"shared_container_name": edge_conf['shared_container_name'],
"user_storage_account_tag_name": edge_conf['user_storage_account_name'],
"tunnel_port": "22",
"socks_port": "1080",
"notebook_sg": edge_conf['notebook_security_group_name'],
"edge_sg": edge_conf['edge_security_group_name'],
"notebook_subnet": edge_conf['private_subnet_cidr'],
"instance_id": edge_conf['instance_name'],
"full_edge_conf": edge_conf,
"Action": "Create new EDGE server"}
else:
res = {"hostname": edge_conf['instance_dns_name'],
"public_ip": edge_conf['edge_public_ip'],
"ip": edge_conf['edge_private_ip'],
"key_name": edge_conf['key_name'],
"user_storage_account_name": user_storage_account_name,
"user_container_name": edge_conf['user_container_name'],
"shared_storage_account_name": shared_storage_account_name,
"shared_container_name": edge_conf['shared_container_name'],
"user_storage_account_tag_name": edge_conf['user_storage_account_name'],
"datalake_name": datalake_id,
"datalake_tag_name": edge_conf['datalake_store_name'],
"datalake_shared_directory_name": edge_conf['datalake_shared_directory_name'],
"datalake_user_directory_name": edge_conf['datalake_user_directory_name'],
"tunnel_port": "22",
"socks_port": "1080",
"notebook_sg": edge_conf['notebook_security_group_name'],
"edge_sg": edge_conf['edge_security_group_name'],
"notebook_subnet": edge_conf['private_subnet_cidr'],
"instance_id": edge_conf['instance_name'],
"full_edge_conf": edge_conf,
"Action": "Create new EDGE server"}
print(json.dumps(res))
result.write(json.dumps(res))
except:
print("Failed writing results.")
sys.exit(0)
sys.exit(0)
| 66.570492 | 131 | 0.625 | #!/usr/bin/python
# *****************************************************************************
#
# Copyright (c) 2016, EPAM SYSTEMS INC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# ******************************************************************************
import json
from dlab.fab import *
from dlab.meta_lib import *
import sys, time, os
from dlab.actions_lib import *
if __name__ == "__main__":
local_log_filename = "{}_{}_{}.log".format(os.environ['conf_resource'], os.environ['edge_user_name'],
os.environ['request_id'])
local_log_filepath = "/logs/edge/" + local_log_filename
logging.basicConfig(format='%(levelname)-8s [%(asctime)s] %(message)s',
level=logging.DEBUG,
filename=local_log_filepath)
try:
print('Generating infrastructure names and tags')
edge_conf = dict()
edge_conf['service_base_name'] = os.environ['conf_service_base_name']
edge_conf['resource_group_name'] = os.environ['azure_resource_group_name']
edge_conf['key_name'] = os.environ['conf_key_name']
edge_conf['vpc_name'] = os.environ['azure_vpc_name']
edge_conf['region'] = os.environ['azure_region']
edge_conf['subnet_name'] = os.environ['azure_subnet_name']
edge_conf['user_name'] = os.environ['edge_user_name'].replace('_', '-')
edge_conf['user_keyname'] = os.environ['edge_user_name']
edge_conf['private_subnet_name'] = edge_conf['service_base_name'] + '-' + edge_conf['user_name'] + '-subnet'
edge_conf['instance_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + '-edge'
edge_conf['network_interface_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + '-edge-nif'
edge_conf['static_public_ip_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + '-edge-ip'
edge_conf['primary_disk_name'] = edge_conf['instance_name'] + '-disk0'
edge_conf['instance_dns_name'] = 'host-' + edge_conf['instance_name'] + '.' + edge_conf['region'] + '.cloudapp.azure.com'
edge_conf['user_storage_account_name'] = edge_conf['service_base_name'] + '-' + edge_conf[
'user_name'] + '-storage'
edge_conf['user_container_name'] = (edge_conf['service_base_name'] + '-' + edge_conf['user_name'] + '-container').lower()
edge_conf['shared_storage_account_name'] = edge_conf['service_base_name'] + '-shared-storage'
edge_conf['shared_container_name'] = (edge_conf['service_base_name'] + '-shared-container').lower()
edge_conf['datalake_store_name'] = edge_conf['service_base_name'] + '-ssn-datalake'
edge_conf['datalake_shared_directory_name'] = edge_conf['service_base_name'] + '-shared-folder'
edge_conf['datalake_user_directory_name'] = '{0}-{1}-folder'.format(edge_conf['service_base_name'], edge_conf['user_name'])
edge_conf['edge_security_group_name'] = edge_conf['instance_name'] + '-sg'
edge_conf['notebook_security_group_name'] = edge_conf['service_base_name'] + "-" + edge_conf['user_name'] + \
'-nb-sg'
edge_conf['master_security_group_name'] = edge_conf['service_base_name'] + '-' \
+ edge_conf['user_name'] + '-dataengine-master-sg'
edge_conf['slave_security_group_name'] = edge_conf['service_base_name'] + '-' \
+ edge_conf['user_name'] + '-dataengine-slave-sg'
edge_conf['dlab_ssh_user'] = os.environ['conf_os_user']
keyfile_name = "{}{}.pem".format(os.environ['conf_key_dir'], edge_conf['key_name'])
edge_conf['private_subnet_cidr'] = AzureMeta().get_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name']).address_prefix
edge_conf['edge_public_ip'] = AzureMeta().get_instance_public_ip_address(edge_conf['resource_group_name'],
edge_conf['instance_name'])
edge_conf['edge_private_ip'] = AzureMeta().get_private_ip_address(edge_conf['resource_group_name'],
edge_conf['instance_name'])
instance_hostname = AzureMeta().get_private_ip_address(edge_conf['resource_group_name'],
edge_conf['instance_name'])
except Exception as err:
append_result("Failed to generate infrastructure names", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
if os.environ['conf_os_family'] == 'debian':
initial_user = 'ubuntu'
sudo_group = 'sudo'
if os.environ['conf_os_family'] == 'redhat':
initial_user = 'ec2-user'
sudo_group = 'wheel'
logging.info('[CREATING DLAB SSH USER]')
print('[CREATING DLAB SSH USER]')
params = "--hostname {} --keyfile {} --initial_user {} --os_user {} --sudo_group {}".format\
(instance_hostname, os.environ['conf_key_dir'] + os.environ['conf_key_name'] + ".pem", initial_user,
edge_conf['dlab_ssh_user'], sudo_group)
try:
local("~/scripts/{}.py {}".format('create_ssh_user', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed creating ssh user 'dlab'.", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
print('[INSTALLING PREREQUISITES]')
logging.info('[INSTALLING PREREQUISITES]')
params = "--hostname {} --keyfile {} --user {} --region {}".\
format(instance_hostname, keyfile_name, edge_conf['dlab_ssh_user'], os.environ['azure_region'])
try:
local("~/scripts/{}.py {}".format('install_prerequisites', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed installing apps: apt & pip.", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
print('[INSTALLING HTTP PROXY]')
logging.info('[INSTALLING HTTP PROXY]')
additional_config = {"exploratory_subnet": edge_conf['private_subnet_cidr'],
"template_file": "/root/templates/squid.conf"}
params = "--hostname {} --keyfile {} --additional_config '{}' --user {}" \
.format(instance_hostname, keyfile_name, json.dumps(additional_config), edge_conf['dlab_ssh_user'])
try:
local("~/scripts/{}.py {}".format('configure_http_proxy', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed installing http proxy.", str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
print('[INSTALLING USERs KEY]')
logging.info('[INSTALLING USERs KEY]')
additional_config = {"user_keyname": edge_conf['user_keyname'],
"user_keydir": os.environ['conf_key_dir']}
params = "--hostname {} --keyfile {} --additional_config '{}' --user {}".format(
instance_hostname, keyfile_name, json.dumps(additional_config), edge_conf['dlab_ssh_user'])
try:
local("~/scripts/{}.py {}".format('install_user_key', params))
except:
traceback.print_exc()
raise Exception
except Exception as err:
append_result("Failed installing users key. Excpeption: " + str(err))
AzureActions().remove_instance(edge_conf['resource_group_name'], edge_conf['instance_name'])
AzureActions().remove_subnet(edge_conf['resource_group_name'], edge_conf['vpc_name'],
edge_conf['private_subnet_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['edge_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'], edge_conf['notebook_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['master_security_group_name'])
AzureActions().remove_security_group(edge_conf['resource_group_name'],
edge_conf['slave_security_group_name'])
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
AzureActions().remove_storage_account(edge_conf['resource_group_name'], storage_account.name)
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
AzureActions().remove_datalake_directory(datalake.name, edge_conf['datalake_user_directory_name'])
sys.exit(1)
try:
for storage_account in AzureMeta().list_storage_accounts(edge_conf['resource_group_name']):
if edge_conf['shared_storage_account_name'] == storage_account.tags["Name"]:
shared_storage_account_name = storage_account.name
if edge_conf['user_storage_account_name'] == storage_account.tags["Name"]:
user_storage_account_name = storage_account.name
print('[SUMMARY]')
logging.info('[SUMMARY]')
print("Instance name: {}".format(edge_conf['instance_name']))
print("Hostname: {}".format(edge_conf['instance_dns_name']))
print("Public IP: {}".format(edge_conf['edge_public_ip']))
print("Private IP: {}".format(edge_conf['edge_private_ip']))
print("Key name: {}".format(edge_conf['key_name']))
print("User storage account name: {}".format(user_storage_account_name))
print("User container name: {}".format(edge_conf['user_container_name']))
if os.environ['azure_datalake_enable'] == 'true':
for datalake in AzureMeta().list_datalakes(edge_conf['resource_group_name']):
if edge_conf['datalake_store_name'] == datalake.tags["Name"]:
datalake_id = datalake.name
print("Data Lake name: {}".format(datalake_id))
print("Data Lake tag name: {}".format(edge_conf['datalake_store_name']))
print("Data Lake Store user directory name: {}".format(edge_conf['datalake_user_directory_name']))
print("Notebook SG: {}".format(edge_conf['notebook_security_group_name']))
print("Edge SG: {}".format(edge_conf['edge_security_group_name']))
print("Notebook subnet: {}".format(edge_conf['private_subnet_cidr']))
with open("/root/result.json", 'w') as result:
if os.environ['azure_datalake_enable'] == 'false':
res = {"hostname": edge_conf['instance_dns_name'],
"public_ip": edge_conf['edge_public_ip'],
"ip": edge_conf['edge_private_ip'],
"key_name": edge_conf['key_name'],
"user_storage_account_name": user_storage_account_name,
"user_container_name": edge_conf['user_container_name'],
"shared_storage_account_name": shared_storage_account_name,
"shared_container_name": edge_conf['shared_container_name'],
"user_storage_account_tag_name": edge_conf['user_storage_account_name'],
"tunnel_port": "22",
"socks_port": "1080",
"notebook_sg": edge_conf['notebook_security_group_name'],
"edge_sg": edge_conf['edge_security_group_name'],
"notebook_subnet": edge_conf['private_subnet_cidr'],
"instance_id": edge_conf['instance_name'],
"full_edge_conf": edge_conf,
"Action": "Create new EDGE server"}
else:
res = {"hostname": edge_conf['instance_dns_name'],
"public_ip": edge_conf['edge_public_ip'],
"ip": edge_conf['edge_private_ip'],
"key_name": edge_conf['key_name'],
"user_storage_account_name": user_storage_account_name,
"user_container_name": edge_conf['user_container_name'],
"shared_storage_account_name": shared_storage_account_name,
"shared_container_name": edge_conf['shared_container_name'],
"user_storage_account_tag_name": edge_conf['user_storage_account_name'],
"datalake_name": datalake_id,
"datalake_tag_name": edge_conf['datalake_store_name'],
"datalake_shared_directory_name": edge_conf['datalake_shared_directory_name'],
"datalake_user_directory_name": edge_conf['datalake_user_directory_name'],
"tunnel_port": "22",
"socks_port": "1080",
"notebook_sg": edge_conf['notebook_security_group_name'],
"edge_sg": edge_conf['edge_security_group_name'],
"notebook_subnet": edge_conf['private_subnet_cidr'],
"instance_id": edge_conf['instance_name'],
"full_edge_conf": edge_conf,
"Action": "Create new EDGE server"}
print(json.dumps(res))
result.write(json.dumps(res))
except:
print("Failed writing results.")
sys.exit(0)
sys.exit(0)
| 0 | 0 | 0 |
0360b4a0c14622785e2c5d98e3fb589264013d5d | 5,846 | py | Python | samples/cgc/backdoor/01_nacl.py | EarthCompass/patchkit | 7d3dd1f6ad410a1bb975b544ccb666c859ec2448 | [
"MIT"
] | 631 | 2016-08-08T01:44:40.000Z | 2022-03-18T17:38:13.000Z | samples/cgc/backdoor/01_nacl.py | EarthCompass/patchkit | 7d3dd1f6ad410a1bb975b544ccb666c859ec2448 | [
"MIT"
] | 32 | 2016-08-08T01:58:09.000Z | 2021-06-17T11:06:51.000Z | samples/cgc/backdoor/01_nacl.py | EarthCompass/patchkit | 7d3dd1f6ad410a1bb975b544ccb666c859ec2448 | [
"MIT"
] | 94 | 2016-08-08T05:10:06.000Z | 2021-06-17T04:55:06.000Z | import os
import sys
from util import read
from util.crypto.rc4 import rc4
from util.patch.syscall import find_syscall_funcs
| 33.988372 | 108 | 0.522237 | import os
import sys
from util import read
from util.crypto.rc4 import rc4
from util.patch.syscall import find_syscall_funcs
def backdoor(pt):
c = read('backdoor/tweetnacl.c')
c = c.replace('#include "tweetnacl.h"', '')
h = read('backdoor/tweetnacl.h')
pubkey = read('backdoor/pubkey.h')
randombytes = r'''
void randombytes(uint8_t *msg, uint64_t len) {
syscall3(SYS_random, (uint32_t)msg, len, 0);
}
'''
# TODO: transmit_all / receive_all?
code = r'''
#define NONCE_LEN 8
#define SM_LEN 0x48
void handshake() {
uint8_t nonce[NONCE_LEN];
syscall3(SYS_random, (uint32_t)nonce, NONCE_LEN, 0);
syscall4(SYS_transmit, 1, (uint32_t)nonce, NONCE_LEN, 0);
uint32_t smlen = SM_LEN;
uint8_t *sm, *m;
if (syscall3(SYS_allocate, smlen, 0, (uint32_t)&sm) ||
(syscall3(SYS_allocate, smlen, 0, (uint32_t)&m))) {
_terminate(1);
}
syscall4(SYS_receive, 0, (uint32_t)sm, smlen, 0);
uint64_t mlen;
int valid = crypto_sign_open(m, &mlen, sm, smlen, pubkey);
if (valid == 0 && memcmp(m, nonce, NONCE_LEN) == 0) {
syscall4(SYS_transmit, 1, 0x4347c000, 4, 0);
}
syscall1(SYS__terminate, 2);
}
'''
code = h + pubkey + code + randombytes + c
backdoor_addr, size = pt.inject(c=code, size=True)
# rc4-encrypt the backdoor so you can't ROP directly into the type2 pov
# this block also intercepts the receive() syscall function
rc4_key = os.urandom(16)
# as part of rc4-encrypting, we relocate the backdoor to the NX page so it doesn't add 1000+ ROP gadgets
xor = rc4(rc4_key)
data = pt.elf.read(backdoor_addr, size)
for i in xrange(len(data)):
data[i] ^= xor.next()
shadow_addr = pt.inject(raw=data, target='nx', silent=True)
pt.patch(backdoor_addr, raw=size * '\x00', silent=True)
# xor key so they can't just pull it out of memory
key_otp = os.urandom(len(rc4_key))
key = ''.join([chr(ord(c) ^ ord(key_otp[i])) for i, c in enumerate(rc4_key)])
str2c = lambda x: ', '.join(map(str, map(ord, x)))
call_backdoor = r'''
void call_backdoor() {
void (*backdoor)() = (void (*)())%d;
char *shadow_addr = (char *)%d;
size_t bd_size = %d;
memcpy(backdoor, shadow_addr, bd_size);
uint8_t state[256];
uint8_t rc4_key[] = {%s};
uint8_t rc4_otp[] = {%s};
int keylen = %d;
for (int i = 0; i < keylen; i++) {
rc4_key[i] ^= rc4_otp[i];
}
ksa(state, rc4_key, keylen);
rc4(state, (uint8_t *)backdoor, bd_size);
backdoor();
// always exit after backdoor so there's not a decrypted type 2 POV in memory you can ROP into
_terminate(0);
}
''' % (backdoor_addr, shadow_addr, size, str2c(key), str2c(key_otp), len(key))
receive_hook_head = r'''
void call_backdoor();
void check_init(char *buf, uint32_t size);
int _receive(int fd, void *buf, uint32_t size, uint32_t *count);
#define WINDOW 9
char bufsave[WINDOW] = {1};
uint32_t first = 1, saved = 1, pos = 1;
int receive(int fd, void *_buf, uint32_t size, uint32_t *count) {
char *buf = (char *)_buf;
// shortcut if we've run and buffer is empty, or on fd > 0
if (fd != 0 || saved == 0) {
return _receive(fd, buf, size, count);
} else if (first) {
first = 0;
if (size < WINDOW) {
int ret = _receive(fd, bufsave, WINDOW, &saved);
if (ret) return ret;
check_init(bufsave, saved);
pos = 0;
} else {
int ret = _receive(fd, buf, size, &saved);
if (ret) return ret;
if (count) *count = saved;
check_init(buf, saved);
saved = 0;
return ret;
}
}
// flush buffer
if (saved > 0 && saved < size) {
memcpy(buf, bufsave + pos, saved);
if (count) *count = saved;
uint32_t tmp;
int ret = _receive(fd, buf + saved, size - saved, &tmp);
saved = 0;
if (ret) return ret;
if (count) *count += tmp;
return ret;
} else if (saved >= size) {
memcpy(buf, bufsave + pos, size);
if (count) *count = size;
saved -= size;
pos += size;
}
return 0;
}'''
receive_hook_tail = r'''
void check_init(char *buf, uint32_t size) {
if (size < 5) return;
char hash[4];
for (int i = 0; i < 4; i++) hash[i] = buf[i];
char *key = "ECAF";
for (int i = 4; i < size; i++) {
for (int j = 0; j < 4; j++) {
hash[j] ^= buf[i];
}
if (*(uint32_t *)hash == *(uint32_t *)key) {
size = i + 1;
// respond with inverted key so POV can seek ahead to handshake
for (int j = 0; j < size; j++) {
buf[j] ^= 0xff;
}
transmit(1, buf, size, 0);
call_backdoor();
}
}
}
''' + call_backdoor
def patch_receive(code, syms):
if syms == ['receive']:
out = []
# TODO: this is really gross text parsing
out.append(receive_hook_head)
for line in code.split('\n'):
if line.startswith('int receive(') and line.endswith('{'):
out.append(line.replace('int receive(', 'int _receive(', 1))
else:
out.append(line)
out.append(receive_hook_tail)
return '\n'.join(out)
pt.binary.linker.onpre(patch_receive)
| 5,697 | 0 | 23 |
72a8d7b86cb93e472da4a88ffcffd54c32dd8924 | 2,209 | py | Python | pynfact/server.py | jacorbal/pynfact | 3dc512ef7209b596353060616c0df2abe19a638f | [
"MIT"
] | null | null | null | pynfact/server.py | jacorbal/pynfact | 3dc512ef7209b596353060616c0df2abe19a638f | [
"MIT"
] | null | null | null | pynfact/server.py | jacorbal/pynfact | 3dc512ef7209b596353060616c0df2abe19a638f | [
"MIT"
] | null | null | null | # vim: set ft=python fileencoding=utf-8 tw=72 fdm=indent foldlevel=1 nowrap:
"""
Simple server for testing purposes.
:copyright: © 2012-2020, J. A. Corbal
:license: MIT
"""
import os
import sys
from http.server import HTTPServer, SimpleHTTPRequestHandler
class Server:
"""Simple server.
.. versionchanged:: 1.2.0a1
Implement ``logging`` instead of printing to ``stdout`` and/or
``stderr``.
"""
def __init__(self, host='127.0.0.1', port=4000, path='_build',
logger=None):
"""Constructor.
:param host: Addres where the server will be listening
:type host: str
:param port: Port where the server will be listening
:type port: str
:param path: Where the static website will be generated
:type path: str
:param logger: Logger where to store activity in
:type logger: logging.Logger
"""
self.port = port
self.host = host
self.path = path
self.logger = logger
def serve(self):
"""Serve a specific directory and waits for keyboard interrupt.
:raise FileNotFoundError: If the deploy directory doesn't exist
:raise KeyboardInterrupt: If the user stops the server (``^C``)
:raise OSError: If ``location:port`` is not valid or in use
"""
try: # Find the deploy directory
os.chdir(self.path)
except FileNotFoundError:
self.logger and self.logger.error(
"Deploy directory not found")
sys.exit(61)
try: # Initialize the server
httpd = HTTPServer((self.host, self.port),
SimpleHTTPRequestHandler)
except OSError:
self.logger and self.logger.error(
"Address not valid or already in use")
sys.exit(62)
self.logger and self.logger.info(
"Serving {}:{} at {}".format(self.host, self.port,
self.path))
try: # Listen until a keyboard interruption
httpd.serve_forever()
except KeyboardInterrupt:
self.logger and self.logger.info("Interrupted!")
| 32.014493 | 76 | 0.586238 | # vim: set ft=python fileencoding=utf-8 tw=72 fdm=indent foldlevel=1 nowrap:
"""
Simple server for testing purposes.
:copyright: © 2012-2020, J. A. Corbal
:license: MIT
"""
import os
import sys
from http.server import HTTPServer, SimpleHTTPRequestHandler
class Server:
"""Simple server.
.. versionchanged:: 1.2.0a1
Implement ``logging`` instead of printing to ``stdout`` and/or
``stderr``.
"""
def __init__(self, host='127.0.0.1', port=4000, path='_build',
logger=None):
"""Constructor.
:param host: Addres where the server will be listening
:type host: str
:param port: Port where the server will be listening
:type port: str
:param path: Where the static website will be generated
:type path: str
:param logger: Logger where to store activity in
:type logger: logging.Logger
"""
self.port = port
self.host = host
self.path = path
self.logger = logger
def serve(self):
"""Serve a specific directory and waits for keyboard interrupt.
:raise FileNotFoundError: If the deploy directory doesn't exist
:raise KeyboardInterrupt: If the user stops the server (``^C``)
:raise OSError: If ``location:port`` is not valid or in use
"""
try: # Find the deploy directory
os.chdir(self.path)
except FileNotFoundError:
self.logger and self.logger.error(
"Deploy directory not found")
sys.exit(61)
try: # Initialize the server
httpd = HTTPServer((self.host, self.port),
SimpleHTTPRequestHandler)
except OSError:
self.logger and self.logger.error(
"Address not valid or already in use")
sys.exit(62)
self.logger and self.logger.info(
"Serving {}:{} at {}".format(self.host, self.port,
self.path))
try: # Listen until a keyboard interruption
httpd.serve_forever()
except KeyboardInterrupt:
self.logger and self.logger.info("Interrupted!")
| 0 | 0 | 0 |
68dd208ec7d8ba0ae708db0beebd344158030e83 | 691 | py | Python | molsysmt/basic/has_attribute.py | uibcdf/MolModMTs | 4f6b6f671a9fa3e73008d1e9c48686d5f20a6573 | [
"MIT"
] | null | null | null | molsysmt/basic/has_attribute.py | uibcdf/MolModMTs | 4f6b6f671a9fa3e73008d1e9c48686d5f20a6573 | [
"MIT"
] | null | null | null | molsysmt/basic/has_attribute.py | uibcdf/MolModMTs | 4f6b6f671a9fa3e73008d1e9c48686d5f20a6573 | [
"MIT"
] | null | null | null | from molsysmt._private.exceptions import *
from molsysmt._private.digestion import *
from molsysmt._private.lists_and_tuples import is_list_or_tuple
| 25.592593 | 79 | 0.738061 | from molsysmt._private.exceptions import *
from molsysmt._private.digestion import *
from molsysmt._private.lists_and_tuples import is_list_or_tuple
def has_attribute(molecular_system, attribute, check=True):
from . import where_is_attribute, is_molecular_system
from molsysmt.tools.attribute import is_attribute
if check:
if not is_molecular_system(item):
raise WrongItemError(attribute)
if not is_attribute(attribute):
raise WrongAttributeError(attribute)
output_item, output_form = where_is_attribute(item, attribute, check=False)
output = False
if output_item is not None:
output = True
return output
| 518 | 0 | 23 |
4aeef765926424bacb89047c3511142f5d667655 | 53,295 | py | Python | api/app/reviews/tests.py | bamtak/Baobab | 5489e97a536a3a1c1e994c333a1c471385116cf6 | [
"Apache-2.0"
] | null | null | null | api/app/reviews/tests.py | bamtak/Baobab | 5489e97a536a3a1c1e994c333a1c471385116cf6 | [
"Apache-2.0"
] | null | null | null | api/app/reviews/tests.py | bamtak/Baobab | 5489e97a536a3a1c1e994c333a1c471385116cf6 | [
"Apache-2.0"
] | null | null | null | from datetime import datetime
import json
from app import db, LOGGER
from app.utils.testing import ApiTestCase
from app.events.models import Event, EventRole
from app.users.models import AppUser, UserCategory, Country
from app.applicationModel.models import ApplicationForm, Question, Section
from app.responses.models import Response, Answer, ResponseReviewer
from app.reviews.models import ReviewForm, ReviewQuestion, ReviewResponse, ReviewScore
from app.utils.errors import REVIEW_RESPONSE_NOT_FOUND, FORBIDDEN, USER_NOT_FOUND
from nose.plugins.skip import SkipTest
from app.organisation.models import Organisation
| 42.910628 | 185 | 0.618013 | from datetime import datetime
import json
from app import db, LOGGER
from app.utils.testing import ApiTestCase
from app.events.models import Event, EventRole
from app.users.models import AppUser, UserCategory, Country
from app.applicationModel.models import ApplicationForm, Question, Section
from app.responses.models import Response, Answer, ResponseReviewer
from app.reviews.models import ReviewForm, ReviewQuestion, ReviewResponse, ReviewScore
from app.utils.errors import REVIEW_RESPONSE_NOT_FOUND, FORBIDDEN, USER_NOT_FOUND
from nose.plugins.skip import SkipTest
from app.organisation.models import Organisation
class ReviewsApiTest(ApiTestCase):
def seed_static_data(self):
self.add_organisation('Deep Learning Indaba 2019', 'blah.png', 'blah_big.png')
self.add_organisation('Deep Learning Indaba 2020', 'blah.png', 'blah_big.png')
user_categories = [
UserCategory('Honours'),
UserCategory('Student'),
UserCategory('MSc'),
UserCategory('PhD')
]
db.session.add_all(user_categories)
db.session.commit()
countries = [
Country('Egypt'),
Country('Botswana'),
Country('Namibia'),
Country('Zimbabwe'),
Country('Mozambique'),
Country('Ghana'),
Country('Nigeria')
]
db.session.add_all(countries)
db.session.commit()
reviewer1 = self.add_user('r1@r.com', 'reviewer', '1', 'Mr', 1, 1, 'M', 'Wits', 'CS', 'NA', 2)
reviewer2 = self.add_user('r2@r.com', 'reviewer', '2', 'Ms', 1, 1, 'F', 'UCT', 'Chem', 'NA', 2)
reviewer3 = self.add_user('r3@r.com', 'reviewer', '3', 'Mr', 1, 1, 'M', 'UKZN', 'Phys', 'NA', 2)
reviewer4 = self.add_user('r4@r.com', 'reviewer', '4', 'Ms', 1, 1, 'F', 'RU', 'Math', 'NA', 2)
candidate1 = self.add_user('c1@c.com', 'candidate', '1', 'Mr', 1, 2, 'M', 'UWC', 'CS', 'NA', 2)
candidate2 = self.add_user('c2@c.com', 'candidate', '2', 'Ms', 3, 4, 'F', 'RU', 'Chem', 'NA', 3)
candidate3 = self.add_user('c3@c.com', 'candidate', '3', 'Mr', 5, 6, 'M', 'UFH', 'Phys', 'NA', 4)
candidate4 = self.add_user('c4@c.com', 'candidate', '4', 'Ms', 7, 8, 'F', 'NWU', 'Math', 'NA', 5)
system_admin = self.add_user('sa@sa.com', 'system_admin', '1', 'Ms', 7, 8, 'F', 'NWU', 'Math', 'NA', 5, is_admin=True)
event_admin = self.add_user('ea@ea.com', 'event_admin', '1', 'Ms', 7, 8, 'F', 'NWU', 'Math', 'NA', 5)
db.session.commit()
events = [
Event('indaba 2019', 'The Deep Learning Indaba 2019, Kenyatta University, Nairobi, Kenya ', datetime(2019, 8, 25), datetime(2019, 8, 31),
'KENYADABA2019', 1, 'abx@indaba.deeplearning','indaba.deeplearning'),
Event('indaba 2020', 'The Deep Learning Indaba 2018, Stellenbosch University, South Africa', datetime(2018, 9, 9), datetime(2018, 9, 15),
'INDABA2020', 2, 'abx@indaba.deeplearning','indaba.deeplearning')
]
db.session.add_all(events)
db.session.commit()
event_roles = [
EventRole('admin', 10, 1),
EventRole('reviewer', 3, 1)
]
db.session.add_all(event_roles)
db.session.commit()
application_forms = [
ApplicationForm(1, True, datetime(2019, 4, 30)),
ApplicationForm(2, False, datetime(2018, 4, 30))
]
db.session.add_all(application_forms)
db.session.commit()
sections = [
Section(1, 'Tell Us a Bit About You', '', 1),
Section(2, 'Tell Us a Bit About You', '', 1)
]
db.session.add_all(sections)
db.session.commit()
options = [
{
"value": "indaba-2017",
"label": "Yes, I attended the 2017 Indaba"
},
{
"value": "indaba-2018",
"label": "Yes, I attended the 2018 Indaba"
},
{
"value": "indaba-2017-2018",
"label": "Yes, I attended both Indabas"
},
{
"value": "none",
"label": "No"
}
]
questions = [
Question(1, 1, 'Why is attending the Deep Learning Indaba 2019 important to you?', 'Enter 50 to 150 words', 1, 'long_text', ''),
Question(1, 1, 'How will you share what you have learnt after the Indaba?', 'Enter 50 to 150 words', 2, 'long_text', ''),
Question(2, 2, 'Have you worked on a project that uses machine learning?', 'Enter 50 to 150 words', 1, 'long_text', ''),
Question(2, 2, 'Would you like to be considered for a travel award?', 'Enter 50 to 150 words', 2, 'long_text', ''),
Question(1, 1, 'Did you attend the 2017 or 2018 Indaba', 'Select an option...', 3, 'multi-choice', None, None, True, None, options)
]
db.session.add_all(questions)
db.session.commit()
closed_review = ReviewForm(2, datetime(2018, 4, 30))
closed_review.close()
review_forms = [
ReviewForm(1, datetime(2019, 4, 30)),
closed_review
]
db.session.add_all(review_forms)
db.session.commit()
review_questions = [
ReviewQuestion(1, 1, None, None, 'multi-choice', None, None, True, 1, None, None, 0),
ReviewQuestion(1, 2, None, None, 'multi-choice', None, None, True, 2, None, None, 0),
ReviewQuestion(2, 3, None, None, 'multi-choice', None, None, True, 1, None, None, 0),
ReviewQuestion(2, 4, None, None, 'information', None, None, False, 2, None, None, 0)
]
db.session.add_all(review_questions)
db.session.commit()
def get_auth_header_for(self, email):
body = {
'email': email,
'password': 'abc'
}
response = self.app.post('api/v1/authenticate', data=body)
data = json.loads(response.data)
header = {'Authorization': data['token']}
return header
def setup_one_reviewer_one_candidate(self):
responses = [
Response(1, 5, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.')
]
db.session.add_all(answers)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1) # assign reviewer 1 to candidate 1 response
]
db.session.add_all(response_reviewers)
db.session.commit()
def test_one_reviewer_one_candidate(self):
self.seed_static_data()
self.setup_one_reviewer_one_candidate()
header = self.get_auth_header_for('r1@r.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_remaining_count'], 1)
def test_one_reviewer_one_candidate_review_summary(self):
self.seed_static_data()
self.setup_one_reviewer_one_candidate()
header = self.get_auth_header_for('ea@ea.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/reviewassignment/summary', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_unallocated'], 2)
def setup_responses_and_no_reviewers(self):
responses = [
Response(1, 5, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.')
]
db.session.add_all(answers)
db.session.commit()
def test_no_response_reviewers(self):
self.seed_static_data()
self.setup_responses_and_no_reviewers()
header = self.get_auth_header_for('r1@r.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_remaining_count'], 0)
def test_no_response_reviewers_reviews_unallocated(self):
self.seed_static_data()
self.setup_responses_and_no_reviewers()
header = self.get_auth_header_for('ea@ea.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/reviewassignment/summary', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_unallocated'], 3)
def setup_one_reviewer_three_candidates(self):
responses = [
Response(1, 5, True),
Response(1, 6, True),
Response(1, 7, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.'),
Answer(2, 1, 'I want to do a PhD.'),
Answer(2, 2, 'I will share by writing a blog.'),
Answer(3, 1, 'I want to solve new problems.'),
Answer(3, 2, 'I will share by tutoring.'),
]
db.session.add_all(answers)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1),
ResponseReviewer(2, 1),
ResponseReviewer(3, 1)
]
db.session.add_all(response_reviewers)
db.session.commit()
def test_one_reviewer_three_candidates(self):
self.seed_static_data()
self.setup_one_reviewer_three_candidates()
header = self.get_auth_header_for('r1@r.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_remaining_count'], 3)
def setup_one_reviewer_three_candidates_and_one_completed_review(self):
responses = [
Response(1, 5, True),
Response(1, 6, True),
Response(1, 7, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.'),
Answer(2, 1, 'I want to do a PhD.'),
Answer(2, 2, 'I will share by writing a blog.'),
Answer(3, 1, 'I want to solve new problems.'),
Answer(3, 2, 'I will share by tutoring.')
]
db.session.add_all(answers)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1),
ResponseReviewer(2, 1),
ResponseReviewer(3, 1)
]
db.session.add_all(response_reviewers)
db.session.commit()
review_response = ReviewResponse(1, 1, 1)
db.session.add(review_response)
db.session.commit()
def test_one_reviewer_three_candidates_and_one_completed_review(self):
self.seed_static_data()
self.setup_one_reviewer_three_candidates_and_one_completed_review()
header = self.get_auth_header_for('r1@r.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_remaining_count'], 2)
def setup_one_reviewer_three_candidates_with_one_withdrawn_response_and_one_unsubmitted_response(self):
withdrawn_response = Response(1, 5, True)
withdrawn_response.withdraw_response()
responses = [
withdrawn_response,
Response(1, 6, False),
Response(1, 7, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.'),
Answer(2, 1, 'I want to do a PhD.'),
Answer(2, 2, 'I will share by writing a blog.'),
Answer(3, 1, 'I want to solve new problems.'),
Answer(3, 2, 'I will share by tutoring.')
]
db.session.add_all(answers)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1),
ResponseReviewer(2, 1),
ResponseReviewer(3, 1)
]
db.session.add_all(response_reviewers)
db.session.commit()
def test_one_reviewer_three_candidates_with_one_withdrawn_response_and_one_unsubmitted_response(self):
self.seed_static_data()
self.setup_one_reviewer_three_candidates_with_one_withdrawn_response_and_one_unsubmitted_response()
header = self.get_auth_header_for('r1@r.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_remaining_count'], 1)
def setup_multiple_reviewers_with_different_subsets_of_candidates_and_reviews_completed(self):
responses = [
Response(1, 5, True),
Response(1, 6, True),
Response(1, 7, True),
Response(1, 8, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.'),
Answer(2, 1, 'I want to do a PhD.'),
Answer(2, 2, 'I will share by writing a blog.'),
Answer(3, 1, 'I want to solve new problems.'),
Answer(3, 2, 'I will share by tutoring.'),
Answer(4, 1, 'I want to exchange ideas with like minded people'),
Answer(4, 2, 'I will mentor people interested in ML.')
]
db.session.add_all(answers)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1),
ResponseReviewer(2, 1),
ResponseReviewer(3, 1),
ResponseReviewer(2, 2),
ResponseReviewer(3, 2),
ResponseReviewer(1, 3),
ResponseReviewer(2, 3),
ResponseReviewer(3, 3),
ResponseReviewer(4, 3),
ResponseReviewer(1, 4)
]
db.session.add_all(response_reviewers)
db.session.commit()
review_responses = [
ReviewResponse(1, 2, 2),
ReviewResponse(1, 3, 1),
ReviewResponse(1, 3, 2),
ReviewResponse(1, 4, 1)
]
db.session.add_all(review_responses)
db.session.commit()
def test_multiple_reviewers_with_different_subsets_of_candidates_and_reviews_completed(self):
self.seed_static_data()
self.setup_multiple_reviewers_with_different_subsets_of_candidates_and_reviews_completed()
params = {'event_id': 1}
header = self.get_auth_header_for('r1@r.com')
response1 = self.app.get('/api/v1/review', headers=header, data=params)
data1 = json.loads(response1.data)
header = self.get_auth_header_for('r2@r.com')
response2 = self.app.get('/api/v1/review', headers=header, data=params)
data2 = json.loads(response2.data)
header = self.get_auth_header_for('r3@r.com')
response3 = self.app.get('/api/v1/review', headers=header, data=params)
data3 = json.loads(response3.data)
header = self.get_auth_header_for('r4@r.com')
response4 = self.app.get('/api/v1/review', headers=header, data=params)
data4 = json.loads(response4.data)
self.assertEqual(data1['reviews_remaining_count'], 3)
self.assertEqual(data2['reviews_remaining_count'], 1)
self.assertEqual(data3['reviews_remaining_count'], 2)
self.assertEqual(data4['reviews_remaining_count'], 0)
def test_skipping(self):
self.seed_static_data()
self.setup_one_reviewer_three_candidates()
params = {'event_id': 1, 'skip': 1}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['response']['user_id'], 6)
self.assertEqual(data['response']['answers'][0]['value'], 'I want to do a PhD.')
self.assertEqual(data['user']['affiliation'], 'RU')
self.assertEqual(data['user']['department'], 'Chem')
self.assertEqual(data['user']['nationality_country'], 'Botswana')
self.assertEqual(data['user']['residence_country'], 'Namibia')
self.assertEqual(data['user']['user_category'], 'Student')
def test_high_skip_defaults_to_last_review(self):
self.seed_static_data()
self.setup_one_reviewer_three_candidates()
params = {'event_id': 1, 'skip': 5}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['response']['user_id'], 7)
self.assertEqual(data['response']['answers'][1]['value'], 'I will share by tutoring.')
self.assertEqual(data['user']['affiliation'], 'UFH')
self.assertEqual(data['user']['department'], 'Phys')
self.assertEqual(data['user']['nationality_country'], 'Zimbabwe')
self.assertEqual(data['user']['residence_country'], 'Mozambique')
self.assertEqual(data['user']['user_category'], 'MSc')
def setup_candidate_who_has_applied_to_multiple_events(self):
responses = [
Response(1, 5, True),
Response(2, 5, True)
]
db.session.add_all(responses)
db.session.commit()
answers = [
Answer(1, 1, 'I will learn alot.'),
Answer(1, 2, 'I will share by doing talks.'),
Answer(2, 3, 'Yes I worked on a vision task.'),
Answer(2, 4, 'Yes I want the travel award.')
]
db.session.add_all(answers)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1),
ResponseReviewer(2, 1)
]
db.session.add_all(response_reviewers)
db.session.commit()
def test_filtering_on_event_when_candidate_has_applied_to_more_than(self):
self.seed_static_data()
self.setup_candidate_who_has_applied_to_multiple_events()
params = {'event_id': 2}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews_remaining_count'], 1)
self.assertEqual(data['response']['user_id'], 5)
self.assertEqual(data['response']['answers'][0]['value'], 'Yes I worked on a vision task.')
self.assertEqual(data['user']['affiliation'], 'UWC')
self.assertEqual(data['user']['department'], 'CS')
self.assertEqual(data['user']['nationality_country'], 'South Africa')
self.assertEqual(data['user']['residence_country'], 'Egypt')
self.assertEqual(data['user']['user_category'], 'Honours')
def setup_multi_choice_answer(self):
response = Response(1, 5, True)
db.session.add(response)
db.session.commit()
answer = Answer(1, 5, 'indaba-2017')
db.session.add(answer)
db.session.commit()
response_reviewer = ResponseReviewer(1, 1)
db.session.add(response_reviewer)
db.session.commit()
def test_multi_choice_answers_use_label_instead_of_value(self):
self.seed_static_data()
self.setup_multi_choice_answer()
params = {'event_id': 1}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/review', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['response']['answers'][0]['value'], 'Yes, I attended the 2017 Indaba')
def test_review_response_not_found(self):
self.seed_static_data()
params = {'id': 55}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/reviewresponse', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(response.status_code, REVIEW_RESPONSE_NOT_FOUND[1])
def setup_review_response(self):
response = Response(1, 5, True)
db.session.add(response)
db.session.commit()
answer = Answer(1, 1, 'To learn alot')
db.session.add(answer)
db.session.commit()
self.review_response = ReviewResponse(1, 1, 1)
self.review_response.review_scores.append(ReviewScore(1, 'answer1'))
self.review_response.review_scores.append(ReviewScore(2, 'answer2'))
db.session.add(self.review_response)
db.session.commit()
db.session.flush()
def test_review_response(self):
self.seed_static_data()
self.setup_review_response()
params = {'id': self.review_response.id}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/reviewresponse', headers=header, data=params)
data = json.loads(response.data)
print(data)
self.assertEqual(data['review_form']['id'], 1)
self.assertEqual(data['review_response']['reviewer_user_id'], 1)
self.assertEqual(data['review_response']['response_id'], 1)
self.assertEqual(data['review_response']['scores'][0]['value'], 'answer1')
self.assertEqual(data['review_response']['scores'][1]['value'], 'answer2')
def test_prevent_saving_review_response_reviewer_was_not_assigned_to_response(self):
self.seed_static_data()
params = json.dumps({'review_form_id': 1, 'response_id': 1, 'scores': [{'review_question_id': 1, 'value': 'test_answer'}]})
header = self.get_auth_header_for('r1@r.com')
response = self.app.post('/api/v1/reviewresponse', headers=header, data=params, content_type='application/json')
self.assertEqual(response.status_code, FORBIDDEN[1])
def setup_response_reviewer(self):
response = Response(1, 5, True)
db.session.add(response)
db.session.commit()
response_reviewer = ResponseReviewer(1, 1)
db.session.add(response_reviewer)
db.session.commit()
def test_saving_review_response(self):
self.seed_static_data()
self.setup_response_reviewer()
params = json.dumps({'review_form_id': 1, 'response_id': 1, 'scores': [{'review_question_id': 1, 'value': 'test_answer'}]})
header = self.get_auth_header_for('r1@r.com')
response = self.app.post('/api/v1/reviewresponse', headers=header, data=params, content_type='application/json')
review_scores = db.session.query(ReviewScore).filter_by(review_response_id=1).all()
self.assertEqual(response.status_code, 201)
self.assertEqual(len(review_scores), 1)
self.assertEqual(review_scores[0].value, 'test_answer')
def setup_existing_review_response(self):
response = Response(1, 5, True)
db.session.add(response)
db.session.commit()
response_reviewer = ResponseReviewer(1, 1)
db.session.add(response_reviewer)
db.session.commit()
review_response = ReviewResponse(1, 1, 1)
review_response.review_scores = [ReviewScore(1, 'test_answer1'), ReviewScore(2, 'test_answer2')]
db.session.add(review_response)
db.session.commit()
def test_updating_review_response(self):
self.seed_static_data()
self.setup_existing_review_response()
params = json.dumps({'review_form_id': 1, 'response_id': 1, 'scores': [{'review_question_id': 1, 'value': 'test_answer3'}, {'review_question_id': 2, 'value': 'test_answer4'}]})
header = self.get_auth_header_for('r1@r.com')
response = self.app.put('/api/v1/reviewresponse', headers=header, data=params, content_type='application/json')
review_scores = db.session.query(ReviewScore).filter_by(review_response_id=1).order_by(ReviewScore.review_question_id).all()
self.assertEqual(response.status_code, 200)
self.assertEqual(len(review_scores), 2)
self.assertEqual(review_scores[0].value, 'test_answer3')
self.assertEqual(review_scores[1].value, 'test_answer4')
def test_user_cant_assign_responsesreviewer_without_system_or_event_admin_role(self):
self.seed_static_data()
params = {'event_id': 1, 'reviewer_user_email': 'r2@r.com', 'num_reviews': 10}
header = self.get_auth_header_for('c1@c.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
self.assertEqual(response.status_code, FORBIDDEN[1])
def test_reviewer_not_found(self):
self.seed_static_data()
params = {'event_id': 1, 'reviewer_user_email': 'non_existent@user.com', 'num_reviews': 10}
header = self.get_auth_header_for('sa@sa.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
self.assertEqual(response.status_code, USER_NOT_FOUND[1])
def test_add_reviewer_with_no_roles(self):
self.seed_static_data()
params = {'event_id': 1, 'reviewer_user_email': 'r1@r.com', 'num_reviews': 10}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
event_roles = db.session.query(EventRole).filter_by(user_id=1, event_id=1).all()
self.assertEqual(len(event_roles), 1)
self.assertEqual(event_roles[0].role, 'reviewer')
def test_add_reviewer_with_a_role(self):
self.seed_static_data()
params = {'event_id': 1, 'reviewer_user_email': 'ea@ea.com', 'num_reviews': 10}
header = self.get_auth_header_for('sa@sa.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
event_roles = db.session.query(EventRole).filter_by(user_id=10, event_id=1).order_by(EventRole.id).all()
self.assertEqual(len(event_roles), 2)
self.assertEqual(event_roles[0].role, 'admin')
self.assertEqual(event_roles[1].role, 'reviewer')
def setup_responses_without_reviewers(self):
responses = [
Response(1, 5, True),
Response(1, 6, True),
Response(1, 7, True),
Response(1, 8, True)
]
db.session.add_all(responses)
db.session.commit()
def test_adding_first_reviewer(self):
self.seed_static_data()
self.setup_responses_without_reviewers()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 4}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).filter_by(reviewer_user_id=3).all()
self.assertEqual(response.status_code, 201)
self.assertEqual(len(response_reviewers), 4)
def test_limit_of_num_reviews(self):
self.seed_static_data()
self.setup_responses_without_reviewers()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 3}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).filter_by(reviewer_user_id=3).all()
self.assertEqual(len(response_reviewers), 3)
def setup_reviewer_with_own_response(self):
responses = [
Response(1, 3, True), # reviewer
Response(1, 5, True) # someone else
]
db.session.add_all(responses)
db.session.commit()
def test_reviewer_does_not_get_assigned_to_own_response(self):
self.seed_static_data()
self.setup_reviewer_with_own_response()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 3}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).filter_by(reviewer_user_id=3).all()
self.assertEqual(len(response_reviewers), 1)
self.assertEqual(response_reviewers[0].response_id, 2)
def setup_withdrawn_and_unsubmitted_responses(self):
responses = [
Response(1, 5, is_submitted=False, is_withdrawn=False),
Response(1, 6, is_submitted=True, is_withdrawn=True),
Response(1, 7, is_submitted=True, is_withdrawn=False)
]
db.session.add_all(responses)
db.session.commit()
def test_withdrawn_and_unsubmitted_responses_are_not_assigned_reviewers(self):
self.seed_static_data()
self.setup_withdrawn_and_unsubmitted_responses()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 3}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).filter_by(reviewer_user_id=3).all()
self.assertEqual(len(response_reviewers), 1)
self.assertEqual(response_reviewers[0].response_id, 3)
def setup_response_with_three_reviewers(self):
response = Response(1, 5, True)
db.session.add(response)
db.session.commit()
response_reviewers = [
ResponseReviewer(1, 1),
ResponseReviewer(1, 2),
ResponseReviewer(1, 4)
]
db.session.add_all(response_reviewers)
db.session.commit()
def test_response_with_three_reviewers_does_not_get_assigned_another_reviewer(self):
self.seed_static_data()
self.setup_response_with_three_reviewers()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 3}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).filter_by(reviewer_user_id=3).all()
self.assertEqual(len(response_reviewers), 0)
def setup_responsereview_with_different_reviewer(self):
response = Response(1, 5, is_submitted=True)
db.session.add(response)
db.session.commit()
response_reviewer = ResponseReviewer(1, 1)
db.session.add(response_reviewer)
db.session.commit()
def test_response_will_get_multiple_reviewers_assigned(self):
self.seed_static_data()
self.setup_responsereview_with_different_reviewer()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 3}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).order_by(ResponseReviewer.reviewer_user_id).all()
self.assertEqual(len(response_reviewers), 2)
self.assertEqual(response_reviewers[0].reviewer_user_id, 1)
self.assertEqual(response_reviewers[1].reviewer_user_id, 3)
def setup_reviewer_is_not_assigned_to_response_more_than_once(self):
response = Response(1,5,is_submitted=True)
db.session.add(response)
db.session.commit()
def setup_count_reviews_allocated_and_completed(self):
db.session.add_all([
EventRole('reviewer', 1, 1),
EventRole('reviewer', 2, 1),
EventRole('reviewer', 3, 1),
EventRole('reviewer', 4, 1)
])
responses = [
Response(1, 5, True), #1
Response(1, 6, True), #2
Response(1, 7, True), #3
Response(1, 8, True), #4
Response(2, 5, True), #5
Response(2, 6, True) #6
]
db.session.add_all(responses)
response_reviewers = [
ResponseReviewer(1, 2),
ResponseReviewer(2, 2),
ResponseReviewer(3, 2),
ResponseReviewer(4, 2),
ResponseReviewer(6, 2),
ResponseReviewer(2, 3),
ResponseReviewer(4, 3),
ResponseReviewer(3, 4),
ResponseReviewer(5, 1),
]
db.session.add_all(response_reviewers)
# review form, review_user_id, response_id
review_responses = [
ReviewResponse(1, 3, 2),
ReviewResponse(1, 3, 4),
ReviewResponse(1, 2, 1),
ReviewResponse(1, 2, 3),
ReviewResponse(1, 2, 4),
ReviewResponse(2, 1, 5),
ReviewResponse(2, 2, 6)
]
db.session.add_all(review_responses)
db.session.commit()
# response 1 - 1 review assigned - 1 complete
# response 2 - 2 reviews - 1 complete
# response 3 - 2 reviews - 1 complete
# response 4 - 2 reviews - 1 complete
# response 5 - 1 review - 1 complete
# response 6 - 1 review - 1 complete
# reviewer 1 - 1 review assigned (1 from event 2) - 1 complete
# reviewer 2 - 5 reviews assigned (1 from event 2)- 3 complete
# reviewer 3 - 2 reviews assigned - 2 complete
# reviewer 4 - 1 review assigned - none complete
# total assigned reviews: 9
# total required review = 6*3 = 18
# total unallocated: 18 - 9 = 9
# total completed reviews: 6
@SkipTest
def test_count_reviews_allocated_and_completed(self):
self.seed_static_data()
self.setup_count_reviews_allocated_and_completed()
header = self.get_auth_header_for('ea@ea.com')
params = {'event_id': 1}
response = self.app.get('/api/v1/reviewassignment', headers=header, data=params)
data = json.loads(response.data)
data = sorted(data, key=lambda k: k['email'])
LOGGER.debug(data)
self.assertEqual(len(data),3)
self.assertEqual(data[0]['email'], 'r2@r.com')
self.assertEqual(data[0]['reviews_allocated'], 4)
self.assertEqual(data[0]['reviews_completed'], 3)
self.assertEqual(data[1]['email'], 'r3@r.com')
self.assertEqual(data[1]['reviews_allocated'], 2)
self.assertEqual(data[1]['reviews_completed'], 2)
self.assertEqual(data[2]['email'], 'r4@r.com')
self.assertEqual(data[2]['reviews_allocated'], 1)
self.assertEqual(data[2]['reviews_completed'], 0)
def test_reviewer_is_not_assigned_to_response_more_than_once(self):
self.seed_static_data()
self.setup_reviewer_is_not_assigned_to_response_more_than_once()
params = {'event_id': 1, 'reviewer_user_email': 'r3@r.com', 'num_reviews': 3}
header = self.get_auth_header_for('ea@ea.com')
response = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response2 = self.app.post('/api/v1/reviewassignment', headers=header, data=params)
response_reviewers = db.session.query(ResponseReviewer).all()
self.assertEqual(len(response_reviewers), 1)
def setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores(self):
second_reviewer = EventRole('reviewer', 2, 1)
db.session.add(second_reviewer)
db.session.commit()
responses = [
Response(1, 5, is_submitted=True),
Response(1, 6, is_submitted=True),
Response(1, 7, is_submitted=True)
]
db.session.add_all(responses)
db.session.commit()
final_verdict_options = [
{'label': 'Yes', 'value': 2},
{'label': 'No', 'value': 0},
{'label': 'Maybe', 'value': 1},
]
verdict_question = ReviewQuestion(1, None, None, 'Final Verdict', 'multi-choice', None, final_verdict_options, True, 3, None, None, 0)
db.session.add(verdict_question)
db.session.commit()
review_responses = [
ReviewResponse(1,3,1),
ReviewResponse(1,3,2),
ReviewResponse(1,2,1),
ReviewResponse(1,2,2),
ReviewResponse(1,3,3)
]
review_responses[0].review_scores = [ReviewScore(1, '23'), ReviewScore(5, '1')]
review_responses[1].review_scores = [ReviewScore(1, '55'), ReviewScore(5, '2')]
review_responses[2].review_scores = [ReviewScore(1, '45'), ReviewScore(2, '67'), ReviewScore(5, 'No')]
review_responses[3].review_scores = [ReviewScore(1, '220'), ReviewScore(5, '2')]
review_responses[4].review_scores = [ReviewScore(1, '221'), ReviewScore(5, '1')]
db.session.add_all(review_responses)
db.session.commit()
def test_review_history_returned(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(len(data['reviews']), 3)
self.assertEqual(data['num_entries'], 3)
self.assertEqual(data['reviews'][0]['review_response_id'], 1)
self.assertEqual(data['reviews'][0]['nationality_country'], 'South Africa')
self.assertEqual(data['reviews'][0]['residence_country'], 'Egypt')
self.assertEqual(data['reviews'][0]['affiliation'], 'UWC')
self.assertEqual(data['reviews'][0]['department'], 'CS')
self.assertEqual(data['reviews'][0]['user_category'], 'Honours')
self.assertEqual(data['reviews'][0]['final_verdict'], 'Maybe')
self.assertEqual(data['reviews'][1]['review_response_id'], 2)
self.assertEqual(data['reviews'][1]['nationality_country'], 'Botswana')
self.assertEqual(data['reviews'][1]['residence_country'], 'Namibia')
self.assertEqual(data['reviews'][1]['affiliation'], 'RU')
self.assertEqual(data['reviews'][1]['department'], 'Chem')
self.assertEqual(data['reviews'][1]['user_category'], 'Student')
self.assertEqual(data['reviews'][1]['final_verdict'], 'Yes')
self.assertEqual(data['reviews'][2]['review_response_id'], 5)
self.assertEqual(data['reviews'][2]['nationality_country'], 'Zimbabwe')
self.assertEqual(data['reviews'][2]['residence_country'], 'Mozambique')
self.assertEqual(data['reviews'][2]['affiliation'], 'UFH')
self.assertEqual(data['reviews'][2]['department'], 'Phys')
self.assertEqual(data['reviews'][2]['user_category'], 'MSc')
self.assertEqual(data['reviews'][2]['final_verdict'], 'Maybe')
def test_brings_back_only_logged_in_reviewer_reviewresponses(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r2@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(len(data['reviews']), 2)
self.assertEqual(data['reviews'][0]['review_response_id'], 3)
self.assertEqual(data['reviews'][1]['review_response_id'], 4)
def test_logged_in_user_not_reviewer(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('c1@c.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
self.assertEqual(response.status_code, FORBIDDEN[1])
def setup_reviewer_with_no_reviewresponses(self):
reviewer = EventRole('reviewer', 1, 1)
db.session.add(reviewer)
db.session.commit()
def test_reviewer_with_no_reviewresponses(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
self.setup_reviewer_with_no_reviewresponses()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['num_entries'], 0)
self.assertEqual(data['reviews'], [])
def test_order_by_reviewresponseid(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['review_response_id'], 1)
self.assertEqual(data['reviews'][1]['review_response_id'], 2)
self.assertEqual(data['reviews'][2]['review_response_id'], 5)
def setup_reviewresponses_with_unordered_timestamps(self):
final_verdict_options = [
{'label': 'Yes', 'value': 2},
{'label': 'No', 'value': 0},
{'label': 'Maybe', 'value': 1},
]
verdict_question = ReviewQuestion(1, None, None, 'Final Verdict', 'multi-choice', None, final_verdict_options, True, 3, None, None, 0)
db.session.add(verdict_question)
db.session.commit()
responses = [
Response(1, 5, is_submitted=True),
Response(1, 6, is_submitted=True),
Response(1, 7, is_submitted=True)
]
db.session.add_all(responses)
db.session.commit()
review_response_1 = ReviewResponse(1,3,1)
review_response_2 = ReviewResponse(1,3,2)
review_response_3 = ReviewResponse(1,3,3)
review_response_1.submitted_timestamp = datetime(2019, 1, 1)
review_response_2.submitted_timestamp = datetime(2018, 1, 1)
review_response_3.submitted_timestamp = datetime(2018, 6, 6)
review_responses = [review_response_1, review_response_2, review_response_3]
review_responses[0].review_scores = [ReviewScore(1, '67'), ReviewScore(5, 'Yes')]
review_responses[1].review_scores = [ReviewScore(1, '23'), ReviewScore(5, 'Yes')]
review_responses[2].review_scores = [ReviewScore(1, '53'), ReviewScore(5, 'Yes')]
db.session.add_all(review_responses)
db.session.commit()
def test_order_by_submittedtimestamp(self):
self.seed_static_data()
self.setup_reviewresponses_with_unordered_timestamps()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'submitted_timestamp'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
LOGGER.debug(data)
self.assertEqual(data['reviews'][0]['submitted_timestamp'], '2018-01-01T00:00:00')
self.assertEqual(data['reviews'][1]['submitted_timestamp'], '2018-06-06T00:00:00')
self.assertEqual(data['reviews'][2]['submitted_timestamp'], '2019-01-01T00:00:00')
def test_order_by_nationalitycountry(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'nationality_country'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['nationality_country'], 'Botswana')
self.assertEqual(data['reviews'][1]['nationality_country'], 'South Africa')
self.assertEqual(data['reviews'][2]['nationality_country'], 'Zimbabwe')
def test_order_by_residencecountry(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'residence_country'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['residence_country'], 'Egypt')
self.assertEqual(data['reviews'][1]['residence_country'], 'Mozambique')
self.assertEqual(data['reviews'][2]['residence_country'], 'Namibia')
def test_order_by_affiliation(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'affiliation'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['affiliation'], 'RU')
self.assertEqual(data['reviews'][1]['affiliation'], 'UFH')
self.assertEqual(data['reviews'][2]['affiliation'], 'UWC')
def test_order_by_department(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'department'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['department'], 'CS') # ascii ordering orders capital letters before lowercase
self.assertEqual(data['reviews'][1]['department'], 'Chem')
self.assertEqual(data['reviews'][2]['department'], 'Phys')
def test_order_by_usercategory(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'user_category'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['user_category'], 'Honours')
self.assertEqual(data['reviews'][1]['user_category'], 'MSc')
self.assertEqual(data['reviews'][2]['user_category'], 'Student')
def test_order_by_finalverdict(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 10, 'sort_column' : 'final_verdict'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['reviews'][0]['final_verdict'], 'Maybe')
self.assertEqual(data['reviews'][1]['final_verdict'], 'Maybe')
self.assertEqual(data['reviews'][2]['final_verdict'], 'Yes')
def setup_two_extra_responses_for_reviewer3(self):
responses = [
Response(1, 8, is_submitted=True),
Response(1, 1, is_submitted=True)
]
db.session.add_all(responses)
db.session.commit()
review_responses = [
ReviewResponse(1,3,4),
ReviewResponse(1,3,5)
]
review_responses[0].review_scores = [ReviewScore(1, '89'), ReviewScore(5, 'Maybe')]
review_responses[1].review_scores = [ReviewScore(1, '75'), ReviewScore(5, 'Yes')]
db.session.add_all(review_responses)
db.session.commit()
def test_first_page_in_pagination(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
self.setup_two_extra_responses_for_reviewer3()
params ={'event_id' : 1, 'page_number' : 0, 'limit' : 2, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(len(data['reviews']), 2)
self.assertEqual(data['num_entries'], 5)
self.assertEqual(data['reviews'][0]['review_response_id'], 1)
self.assertEqual(data['reviews'][1]['review_response_id'], 2)
def test_middle_page_in_pagination(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
self.setup_two_extra_responses_for_reviewer3()
params ={'event_id' : 1, 'page_number' : 1, 'limit' : 2, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(len(data['reviews']), 2)
self.assertEqual(data['num_entries'], 5)
self.assertEqual(data['reviews'][0]['review_response_id'], 5)
self.assertEqual(data['reviews'][1]['review_response_id'], 6)
def test_last_page_in_pagination(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
self.setup_two_extra_responses_for_reviewer3()
params ={'event_id' : 1, 'page_number' : 2, 'limit' : 2, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(len(data['reviews']), 1)
self.assertEqual(data['num_entries'], 5)
self.assertEqual(data['reviews'][0]['review_response_id'], 7)
def test_total_number_of_pages_greater_than_zero(self):
self.seed_static_data()
self.setup_reviewer_responses_finalverdict_reviewquestion_reviewresponses_and_scores()
self.setup_two_extra_responses_for_reviewer3()
params ={'event_id' : 1, 'page_number' : 2, 'limit' : 2, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r3@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['total_pages'], 3)
def test_total_number_of_pages_when_zero(self):
self.seed_static_data()
self.setup_reviewer_with_no_reviewresponses()
params ={'event_id' : 1, 'page_number' : 2, 'limit' : 2, 'sort_column' : 'review_response_id'}
header = self.get_auth_header_for('r1@r.com')
response = self.app.get('/api/v1/reviewhistory', headers=header, data=params)
data = json.loads(response.data)
self.assertEqual(data['total_pages'], 0)
| 50,461 | 2,172 | 24 |
fccd3577beac65370c5b11906b2378b151aeac75 | 10,050 | py | Python | TextAnalyzer.py | GoetzEdinger/TextAnalyzer | ca305163fce52ade2edd20d80902911fc022f175 | [
"MIT"
] | 1 | 2015-07-12T21:31:27.000Z | 2015-07-12T21:31:27.000Z | TextAnalyzer.py | GoetzEdinger/TextAnalyzer | ca305163fce52ade2edd20d80902911fc022f175 | [
"MIT"
] | null | null | null | TextAnalyzer.py | GoetzEdinger/TextAnalyzer | ca305163fce52ade2edd20d80902911fc022f175 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
'''
Created on 09.07.2015
@author: Goetz Edinger
'''
import enchant
import nltk.data
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
import string
import sys
debug = True
dict_sentence_length = {}
dict_word_length = {}
number_of_letters = 0
number_of_words = 0
number_of_sentences = 0
average_length_word = 0
number_of_foreign_words = 0
average_length_sentence = 0
length_shortest_word = 10000
length_longest_word = 0
length_of_all_words = 0
length_shortest_sentence = 10000
length_longest_sentence = 0
length_of_all_sentences = 0
number_of_words_long_sentence = 0
shortest_word = ""
longest_word = ""
########################################################
#
# Split Text into words
# Count the letters of every word
# Count how many words have how many letters
#
########################################################
########################################################
#
# Split Text into sentences
# Count the words of every sentence
# Count how many sentences have how many words
#
########################################################
########################################################
#
# Count foreign words
#
########################################################
########################################################
#
# Display results
#
########################################################
########################################################
#
# Statistics:
#
# Thresholds:
#
# for words:
# green == good ()
# yellow == acceptable ()
# red == not acceptable ()
#
# for sentences:
# green == good ()
# yellow == acceptable ()
# red == not acceptable ()
#
# Calculate the average length of the words
# Calculate the average length of the sentences
#
########################################################
if __name__ == '__main__':
main(sys.argv[1:])
| 36.948529 | 931 | 0.644279 | # -*- coding: utf-8 -*-
'''
Created on 09.07.2015
@author: Goetz Edinger
'''
import enchant
import nltk.data
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
import string
import sys
debug = True
dict_sentence_length = {}
dict_word_length = {}
number_of_letters = 0
number_of_words = 0
number_of_sentences = 0
average_length_word = 0
number_of_foreign_words = 0
average_length_sentence = 0
length_shortest_word = 10000
length_longest_word = 0
length_of_all_words = 0
length_shortest_sentence = 10000
length_longest_sentence = 0
length_of_all_sentences = 0
number_of_words_long_sentence = 0
shortest_word = ""
longest_word = ""
def count_letters(text):
global number_of_letters
number_of_letters = len(text)
########################################################
#
# Split Text into words
# Count the letters of every word
# Count how many words have how many letters
#
########################################################
def tokenize_words(text):
global number_of_words
global dict_word_length
global length_of_all_words
global length_longest_word
global length_shortest_word
global shortest_word
global longest_word
word_tokenize_list = word_tokenize(text)
count_foreign_words(word_tokenize_list)
number_of_words = len(word_tokenize_list)
for word in word_tokenize_list:
if word in [",", ".", ":", ";", "!", "?"]:
number_of_words -= 1
for word in word_tokenize_list:
if word not in [",", ".", ":", ";", "!", "?"]:
# Get the length of the word
word_length = len(word)
# Get the longest word
if word_length > length_longest_word:
length_longest_word = word_length
longest_word = word
# Get the shortest word
if word_length < length_shortest_word:
length_shortest_word = word_length
shortest_word = word
# Add word_length to length_of_all_words which is a variable to compute the average_length_word
length_of_all_words += word_length
# Look up in the Dictionary dict_word_length, if there is already a key with the same length.
if dict_word_length.has_key(word_length):
# If yes, then get the value of that key ...
old_key = dict_word_length.get(word_length)
# ... increase the value ...
new_key = old_key + 1
# ... write the key/value pair to the Dictionary dict_word_length
dict_word_length[word_length] = new_key
else:
# ... add new key (word_length) with value 1
dict_word_length[word_length] = 1
########################################################
#
# Split Text into sentences
# Count the words of every sentence
# Count how many sentences have how many words
#
########################################################
def tokenize_sentences(text):
global length_longest_sentence
global length_shortest_sentence
global dict_sentence_length
global number_of_sentences
global length_of_all_sentences
german_tokenizer = nltk.data.load('tokenizers/punkt/german.pickle')
sent_tokenize_list = german_tokenizer.tokenize(text)
number_of_sentences = len(sent_tokenize_list)
if debug:
print "\nSätze\n"
for sentence in sent_tokenize_list:
if debug:
print sentence
# Get the length of the sentence
sentence_length = len(sentence)
# Add sentence_length to length_of_all_sentences which is a variable to compute the average_length_sentence
length_of_all_sentences += sentence_length
# Get the longest sentence
if sentence_length > length_longest_sentence:
length_longest_sentence = sentence_length
# Get the shortest sentence
if sentence_length < length_shortest_sentence:
length_shortest_sentence = sentence_length
# Look up in the Dictionary dict_sentence_length, if there is already a key with the same length.
if dict_sentence_length.has_key(sentence_length):
# If yes, then get the value of that key ...
old_key = dict_sentence_length.get(sentence_length)
# ... increase the value ...
new_key = old_key + 1
# ... write the key/value pair to the Dictionary dict_sentence_length
dict_sentence_length[sentence_length] = new_key
else:
# ... add new key (sentence_length) with value 1
dict_sentence_length[sentence_length] = 1
########################################################
#
# Count foreign words
#
########################################################
def count_foreign_words(word_list):
global number_of_foreign_words
d = enchant.Dict("de_DE")
if debug:
print "Fremdwörter\n"
for word in word_list:
if not d.check(word):
if word not in [",", ".", ":", ";", "!", "?"]:
number_of_foreign_words += 1
if debug:
print word
########################################################
#
# Display results
#
########################################################
def display_results():
global number_of_letters
global number_of_words
global number_of_sentences
global average_length_word
global number_of_foreign_words
global average_length_sentence
global length_shortest_word
global length_longest_word
global length_shortest_sentence
global length_longest_sentence
global shortest_word
global longest_word
if debug:
print "Number of letters : " + str(number_of_letters)
print "Number of words : " + str(number_of_words)
print "Number of sentences : " + str(number_of_sentences)
print "Average length of words : " + str(average_length_word)
print "Number of foreign words : " + str(number_of_foreign_words)
print "Average length of sentences : " + str(average_length_sentence)
print "Length of the shortest word : " + str(length_shortest_word)
print "Shortest word : " + shortest_word
print "Length of the longest word : " + str(length_longest_word)
print "Longest word : " + longest_word
print "Length of the shortest sentence: " + str(length_shortest_sentence)
print "Length of the longest sentence : " + str(length_longest_sentence)
########################################################
#
# Statistics:
#
# Thresholds:
#
# for words:
# green == good ()
# yellow == acceptable ()
# red == not acceptable ()
#
# for sentences:
# green == good ()
# yellow == acceptable ()
# red == not acceptable ()
#
# Calculate the average length of the words
# Calculate the average length of the sentences
#
########################################################
def statistics():
global length_of_all_words
global number_of_words
global average_length_word
global length_of_all_sentences
global number_of_sentences
global average_length_sentence
# Calculate the average length of the words
average_length_word = length_of_all_words/number_of_words
# Calculate the average length of the sentences
average_length_sentence = length_of_all_sentences/number_of_sentences
def get_text_from_file(filename):
text = ""
try:
file = open(filename, 'r')
text = file.read()
return text
except:
return text
def main(argv):
text = """Raum- und Zeitangaben sind in der Relativitätstheorie keine universell gültigen Ordnungsstrukturen. Vielmehr werden der räumliche und zeitliche Abstand zweier Ereignisse oder auch deren Gleichzeitigkeit von Beobachtern mit verschiedenen Bewegungszuständen unterschiedlich beurteilt. Bewegte Objekte erweisen sich im Vergleich zum Ruhezustand in Bewegungsrichtung als verkürzt und bewegte Uhren als verlangsamt. Da jedoch jeder gleichförmig bewegte Beobachter den Standpunkt vertreten kann, er sei in Ruhe, beruhen diese Beobachtungen auf Gegenseitigkeit, das heißt, zwei relativ zueinander bewegte Beobachter sehen die Uhren des jeweils anderen langsamer gehen. Außerdem sind aus ihrer Sicht die Meterstäbe des jeweils anderen kürzer als ein Meter, wenn sie längs der Bewegungsrichtung ausgerichtet sind. Die Frage, wer die Situation korrekt beschreibt, ist hierbei prinzipiell nicht zu beantworten und daher sinnlos.
Diese Längenkontraktion und Zeitdilatation lassen sich vergleichsweise anschaulich anhand von Minkowski-Diagrammen und anhand des bekannten Zwillingsparadoxons nachvollziehen. In der mathematischen Formulierung ergeben sie sich aus der Lorentz-Transformation, die den Zusammenhang zwischen den Raum- und Zeitkoordinaten der verschiedenen Beobachter beschreibt. Diese Transformation lässt sich direkt aus den beiden obigen Axiomen und der Annahme, dass sie linear ist, herleiten.
Die meisten dieser relativistisch erklärbaren Phänomene machen sich erst bei Geschwindigkeiten bemerkbar, die im Vergleich zur Lichtgeschwindigkeit nennenswert groß sind. Solche Geschwindigkeiten werden im Alltag nicht annähernd erreicht. John Smith ist ein Märchenerzähler. Der 1. Mai ist ein Feiertag. Hol's der Kuckuck!"""
if len(argv) > 0:
text = get_text_from_file(argv[0])
if len(argv) > 1:
print "usage: python TextAnalyzer"
print "or"
print "python TextAnalyzer <filename>"
else:
if len(text) > 2:
count_letters(text)
tokenize_words(text)
tokenize_sentences(text)
statistics()
display_results()
else:
print "Something went wrong ..."
if __name__ == '__main__':
main(sys.argv[1:])
| 7,907 | 0 | 184 |
6dcb4bfbb7a76895bfedb5f55a7a7983bbd60f32 | 11,375 | py | Python | fdk/context.py | jlowe000/fdk-python | d7b96bcb974733792cc29d0afd3d86b3f120ac54 | [
"ECL-2.0",
"Apache-2.0"
] | 44 | 2017-11-03T14:59:07.000Z | 2022-01-17T18:58:07.000Z | fdk/context.py | jlowe000/fdk-python | d7b96bcb974733792cc29d0afd3d86b3f120ac54 | [
"ECL-2.0",
"Apache-2.0"
] | 66 | 2017-11-28T16:01:55.000Z | 2021-07-28T11:20:13.000Z | fdk/context.py | jlowe000/fdk-python | d7b96bcb974733792cc29d0afd3d86b3f120ac54 | [
"ECL-2.0",
"Apache-2.0"
] | 16 | 2017-12-08T15:58:43.000Z | 2022-01-28T16:08:36.000Z | #
# Copyright (c) 2019, 2020 Oracle and/or its affiliates. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import datetime as dt
import io
import os
import random
from fdk import constants
from fdk import headers as hs
from fdk import log
from collections import namedtuple
def context_from_format(format_def: str, **kwargs) -> (
InvokeContext, io.BytesIO):
"""
Creates a context from request
:param format_def: function format
:type format_def: str
:param kwargs: request-specific map of parameters
:return: invoke context and data
:rtype: tuple
"""
app_id = os.environ.get(constants.FN_APP_ID)
fn_id = os.environ.get(constants.FN_ID)
app_name = os.environ.get(constants.FN_APP_NAME)
fn_name = os.environ.get(constants.FN_NAME)
# the tracing enabled env variable is passed as a "0" or "1" string
# and therefore needs to be converted appropriately.
is_tracing_enabled = os.environ.get(constants.OCI_TRACING_ENABLED)
is_tracing_enabled = (
bool(int(is_tracing_enabled))
if is_tracing_enabled is not None
else False
)
trace_collector_url = os.environ.get(constants.OCI_TRACE_COLLECTOR_URL)
if format_def == constants.HTTPSTREAM:
data = kwargs.get("data")
headers = kwargs.get("headers")
# zipkin tracing http headers
trace_id = span_id = parent_span_id = is_sampled = trace_flags = None
tracing_context = None
if is_tracing_enabled:
# we generate the trace_id if tracing is enabled
# but the traceId zipkin header is missing.
trace_id = headers.get(constants.X_B3_TRACEID)
trace_id = generate_id() if trace_id is None else trace_id
span_id = headers.get(constants.X_B3_SPANID)
parent_span_id = headers.get(constants.X_B3_PARENTSPANID)
# span_id is also generated if the zipkin header is missing.
span_id = generate_id() if span_id is None else span_id
# is_sampled should be a boolean in the form of a "0/1" but
# legacy samples have them as "False/True"
is_sampled = headers.get(constants.X_B3_SAMPLED)
is_sampled = int(is_sampled) if is_sampled is not None else 1
# not currently used but is defined by the zipkin headers standard
trace_flags = headers.get(constants.X_B3_FLAGS)
# tracing context will be an empty object
# if tracing is not enabled or the flag is missing.
# this prevents the customer code from failing if they decide to
# disable tracing. An empty tracing context will not
# emit spans due to is_sampled being None.
tracing_context = TracingContext(
is_tracing_enabled,
trace_collector_url,
trace_id,
span_id,
parent_span_id,
is_sampled,
trace_flags
)
method = headers.get(constants.FN_HTTP_METHOD)
request_url = headers.get(constants.FN_HTTP_REQUEST_URL)
deadline = headers.get(constants.FN_DEADLINE)
call_id = headers.get(constants.FN_CALL_ID)
content_type = headers.get(constants.CONTENT_TYPE)
ctx = InvokeContext(
app_id, app_name, fn_id, fn_name, call_id,
content_type=content_type,
deadline=deadline,
config=os.environ,
headers=headers,
method=method,
request_url=request_url,
fn_format=constants.HTTPSTREAM,
tracing_context=tracing_context,
)
return ctx, data
| 32.78098 | 78 | 0.63833 | #
# Copyright (c) 2019, 2020 Oracle and/or its affiliates. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import datetime as dt
import io
import os
import random
from fdk import constants
from fdk import headers as hs
from fdk import log
from collections import namedtuple
class InvokeContext(object):
def __init__(self, app_id, app_name, fn_id, fn_name, call_id,
content_type="application/octet-stream",
deadline=None, config=None,
headers=None, request_url=None,
method="POST", fn_format=None,
tracing_context=None):
"""
Request context here to be a placeholder
for request-specific attributes
:param app_id: Fn App ID
:type app_id: str
:param app_name: Fn App name
:type app_name: str
:param fn_id: Fn App Fn ID
:type fn_id: str
:param fn_name: Fn name
:type fn_name: str
:param call_id: Fn call ID
:type call_id: str
:param content_type: request content type
:type content_type: str
:param deadline: request deadline
:type deadline: str
:param config: an app/fn config
:type config: dict
:param headers: request headers
:type headers: dict
:param request_url: request URL
:type request_url: str
:param method: request method
:type method: str
:param fn_format: function format
:type fn_format: str
:param tracing_context: tracing context
:type tracing_context: TracingContext
"""
self.__app_id = app_id
self.__fn_id = fn_id
self.__call_id = call_id
self.__config = config if config else {}
self.__headers = headers if headers else {}
self.__http_headers = {}
self.__deadline = deadline
self.__content_type = content_type
self._request_url = request_url
self._method = method
self.__response_headers = {}
self.__fn_format = fn_format
self.__app_name = app_name
self.__fn_name = fn_name
self.__tracing_context = tracing_context if tracing_context else None
log.log("request headers. gateway: {0} {1}"
.format(self.__is_gateway(), headers))
if self.__is_gateway():
self.__headers = hs.decap_headers(headers, True)
self.__http_headers = hs.decap_headers(headers, False)
def AppID(self):
return self.__app_id
def AppName(self):
return self.__app_name
def FnID(self):
return self.__fn_id
def FnName(self):
return self.__fn_name
def CallID(self):
return self.__call_id
def Config(self):
return self.__config
def Headers(self):
return self.__headers
def HTTPHeaders(self):
return self.__http_headers
def Format(self):
return self.__fn_format
def TracingContext(self):
return self.__tracing_context
def Deadline(self):
if self.__deadline is None:
now = dt.datetime.now(dt.timezone.utc).astimezone()
now += dt.timedelta(0, float(constants.DEFAULT_DEADLINE))
return now.isoformat()
return self.__deadline
def SetResponseHeaders(self, headers, status_code):
log.log("setting headers. gateway: {0}".format(self.__is_gateway()))
if self.__is_gateway():
headers = hs.encap_headers(headers, status=status_code)
for k, v in headers.items():
self.__response_headers[k.lower()] = v
def GetResponseHeaders(self):
return self.__response_headers
def RequestURL(self):
return self._request_url
def Method(self):
return self._method
def __is_gateway(self):
return (constants.FN_INTENT in self.__headers
and self.__headers.get(constants.FN_INTENT)
== constants.INTENT_HTTP_REQUEST)
class TracingContext(object):
def __init__(self, is_tracing_enabled, trace_collector_url,
trace_id, span_id, parent_span_id,
is_sampled, flags):
"""
Tracing context here to be a placeholder
for tracing-specific attributes
:param is_tracing_enabled: tracing enabled flag
:type is_tracing_enabled: bool
:param trace_collector_url: APM Trace Collector Endpoint URL
:type trace_collector_url: str
:param trace_id: Trace ID
:type trace_id: str
:param span_id: Span ID
:type span_id: str
:param parent_span_id: Parent Span ID
:type parent_span_id: str
:param is_sampled: Boolean for emmitting spans
:type is_sampled: int (0 or 1)
:param flags: Debug flags
:type flags: int (0 or 1)
"""
self.__is_tracing_enabled = is_tracing_enabled
self.__trace_collector_url = trace_collector_url
self.__trace_id = trace_id
self.__span_id = span_id
self.__parent_span_id = parent_span_id
self.__is_sampled = is_sampled
self.__flags = flags
self.__app_name = os.environ.get(constants.FN_APP_NAME)
self.__app_id = os.environ.get(constants.FN_APP_ID)
self.__fn_name = os.environ.get(constants.FN_NAME)
self.__fn_id = os.environ.get(constants.FN_ID)
self.__zipkin_attrs = self.__create_zipkin_attrs(is_tracing_enabled)
def is_tracing_enabled(self):
return self.__is_tracing_enabled
def trace_collector_url(self):
return self.__trace_collector_url
def trace_id(self):
return self.__trace_id
def span_id(self):
return self.__span_id
def parent_span_id(self):
return self.__parent_span_id
def is_sampled(self):
return bool(self.__is_sampled)
def flags(self):
return self.__flags
def zipkin_attrs(self):
return self.__zipkin_attrs
# this is a helper method specific for py_zipkin
def __create_zipkin_attrs(self, is_tracing_enabled):
ZipkinAttrs = namedtuple(
"ZipkinAttrs",
"trace_id, span_id, parent_span_id, is_sampled, flags"
)
trace_id = self.__trace_id
span_id = self.__span_id
parent_span_id = self.__parent_span_id
is_sampled = bool(self.__is_sampled)
trace_flags = self.__flags
# As the fnLb sends the parent_span_id as the span_id
# assign the parent span id as the span id.
if is_tracing_enabled:
parent_span_id = span_id
span_id = generate_id()
zipkin_attrs = ZipkinAttrs(
trace_id,
span_id,
parent_span_id,
is_sampled,
trace_flags
)
return zipkin_attrs
def service_name(self, override=None):
# in case of missing app and function name env variables
service_name = (
override
if override is not None
else str(self.__app_name) + "::" + str(self.__fn_name)
)
return service_name.lower()
def annotations(self):
annotations = {
"generatedBy": "faas",
"appName": self.__app_name,
"appID": self.__app_id,
"fnName": self.__fn_name,
"fnID": self.__fn_id,
}
return annotations
def generate_id():
return "{:016x}".format(random.getrandbits(64))
def context_from_format(format_def: str, **kwargs) -> (
InvokeContext, io.BytesIO):
"""
Creates a context from request
:param format_def: function format
:type format_def: str
:param kwargs: request-specific map of parameters
:return: invoke context and data
:rtype: tuple
"""
app_id = os.environ.get(constants.FN_APP_ID)
fn_id = os.environ.get(constants.FN_ID)
app_name = os.environ.get(constants.FN_APP_NAME)
fn_name = os.environ.get(constants.FN_NAME)
# the tracing enabled env variable is passed as a "0" or "1" string
# and therefore needs to be converted appropriately.
is_tracing_enabled = os.environ.get(constants.OCI_TRACING_ENABLED)
is_tracing_enabled = (
bool(int(is_tracing_enabled))
if is_tracing_enabled is not None
else False
)
trace_collector_url = os.environ.get(constants.OCI_TRACE_COLLECTOR_URL)
if format_def == constants.HTTPSTREAM:
data = kwargs.get("data")
headers = kwargs.get("headers")
# zipkin tracing http headers
trace_id = span_id = parent_span_id = is_sampled = trace_flags = None
tracing_context = None
if is_tracing_enabled:
# we generate the trace_id if tracing is enabled
# but the traceId zipkin header is missing.
trace_id = headers.get(constants.X_B3_TRACEID)
trace_id = generate_id() if trace_id is None else trace_id
span_id = headers.get(constants.X_B3_SPANID)
parent_span_id = headers.get(constants.X_B3_PARENTSPANID)
# span_id is also generated if the zipkin header is missing.
span_id = generate_id() if span_id is None else span_id
# is_sampled should be a boolean in the form of a "0/1" but
# legacy samples have them as "False/True"
is_sampled = headers.get(constants.X_B3_SAMPLED)
is_sampled = int(is_sampled) if is_sampled is not None else 1
# not currently used but is defined by the zipkin headers standard
trace_flags = headers.get(constants.X_B3_FLAGS)
# tracing context will be an empty object
# if tracing is not enabled or the flag is missing.
# this prevents the customer code from failing if they decide to
# disable tracing. An empty tracing context will not
# emit spans due to is_sampled being None.
tracing_context = TracingContext(
is_tracing_enabled,
trace_collector_url,
trace_id,
span_id,
parent_span_id,
is_sampled,
trace_flags
)
method = headers.get(constants.FN_HTTP_METHOD)
request_url = headers.get(constants.FN_HTTP_REQUEST_URL)
deadline = headers.get(constants.FN_DEADLINE)
call_id = headers.get(constants.FN_CALL_ID)
content_type = headers.get(constants.CONTENT_TYPE)
ctx = InvokeContext(
app_id, app_name, fn_id, fn_name, call_id,
content_type=content_type,
deadline=deadline,
config=os.environ,
headers=headers,
method=method,
request_url=request_url,
fn_format=constants.HTTPSTREAM,
tracing_context=tracing_context,
)
return ctx, data
| 2,729 | 4,417 | 69 |
e8b50469be455015225ee1e1a7a899709b2a0f60 | 1,622 | py | Python | models/user.py | CodeByMini/thefriendzone | 84c3dd14ba2b0be7cf3cd681f761d3d6780498d4 | [
"Apache-2.0"
] | null | null | null | models/user.py | CodeByMini/thefriendzone | 84c3dd14ba2b0be7cf3cd681f761d3d6780498d4 | [
"Apache-2.0"
] | null | null | null | models/user.py | CodeByMini/thefriendzone | 84c3dd14ba2b0be7cf3cd681f761d3d6780498d4 | [
"Apache-2.0"
] | null | null | null | from models.status import Status
from datetime import datetime
class User:
"""
The User object represents a person,
bot or entity registered as a user
in the application.
username:
(str)
password:
(str)
last_seen:
(str)
timestamp when the user was latest
recorded as active. Mutates with update_last_seen().
buddies:
(dict <str, User>)
status:
(Status enum)
"""
@property
@username.setter
@property
@password.setter
@property
@property | 22.84507 | 71 | 0.600493 | from models.status import Status
from datetime import datetime
class User:
"""
The User object represents a person,
bot or entity registered as a user
in the application.
username:
(str)
password:
(str)
last_seen:
(str)
timestamp when the user was latest
recorded as active. Mutates with update_last_seen().
buddies:
(dict <str, User>)
status:
(Status enum)
"""
def __init__(self, username, password):
self._username = username
self._password = password
self._last_seen = None
self._buddies = dict()
self._status = Status.offline
def __repr__(self):
return f"User(username: {self._username}"
def __str__(self):
return f"{self._username}, last seen: {self._last_seen}"
@property
def username(self):
return self._username
@username.setter
def username(self, val):
self._username = val
@property
def password(self):
return self._password
@password.setter
def password(self, val):
self._password = val
@property
def buddies(self):
return self._buddies
@property
def last_seen(self):
return self._last_seen
def update_last_seen(self):
self._last_seen = datetime.now().strftime("%m/%d/%Y, %H:%M:%S")
def add_buddy(self, buddy):
self._buddies['buddies'] = buddy
def remove_buddy(self, username) -> bool:
try:
del self._buddies[username]
except KeyError:
return False
return True | 740 | 0 | 317 |
ebc9c14fe5a89e7e13d7881bbe679c8d68478cf4 | 1,972 | py | Python | entertainmentCenter.py | Arie3301/udacity-movietrailers | b23b1981ba71fd1535e7a77aa4332413586ac56e | [
"MIT"
] | null | null | null | entertainmentCenter.py | Arie3301/udacity-movietrailers | b23b1981ba71fd1535e7a77aa4332413586ac56e | [
"MIT"
] | null | null | null | entertainmentCenter.py | Arie3301/udacity-movietrailers | b23b1981ba71fd1535e7a77aa4332413586ac56e | [
"MIT"
] | null | null | null | import media
import fresh_tomatoes
def main():
"""Generate an HTML page to show movies with corresponding information."""
toy_story = media.Movie(
"Toy Story",
"A story of a boy and his toys that come to life.",
"https://upload.wikimedia.org/wikipedia/en/1/13/Toy_Story.jpg",
"https://www.youtube.com/watch?v=KYz2wyBy3kc"
)
forrest_gump = media.Movie(
"Forrest Gump",
"A story of an imbecile who makes history.",
"https://upload.wikimedia.org/wikipedia/en/6/67/Forrest_Gump_poster.jpg", # NOQA
"https://www.youtube.com/watch?v=77ij5gCTjYU"
)
goodfellas = media.Movie(
"Goodfellas",
"The rise and fall of a gangster.",
"https://upload.wikimedia.org/wikipedia/en/7/7b/Goodfellas.jpg",
"https://www.youtube.com/watch?v=qo5jJpHtI1Y"
)
lion_king = media.Movie(
"The Lion King",
"Hamlet on the Savanah.",
"https://upload.wikimedia.org/wikipedia/en/3/3d/The_Lion_King_poster.jpg", # NOQA
"https://www.youtube.com/watch?v=4sj1MT05lAA"
)
hercules = media.Movie(
"Hercules",
"The son of Zeus must earn his place back among the gods.",
"https://upload.wikimedia.org/wikipedia/en/6/65/Hercules_%281997_film%29_poster.jpg", # NOQA
"https://www.youtube.com/watch?v=ZvtspevZxpg"
)
oceans_11 = media.Movie(
"Ocean's Eleven",
"A gang of eleven makes a casino heist.",
"https://upload.wikimedia.org/wikipedia/en/6/68/Ocean%27s_Eleven_2001_Poster.jpg", # NOQA
"https://www.youtube.com/watch?v=imm6OR605UI"
)
# fresh_tomatoes.open_movies_page() requires its argument in list format
movies = [
toy_story,
forrest_gump,
goodfellas,
lion_king,
hercules,
oceans_11
]
fresh_tomatoes.open_movies_page(movies)
if __name__ == "__main__":
main()
| 30.8125 | 101 | 0.619168 | import media
import fresh_tomatoes
def main():
"""Generate an HTML page to show movies with corresponding information."""
toy_story = media.Movie(
"Toy Story",
"A story of a boy and his toys that come to life.",
"https://upload.wikimedia.org/wikipedia/en/1/13/Toy_Story.jpg",
"https://www.youtube.com/watch?v=KYz2wyBy3kc"
)
forrest_gump = media.Movie(
"Forrest Gump",
"A story of an imbecile who makes history.",
"https://upload.wikimedia.org/wikipedia/en/6/67/Forrest_Gump_poster.jpg", # NOQA
"https://www.youtube.com/watch?v=77ij5gCTjYU"
)
goodfellas = media.Movie(
"Goodfellas",
"The rise and fall of a gangster.",
"https://upload.wikimedia.org/wikipedia/en/7/7b/Goodfellas.jpg",
"https://www.youtube.com/watch?v=qo5jJpHtI1Y"
)
lion_king = media.Movie(
"The Lion King",
"Hamlet on the Savanah.",
"https://upload.wikimedia.org/wikipedia/en/3/3d/The_Lion_King_poster.jpg", # NOQA
"https://www.youtube.com/watch?v=4sj1MT05lAA"
)
hercules = media.Movie(
"Hercules",
"The son of Zeus must earn his place back among the gods.",
"https://upload.wikimedia.org/wikipedia/en/6/65/Hercules_%281997_film%29_poster.jpg", # NOQA
"https://www.youtube.com/watch?v=ZvtspevZxpg"
)
oceans_11 = media.Movie(
"Ocean's Eleven",
"A gang of eleven makes a casino heist.",
"https://upload.wikimedia.org/wikipedia/en/6/68/Ocean%27s_Eleven_2001_Poster.jpg", # NOQA
"https://www.youtube.com/watch?v=imm6OR605UI"
)
# fresh_tomatoes.open_movies_page() requires its argument in list format
movies = [
toy_story,
forrest_gump,
goodfellas,
lion_king,
hercules,
oceans_11
]
fresh_tomatoes.open_movies_page(movies)
if __name__ == "__main__":
main()
| 0 | 0 | 0 |
a4ae68cc8e486e00feeb0701d05b2910a94a4ebd | 4,624 | py | Python | manage.py | alessiosavi/PyParser | 788f783bd17ab5d63cec62af6d54b94f2b1737c9 | [
"Apache-2.0"
] | null | null | null | manage.py | alessiosavi/PyParser | 788f783bd17ab5d63cec62af6d54b94f2b1737c9 | [
"Apache-2.0"
] | null | null | null | manage.py | alessiosavi/PyParser | 788f783bd17ab5d63cec62af6d54b94f2b1737c9 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
import argparse
import os
import signal
import subprocess
import sys
# Project defaults
from utils import utils
# ===== LOAD CONFIGURATION FILE =====
CONFIG_FILE = "conf/conf.json"
CFG, log, = utils.init_main_data(CONFIG_FILE)
if CFG is None or log is None:
print("Unable to load {}".format(CONFIG_FILE))
sys.exit(2)
DEFAULT_IP = "{}:{}".format(CFG['network']['host'], CFG['network']["port"])
FLASK_APP = 'server/__init__.py'
cm = CommandManager()
cm.add(Command(
"build",
"compiles python files in project into .pyc binaries",
lambda c: 'python -m compileall .'))
cm.add(Command(
"start",
"runs server with gunicorn in a production setting",
lambda c: 'gunicorn -b {0}:{1} --timeout 99999 server:app'.format(c['host'], c['port']),
{
'FLASK_APP': FLASK_APP,
'FLASK_DEBUG': 'false'
}))
cm.add(Command(
"run",
"runs dev server using Flask's native debugger & backend reloader",
lambda c: 'python -m flask run --host={0} --port={1} --debugger --reload'.format(c['host'], c['port']),
{
'FLASK_APP': FLASK_APP,
'FLASK_DEBUG': 'true'
}))
cm.add(Command(
"livereload",
"runs dev server using livereload for dynamic webpage reloading",
lambda c: 'python -m flask run',
{
'FLASK_APP': FLASK_APP,
'FLASK_LIVE_RELOAD': 'true',
}))
cm.add(Command(
"debug",
"runs dev server in debug mode; use with an IDE's remote debugger",
lambda c: 'python -m flask run --host={0} --port={1} --no-debugger --no-reload'.format(c['host'], c['port']),
{
'FLASK_APP': FLASK_APP,
'FLASK_DEBUG': 'true'
}))
cm.add(Command(
"test",
"runs all tests inside of `tests` directory",
lambda c: 'python -m unittest discover -s tests -p "*.py"'))
# Create and format argument parser for CLI
parser = argparse.ArgumentParser(description=cm.availableCommands(),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("subcommand", help="subcommand to run (see list above)")
parser.add_argument("ipaddress", nargs='?', default=DEFAULT_IP,
help="address and port to run on (i.e. {0})".format(DEFAULT_IP))
# Take in command line input for configuration
try:
args = parser.parse_args()
cmd = args.subcommand
addr = args.ipaddress.split(':')
cm.configure({
'host': addr[0],
'port': addr[1],
})
cm.run(cmd)
except KeyboardInterrupt:
if 'FLASK_LIVE_RELOAD' in os.environ and os.environ['FLASK_LIVE_RELOAD'] == 'true':
livereload_check()
except:
if len(sys.argv) == 1:
log.debug(cm.availableCommands())
sys.exit(0)
| 29.832258 | 113 | 0.601644 | # -*- coding: utf-8 -*-
import argparse
import os
import signal
import subprocess
import sys
# Project defaults
from utils import utils
# ===== LOAD CONFIGURATION FILE =====
CONFIG_FILE = "conf/conf.json"
CFG, log, = utils.init_main_data(CONFIG_FILE)
if CFG is None or log is None:
print("Unable to load {}".format(CONFIG_FILE))
sys.exit(2)
DEFAULT_IP = "{}:{}".format(CFG['network']['host'], CFG['network']["port"])
FLASK_APP = 'server/__init__.py'
class Command:
def __init__(self, name, descr, runcmd, env=None, conf=None):
if conf is None:
conf = {}
if env is None:
env = {}
self.name = name
self.descr = descr
self.runcmd = runcmd
self.env = env
self.conf = conf
def run(self, conf):
cmd = self.runcmd(conf)
env = os.environ
env.update(conf)
env.update(self.env)
subprocess.call(cmd, env=env, shell=True)
class CommandManager:
def __init__(self):
self.commands = {}
def add(self, command):
self.commands[command.name] = command
def configure(self, conf):
self.conf = conf
def run(self, command):
if command in self.commands:
self.commands[command].run(self.conf)
else:
log.debug("invalid command specified\n")
log.debug(self.availableCommands())
def availableCommands(self):
commands = sorted(self.commands.values(), key=lambda c: c.name)
space = max([len(c.name) for c in commands]) + 2
description = 'available subcommands:\n'
for c in commands:
description += ' ' + c.name + ' ' * (space - len(c.name)) + c.descr + '\n'
return description
cm = CommandManager()
cm.add(Command(
"build",
"compiles python files in project into .pyc binaries",
lambda c: 'python -m compileall .'))
cm.add(Command(
"start",
"runs server with gunicorn in a production setting",
lambda c: 'gunicorn -b {0}:{1} --timeout 99999 server:app'.format(c['host'], c['port']),
{
'FLASK_APP': FLASK_APP,
'FLASK_DEBUG': 'false'
}))
cm.add(Command(
"run",
"runs dev server using Flask's native debugger & backend reloader",
lambda c: 'python -m flask run --host={0} --port={1} --debugger --reload'.format(c['host'], c['port']),
{
'FLASK_APP': FLASK_APP,
'FLASK_DEBUG': 'true'
}))
cm.add(Command(
"livereload",
"runs dev server using livereload for dynamic webpage reloading",
lambda c: 'python -m flask run',
{
'FLASK_APP': FLASK_APP,
'FLASK_LIVE_RELOAD': 'true',
}))
cm.add(Command(
"debug",
"runs dev server in debug mode; use with an IDE's remote debugger",
lambda c: 'python -m flask run --host={0} --port={1} --no-debugger --no-reload'.format(c['host'], c['port']),
{
'FLASK_APP': FLASK_APP,
'FLASK_DEBUG': 'true'
}))
cm.add(Command(
"test",
"runs all tests inside of `tests` directory",
lambda c: 'python -m unittest discover -s tests -p "*.py"'))
# Create and format argument parser for CLI
parser = argparse.ArgumentParser(description=cm.availableCommands(),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("subcommand", help="subcommand to run (see list above)")
parser.add_argument("ipaddress", nargs='?', default=DEFAULT_IP,
help="address and port to run on (i.e. {0})".format(DEFAULT_IP))
def livereload_check():
check = subprocess.call("lsof -n -i4TCP:{}".format(CFG['network']["port"]), shell=True)
if check == 0:
output = subprocess.check_output(
"ps -ef | egrep 'python manage.py|gunicorn' | egrep -v grep | awk '{print $3}'",
shell=True).decode().strip()
for _pid in output.split("\n"):
pypid = int(_pid)
os.kill(pypid, signal.SIGKILL)
log.debug("Discovered rogue Python process: {0}".format(pypid))
log.debug("Killing PID {0}...".format(pypid))
else:
log.debug(" No rogue Python process running")
# Take in command line input for configuration
try:
args = parser.parse_args()
cmd = args.subcommand
addr = args.ipaddress.split(':')
cm.configure({
'host': addr[0],
'port': addr[1],
})
cm.run(cmd)
except KeyboardInterrupt:
if 'FLASK_LIVE_RELOAD' in os.environ and os.environ['FLASK_LIVE_RELOAD'] == 'true':
livereload_check()
except:
if len(sys.argv) == 1:
log.debug(cm.availableCommands())
sys.exit(0)
| 1,655 | -7 | 256 |
1e2d804940cb39ed13692c328011679d3bd4c173 | 2,285 | py | Python | examples/textbook/salpeter.py | joshuawall/amuse | c2034074ee76c08057c4faa96c32044ab40952e9 | [
"Apache-2.0"
] | 1 | 2019-12-28T22:47:51.000Z | 2019-12-28T22:47:51.000Z | examples/textbook/salpeter.py | joshuawall/amuse | c2034074ee76c08057c4faa96c32044ab40952e9 | [
"Apache-2.0"
] | null | null | null | examples/textbook/salpeter.py | joshuawall/amuse | c2034074ee76c08057c4faa96c32044ab40952e9 | [
"Apache-2.0"
] | 2 | 2021-11-19T04:41:37.000Z | 2021-11-20T02:11:17.000Z | from amuse.lab import new_powerlaw_mass_distribution
###BOOKLISTSTART###
import numpy
import math
from amuse.units import units
from matplotlib import pyplot
from prepare_figure import figure_frame, get_distinct
###BOOKLISTSTOP###
if __name__ in ('__main__', '__plot__'):
o, arguments = new_option_parser().parse_args()
numpy.random.seed(31415)
generate_power_law_mass_function(**o.__dict__)
| 36.854839 | 76 | 0.623632 | from amuse.lab import new_powerlaw_mass_distribution
def generate_power_law_mass_function(N, Mmin, Mmax, ximf):
masses = new_powerlaw_mass_distribution(N, Mmin, Mmax, ximf)
plot_mass_function(masses, ximf)
###BOOKLISTSTART###
import numpy
import math
from amuse.units import units
from matplotlib import pyplot
from prepare_figure import figure_frame, get_distinct
def plot_mass_function(masses, ximf):
Mmin = masses.min()
Mmax = masses.max()
lm = math.log10(0.5*Mmin.value_in(units.MSun))
lM = math.log10(1.5*Mmax.value_in(units.MSun))
bins = 10**numpy.linspace(lm, lM, 51)
Nbin, bin_edges= numpy.histogram(masses.value_in(units.MSun), bins=bins)
y = Nbin / (bin_edges[1:] - bin_edges[:-1])
x = (bin_edges[1:] + bin_edges[:-1]) / 2.0
for i in range(len(y)):
y[i] = max(y[i], 1.e-10)
fig, ax = figure_frame("M$_\odot$", "N", xsize=12, ysize=8)
colors = get_distinct(2)
pyplot.scatter(x, y, s=100, c=colors[0], lw=0)
c = ((Mmax.value_in(units.MSun)**(ximf+1)) \
- (Mmin.value_in(units.MSun)**(ximf+1))) / (ximf+1)
pyplot.plot(x, len(masses)/c * (x**ximf), c=colors[1])
pyplot.loglog()
save_file = "salpeter.png"
pyplot.savefig(save_file)
print '\nSaved figure in file', save_file,'\n'
pyplot.show()
###BOOKLISTSTOP###
def new_option_parser():
from amuse.units.optparse import OptionParser
result = OptionParser()
result.add_option("-N", dest="N", type="int",default = 1000,
help="number of stars [10]")
result.add_option("-m", unit=units.MSun,
dest="Mmin", type="float",default = 1|units.MSun,
help="minimum mass of the mass function [0.1] %unit")
result.add_option("-M", unit=units.MSun,
dest="Mmax", type="float",default = 100|units.MSun,
help="maximum mass of the mass function [100] %unit")
result.add_option("-x",
dest="ximf", type="float",default = -2.35,
help="mass function slope [-2.35]")
return result
if __name__ in ('__main__', '__plot__'):
o, arguments = new_option_parser().parse_args()
numpy.random.seed(31415)
generate_power_law_mass_function(**o.__dict__)
| 1,808 | 0 | 69 |
fca339414723ecddd70b58456b989c6b107a2352 | 3,396 | py | Python | gpxtrackposter/year_range.py | narfel/GpxTrackPoster | 57aa34feb43f8b76ef40d1500067e532d2ff439a | [
"MIT"
] | 1 | 2021-07-09T02:57:08.000Z | 2021-07-09T02:57:08.000Z | gpxtrackposter/year_range.py | narfel/GpxTrackPoster | 57aa34feb43f8b76ef40d1500067e532d2ff439a | [
"MIT"
] | null | null | null | gpxtrackposter/year_range.py | narfel/GpxTrackPoster | 57aa34feb43f8b76ef40d1500067e532d2ff439a | [
"MIT"
] | null | null | null | """Represent a range of years, with ability to update based on a track"""
# Copyright 2016-2020 Florian Pigorsch & Contributors. All rights reserved.
#
# Use of this source code is governed by a MIT-style
# license that can be found in the LICENSE file.
import datetime
import re
import typing
class YearRange:
"""Represent a range of years, with ability to update based on a track
Attributes:
from_year: First year in range (lower)
to_year: Last year in range (higher)
Methods:
parse: Parse a string into lower and upper bounds
add: Adjust bounds based on a track
contains: If track is contained in the range
count: Number of years in range
"""
def __init__(self) -> None:
"""Inits YearRange with empty bounds -- to be built after init"""
self.from_year: typing.Optional[int] = None
self.to_year: typing.Optional[int] = None
def parse(self, s: str) -> bool:
"""Parse a plaintext range of years into a pair of years
Attempt to turn the input string into a pair of year values, from_year and to_year. If one
year is passed, both from_year and to_year will be set to that year. If a range like
'2016-2018' is passed, from_year will be set to 2016, and to_year will be set to 2018.
Args:
s: A string representing a range of years or a single year
Returns:
True if the range was successfully parsed, False if not.
"""
if s == "all":
self.from_year = None
self.to_year = None
return True
m = re.match(r"^\d+$", s)
if m:
self.from_year = int(s)
self.to_year = self.from_year
return True
m = re.match(r"^(\d+)-(\d+)$", s)
if m:
y1, y2 = int(m.group(1)), int(m.group(2))
if y1 <= y2:
self.from_year = y1
self.to_year = y2
return True
return False
def add(self, t: datetime.datetime) -> None:
"""For the given t, update from_year and to_year to include that timestamp"""
if self.from_year is None:
self.from_year = t.year
self.to_year = t.year
return
assert self.from_year is not None
assert self.to_year is not None
if t.year < self.from_year:
self.from_year = t.year
elif t.year > self.to_year:
self.to_year = t.year
def contains(self, t: datetime.datetime) -> bool:
"""Return True if current year range contains t, False if not"""
if self.from_year is None:
return True
assert self.from_year is not None
assert self.to_year is not None
return self.from_year <= t.year <= self.to_year
def count(self) -> int:
"""Return number of years contained in the current range"""
if self.from_year is None:
return 0
assert self.to_year is not None
return 1 + self.to_year - self.from_year
| 32.653846 | 98 | 0.594229 | """Represent a range of years, with ability to update based on a track"""
# Copyright 2016-2020 Florian Pigorsch & Contributors. All rights reserved.
#
# Use of this source code is governed by a MIT-style
# license that can be found in the LICENSE file.
import datetime
import re
import typing
class YearRange:
"""Represent a range of years, with ability to update based on a track
Attributes:
from_year: First year in range (lower)
to_year: Last year in range (higher)
Methods:
parse: Parse a string into lower and upper bounds
add: Adjust bounds based on a track
contains: If track is contained in the range
count: Number of years in range
"""
def __init__(self) -> None:
"""Inits YearRange with empty bounds -- to be built after init"""
self.from_year: typing.Optional[int] = None
self.to_year: typing.Optional[int] = None
def parse(self, s: str) -> bool:
"""Parse a plaintext range of years into a pair of years
Attempt to turn the input string into a pair of year values, from_year and to_year. If one
year is passed, both from_year and to_year will be set to that year. If a range like
'2016-2018' is passed, from_year will be set to 2016, and to_year will be set to 2018.
Args:
s: A string representing a range of years or a single year
Returns:
True if the range was successfully parsed, False if not.
"""
if s == "all":
self.from_year = None
self.to_year = None
return True
m = re.match(r"^\d+$", s)
if m:
self.from_year = int(s)
self.to_year = self.from_year
return True
m = re.match(r"^(\d+)-(\d+)$", s)
if m:
y1, y2 = int(m.group(1)), int(m.group(2))
if y1 <= y2:
self.from_year = y1
self.to_year = y2
return True
return False
def clear(self) -> None:
self.from_year = None
self.to_year = None
def add(self, t: datetime.datetime) -> None:
"""For the given t, update from_year and to_year to include that timestamp"""
if self.from_year is None:
self.from_year = t.year
self.to_year = t.year
return
assert self.from_year is not None
assert self.to_year is not None
if t.year < self.from_year:
self.from_year = t.year
elif t.year > self.to_year:
self.to_year = t.year
def contains(self, t: datetime.datetime) -> bool:
"""Return True if current year range contains t, False if not"""
if self.from_year is None:
return True
assert self.from_year is not None
assert self.to_year is not None
return self.from_year <= t.year <= self.to_year
def count(self) -> int:
"""Return number of years contained in the current range"""
if self.from_year is None:
return 0
assert self.to_year is not None
return 1 + self.to_year - self.from_year
def iter(self) -> typing.Generator[int, None, None]:
if self.from_year is None:
return
assert self.to_year is not None
for year in range(self.from_year, self.to_year + 1):
yield year
| 271 | 0 | 54 |
efb255992679f342c7ebd054a3ecc97828dfdb96 | 112 | py | Python | moses/char_rnn/__init__.py | samadejacobs/moses | 1fda9a06ef645f533191990cd10834e52ec29a37 | [
"MIT"
] | null | null | null | moses/char_rnn/__init__.py | samadejacobs/moses | 1fda9a06ef645f533191990cd10834e52ec29a37 | [
"MIT"
] | null | null | null | moses/char_rnn/__init__.py | samadejacobs/moses | 1fda9a06ef645f533191990cd10834e52ec29a37 | [
"MIT"
] | 3 | 2020-03-31T16:07:20.000Z | 2021-06-21T16:15:57.000Z | from .config import get_parser as char_rnn_parser
from .model import CharRNN
from .trainer import CharRNNTrainer | 37.333333 | 49 | 0.857143 | from .config import get_parser as char_rnn_parser
from .model import CharRNN
from .trainer import CharRNNTrainer | 0 | 0 | 0 |
5b54b094c6f4a462492a448757b9d3954d49c8d5 | 1,109 | py | Python | pathSum.py | pflun/learningAlgorithms | 3101e989488dfc8a56f1bf256a1c03a837fe7d97 | [
"MIT"
] | null | null | null | pathSum.py | pflun/learningAlgorithms | 3101e989488dfc8a56f1bf256a1c03a837fe7d97 | [
"MIT"
] | null | null | null | pathSum.py | pflun/learningAlgorithms | 3101e989488dfc8a56f1bf256a1c03a837fe7d97 | [
"MIT"
] | null | null | null | # Definition for a binary tree node.
from sortedArrayToBST import Solution
test = Solution()
head_node = test.sortedArrayToBST([1, 2, 3, 4, 5, 6, 7])
test1 = Solution1()
print test1.pathSum(head_node, 7)
# 4
# 2 6
# 1 3 5 7 | 27.04878 | 93 | 0.555455 | # Definition for a binary tree node.
from sortedArrayToBST import Solution
class TreeNode(object):
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution1(object):
def __init__(self):
# Tip: instance variable unique to each instance (not really related to this problem)
self.store = []
def pathSum(self, root, sum):
ls = []
self.dfs(root, sum, ls)
return self.store
def dfs(self, root, target, ls):
if root:
if not root.left and not root.right:
if root.val == target:
ls.append(root.val)
self.store.append(ls)
# Tip: ls + [root.val] to pass tmp path
if root.left:
self.dfs(root.left, target - root.val, ls + [root.val])
if root.right:
self.dfs(root.right, target - root.val, ls + [root.val])
test = Solution()
head_node = test.sortedArrayToBST([1, 2, 3, 4, 5, 6, 7])
test1 = Solution1()
print test1.pathSum(head_node, 7)
# 4
# 2 6
# 1 3 5 7 | 721 | 5 | 152 |
49ef48b7da7d171f74104bc9e7bd0238f46fb42b | 2,239 | py | Python | query_hosts_create_tags.py | nadaj/Miscellany | 0c7beb9cb9f3552cf25075d847e5d3bd28054c71 | [
"BSD-3-Clause"
] | 155 | 2017-01-04T16:45:57.000Z | 2022-03-24T18:19:27.000Z | query_hosts_create_tags.py | nadaj/Miscellany | 0c7beb9cb9f3552cf25075d847e5d3bd28054c71 | [
"BSD-3-Clause"
] | 42 | 2017-03-02T21:20:10.000Z | 2021-10-13T13:10:03.000Z | query_hosts_create_tags.py | nadaj/Miscellany | 0c7beb9cb9f3552cf25075d847e5d3bd28054c71 | [
"BSD-3-Clause"
] | 127 | 2015-12-21T19:32:58.000Z | 2022-03-10T12:03:36.000Z | import json
import os
import requests
import pprint
from datadog import initialize, api
# source /opt/datadog/datadog-agent/venv/bin/activate
DD_API_KEY = os.getenv('DD_API_KEY', '')
DD_APP_KEY = os.getenv('DD_APP_KEY', '')
options = {
'api_key': DD_API_KEY,
'app_key': DD_APP_KEY
}
initialize(**options)
initial_filter_string = '' # string to query datadog api for matching hosts. this may return more hosts than you are looking for.
query_key = 'host_name' # any key of the host object (i.e. 'platform', 'id') to use when iterating over the search results. host_name is default.
query_string = 'splunk' # query string to run against search string
create_tags = False # set to true to create tags on matchings hosts
host_count = api.Hosts.search(filter=initial_filter_string)['total_matching']
print('%r hosts matching initial_filter_string' % host_count)
num_req = host_count // 100 + 1
print('%r number of api requests to query all matching hosts' % num_req)
matching_hosts = []
start_index = 0
for i in range(1, num_req+1):
print('api request %r of %r' % (i, num_req))
host_list = api.Hosts.search(filter=initial_filter_string, sort_field='apps', count=100, start=start_index)['host_list']
start_index += 100
for host in host_list:
matching_hosts.append(host)
print('Matching host count: %r' % len(matching_hosts))
# run a second, more granular query to identify hosts
all_hostnames = []
hosts_to_tag = []
for host in matching_hosts:
host_name = host['host_name'] # get the host name for adding tags
all_hostnames.append(host_name)
value = host[query_key] # ex host['platform'], hosts['id'], etc
if query_string in value: # contains, startswith, endswith, etc
hosts_to_tag.append(host_name)
print('identified host %s' % host_name)
print('Host count: %r' % host_count)
print('Unique host names: %r' % len(set(all_hostnames)))
print('Hosts to tag: %r' % len(hosts_to_tag))
if create_tags:
# add a tag to hosts
for host_name in hosts_to_tag:
res = api.Tag.create(host_name, tags=['tag_name:tag_value'])
if 'errors' in res:
print(res['errors'])
else:
print("Set 'create_tags' = True to create tags for matchings hosts.")
| 34.984375 | 146 | 0.712372 | import json
import os
import requests
import pprint
from datadog import initialize, api
# source /opt/datadog/datadog-agent/venv/bin/activate
DD_API_KEY = os.getenv('DD_API_KEY', '')
DD_APP_KEY = os.getenv('DD_APP_KEY', '')
options = {
'api_key': DD_API_KEY,
'app_key': DD_APP_KEY
}
initialize(**options)
initial_filter_string = '' # string to query datadog api for matching hosts. this may return more hosts than you are looking for.
query_key = 'host_name' # any key of the host object (i.e. 'platform', 'id') to use when iterating over the search results. host_name is default.
query_string = 'splunk' # query string to run against search string
create_tags = False # set to true to create tags on matchings hosts
host_count = api.Hosts.search(filter=initial_filter_string)['total_matching']
print('%r hosts matching initial_filter_string' % host_count)
num_req = host_count // 100 + 1
print('%r number of api requests to query all matching hosts' % num_req)
matching_hosts = []
start_index = 0
for i in range(1, num_req+1):
print('api request %r of %r' % (i, num_req))
host_list = api.Hosts.search(filter=initial_filter_string, sort_field='apps', count=100, start=start_index)['host_list']
start_index += 100
for host in host_list:
matching_hosts.append(host)
print('Matching host count: %r' % len(matching_hosts))
# run a second, more granular query to identify hosts
all_hostnames = []
hosts_to_tag = []
for host in matching_hosts:
host_name = host['host_name'] # get the host name for adding tags
all_hostnames.append(host_name)
value = host[query_key] # ex host['platform'], hosts['id'], etc
if query_string in value: # contains, startswith, endswith, etc
hosts_to_tag.append(host_name)
print('identified host %s' % host_name)
print('Host count: %r' % host_count)
print('Unique host names: %r' % len(set(all_hostnames)))
print('Hosts to tag: %r' % len(hosts_to_tag))
if create_tags:
# add a tag to hosts
for host_name in hosts_to_tag:
res = api.Tag.create(host_name, tags=['tag_name:tag_value'])
if 'errors' in res:
print(res['errors'])
else:
print("Set 'create_tags' = True to create tags for matchings hosts.")
| 0 | 0 | 0 |
5100719deadc1ab5f5956fa7c4b8e9f8c772c93c | 567 | py | Python | linter.py | CudaText-addons/cuda_lint_innosetup | 159b119f5c007f36e29c1d005188c6ec2ece1784 | [
"MIT"
] | 1 | 2021-05-08T07:45:19.000Z | 2021-05-08T07:45:19.000Z | linter.py | CudaText-addons/cuda_lint_innosetup | 159b119f5c007f36e29c1d005188c6ec2ece1784 | [
"MIT"
] | null | null | null | linter.py | CudaText-addons/cuda_lint_innosetup | 159b119f5c007f36e29c1d005188c6ec2ece1784 | [
"MIT"
] | null | null | null | #
# linter.py
# Linter for SublimeLinter3, a code checking framework for Sublime Text 3
#
# Written by Jan T. Sott
# Copyright (c) 2017 Jan T. Sott
#
# License: MIT
# Ported to CudaLint by Alexey T.
#
from cuda_lint import Linter, util
class Iscc(Linter):
"""Provides an interface to the ISCC executable."""
cmd = ('ISCC.exe', '/Q', '/O-', '@')
syntax = 'Inno Setup'
regex = (
r'^Error on line (?P<line>\d+) in (?P<file>.*\.iss): (?P<message>.+)$'
)
multiline = True
error_stream = util.STREAM_STDERR
line_col_base = (0, 1)
| 22.68 | 78 | 0.61552 | #
# linter.py
# Linter for SublimeLinter3, a code checking framework for Sublime Text 3
#
# Written by Jan T. Sott
# Copyright (c) 2017 Jan T. Sott
#
# License: MIT
# Ported to CudaLint by Alexey T.
#
from cuda_lint import Linter, util
class Iscc(Linter):
"""Provides an interface to the ISCC executable."""
cmd = ('ISCC.exe', '/Q', '/O-', '@')
syntax = 'Inno Setup'
regex = (
r'^Error on line (?P<line>\d+) in (?P<file>.*\.iss): (?P<message>.+)$'
)
multiline = True
error_stream = util.STREAM_STDERR
line_col_base = (0, 1)
| 0 | 0 | 0 |
66146d833c1b018ec787f7efcec2dd545d37bf12 | 999 | py | Python | Authentication/tests.py | MuhammadSalahAli/TolkProject | 38e655be8f1776c216f1e7b3a727b39a1ea06cfb | [
"MIT"
] | 36 | 2021-01-18T11:58:17.000Z | 2021-12-09T21:40:29.000Z | Authentication/tests.py | MuhammadSalahAli/TolkProject | 38e655be8f1776c216f1e7b3a727b39a1ea06cfb | [
"MIT"
] | 2 | 2021-07-02T04:07:58.000Z | 2021-08-22T21:23:33.000Z | Authentication/tests.py | MuhammadSalahAli/TolkProject | 38e655be8f1776c216f1e7b3a727b39a1ea06cfb | [
"MIT"
] | 7 | 2021-08-09T02:21:49.000Z | 2022-02-06T11:04:04.000Z | from django.test import TestCase
from django.contrib.auth import get_user_model
import json
User = get_user_model()
| 37 | 113 | 0.697698 | from django.test import TestCase
from django.contrib.auth import get_user_model
import json
User = get_user_model()
class TestAuthentication(TestCase):
def setUp(self) -> None:
self.user = User.objects.create_user(email='guest_1@user.com')
self.user.set_password('password')
self.user.save()
def test_bad_credentials(self):
response = self.client.post('/api/login/', data={'email': 'guest_1user.com', 'password': 'bad password'})
self.assertEqual(response.status_code, 400)
is_authenticated = self.client.get('/api/authenticated/').data['is_authenticated']
self.assertFalse(is_authenticated)
def test_user_authentication(self):
response = self.client.post('/api/login/', data={'email': 'guest_1@user.com', 'password': 'password'})
self.assertEqual(response.status_code, 202)
is_authenticated = self.client.get('/api/authenticated/').data['is_authenticated']
self.assertTrue(is_authenticated)
| 763 | 14 | 104 |
decee34e0333f716cf45f2e9c465d3d7ff1c09fd | 2,109 | py | Python | core/urls.py | Aju100/WikimediaDataProject | f31e7856e49f510628e2ce65f40844b1f762a546 | [
"MIT"
] | 1 | 2021-06-08T02:37:04.000Z | 2021-06-08T02:37:04.000Z | core/urls.py | Aju100/WikimediaDataProject | f31e7856e49f510628e2ce65f40844b1f762a546 | [
"MIT"
] | null | null | null | core/urls.py | Aju100/WikimediaDataProject | f31e7856e49f510628e2ce65f40844b1f762a546 | [
"MIT"
] | null | null | null | from django.urls import path, include
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('populated_cities', views.populated_cities_query, name ='populated_cities'),
path('start_tour_cities', views.start_tour_cities, name ='start_tour_cities'),
path('stop_tour_cities', views.stop_tour_cities, name ='stop_tour_cities'),
path('premierLeague_stadiums', views.premierLeague_stadiums_query, name ='premierLeague_stadiums'),
path('start_tour_stadiums', views.start_tour_stadiums, name ='start_tour_stadiums'),
path('stop_tour_stadiums', views.stop_tour_stadiums, name ='stop_tour_stadiums'),
path('longest_rivers', views.longest_rivers_query, name ='longest_rivers'),
path('tour_experience', views.tour_experience, name ='tour_experience'),
path('line_track_experience', views.line_track_experience, name ='line_track_experience'),
path('stop_experience', views.stop_experience, name ='stop_experience'),
path('spanish_airports', views.spanish_airports_query, name ='spanish_airports'),
path('start_tour_airports', views.start_tour_airports, name ='start_tour_airports'),
path('stop_tour_airports', views.stop_tour_airports, name ='stop_tour_airports'),
path('summer_olympic_games_aux', views.olympic_games_query_aux, name ='summer_olympic_games_aux'),
path('summer_olympic_games', views.olympic_games_query, name ='summer_olympic_games'),
path('try_demo', views.try_demo, name ='try_demo'),
path('start_lleida_tour', views.start_lleida_tour, name ='start_lleida_tour'),
path('start_bayern_tour', views.start_bayern_tour, name ='start_bayern_tour'),
path('start_barcelona92', views.start_barcelona92, name ='start_barcelona92'),
path('stop_tour_demo', views.stop_tour_demo, name ='stop_tour_demo'),
path('clear_KML_folder', views.clear_KML_folder, name ='clear_KML_folder'),
path('stop_current_tour', views.stop_current_tour, name ='stop_current_tour'),
path('relaunch_LG', views.relaunch_LG, name ='relaunch_LG'),
path('clear_LG_cache', views.clear_LG_cache, name ='clear_LG_cache')
] | 70.3 | 103 | 0.766714 | from django.urls import path, include
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('populated_cities', views.populated_cities_query, name ='populated_cities'),
path('start_tour_cities', views.start_tour_cities, name ='start_tour_cities'),
path('stop_tour_cities', views.stop_tour_cities, name ='stop_tour_cities'),
path('premierLeague_stadiums', views.premierLeague_stadiums_query, name ='premierLeague_stadiums'),
path('start_tour_stadiums', views.start_tour_stadiums, name ='start_tour_stadiums'),
path('stop_tour_stadiums', views.stop_tour_stadiums, name ='stop_tour_stadiums'),
path('longest_rivers', views.longest_rivers_query, name ='longest_rivers'),
path('tour_experience', views.tour_experience, name ='tour_experience'),
path('line_track_experience', views.line_track_experience, name ='line_track_experience'),
path('stop_experience', views.stop_experience, name ='stop_experience'),
path('spanish_airports', views.spanish_airports_query, name ='spanish_airports'),
path('start_tour_airports', views.start_tour_airports, name ='start_tour_airports'),
path('stop_tour_airports', views.stop_tour_airports, name ='stop_tour_airports'),
path('summer_olympic_games_aux', views.olympic_games_query_aux, name ='summer_olympic_games_aux'),
path('summer_olympic_games', views.olympic_games_query, name ='summer_olympic_games'),
path('try_demo', views.try_demo, name ='try_demo'),
path('start_lleida_tour', views.start_lleida_tour, name ='start_lleida_tour'),
path('start_bayern_tour', views.start_bayern_tour, name ='start_bayern_tour'),
path('start_barcelona92', views.start_barcelona92, name ='start_barcelona92'),
path('stop_tour_demo', views.stop_tour_demo, name ='stop_tour_demo'),
path('clear_KML_folder', views.clear_KML_folder, name ='clear_KML_folder'),
path('stop_current_tour', views.stop_current_tour, name ='stop_current_tour'),
path('relaunch_LG', views.relaunch_LG, name ='relaunch_LG'),
path('clear_LG_cache', views.clear_LG_cache, name ='clear_LG_cache')
] | 0 | 0 | 0 |
c91cf546e259967fe077b3bc9f5679adb51c38a3 | 1,130 | py | Python | ParagraphRefact.py | willard1218/LearnEnglish | c9a908ffdff097b2c819540a9a2a761c91c1eead | [
"Apache-2.0"
] | null | null | null | ParagraphRefact.py | willard1218/LearnEnglish | c9a908ffdff097b2c819540a9a2a761c91c1eead | [
"Apache-2.0"
] | null | null | null | ParagraphRefact.py | willard1218/LearnEnglish | c9a908ffdff097b2c819540a9a2a761c91c1eead | [
"Apache-2.0"
] | null | null | null | import Enum
RF = Enum.enum(ORIGINAL=0, PIECE=1, EN_LINE1=2, EN_LINE2=3) | 28.974359 | 72 | 0.69646 | import Enum
RF = Enum.enum(ORIGINAL=0, PIECE=1, EN_LINE1=2, EN_LINE2=3)
def refactWithLine(talk, countOfLine):
count = 0
talk.ResetStartOfParagraph(False)
subtitle = talk.subtitles
subtitle[0].startOfParagraph = True
for i in xrange(1,len(subtitle)):
if(subtitle[i].content[-1] == "." or subtitle[i].content[-1] == "?"):
count = count + 1
if(i < len(subtitle)-1 and count == countOfLine):
subtitle[i+1].startOfParagraph = True
count = 0
def methodOriginal(enTalk, chTalk):
return
def methodPIECE(enTalk, chTalk):
chTalk.ResetStartOfParagraph(True)
enTalk.ResetStartOfParagraph(True)
def methodEN_LINE1(enTalk, chTalk):
chTalk.ResetStartOfParagraph(True)
refactWithLine(enTalk, 1)
def methodEN_LINE2(enTalk, chTalk):
chTalk.ResetStartOfParagraph(True)
refactWithLine(enTalk, 2)
def RefactStartOfParagraph(enTalk, chTalk, refactType = RF.ORIGINAL):
doMethod = {
RF.ORIGINAL : methodOriginal,
RF.PIECE : methodPIECE,
RF.EN_LINE1 : methodEN_LINE1,
RF.EN_LINE2 : methodEN_LINE2,
}
doMethod[int(refactType)](enTalk, chTalk) | 899 | 0 | 158 |
793d195c49c731b9089c998b075845197f2a39b1 | 13,520 | py | Python | py/Simulator/QuestaSimulator.py | cjchin/PoC | dbb07f85648dc4d5e66696699071750fcc08c8f7 | [
"Apache-2.0"
] | 2 | 2018-03-17T18:14:52.000Z | 2021-05-02T06:47:16.000Z | py/Simulator/QuestaSimulator.py | cjchin/PoC | dbb07f85648dc4d5e66696699071750fcc08c8f7 | [
"Apache-2.0"
] | null | null | null | py/Simulator/QuestaSimulator.py | cjchin/PoC | dbb07f85648dc4d5e66696699071750fcc08c8f7 | [
"Apache-2.0"
] | 1 | 2021-05-02T06:47:19.000Z | 2021-05-02T06:47:19.000Z | # EMACS settings: -*- tab-width: 2; indent-tabs-mode: t; python-indent-offset: 2 -*-
# vim: tabstop=2:shiftwidth=2:noexpandtab
# kate: tab-width 2; replace-tabs off; indent-width 2;
#
# ==============================================================================
# Authors: Patrick Lehmann
# Martin Zabel
#
# Python Module: Mentor QuestaSim simulator.
#
# License:
# ==============================================================================
# Copyright 2007-2016 Technische Universitaet Dresden - Germany
# Chair of VLSI-Design, Diagnostics and Architecture
#
# 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.
# ==============================================================================
#
# load dependencies
from pathlib import Path
from textwrap import dedent
from Base.Project import FileTypes, ToolChain, Tool
from DataBase.Config import Vendors
from ToolChains.Mentor.QuestaSim import QuestaSim, QuestaSimException
from Simulator import VHDL_TESTBENCH_LIBRARY_NAME, SimulatorException, SkipableSimulatorException, SimulationSteps, Simulator as BaseSimulator
__api__ = [
'Simulator'
]
__all__ = __api__
| 46.30137 | 161 | 0.723817 | # EMACS settings: -*- tab-width: 2; indent-tabs-mode: t; python-indent-offset: 2 -*-
# vim: tabstop=2:shiftwidth=2:noexpandtab
# kate: tab-width 2; replace-tabs off; indent-width 2;
#
# ==============================================================================
# Authors: Patrick Lehmann
# Martin Zabel
#
# Python Module: Mentor QuestaSim simulator.
#
# License:
# ==============================================================================
# Copyright 2007-2016 Technische Universitaet Dresden - Germany
# Chair of VLSI-Design, Diagnostics and Architecture
#
# 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.
# ==============================================================================
#
# load dependencies
from pathlib import Path
from textwrap import dedent
from Base.Project import FileTypes, ToolChain, Tool
from DataBase.Config import Vendors
from ToolChains.Mentor.QuestaSim import QuestaSim, QuestaSimException
from Simulator import VHDL_TESTBENCH_LIBRARY_NAME, SimulatorException, SkipableSimulatorException, SimulationSteps, Simulator as BaseSimulator
__api__ = [
'Simulator'
]
__all__ = __api__
class Simulator(BaseSimulator):
TOOL_CHAIN = ToolChain.Mentor_QuestaSim
TOOL = Tool.Mentor_vSim
def __init__(self, host, dryRun, simulationSteps):
# A separate elaboration step is not implemented in QuestaSim
simulationSteps &= ~SimulationSteps.Elaborate
super().__init__(host, dryRun, simulationSteps)
vSimSimulatorFiles = host.PoCConfig['CONFIG.DirectoryNames']['QuestaSimFiles']
self.Directories.Working = host.Directories.Temp / vSimSimulatorFiles
self.Directories.PreCompiled = host.Directories.PreCompiled / vSimSimulatorFiles
if (SimulationSteps.CleanUpBefore in self._simulationSteps):
pass
if (SimulationSteps.Prepare in self._simulationSteps):
self._PrepareSimulationEnvironment()
self._PrepareSimulator()
def _PrepareSimulator(self):
# create the QuestaSim executable factory
self.LogVerbose("Preparing Mentor simulator.")
# for sectionName in ['INSTALL.Mentor.QuestaSim', 'INSTALL.Mentor.ModelSim', 'INSTALL.Altera.ModelSim']:
# if (len(self.Host.PoCConfig.options(sectionName)) != 0):
# break
# else:
# XXX: check SectionName if ModelSim is configured
# raise NotConfiguredException(
# "Neither Mentor Graphics QuestaSim, ModelSim PE nor ModelSim Altera-Edition are configured on this system.")
# questaSection = self.Host.PoCConfig[sectionName]
# binaryPath = Path(questaSection['BinaryDirectory'])
# version = questaSection['Version']
binaryPath = Path(self.Host.PoCConfig['INSTALL.ModelSim']['BinaryDirectory'])
version = self.Host.PoCConfig['INSTALL.ModelSim']['Version']
self._toolChain = QuestaSim(self.Host.Platform, self.DryRun, binaryPath, version, logger=self.Logger)
def Run(self, testbench, board, vhdlVersion, vhdlGenerics=None):
# TODO: refactor into a ModelSim module, shared by QuestaSim and Cocotb (-> MixIn class)?
# select modelsim.ini
self._modelsimIniPath = self.Directories.PreCompiled
if board.Device.Vendor is Vendors.Altera:
self._modelsimIniPath /= self.Host.PoCConfig['CONFIG.DirectoryNames']['AlteraSpecificFiles']
elif board.Device.Vendor is Vendors.Lattice:
self._modelsimIniPath /= self.Host.PoCConfig['CONFIG.DirectoryNames']['LatticeSpecificFiles']
elif board.Device.Vendor is Vendors.Xilinx:
self._modelsimIniPath /= self.Host.PoCConfig['CONFIG.DirectoryNames']['XilinxSpecificFiles']
self._modelsimIniPath /= "modelsim.ini"
if not self._modelsimIniPath.exists():
raise SimulatorException("Modelsim ini file '{0!s}' not found.".format(self._modelsimIniPath)) \
from FileNotFoundError(str(self._modelsimIniPath))
super().Run(testbench, board, vhdlVersion, vhdlGenerics)
def _RunAnalysis(self, _):
# create a QuestaVHDLCompiler instance
vlib = self._toolChain.GetVHDLLibraryTool()
for lib in self._pocProject.VHDLLibraries:
vlib.Parameters[vlib.SwitchLibraryName] = lib.Name
vlib.CreateLibrary()
# create a QuestaVHDLCompiler instance
vcom = self._toolChain.GetVHDLCompiler()
vcom.Parameters[vcom.FlagQuietMode] = True
vcom.Parameters[vcom.FlagExplicit] = True
vcom.Parameters[vcom.FlagRangeCheck] = True
vcom.Parameters[vcom.SwitchModelSimIniFile] = self._modelsimIniPath.as_posix()
vcom.Parameters[vcom.SwitchVHDLVersion] = repr(self._vhdlVersion)
recompileScriptContent = dedent("""\
puts "Recompiling..."
""")
# run vcom compile for each VHDL file
for file in self._pocProject.Files(fileType=FileTypes.VHDLSourceFile):
if (not file.Path.exists()): raise SimulatorException("Cannot analyse '{0!s}'.".format(file.Path)) from FileNotFoundError(str(file.Path))
vcomLogFile = self.Directories.Working / (file.Path.stem + ".vcom.log")
vcom.Parameters[vcom.SwitchVHDLLibrary] = file.LibraryName
vcom.Parameters[vcom.ArgLogFile] = vcomLogFile
vcom.Parameters[vcom.ArgSourceFile] = file.Path
try:
vcom.Compile()
except QuestaSimException as ex:
raise SimulatorException("Error while compiling '{0!s}'.".format(file.Path)) from ex
if vcom.HasErrors:
raise SkipableSimulatorException("Error while compiling '{0!s}'.".format(file.Path))
# delete empty log files
if (vcomLogFile.stat().st_size == 0):
try:
vcomLogFile.unlink()
except OSError as ex:
raise SimulatorException("Error while deleting '{0!s}'.".format(vcomLogFile)) from ex
# collecting all compile commands in a buffer
recompileScriptContent += dedent("""\
puts " Compiling '{file}'..."
{tcl}
""").format(
file=file.Path.as_posix(),
tcl=vcom.GetTclCommand()
)
recompileScriptContent += dedent("""\
puts "Recompilation done"
puts "Restarting simulation..."
restart -force
puts "Simulation is restarted."
""")
recompileScriptContent = recompileScriptContent.replace("\\", "/") # WORKAROUND: to convert all paths to Tcl compatible paths.
recompileScriptPath = self.Directories.Working / "recompile.do"
self.LogDebug("Writing recompile script to '{0!s}'".format(recompileScriptPath))
with recompileScriptPath.open('w') as fileHandle:
fileHandle.write(recompileScriptContent)
def _RunSimulation(self, testbench):
if (SimulationSteps.ShowWaveform in self._simulationSteps):
return self._RunSimulationWithGUI(testbench)
tclBatchFilePath = self.Host.Directories.Root / self.Host.PoCConfig[testbench.ConfigSectionName]['vSimBatchScript']
tclDefaultBatchFilePath = self.Host.Directories.Root / self.Host.PoCConfig[testbench.ConfigSectionName]['vSimDefaultBatchScript']
# create a QuestaSimulator instance
vsim = self._toolChain.GetSimulator()
vsim.Parameters[vsim.SwitchModelSimIniFile] = self._modelsimIniPath.as_posix()
# vsim.Parameters[vsim.FlagOptimization] = True # FIXME:
vsim.Parameters[vsim.FlagReportAsError] = "3473"
vsim.Parameters[vsim.SwitchTimeResolution] = "1fs"
vsim.Parameters[vsim.FlagCommandLineMode] = True
vsim.Parameters[vsim.SwitchTopLevel] = "{0}.{1}".format(VHDL_TESTBENCH_LIBRARY_NAME, testbench.ModuleName)
# find a Tcl batch script for the BATCH mode
vsimBatchCommand = ""
if (tclBatchFilePath.exists()):
self.LogDebug("Found Tcl script for BATCH mode: '{0!s}'".format(tclBatchFilePath))
vsimBatchCommand += "do {0};".format(tclBatchFilePath.as_posix())
elif (tclDefaultBatchFilePath.exists()):
self.LogDebug("Falling back to default Tcl script for BATCH mode: '{0!s}'".format(tclDefaultBatchFilePath))
vsimBatchCommand += "do {0};".format(tclDefaultBatchFilePath.as_posix())
else:
raise QuestaSimException("No Tcl batch script for BATCH mode found.") \
from FileNotFoundError(str(tclDefaultBatchFilePath))
vsim.Parameters[vsim.SwitchBatchCommand] = vsimBatchCommand
testbench.Result = vsim.Simulate()
def _RunSimulationWithGUI(self, testbench):
tclGUIFilePath = self.Host.Directories.Root / self.Host.PoCConfig[testbench.ConfigSectionName]['vSimGUIScript']
tclWaveFilePath = self.Host.Directories.Root / self.Host.PoCConfig[testbench.ConfigSectionName]['vSimWaveScript']
tclDefaultGUIFilePath = self.Host.Directories.Root / self.Host.PoCConfig[testbench.ConfigSectionName]['vSimDefaultGUIScript']
tclDefaultWaveFilePath = self.Host.Directories.Root / self.Host.PoCConfig[testbench.ConfigSectionName]['vSimDefaultWaveScript']
# create a QuestaSimulator instance
vsim = self._toolChain.GetSimulator()
vsim.Parameters[vsim.SwitchModelSimIniFile] = self._modelsimIniPath.as_posix()
# vsim.Parameters[vsim.FlagOptimization] = True # FIXME:
vsim.Parameters[vsim.FlagReportAsError] = "3473"
vsim.Parameters[vsim.SwitchTimeResolution] = "1fs"
vsim.Parameters[vsim.FlagGuiMode] = True
vsim.Parameters[vsim.SwitchTopLevel] = "{0}.{1}".format(VHDL_TESTBENCH_LIBRARY_NAME, testbench.ModuleName)
# vsim.Parameters[vsim.SwitchTitle] = testbenchName
vsimDefaultWaveCommands = "add wave *"
# find a Tcl batch script to load predefined signals in the waveform window
vsimBatchCommand = ""
self.LogDebug("'{0!s}'\n '{1!s}'".format(tclWaveFilePath, self.Host.Directories.Root))
if (tclWaveFilePath != self.Host.Directories.Root):
if (tclWaveFilePath.exists()):
self.LogDebug("Found waveform script: '{0!s}'".format(tclWaveFilePath))
vsimBatchCommand = "do {0};".format(tclWaveFilePath.as_posix())
elif (tclDefaultWaveFilePath != self.Host.Directories.Root):
if (tclDefaultWaveFilePath.exists()):
self.LogDebug("Found default waveform script: '{0!s}'".format(tclDefaultWaveFilePath))
vsimBatchCommand = "do {0};".format(tclDefaultWaveFilePath.as_posix())
else:
self.LogDebug("Couldn't find default waveform script: '{0!s}'. Loading default command '{1}'.".format(tclDefaultWaveFilePath, vsimDefaultWaveCommands))
vsimBatchCommand = "{0};".format(vsimDefaultWaveCommands)
else:
self.LogDebug("Couldn't find waveform script: '{0!s}'. Loading default command '{1}'.".format(tclWaveFilePath, vsimDefaultWaveCommands))
vsim.Parameters[vsim.SwitchBatchCommand] = "{0};".format(vsimDefaultWaveCommands)
elif (tclDefaultWaveFilePath != self.Host.Directories.Root):
if (tclDefaultWaveFilePath.exists()):
self.LogDebug("Falling back to default waveform script: '{0!s}'".format(tclDefaultWaveFilePath))
vsimBatchCommand = "do {0};".format(tclDefaultWaveFilePath.as_posix())
else:
self.LogDebug("Couldn't find default waveform script: '{0!s}'. Loading default command '{1}'.".format(tclDefaultWaveFilePath, vsimDefaultWaveCommands))
vsimBatchCommand = "{0};".format(vsimDefaultWaveCommands)
else:
self.LogWarning("No waveform script specified. Loading default command '{1}'.".format(vsimDefaultWaveCommands))
vsimBatchCommand = "{0};".format(vsimDefaultWaveCommands)
# find a Tcl batch script for the GUI mode
vsimRunScript = ""
if (tclGUIFilePath.exists()):
self.LogDebug("Found Tcl script for GUI mode: '{0!s}'".format(tclGUIFilePath))
vsimRunScript = tclGUIFilePath.as_posix()
vsimBatchCommand += "do {0};".format(vsimRunScript)
elif (tclDefaultGUIFilePath.exists()):
self.LogDebug("Falling back to default Tcl script for GUI mode: '{0!s}'".format(tclDefaultGUIFilePath))
vsimRunScript = tclDefaultGUIFilePath.as_posix()
vsimBatchCommand += "do {0};".format(vsimRunScript)
else:
raise QuestaSimException("No Tcl batch script for GUI mode found.") \
from FileNotFoundError(str(tclDefaultGUIFilePath))
vsim.Parameters[vsim.SwitchBatchCommand] = vsimBatchCommand
# writing a relaunch file
recompileScriptPath = self.Directories.Working / "recompile.do"
relaunchScriptPath = self.Directories.Working / "relaunch.do"
saveWaveformScriptPath = self.Directories.Working / "saveWaveform.do"
relaunchScriptContent = dedent("""\
puts "Loading recompile script '{recompileScript}'..."
do {recompileScript}
puts "Loading run script '{runScript}'..."
do {runScript}
""").format(
recompileScript=recompileScriptPath.as_posix(),
runScript=vsimRunScript
)
self.LogDebug("Writing relaunch script to '{0!s}'".format(relaunchScriptPath))
with relaunchScriptPath.open('w') as fileHandle:
fileHandle.write(relaunchScriptContent)
# writing a saveWaveform file
saveWaveformScriptContent = dedent("""\
puts "Saving waveform settings to '{waveformFile}'..."
write format wave -window .main_pane.wave.interior.cs.body.pw.wf {waveformFile}
""").format(
waveformFile=tclWaveFilePath.as_posix()
)
self.LogDebug("Writing saveWaveform script to '{0!s}'".format(saveWaveformScriptPath))
with saveWaveformScriptPath.open('w') as fileHandle:
fileHandle.write(saveWaveformScriptContent)
testbench.Result = vsim.Simulate()
| 11,506 | 236 | 23 |
af19a1538ffdc439b60f50fdf727d12a925a93f3 | 128 | py | Python | yc228/908.py | c-yan/yukicoder | cdbbd65402177225dd989df7fe01f67908484a69 | [
"MIT"
] | null | null | null | yc228/908.py | c-yan/yukicoder | cdbbd65402177225dd989df7fe01f67908484a69 | [
"MIT"
] | null | null | null | yc228/908.py | c-yan/yukicoder | cdbbd65402177225dd989df7fe01f67908484a69 | [
"MIT"
] | null | null | null | S = input()
if all(c == ' ' for c in S[1::2]) and all('a' <= c <= 'z' for c in S[::2]):
print('Yes')
else:
print('No')
| 18.285714 | 75 | 0.445313 | S = input()
if all(c == ' ' for c in S[1::2]) and all('a' <= c <= 'z' for c in S[::2]):
print('Yes')
else:
print('No')
| 0 | 0 | 0 |
7b6e6c533e62117ccdb9f1743db9f70ee1c32542 | 5,070 | py | Python | 2dacousti_fdtd.py | dhufe/2d_ftdt_acoustics | 062ff13399cc3b9db360e047bd1deb7c0e209397 | [
"MIT"
] | null | null | null | 2dacousti_fdtd.py | dhufe/2d_ftdt_acoustics | 062ff13399cc3b9db360e047bd1deb7c0e209397 | [
"MIT"
] | null | null | null | 2dacousti_fdtd.py | dhufe/2d_ftdt_acoustics | 062ff13399cc3b9db360e047bd1deb7c0e209397 | [
"MIT"
] | null | null | null | #
#
#
#
#
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.animation as animation
## Computing grid and resolutions
# Grid size x-direction
NX = 500
# Grid size y-direction
NY = 500
## Properties of the acoustic excitation
# Frequency
freq = 60.0e3 # Hz
# wave speed
cmax = 343 # m/s
# wave length
lamda = cmax / freq
# Spatial resolution
# spatial stability critria: dx must be smaller or equal than lambda_min / 20
# where lambda_min is the shortest wavelength in the model!
dx = lamda/40 #.01
# Time domain resolution
# time stability criteria: dt must be smaller or equal than dx / ( sqrt(2) * c_max )
# where c_max is the highest wave speed in the model!
dt = dx/(cmax*np.sqrt(2) ) # 20.0e-6
# mesh grid
x = np.arange ( 0, (NX)*dx, dx )
y = np.arange ( 0, (NY)*dx, dx )
xx, yy = np.meshgrid( y, x )
## Properties of the fluid like density and viscosity
# Density
rho = 1.241
# Bulk viscosity: c^2 x rho
kappa = np.power(cmax, 2.0) * rho #142.0e3
## Computing magnitutes for two dimensional particle velocity and pressure
Vx = np.zeros ( ( NX + 1, NY ) )
Vy = np.zeros ( ( NX , NY + 1 ) )
P = np.zeros ( ( NX , NY ) )
NFrames = 3000
SourceWidth = 20
SourceHeight = 10
# setup indices
ind = np.full(( NX, NY), False, dtype=bool)
# setup indices
ind = np.full(( NX, NY), False, dtype=bool)
Pmx = int(np.floor(NX/4 - 10 + 1 ))
Pmy = int(np.floor(NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
Pmx = int(np.floor(NX/2 - 10 + 1 ))
Pmy = int(np.floor(NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
Pmx = int(np.floor(3*NX/4 - 10 + 1 ))
Pmy = int(np.floor( NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
Pmx = int(np.floor(NX - 30 + 1 ))
Pmy = int(np.floor(NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
# Pxs = int(np.floor(NX/2 - SourceWidth/2 + 1 ))
# Pxe = int(np.floor(NX/2 + SourceWidth/2 + 1 ))
# Pys = int(np.floor(NY/2 - SourceHeight/2 + 1 ))
# Pye = int(np.floor(NY/2 + SourceHeight/2 + 1 ))
# ind[ Pxs:Pxe, Pys:Pys ] = True
## Visual stuff
# Colormap
colormap = 'RdBu'
# Plot creation
fig = plt.figure(figsize=(12.8, 7.2))
ax = fig.add_subplot(1,1,1)
cax = ax.pcolormesh( xx, yy, P, vmin=-1, vmax=1, cmap=colormap)
ax.set_xlabel ( r'Position $x$ / $m$' )
ax.set_ylabel ( r'Position $y$ / $m$' )
ax.set_xlim ( y[0], y[-1] )
ax.set_ylim ( x[0], x[-1] )
fig.colorbar(cax)
fig.tight_layout()
image_step = 400
## help variables
dt_over_rho_x_dx = dt / ( rho * dx )
kappa_x_dt_over_dx = kappa * dt / dx
print ( 'Spatial stepsize %00.3f mm.' % ( dx*1e3 ) )
print ( 'Time stepsize %00.3f us.' % ( dt*1e6 ) )
print ( 'Volume elasticity %03.3f .' % ( kappa*1e-3 ) )
print ( 'Pulse width %d ' % ( int((1.0/freq)/dt) ) )
n = 0
anim = animation.FuncAnimation(fig, updatefig, frames=NFrames-1,interval=image_step, blit=True)
anim
Writer = animation.writers['ffmpeg']
writer = Writer ( fps=100, metadata=dict(artist='dkotscha' ), bitrate=6000)
anim.save('ftdt_acoustic_one_source_2d.mp4', writer=writer )
plt.close()
| 25.477387 | 99 | 0.559369 | #
#
#
#
#
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def circle(indices, xm, ym, r):
x = r - 1
y = 0
dx = 1
dy = 1
err = dx - (r * 2)
while ( x >= y):
indices [ xm + x , ym + y] = True
indices [ xm + y , ym + x] = True
indices [ xm - y , ym + x] = True
indices [ xm - x , ym + y] = True
indices [ xm - x , ym - y] = True
indices [ xm - y , ym - x] = True
indices [ xm + y , ym - x] = True
indices [ xm + x , ym - y] = True
if err <= 0:
y+=1
err += dy
dy += 2
if err > 0:
x-=1
dy += 2
err += dx - ( 2*r )
## Computing grid and resolutions
# Grid size x-direction
NX = 500
# Grid size y-direction
NY = 500
## Properties of the acoustic excitation
# Frequency
freq = 60.0e3 # Hz
# wave speed
cmax = 343 # m/s
# wave length
lamda = cmax / freq
# Spatial resolution
# spatial stability critria: dx must be smaller or equal than lambda_min / 20
# where lambda_min is the shortest wavelength in the model!
dx = lamda/40 #.01
# Time domain resolution
# time stability criteria: dt must be smaller or equal than dx / ( sqrt(2) * c_max )
# where c_max is the highest wave speed in the model!
dt = dx/(cmax*np.sqrt(2) ) # 20.0e-6
# mesh grid
x = np.arange ( 0, (NX)*dx, dx )
y = np.arange ( 0, (NY)*dx, dx )
xx, yy = np.meshgrid( y, x )
## Properties of the fluid like density and viscosity
# Density
rho = 1.241
# Bulk viscosity: c^2 x rho
kappa = np.power(cmax, 2.0) * rho #142.0e3
## Computing magnitutes for two dimensional particle velocity and pressure
Vx = np.zeros ( ( NX + 1, NY ) )
Vy = np.zeros ( ( NX , NY + 1 ) )
P = np.zeros ( ( NX , NY ) )
NFrames = 3000
SourceWidth = 20
SourceHeight = 10
# setup indices
ind = np.full(( NX, NY), False, dtype=bool)
# setup indices
ind = np.full(( NX, NY), False, dtype=bool)
Pmx = int(np.floor(NX/4 - 10 + 1 ))
Pmy = int(np.floor(NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
Pmx = int(np.floor(NX/2 - 10 + 1 ))
Pmy = int(np.floor(NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
Pmx = int(np.floor(3*NX/4 - 10 + 1 ))
Pmy = int(np.floor( NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
Pmx = int(np.floor(NX - 30 + 1 ))
Pmy = int(np.floor(NY/2 + 1 ))
circle ( ind, Pmx, Pmy, 20 )
# Pxs = int(np.floor(NX/2 - SourceWidth/2 + 1 ))
# Pxe = int(np.floor(NX/2 + SourceWidth/2 + 1 ))
# Pys = int(np.floor(NY/2 - SourceHeight/2 + 1 ))
# Pye = int(np.floor(NY/2 + SourceHeight/2 + 1 ))
# ind[ Pxs:Pxe, Pys:Pys ] = True
## Visual stuff
# Colormap
colormap = 'RdBu'
# Plot creation
fig = plt.figure(figsize=(12.8, 7.2))
ax = fig.add_subplot(1,1,1)
cax = ax.pcolormesh( xx, yy, P, vmin=-1, vmax=1, cmap=colormap)
ax.set_xlabel ( r'Position $x$ / $m$' )
ax.set_ylabel ( r'Position $y$ / $m$' )
ax.set_xlim ( y[0], y[-1] )
ax.set_ylim ( x[0], x[-1] )
fig.colorbar(cax)
fig.tight_layout()
image_step = 400
## help variables
dt_over_rho_x_dx = dt / ( rho * dx )
kappa_x_dt_over_dx = kappa * dt / dx
print ( 'Spatial stepsize %00.3f mm.' % ( dx*1e3 ) )
print ( 'Time stepsize %00.3f us.' % ( dt*1e6 ) )
print ( 'Volume elasticity %03.3f .' % ( kappa*1e-3 ) )
print ( 'Pulse width %d ' % ( int((1.0/freq)/dt) ) )
n = 0
def updatefig (n):
# Updating particle velocities
for i in range (2,NX):
for j in range ( 1, NY ):
Vx[i,j] -= dt_over_rho_x_dx * ( P[i,j] - P[i-1,j] )
for i in range (1,NX):
for j in range ( 2, NY):
Vy[i,j] -= dt_over_rho_x_dx * ( P[i,j] - P[i,j-1] )
# Update sound pressure
for i in range (1, NX):
for j in range (1,NY):
P[i,j] -= ( ( Vx[i+1,j] - Vx[i,j] ) + ( Vy[i,j+1] - Vy[i,j] ) )
# Acoustic source ( during one period)
if (n < (1.0/freq)/dt):
P[ ind ] += .33*(1.0-np.cos(2.0*np.pi*freq*n*dt))/2.0 * np.sin(2.0*np.pi*freq*n*dt)
if (n < (1.0/(.5*freq))/dt):
P[ ind ] += .33*(1.0-np.cos(2.0*np.pi*.5*freq*n*dt))/2.0 * np.sin(2.0*np.pi*.5*freq*n*dt)
if (n < (1.0/(.25*freq))/dt):
P[ ind ] += .33*(1.0-np.cos(2.0*np.pi*.25*freq*n*dt))/2.0 * np.sin(2.0*np.pi*.25*freq*n*dt)
if (( n + 1 ) % 100 == 1) or ( n == 0):
print ( '--- processing step %03d ---' % ( n ))
# Updating the current calculation step
n += 1
# Updating data
cax.set_array ( P.flatten() )
#cax2.set_array ( np.abs(P).flatten() )
#ax.set_title("circular membrane: l={}, m={}-Mode".format(l+1,m))
ax.set_title("Time step {} ms".format( int((n*dt*1e3*10))/10.0 ) )
#ax[1].set_title("Time step {} ms".format( int((n*dt*1e3*10))/10.0 ) )
return cax,
anim = animation.FuncAnimation(fig, updatefig, frames=NFrames-1,interval=image_step, blit=True)
anim
Writer = animation.writers['ffmpeg']
writer = Writer ( fps=100, metadata=dict(artist='dkotscha' ), bitrate=6000)
anim.save('ftdt_acoustic_one_source_2d.mp4', writer=writer )
plt.close()
| 1,979 | 0 | 46 |
ddbe5d981cc7c179c7df3962dd94212f2262bf54 | 629 | py | Python | MOnewUser/env/lib/python3.8/site-packages/eth_hash/abc.py | julio-cesar-leitao/PGP-PKI | 9bdeb4606534d6dacba8356b223a20e204399d32 | [
"MIT"
] | 68 | 2018-02-08T20:46:16.000Z | 2022-03-18T19:36:09.000Z | MOnewUser/env/lib/python3.8/site-packages/eth_hash/abc.py | julio-cesar-leitao/PGP-PKI | 9bdeb4606534d6dacba8356b223a20e204399d32 | [
"MIT"
] | 21 | 2018-02-07T20:45:06.000Z | 2021-09-24T19:57:52.000Z | MOnewUser/env/lib/python3.8/site-packages/eth_hash/abc.py | julio-cesar-leitao/PGP-PKI | 9bdeb4606534d6dacba8356b223a20e204399d32 | [
"MIT"
] | 46 | 2018-02-08T15:15:40.000Z | 2022-03-28T18:49:32.000Z | from abc import (
ABC,
abstractmethod,
)
from typing import (
Union,
)
| 17.472222 | 72 | 0.583466 | from abc import (
ABC,
abstractmethod,
)
from typing import (
Union,
)
class PreImageAPI(ABC):
@abstractmethod
def __init__(self, value: bytes) -> None:
...
@abstractmethod
def update(self, value: bytes) -> None:
...
@abstractmethod
def digest(self) -> bytes:
...
@abstractmethod
def copy(self) -> 'PreImageAPI':
...
class BackendAPI(ABC):
@abstractmethod
def keccak256(self, in_data: Union[bytearray, bytes]) -> bytes:
...
@abstractmethod
def preimage(self, in_data: Union[bytearray, bytes]) -> PreImageAPI:
...
| 215 | 283 | 46 |
ef3d6bc838bd24e2609fe5471781cac8f97d2759 | 12,070 | py | Python | GP_model.py | JixiangChen-Jimmy/DMEA | 886ca864fa6b2f51867688035870f1ec344d1090 | [
"MIT"
] | null | null | null | GP_model.py | JixiangChen-Jimmy/DMEA | 886ca864fa6b2f51867688035870f1ec344d1090 | [
"MIT"
] | null | null | null | GP_model.py | JixiangChen-Jimmy/DMEA | 886ca864fa6b2f51867688035870f1ec344d1090 | [
"MIT"
] | null | null | null | import GPy
from GPyOpt.util.general import get_quantiles
import GPyOpt
import numpy as np
from math import pow, log, sqrt
import pandas as pd
# train_x : history x
# train_y : history y
# num_init : The number of initial sample points
# lsx : the collected x in last round
# lsy : the text function value corresponding to the collected x in last round
# P : Represents the historical confidence level of the acquisition functions in acquisition library
# domain : refer to the information of the variable of BayesianOptimization called by GPyOpt
# f : refer to the optimization objectives information of BayesianOptimization called by GPyOpt
# num_obj : The selected number of acquisition functions that build up multi-objective optimization.
# eta is a hyperparameters
| 43.731884 | 121 | 0.505965 | import GPy
from GPyOpt.util.general import get_quantiles
import GPyOpt
import numpy as np
from math import pow, log, sqrt
import pandas as pd
# train_x : history x
# train_y : history y
# num_init : The number of initial sample points
# lsx : the collected x in last round
# lsy : the text function value corresponding to the collected x in last round
# P : Represents the historical confidence level of the acquisition functions in acquisition library
# domain : refer to the information of the variable of BayesianOptimization called by GPyOpt
# f : refer to the optimization objectives information of BayesianOptimization called by GPyOpt
# num_obj : The selected number of acquisition functions that build up multi-objective optimization.
# eta is a hyperparameters
class GP:
def __init__(self, iter, train_x, train_y, exx, exy, k, num_init, lsx, lsy, P, f,
domain, model, eta=0.9):
self.train_x = train_x.copy()
self.train_y = train_y.copy()
self.mean = np.mean(exy)
self.std = np.std(exy)
self.num_train = exx.shape[0]
self.lsx = lsx.copy()
self.lsy = lsy.copy()
self.exx = exx.copy()
self.exy = exy.copy()
self.dim = self.exx.shape[1]
self.k = k
self.num_init = num_init
self.P = P
self.eta = eta
self.domain = domain
self.f = f
self.iter = iter
self.m = model
self.tau = np.min(train_y)
self.burnin = 200
self.n_samples = 10
self.subsample_interval = 10
self.sample()
self.update()
self.mean = np.mean(train_y)
self.std = np.std(train_y)
self.train_y = (train_y.copy() - self.mean) / self.std
self.num_train = train_x.shape[0]
kern = GPy.kern.Matern52(input_dim=self.dim, ARD=True)
self.m = GPy.models.GPRegression(self.train_x, self.train_y, kern, noise_var=0)
def update(self):
tx = self.train_x.tolist()
ty = self.train_y.ravel().tolist()
lsy = self.lsy.ravel().tolist()
self.lsbesty = np.min(self.exy)
history_data = pd.DataFrame({'x': tx, 'y': ty})
history_data.sort_values(by='y', ascending=True, inplace=True)
history_data.reset_index(drop=True, inplace=True)
self.rank = []
self.hq_x = []
self.phi_alpha = []
alpha = 3
for i in lsy:
if len(history_data.index[history_data['y'] < i]) > 0:
self.rank.append(history_data.index[history_data['y'] < i][-1] + 1)
else:
self.rank.append(0)
if self.rank[-1] <= int(alpha):
self.hq_x.append(1)
else:
self.hq_x.append(0)
self.phii = self.hq_x
self.rank = np.array(self.rank)
self.hq_x = np.array(self.hq_x)
# Stores information about the collection library
self.acqusition_type = ['MPI', 'EI', 'LCB', 'LCB', 'LCB', 'LCB', 'LCB']
self.hyperpara = [0.001, 0.001, [0.5, 0.5], [0.5, 0.05], [5, 0.1], [10, 0.1], [30, 0.1]]
# LP_recommend_num is the regarding threshold of recommended number when using LP-alpha(x) acquisition functions
# beta is equal to 0.5
LP_recommend_num = int(0.5 * self.k) # k / 2
current_P = []
for i in range(len(self.acqusition_type)):
if self.acqusition_type[i] == 'LCB':
self.set_kappa(self.hyperpara[i][0], self.hyperpara[i][1])
LP_checker = GPyOpt.methods.BayesianOptimization(f=self.f,
domain=self.domain,
acquisition_type=self.acqusition_type[i],
normalize_Y=True,
X=self.exx,
Y=self.exy,
evaluator_type='local_penalization',
batch_size=LP_recommend_num,
acquisition_weight=self.kappa)
else:
LP_checker = GPyOpt.methods.BayesianOptimization(f=self.f,
domain=self.domain,
acquisition_type=self.acqusition_type[i],
normalize_Y=True,
X=self.exx,
Y=self.exy,
evaluator_type='local_penalization',
batch_size=LP_recommend_num,
acquisition_jitter=self.hyperpara[i])
LP_checker.run_optimization(max_iter=1)
LP_acq_x = LP_checker.suggested_sample
# acq_recommend_bound is the threshold at which the acquisition function recommends the sample points or not
acq_recommend_bound = np.inf
for x in LP_acq_x:
pys, pss = self.predict(x)
if self.acqusition_type[i] == 'LCB':
acq_calc = -self.LCB(pys, pss)
elif self.acqusition_type[i] == 'MPI':
acq_calc = self.PI(pys, pss, self.hyperpara[i])
else:
acq_calc = self.EI(pys, pss, self.hyperpara[i])
if acq_calc < acq_recommend_bound:
acq_recommend_bound = acq_calc
# acq_val is the value of the acquisition function
# under the current acquisition function of the previous round of sampling points
acq_val = []
phi_alpha = []
for x in self.lsx:
pys, pss = self.predict(x)
if self.acqusition_type[i] == 'LCB':
acq_calc = -self.LCB(pys, pss)
elif self.acqusition_type[i] == 'MPI':
acq_calc = self.PI(pys, pss, self.hyperpara[i])
else:
acq_calc = self.EI(pys, pss, self.hyperpara[i])
acq_val.append(acq_calc)
if acq_calc >= acq_recommend_bound - 1e-7:
phi_alpha.append(1)
else:
phi_alpha.append(0)
phi_alpha = np.array(phi_alpha)
self.phi_alpha.append(phi_alpha.tolist())
# cp is used to calculate the penalty value of current acquisition function
cp = 0
for i in range(self.lsx.shape[0]):
cp += abs(self.hq_x[i] - phi_alpha[i]) * abs(self.lsbesty - lsy[i])
if self.hq_x[i] == 1 and phi_alpha[i] == 1:
cp += lsy[i] - self.lsbesty
current_P.append(cp)
current_P = np.array(current_P)
# self.CP is a comprehensive information that synthesizes the historical penalty value of acquisition functions
# and the penalty value obtained by the previous round of sampling points,
# and needs to be stored by calling the main function
self.CP = self.eta * self.P + current_P
df_CP = pd.DataFrame({'cp': self.CP.tolist()})
# self.current_choice stores the subscripts of the num_obj acquisition functions selected in the acquisition
# function library.
# self.confidence returns the confidence level obtained by recombining the penalty value of the selected
# acquisition functions in the current round, which is a one-dimensional ndarray
# num_obj : The number of selected acquisition functions that build up multi-objective optimization
# num_obj is equal to 3
self.current_choice = df_CP.index[df_CP['cp'].rank(method='first') - 3 < 1e-3].tolist()
self.T = []
minn = np.min(self.CP)
for i in self.current_choice:
self.T.append(self.CP[i])
self.T = np.array(self.T)
self.T = (self.T + 1e-4) / np.sum((self.T + 1e-4))
def sample(self):
self.m.optimize_restarts(num_restarts=10)
if not 0:
self.s = np.array(np.array(self.m[:]))
self.s = self.s.reshape(1, self.s.size)
self.ms = np.array([self.m])
else:
hmc = GPy.inference.mcmc.HMC(self.m, stepsize=5e-2)
s = hmc.sample(num_samples=self.n_samples * self.subsample_interval)
self.s = s[0::self.subsample_interval]
self.ms = []
for i in range(self.s.shape[0]):
samp_kern = GPy.kern.Matern52(input_dim=self.dim, ARD=True)
samp_m = GPy.models.GPRegression(self.train_x, self.train_y, samp_kern)
samp_m[:] = self.s[i]
samp_m.parameters_changed()
self.ms = np.append(self.ms, samp_m)
def predict_sample(self, x, hyp_vec):
self.m.kern.variance = hyp_vec[0]
self.m.kern.lengthscale = hyp_vec[1:1 + self.dim]
self.m.likelihood.variance = hyp_vec[1 + self.dim]
py, ps2 = self.m.predict(x.reshape(1, x.size))
py = self.mean + (py * self.std)
ps2 = ps2 * (self.std ** 2)
return py, ps2
def set_kappa(self, upsilon, delta):
num_train = self.num_train
t = 1 + max(int((num_train - self.num_init) / self.k), 0)
self.kappa = sqrt(
upsilon * 2 * log(pow(t, 2.0 + self.dim / 2.0) * 3 * pow(np.pi, 2) / (3 * delta))) # kappa of LCB
def predict(self, x):
num_samples = self.s.shape[0]
pys = np.zeros((num_samples, 1))
pss = np.zeros((num_samples, 1))
for i in range(num_samples):
m, v = self.ms[i].predict(x.reshape(1, x.size))
pys[i] = m[0][0]
pss[i] = v[0][0]
pys = self.mean + (pys * self.std)
pss = pss * (self.std ** 2)
return pys, np.sqrt(pss)
def LCB(self, pys, pss):
num_samples = pys.shape[0]
acq = 0
for i in range(num_samples):
y = pys[i]
s = pss[i]
lcb = y - self.kappa * s
acq += lcb
acq /= self.s.shape[0]
return acq
def EI(self, pys, pss, eps):
num_samples = pys.shape[0]
acq = 0
for i in range(num_samples):
y = pys[i]
s = pss[i]
phi, Phi, u = get_quantiles(eps, self.tau, y, s)
f_acqu = s * (u * Phi + phi)
acq += f_acqu
acq /= self.s.shape[0]
return acq
def PI(self, pys, pss, eps):
num_samples = pys.shape[0]
acq = 0
for i in range(num_samples):
y = pys[i]
s = pss[i]
_, Phi, _ = get_quantiles(eps, self.tau, y, s)
f_acqu = Phi
acq += f_acqu
acq /= self.s.shape[0]
return acq
def MACE_acq(self, x):
pys, pss = self.predict(x)
list = []
for i in self.current_choice:
if self.acqusition_type[i] == 'LCB':
self.set_kappa(self.hyperpara[i][0], self.hyperpara[i][1])
lcb = self.LCB(pys, pss)
list.append([lcb, 0])
elif self.acqusition_type[i] == 'MPI':
pi = self.PI(pys, pss, self.hyperpara[i])
list.append([pi, 1])
else:
ei = self.EI(pys, pss, self.hyperpara[i])
list.append([ei, 1])
return list
| 10,980 | -12 | 312 |
ddb0483295a6a1a976fc5fd283466768897fe4c3 | 350 | py | Python | definition_theme/__manifest__.py | talway/OdooERPApp | 2c44baa8ad020550098e18e630dbc57d3927d1ca | [
"Apache-2.0"
] | null | null | null | definition_theme/__manifest__.py | talway/OdooERPApp | 2c44baa8ad020550098e18e630dbc57d3927d1ca | [
"Apache-2.0"
] | null | null | null | definition_theme/__manifest__.py | talway/OdooERPApp | 2c44baa8ad020550098e18e630dbc57d3927d1ca | [
"Apache-2.0"
] | 2 | 2019-11-11T12:34:03.000Z | 2020-09-21T07:25:41.000Z | # -*- coding: utf-8 -*-
{
'name': "自定义主题界面风格",
'summary': """自定义界面风格模块""",
'description': """用以自定义Odoo界面风格的模块""",
'author': "SuXueFeng",
'website': "https://www.sxfblog.com",
'category': 'style',
'version': '0.1',
'depends': ['base'],
'data': [
'views/WebAssetsBackend.xml',
],
'demo': [
],
}
| 19.444444 | 42 | 0.488571 | # -*- coding: utf-8 -*-
{
'name': "自定义主题界面风格",
'summary': """自定义界面风格模块""",
'description': """用以自定义Odoo界面风格的模块""",
'author': "SuXueFeng",
'website': "https://www.sxfblog.com",
'category': 'style',
'version': '0.1',
'depends': ['base'],
'data': [
'views/WebAssetsBackend.xml',
],
'demo': [
],
}
| 0 | 0 | 0 |
4e7b9e9936d24d03c5bc95bfa26b6063c5eacaab | 195 | py | Python | tests/run_flake8/noerror.py | 10sr/flake8-no-implicit-concat | 11db2327ffc122d9481c6e03a77cf62b1dc85d25 | [
"MIT"
] | 15 | 2020-05-21T19:39:58.000Z | 2022-03-22T11:04:12.000Z | tests/run_flake8/noerror.py | 10sr/flake8-no-implicit-concat | 11db2327ffc122d9481c6e03a77cf62b1dc85d25 | [
"MIT"
] | 43 | 2020-05-20T05:19:20.000Z | 2021-11-25T05:34:51.000Z | tests/run_flake8/noerror.py | 10sr/flake8-no-implicit-concat | 11db2327ffc122d9481c6e03a77cf62b1dc85d25 | [
"MIT"
] | 1 | 2020-08-25T23:04:08.000Z | 2020-08-25T23:04:08.000Z | a = "aaa"
b = "bbb" + "ccc"
d = """ddd
eee
fff""" + "ggg"
l = ["hhh",
"iii" +
"jjj"]
noqa = "k" "l" # noqa: NIC001
noqa = ("k" # noqa: NIC002
"l")
print("abc" + "def", "ghi")
| 15 | 30 | 0.410256 | a = "aaa"
b = "bbb" + "ccc"
d = """ddd
eee
fff""" + "ggg"
l = ["hhh",
"iii" +
"jjj"]
noqa = "k" "l" # noqa: NIC001
noqa = ("k" # noqa: NIC002
"l")
print("abc" + "def", "ghi")
| 0 | 0 | 0 |
67bca61bcc5eccb73a28567b84e02d4c1c3a79b7 | 1,786 | py | Python | bookmark_website/bookmark_app/migrations/0001_initial.py | thejeshpr/bookmarks | eff748c0e7a6e3d211d3e87b25ffefca612726ce | [
"MIT"
] | null | null | null | bookmark_website/bookmark_app/migrations/0001_initial.py | thejeshpr/bookmarks | eff748c0e7a6e3d211d3e87b25ffefca612726ce | [
"MIT"
] | null | null | null | bookmark_website/bookmark_app/migrations/0001_initial.py | thejeshpr/bookmarks | eff748c0e7a6e3d211d3e87b25ffefca612726ce | [
"MIT"
] | null | null | null | # Generated by Django 2.2.7 on 2019-11-18 16:44
from django.db import migrations, models
import django.db.models.deletion
| 42.52381 | 169 | 0.595185 | # Generated by Django 2.2.7 on 2019-11-18 16:44
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(help_text='Category Name', max_length=50, unique=True)),
('slug', models.CharField(max_length=50, unique=True)),
('timestamp', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
],
options={
'verbose_name_plural': 'Categories',
},
),
migrations.CreateModel(
name='Bookmark',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('desc', models.TextField(blank=True, help_text='Description', null=True)),
('title', models.CharField(blank=True, help_text='bookmark title', max_length=500, null=True, unique=True)),
('slug', models.CharField(max_length=500, unique=True)),
('url', models.URLField(blank=True, help_text='URL', max_length=2000, null=True)),
('timestamp', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
('category', models.ForeignKey(help_text='Category', on_delete=django.db.models.deletion.CASCADE, related_name='bookmarks', to='bookmark_app.Category')),
],
),
]
| 0 | 1,639 | 23 |
f675775f12c8350ddef35bc639c03e2223ccc8a6 | 1,931 | py | Python | setup.py | calibear20/NHentai-API | c543f96f4088dd0f25842e9935f2f6c84317dc55 | [
"MIT"
] | null | null | null | setup.py | calibear20/NHentai-API | c543f96f4088dd0f25842e9935f2f6c84317dc55 | [
"MIT"
] | null | null | null | setup.py | calibear20/NHentai-API | c543f96f4088dd0f25842e9935f2f6c84317dc55 | [
"MIT"
] | null | null | null | from setuptools import setup
import os
env = os.getenv('environment_name') or 'dev'
if env == 'prd':
setup(
name='NHentai-API',
version='0.0.17',
description='NHentai Python API made using webscraping.',
long_description=readme(),
long_description_content_type='text/markdown',
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
url='https://github.com/AlexandreSenpai/NHentai-API',
author='AlexandreSenpai',
author_email='alexandrebsramos@hotmail.com',
keywords='Tagshentai, nhentai, nhentai.net, API, NSFW',
license='MIT',
packages=['NHentai', 'NHentai.entities', 'NHentai.utils'],
install_requires=['requests', 'beautifulsoup4', 'aiohttp', 'expiringdict'],
include_package_data=True,
zip_safe=False
)
elif env == 'dev':
setup(
name='dev-nhentai-build',
version='0.0.6',
description='NHentai Python API made using webscraping.',
long_description=readme(),
long_description_content_type='text/markdown',
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
url='https://github.com/AlexandreSenpai/NHentai-API',
author='AlexandreSenpai',
author_email='alexandrebsramos@hotmail.com',
keywords='Tagshentai, nhentai, nhentai.net, API, NSFW',
license='MIT',
packages=['dev_nhentai', 'dev_nhentai.entities', 'dev_nhentai.utils'],
install_requires=['requests', 'beautifulsoup4', 'aiohttp', 'expiringdict'],
include_package_data=True,
zip_safe=False
)
| 35.759259 | 83 | 0.612118 | from setuptools import setup
import os
def readme():
with open('README.md') as f:
return f.read()
env = os.getenv('environment_name') or 'dev'
if env == 'prd':
setup(
name='NHentai-API',
version='0.0.17',
description='NHentai Python API made using webscraping.',
long_description=readme(),
long_description_content_type='text/markdown',
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
url='https://github.com/AlexandreSenpai/NHentai-API',
author='AlexandreSenpai',
author_email='alexandrebsramos@hotmail.com',
keywords='Tagshentai, nhentai, nhentai.net, API, NSFW',
license='MIT',
packages=['NHentai', 'NHentai.entities', 'NHentai.utils'],
install_requires=['requests', 'beautifulsoup4', 'aiohttp', 'expiringdict'],
include_package_data=True,
zip_safe=False
)
elif env == 'dev':
setup(
name='dev-nhentai-build',
version='0.0.6',
description='NHentai Python API made using webscraping.',
long_description=readme(),
long_description_content_type='text/markdown',
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
url='https://github.com/AlexandreSenpai/NHentai-API',
author='AlexandreSenpai',
author_email='alexandrebsramos@hotmail.com',
keywords='Tagshentai, nhentai, nhentai.net, API, NSFW',
license='MIT',
packages=['dev_nhentai', 'dev_nhentai.entities', 'dev_nhentai.utils'],
install_requires=['requests', 'beautifulsoup4', 'aiohttp', 'expiringdict'],
include_package_data=True,
zip_safe=False
)
| 49 | 0 | 23 |
8f607160751ecb6032d08116ea48d35715e78810 | 25,103 | py | Python | tests/daemon/test_walletd.py | scottdonaldau/QRL | fb78c1cdf227330ace46f590a36cc6a52c7af3fe | [
"MIT"
] | null | null | null | tests/daemon/test_walletd.py | scottdonaldau/QRL | fb78c1cdf227330ace46f590a36cc6a52c7af3fe | [
"MIT"
] | null | null | null | tests/daemon/test_walletd.py | scottdonaldau/QRL | fb78c1cdf227330ace46f590a36cc6a52c7af3fe | [
"MIT"
] | null | null | null | # coding=utf-8
# Distributed under the MIT software license, see the accompanying
# file LICENSE or http://www.opensource.org/licenses/mit-license.php.
from unittest import TestCase
from mock import Mock
from pyqrllib.pyqrllib import bin2hstr, hstr2bin
from qrl.daemon.walletd import WalletD
from qrl.generated import qrl_pb2
from qrl.core.txs.TransferTransaction import TransferTransaction
from qrl.core.Wallet import WalletDecryptionError
from qrl.core.misc import logger
from tests.misc.helper import set_qrl_dir, get_alice_xmss, get_bob_xmss
logger.initialize_default()
| 44.508865 | 120 | 0.569374 | # coding=utf-8
# Distributed under the MIT software license, see the accompanying
# file LICENSE or http://www.opensource.org/licenses/mit-license.php.
from unittest import TestCase
from mock import Mock
from pyqrllib.pyqrllib import bin2hstr, hstr2bin
from qrl.daemon.walletd import WalletD
from qrl.generated import qrl_pb2
from qrl.core.txs.TransferTransaction import TransferTransaction
from qrl.core.Wallet import WalletDecryptionError
from qrl.core.misc import logger
from tests.misc.helper import set_qrl_dir, get_alice_xmss, get_bob_xmss
logger.initialize_default()
class TestWalletD(TestCase):
def __init__(self, *args, **kwargs):
self.passphrase = '你好'
self.qaddress = "Q010400ff39df1ba4d1d5b8753e6d04c51c34b95b01fc3650c10ca7b296a18bdc105412c59d0b3b"
self.hex_seed = "0104008441d43524996f76236141d16b7b324323abf796e77ad" \
"7c874622a82f5744bb803f9b404d25733d0db82be7ac6f3c4cf"
self.mnemonic = "absorb drank lute brick cure evil inept group grey " \
"breed hood reefy eager depict weed image law legacy " \
"jockey calm lover freeze fact lively wide dread spiral " \
"jaguar span rinse salty pulsar violet fare"
super(TestWalletD, self).__init__(*args, **kwargs)
def test_init(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
self.assertIsNotNone(walletd)
def test_qaddress_to_address(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = "Q010600968c3408cba5192d75c11cec909e803fc590e82463216b5a04ce8e447f76b4e02c0d3d81"
address = walletd.qaddress_to_address(qaddress)
self.assertEqual(qaddress[1:], bin2hstr(address))
def test_authenticate(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd.authenticate()
walletd._wallet = Mock()
walletd._wallet.encrypted = Mock(return_value=True)
with self.assertRaises(ValueError):
walletd.authenticate()
def test_encrypt_last_item(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd.authenticate()
walletd.add_new_address(height=4)
self.assertFalse(walletd.get_wallet_info()[2])
walletd._passphrase = self.passphrase
walletd._encrypt_last_item()
self.assertTrue(walletd.get_wallet_info()[2])
def test_get_wallet_index_xmss(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
index, xmss = walletd._get_wallet_index_xmss(qaddress, 0)
self.assertEqual(index, 0)
self.assertEqual(xmss.qaddress, qaddress)
def test_add_new_address(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
self.assertEqual(len(walletd.list_address()), 1)
def test_add_new_address2(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
self.assertEqual(len(walletd.list_address()), 1)
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
self.assertEqual(len(walletd.list_address()), 2)
def test_add_address_from_seed(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress1 = walletd.add_address_from_seed(seed=self.hex_seed) # Using hexseed
self.assertEqual(self.qaddress, qaddress1)
self.assertEqual(len(walletd.list_address()), 1)
def test_add_address_from_seed2(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress1 = walletd.add_address_from_seed(seed=self.hex_seed) # Using hexseed
self.assertEqual(self.qaddress, qaddress1)
self.assertEqual(len(walletd.list_address()), 1)
walletd.remove_address(self.qaddress)
self.assertEqual(len(walletd.list_address()), 0)
qaddress2 = walletd.add_address_from_seed(seed=self.mnemonic) # Using mnemonic
self.assertEqual(self.qaddress, qaddress2)
self.assertEqual(len(walletd.list_address()), 1)
def test_list_address(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
self.assertEqual(len(walletd.list_address()), 1)
list_address = walletd.list_address()
self.assertEqual(list_address[0], qaddress)
def test_remove_address(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
self.assertEqual(len(walletd.list_address()), 1)
result = walletd.remove_address(qaddress)
self.assertTrue(result)
self.assertEqual(len(walletd.list_address()), 0)
def test_remove_address2(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
self.assertEqual(len(walletd.list_address()), 1)
result = walletd.remove_address(qaddress)
self.assertTrue(result)
self.assertEqual(len(walletd.list_address()), 0)
result = walletd.remove_address("Q123")
self.assertFalse(result)
self.assertEqual(len(walletd.list_address()), 0)
def test_get_recovery_seeds(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
self.assertEqual(qaddress[0], 'Q')
seeds = walletd.get_recovery_seeds(qaddress)
self.assertIsInstance(seeds, tuple)
walletd.remove_address(qaddress)
self.assertEqual(len(walletd.list_address()), 0)
qaddress2 = walletd.add_address_from_seed(seeds[0]) # Using Hex Seed
self.assertEqual(qaddress, qaddress2)
walletd.remove_address(qaddress2)
self.assertEqual(len(walletd.list_address()), 0)
qaddress2 = walletd.add_address_from_seed(seeds[1]) # Using Mnemonic
self.assertEqual(qaddress, qaddress2)
walletd.remove_address(qaddress2)
self.assertEqual(len(walletd.list_address()), 0)
def test_get_wallet_info(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
version, len_address_items, encrypted = walletd.get_wallet_info()
self.assertEqual(version, 1)
self.assertEqual(len_address_items, 0)
self.assertFalse(encrypted)
def test_push_transaction(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
alice_xmss = get_alice_xmss()
bob_xmss = get_bob_xmss()
tx = TransferTransaction.create(addrs_to=[bob_xmss.address],
amounts=[1],
fee=1,
xmss_pk=alice_xmss.pk)
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
walletd._push_transaction(tx, alice_xmss)
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.UNKNOWN))
with self.assertRaises(Exception):
walletd._push_transaction(tx, alice_xmss)
def test_relay_transfer_txn(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
alice_xmss = get_alice_xmss(4)
bob_xmss = get_bob_xmss(4)
qaddresses_to = [alice_xmss.qaddress, bob_xmss.qaddress]
amounts = [1000000000, 1000000000]
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_transfer_txn(qaddresses_to=qaddresses_to,
amounts=amounts,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
def test_relay_transfer_txn2(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
walletd.encrypt_wallet(self.passphrase)
walletd.unlock_wallet(self.passphrase)
alice_xmss = get_alice_xmss(4)
bob_xmss = get_bob_xmss(4)
qaddresses_to = [alice_xmss.qaddress, bob_xmss.qaddress]
amounts = [1000000000, 1000000000]
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_transfer_txn(qaddresses_to=qaddresses_to,
amounts=amounts,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.relay_transfer_txn(qaddresses_to=qaddresses_to,
amounts=amounts,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
def test_relay_message_txn(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_message_txn(message='Hello QRL!',
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
def test_relay_message_txn2(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
walletd.encrypt_wallet(self.passphrase)
walletd.unlock_wallet(self.passphrase)
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_message_txn(message='Hello QRL!',
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.relay_message_txn(message='Hello QRL!',
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
def test_relay_token_txn(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
alice_xmss = get_alice_xmss(4)
bob_xmss = get_bob_xmss(4)
qaddresses = [alice_xmss.qaddress, bob_xmss.qaddress]
amounts = [1000000000, 1000000000]
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_token_txn(symbol='QRL',
name='Quantum Resistant Ledger',
owner_qaddress=alice_xmss.qaddress,
decimals=5,
qaddresses=qaddresses,
amounts=amounts,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
def test_relay_token_txn2(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
walletd.encrypt_wallet(self.passphrase)
walletd.unlock_wallet(self.passphrase)
alice_xmss = get_alice_xmss(4)
bob_xmss = get_bob_xmss(4)
qaddresses = [alice_xmss.qaddress, bob_xmss.qaddress]
amounts = [1000000000, 1000000000]
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_token_txn(symbol='QRL',
name='Quantum Resistant Ledger',
owner_qaddress=alice_xmss.qaddress,
decimals=5,
qaddresses=qaddresses,
amounts=amounts,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.relay_token_txn(symbol='QRL',
name='Quantum Resistant Ledger',
owner_qaddress=alice_xmss.qaddress,
decimals=5,
qaddresses=qaddresses,
amounts=amounts,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
def test_relay_transfer_token_txn(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
walletd.encrypt_wallet(self.passphrase)
walletd.unlock_wallet(self.passphrase)
alice_xmss = get_alice_xmss(4)
bob_xmss = get_bob_xmss(4)
qaddresses_to = [alice_xmss.qaddress, bob_xmss.qaddress]
amounts = [1000000000, 1000000000]
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_transfer_token_txn(qaddresses_to=qaddresses_to,
amounts=amounts,
token_txhash='',
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.relay_transfer_token_txn(qaddresses_to=qaddresses_to,
amounts=amounts,
token_txhash='',
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
def test_relay_slave_txn(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address(height=4)
walletd.encrypt_wallet(self.passphrase)
walletd.unlock_wallet(self.passphrase)
alice_xmss = get_alice_xmss(4)
slave_pks = [alice_xmss.pk]
access_types = [0]
walletd._public_stub.PushTransaction = Mock(
return_value=qrl_pb2.PushTransactionResp(error_code=qrl_pb2.PushTransactionResp.SUBMITTED))
tx = walletd.relay_slave_txn(slave_pks=slave_pks,
access_types=access_types,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
self.assertIsNotNone(tx)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.relay_slave_txn(slave_pks=slave_pks,
access_types=access_types,
fee=100000000,
master_qaddress=None,
signer_address=qaddress,
ots_index=0)
def test_encrypt_wallet(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
with self.assertRaises(ValueError):
walletd.encrypt_wallet(passphrase=self.passphrase)
walletd.add_new_address()
walletd.encrypt_wallet(passphrase=self.passphrase)
with self.assertRaises(Exception):
walletd.encrypt_wallet(passphrase=self.passphrase)
def test_lock_wallet(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd.add_new_address()
walletd.encrypt_wallet(passphrase=self.passphrase)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.add_new_address()
def test_unlock_wallet(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd.add_new_address()
walletd.encrypt_wallet(passphrase=self.passphrase)
walletd.lock_wallet()
with self.assertRaises(ValueError):
walletd.add_new_address()
with self.assertRaises(WalletDecryptionError):
walletd.unlock_wallet(passphrase='pass123')
walletd.unlock_wallet(passphrase=self.passphrase)
walletd.add_new_address()
self.assertEqual(len(walletd.list_address()), 2)
def test_change_passphrase(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
qaddress = walletd.add_new_address()
walletd.encrypt_wallet(passphrase=self.passphrase)
walletd.lock_wallet()
passphrase2 = 'pass000'
with self.assertRaises(ValueError):
walletd.change_passphrase(old_passphrase='pass123', new_passphrase='pass234')
walletd.change_passphrase(old_passphrase=self.passphrase, new_passphrase=passphrase2)
with self.assertRaises(WalletDecryptionError):
walletd.unlock_wallet(passphrase=self.passphrase)
walletd.unlock_wallet(passphrase=passphrase2)
qaddresses = walletd.list_address()
self.assertEqual(len(qaddresses), 1)
self.assertEqual(qaddresses[0], qaddress)
def test_get_transactions_by_address(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd._public_stub.GetTransactionsByAddress = Mock(
return_value=qrl_pb2.GetTransactionsByAddressResp(mini_transactions=[],
balance=0))
mini_transactions, balance = walletd.get_transactions_by_address(qaddress=get_alice_xmss(4).qaddress)
self.assertEqual(len(mini_transactions), 0)
self.assertEqual(balance, 0)
def test_get_transaction(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
tx = qrl_pb2.Transaction()
tx.fee = 10
tx.transaction_hash = b'1234'
tx.message.message_hash = b'hello'
pk = '01020016ecb9f39b9f4275d5a49e232346a15ae2fa8c50a2927daeac189b8c5f2d1' \
'8bc4e3983bd564298c49ae2e7fa6e28d4b954d8cd59398f1225b08d6144854aee0e'
tx.public_key = bytes(hstr2bin(pk))
walletd._public_stub.GetTransaction = Mock(
return_value=qrl_pb2.GetTransactionResp(tx=tx, confirmations=10))
tx, confirmations = walletd.get_transaction(tx_hash='1234')
self.assertIsNotNone(tx)
self.assertEqual(tx.transaction_hash, bin2hstr(b'1234'))
self.assertEqual(confirmations, 10)
def test_get_balance(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd._public_stub.GetBalance = Mock(
return_value=qrl_pb2.GetBalanceResp(balance=1000))
balance = walletd.get_balance(self.qaddress)
self.assertEqual(balance, 1000)
def test_get_ots(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd._public_stub.GetOTS = Mock(
return_value=qrl_pb2.GetOTSResp(ots_bitfield=[b'\x00'] * 10, next_unused_ots_index=1))
ots_bitfield, next_unused_ots_index = walletd.get_ots(self.qaddress)
self.assertEqual(ots_bitfield, [b'\x00'] * 10)
self.assertEqual(next_unused_ots_index, 1)
def test_get_height(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
walletd._public_stub.GetHeight = Mock(
return_value=qrl_pb2.GetHeightResp(height=1001))
height = walletd.get_height()
self.assertEqual(height, 1001)
def test_get_block(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
block = qrl_pb2.Block()
block.header.hash_header = b'001122'
block.header.block_number = 1
walletd._public_stub.GetBlock = Mock(
return_value=qrl_pb2.GetBlockResp(block=block))
b = walletd.get_block('001122')
self.assertEqual(b.header.hash_header, bin2hstr(block.header.hash_header))
self.assertEqual(b.header.block_number, block.header.block_number)
def test_get_block_by_number(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
block = qrl_pb2.Block()
block.header.hash_header = b'001122'
block.header.block_number = 1
walletd._public_stub.GetBlockByNumber = Mock(
return_value=qrl_pb2.GetBlockResp(block=block))
b = walletd.get_block_by_number(1)
self.assertEqual(b.header.hash_header, bin2hstr(block.header.hash_header))
self.assertEqual(b.header.block_number, block.header.block_number)
def test_get_address_from_pk(self):
with set_qrl_dir("wallet_ver1"):
walletd = WalletD()
pk = '01020016ecb9f39b9f4275d5a49e232346a15ae2fa8c50a2927daeac189b8c5f2d1' \
'8bc4e3983bd564298c49ae2e7fa6e28d4b954d8cd59398f1225b08d6144854aee0e'
address = walletd.get_address_from_pk(pk)
self.assertEqual(address, 'Q010200670246b0026436b717f199e3ec5320ba6ab61d5eddff811ac199a9e9b871d3280178b343')
| 23,528 | 7 | 994 |
d1d129d88ef1dad9ac4e5229af7bdc3ff834ffcc | 540 | py | Python | fbd/migrations/versions/23184a5b4714_.py | olety/FBG | 337c81ed661c11ee7283cffff63b1949363a8151 | [
"MIT"
] | null | null | null | fbd/migrations/versions/23184a5b4714_.py | olety/FBG | 337c81ed661c11ee7283cffff63b1949363a8151 | [
"MIT"
] | 11 | 2017-05-26T13:36:09.000Z | 2021-08-17T14:37:32.000Z | fbd/migrations/versions/23184a5b4714_.py | olety/FBD | 337c81ed661c11ee7283cffff63b1949363a8151 | [
"MIT"
] | null | null | null | """
Revision ID: 23184a5b4714
Revises: 2fcbb8a6de94
Create Date: 2017-05-29 17:13:59.813275
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '23184a5b4714'
down_revision = '2fcbb8a6de94'
branch_labels = None
depends_on = None
| 18.62069 | 65 | 0.681481 | """
Revision ID: 23184a5b4714
Revises: 2fcbb8a6de94
Create Date: 2017-05-29 17:13:59.813275
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '23184a5b4714'
down_revision = '2fcbb8a6de94'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
pass
# ### end Alembic commands ###
| 208 | 0 | 46 |
2ec8e0ea45f5424c33d13abaa9c4a1a92f1bdc80 | 2,011 | py | Python | dics/naver.py | jones3kd/Anki-Korean-Lookup-Addon | 5fe0717876c042b145bd2d208ad3a510626837e3 | [
"MIT"
] | null | null | null | dics/naver.py | jones3kd/Anki-Korean-Lookup-Addon | 5fe0717876c042b145bd2d208ad3a510626837e3 | [
"MIT"
] | null | null | null | dics/naver.py | jones3kd/Anki-Korean-Lookup-Addon | 5fe0717876c042b145bd2d208ad3a510626837e3 | [
"MIT"
] | null | null | null | """
To do
get hanja
get multiple definitions on page
example sentences
"""
from bs4 import BeautifulSoup
import urllib.request
import urllib.parse
import time
import re
import random
defin = []
class NaverDict:
"""
This class looks up korean vocabulary words using the
Naver korean-english dictionary.
"""
def get_def(self, org_word):
"""
Looks up the word givein the params and returns a
list of tuples of definitons because
there may be more than one definition for words.
If the definition was not found returns None
"""
kor_word = None
eng_defs = ""
eng_def = ""
hanja = None
#convert word from hangul into the percent characters
#example converts korean word to %ED%95%9C%EB%B2%88
word = urllib.parse.quote(org_word)
#put word in url
url = self.url.replace('[word]', word)
#time.sleep(random.randrange(4,10))
html = urllib.request.urlopen(url)
soup = BeautifulSoup(html, 'html.parser')
#returns a list of tag objects
kor_word = soup.find(True, {'class':['first']})
try:
kor_word = kor_word.span.a.string
eng_defs = soup.find(True, {'class':['list_e2']}).dd.div.p.span
#print(eng_def.children)
for string in eng_defs.stripped_strings:
eng_def += str(string) + " "
#eng_def = eng_def.dd.div.p.string
except Exception as e:
#could not find word in naver dic
return None
#dont forget to get the hanja
if kor_word is None:
kor_word = org_word
return [(org_word, kor_word, eng_def)]
naver_dic = NaverDict()
word = input("enter word: ")
print(naver_dic.get_def(word))
| 26.116883 | 89 | 0.610144 | """
To do
get hanja
get multiple definitions on page
example sentences
"""
from bs4 import BeautifulSoup
import urllib.request
import urllib.parse
import time
import re
import random
defin = []
class NaverDict:
"""
This class looks up korean vocabulary words using the
Naver korean-english dictionary.
"""
def __init__(self):
#will replace [word] with the actual korean
#word we want to query
self.url = 'http://endic.naver.com/search.nhn?sLn=en&isOnlyViewEE=N&query=[word]'
def get_def(self, org_word):
"""
Looks up the word givein the params and returns a
list of tuples of definitons because
there may be more than one definition for words.
If the definition was not found returns None
"""
kor_word = None
eng_defs = ""
eng_def = ""
hanja = None
#convert word from hangul into the percent characters
#example converts korean word to %ED%95%9C%EB%B2%88
word = urllib.parse.quote(org_word)
#put word in url
url = self.url.replace('[word]', word)
#time.sleep(random.randrange(4,10))
html = urllib.request.urlopen(url)
soup = BeautifulSoup(html, 'html.parser')
#returns a list of tag objects
kor_word = soup.find(True, {'class':['first']})
try:
kor_word = kor_word.span.a.string
eng_defs = soup.find(True, {'class':['list_e2']}).dd.div.p.span
#print(eng_def.children)
for string in eng_defs.stripped_strings:
eng_def += str(string) + " "
#eng_def = eng_def.dd.div.p.string
except Exception as e:
#could not find word in naver dic
return None
#dont forget to get the hanja
if kor_word is None:
kor_word = org_word
return [(org_word, kor_word, eng_def)]
naver_dic = NaverDict()
word = input("enter word: ")
print(naver_dic.get_def(word))
| 171 | 0 | 27 |
05a2e39a60bc35bf0b3e55b2cd50b1737d5d4513 | 17,967 | py | Python | criticality_recognition/phenomena_extraction.py | lu-w/criticality-recognition | 5ad2e12699ad4bf2d7f60ce9e30f26110adce436 | [
"MIT"
] | 4 | 2022-03-13T19:33:43.000Z | 2022-03-15T22:20:36.000Z | criticality_recognition/phenomena_extraction.py | lu-w/criticality-recognition | 5ad2e12699ad4bf2d7f60ce9e30f26110adce436 | [
"MIT"
] | null | null | null | criticality_recognition/phenomena_extraction.py | lu-w/criticality-recognition | 5ad2e12699ad4bf2d7f60ce9e30f26110adce436 | [
"MIT"
] | null | null | null | import logging
import owlready2
import tqdm
import auto.auto
logger = logging.getLogger(__name__)
_CACHED_CP_CLASSES = dict()
class Scene_Criticality_Phenomenon(Criticality_Phenomenon):
"""
Concretization for scene-level criticality phenomena. Note that member traffic_model is a list of scenes.
"""
class Scenario_Criticality_Phenomenon(Criticality_Phenomenon):
"""
Concretization for scenario-level criticality phenomena.
"""
def phenomena_scenario(scenario: list or owlready2.World) -> list:
"""
scenario: Either a list of worlds, each world representing a single scene or a single world representing a whole
scenario
returns: A list of criticality phenomena objects (either scene or scenario) sorted by temporal occurrence
"""
cps = []
if type(scenario) == list:
for scene in scenario:
tm = auto.auto.get_ontology(auto.auto.Ontology.Traffic_Model, scene)
cp_ont = auto.auto.get_ontology(auto.auto.Ontology.Criticality_Phenomena, scene)
scenes = list(tm.search(type=tm.Scene))
if len(scenes) > 0:
for scene_cp in scenario.search(type=cp_ont.Criticality_Phenomenon):
scene_cp_obj = Scene_Criticality_Phenomenon(scenes[0], scene_cp, None) # TODO
cps.append(scene_cp_obj)
else:
raise ValueError("No scenes found in scene world " + str(scene))
elif type(scenario) == owlready2.World:
tm = auto.auto.get_ontology(auto.auto.Ontology.Traffic_Model, scenario)
ac = auto.auto.get_ontology(auto.auto.Ontology.Act, scenario)
ti = auto.auto.get_ontology(auto.auto.Ontology.Time, scenario)
cp_ont = auto.auto.get_ontology(auto.auto.Ontology.Criticality_Phenomena, scenario)
scenarios = list(scenario.search(type=tm.Scenario))
if len(scenarios) > 0:
if logger.level == logging.DEBUG:
search_space = tqdm.tqdm(list(scenario.search(type=cp_ont.Criticality_Phenomenon)))
else:
search_space = scenario.search(type=cp_ont.Criticality_Phenomenon)
for cp in search_space:
cp_clss = list(filter(lambda x: x in scenario.classes(),
[y for y in cp.INDIRECT_is_a if hasattr(y, "namespace") and y.namespace.base_iri
== "http://purl.org/auto/criticality_phenomena#"]))
most_specific_cp_clss = [cp_cls for cp_cls in cp_clss if hasattr(cp_cls, "__subclasses__") and
len(set(cp_cls.__subclasses__()).intersection(set(cp_clss))) == 0]
for cp_cls in most_specific_cp_clss:
objects = [None]
if len(cp_cls.object_extraction_code) > 0:
try:
local_dict = {"subject": cp, "subjects": []}
exec(cp_cls.object_extraction_code[0], globals(), local_dict)
objects = local_dict["objects"] or []
except Exception as e:
logger.error("Invalid object extraction code in OWL during CP extraction of: " + str(cp) +
" (" + str(cp_cls) + "): " + str(e))
for object_dict in objects:
if set(cp.INDIRECT_is_a).intersection({ac.Activity, ti.Interval}):
scenario_cp_obj = Scenario_Criticality_Phenomenon(scenarios[0], cp, cp_cls, object_dict)
cps.append(scenario_cp_obj)
elif len(cp.in_traffic_model) > 0 and tm.Scene in cp.in_traffic_model[0].INDIRECT_is_a:
scene_cp_obj = Scene_Criticality_Phenomenon(cp.in_traffic_model, cp, cp_cls, object_dict)
cps.append(scene_cp_obj)
else:
raise ValueError("CP with no scene or scenario found: " + str(cp))
else:
raise ValueError("No scenario found in scenario world " + str(scenario))
else:
raise ValueError("Wrong scenario type: neither a list nor a world, but found " + str(type(scenario)))
cps.sort(key=lambda x: x.time if type(x) is Scene_Criticality_Phenomenon else x.time[0])
return cps
def list_cps(cps: list, output_format="natural", world=None, print_non_visualizable_info=False) -> str:
"""
Lists the given criticality phenomena in a given output format. If a world is given, checks if the phenomena are
visualizable in the traffic model of the world.
If format is natural, it returns a string of lines where each line represents the string represention of the CP.
If format is csv, it returns a string of ;-separated CSV lines including a file header of the format
'Start Time;End Time;Criticality Phenomenon;Subject(s);Object(s)[;Visualizable In Scene]'.
:param cps: A list of criticality phenomena
:param output_format: "natural" or ("csv" or "csv-file" (no difference))
:param world: The world with the traffic model of the CP list
:param print_non_visualizable_info: Whether to check if CPs
:return: A string with each line representing a criticality phenomenon
"""
output = ""
scene_cps = []
if print_non_visualizable_info and world:
tm = auto.auto.get_ontology(auto.auto.Ontology.Traffic_Model, world)
scenes = list(filter(lambda x: tm.Scene in x.is_a, world.search(type=tm.Scenario)[0].has_traffic_model))
for scene in scenes:
scene_cps += [cp for cp in cps if cp.is_representable_in_scene(scene)]
if output_format == "natural":
output += "Result:\n"
elif output_format == "csv" or output_format == "csv-file":
csv_header = "Start Time;End Time;Criticality Phenomenon;Subject(s);Object(s)"
if print_non_visualizable_info and world:
csv_header += ";Visualizable In Scene"
output += csv_header + "\n"
if len(cps) > 0:
for cp in cps:
if output_format == "natural":
visualizable = ""
if print_non_visualizable_info and world:
if cp not in scene_cps:
visualizable = "[Non visualizable] "
output += visualizable + str(cp) + "\n"
elif output_format == "csv" or output_format == "csv-file":
visualizable = ""
if print_non_visualizable_info and world:
visualizable = ";" + str(cp in scene_cps)
output += cp.to_csv() + visualizable + "\n"
elif output_format == "natural":
output += "No criticality phenomenon found.\n"
return output[:-1]
def _get_individual_id(individual) -> str:
"""
Returns a unique identifier as string for the given individual.
:param individual: The individual to get the ID for.
:return: A string representing the ID.
"""
if hasattr(individual, "identifier") and (isinstance(individual.identifier, list) and
len(individual.identifier) > 0 and
type(individual.identifier[0]) in [int, str]) or (
type(individual.identifier) in [int, str]):
return str(individual.identifier[0])
else:
return str(individual)
def get_most_specific_classes(list_of_individuals, caching=True):
"""
Helper function that looks up the subsumption hierarchy and returns the most specific classes of a list of
individuals(i.e. removes all classes that are a parent of some class of the individuals). It looks only at the
subsumption hierarchy spanned by the domain (L1-L6) and perception, physics, and act ontologies.
:param list_of_individuals: A list of individuals
:return: A list of tuples containing the individual in the first entry and a list of most specific classes in the
second entry (as strings)
"""
res = []
noncached_list_of_individuals = []
if caching:
for i in list_of_individuals:
if i in _CACHED_CP_CLASSES.keys():
i_id = _get_individual_id(i)
res.append((i_id, _CACHED_CP_CLASSES[i]))
else:
noncached_list_of_individuals.append(i)
relevant_iris = [auto.auto.Ontology.L1_Core.value, auto.auto.Ontology.L2_Core.value,
auto.auto.Ontology.L3_Core.value, auto.auto.Ontology.L4_Core.value,
auto.auto.Ontology.L5_Core.value, auto.auto.Ontology.L6_Core.value, auto.auto.Ontology.L1_DE.value,
auto.auto.Ontology.L2_DE.value, auto.auto.Ontology.L3_DE.value, auto.auto.Ontology.L4_DE.value,
auto.auto.Ontology.L5_DE.value, auto.auto.Ontology.L6_DE.value]
relevant_additional_iris = [auto.auto.Ontology.Perception.value, auto.auto.Ontology.Physics.value,
auto.auto.Ontology.Act.value]
for individual in noncached_list_of_individuals:
relevant_classes = [x for x in individual.namespace.ontology.classes() if x.namespace.base_iri in relevant_iris]
relevant_additional_classes = [x for x in individual.namespace.ontology.classes() if x.namespace.base_iri in
relevant_additional_iris]
individual_clss = list(filter(lambda x: x in relevant_classes, individual.INDIRECT_is_a))
if len(individual_clss) == 0:
# Retry finding something outside of domain ontologies, e.g. physics
individual_clss = list(filter(lambda x: x in relevant_additional_classes, individual.INDIRECT_is_a))
individual_id = _get_individual_id(individual)
most_specific_individual_clss = [str(individual_cls) for individual_cls in individual_clss if
hasattr(individual_cls, "__subclasses__") and len(
set(individual_cls.__subclasses__()).intersection(set(individual_clss)))
== 0]
res.append((individual_id, most_specific_individual_clss))
if caching:
_CACHED_CP_CLASSES[individual] = most_specific_individual_clss
return res
| 53.31454 | 120 | 0.611009 | import logging
import owlready2
import tqdm
import auto.auto
logger = logging.getLogger(__name__)
_CACHED_CP_CLASSES = dict()
class Criticality_Phenomenon:
def __init__(self, traffic_model, cp, cp_cls, objects=None):
"""
Constructor for criticality phenomena. Information about criticality phenomena are stored as follows:
- traffic_model: The scene or scenario in which the CP occurs.
- cp: The CP individual from the ontology (owlready2 object).
- cp_cls: The class of the criticality phenomenon from the ontology (owlready2 class).
- time: Either an interval tuple (start, end) or a time point as float.
- subjects: A list of subjects (individuals within the ontology) of the CP.
- objects: A dictionary mapping object properties to lists of objects (individuals within the ontology), e.g.
{"prop1": [obj1, obj2], "prop2": [obj2, obj3]}.
:param traffic_model: The scene or scenario in which the criticality phenomenon occurs.
:param cp: An individual from the ABox that is a criticality phenomenon
:param cp_cls: The class of the TBox of the criticality phenomenon (i.e. of cp)
:param objects: If the objects of the criticality phenomenon were already extracted previously, they can be
passed here as a dict, assigning each object property (str) a list of objects, e.g. {"prop1": [obj1, obj2],
"prop2": [obj2, obj3]}. If None is given, objects will be extracted in the constructor.
"""
self.traffic_model = traffic_model
self.cp = cp
self.cp_cls = cp_cls
self.time = 0
ac = auto.auto.get_ontology(auto.auto.Ontology.Act, self.cp.namespace.world)
# Subjects
if len(self.cp_cls.subject_extraction_code) > 0:
try:
local_dict = {"subject": self.cp, "subjects": []}
exec(self.cp_cls.subject_extraction_code[0], globals(), local_dict)
self.subjects = local_dict["subjects"] or []
except Exception as e:
logger.error("Invalid subject extraction code in OWL during CP extraction of: " + str(self.cp) + " (" +
str(self.cp_cls) + "): " + str(e))
self.subjects = []
elif ac.Activity in self.cp.INDIRECT_is_a and len(self.cp.conducted_by) > 0:
self.subjects = self.cp.conducted_by
else:
self.subjects = [self.cp]
# Predicate
self.predicate = str(self.cp_cls).replace("criticality_phenomena.", "")
if len(self.cp_cls.label.en) > 0:
self.predicate += " (" + self.cp_cls.label.en[0] + ")"
# Objects
if objects is None and len(self.cp_cls.object_extraction_code) > 0:
try:
local_dict = {"subject": self.cp, "objects": []}
exec(self.cp_cls.object_extraction_code[0], globals(), local_dict)
self.objects = local_dict["objects"] or []
except Exception as e:
logger.error("Invalid object extraction code in OWL during CP extraction of: " + str(self.cp) + " (" +
str(self.cp_cls) + "): " + str(e))
self.objects = []
elif objects is None and ac.Activity in self.cp.INDIRECT_is_a:
self.objects = {"participants": self.cp.has_participant}
else:
self.objects = objects or dict()
def __str__(self) -> str:
"""
Returns a string representation of the criticality phenomenon in the form 't = X: subject(s) -- predicate -->
object(s)'.
:return: String representation as described above.
"""
# Time
label = "t = " + str(self.at_time()) + ": "
# Subjects
if len(self.subjects) > 0:
subj_and_classes = get_most_specific_classes(self.subjects)
label += ", ".join([str(x[0]) + " (" + ", ".join(x[1]) + ")" for x in subj_and_classes]) + " -- "
# Predicate
label += self.predicate
# Objects
for obj_predicate in self.objects.keys():
obj_and_classes = get_most_specific_classes(self.objects[obj_predicate])
label += " -- " + obj_predicate + " --> " + ", ".join([str(x[0]) + " (" + ", ".join(x[1]) + ")" for x in
obj_and_classes])
return label
def to_csv(self) -> str:
"""
Returns a CSV representation (semicolon-separated) of the criticality phenomenon in the form 'time; predicate;
subject(s); object(s)'.
:return: CSV as a string as described above
"""
# Time
time = self.at_time()
if isinstance(time, tuple):
start, end = time
else:
start, end = time, time
csv_res = str(start) + ";" + str(end) + ";"
# Predicate
csv_res += self.predicate + ";"
# Subjects
if len(self.subjects) > 0:
subj_and_classes = get_most_specific_classes(self.subjects)
csv_res += ", ".join([str(x[0]) + " (" + ", ".join(x[1]) + ")" for x in subj_and_classes])
csv_res += ";"
# Objects
for obj_predicate in self.objects.keys():
obj_and_classes = get_most_specific_classes(self.objects[obj_predicate])
csv_res += obj_predicate + ": " + ", ".join([str(x[0]) + " (" + ", ".join(x[1]) + ")" for x in
obj_and_classes]) + " | "
if csv_res.endswith(" | "):
csv_res = csv_res[:-3]
return csv_res
def at_time(self):
"""
Interface: returns the time at which the criticality phenomenon is occurring.
:return: The time at which self occurs.
"""
return self.time
def is_representable_in_scene(self, scene) -> bool:
"""
Returns True iff the criticality phenomenon is visualizable or representable in a given scene, i.e. iff
all subjects and objects of the phenomenon are within the scene.
:param scene: The scene to check against.
:return: True iff self is representable in scene.
"""
if len(self.subjects) > 0:
for subj in self.subjects + [y for x in self.objects.values() for y in x]:
if scene not in subj.in_traffic_model:
return False
return True
return False
class Scene_Criticality_Phenomenon(Criticality_Phenomenon):
"""
Concretization for scene-level criticality phenomena. Note that member traffic_model is a list of scenes.
"""
def __init__(self, traffic_model: list, cp, cp_cls, objects=None):
if len(traffic_model) == 0:
raise ValueError("Scene CP with scenes")
Criticality_Phenomenon.__init__(self, traffic_model, cp, cp_cls, objects)
self.time = traffic_model[0].inTimePosition[0].numericPosition[0]
class Scenario_Criticality_Phenomenon(Criticality_Phenomenon):
"""
Concretization for scenario-level criticality phenomena.
"""
def __init__(self, traffic_model, cp, cp_cls, objects=None):
Criticality_Phenomenon.__init__(self, traffic_model, cp, cp_cls, objects)
if hasattr(self.cp, "hasBeginning") and hasattr(self.cp, "hasEnd") and len(self.cp.hasBeginning) > 0 and \
len(self.cp.hasBeginning[0].inTimePosition) > 0 and \
len(self.cp.hasBeginning[0].inTimePosition[0].numericPosition) > 0 and len(self.cp.hasEnd) > 0 and \
len(self.cp.hasEnd[0].inTimePosition) > 0 and \
len(self.cp.hasEnd[0].inTimePosition[0].numericPosition) > 0:
self.time = (self.cp.hasBeginning[0].inTimePosition[0].numericPosition[0],
self.cp.hasEnd[0].inTimePosition[0].numericPosition[0])
else:
self.time = (self.traffic_model.hasBeginning[0].inTimePosition[0].numericPosition[0],
self.traffic_model.hasEnd[0].inTimePosition[0].numericPosition[0])
def phenomena_scenario(scenario: list or owlready2.World) -> list:
"""
scenario: Either a list of worlds, each world representing a single scene or a single world representing a whole
scenario
returns: A list of criticality phenomena objects (either scene or scenario) sorted by temporal occurrence
"""
cps = []
if type(scenario) == list:
for scene in scenario:
tm = auto.auto.get_ontology(auto.auto.Ontology.Traffic_Model, scene)
cp_ont = auto.auto.get_ontology(auto.auto.Ontology.Criticality_Phenomena, scene)
scenes = list(tm.search(type=tm.Scene))
if len(scenes) > 0:
for scene_cp in scenario.search(type=cp_ont.Criticality_Phenomenon):
scene_cp_obj = Scene_Criticality_Phenomenon(scenes[0], scene_cp, None) # TODO
cps.append(scene_cp_obj)
else:
raise ValueError("No scenes found in scene world " + str(scene))
elif type(scenario) == owlready2.World:
tm = auto.auto.get_ontology(auto.auto.Ontology.Traffic_Model, scenario)
ac = auto.auto.get_ontology(auto.auto.Ontology.Act, scenario)
ti = auto.auto.get_ontology(auto.auto.Ontology.Time, scenario)
cp_ont = auto.auto.get_ontology(auto.auto.Ontology.Criticality_Phenomena, scenario)
scenarios = list(scenario.search(type=tm.Scenario))
if len(scenarios) > 0:
if logger.level == logging.DEBUG:
search_space = tqdm.tqdm(list(scenario.search(type=cp_ont.Criticality_Phenomenon)))
else:
search_space = scenario.search(type=cp_ont.Criticality_Phenomenon)
for cp in search_space:
cp_clss = list(filter(lambda x: x in scenario.classes(),
[y for y in cp.INDIRECT_is_a if hasattr(y, "namespace") and y.namespace.base_iri
== "http://purl.org/auto/criticality_phenomena#"]))
most_specific_cp_clss = [cp_cls for cp_cls in cp_clss if hasattr(cp_cls, "__subclasses__") and
len(set(cp_cls.__subclasses__()).intersection(set(cp_clss))) == 0]
for cp_cls in most_specific_cp_clss:
objects = [None]
if len(cp_cls.object_extraction_code) > 0:
try:
local_dict = {"subject": cp, "subjects": []}
exec(cp_cls.object_extraction_code[0], globals(), local_dict)
objects = local_dict["objects"] or []
except Exception as e:
logger.error("Invalid object extraction code in OWL during CP extraction of: " + str(cp) +
" (" + str(cp_cls) + "): " + str(e))
for object_dict in objects:
if set(cp.INDIRECT_is_a).intersection({ac.Activity, ti.Interval}):
scenario_cp_obj = Scenario_Criticality_Phenomenon(scenarios[0], cp, cp_cls, object_dict)
cps.append(scenario_cp_obj)
elif len(cp.in_traffic_model) > 0 and tm.Scene in cp.in_traffic_model[0].INDIRECT_is_a:
scene_cp_obj = Scene_Criticality_Phenomenon(cp.in_traffic_model, cp, cp_cls, object_dict)
cps.append(scene_cp_obj)
else:
raise ValueError("CP with no scene or scenario found: " + str(cp))
else:
raise ValueError("No scenario found in scenario world " + str(scenario))
else:
raise ValueError("Wrong scenario type: neither a list nor a world, but found " + str(type(scenario)))
cps.sort(key=lambda x: x.time if type(x) is Scene_Criticality_Phenomenon else x.time[0])
return cps
def list_cps(cps: list, output_format="natural", world=None, print_non_visualizable_info=False) -> str:
"""
Lists the given criticality phenomena in a given output format. If a world is given, checks if the phenomena are
visualizable in the traffic model of the world.
If format is natural, it returns a string of lines where each line represents the string represention of the CP.
If format is csv, it returns a string of ;-separated CSV lines including a file header of the format
'Start Time;End Time;Criticality Phenomenon;Subject(s);Object(s)[;Visualizable In Scene]'.
:param cps: A list of criticality phenomena
:param output_format: "natural" or ("csv" or "csv-file" (no difference))
:param world: The world with the traffic model of the CP list
:param print_non_visualizable_info: Whether to check if CPs
:return: A string with each line representing a criticality phenomenon
"""
output = ""
scene_cps = []
if print_non_visualizable_info and world:
tm = auto.auto.get_ontology(auto.auto.Ontology.Traffic_Model, world)
scenes = list(filter(lambda x: tm.Scene in x.is_a, world.search(type=tm.Scenario)[0].has_traffic_model))
for scene in scenes:
scene_cps += [cp for cp in cps if cp.is_representable_in_scene(scene)]
if output_format == "natural":
output += "Result:\n"
elif output_format == "csv" or output_format == "csv-file":
csv_header = "Start Time;End Time;Criticality Phenomenon;Subject(s);Object(s)"
if print_non_visualizable_info and world:
csv_header += ";Visualizable In Scene"
output += csv_header + "\n"
if len(cps) > 0:
for cp in cps:
if output_format == "natural":
visualizable = ""
if print_non_visualizable_info and world:
if cp not in scene_cps:
visualizable = "[Non visualizable] "
output += visualizable + str(cp) + "\n"
elif output_format == "csv" or output_format == "csv-file":
visualizable = ""
if print_non_visualizable_info and world:
visualizable = ";" + str(cp in scene_cps)
output += cp.to_csv() + visualizable + "\n"
elif output_format == "natural":
output += "No criticality phenomenon found.\n"
return output[:-1]
def _get_individual_id(individual) -> str:
"""
Returns a unique identifier as string for the given individual.
:param individual: The individual to get the ID for.
:return: A string representing the ID.
"""
if hasattr(individual, "identifier") and (isinstance(individual.identifier, list) and
len(individual.identifier) > 0 and
type(individual.identifier[0]) in [int, str]) or (
type(individual.identifier) in [int, str]):
return str(individual.identifier[0])
else:
return str(individual)
def get_most_specific_classes(list_of_individuals, caching=True):
"""
Helper function that looks up the subsumption hierarchy and returns the most specific classes of a list of
individuals(i.e. removes all classes that are a parent of some class of the individuals). It looks only at the
subsumption hierarchy spanned by the domain (L1-L6) and perception, physics, and act ontologies.
:param list_of_individuals: A list of individuals
:return: A list of tuples containing the individual in the first entry and a list of most specific classes in the
second entry (as strings)
"""
res = []
noncached_list_of_individuals = []
if caching:
for i in list_of_individuals:
if i in _CACHED_CP_CLASSES.keys():
i_id = _get_individual_id(i)
res.append((i_id, _CACHED_CP_CLASSES[i]))
else:
noncached_list_of_individuals.append(i)
relevant_iris = [auto.auto.Ontology.L1_Core.value, auto.auto.Ontology.L2_Core.value,
auto.auto.Ontology.L3_Core.value, auto.auto.Ontology.L4_Core.value,
auto.auto.Ontology.L5_Core.value, auto.auto.Ontology.L6_Core.value, auto.auto.Ontology.L1_DE.value,
auto.auto.Ontology.L2_DE.value, auto.auto.Ontology.L3_DE.value, auto.auto.Ontology.L4_DE.value,
auto.auto.Ontology.L5_DE.value, auto.auto.Ontology.L6_DE.value]
relevant_additional_iris = [auto.auto.Ontology.Perception.value, auto.auto.Ontology.Physics.value,
auto.auto.Ontology.Act.value]
for individual in noncached_list_of_individuals:
relevant_classes = [x for x in individual.namespace.ontology.classes() if x.namespace.base_iri in relevant_iris]
relevant_additional_classes = [x for x in individual.namespace.ontology.classes() if x.namespace.base_iri in
relevant_additional_iris]
individual_clss = list(filter(lambda x: x in relevant_classes, individual.INDIRECT_is_a))
if len(individual_clss) == 0:
# Retry finding something outside of domain ontologies, e.g. physics
individual_clss = list(filter(lambda x: x in relevant_additional_classes, individual.INDIRECT_is_a))
individual_id = _get_individual_id(individual)
most_specific_individual_clss = [str(individual_cls) for individual_cls in individual_clss if
hasattr(individual_cls, "__subclasses__") and len(
set(individual_cls.__subclasses__()).intersection(set(individual_clss)))
== 0]
res.append((individual_id, most_specific_individual_clss))
if caching:
_CACHED_CP_CLASSES[individual] = most_specific_individual_clss
return res
| 1,227 | 6,336 | 75 |
fb774c8a7a6083cfa0aad97f1b5c322164dcbec0 | 3,818 | py | Python | bakthread.py | ifuzx/bakscan | 3d5e36a390b65e29fe89ec1578bda13f9ac92406 | [
"Unlicense"
] | null | null | null | bakthread.py | ifuzx/bakscan | 3d5e36a390b65e29fe89ec1578bda13f9ac92406 | [
"Unlicense"
] | null | null | null | bakthread.py | ifuzx/bakscan | 3d5e36a390b65e29fe89ec1578bda13f9ac92406 | [
"Unlicense"
] | null | null | null | import re
import requests
import threading
import queue as Queue
from requests.packages.urllib3.exceptions import InsecureRequestWarning
| 33.491228 | 138 | 0.499214 | import re
import requests
import threading
import queue as Queue
from requests.packages.urllib3.exceptions import InsecureRequestWarning
class myThread(threading.Thread):
def __init__(self, name, q):
threading.Thread.__init__(self)
self.name = name
self.q = q
def run(self):
while True:
try:
bakburp(self.name, self.q)
except:
break
def bakburp(threadName, q):
geturl = q.get(timeout=10)
html=""
try:
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
conn = requests.get(geturl,verify=False, allow_redirects=False,timeout=30)
conn.encoding = 'utf-8'
html = conn.status_code
s = requests.session()
if html == 200:
err = len(conn.content)
if err < 1000000:
print('\r'+f'\033[33m[*]\033[33mCode：%s -----（可能误报)--大小：%s ----- 目标：%s \n' % (html,err,geturl),end='', flush=True)
else:
print('\r'+f'\033[32m[*]\033[32m Code：%s ----- 文件存在--大小：%s ----- 目标：%s \n' % (html, err, geturl), end='',flush=True)
# print('Code：%s ----- 文件存在--大小：%s ----- 目标：%s' % (html, err, geturl) )
s.close()
conn.close()
else:
print('\r'+geturl+'\r',end='', flush=True)
s.close()
conn.close()
except Exception as e:
print('\r'+geturl+'\r',end='', flush=True)
def run():
headers = {
'Upgrade-Insecure-Requests': '1',
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36'
}
inurl = {"/www.zip", "/www.rar", "/www.tar.gz", "/wwwroot.zip", "/wwwroot.rar", "/wwwroot.tar.gz", "/web.zip",
"/web.rar", "/web.tar.gz", "/.svn" , "/.git" , "/.DS_Store", "/1.zip", "/1.rar","/1.rar" }
hz = {".zip", ".rar", ".tar.gz"}
threadLock = threading.Lock()
link_list = open(r'./runoob.txt', 'r').readlines()
newlink_list =[]
data = []
for i in link_list:
i=i.replace("\n","")
for s in inurl:
url = i + s
newlink_list.append(url)
compile_rule = re.compile(r'(?<![\.\d])(?:\d{1,3}\.){3}\d{1,3}(?![\.\d])')
# if re.compile(r'\d+[\.]\d+[\.]\d+[\.]\d+'，)
if re.findall(compile_rule, i):
continue
if i.startswith("https://"):
ex_domain = i.replace("https://", "").split('.')
# print(ex_domain)
for index in hz:
url = i +'/' + i.replace("https://", "") + index
newlink_list.append(url)
for top in ex_domain:
for end in hz:
url = i + '/' + top + end
newlink_list.append(url)
elif i.startswith("http://"):
ex_domain = i.replace("http://", "").split('.')
for index in hz:
url = i + '/' + i.replace("http://", "") + index
newlink_list.append(url)
for top in ex_domain:
for end in hz:
url = i + '/'+ top+end
newlink_list.append(url)
# for i in newlink_list:
# print(i)
# 创建50个线程名
thnum = 100
threadList = []
num=0
while num <= thnum:
threadList.append("thread" + str(num))
num+=1
# 设置队列长度
workQueue = Queue.Queue(3000)
# 线程池# 创建新线程
threads = []
for tName in threadList:
thread = myThread(tName, workQueue)
thread.start()
# threadLock.acquire()
# threadLock.release()
threads.append(thread)
# 将url填充到队列
for url in newlink_list:
workQueue.put(url)
# 等待所有线程完成
for t in threads:
t.join()
print("end")
| 3,685 | 12 | 122 |
e9bfb50a19a589a69f5030410dca17dc1c9ab50c | 3,539 | py | Python | hera_librarian/tests/test_cli.py | BrianJKoopman/librarian | 2d4164b60fe41d1a192f03d8baad12c0d11d0394 | [
"BSD-2-Clause"
] | 5 | 2016-02-17T17:51:05.000Z | 2021-12-09T19:08:17.000Z | hera_librarian/tests/test_cli.py | BrianJKoopman/librarian | 2d4164b60fe41d1a192f03d8baad12c0d11d0394 | [
"BSD-2-Clause"
] | 60 | 2016-01-29T20:40:04.000Z | 2021-05-28T14:13:18.000Z | hera_librarian/tests/test_cli.py | simonsobs/librarian | 188370d84a988f898bb9de4593078c1868602f08 | [
"BSD-2-Clause"
] | 6 | 2017-11-13T10:40:34.000Z | 2020-07-10T00:42:57.000Z | # -*- mode: python; coding: utf-8 -*-
# Copyright 2019 the HERA Collaboration
# Licensed under the 2-clause BSD License
"""Test code in hera_librarian/cli.py
"""
import pytest
import os
import hera_librarian
from hera_librarian import cli
| 26.214815 | 81 | 0.65414 | # -*- mode: python; coding: utf-8 -*-
# Copyright 2019 the HERA Collaboration
# Licensed under the 2-clause BSD License
"""Test code in hera_librarian/cli.py
"""
import pytest
import os
import hera_librarian
from hera_librarian import cli
def test_die(capsys):
# test without specifying replacement args
with pytest.raises(SystemExit) as e:
cli.die("my error")
captured = capsys.readouterr()
assert e.type == SystemExit
assert e.value.code == 1
assert captured.err == "error: my error\n"
# test with replacement args
with pytest.raises(SystemExit) as e:
cli.die("my %s", "error")
captured = capsys.readouterr()
assert e.type == SystemExit
assert e.value.code == 1
assert captured.err == "error: my error\n"
return
def test_print_table(capsys):
# define dicts
dict1 = {"name": "foo", "size": 10}
dict2 = {"name": "bar", "size": 12}
dict_list = [dict1, dict2]
col_list = ["name", "size"]
col_names = ["Name of file", "Size of file"]
# test without specifying order
cli.print_table(dict_list)
captured = capsys.readouterr()
stdout = captured.out
correct_table = """name | size
---- | ----
foo | 10
bar | 12
"""
assert stdout == correct_table
# test without column names
cli.print_table(dict_list, col_list)
captured = capsys.readouterr()
stdout = captured.out
assert stdout == correct_table
# test with column names
cli.print_table(dict_list, col_list, col_names)
captured = capsys.readouterr()
stdout = captured.out
correct_table = """Name of file | Size of file
------------ | ------------
foo | 10
bar | 12
"""
assert stdout == correct_table
# test using the wrong number of column headers
with pytest.raises(ValueError):
cli.print_table(dict_list, col_list, col_names[:1])
return
def test_sizeof_fmt():
# test a few known values
bts = 512
assert cli.sizeof_fmt(bts) == "512.0 B"
bts = 1024
assert cli.sizeof_fmt(bts) == "1.0 kB"
bts = 1024**2
assert cli.sizeof_fmt(bts) == "1.0 MB"
bts = 1024**3
assert cli.sizeof_fmt(bts) == "1.0 GB"
bts = 1024**4
assert cli.sizeof_fmt(bts) == "1.0 TB"
bts = 1024**5
assert cli.sizeof_fmt(bts) == "1.0 PB"
bts = 1024**6
assert cli.sizeof_fmt(bts) == "1.0 EB"
bts = 1024**7
assert cli.sizeof_fmt(bts) == "1.0 ZB"
bts = 1024**8
assert cli.sizeof_fmt(bts) == "1.0 YB"
return
def test_generate_parser():
ap = cli.generate_parser()
# make sure we have all the subparsers we're expecting
available_subparsers = tuple(ap._subparsers._group_actions[0].choices.keys())
assert "add-file-event" in available_subparsers
assert "add-obs" in available_subparsers
assert "launch-copy" in available_subparsers
assert "assign-sessions" in available_subparsers
assert "delete-files" in available_subparsers
assert "locate-file" in available_subparsers
assert "initiate-offload" in available_subparsers
assert "offload-helper" in available_subparsers
assert "search-files" in available_subparsers
assert "set-file-deletion-policy" in available_subparsers
assert "stage-files" in available_subparsers
assert "upload" in available_subparsers
return
def test_main(script_runner):
version = hera_librarian.__version__
ret = script_runner.run("librarian", "-V")
assert ret.stdout == "librarian {}\n".format(version)
| 3,177 | 0 | 115 |
15f861d098e92c2a6abe585f495d1e9b5cf29ce8 | 8,031 | py | Python | models/architectures.py | hsqmlzno1/Transferable-E2E-ABSA | 6083af24892c93703ed6695aff277d54d4b426c6 | [
"MIT"
] | 73 | 2019-08-14T09:25:02.000Z | 2022-03-25T07:19:45.000Z | models/architectures.py | hsqmlzno1/Transferable-E2E-ABSA | 6083af24892c93703ed6695aff277d54d4b426c6 | [
"MIT"
] | 3 | 2020-02-25T06:39:52.000Z | 2020-12-26T12:40:23.000Z | models/architectures.py | hsqmlzno1/Transferable-E2E-ABSA | 6083af24892c93703ed6695aff277d54d4b426c6 | [
"MIT"
] | 13 | 2019-09-22T03:45:21.000Z | 2021-11-22T06:52:28.000Z |
import tensorflow as tf
from nn_utils import *
import numpy as np
import nn_utils
| 48.08982 | 163 | 0.528826 |
import tensorflow as tf
from nn_utils import *
import numpy as np
import nn_utils
class SuperNN(object):
def __init__(self,
args,
word2vec,
init=None,
scope=None):
self.args = args
self.word2vec = word2vec
self.init = init
self.scope = scope
self.hops = args.hops
self.max_len = args.max_len
self.input_win = args.input_win
self.dim_w = args.dim_w
self.dim_asp_h = args.dim_asp_h
self.dim_opn_h = args.dim_opn_h
self.dim_ts_h = args.dim_ts_h
self.dim_rel = args.dim_rel
self.dim_ote_y = args.dim_ote_y
self.dim_ts_y = args.dim_ts_y
self.dim_lm_y = args.dim_lm_y
self.ote_tag_vocab = args.ote_tag_vocab
self.ts_tag_vocab = args.ts_tag_vocab
self.build_vars()
def build_vars(self):
with tf.variable_scope(self.scope):
self.Wa = tf.Variable(self.init([2*self.dim_asp_h+2*self.dim_asp_h, 2*self.dim_asp_h]), name='Wa')
self.Wo = tf.Variable(self.init([2*self.dim_asp_h+2*self.dim_opn_h, 2*self.dim_asp_h]), name='Wo')
self.bias_a = tf.Variable(self.init([2*self.dim_asp_h, ]), name='bias_a')
self.bias_o = tf.Variable(self.init([2*self.dim_asp_h, ]), name='bias_o')
# relation matrices between aspect&aspect, opinion&opinion, aspect&opinion
self.Ta = tf.Variable(self.init([self.dim_rel, 2*self.dim_asp_h, 2*self.dim_asp_h]), name='Ta')
self.To = tf.Variable(self.init([self.dim_rel, 2*self.dim_opn_h, 2*self.dim_opn_h]), name='To')
self.Tao = tf.Variable(self.init([self.dim_rel, 2*self.dim_asp_h, 2*self.dim_opn_h]), name='Tao')
self.va = tf.Variable(self.init([2*self.dim_rel]), name='v_a')
self.vo = tf.Variable(self.init([2*self.dim_rel]), name='v_o')
def __call__(self, win_reviews, batch_length, ma_0, mo_0, dropout_rate, reuse=False):
with tf.variable_scope(self.scope, reuse=reuse):
with tf.variable_scope("Embedding_layer"):
mask = tf.cast(tf.sign(win_reviews), tf.float32)
self.input_emb = tf.nn.embedding_lookup(self.word2vec, win_reviews) #(b, m, win, d)
self.input_emb = tf.nn.dropout(self.input_emb, dropout_rate)
with tf.variable_scope('LSTM-OTE'):
fw_cell = tf.contrib.rnn.LSTMCell(self.dim_asp_h)
bw_cell = tf.contrib.rnn.LSTMCell(self.dim_asp_h)
outputs, states = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, self.input_emb,
sequence_length=batch_length,
dtype=tf.float32)
asp_h = tf.concat(outputs, -1) #(b, m, 2*dim_asp_h)
with tf.variable_scope('Attention'):
ma_t = tf.tile(ma_0, [tf.shape(asp_h)[0], 1]) #(b, 2*dim_asp_h)
mo_t = tf.tile(mo_0, [tf.shape(asp_h)[0], 1]) #(b, 2*dim_opn_h)
za_list, zo_list = [], []
ma_list, mo_list = [], []
ma_list.append(ma_t) # (b, 2*dim_asp_h)
mo_list.append(mo_t) # (b, 2*dim_opn_h)
for l in range(self.hops):
za = tf.concat([self.tensor_product(asp_h, ma_t, self.Ta, self.Wa, self.bias_a, dropout_rate), #(b, m, 2*dim_rel)
self.tensor_product(asp_h, mo_t, self.Tao, self.Wo, self.bias_o, dropout_rate)], #(b, m, 2*dim_rel)
-1) #(b, m, 2*dim_rel)
zo = tf.concat([self.tensor_product(asp_h, mo_t, self.To, self.Wo, self.bias_o, dropout_rate), #(b, m, 2*dim_rel)
self.tensor_product(asp_h, ma_t, tf.transpose(self.Tao, [0, 2, 1]), self.Wa, self.bias_a, dropout_rate)], #(b, m, 2*dim_rel)
-1) #(b, m, 2*dim_rel)
za_l = tf.reshape(za, [-1, 2*self.dim_rel]) #(b*m, 2*dim_rel)
zo_l = tf.reshape(zo, [-1, 2*self.dim_rel]) #(b*m, 2*dim_rel)
za_list.append(za_l)
zo_list.append(zo_l)
ea_l = tf.reduce_sum(tf.multiply(za_l, self.va), -1) #(b*m)
eo_l = tf.reduce_sum(tf.multiply(zo_l, self.vo), -1) #(b*m)
ea_l = tf.reshape(ea_l, [-1, self.max_len]) #(b,m)
eo_l = tf.reshape(eo_l, [-1, self.max_len]) #(b,m)
alpha_a = tf.expand_dims(nn_utils.mask_softmax(ea_l, axis=1, mask=mask), -1) #(b,m,1)
alpha_o = tf.expand_dims(nn_utils.mask_softmax(eo_l, axis=1, mask=mask), -1) #(b,m,1)
a_summary = tf.reduce_sum(asp_h * alpha_a, 1) #(b, 2*dim_ote_h)
ma_t = ma_t + a_summary
o_summary = tf.reduce_sum(asp_h * alpha_o, 1) #(b, 2*dim_ote_h)
mo_t = mo_t + o_summary
ma_list.append(ma_t) #(b, 2*dim_asp_h)
mo_list.append(mo_t) #(b, 2*dim_opn_h)
asp_h = za_list[-1]
opn_h = zo_list[-1]
asp_h = tf.reshape(asp_h, [-1, self.max_len, 2*self.dim_rel])
opn_h = tf.reshape(opn_h, [-1, self.max_len, 2*self.dim_rel])
with tf.variable_scope('LSTM-TS'):
fw_cell = tf.contrib.rnn.LSTMCell(self.dim_ts_h)
bw_cell = tf.contrib.rnn.LSTMCell(self.dim_ts_h)
outputs, states = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, asp_h,
sequence_length=batch_length,
dtype=tf.float32)
ts_h = tf.concat(outputs, -1) #(b, m, 2*dim_opn_h)
with tf.variable_scope('FC_layer'):
asp_h = tf.nn.dropout(asp_h, dropout_rate) #(b, m, 2*dim_ote)
opn_h = tf.nn.dropout(opn_h, dropout_rate) #(b, m, 2*dim_ts)
ts_h = tf.nn.dropout(ts_h, dropout_rate) #(b, m, 2*dim_asp_h+2*dim_opn_h)
asp_h = tf.reshape(asp_h, [-1, asp_h.shape.as_list()[-1]])
opn_h = tf.reshape(opn_h, [-1, opn_h.shape.as_list()[-1]])
ts_h = tf.reshape(ts_h, [-1, ts_h.shape.as_list()[-1]])
asp_pred = nn_utils.fc_layer(asp_h, output_dim=self.dim_ote_y, scope="asp_tagger", reuse=reuse)
opn_pred = nn_utils.fc_layer(opn_h, output_dim=self.dim_lm_y, scope="opn_tagger", reuse=reuse)
ts_pred = nn_utils.fc_layer(ts_h, output_dim=self.dim_ts_y, scope="ts_tagger", reuse=reuse)
return asp_h, ts_h, asp_pred, opn_pred, ts_pred, a_summary, o_summary, tf.squeeze(alpha_a, 2), tf.squeeze(alpha_o, 2)
def tensor_product(self, a, b, T, W, bias, dropout_rate, activation=None):
_, element_size, a_dim = a.shape.as_list()
_, b_dim = b.shape.as_list()
emb_dim = T.shape.as_list()[-1]
T_dropout = tf.nn.dropout(T, dropout_rate)
a_re = tf.reshape(a, [-1, a_dim]) #(b*m, d_a)
b_tile = tf.tile(tf.expand_dims(b, 1), [1, element_size, 1]) #(b, m, d_b)
ab_fusion = tf.reshape(tf.concat([a, b_tile], -1), [-1, a_dim+b_dim]) #(b*m, d_a+d_b)
a_re = a_re + tf.nn.relu(tf.matmul(ab_fusion, W)+ bias) #(b*m, d_a)
output = tf.concat([tf.matmul(
tf.reshape(tf.matmul(a_re, T_k), [-1, element_size, emb_dim]),
tf.expand_dims(b, -1))
for T_k in tf.unstack(T_dropout, axis=0)], -1)
if activation != None:
output = activation(output)
return output | 7,801 | 1 | 140 |