text stringlengths 12 1.05M | repo_name stringlengths 5 86 | path stringlengths 4 191 | language stringclasses 1 value | license stringclasses 15 values | size int32 12 1.05M | keyword listlengths 1 23 | text_hash stringlengths 64 64 |
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import py
import sys
import pytest
class CommonFSTests(object):
def test_constructor_equality(self, path1):
p = path1.__class__(path1)
assert p == path1
def test_eq_nonstring(self, path1):
p1 = path1.join('sampledir')
p2 = path1.join('sampledir')
assert p1 == p2
def test_new_identical(self, path1):
assert path1 == path1.new()
def test_join(self, path1):
p = path1.join('sampledir')
strp = str(p)
assert strp.endswith('sampledir')
assert strp.startswith(str(path1))
def test_join_normalized(self, path1):
newpath = path1.join(path1.sep+'sampledir')
strp = str(newpath)
assert strp.endswith('sampledir')
assert strp.startswith(str(path1))
newpath = path1.join((path1.sep*2) + 'sampledir')
strp = str(newpath)
assert strp.endswith('sampledir')
assert strp.startswith(str(path1))
def test_join_noargs(self, path1):
newpath = path1.join()
assert path1 == newpath
def test_add_something(self, path1):
p = path1.join('sample')
p = p + 'dir'
assert p.check()
assert p.exists()
assert p.isdir()
assert not p.isfile()
def test_parts(self, path1):
newpath = path1.join('sampledir', 'otherfile')
par = newpath.parts()[-3:]
assert par == [path1, path1.join('sampledir'), newpath]
revpar = newpath.parts(reverse=True)[:3]
assert revpar == [newpath, path1.join('sampledir'), path1]
def test_common(self, path1):
other = path1.join('sampledir')
x = other.common(path1)
assert x == path1
#def test_parents_nonexisting_file(self, path1):
# newpath = path1 / 'dirnoexist' / 'nonexisting file'
# par = list(newpath.parents())
# assert par[:2] == [path1 / 'dirnoexist', path1]
def test_basename_checks(self, path1):
newpath = path1.join('sampledir')
assert newpath.check(basename='sampledir')
assert newpath.check(notbasename='xyz')
assert newpath.basename == 'sampledir'
def test_basename(self, path1):
newpath = path1.join('sampledir')
assert newpath.check(basename='sampledir')
assert newpath.basename, 'sampledir'
def test_dirname(self, path1):
newpath = path1.join('sampledir')
assert newpath.dirname == str(path1)
def test_dirpath(self, path1):
newpath = path1.join('sampledir')
assert newpath.dirpath() == path1
def test_dirpath_with_args(self, path1):
newpath = path1.join('sampledir')
assert newpath.dirpath('x') == path1.join('x')
def test_newbasename(self, path1):
newpath = path1.join('samplefile')
newbase = newpath.new(basename="samplefile2")
assert newbase.basename == "samplefile2"
assert newbase.dirpath() == newpath.dirpath()
def test_not_exists(self, path1):
assert not path1.join('does_not_exist').check()
assert path1.join('does_not_exist').check(exists=0)
def test_exists(self, path1):
assert path1.join("samplefile").check()
assert path1.join("samplefile").check(exists=1)
assert path1.join("samplefile").exists()
assert path1.join("samplefile").isfile()
assert not path1.join("samplefile").isdir()
def test_dir(self, path1):
#print repr(path1.join("sampledir"))
assert path1.join("sampledir").check(dir=1)
assert path1.join('samplefile').check(notdir=1)
assert not path1.join("samplefile").check(dir=1)
assert path1.join("samplefile").exists()
assert not path1.join("samplefile").isdir()
assert path1.join("samplefile").isfile()
def test_fnmatch_file(self, path1):
assert path1.join("samplefile").check(fnmatch='s*e')
assert path1.join("samplefile").fnmatch('s*e')
assert not path1.join("samplefile").fnmatch('s*x')
assert not path1.join("samplefile").check(fnmatch='s*x')
#def test_fnmatch_dir(self, path1):
# pattern = path1.sep.join(['s*file'])
# sfile = path1.join("samplefile")
# assert sfile.check(fnmatch=pattern)
def test_relto(self, path1):
l=path1.join("sampledir", "otherfile")
assert l.relto(path1) == l.sep.join(["sampledir", "otherfile"])
assert l.check(relto=path1)
assert path1.check(notrelto=l)
assert not path1.check(relto=l)
def test_bestrelpath(self, path1):
curdir = path1
sep = curdir.sep
s = curdir.bestrelpath(curdir)
assert s == "."
s = curdir.bestrelpath(curdir.join("hello", "world"))
assert s == "hello" + sep + "world"
s = curdir.bestrelpath(curdir.dirpath().join("sister"))
assert s == ".." + sep + "sister"
assert curdir.bestrelpath(curdir.dirpath()) == ".."
assert curdir.bestrelpath("hello") == "hello"
def test_relto_not_relative(self, path1):
l1=path1.join("bcde")
l2=path1.join("b")
assert not l1.relto(l2)
assert not l2.relto(l1)
@py.test.mark.xfail("sys.platform.startswith('java')")
def test_listdir(self, path1):
l = path1.listdir()
assert path1.join('sampledir') in l
assert path1.join('samplefile') in l
py.test.raises(py.error.ENOTDIR,
"path1.join('samplefile').listdir()")
def test_listdir_fnmatchstring(self, path1):
l = path1.listdir('s*dir')
assert len(l)
assert l[0], path1.join('sampledir')
def test_listdir_filter(self, path1):
l = path1.listdir(lambda x: x.check(dir=1))
assert path1.join('sampledir') in l
assert not path1.join('samplefile') in l
def test_listdir_sorted(self, path1):
l = path1.listdir(lambda x: x.check(basestarts="sample"), sort=True)
assert path1.join('sampledir') == l[0]
assert path1.join('samplefile') == l[1]
assert path1.join('samplepickle') == l[2]
def test_visit_nofilter(self, path1):
l = []
for i in path1.visit():
l.append(i.relto(path1))
assert "sampledir" in l
assert path1.sep.join(["sampledir", "otherfile"]) in l
def test_visit_norecurse(self, path1):
l = []
for i in path1.visit(None, lambda x: x.basename != "sampledir"):
l.append(i.relto(path1))
assert "sampledir" in l
assert not path1.sep.join(["sampledir", "otherfile"]) in l
@pytest.mark.parametrize('fil', ['*dir', u'*dir',
pytest.mark.skip("sys.version_info <"
" (3,6)")(b'*dir')])
def test_visit_filterfunc_is_string(self, path1, fil):
l = []
for i in path1.visit(fil):
l.append(i.relto(path1))
assert len(l), 2
assert "sampledir" in l
assert "otherdir" in l
@py.test.mark.xfail("sys.platform.startswith('java')")
def test_visit_ignore(self, path1):
p = path1.join('nonexisting')
assert list(p.visit(ignore=py.error.ENOENT)) == []
def test_visit_endswith(self, path1):
l = []
for i in path1.visit(lambda x: x.check(endswith="file")):
l.append(i.relto(path1))
assert path1.sep.join(["sampledir", "otherfile"]) in l
assert "samplefile" in l
def test_endswith(self, path1):
assert path1.check(notendswith='.py')
x = path1.join('samplefile')
assert x.check(endswith='file')
def test_cmp(self, path1):
path1 = path1.join('samplefile')
path2 = path1.join('samplefile2')
assert (path1 < path2) == ('samplefile' < 'samplefile2')
assert not (path1 < path1)
def test_simple_read(self, path1):
x = path1.join('samplefile').read('r')
assert x == 'samplefile\n'
def test_join_div_operator(self, path1):
newpath = path1 / '/sampledir' / '/test//'
newpath2 = path1.join('sampledir', 'test')
assert newpath == newpath2
def test_ext(self, path1):
newpath = path1.join('sampledir.ext')
assert newpath.ext == '.ext'
newpath = path1.join('sampledir')
assert not newpath.ext
def test_purebasename(self, path1):
newpath = path1.join('samplefile.py')
assert newpath.purebasename == 'samplefile'
def test_multiple_parts(self, path1):
newpath = path1.join('samplefile.py')
dirname, purebasename, basename, ext = newpath._getbyspec(
'dirname,purebasename,basename,ext')
assert str(path1).endswith(dirname) # be careful with win32 'drive'
assert purebasename == 'samplefile'
assert basename == 'samplefile.py'
assert ext == '.py'
def test_dotted_name_ext(self, path1):
newpath = path1.join('a.b.c')
ext = newpath.ext
assert ext == '.c'
assert newpath.ext == '.c'
def test_newext(self, path1):
newpath = path1.join('samplefile.py')
newext = newpath.new(ext='.txt')
assert newext.basename == "samplefile.txt"
assert newext.purebasename == "samplefile"
def test_readlines(self, path1):
fn = path1.join('samplefile')
contents = fn.readlines()
assert contents == ['samplefile\n']
def test_readlines_nocr(self, path1):
fn = path1.join('samplefile')
contents = fn.readlines(cr=0)
assert contents == ['samplefile', '']
def test_file(self, path1):
assert path1.join('samplefile').check(file=1)
def test_not_file(self, path1):
assert not path1.join("sampledir").check(file=1)
assert path1.join("sampledir").check(file=0)
def test_non_existent(self, path1):
assert path1.join("sampledir.nothere").check(dir=0)
assert path1.join("sampledir.nothere").check(file=0)
assert path1.join("sampledir.nothere").check(notfile=1)
assert path1.join("sampledir.nothere").check(notdir=1)
assert path1.join("sampledir.nothere").check(notexists=1)
assert not path1.join("sampledir.nothere").check(notfile=0)
# pattern = path1.sep.join(['s*file'])
# sfile = path1.join("samplefile")
# assert sfile.check(fnmatch=pattern)
def test_size(self, path1):
url = path1.join("samplefile")
assert url.size() > len("samplefile")
def test_mtime(self, path1):
url = path1.join("samplefile")
assert url.mtime() > 0
def test_relto_wrong_type(self, path1):
py.test.raises(TypeError, "path1.relto(42)")
def test_load(self, path1):
p = path1.join('samplepickle')
obj = p.load()
assert type(obj) is dict
assert obj.get('answer',None) == 42
def test_visit_filesonly(self, path1):
l = []
for i in path1.visit(lambda x: x.check(file=1)):
l.append(i.relto(path1))
assert not "sampledir" in l
assert path1.sep.join(["sampledir", "otherfile"]) in l
def test_visit_nodotfiles(self, path1):
l = []
for i in path1.visit(lambda x: x.check(dotfile=0)):
l.append(i.relto(path1))
assert "sampledir" in l
assert path1.sep.join(["sampledir", "otherfile"]) in l
assert not ".dotfile" in l
def test_visit_breadthfirst(self, path1):
l = []
for i in path1.visit(bf=True):
l.append(i.relto(path1))
for i, p in enumerate(l):
if path1.sep in p:
for j in range(i, len(l)):
assert path1.sep in l[j]
break
else:
py.test.fail("huh")
def test_visit_sort(self, path1):
l = []
for i in path1.visit(bf=True, sort=True):
l.append(i.relto(path1))
for i, p in enumerate(l):
if path1.sep in p:
break
assert l[:i] == sorted(l[:i])
assert l[i:] == sorted(l[i:])
def test_endswith(self, path1):
def chk(p):
return p.check(endswith="pickle")
assert not chk(path1)
assert not chk(path1.join('samplefile'))
assert chk(path1.join('somepickle'))
def test_copy_file(self, path1):
otherdir = path1.join('otherdir')
initpy = otherdir.join('__init__.py')
copied = otherdir.join('copied')
initpy.copy(copied)
try:
assert copied.check()
s1 = initpy.read()
s2 = copied.read()
assert s1 == s2
finally:
if copied.check():
copied.remove()
def test_copy_dir(self, path1):
otherdir = path1.join('otherdir')
copied = path1.join('newdir')
try:
otherdir.copy(copied)
assert copied.check(dir=1)
assert copied.join('__init__.py').check(file=1)
s1 = otherdir.join('__init__.py').read()
s2 = copied.join('__init__.py').read()
assert s1 == s2
finally:
if copied.check(dir=1):
copied.remove(rec=1)
def test_remove_file(self, path1):
d = path1.ensure('todeleted')
assert d.check()
d.remove()
assert not d.check()
def test_remove_dir_recursive_by_default(self, path1):
d = path1.ensure('to', 'be', 'deleted')
assert d.check()
p = path1.join('to')
p.remove()
assert not p.check()
def test_ensure_dir(self, path1):
b = path1.ensure_dir("001", "002")
assert b.basename == "002"
assert b.isdir()
def test_mkdir_and_remove(self, path1):
tmpdir = path1
py.test.raises(py.error.EEXIST, tmpdir.mkdir, 'sampledir')
new = tmpdir.join('mktest1')
new.mkdir()
assert new.check(dir=1)
new.remove()
new = tmpdir.mkdir('mktest')
assert new.check(dir=1)
new.remove()
assert tmpdir.join('mktest') == new
def test_move_file(self, path1):
p = path1.join('samplefile')
newp = p.dirpath('moved_samplefile')
p.move(newp)
try:
assert newp.check(file=1)
assert not p.check()
finally:
dp = newp.dirpath()
if hasattr(dp, 'revert'):
dp.revert()
else:
newp.move(p)
assert p.check()
def test_move_dir(self, path1):
source = path1.join('sampledir')
dest = path1.join('moveddir')
source.move(dest)
assert dest.check(dir=1)
assert dest.join('otherfile').check(file=1)
assert not source.join('sampledir').check()
def test_fspath_protocol_match_strpath(self, path1):
assert path1.__fspath__() == path1.strpath
def test_fspath_func_match_strpath(self, path1):
try:
from os import fspath
except ImportError:
from py._path.common import fspath
assert fspath(path1) == path1.strpath
@py.test.mark.skip("sys.version_info < (3,6)")
def test_fspath_open(self, path1):
f = path1.join('opentestfile')
open(f)
@py.test.mark.skip("sys.version_info < (3,6)")
def test_fspath_fsencode(self, path1):
from os import fsencode
assert fsencode(path1) == fsencode(path1.strpath)
def setuptestfs(path):
if path.join('samplefile').check():
return
#print "setting up test fs for", repr(path)
samplefile = path.ensure('samplefile')
samplefile.write('samplefile\n')
execfile = path.ensure('execfile')
execfile.write('x=42')
execfilepy = path.ensure('execfile.py')
execfilepy.write('x=42')
d = {1:2, 'hello': 'world', 'answer': 42}
path.ensure('samplepickle').dump(d)
sampledir = path.ensure('sampledir', dir=1)
sampledir.ensure('otherfile')
otherdir = path.ensure('otherdir', dir=1)
otherdir.ensure('__init__.py')
module_a = otherdir.ensure('a.py')
module_a.write('from .b import stuff as result\n')
module_b = otherdir.ensure('b.py')
module_b.write('stuff="got it"\n')
module_c = otherdir.ensure('c.py')
module_c.write('''import py;
import otherdir.a
value = otherdir.a.result
''')
module_d = otherdir.ensure('d.py')
module_d.write('''import py;
from otherdir import a
value2 = a.result
''')
| asajeffrey/servo | tests/wpt/web-platform-tests/tools/third_party/py/testing/path/common.py | Python | mpl-2.0 | 16,410 | [
"VisIt"
] | 784ae43f0e784f384f9a1552fcd1399245841c0d542dc062bf273393376f8f28 |
#!/usr/bin/env python
# encoding: utf-8
# PYTHON_ARGCOMPLETE_OK
# === IMPORTANT ====
# NOTE: In order to support non-ASCII file names,
# your system's locale MUST be set to 'utf-8'
# CAVEAT: DOESN'T seem to work with proxy, the underlying reason being
# the 'requests' package used for http communication doesn't seem
# to work properly with proxies, reason unclear.
# NOTE: It seems Baidu doesn't handle MD5 quite right after combining files,
# so it may return erroneous MD5s. Perform a rapidupload again may fix the problem.
# That's why I changed default behavior to no-verification.
# NOTE: syncup / upload, syncdown / downdir are partially duplicates
# the difference: syncup/down compare and perform actions
# while down/up just proceed to download / upload (but still compare during actions)
# so roughly the same, except that sync can delete extra files
#
# TODO: Dry run?
# TODO: Use batch functions for better performance
# == NOTE ==
#Proxy is supported by the underlying Requests library, you can activate HTTP proxies by setting the HTTP_PROXY and HTTPS_PROXY environment variables respectively as follows:
#HTTP_PROXY=http://user:password@domain
#HTTPS_PROXY=http://user:password@domain
#(More information: http://docs.python-requests.org/en/master/user/advanced/#proxies)
#Though from my experience, it seems that some proxy servers may not be supported properly.
# from __future__ imports must occur at the beginning of the file
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
import os
import sys
import io
import signal
import time
import shutil
import tempfile
import posixpath
import json
import hashlib
import base64
import re
import pprint
import socket
import subprocess
#from collections import OrderedDict
from functools import partial
from argparse import ArgumentParser
from argparse import RawDescriptionHelpFormatter
# unify Python 2 and 3
if sys.version_info[0] == 2:
import urllib as ulp
import cPickle as pickle
pickleload = pickle.load
elif sys.version_info[0] == 3:
import urllib.parse as ulp
import pickle
unicode = str
basestring = str
long = int
raw_input = input
pickleload = partial(pickle.load, encoding="bytes")
from . import const
from . import gvar
from . import printer_console
from .cached import (cached, stringifypickle, md5, crc32, slice_md5)
from .struct import PathDictTree
from .util import (
iswindows,
perr, pwarn, pinfo, pdbg,
jsondump, jsonload, formatex, rb,
joinpath, get_pcs_path, print_pcs_list, str2bool, str2int,
human_size, interpret_size, ls_time, ls_type,
makedir, removedir, movefile, removefile, getfilesize,
MyPrettyPrinter)
from .chkreq import (check_requirements, CheckResult)
from .requester import RequestsRequester
pr = printer_console.pr
prcolor = printer_console.prcolor
ask = printer_console.ask
pprgr = printer_console.pprgr
# there was a WantWriteError uncaught exception for Urllib3:
# https://github.com/shazow/urllib3/pull/412
# it was fixed here:
# https://github.com/shazow/urllib3/pull/413
# commit:
# https://github.com/shazow/urllib3/commit/a89dda000ed144efeb6be4e0b417c0465622fe3f
# and this was included in this commit in the Requests library
# https://github.com/kennethreitz/requests/commit/7aa6c62d6d917e11f81b166d1d6c9e60340783ac
# which was included in version 2.5.0 or above
# so minimum 2.5.0 is required
import requests
try:
from requests.packages.urllib3.exceptions import ReadTimeoutError
except:
try:
from urllib3.exceptions import ReadTimeoutError
ReadTimeoutError
except:
perr("Something seems wrong with the urllib3 installation.\nQuitting")
sys.exit(const.EFatal)
class ByPy(object):
'''The main class of the bypy program'''
# TODO: Apply to configdir instead of ~/.bypy
@staticmethod
def migratesettings():
result = const.ENoError
if os.path.exists(const.OldByPyCertsPath):
removefile(const.OldByPyCertsPath)
filesToMove = [
[const.OldTokenFilePath, const.TokenFilePath],
[const.OldPicklePath, const.PicklePath]
]
result = makedir(const.ConfigDir, 0o700) and result # make it secretive
# this directory must exist
if result != const.ENoError:
perr("Fail to create config directory '{}'".format(const.ConfigDir))
return result
for tomove in filesToMove:
oldfile = tomove[0]
newfile = tomove[1]
if os.path.exists(oldfile):
dst = newfile
if os.path.exists(newfile):
dst = dst + '.old'
result = movefile(oldfile, dst) and result
# we move to JSON for hash caching for better portability
# http://www.benfrederickson.com/dont-pickle-your-data/
# https://kovshenin.com/2010/pickle-vs-json-which-is-faster/
# JSON even outpeforms Pickle and definitely much more portable
# DON'T bother with pickle.
if os.path.exists(const.PicklePath):
oldcache = {}
try:
with io.open(const.PicklePath, 'rb') as f:
oldcache = pickleload(f)
stringifypickle(oldcache)
cached.loadcache(oldcache)
cached.savecache(True)
pinfo("Contents of Pickle (old format hash cache) '{}' "
"has been merged to '{}'".format(const.PicklePath, const.HashCachePath))
mergedfile = const.PicklePath + '.merged'
ok = movefile(const.PicklePath, mergedfile)
if ok == const.ENoError:
pinfo("Pickle (old format hash cache) '{}' "
"has been renamed to '{}".format(const.PicklePath, mergedfile))
else:
perr("Failed to move Pickle (old format hash cache) '{}' to '{}'".format(const.PicklePath, mergedfile))
except (
pickle.PickleError,
# the following is for dealing with corrupted cache file
EOFError, TypeError, ValueError):
invalidfile = const.PicklePath + '.invalid'
ok = movefile(const.PicklePath, invalidfile)
perr("{} invalid Pickle (old format hash cache) file '{}' to '{}'".format(
"Moved" if ok == const.ENoError else "Failed to move",
const.PicklePath, invalidfile))
return result
# TODO: save settings here?
def quit(retcode = const.ENoError):
# saving is the most important
# we save, but don't clean, why?
# think about unmount path, moved files,
# once we discard the information, they are gone.
# so unless the user specifically request a clean,
# we don't act too smart.
#cached.cleancache()
cached.savecache()
# if we flush() on Ctrl-C, we get
# IOError: [Errno 32] Broken pipe
sys.stdout.flush()
sys.exit(retcode)
def savesetting(self):
try:
jsondump(self.__setting, self.__settingpath)
except Exception as ex:
perr("Failed to save settings.\n{}".format(formatex(ex)))
# complaining is enough, no need more actions as this is non-critical
# TODO: this constructor is getting fat ...
def __init__(self,
slice_size = const.DefaultSliceSize,
dl_chunk_size = const.DefaultDlChunkSize,
verify = True,
retry = 5, timeout = None,
quit_when_fail = False,
resumedownload = True,
extraupdate = lambda: (),
incregex = '',
ondup = '',
followlink = True,
checkssl = True,
cacerts = None,
rapiduploadonly = False,
mirror = None,
resumedl_revertcount = const.DefaultResumeDlRevertCount,
verbose = 0, debug = False,
configdir = const.ConfigDir,
requester = RequestsRequester,
apikey = const.ApiKey,
downloader = "",
downloader_args = "",
secretkey = const.SecretKey):
super(ByPy, self).__init__()
# handle backward compatibility, a.k.a. history debt
sr = ByPy.migratesettings()
if sr != const.ENoError:
# bail out
perr("Failed to migrate old settings.")
quit(const.EMigrationFailed)
self.__configdir = configdir.rstrip("/\\ ")
# os.path.join() may not handle unicode well on Python 2.7
self.__tokenpath = configdir + os.sep + const.TokenFileName
self.__settingpath = configdir + os.sep + const.SettingFileName
self.__setting = {}
self.__downloader = downloader.lower().strip()
if downloader_args:
self.__downloader_args = downloader_args
else:
if downloader in const.DownloaderDefaultArgs:
self.__downloader_args = const.DownloaderDefaultArgs[downloader]
else:
self.__downloader_args = ''
if os.path.exists(self.__settingpath):
try:
self.__setting = jsonload(self.__settingpath)
except Exception as ex:
perr("Error loading settings: {}, using default settings".format(formatex(ex)))
self.__hashcachepath = configdir + os.sep + const.HashCacheFileName
cached.hashcachepath = self.__hashcachepath
self.__certspath = os.path.join(os.path.dirname(__file__), const.ByPyCertsFileName)
self.__requester = requester
self.__apikey = apikey
self.__secretkey = secretkey
self.__use_server_auth = not secretkey
global pcsurl
global cpcsurl
global dpcsurl
if mirror and mirror.lower() != const.PcsDomain:
pcsurl = 'https://' + mirror + const.RestApiPath
cpcsurl = pcsurl
dpcsurl = pcsurl
# using a mirror, which has name mismatch SSL error,
# so need to disable SSL check
pwarn("Mirror '{}' used instead of the default PCS server url '{}', ".format(pcsurl, const.PcsUrl) + \
"we have to disable the SSL cert check in this case.")
checkssl = False
else:
# use the default domain
pcsurl = const.PcsUrl
cpcsurl = const.CPcsUrl
dpcsurl = const.DPcsUrl
self.__slice_size = slice_size
self.__dl_chunk_size = dl_chunk_size
self.__verify = verify
self.__retry = retry
self.__quit_when_fail = quit_when_fail
self.__timeout = timeout
self.__resumedownload = resumedownload
self.__extraupdate = extraupdate
self.__incregex = incregex
self.__incregmo = re.compile(incregex)
if ondup and len(ondup) > 0:
self.__ondup = ondup[0].upper()
else:
self.__ondup = 'O' # O - Overwrite* S - Skip P - Prompt
self.__followlink = followlink;
self.__rapiduploadonly = rapiduploadonly
self.__resumedl_revertcount = resumedl_revertcount
self.__checkssl = checkssl
if self.__checkssl:
# sort of undocumented by requests
# http://stackoverflow.com/questions/10667960/python-requests-throwing-up-sslerror
if cacerts is not None:
if os.path.isfile(cacerts):
self.__certspath = cacerts
else:
perr("Invalid CA Bundle '{}' specified")
# falling through here means no customized CA Certs specified
if self.__checkssl is True:
# use our own CA Bundle if possible
if os.path.isfile(self.__certspath):
self.__checkssl = self.__certspath
else:
# Well, disable cert verification
pwarn(
"** SSL Certificate Verification has been disabled **\n\n"
"If you are confident that your CA Bundle can verify "
"Baidu PCS's certs, you can run the prog with the '" + const.CaCertsOption + \
" <your ca cert path>' argument to enable SSL cert verification.\n\n"
"However, most of the time, you can ignore this warning, "
"you are not going to send sensitive data to the cloud plainly right?")
self.__checkssl = False
if not checkssl:
requester.disable_warnings()
# these two variables are without leadning double underscaore "__" as to export the as public,
# so if any code using this class can check the current verbose / debug level
cached.verbose = self.verbose = verbose
cached.debug = self.debug = debug
cached.loadcache()
requester.set_logging_level(debug)
# useful info for debugging
if debug > 0:
pr("----")
pr("Verbose level = {}".format(verbose))
pr("Debug level = {}".format(debug))
# these informations are useful for debugging
pr("Config directory: '{}'".format(self.__configdir))
pr("Token file: '{}'".format(self.__tokenpath))
pr("Hash Cache file: '{}'".format(self.__hashcachepath))
pr("App root path at Baidu Yun '{}'".format(const.AppPcsPath))
pr("sys.stdin.encoding = {}".format(sys.stdin.encoding))
pr("sys.stdout.encoding = {}".format(sys.stdout.encoding))
pr("sys.stderr.encoding = {}".format(sys.stderr.encoding))
pr("----\n")
# the prophet said: thou shalt initialize
self.__existing_size = 0
self.__json = {}
self.__access_token = ''
self.__remote_json = {}
self.__slice_md5s = []
self.__cookies = {}
# TODO: whether this works is still to be tried out
self.__isrev = False
self.__rapiduploaded = False
# store the response object, mainly for testing.
self.response = object()
# store function-specific result data
self.result = {}
if not self.__load_local_json():
# no need to call __load_local_json() again as __auth() will load the json & acess token.
result = self.__auth()
if result != const.ENoError:
perr("Program authorization FAILED.\n"
"You need to authorize this program before using any PCS functions.\n"
"Quitting...\n")
quit(result)
for proxy in ['HTTP_PROXY', 'HTTPS_PROXY']:
if proxy in os.environ:
pr("{} used: {}".format(proxy, os.environ[proxy]))
def pv(self, msg, **kwargs):
if self.verbose:
pr(msg)
def pd(self, msg, level = 1, **kwargs):
if self.debug >= level:
pdbg(msg, kwargs)
def shalloverwrite(self, prompt):
if self.__ondup == 'S':
return False
elif self.__ondup == 'P':
ans = ask(prompt, False).upper()
if not ans.startswith('Y'):
return False
return True
def __print_error_json(self, r):
try:
dj = r.json()
if 'error_code' in dj and 'error_msg' in dj:
ec = dj['error_code']
et = dj['error_msg']
msg = ''
if ec == const.IEMD5NotFound:
pf = pinfo
msg = et
else:
pf = perr
msg = "Error JSON returned:{}\nError code: {}\nError Description: {}".format(dj, ec, et)
pf(msg)
except Exception as ex:
perr('Error parsing JSON Error Code from:\n{}\n{}'.format(rb(r.text), formatex(ex)))
def __dump_exception(self, ex, url, pars, r, act):
if self.debug or self.verbose:
perr("Error accessing '{}'".format(url))
if self.debug:
perr(formatex(ex))
perr("Function: {}".format(act.__name__))
perr("Website parameters: {}".format(pars))
if r != None:
# just playing it safe
if hasattr(r, 'url'):
perr("Full URL: {}".format(r.url))
if hasattr(r, 'status_code') and hasattr(r, 'text'):
perr("HTTP Response Status Code: {}".format(r.status_code))
if (r.status_code != 200 and r.status_code != 206) \
or (not ('method' in pars and pars['method'] == 'download') \
and url.find('method=download') == -1 \
and url.find('baidupcs.com/file/') == -1):
self.__print_error_json(r)
perr("Website returned: {}".format(rb(r.text)))
# child class override this to to customize error handling
def __handle_more_response_error(self, r, sc, ec, act, actargs):
return const.ERequestFailed
# TODO: the 'act' param is hacky
def __get_json(self, r, act, defaultec = const.ERequestFailed):
try:
j = r.json()
self.pd("Website returned JSON: {}".format(j))
if 'error_code' in j:
return j['error_code']
else:
return defaultec
except ValueError:
if hasattr(r, 'text'):
self.pd("Website Response: {}".format(rb(r.text)))
if act == self.__cdl_act:
return const.IETaskNotFound
return defaultec
def __request_work_die(self, ex, url, pars, r, act):
result = const.EFatal
self.__dump_exception(ex, url, pars, r, act)
perr("Fatal Exception, no way to continue.\nQuitting...\n")
perr("If the error is reproducible, run the program with `-dv` arguments again to get more info.\n")
quit(result)
# we eat the exception, and use return code as the only
# error notification method, we don't want to mix them two
#raise # must notify the caller about the failure
def __request_work(self, url, pars, act, method, actargs = None, addtoken = True, dumpex = True, **kwargs):
result = const.ENoError
r = None
self.__extraupdate()
parsnew = pars.copy()
if addtoken:
parsnew['access_token'] = self.__access_token
try:
self.pd(method + ' ' + url)
self.pd("actargs: {}".format(actargs))
self.pd("Params: {}".format(pars))
r = self.__requester.request(method, url, params = parsnew, timeout = self.__timeout, verify = self.__checkssl, **kwargs)
self.response = r
sc = r.status_code
self.pd("Full URL: {}".format(r.url))
self.pd("HTTP Status Code: {}".format(sc))
# BUGFIX: DON'T do this, if we are downloading a big file,
# the program will eat A LOT of memeory and potentially hang / get killed
#self.pd("Request Headers: {}".format(pprint.pformat(r.request.headers)), 2)
#self.pd("Response Header: {}".format(pprint.pformat(r.headers)), 2)
#self.pd("Response: {}".format(rb(r.text)), 3)
if sc == requests.codes.ok or sc == 206: # 206 Partial Content
if sc == requests.codes.ok:
# #162 https://github.com/houtianze/bypy/pull/162
# handle response like this: {"error_code":0,"error_msg":"no error","request_id":70768340515255385}
if not ('method' in pars and pars['method'] == 'download'):
try:
j = r.json()
if 'error_code' in j and j['error_code'] == 0 and 'error_msg' in j and j['error_msg'] == 'no error':
self.pd("Unexpected response: {}".format(j))
return const.ERequestFailed
except Exception as ex:
perr(formatex(ex))
# TODO: Shall i return this?
return const.ERequestFailed
self.pd("200 OK, processing action")
else:
self.pd("206 Partial Content (this is OK), processing action")
result = act(r, actargs)
if result == const.ENoError:
self.pd("Request all goes fine")
else:
ec = self.__get_json(r, act)
# 6 (sc: 403): No permission to access user data
# 110 (sc: 401): Access token invalid or no longer valid
# 111 (sc: 401): Access token expired
if ec == 111 or ec == 110 or ec == 6: # and sc == 401:
self.pd("ec = {}".format(ec))
self.pd("Need to refresh token, refreshing")
if const.ENoError == self.__refresh_token(): # refresh the token and re-request
# TODO: avoid infinite recursive loops
# TODO: properly pass retry
result = self.__request(url, pars, act, method, actargs, True, addtoken, dumpex, **kwargs)
else:
result = const.EFatal
perr("FATAL: Token refreshing failed, can't continue.\nQuitting...\n")
quit(result)
# File md5 not found, you should use upload API to upload the whole file.
elif ec == const.IEMD5NotFound: # and sc == 404:
self.pd("MD5 not found, rapidupload failed")
result = ec
# superfile create failed
elif ec == const.IESuperfileCreationFailed \
or ec == const.IEBlockMissInSuperFile2:
self.pd("Failed to combine files from MD5 slices")
result = ec
# errors that make retrying meaningless
elif (
ec == 31061 or # sc == 400 file already exists
ec == 31062 or # sc == 400 file name is invalid
ec == 31063 or # sc == 400 file parent path does not exist
ec == 31064 or # sc == 403 file is not authorized
ec == 31065 or # sc == 400 directory is full
ec == 31066 or # sc == 403 (indeed 404) file does not exist
ec == const.IETaskNotFound or # 36016 or # sc == 404 Task was not found
# the following was found by xslidian, but i have never ecountered before
ec == 31390): # sc == 404 # {"error_code":31390,"error_msg":"Illegal File"} # r.url.find('http://bcscdn.baidu.com/bcs-cdn/wenxintishi') == 0
result = ec
# TODO: Move this out to cdl_cancel() ?
#if ec == const.IETaskNotFound:
# pr(r.json())
if dumpex:
self.__dump_exception(None, url, pars, r, act)
else:
# gate for child classes to customize behaviors
# the function should return ERequestFailed if it doesn't handle the case
result = self.__handle_more_response_error(r, sc, ec, act, actargs)
if result == const.ERequestFailed and dumpex:
self.__dump_exception(None, url, pars, r, act)
except (requests.exceptions.RequestException,
socket.error,
ReadTimeoutError) as ex:
# If certificate check failed, no need to continue
# but prompt the user for work-around and quit
# why so kludge? because requests' SSLError doesn't set
# the errno and strerror due to using **kwargs,
# so we are forced to use string matching
if isinstance(ex, requests.exceptions.SSLError) \
and re.match(r'^\[Errno 1\].*error:14090086.*:certificate verify failed$', str(ex), re.I):
# [Errno 1] _ssl.c:504: error:14090086:SSL routines:SSL3_GET_SERVER_CERTIFICATE:certificate verify failed
result = const.EFatal
self.__dump_exception(ex, url, pars, r, act)
perr("\n\n== Baidu's Certificate Verification Failure ==\n"
"We couldn't verify Baidu's SSL Certificate.\n"
"It's most likely that the system doesn't have "
"the corresponding CA certificate installed.\n"
"There are two ways of solving this:\n"
"Either) Run this prog with the '" + const.CaCertsOption + \
" <path to " + const.ByPyCertsFileName + "> argument "
"(" + const.ByPyCertsFileName + " comes along with this prog). "
"This is the secure way. "
"However, it won't work after 2020-02-08 when "
"the certificat expires.\n"
"Or) Run this prog with the '" + const.DisableSslCheckOption + \
"' argument. This supresses the CA cert check "
"and always works.\n")
quit(result)
# why so kludge? because requests' SSLError doesn't set
# the errno and strerror due to using **kwargs,
# so we are forced to use string matching
if isinstance(ex, requests.exceptions.SSLError) \
and re.match(r'^\[Errno 1\].*error:14090086.*:certificate verify failed$', str(ex), re.I):
# [Errno 1] _ssl.c:504: error:14090086:SSL routines:SSL3_GET_SERVER_CERTIFICATE:certificate verify failed
perr("\n*** We probably don't have Baidu's CA Certificate ***\n" \
"This in fact doesn't matter most of the time.\n\n" \
"However, if you are _really_ concern about it, you can:\n" \
"Either) Run this prog with the '" + const.CaCertsOption + \
" <path to bypy.cacerts.pem>' " \
"argument. This is the secure way.\n" \
"Or) Run this prog with the '" + const.DisableSslCheckOption + \
"' argument. This suppresses the CA cert check.\n")
result = const.ERequestFailed
if dumpex:
self.__dump_exception(ex, url, pars, r, act)
# TODO: put this check into the specific funcitons?
except ValueError as ex:
if ex.message == 'No JSON object could be decoded':
result = const.ERequestFailed
if dumpex:
self.__dump_exception(ex, url, pars, r, act)
else:
result = const.EFatal
self.__request_work_die(ex, url, pars, r, act)
except Exception as ex:
# OpenSSL SysCallError
if ex.args == (10054, 'WSAECONNRESET') \
or ex.args == (10053, 'WSAECONNABORTED') \
or ex.args == (104, 'ECONNRESET') \
or ex.args == (110, 'ETIMEDOUT') \
or ex.args == (32, 'EPIPE'):
result = const.ERequestFailed
if dumpex:
self.__dump_exception(ex, url, pars, r, act)
else:
result = const.EFatal
self.__request_work_die(ex, url, pars, r, act)
return result
def __request(self, url, pars, act, method, actargs = None, retry = True, addtoken = True, dumpex = True, **kwargs):
tries = 1
if retry:
tries = self.__retry
result = const.ERequestFailed
# Change the User-Agent to avoid server fuss
kwnew = kwargs.copy()
if 'headers' not in kwnew:
kwnew['headers'] = { 'User-Agent': const.UserAgent }
# Now, allow to User-Agent to be set in the caller, instead of always using the default UserAgent value.
if 'User-Agent' not in kwnew['headers']:
kwnew['headers']['User-Agent'] = const.UserAgent
i = 0
while True:
result = self.__request_work(url, pars, act, method, actargs, addtoken, dumpex, **kwnew)
i += 1
# only ERequestFailed needs retry, other error still directly return
if result == const.ERequestFailed:
if i < tries:
# algo changed: delay more after each failure
delay = const.RetryDelayInSec * i
perr("Waiting {} seconds before retrying...".format(delay))
time.sleep(delay)
perr("Request Try #{} / {}".format(i + 1, tries))
else:
result = const.EMaxRetry
perr("Maximum number ({}) of tries failed.".format(tries))
if self.__quit_when_fail:
quit(const.EMaxRetry)
break
else:
break
return result
def __get(self, url, pars, act, actargs = None, retry = True, addtoken = True, dumpex = True, **kwargs):
return self.__request(url, pars, act, 'GET', actargs, retry, addtoken, dumpex, **kwargs)
def __post(self, url, pars, act, actargs = None, retry = True, addtoken = True, dumpex = True, **kwargs):
return self.__request(url, pars, act, 'POST', actargs, retry, addtoken, dumpex, **kwargs)
# direction: True - upload, False - download
def __shallinclude(self, lpath, rpath, direction):
arrow = '==>' if direction else '<=='
checkpath = lpath if direction else rpath
# TODO: bad practice, see os.access() document for more info
if direction: # upload
if not os.path.exists(lpath):
perr("'{}' {} '{}' skipped since local path no longer exists".format(
lpath, arrow, rpath));
return False
else: # download
if os.path.exists(lpath) and (not os.access(lpath, os.R_OK)):
perr("'{}' {} '{}' skipped due to permission".format(
lpath, arrow, rpath));
return False
if '\\' in os.path.basename(checkpath):
perr("'{}' {} '{}' skipped due to problemic '\\' in the path".format(
lpath, arrow, rpath));
return False
include = (not self.__incregex) or self.__incregmo.match(checkpath)
if not include:
self.pv("'{}' {} '{}' skipped as it's not included in the regex pattern".format(
lpath, arrow, rpath));
return include
ListFormatDict = {
'$t' : (lambda json: ls_type(json['isdir'])),
'$f' : (lambda json: json['path'].split('/')[-1]),
'$c' : (lambda json: ls_time(json['ctime'])),
'$m' : (lambda json: ls_time(json['mtime'])),
'$d' : (lambda json: str(json['md5'] if 'md5' in json else '')),
'$s' : (lambda json: str(json['size'])),
'$i' : (lambda json: str(json['fs_id'])),
'$b' : (lambda json: str(json['block_list'] if 'block_list' in json else '')),
'$u' : (lambda json: 'HasSubDir' if 'ifhassubdir' in json and json['ifhassubdir'] else 'NoSubDir'),
'$$' : (lambda json: '$')
}
def __replace_list_format(self, fmt, j):
output = fmt
for k, v in ByPy.ListFormatDict.items():
output = output.replace(k, v(j))
return output
def __load_local_json(self):
try:
self.__json = jsonload(self.__tokenpath)
self.__access_token = self.__json['access_token']
self.pd("Token loaded:")
self.pd(self.__json)
return True
except IOError as ex:
self.pd("Error while loading baidu pcs token.\n{}".format(formatex(ex)))
return False
def __store_json_only(self, j):
self.__json = j
self.__access_token = self.__json['access_token']
self.pd("access token: " + self.__access_token)
self.pd("Authorize JSON:")
self.pd(self.__json)
tokenmode = 0o600
try:
jsondump(self.__json, self.__tokenpath)
os.chmod(self.__tokenpath, tokenmode)
return const.ENoError
except Exception as ex:
perr("Exception occured while trying to store access token:\n{}".format(
formatex(ex)))
return const.EFileWrite
def __prompt_clean(self):
pinfo('-' * 64)
pinfo("""This is most likely caused by authorization errors.
Possible causes:
- You didn't run this program for a long time (more than a month).
- You changed your Baidu password after authorizing this program.
- You didn't give this program the 'netdisk' access while authorizing.
- ...
Possible fixes:
1. Remove the authorization token by running with the parameter '{}', and then re-run this program.
2. If (1) still doesn't solve the problem, you may have to go to:
https://passport.baidu.com/accountbind
and remove the authorization of this program, and then re-run this program.""".format(const.CleanOptionShort))
return const.EInvalidJson
def __store_json(self, r):
j = {}
try:
j = r.json()
except Exception as ex:
perr("Failed to decode JSON:\n{}".format(formatex(ex)))
perr("Error response:\n{}".format(r.text));
return self.__prompt_clean()
return self.__store_json_only(j)
def __server_auth_act(self, r, args):
return self.__store_json(r)
def __server_auth(self):
params = {
'client_id' : self.__apikey,
'response_type' : 'code',
'redirect_uri' : 'oob',
'scope' : 'basic netdisk' }
pars = ulp.urlencode(params)
msg = 'Please visit:\n{}\nAnd authorize this app'.format(const.ServerAuthUrl + '?' + pars) + \
'\nPaste the Authorization Code here within 10 minutes.'
auth_code = ask(msg).strip()
self.pd("auth_code: {}".format(auth_code))
pr('Authorizing, please be patient, it may take upto {} seconds...'.format(self.__timeout))
pars = {
'code' : auth_code,
'redirect_uri' : 'oob' }
result = None
for auth in const.AuthServerList:
(url, retry, msg) = auth
pr(msg)
result = self.__get(url, pars, self.__server_auth_act, retry = retry, addtoken = False)
if result == const.ENoError:
break
if result == const.ENoError:
pr("Successfully authorized")
else:
perr("Fatal: All server authorizations failed.")
self.__prompt_clean()
return result
def __device_auth_act(self, r, args):
dj = r.json()
return self.__get_token(dj)
def __device_auth(self):
pars = {
'client_id' : self.__apikey,
'response_type' : 'device_code',
'scope' : 'basic netdisk'}
return self.__get(const.DeviceAuthUrl, pars, self.__device_auth_act, addtoken = False)
def __auth(self):
if self.__use_server_auth:
return self.__server_auth()
else:
return self.__device_auth()
def __get_token_act(self, r, args):
return self.__store_json(r)
def __get_token(self, deviceJson):
# msg = "Please visit:{}\n" + deviceJson['verification_url'] + \
# "\nwithin " + str(deviceJson['expires_in']) + " seconds\n"
# "Input the CODE: {}\n".format(deviceJson['user_code'])" + \
# "and Authorize this little app.\n"
# "Press [Enter] when you've finished\n"
msg = "Please visit:\n{}\nwithin {} seconds\n" \
"Input the CODE: {}\n" \
"and Authorize this little app.\n" \
"Press [Enter] when you've finished\n".format(
deviceJson['verification_url'],
str(deviceJson['expires_in']),
deviceJson['user_code'])
ask(msg)
pars = {
'grant_type' : 'device_token',
'code' : deviceJson['device_code'],
'client_id' : self.__apikey,
'client_secret' : self.__secretkey}
return self.__get(const.TokenUrl, pars, self.__get_token_act, addtoken = False)
def __refresh_token_act(self, r, args):
return self.__store_json(r)
def __refresh_token(self):
if self.__use_server_auth:
pr('Refreshing, please be patient, it may take upto {} seconds...'.format(self.__timeout))
pars = {
'grant_type' : 'refresh_token',
'refresh_token' : self.__json['refresh_token'] }
result = None
for refresh in const.RefreshServerList:
(url, retry, msg) = refresh
pr(msg)
result = self.__get(url, pars, self.__refresh_token_act, retry = retry, addtoken = False)
if result == const.ENoError:
break
if result == const.ENoError:
pr("Token successfully refreshed")
else:
perr("Token-refreshing on all the servers failed")
self.__prompt_clean()
return result
else:
pars = {
'grant_type' : 'refresh_token',
'refresh_token' : self.__json['refresh_token'],
'client_secret' : self.__secretkey,
'client_id' : self.__apikey}
return self.__post(const.TokenUrl, pars, self.__refresh_token_act)
def __walk_normal_file(self, dir):
#dirb = dir.encode(FileSystemEncoding)
for walk in os.walk(dir, followlinks=self.__followlink):
normalfiles = [t for t in walk[-1]
if os.path.isfile(os.path.join(walk[0], t))]
normalfiles.sort()
normalwalk = walk[:-1] + (normalfiles,)
yield normalwalk
def __quota_act(self, r, args):
j = r.json()
pr('Quota: ' + human_size(j['quota']))
pr('Used: ' + human_size(j['used']))
return const.ENoError
def help(self, command): # this comes first to make it easy to spot
''' Usage: help <command> - provide some information for the command '''
for i, v in ByPy.__dict__.items():
if callable(v) and v.__doc__ and v.__name__ == command :
help = v.__doc__.strip()
pos = help.find(const.HelpMarker)
if pos != -1:
pr("Usage: " + help[pos + len(const.HelpMarker):].strip())
def refreshtoken(self):
''' Usage: refreshtoken - refresh the access token '''
return self.__refresh_token()
def info(self):
return self.quota()
def quota(self):
''' Usage: quota/info - displays the quota information '''
pars = {
'method' : 'info' }
return self.__get(pcsurl + 'quota', pars, self.__quota_act)
# return:
# 0: local and remote files are of same size
# 1: local file is larger
# 2: remote file is larger
# -1: inconclusive (probably invalid remote json)
def __compare_size(self, lsize, rjson):
if 'size' in rjson:
rsize = rjson['size']
if lsize == rsize:
return 0;
elif lsize > rsize:
return 1;
else:
return 2
else:
return -1
def __verify_current_file(self, j, gotlmd5):
# if we really don't want to verify
if self.__current_file == '/dev/null' and not self.__verify:
return const.ENoError
rsize = 0
rmd5 = 0
# always perform size check even __verify is False
if 'size' in j:
rsize = j['size']
else:
perr("Unable to verify JSON: '{}', as no 'size' entry found".format(j))
return const.EHashMismatch
if 'md5' in j:
rmd5 = j['md5']
#elif 'block_list' in j and len(j['block_list']) > 0:
# rmd5 = j['block_list'][0]
#else:
# # quick hack for meta's 'block_list' field
# pwarn("No 'md5' nor 'block_list' found in json:\n{}".format(j))
# pwarn("Assuming MD5s match, checking size ONLY.")
# rmd5 = self.__current_file_md5
else:
perr("Unable to verify JSON: '{}', as no 'md5' entry found".format(j))
return const.EHashMismatch
self.pd("Comparing local file '{}' and remote file '{}'".format(
self.__current_file, j['path']))
self.pd("Local file size : {}".format(self.__current_file_size))
self.pd("Remote file size: {}".format(rsize))
if self.__current_file_size == rsize:
self.pd("Local file and remote file sizes match")
if self.__verify:
if not gotlmd5:
self.__current_file_md5 = md5(self.__current_file)
self.pd("Local file MD5 : {}".format(self.__current_file_md5))
self.pd("Remote file MD5: {}".format(rmd5))
if self.__current_file_md5 == rmd5:
self.pd("Local file and remote file hashes match")
return const.ENoError
else:
pinfo("Local file and remote file hashes DON'T match")
return const.EHashMismatch
else:
return const.ENoError
else:
pinfo("Local file and remote file sizes DON'T match")
return const.EHashMismatch
def __get_file_info_act(self, r, args):
try:
remotefile = args
j = r.json()
self.pd("List json: {}".format(j))
l = j['list']
for f in l:
if f['path'] == remotefile: # case-sensitive
self.__remote_json = f
self.pd("File info json: {}".format(self.__remote_json))
return const.ENoError;
return const.EFileNotFound
except KeyError as ex:
perr(formatex(ex))
return const.ERequestFailed
# the 'meta' command sucks, since it doesn't supply MD5 ...
# now the JSON is written to self.__remote_json, due to Python call-by-reference chaos
# https://stackoverflow.com/questions/986006/python-how-do-i-pass-a-variable-by-reference
# as if not enough confusion in Python call-by-reference
def __get_file_info(self, remotefile, **kwargs):
if remotefile == const.AppPcsPath: # root path
# fake it
rj = {}
rj['isdir'] = 1
rj['ctime'] = 0
rj['fs_id'] = 0
rj['mtime'] = 0
rj['path'] = const.AppPcsPath
rj['md5'] = ''
rj['size'] = 0
self.__remote_json = rj
self.pd("File info json: {}".format(self.__remote_json))
return const.ENoError
rdir, rfile = posixpath.split(remotefile)
self.pd("__get_file_info(): rdir : {} | rfile: {}".format(rdir, rfile))
if rdir and rfile:
pars = {
'method' : 'list',
'path' : rdir,
'by' : 'name', # sort in case we can use binary-search, etc in the futrue.
'order' : 'asc' }
return self.__get(pcsurl + 'file', pars, self.__get_file_info_act, remotefile, **kwargs)
else:
perr("Invalid remotefile '{}' specified.".format(remotefile))
return const.EArgument
def get_file_info(self, remotefile = '/'):
rpath = get_pcs_path(remotefile)
return self.__get_file_info(rpath)
def __list_act(self, r, args):
(remotedir, fmt) = args
j = r.json()
pr("{} ({}):".format(remotedir, fmt))
for f in j['list']:
pr(self.__replace_list_format(fmt, f))
return const.ENoError
def ls(self, remotepath = '',
fmt = '$t $f $s $m $d',
sort = 'name', order = 'asc'):
return self.list(remotepath, fmt, sort, order)
def list(self, remotepath = '',
fmt = '$t $f $s $m $d',
sort = 'name', order = 'asc'):
''' Usage: list/ls [remotepath] [format] [sort] [order] - list the 'remotepath' directory at Baidu PCS
remotepath - the remote path at Baidu PCS. default: root directory '/'
format - specifies how the list are displayed
$t - Type: Directory ('D') or File ('F')
$f - File name
$c - Creation time
$m - Modification time
$d - MD5 hash
$s - Size
$$ - The '$' sign
So '$t - $f - $s - $$' will display "Type - File - Size - $'
Default format: '$t $f $s $m $d'
sort - sorting by [name, time, size]. default: 'name'
order - sorting order [asc, desc]. default: 'asc'
'''
rpath = get_pcs_path(remotepath)
pars = {
'method' : 'list',
'path' : rpath,
'by' : sort,
'order' : order }
return self.__get(pcsurl + 'file', pars, self.__list_act, (rpath, fmt))
def __meta_act(self, r, args):
return self.__list_act(r, args)
def __meta(self, rpath, fmt):
pars = {
'method' : 'meta',
'path' : rpath }
return self.__get(pcsurl + 'file', pars,
self.__meta_act, (rpath, fmt))
# multi-file meta is not implemented for its low usage
def meta(self, remotepath = '/', fmt = '$t $u $f $s $c $m $i $b'):
''' Usage: meta <remotepath> [format] - \
get information of the given path (dir / file) at Baidu Yun.
remotepath - the remote path
format - specifies how the list are displayed
it supports all the format variables in the 'list' command, and additionally the followings:
$i - fs_id
$b - MD5 block_list
$u - Has sub directory or not
'''
rpath = get_pcs_path(remotepath)
return self.__meta(rpath, fmt)
# this 'is_revision' parameter sometimes gives the following error (e.g. for rapidupload):
# {u'error_code': 31066, u'error_msg': u'file does not exist'}
# and maintain it is also an extra burden, so it's disabled for now
def __add_isrev_param(self, ondup, pars):
pass
#if self.__isrev and ondup != 'newcopy':
# pars['is_revision'] = 1
def __combine_file_act(self, r, args):
result = self.__verify_current_file(r.json(), False)
if result == const.ENoError:
self.pv("'{}' =C=> '{}' OK.".format(self.__current_file, args))
else:
perr("'{}' =C=> '{}' FAILED.".format(self.__current_file, args))
# save the md5 list, in case we add in resume function later to this program
self.__last_slice_md5s = self.__slice_md5s
self.__slice_md5s = []
return result
def __combine_file(self, remotepath, ondup = 'overwrite'):
pars = {
'method' : 'createsuperfile',
'path' : remotepath,
'ondup' : ondup }
self.__add_isrev_param(ondup, pars)
# always print this, so that we can use these data to combine file later
pr("Combining the following MD5 slices:")
for m in self.__slice_md5s:
pr(m)
param = { 'block_list' : self.__slice_md5s }
return self.__post(pcsurl + 'file',
pars, self.__combine_file_act,
remotepath,
data = { 'param' : json.dumps(param) } )
def __upload_slice_act(self, r, args):
j = r.json()
# slices must be verified and re-upload if MD5s don't match,
# otherwise, it makes the uploading slower at the end
rsmd5 = j['md5']
self.pd("Uploaded MD5 slice: " + rsmd5)
if self.__current_slice_md5 == rsmd5:
self.__slice_md5s.append(rsmd5)
self.pv("'{}' >>==> '{}' OK.".format(self.__current_file, args))
return const.ENoError
else:
perr("'{}' >>==> '{}' FAILED.".format(self.__current_file, args))
return const.EHashMismatch
def __upload_slice(self, remotepath):
pars = {
'method' : 'upload',
'type' : 'tmpfile'}
return self.__post(cpcsurl + 'file',
pars, self.__upload_slice_act, remotepath,
# want to be proper? properness doesn't work (search this sentence for more occurence)
#files = { 'file' : (os.path.basename(self.__current_file), self.__current_slice) } )
files = { 'file' : ('file', self.__current_slice) } )
def __update_progress_entry(self, fullpath):
progress = jsonload(const.ProgressPath)
self.pd("Updating slice upload progress for {}".format(fullpath))
progress[fullpath] = (self.__slice_size, self.__slice_md5s)
jsondump(progress, const.ProgressPath)
def __delete_progress_entry(self, fullpath):
progress = jsonload(const.ProgressPath)
# http://stackoverflow.com/questions/11277432/how-to-remove-a-key-from-a-python-dictionary
#del progress[fullpath]
self.pd("Removing slice upload progress for {}".format(fullpath))
progress.pop(fullpath, None)
jsondump(progress, const.ProgressPath)
def __upload_file_slices(self, localpath, remotepath, ondup = 'overwrite'):
pieces = const.MaxSlicePieces
slice = self.__slice_size
if self.__current_file_size <= self.__slice_size * const.MaxSlicePieces:
# slice them using slice size
pieces = (self.__current_file_size + self.__slice_size - 1 ) // self.__slice_size
else:
# the following comparision is done in the caller:
# elif self.__current_file_size <= MaxSliceSize * MaxSlicePieces:
# no choice, but need to slice them to 'MaxSlicePieces' pieces
slice = (self.__current_file_size + const.MaxSlicePieces - 1) // const.MaxSlicePieces
self.pd("Slice size: {}, Pieces: {}".format(slice, pieces))
i = 0
ec = const.ENoError
fullpath = os.path.abspath(self.__current_file)
progress = {}
initial_offset = 0
if not os.path.exists(const.ProgressPath):
jsondump(progress, const.ProgressPath)
progress = jsonload(const.ProgressPath)
if fullpath in progress:
self.pd("Find the progress entry resume uploading")
(slice, md5s) = progress[fullpath]
self.__slice_md5s = []
with io.open(self.__current_file, 'rb') as f:
self.pd("Verifying the md5s. Total count = {}".format(len(md5s)))
for md in md5s:
cslice = f.read(slice)
cm = hashlib.md5(cslice)
if (cm.hexdigest() == md):
self.pd("{} verified".format(md))
# TODO: a more rigorous check would be also verifying
# slices exist at Baidu Yun as well (rapidupload test?)
# but that's a bit complex. for now, we don't check
# this but simply delete the progress entry if later
# we got error combining the slices.
self.__slice_md5s.append(md)
else:
break
self.pd("verified md5 count = {}".format(len(self.__slice_md5s)))
i = len(self.__slice_md5s)
initial_offset = i * slice
self.pd("Start from offset {}".format(initial_offset))
with io.open(self.__current_file, 'rb') as f:
start_time = time.time()
f.seek(initial_offset, os.SEEK_SET)
while i < pieces:
self.__current_slice = f.read(slice)
m = hashlib.md5()
m.update(self.__current_slice)
self.__current_slice_md5 = m.hexdigest()
self.pd("Uploading MD5 slice: {}, #{} / {}".format(
self.__current_slice_md5,
i + 1, pieces))
j = 0
while True:
ec = self.__upload_slice(remotepath)
if ec == const.ENoError:
self.pd("Slice MD5 match, continuing next slice")
pprgr(f.tell(), self.__current_file_size, start_time, initial_offset)
self.__update_progress_entry(fullpath)
break
elif j < self.__retry:
j += 1
# TODO: Improve or make it DRY with the __request retry logic
perr("Slice MD5 mismatch, waiting {} seconds before retrying...".format(const.RetryDelayInSec))
time.sleep(const.RetryDelayInSec)
perr("Retrying #{} / {}".format(j + 1, self.__retry))
else:
self.__slice_md5s = []
break
if ec != const.ENoError:
break
i += 1
if ec != const.ENoError:
return ec
else:
#self.pd("Sleep 2 seconds before combining, just to be safer.")
#time.sleep(2)
ec = self.__combine_file(remotepath, ondup = 'overwrite')
if ec == const.ENoError \
or ec == const.IESuperfileCreationFailed \
or ec == const.IEBlockMissInSuperFile2 \
or ec == const.EMaxRetry: # to handle undocumented error codes if any
# we delete on success or failure caused by
# the slices uploaded expired / became invalid
# (needed for a fresh re-upload later)
self.__delete_progress_entry(fullpath)
return ec
def __rapidupload_file_act(self, r, args):
if self.__verify:
self.pd("Not strong-consistent, sleep 1 second before verification")
time.sleep(1)
return self.__verify_current_file(r.json(), True)
else:
return const.ENoError
def __rapidupload_file_post(self, rpath, size, md5str, slicemd5str, crcstr, ondup = 'overwrite'):
pars = {
'method' : 'rapidupload',
'path' : rpath,
'content-length' : size,
'content-md5' : md5str,
'slice-md5' : slicemd5str,
'content-crc32' : crcstr,
'ondup' : ondup }
self.__add_isrev_param(ondup, pars)
self.pd("RapidUploading Length: {} MD5: {}, Slice-MD5: {}, CRC: {}".format(
size, md5str, slicemd5str, crcstr))
return self.__post(pcsurl + 'file', pars, self.__rapidupload_file_act)
def __get_hashes_for_rapidupload(self, lpath, setlocalfile = False):
if setlocalfile:
self.__current_file = lpath
self.__current_file_size = getfilesize(lpath)
self.__current_file_md5 = md5(self.__current_file)
self.__current_file_slice_md5 = slice_md5(self.__current_file)
self.__current_file_crc32 = crc32(self.__current_file)
def __rapidupload_file(self, lpath, rpath, ondup = 'overwrite', setlocalfile = False):
self.__get_hashes_for_rapidupload(lpath, setlocalfile)
md5str = self.__current_file_md5
slicemd5str = self.__current_file_slice_md5
crcstr = hex(self.__current_file_crc32)
return self.__rapidupload_file_post(rpath, self.__current_file_size, md5str, slicemd5str, crcstr, ondup)
def __upload_one_file_act(self, r, args):
result = self.__verify_current_file(r.json(), False)
if result == const.ENoError:
self.pv("'{}' ==> '{}' OK.".format(self.__current_file, args))
else:
perr("'{}' ==> '{}' FAILED.".format(self.__current_file, args))
return result
def __upload_one_file(self, localpath, remotepath, ondup = 'overwrite'):
pars = {
'method' : 'upload',
'path' : remotepath,
'ondup' : ondup }
self.__add_isrev_param(ondup, pars)
with io.open(localpath, 'rb') as f:
return self.__post(cpcsurl + 'file',
pars, self.__upload_one_file_act, remotepath,
# wants to be proper? properness doesn't work
# there seems to be a bug at Baidu's handling of http text:
# Content-Disposition: ... filename=utf-8''yourfile.ext
# (pass '-ddd' to this program to verify this)
# when you specify a unicode file name, which will be encoded
# using the utf-8'' syntax
# so, we put a work-around here: we always call our file 'file'
# NOTE: an empty file name '' doesn't seem to work, so we
# need to give it a name at will, but empty one.
# apperantly, Baidu PCS doesn't use this file name for
# checking / verification, so we are probably safe here.
#files = { 'file' : (os.path.basename(localpath), f) })
files = { 'file' : ('file', f) })
#TODO: upload empty directories as well?
def __walk_upload(self, localpath, remotepath, ondup, walk):
(dirpath, dirnames, filenames) = walk
rdir = os.path.relpath(dirpath, localpath)
if rdir == '.':
rdir = ''
else:
rdir = rdir.replace('\\', '/')
rdir = (remotepath + '/' + rdir).rstrip('/') # '/' bites
result = const.ENoError
for name in filenames:
#lfile = os.path.join(dirpath, name)
lfile = joinpath(dirpath, name)
self.__current_file = lfile
self.__current_file_size = getfilesize(lfile)
rfile = rdir + '/' + name.replace('\\', '/')
# if the corresponding file matches at Baidu Yun, then don't upload
upload = True
self.__isrev = False
self.__remote_json = {}
subresult = self.__get_file_info(rfile, dumpex = False)
if subresult == const.ENoError: # same-name remote file exists
self.__isrev = True
if const.ENoError == self.__verify_current_file(self.__remote_json, False):
# the two files are the same
upload = False
self.pv("Remote file '{}' already exists, skip uploading".format(rfile))
else: # the two files are different
if not self.shalloverwrite("Remote file '{}' exists but is different, "
"do you want to overwrite it? [y/N]".format(rfile)):
upload = False
self.__isrev = False
if upload:
fileresult = self.__upload_file(lfile, rfile, ondup)
if fileresult != const.ENoError:
result = fileresult # we still continue
else:
pinfo("Remote file '{}' exists and is the same, skip uploading".format(rfile))
# next / continue
return result
def __upload_dir(self, localpath, remotepath, ondup = 'overwrite'):
result = const.ENoError
self.pd("Uploading directory '{}' to '{}'".format(localpath, remotepath))
# it's so minor that we don't care about the return value
#self.__mkdir(remotepath, retry = False, dumpex = False)
#for walk in os.walk(localpath, followlinks=self.__followlink):
for walk in self.__walk_normal_file(localpath):
thisresult = self.__walk_upload(localpath, remotepath, ondup, walk)
# we continue even if some upload failed, but keep the last error code
if thisresult != const.ENoError:
result = thisresult
return result
def __upload_file(self, localpath, remotepath, ondup = 'overwrite'):
# TODO: this is a quick patch
if not self.__shallinclude(localpath, remotepath, True):
# since we are not going to upload it, there is no error
return const.ENoError
self.__current_file = localpath
self.__current_file_size = getfilesize(localpath)
result = const.ENoError
if self.__current_file_size > const.MinRapidUploadFileSize:
self.pd("'{}' is being RapidUploaded.".format(self.__current_file))
result = self.__rapidupload_file(localpath, remotepath, ondup)
if result == const.ENoError:
self.pv("RapidUpload: '{}' =R=> '{}' OK.".format(localpath, remotepath))
self.__rapiduploaded = True
else:
self.__rapiduploaded = False
if not self.__rapiduploadonly:
self.pd("'{}' can't be RapidUploaded, now trying normal uploading.".format(
self.__current_file))
# rapid upload failed, we have to upload manually
if self.__current_file_size <= self.__slice_size:
self.pd("'{}' is being non-slicing uploaded.".format(self.__current_file))
# no-slicing upload
result = self.__upload_one_file(localpath, remotepath, ondup)
elif self.__current_file_size <= const.MaxSliceSize * const.MaxSlicePieces:
# slice them using slice size
self.pd("'{}' is being slicing uploaded.".format(self.__current_file))
result = self.__upload_file_slices(localpath, remotepath, ondup)
else:
result = const.EFileTooBig
perr("Error: size of file '{}' - {} is too big".format(
self.__current_file,
self.__current_file_size))
else:
self.pv("'{}' can't be rapidly uploaded, so it's skipped since we are in the rapid-upload-only mode.".format(localpath))
return result
elif not self.__rapiduploadonly:
# very small file, must be uploaded manually and no slicing is needed
self.pd("'{}' is small and being non-slicing uploaded.".format(self.__current_file))
return self.__upload_one_file(localpath, remotepath, ondup)
def upload(self, localpath = '', remotepath = '', ondup = "overwrite"):
''' Usage: upload [localpath] [remotepath] [ondup] - \
upload a file or directory (recursively)
localpath - local path, is the current directory '.' if not specified
remotepath - remote path at Baidu Yun (after app root directory at Baidu Yun)
ondup - what to do upon duplication ('overwrite' or 'newcopy'), default: 'overwrite'
'''
# copying since Python is call-by-reference by default,
# so we shall not modify the passed-in parameters
lpath = localpath.rstrip('\\/ ') # no trailing slashes
lpathbase = os.path.basename(lpath)
rpath = remotepath
if not lpath:
# so, if you don't specify the local path, it will always be the current directory
# and thus isdir(localpath) is always true
lpath = os.path.abspath(".")
self.pd("localpath not set, set it to current directory '{}'".format(localpath))
if os.path.isfile(lpath):
self.pd("Uploading file '{}'".format(lpath))
if not rpath or rpath == '/': # to root we go
rpath = lpathbase
if rpath[-1] == '/': # user intends to upload to this DIR
rpath = get_pcs_path(rpath + lpathbase)
else:
rpath = get_pcs_path(rpath)
# avoid uploading a file and destroy a directory by accident
subresult = self.__get_file_info(rpath)
if subresult == const.ENoError: # remote path exists, check is dir or file
if self.__remote_json['isdir']: # do this only for dir
rpath += '/' + lpathbase # rpath is guaranteed no '/' ended
self.pd("remote path is '{}'".format(rpath))
return self.__upload_file(lpath, rpath, ondup)
elif os.path.isdir(lpath):
self.pd("Uploading directory '{}' recursively".format(lpath))
rpath = get_pcs_path(rpath)
return self.__upload_dir(lpath, rpath, ondup)
else:
perr("Error: invalid local path '{}' for uploading specified.".format(localpath))
return const.EParameter
# The parameter 'localfile' is a bit kluge as it carries double meanings,
# but this is to be command line friendly (one can not input an empty string '' from the command line),
# so let's just leave it like this unless we can devise a cleverer / clearer weay
def combine(self, remotefile, localfile = '*', *args):
''' Usage: combine <remotefile> [localfile] [md5s] - \
try to create a file at PCS by combining slices, having MD5s specified
remotefile - remote file at Baidu Yun (after app root directory at Baidu Yun)
localfile - local file to verify against, passing in a star '*' or '/dev/null' means no verification
md5s - MD5 digests of the slices, can be:
- list of MD5 hex strings separated by spaces
- a string in the form of 'l<path>' where <path> points to a text file containing MD5 hex strings separated by spaces or line-by-line
'''
self.__slice_md5s = []
if args:
if args[0].upper() == 'L':
try:
with io.open(args[1:], 'r', encoding = 'utf-8') as f:
contents = f.read()
digests = filter(None, contents.split())
for d in digests:
self.__slice_md5s.append(d)
except IOError as ex:
perr("Exception occured while reading file '{}'.\n{}".format(
localfile, formatex(ex)))
else:
for arg in args:
self.__slice_md5s.append(arg)
else:
perr("You MUST provide the MD5s hex strings through arguments or a file.")
return const.EArgument
original_verify = self.__verify
if not localfile or localfile == '*' or localfile == '/dev/null':
self.__current_file = '/dev/null' # Force no verify
self.__verify = False
else:
self.__current_file = localfile
self.__current_file_size = getfilesize(localfile)
result = self.__combine_file(get_pcs_path(remotefile))
self.__verify = original_verify
return result
# no longer used
def __get_meta_act(self, r, args):
parse_ok = False
j = r.json()
if 'list' in j:
lj = j['list']
if len(lj) > 0:
self.__remote_json = lj[0] # TODO: ugly patch
# patch for inconsistency between 'list' and 'meta' json
#self.__remote_json['md5'] = self.__remote_json['block_list'].strip('[]"')
self.pd("self.__remote_json: {}".format(self.__remote_json))
parse_ok = True
return const.ENoError
if not parse_ok:
self.__remote_json = {}
perr("Invalid JSON:\n{}".format(j))
return const.EInvalidJson
# no longer used
def __get_meta(self, remotefile):
pars = {
'method' : 'meta',
'path' : remotefile }
return self.__get(
pcsurl + 'file', pars,
self.__get_meta_act)
# NO LONGER IN USE
def __downfile_act(self, r, args):
rfile, offset = args
with io.open(self.__current_file, 'r+b' if offset > 0 else 'wb') as f:
if offset > 0:
f.seek(offset)
rsize = self.__remote_json['size']
start_time = time.time()
for chunk in r.iter_content(chunk_size = self.__dl_chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
f.flush()
pprgr(f.tell(), rsize, start_time)
# https://stackoverflow.com/questions/7127075/what-exactly-the-pythons-file-flush-is-doing
#os.fsync(f.fileno())
# No exception above, then everything goes fine
result = const.ENoError
if self.__verify:
self.__current_file_size = getfilesize(self.__current_file)
result = self.__verify_current_file(self.__remote_json, False)
if result == const.ENoError:
self.pv("'{}' <== '{}' OK".format(self.__current_file, rfile))
else:
perr("'{}' <== '{}' FAILED".format(self.__current_file, rfile))
return result
def __downchunks_act(self, r, args):
rfile, offset, rsize, start_time = args
expectedBytes = self.__dl_chunk_size
if rsize - offset < self.__dl_chunk_size:
expectedBytes = rsize - offset
if len(r.content) != expectedBytes:
return const.ERequestFailed
else:
with io.open(self.__current_file, 'r+b' if offset > 0 else 'wb') as f:
if offset > 0:
f.seek(offset)
f.write(r.content)
pos = f.tell()
pprgr(pos, rsize, start_time, existing = self.__existing_size)
if pos - offset == expectedBytes:
return const.ENoError
else:
return const.EFileWrite
def __down_aria2c(self, remotefile, localfile):
url = "{}{}".format(dpcsurl, "file")
# i think encoding in UTF-8 before escaping is presumably the best practice
# http://stackoverflow.com/a/913653/404271
pars = {
"method": "download",
"path": remotefile.encode('utf-8'),
"access_token": self.__access_token,
}
full_url = "{}?{}".format(url, ulp.urlencode(pars))
cmd = "aria2c --user-agent='{}' {} -o '{}' '{}'".format(const.UserAgent, self.__downloader_args, localfile, full_url)
self.pd("call: {}".format(cmd))
ret = subprocess.call(cmd, shell = True)
self.pd("aria2c exited with status: {}".format(ret))
# TODO: a finer map return codes to our internal errors
if ret != const.ENoError:
ret == const.ERequestFailed
return ret
# requirment: self.__remote_json is already gotten
def __downchunks(self, rfile, start):
rsize = self.__remote_json['size']
pars = {
'method' : 'download',
# Do they cause some side effects?
#'app_id': 250528,
#'check_blue' : '1',
#'ec' : '1',
'path' : rfile }
offset = start
self.__existing_size = offset
start_time = time.time()
while True:
nextoffset = offset + self.__dl_chunk_size
if nextoffset < rsize:
headers = { "Range" : "bytes={}-{}".format(
offset, nextoffset - 1) }
elif offset > 0:
headers = { "Range" : "bytes={}-".format(offset) }
elif rsize >= 1: # offset == 0
# Fix chunked + gzip response,
# seems we need to specify the Range for the first chunk as well:
# https://github.com/houtianze/bypy/pull/161
#headers = { "Range" : "bytes=0-".format(rsize - 1) }
headers = { "Range" : "bytes=0-{}".format(rsize - 1) }
else:
headers = {}
# this _may_ solve #163: { "error_code":31326, "error_msg":"anti hotlinking"}
if 'Range' in headers:
rangemagic = base64.standard_b64encode(headers['Range'][6:].encode('utf-8'))
self.pd("headers['Range'][6:]: {} {}".format(headers['Range'][6:], rangemagic))
#pars['ru'] = rangemagic
#headers['User-Agent'] = 'netdisk;5.2.7.2;PC;PC-Windows;6.2.9200;WindowsBaiduYunGuanJia'
subresult = self.__get(dpcsurl + 'file', pars,
self.__downchunks_act, (rfile, offset, rsize, start_time), headers = headers, cookies = self.__cookies)
if subresult != const.ENoError:
return subresult
if nextoffset < rsize:
offset += self.__dl_chunk_size
else:
break
# No exception above, then everything goes fine
result = const.ENoError
if self.__verify:
self.__current_file_size = getfilesize(self.__current_file)
result = self.__verify_current_file(self.__remote_json, False)
if result == const.ENoError:
self.pv("'{}' <== '{}' OK".format(self.__current_file, rfile))
else:
perr("'{}' <== '{}' FAILED".format(self.__current_file, rfile))
return result
def __downfile(self, remotefile, localfile):
# TODO: this is a quick patch
if not self.__shallinclude(localfile, remotefile, False):
# since we are not going to download it, there is no error
return const.ENoError
result = const.ENoError
rfile = remotefile
self.__remote_json = {}
self.pd("Downloading '{}' as '{}'".format(rfile, localfile))
self.__current_file = localfile
#if self.__verify or self.__resumedownload:
self.pd("Getting info of remote file '{}' for later verification".format(rfile))
result = self.__get_file_info(rfile)
if result != const.ENoError:
return result
offset = 0
self.pd("Checking if we already have the copy locally")
if os.path.isfile(localfile):
self.pd("Same-name local file '{}' exists, checking if contents match".format(localfile))
self.__current_file_size = getfilesize(self.__current_file)
if const.ENoError == self.__verify_current_file(self.__remote_json, False):
self.pd("Same local file '{}' already exists, skip downloading".format(localfile))
return const.ENoError
else:
if not self.shalloverwrite("Same-name locale file '{}' exists but is different, "
"do you want to overwrite it? [y/N]".format(localfile)):
pinfo("Same-name local file '{}' exists but is different, skip downloading".format(localfile))
return const.ENoError
if self.__resumedownload and \
self.__compare_size(self.__current_file_size, self.__remote_json) == 2:
if self.__resumedl_revertcount < 0:
if self.__current_file_size:
offset = self.__current_file_size
else:
# revert back at least self.__resumedl_revertcount download chunk(s), default: one
pieces = self.__current_file_size // self.__dl_chunk_size
if pieces > self.__resumedl_revertcount:
offset = (pieces - self.__resumedl_revertcount) * self.__dl_chunk_size
elif os.path.isdir(localfile):
if not self.shalloverwrite("Same-name directory '{}' exists, "
"do you want to remove it? [y/N]".format(localfile)):
pinfo("Same-name directory '{}' exists, skip downloading".format(localfile))
return const.ENoError
self.pv("Directory with the same name '{}' exists, removing ...".format(localfile))
result = removedir(localfile, self.verbose)
if result == const.ENoError:
self.pv("Removed")
else:
perr("Error removing the directory '{}'".format(localfile))
return result
ldir, file = os.path.split(localfile)
if ldir and not os.path.exists(ldir):
result = makedir(ldir, verbose = self.verbose)
if result != const.ENoError:
perr("Fail to make directory '{}'".format(ldir))
return result
if self.__downloader[:5] == const.DownloaderAria2:
return self.__down_aria2c(rfile, localfile)
else:
return self.__downchunks(rfile, offset)
def downfile(self, remotefile, localpath = ''):
''' Usage: downfile <remotefile> [localpath] - \
download a remote file.
remotefile - remote file at Baidu Yun (after app root directory at Baidu Yun)
localpath - local path.
if it ends with '/' or '\\', it specifies the local directory
if it specifies an existing directory, it is the local directory
if not specified, the local directory is the current directory '.'
otherwise, it specifies the local file name
To stream a file using downfile, you can use the 'mkfifo' trick with omxplayer etc.:
mkfifo /tmp/omx
bypy.py downfile <remotepath> /tmp/omx &
omxplayer /tmp/omx
'''
localfile = localpath
if not localpath:
localfile = os.path.basename(remotefile)
elif localpath[-1] == '\\' or \
localpath[-1] == '/' or \
os.path.isdir(localpath):
#localfile = os.path.join(localpath, os.path.basename(remotefile))
localfile = joinpath(localpath, os.path.basename(remotefile))
else:
localfile = localpath
pcsrpath = get_pcs_path(remotefile)
return self.__downfile(pcsrpath, localfile)
def __stream_act_actual(self, r, args):
pipe, csize = args
with io.open(pipe, 'wb') as f:
for chunk in r.iter_content(chunk_size = csize):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
f.flush()
# https://stackoverflow.com/questions/7127075/what-exactly-the-pythons-file-flush-is-doing
#os.fsync(f.fileno())
def __streaming_act(self, r, args):
return self.__stream_act_actual(r, args)
# NOT WORKING YET
def streaming(self, remotefile, localpipe, fmt = 'M3U8_480_360', chunk = 4 * const.OneM):
''' Usage: stream <remotefile> <localpipe> [format] [chunk] - \
stream a video / audio file converted to M3U format at cloud side, to a pipe.
remotefile - remote file at Baidu Yun (after app root directory at Baidu Yun)
localpipe - the local pipe file to write to
format - output video format (M3U8_320_240 | M3U8_480_224 | \
M3U8_480_360 | M3U8_640_480 | M3U8_854_480). Default: M3U8_480_360
chunk - chunk (initial buffering) size for streaming (default: 4M)
To stream a file, you can use the 'mkfifo' trick with omxplayer etc.:
mkfifo /tmp/omx
bypy.py downfile <remotepath> /tmp/omx &
omxplayer /tmp/omx
*** NOT WORKING YET ****
'''
pars = {
'method' : 'streaming',
'path' : get_pcs_path(remotefile),
'type' : fmt }
return self.__get(pcsurl + 'file', pars,
self.__streaming_act, (localpipe, chunk), stream = True)
def __walk_remote_dir_act(self, r, args):
dirjs, filejs = args
j = r.json()
if 'list' not in j:
self.pd("Key 'list' not found in the response of directory listing request:\n{}".format(j))
return const.ERequestFailed
paths = j['list']
for path in paths:
if path['isdir']:
dirjs.append(path)
else:
filejs.append(path)
return const.ENoError
def __walk_remote_dir_recur(self, remotepath, proceed, remoterootpath, args = None, skip_remote_only_dirs = False):
pars = {
'method' : 'list',
'path' : remotepath,
'by' : 'name',
'order' : 'asc' }
# Python parameters are by-reference and mutable, so they are 'out' by default
dirjs = []
filejs = []
result = self.__get(pcsurl + 'file', pars, self.__walk_remote_dir_act, (dirjs, filejs))
self.pd("Remote dirs: {}".format(dirjs))
self.pd("Remote files: {}".format(filejs))
if result == const.ENoError:
subresult = proceed(remotepath, dirjs, filejs, args)
if subresult != const.ENoError:
self.pd("Error: {} while proceeding remote path'{}'".format(
subresult, remotepath))
result = subresult # we continue
for dirj in dirjs:
crpath = dirj['path'] # crpath - current remote path
if skip_remote_only_dirs and remoterootpath != None and \
self.__local_dir_contents.get(posixpath.relpath(crpath, remoterootpath)) == None:
self.pd("Skipping remote-only sub-directory '{}'.".format(crpath))
continue
subresult = self.__walk_remote_dir_recur(crpath, proceed, remoterootpath, args, skip_remote_only_dirs)
if subresult != const.ENoError:
self.pd("Error: {} while sub-walking remote dirs'{}'".format(
subresult, dirjs))
result = subresult
return result
def __walk_remote_dir(self, remotepath, proceed, args = None, skip_remote_only_dirs = False):
return self.__walk_remote_dir_recur(remotepath, proceed, remotepath, args, skip_remote_only_dirs)
def __prepare_local_dir(self, localdir):
result = const.ENoError
if os.path.isfile(localdir):
result = removefile(localdir, self.verbose)
if result == const.ENoError:
if localdir and not os.path.exists(localdir):
result = makedir(localdir, verbose = self.verbose)
return result
def __proceed_downdir(self, remotepath, dirjs, filejs, args):
result = const.ENoError
rootrpath, localpath = args
rlen = len(remotepath) + 1 # '+ 1' for the trailing '/', it bites.
rootlen = len(rootrpath) + 1 # ditto
result = self.__prepare_local_dir(localpath)
if result != const.ENoError:
perr("Fail to create prepare local directory '{}' for downloading, ABORT".format(localpath))
return result
for dirj in dirjs:
reldir = dirj['path'][rlen:]
#ldir = os.path.join(localpath, reldir)
ldir = joinpath(localpath, reldir)
result = self.__prepare_local_dir(ldir)
if result != const.ENoError:
perr("Fail to create prepare local directory '{}' for downloading, ABORT".format(ldir))
return result
for filej in filejs:
rfile = filej['path']
relfile = rfile[rootlen:]
#lfile = os.path.join(localpath, relfile)
lfile = joinpath(localpath, relfile)
self.__downfile(rfile, lfile)
return result
def __downdir(self, rpath, lpath):
return self.__walk_remote_dir(rpath, self.__proceed_downdir, (rpath, lpath))
def downdir(self, remotepath = None, localpath = None):
''' Usage: downdir [remotedir] [localdir] - \
download a remote directory (recursively)
remotedir - remote directory at Baidu Yun (after app root directory), if not specified, it is set to the root directory at Baidu Yun
localdir - local directory. if not specified, it is set to the current directory
'''
rpath = get_pcs_path(remotepath)
lpath = localpath
if not lpath:
lpath = '' # empty string does it, no need '.'
lpath = lpath.rstrip('/\\ ')
return self.__downdir(rpath, lpath)
def download(self, remotepath = '/', localpath = ''):
''' Usage: download [remotepath] [localpath] - \
download a remote directory (recursively) / file
remotepath - remote path at Baidu Yun (after app root directory), if not specified, it is set to the root directory at Baidu Yun
localpath - local path. if not specified, it is set to the current directory
'''
subr = self.get_file_info(remotepath)
if const.ENoError == subr:
if 'isdir' in self.__remote_json:
if self.__remote_json['isdir']:
return self.downdir(remotepath, localpath)
else:
return self.downfile(remotepath, localpath)
else:
perr("Malformed path info JSON '{}' returned".format(self.__remote_json))
return const.EFatal
elif const.EFileNotFound == subr:
perr("Remote path '{}' does not exist".format(remotepath))
return subr
else:
perr("Error {} while getting info for remote path '{}'".format(subr, remotepath))
return subr
def __mkdir_act(self, r, args):
if self.verbose:
j = r.json()
pr("path, ctime, mtime, fs_id")
pr("{path}, {ctime}, {mtime}, {fs_id}".format(**j))
return const.ENoError
def __mkdir(self, rpath, **kwargs):
# TODO: this is a quick patch
# the code still works because Baidu Yun doesn't require
# parent directory to exist remotely to upload / create a file
if not self.__shallinclude('.', rpath, True):
return const.ENoError
self.pd("Making remote directory '{}'".format(rpath))
pars = {
'method' : 'mkdir',
'path' : rpath }
return self.__post(pcsurl + 'file', pars, self.__mkdir_act, **kwargs)
def mkdir(self, remotepath):
''' Usage: mkdir <remotedir> - \
create a directory at Baidu Yun
remotedir - the remote directory
'''
rpath = get_pcs_path(remotepath)
return self.__mkdir(rpath)
def __move_act(self, r, args):
j = r.json()
list = j['extra']['list']
fromp = list[0]['from']
to = list[0]['to']
self.pd("Remote move: '{}' =mm-> '{}' OK".format(fromp, to))
# aliases
def mv(self, fromp, to):
return self.move(fromp, to)
def rename(self, fromp, to):
return self.move(fromp, to)
def ren(self, fromp, to):
return self.move(fromp, to)
def move(self, fromp, to):
''' Usage: move/mv/rename/ren <from> <to> - \
move a file / dir remotely at Baidu Yun
from - source path (file / dir)
to - destination path (file / dir)
'''
frompp = get_pcs_path(fromp)
top = get_pcs_path(to)
pars = {
'method' : 'move',
'from' : frompp,
'to' : top }
self.pd("Remote moving: '{}' =mm=> '{}'".format(fromp, to))
return self.__post(pcsurl + 'file', pars, self.__move_act)
def __copy_act(self, r, args):
j = r.json()
for list in j['extra']['list']:
fromp = list['from']
to = list['to']
self.pd("Remote copy: '{}' =cc=> '{}' OK".format(fromp, to))
return const.ENoError
# alias
def cp(self, fromp, to):
return self.copy(fromp, to)
def copy(self, fromp, to):
''' Usage: copy/cp <from> <to> - \
copy a file / dir remotely at Baidu Yun
from - source path (file / dir)
to - destination path (file / dir)
'''
frompp = get_pcs_path(fromp)
top = get_pcs_path(to)
pars = {
'method' : 'copy',
'from' : frompp,
'to' : top }
self.pd("Remote copying '{}' =cc=> '{}'".format(frompp, top))
return self.__post(pcsurl + 'file', pars, self.__copy_act)
def __delete_act(self, r, args):
rid = r.json()['request_id']
if rid:
pr("Deletion request '{}' OK".format(rid))
pr("Usage 'list' command to confirm")
return const.ENoError
else:
perr("Deletion failed")
return const.EFailToDeleteFile
def __delete(self, rpath):
pars = {
'method' : 'delete',
'path' : rpath }
self.pd("Remote deleting: '{}'".format(rpath))
return self.__post(pcsurl + 'file', pars, self.__delete_act)
def __delete_children_act(self, r, args):
result = const.ENoError
j = r.json()
for f in j['list']:
# we continue even if some upload failed, but keep the last error code
thisresult = self.__delete(f['path'])
if thisresult != const.ENoError:
result = thisresult
return result
def __delete_children(self, rpath):
pars = {
'method' : 'list',
'path' : rpath}
return self.__get(pcsurl + 'file', pars, self.__delete_children_act, None)
# aliases
def remove(self, remotepath):
return self.delete(remotepath)
def rm(self, remotepath):
return self.delete(remotepath)
def delete(self, remotepath):
''' Usage: delete/remove/rm <remotepath> - \
delete a file / dir remotely at Baidu Yun
remotepath - destination path (file / dir)
'''
rpath = get_pcs_path(remotepath)
#if is_pcs_root_path(rpath):
# return self.__delete_children(rpath)
#else:
return self.__delete(rpath)
def __search_act(self, r, args):
print_pcs_list(r.json())
return const.ENoError
def search(self, keyword, remotepath = None, recursive = True):
''' Usage: search <keyword> [remotepath] [recursive] - \
search for a file using keyword at Baidu Yun
keyword - the keyword to search
remotepath - remote path at Baidu Yun, if not specified, it's app's root directory
resursive - search recursively or not. default is true
'''
rpath = get_pcs_path(remotepath)
pars = {
'method' : 'search',
'path' : rpath,
'wd' : keyword,
're' : '1' if str2bool(recursive) else '0'}
self.pd("Searching: '{}'".format(rpath))
return self.__get(pcsurl + 'file', pars, self.__search_act)
def __listrecycle_act(self, r, args):
print_pcs_list(r.json())
return const.ENoError
def listrecycle(self, start = 0, limit = 1000):
''' Usage: listrecycle [start] [limit] - \
list the recycle contents
start - starting point, default: 0
limit - maximum number of items to display. default: 1000
'''
pars = {
'method' : 'listrecycle',
'start' : str2int(start),
'limit' : str2int(limit) }
self.pd("Listing recycle '{}'")
return self.__get(pcsurl + 'file', pars, self.__listrecycle_act)
def __restore_act(self, r, args):
path = args
pr("'{}' found and restored".format(path))
return const.ENoError
def __restore_search_act(self, r, args):
path = args
flist = r.json()['list']
fsid = None
for f in flist:
if os.path.normpath(f['path'].lower()) == os.path.normpath(path.lower()):
fsid = f['fs_id']
self.pd("fs_id for restoring '{}' found".format(fsid))
break
if fsid:
pars = {
'method' : 'restore',
'fs_id' : fsid }
return self.__post(pcsurl + 'file', pars, self.__restore_act, path)
else:
perr("'{}' not found in the recycle bin".format(path))
def restore(self, remotepath):
''' Usage: restore <remotepath> - \
restore a file from the recycle bin
remotepath - the remote path to restore
'''
rpath = get_pcs_path(remotepath)
# by default, only 1000 items, more than that sounds a bit crazy
pars = {
'method' : 'listrecycle' }
self.pd("Searching for fs_id to restore")
return self.__get(pcsurl + 'file', pars, self.__restore_search_act, rpath)
def __proceed_local_gather(self, dirlen, walk):
#names.sort()
(dirpath, dirnames, filenames) = walk
files = []
for name in filenames:
#fullname = os.path.join(dirpath, name)
fullname = joinpath(dirpath, name)
# ignore broken symbolic links
if not os.path.exists(fullname):
self.pd("Local path '{}' does not exist (broken symbolic link?)".format(fullname))
continue
files.append((name, getfilesize(fullname), md5(fullname)))
reldir = dirpath[dirlen:].replace('\\', '/')
place = self.__local_dir_contents.get(reldir)
for dir in dirnames:
place.add(dir, PathDictTree('D'))
for file in files:
place.add(file[0], PathDictTree('F', size = file[1], md5 = file[2]))
return const.ENoError
def __gather_local_dir(self, dir):
self.__local_dir_contents = PathDictTree()
#for walk in os.walk(dir, followlinks=self.__followlink):
for walk in self.__walk_normal_file(dir):
self.__proceed_local_gather(len(dir), walk)
self.pd(self.__local_dir_contents)
def __proceed_remote_gather(self, remotepath, dirjs, filejs, args = None):
# NOTE: the '+ 1' is due to the trailing slash '/'
# be careful about the trailing '/', it bit me once, bitterly
rootrdir = args
rootlen = len(rootrdir)
dlen = len(remotepath) + 1
for d in dirjs:
self.__remote_dir_contents.get(remotepath[rootlen:]).add(
d['path'][dlen:], PathDictTree('D', size = d['size'], md5 = d['md5']))
for f in filejs:
self.__remote_dir_contents.get(remotepath[rootlen:]).add(
f['path'][dlen:], PathDictTree('F', size = f['size'], md5 = f['md5']))
return const.ENoError
def __gather_remote_dir(self, rdir, skip_remote_only_dirs = False):
self.__remote_dir_contents = PathDictTree()
self.__walk_remote_dir(rdir, self.__proceed_remote_gather, rdir, skip_remote_only_dirs)
self.pd("---- Remote Dir Contents ---")
self.pd(self.__remote_dir_contents)
def __compare(self, remotedir = None, localdir = None, skip_remote_only_dirs = False):
if not localdir:
localdir = '.'
self.pv("Gathering local directory ...")
self.__gather_local_dir(localdir)
self.pv("Done")
self.pv("Gathering remote directory ...")
self.__gather_remote_dir(remotedir, skip_remote_only_dirs)
self.pv("Done")
self.pv("Comparing ...")
# list merge, where Python shines
commonsame = []
commondiff = []
localonly = []
remoteonly = []
# http://stackoverflow.com/questions/1319338/combining-two-lists-and-removing-duplicates-without-removing-duplicates-in-orig
lps = self.__local_dir_contents.allpath()
rps = self.__remote_dir_contents.allpath()
dps = set(rps) - set(lps)
allpath = lps + list(dps)
for p in allpath:
local = self.__local_dir_contents.get(p)
remote = self.__remote_dir_contents.get(p)
if local is None: # must be in the remote dir, since p is from allpath
remoteonly.append((remote.type, p))
elif remote is None:
localonly.append((local.type, p))
else: # all here
same = False
if local.type == 'D' and remote.type == 'D':
type = 'D'
same = True
elif local.type == 'F' and remote.type == 'F':
type = 'F'
if local.extra['size'] == remote.extra['size'] and \
local.extra['md5'] == remote.extra['md5']:
same = True
else:
same = False
else:
type = local.type + remote.type
same = False
if same:
commonsame.append((type, p))
else:
commondiff.append((type, p))
self.pv("Done")
return commonsame, commondiff, localonly, remoteonly
def compare(self, remotedir = None, localdir = None, skip_remote_only_dirs = False):
''' Usage: compare [remotedir] [localdir] - \
compare the remote directory with the local directory
remotedir - the remote directory at Baidu Yun (after app's directory). \
if not specified, it defaults to the root directory.
localdir - the local directory, if not specified, it defaults to the current directory.
skip_remote_only_dirs - skip remote-only sub-directories (faster if the remote \
directory is much larger than the local one). it defaults to False.
'''
same, diff, local, remote = self.__compare(get_pcs_path(remotedir), localdir, str2bool(skip_remote_only_dirs))
pr("==== Same files ===")
for c in same:
pr("{} - {}".format(c[0], c[1]))
pr("==== Different files ===")
for d in diff:
pr("{} - {}".format(d[0], d[1]))
pr("==== Local only ====")
for l in local:
pr("{} - {}".format(l[0], l[1]))
pr("==== Remote only ====")
for r in remote:
pr("{} - {}".format(r[0], r[1]))
pr("\nStatistics:")
pr("--------------------------------")
pr("Same: {}".format(len(same)));
pr("Different: {}".format(len(diff)));
pr("Local only: {}".format(len(local)));
pr("Remote only: {}".format(len(remote)));
self.result['same'] = same
self.result['diff'] = diff
self.result['local'] = local
self.result['remote'] = remote
return const.ENoError
def syncdown(self, remotedir = '', localdir = '', deletelocal = False):
''' Usage: syncdown [remotedir] [localdir] [deletelocal] - \
sync down from the remote directory to the local directory
remotedir - the remote directory at Baidu Yun (after app's directory) to sync from. \
if not specified, it defaults to the root directory
localdir - the local directory to sync to if not specified, it defaults to the current directory.
deletelocal - delete local files that are not inside Baidu Yun directory, default is False
'''
result = const.ENoError
rpath = get_pcs_path(remotedir)
same, diff, local, remote = self.__compare(rpath, localdir)
# clear the way
for d in diff:
t = d[0]
p = d[1]
#lcpath = os.path.join(localdir, p) # local complete path
lcpath = joinpath(localdir, p) # local complete path
rcpath = rpath + '/' + p # remote complete path
if t == 'DF':
result = removedir(lcpath, self.verbose)
subresult = self.__downfile(rcpath, lcpath)
if subresult != const.ENoError:
result = subresult
elif t == 'FD':
result = removefile(lcpath, self.verbose)
subresult = makedir(lcpath, verbose = self.verbose)
if subresult != const.ENoError:
result = subresult
else: # " t == 'F' " must be true
result = self.__downfile(rcpath, lcpath)
for r in remote:
t = r[0]
p = r[1]
#lcpath = os.path.join(localdir, p) # local complete path
lcpath = joinpath(localdir, p) # local complete path
rcpath = rpath + '/' + p # remote complete path
if t == 'F':
subresult = self.__downfile(rcpath, lcpath)
if subresult != const.ENoError:
result = subresult
else: # " t == 'D' " must be true
subresult = makedir(lcpath, verbose = self.verbose)
if subresult != const.ENoError:
result = subresult
if str2bool(deletelocal):
for l in local:
# use os.path.isfile()/isdir() instead of l[0], because we need to check file/dir existence.
# as we may have removed the parent dir previously during the iteration
#p = os.path.join(localdir, l[1])
p = joinpath(localdir, l[1])
if os.path.isfile(p):
subresult = removefile(p, self.verbose)
if subresult != const.ENoError:
result = subresult
elif os.path.isdir(p):
subresult = removedir(p, self.verbose)
if subresult != const.ENoError:
result = subresult
return result
def syncup(self, localdir = '', remotedir = '', deleteremote = False):
''' Usage: syncup [localdir] [remotedir] [deleteremote] - \
sync up from the local directory to the remote directory
localdir - the local directory to sync from if not specified, it defaults to the current directory.
remotedir - the remote directory at Baidu Yun (after app's directory) to sync to. \
if not specified, it defaults to the root directory
deleteremote - delete remote files that are not inside the local directory, default is False
'''
result = const.ENoError
rpath = get_pcs_path(remotedir)
#rpartialdir = remotedir.rstrip('/ ')
same, diff, local, remote = self.__compare(rpath, localdir, True)
# clear the way
for d in diff:
t = d[0] # type
p = d[1] # path
#lcpath = os.path.join(localdir, p) # local complete path
lcpath = joinpath(localdir, p) # local complete path
rcpath = rpath + '/' + p # remote complete path
if self.shalloverwrite("Do you want to overwrite '{}' at Baidu Yun? [y/N]".format(p)):
# this path is before get_pcs_path() since delete() expects so.
#result = self.delete(rpartialdir + '/' + p)
result = self.__delete(rcpath)
# self.pd("diff type: {}".format(t))
# self.__isrev = True
# if t != 'F':
# result = self.move(remotedir + '/' + p, remotedir + '/' + p + '.moved_by_bypy.' + time.strftime("%Y%m%d%H%M%S"))
# self.__isrev = False
if t == 'F' or t == 'FD':
subresult = self.__upload_file(lcpath, rcpath)
if subresult != const.ENoError:
result = subresult
else: # " t == 'DF' " must be true
subresult = self.__mkdir(rcpath)
if subresult != const.ENoError:
result = subresult
else:
pinfo("Uploading '{}' skipped".format(lcpath))
for l in local:
t = l[0]
p = l[1]
#lcpath = os.path.join(localdir, p) # local complete path
lcpath = joinpath(localdir, p) # local complete path
rcpath = rpath + '/' + p # remote complete path
self.pd("local type: {}".format(t))
self.__isrev = False
if t == 'F':
subresult = self.__upload_file(lcpath, rcpath)
if subresult != const.ENoError:
result = subresult
else: # " t == 'D' " must be true
subresult = self.__mkdir(rcpath)
if subresult != const.ENoError:
result = subresult
if str2bool(deleteremote):
# i think the list is built top-down, so directories appearing later are either
# children or another set of directories
pp = '\\' # previous path, setting to '\\' make sure it won't be found in the first step
for r in remote:
#p = rpartialdir + '/' + r[1]
p = rpath + '/' + r[1]
if 0 != p.find(pp): # another path
#subresult = self.delete(p)
subresult = self.__delete(p)
if subresult != const.ENoError:
result = subresult
pp = p
return result
def dumpcache(self):
''' Usage: dumpcache - display file hash cache'''
if cached.cacheloaded:
#pprint.pprint(cached.cache)
MyPrettyPrinter().pprint(cached.cache)
return const.ENoError
else:
perr("Cache not loaded.")
return const.ECacheNotLoaded
def cleancache(self):
''' Usage: cleancache - remove invalid entries from hash cache file'''
if os.path.exists(self.__hashcachepath):
try:
# backup first
backup = self.__hashcachepath + '.lastclean'
shutil.copy(self.__hashcachepath, backup)
self.pd("Hash Cache file '{}' backed up as '{}".format(
self.__hashcachepath, backup))
cached.cleancache()
return const.ENoError
except Exception as ex:
perr(formatex(ex))
return const.EException
else:
return const.EFileNotFound
def __cdl_act(self, r, args):
try:
pr(pprint.pformat(r.json()))
return const.ENoError
except:
pr(pprint.pformat({ 'text': rb(r.text) }))
return const.IETaskNotFound
def __prepare_cdl_add(self, source_url, rpath, timeout):
pr("Adding cloud download task:")
pr("{} =cdl=> {}".format(source_url, rpath))
pars = {
'method': 'add_task',
'source_url': source_url,
'save_path': rpath,
'timeout': 3600 }
return pars
def __cdl_add(self, source_url, rpath, timeout):
pars = self.__prepare_cdl_add(source_url, rpath, timeout)
return self.__post(pcsurl + 'services/cloud_dl', pars, self.__cdl_act)
def __get_cdl_dest(self, source_url, save_path):
rpath = get_pcs_path(save_path)
# download to /apps/bypy root
if rpath == const.AppPcsPath \
or (const.ENoError == self.__get_file_info(rpath) \
and self.__remote_json['isdir']):
filename = source_url.split('/')[-1]
rpath += '/' + filename
return rpath
def cdl_add(self, source_url, save_path = '/', timeout = 3600):
''' Usage: cdl_add <source_url> [save_path] [timeout] - add an offline (cloud) download task
source_url - the URL to download file from.
save_path - path on PCS to save file to. default is to save to root directory '/'.
timeout - timeout in seconds. default is 3600 seconds.
'''
rpath = self.__get_cdl_dest(source_url, save_path)
return self.__cdl_add(source_url, rpath, timeout)
def __get_cdl_query_pars(self, task_ids, op_type):
pars = {
'method': 'query_task',
'task_ids': task_ids,
'op_type': op_type}
return pars
def __cdl_query(self, task_ids, op_type):
pars = self.__get_cdl_query_pars(task_ids, op_type)
return self.__post(pcsurl + 'services/cloud_dl', pars, self.__cdl_act)
def cdl_query(self, task_ids, op_type = 1):
''' Usage: cdl_query <task_ids> - query existing offline (cloud) download tasks
task_ids - task ids seperated by comma (,).
op_type - 0 for task info; 1 for progress info. default is 1
'''
return self.__cdl_query(task_ids, op_type)
def __cdl_mon_act(self, r, args):
try:
task_id, start_time, done = args
j = r.json()
ti = j['task_info'][str(task_id)]
if ('file_size' not in ti) or ('finished_size' not in ti):
done[0] = True
pr(j)
else:
total = int(ti['file_size'])
finish = int(ti['finished_size'])
done[0] = (total != 0 and (total == finish))
pprgr(finish, total, start_time)
if done[0]:
pr(pprint.pformat(j))
return const.ENoError
except Exception as ex:
perr("Exception while monitoring offline (cloud) download task:\n{}".format(formatex(ex)))
perr("Baidu returned:\n{}".format(rb(r.text)))
return const.EInvalidJson
def __cdl_addmon_act(self, r, args):
try:
args[0] = r.json()
pr(pprint.pformat(args[0]))
return const.ENoError
except Exception as ex:
perr("Exception while adding offline (cloud) download task:\n{}".format(formatex(ex)))
perr("Baidu returned:\n{}".format(rb(r.text)))
return const.EInvalidJson
def __cdl_sighandler(self, signum, frame):
pr("Cancelling offline (cloud) download task: {}".format(self.__cdl_task_id))
result = self.__cdl_cancel(self.__cdl_task_id)
pr("Result: {}".format(result))
quit(const.EAbort)
def __cdl_addmon(self, source_url, rpath, timeout = 3600):
pars = self.__prepare_cdl_add(source_url, rpath, timeout)
jc = [{}] # out param
result = self.__post(pcsurl + 'services/cloud_dl',
pars, self.__cdl_addmon_act, jc)
if result == const.ENoError:
if not 'task_id' in jc[0]:
return const.EInvalidJson
task_id = jc[0]['task_id']
pars = self.__get_cdl_query_pars(task_id, 1)
start_time = time.time()
done = [ False ] # out param
# cancel task on Ctrl-C
pr("Press Ctrl-C to cancel the download task")
self.__cdl_task_id = task_id
setsighandler(signal.SIGINT, self.__cdl_sighandler)
setsighandler(signal.SIGHUP, self.__cdl_sighandler)
try:
while True:
result = self.__post(
pcsurl + 'services/cloud_dl', pars, self.__cdl_mon_act,
(task_id, start_time, done))
if result == const.ENoError:
if done[0] == True:
return const.ENoError
else:
return result
time.sleep(5)
except KeyboardInterrupt:
pr("Canceling offline (cloud) downloa task: {}".format(task_id))
self.__cdl_cancel(task_id)
return const.EAbort
else:
return result
def cdl_addmon(self, source_url, save_path = '/', timeout = 3600):
''' Usage: cdl_addmon <source_url> [save_path] [timeout] - add an offline (cloud) download task and monitor the download progress
source_url - the URL to download file from.
save_path - path on PCS to save file to. default is to save to root directory '/'.
timeout - timeout in seconds. default is 3600 seconds.
'''
rpath = self.__get_cdl_dest(source_url, save_path)
return self.__cdl_addmon(source_url, rpath, timeout)
def __cdl_list(self):
pars = {
'method': 'list_task' }
return self.__post(pcsurl + 'services/cloud_dl', pars, self.__cdl_act)
def cdl_list(self):
''' Usage: cdl_list - list offline (cloud) download tasks
'''
return self.__cdl_list()
def __cdl_cancel(self, task_id):
pars = {
'method': 'cancel_task',
'task_id': task_id }
return self.__post(pcsurl + 'services/cloud_dl', pars, self.__cdl_act)
def cdl_cancel(self, task_id):
''' Usage: cdl_cancel <task_id> - cancel an offline (cloud) download task
task_id - id of the task to be canceled.
'''
return self.__cdl_cancel(task_id)
def __get_accept_cmd(self, rpath):
md5str = self.__current_file_md5
slicemd5str = self.__current_file_slice_md5
crcstr = hex(self.__current_file_crc32)
remotepath = rpath[const.AppPcsPathLen:]
if len(remotepath) == 0:
remotepath = 'PATH_NAME_MISSING'
cmd = "bypy accept {} {} {} {} {}".format(
remotepath, self.__current_file_size, md5str, slicemd5str, crcstr)
return cmd
def __share_local_file(self, lpath, rpath, fast):
filesize = getfilesize(lpath)
if filesize < const.MinRapidUploadFileSize:
perr("File size ({}) of '{}' is too small (must be greater or equal than {}) to be shared".format(
human_size(filesize), lpath, human_size(const.MinRapidUploadFileSize)))
return const.EParameter
if fast:
self.__get_hashes_for_rapidupload(lpath, setlocalfile = True)
pr(self.__get_accept_cmd(rpath))
return const.ENoError
ulrpath = const.RemoteTempDir + '/' + posixpath.basename(lpath)
result = self.__upload_file(lpath, ulrpath)
if result != const.ENoError:
perr("Unable to share as uploading failed")
return result
if not self.__rapiduploaded:
i = 0
while i < const.ShareRapidUploadRetries:
i += 1
result = self.__rapidupload_file(lpath, ulrpath, setlocalfile = True)
if result == const.ENoError: # or result == IEMD5NotFound: # retrying if MD5 not found _may_ make the file available?
break;
else:
self.pd("Retrying #{} for sharing '{}'".format(i, lpath))
time.sleep(1)
if result == const.ENoError:
pr(self.__get_accept_cmd(rpath))
return const.ENoError
elif result == const.IEMD5NotFound:
pr("# Sharing (RapidUpload) not possible for '{}', error: {}".format(lpath, result))
return result
else:
pr("# Error sharing '{}', error: {}".format(lpath, result))
return result
def __share_local_dir(self, lpath, rpath, fast):
result = const.ENoError
for walk in self.__walk_normal_file(lpath):
(dirpath, dirnames, filenames) = walk
for filename in filenames:
rpart = os.path.relpath(dirpath, lpath)
if rpart == '.':
rpart = ''
subr = self.__share_local_file(
joinpath(dirpath, filename),
posixpath.join(rpath, rpart, filename),
fast)
if subr != const.ENoError:
result = subr
return result
# assuming the caller asks once only
def __ok_to_use_remote_temp_dir(self):
if const.SettingKey_OverwriteRemoteTempDir in self.__setting and \
self.__setting[const.SettingKey_OverwriteRemoteTempDir]:
return True
# need to check existence of the remote temp dir
fir = self.__get_file_info(const.RemoteTempDir)
if fir == const.ENoError: # path exists
msg = '''
In order to use this functionality, we need to use a temporary directory '{}' \
at Baidu Cloud Storage. However, this remote path exists already.
Is it OK to empty this directory? (Unless coincidentally you uploaded files there, \
it's probably safe to allow this)
y/N/a (yes/NO/always)?
'''.format(const.RemoteTempDir).strip()
ans = ask(msg).lower()
if len(ans) >= 0:
a = ans[0]
if a == 'y':
return True
elif a == 'a':
# persist
self.__setting[const.SettingKey_OverwriteRemoteTempDir] = True
self.savesetting()
return True
else:
return False
else:
return False
elif fir == const.EFileNotFound:
return True
elif fir == const.ERequestFailed:
perr("Request to get info of the remote temp dir '{}' failed, can't continue.".format(const.RemoteTempDir))
return False
else:
assert 0 == "Future work handling (more return code) needed"
return False
def __share_local(self, lpath, rpath, fast):
if not os.path.exists(lpath):
perr("Local path '{}' does not exist.".format(lpath))
return const.EParameter
if not self.__ok_to_use_remote_temp_dir():
perr("Can't continue unless you allow the program to use the remote temporary directory '{}'".format(const.RemoteTempDir))
return const.EUserRejected
if fast:
pr("# fast (unverified) sharing, no network I/O needed (for local sharing), but the other person may not be able to accept some of your files")
if os.path.isfile(lpath):
# keep the same file name
lname = os.path.basename(lpath)
rfile = joinpath(rpath, lname, '/')
return self.__share_local_file(lpath, rfile, fast)
elif os.path.isdir(lpath):
return self.__share_local_dir(lpath, rpath, fast)
else:
perr("Local path '{}' is not a file or directory.".format(lpath))
return const.EParameter
def __share_remote_file(self, tmpdir, rpath, srpath, fast):
rdir, rfile = posixpath.split(rpath)
lpath = joinpath(tmpdir, rfile)
subr = self.__downfile(rpath, lpath)
if subr != const.ENoError:
perr("Fatal: Error {} while downloading remote file '{}'".format(subr, rpath))
return subr
return self.__share_local_file(lpath, srpath, fast)
def __proceed_share_remote(self, rpath, dirjs, filejs, args):
remoterootlen, tmpdir, srpath, fast = args
result = const.ENoError
for filej in filejs:
rfile = filej['path']
subr = self.__share_remote_file(tmpdir, rfile, joinpath(srpath, rfile[remoterootlen:], sep = '/'), fast)
if subr != const.ENoError:
result = subr
return result
def __share_remote_dir(self, tmpdir, rpath, srpath, fast):
return self.__walk_remote_dir(rpath, self.__proceed_share_remote, (len(rpath), tmpdir, srpath, fast))
def __share_remote(self, tmpdir, rpath, srpath, fast): # srpath - share remote path (full path)
subr = self.__get_file_info(rpath)
if const.ENoError == subr:
if 'isdir' in self.__remote_json:
if self.__remote_json['isdir']:
return self.__share_remote_dir(tmpdir, rpath, srpath, fast)
else:
return self.__share_remote_file(tmpdir, rpath, srpath, fast)
else:
perr("Malformed path info JSON '{}' returned".format(self.__remote_json))
return const.EFatal
elif const.EFileNotFound == subr:
perr("Remote path '{}' does not exist".format(rpath))
return subr
else:
perr("Error {} while getting info for remote path '{}'".format(subr, rpath))
return subr
def share(self, path = '.', sharepath = '/', islocal = True, fast = False):
islocal = str2bool(islocal)
fast = str2bool(fast)
if islocal:
lpath = path
rpath = get_pcs_path(sharepath)
result = self.__share_local(lpath, rpath, fast)
if not fast:
# not critical
self.__delete(const.RemoteTempDir)
return result
else:
rpath = get_pcs_path(path)
srpath = get_pcs_path(sharepath)
tmpdir = tempfile.mkdtemp(prefix = 'bypy_')
self.pd("Using local temporary directory '{}' for sharing".format(tmpdir))
try:
result = self.__share_remote(tmpdir, rpath, srpath, fast)
except Exception as ex:
result = const.EFatal
perr("Exception while sharing remote path '{}'.\n{}".format(
rpath, formatex(ex)))
finally:
removedir(tmpdir)
return result
def __accept(self, rpath, size, md5str, slicemd5str, crcstr):
# we have no file to verify against
verify = self.__verify
self.__verify = False
result = self.__rapidupload_file_post(rpath, size, md5str, slicemd5str, crcstr)
self.__verify = verify
if result == const.ENoError:
self.pv("Accepted: {}".format(rpath))
else:
perr("Unable to accept: {}, Error: {}".format(rpath, result))
return result
def accept(self, remotepath, size, md5str, slicemd5str, crcstr):
rpath = get_pcs_path(remotepath)
return self.__accept(rpath, size, md5str, slicemd5str, crcstr)
def sighandler(signum, frame):
pr("Signal {} received, Abort".format(signum))
quit(const.EAbort)
# http://www.gnu.org/software/libc/manual/html_node/Basic-Signal-Handling.html
def setsighandler(signum, handler):
oldhandler = signal.signal(signum, handler)
if oldhandler == signal.SIG_IGN:
signal.signal(signum, signal.SIG_IGN)
return oldhandler
def setuphandlers():
if iswindows():
# setsighandler(signal.CTRL_C_EVENT, sighandler)
# setsighandler(signal.CTRL_BREAK_EVENT, sighandler)
# bug, see: http://bugs.python.org/issue9524
pass
else:
setsighandler(signal.SIGBUS, sighandler)
setsighandler(signal.SIGHUP, sighandler)
# https://stackoverflow.com/questions/108183/how-to-prevent-sigpipes-or-handle-them-properly
setsighandler(signal.SIGPIPE, signal.SIG_IGN)
setsighandler(signal.SIGQUIT, sighandler)
setsighandler(signal.SIGSYS, sighandler)
setsighandler(signal.SIGABRT, sighandler)
setsighandler(signal.SIGFPE, sighandler)
setsighandler(signal.SIGILL, sighandler)
setsighandler(signal.SIGINT, sighandler)
setsighandler(signal.SIGSEGV, sighandler)
setsighandler(signal.SIGTERM, sighandler)
def getparser():
#name = os.path.basename(sys.argv[0])
version = "v%s" % const.__version__
version_message = '%%(prog)s %s' % (version)
desc = "{} - {}".format(version_message, const.__desc__)
# setup argument parser
epilog = "Commands:\n"
summary = []
for k, v in ByPy.__dict__.items():
if callable(v):
if v.__doc__:
help = v.__doc__.strip()
pos = help.find(const.HelpMarker)
if pos != -1:
pos_body = pos + len(const.HelpMarker)
helpbody = help[pos_body:]
helpline = helpbody.split('\n')[0].strip() + '\n'
if helpline.find('help') == 0:
summary.insert(0, helpline)
else:
summary.append(helpline)
#commands.append(v.__name__) # append command name
remaining = summary[1:]
remaining.sort()
summary = [summary[0]] + remaining
epilog += ''.join(summary)
parser = ArgumentParser(
prog = const.__title__,
description=desc,
formatter_class=RawDescriptionHelpFormatter, epilog=epilog)
# help, version, program information etc
parser.add_argument('-V', '--version', action='version', version=version_message)
# debug, logging
parser.add_argument("-d", "--debug",
dest="debug", action="count", default=0,
help="set debugging level (-dd to increase debugging level, -ddd to enable HTPP traffic debugging as well (very talkative)) [default: %(default)s]")
parser.add_argument("-v", "--verbose", dest="verbose", default=0, action="count", help="set verbosity level [default: %(default)s]")
# program tunning, configration (those will be passed to class ByPy)
parser.add_argument("-r", "--retry",
dest="retry", default=5, type=int,
help="number of retry attempts on network error [default: %(default)i times]")
parser.add_argument("-q", "--quit-when-fail",
dest="quit", default=False, type=str2bool,
help="quit when maximum number of retry failed [default: %(default)s]")
parser.add_argument("-t", "--timeout",
dest="timeout", default=60.0, type=float,
help="network timeout in seconds [default: %(default)s]")
parser.add_argument("-s", "--slice",
dest="slice", default=const.DefaultSliceSize,
help="size of file upload slice (can use '1024', '2k', '3MB', etc) [default: {} MB]".format(const.DefaultSliceInMB))
parser.add_argument("--chunk",
dest="chunk", default=const.DefaultDlChunkSize,
help="size of file download chunk (can use '1024', '2k', '3MB', etc) [default: {} MB]".format(const.DefaultDlChunkSize // const.OneM))
parser.add_argument("-e", "--verify",
dest="verify", action="store_true",
help="verify upload / download [default : %(default)s]")
parser.add_argument("-f", "--force-hash",
dest="forcehash", action="store_true",
help="force file MD5 / CRC32 calculation instead of using cached value")
parser.add_argument("--resume-download",
dest="resumedl", default=True, type=str2bool,
help="resume instead of restarting when downloading if local file already exists [default: %(default)s]")
parser.add_argument("--include-regex",
dest="incregex", default='',
help="regular expression of files to include. if not specified (default), everything is included. for download, the regex applies to the remote files; for upload, the regex applies to the local files. to exclude files, think about your regex, some tips here: https://stackoverflow.com/questions/406230/regular-expression-to-match-string-not-containing-a-word [default: %(default)s]")
parser.add_argument("--on-dup",
dest="ondup", default='overwrite',
help="what to do when the same file / folder exists in the destination: 'overwrite', 'skip', 'prompt' [default: %(default)s]")
parser.add_argument("--no-symlink",
dest="followlink", action="store_false",
help="DON'T follow symbol links when uploading / syncing up")
parser.add_argument(const.DisableSslCheckOption,
dest="checkssl", action="store_false",
help="DON'T verify host SSL cerificate")
parser.add_argument(const.CaCertsOption,
dest="cacerts", default=None,
help="Specify the path for CA Bundle [default: %(default)s]")
parser.add_argument("--mirror",
dest="mirror", default=None,
help="Specify the PCS mirror (e.g. bj.baidupcs.com. Open 'https://pcs.baidu.com/rest/2.0/pcs/manage?method=listhost' to get the list) to use. [default: " + const.PcsDomain + "]")
parser.add_argument("--rapid-upload-only",
dest="rapiduploadonly", action="store_true",
help="only upload large files that can be rapidly uploaded")
parser.add_argument("--resume-download-revert-back",
dest="resumedl_revertcount", default=const.DefaultResumeDlRevertCount,
type=int, metavar='RCOUNT',
help="Revert back at least %(metavar)s download chunk(s) and align to chunk boundary when resuming the download. A negative value means NO reverts. [default: %(default)s]")
# support aria2c
parser.add_argument("--downloader",
dest="downloader", default="",
help="downloader to use (use python if not specified). valid values: {} [default: %(default)s]".format(const.Downloaders))
parser.add_argument("--downloader-arguments",
dest="downloader_args", default="",
help="arguments for the downloader, default values: {} [default: %(default)s]".format(
const.DownloaderDefaultArgs))
# expose this to provide a primitive multi-user support
parser.add_argument("--config-dir", dest="configdir", default=const.ConfigDir, help="specify the config path [default: %(default)s]")
# action
parser.add_argument(const.CleanOptionShort, const.CleanOptionLong,
dest="clean", action="count", default=0,
help="clean settings (remove the token file), -cc: clean settings and hash cache")
# the MAIN parameter - what command to perform
parser.add_argument("command", nargs='*', help = "operations (quota, list, etc)")
return parser;
def clean_prog_files(cleanlevel, verbose, tokenpath = const.TokenFilePath):
result = removefile(tokenpath, verbose)
if result == const.ENoError:
pr("Token file '{}' removed. You need to re-authorize "
"the application upon next run".format(tokenpath))
else:
perr("Failed to remove the token file '{}'".format(tokenpath))
perr("You need to remove it manually")
if cleanlevel >= 2:
subresult = os.remove(cached.hashcachepath)
if subresult == const.ENoError:
pr("Hash Cache File '{}' removed.".format(cached.hashcachepath))
else:
perr("Failed to remove the Hash Cache File '{}'".format(cached.hashcachepath))
perr("You need to remove it manually")
result = subresult
return result
def main(argv=None): # IGNORE:C0111
''' Main Entry '''
reqres = check_requirements()
if reqres == CheckResult.Error:
quit(const.EFatal)
try:
result = const.ENoError
if argv is None:
argv = sys.argv
else:
sys.argv.extend(argv)
setuphandlers()
parser = getparser()
args = parser.parse_args()
# house-keeping reminder
# TODO: may need to move into ByPy for customized config dir
if os.path.exists(const.HashCachePath):
cachesize = getfilesize(const.HashCachePath)
if cachesize > 10 * const.OneM or cachesize == -1:
pr((
"*** WARNING ***\n"
"Hash Cache file '{0}' is very large ({1}).\n"
"This may affect program's performance (high memory consumption).\n"
"You can first try to run 'bypy.py cleancache' to slim the file.\n"
"But if the file size won't reduce (this warning persists),"
" you may consider deleting / moving the Hash Cache file '{0}'\n"
"*** WARNING ***\n\n\n").format(const.HashCachePath, human_size(cachesize)))
# check for situations that require no ByPy object creation first
if args.clean >= 1:
return clean_prog_files(args.clean, args.verbose)
# some arguments need some processing
try:
slice_size = interpret_size(args.slice)
except (ValueError, KeyError):
pr("Error: Invalid slice size specified '{}'".format(args.slice))
return const.EArgument
try:
chunk_size = interpret_size(args.chunk)
except (ValueError, KeyError):
pr("Error: Invalid slice size specified '{}'".format(args.slice))
return const.EArgument
if len(args.command) <= 0 or \
(len(args.command) == 1 and args.command[0].lower() == 'help'):
parser.print_help()
return const.EArgument
elif args.command[0] in ByPy.__dict__: # dir(ByPy), dir(by)
timeout = args.timeout or None
cached.usecache = not args.forcehash
# we construct a ByPy object here.
# if you want to try PanAPI, simply replace ByPy with PanAPI, and all the bduss related function _should_ work
# I didn't use PanAPI here as I have never tried out those functions inside
by = ByPy(slice_size = slice_size, dl_chunk_size = chunk_size,
verify = args.verify,
retry = args.retry, timeout = timeout,
quit_when_fail = args.quit,
resumedownload = args.resumedl,
incregex = args.incregex,
ondup = args.ondup,
followlink = args.followlink,
checkssl = args.checkssl,
cacerts = args.cacerts,
rapiduploadonly = args.rapiduploadonly,
mirror = args.mirror,
configdir = args.configdir,
resumedl_revertcount = args.resumedl_revertcount,
downloader = args.downloader,
downloader_args = args.downloader_args,
verbose = args.verbose, debug = args.debug)
uargs = []
for arg in args.command[1:]:
if sys.version_info[0] < 3:
uargs.append(unicode(arg, gvar.SystemEncoding))
else:
uargs.append(arg)
result = getattr(by, args.command[0])(*uargs)
else:
pr("Error: Command '{}' not available.".format(args.command[0]))
parser.print_help()
return const.EParameter
except KeyboardInterrupt:
# handle keyboard interrupt
pr("KeyboardInterrupt")
pr("Abort")
except Exception as ex:
# NOTE: Capturing the exeption as 'ex' seems matters, otherwise this:
# except Exception ex:
# will sometimes give exception ...
perr("Exception occurred:\n{}".format(formatex(ex)))
pr("Abort")
raise
quit(result)
if __name__ == "__main__":
main()
# vim: tabstop=4 noexpandtab shiftwidth=4 softtabstop=4 ff=unix fileencoding=utf-8
| liuycsd/bypy | bypy/bypy.py | Python | mit | 111,744 | [
"VisIt"
] | 0fe88c92c12aef07620d4490c92c5f8ed12ba0fc9c65584aaa5c975e1f993b14 |
# proxy module
from __future__ import absolute_import
from mayavi.version import *
| enthought/etsproxy | enthought/mayavi/version.py | Python | bsd-3-clause | 83 | [
"Mayavi"
] | 7602a2343d7c445e720c1be6d0988731f35242e21c78dc539819674dfcde6347 |
""" A script for updating the patient organization of a user.
This is a manage.py command. Run with --help for documentation.
Example usage:
To run on localhost:
> manage.py print_all_visits --org maventy
To run on production:
> manage.py print_all_visits --org maventy --remote
"""
import logging
from optparse import make_option
import sys
from google.appengine.ext import db
from healthdb import models
from healthdb.management.commands.commandutil import ManageCommand
def print_all_visits(org):
patient_cache, visits = models.Visit.get_all_visits(org)
print "%s" % models.Visit.export_csv_header()
num = 0
for visit in visits:
num += 1
if (num % 100) == 0: logging.info("printed %d visits" % num)
print(visit.export_csv_line(patient_cache.get_patient(
visit.parent_key())))
logging.info("printed %d visits" % num)
class Command(ManageCommand):
option_list = ManageCommand.option_list + (
make_option('--organization', dest='organization',
help='Organization'),
)
help = 'print all visits'
def handle(self, *app_labels, **options):
self.connect(*app_labels, **options)
print_all_visits(options.get('organization'))
| avastjohn/maventy_new | healthdb/management/commands/print_all_visits.py | Python | bsd-3-clause | 1,191 | [
"VisIt"
] | b5f51d2f99070d651de5c6b067f4b06da289337bf297590a8f22d72c57b71abc |
import numpy
import sys
from mmdlab.datareader import readfile_metgas_boxes
from mmdlab.utils import showable
from mmdlab.utils import saveimg
from mmdlab.utils import offscreen
from mmdlab.utils import init_mlab_scene
from mayavi import mlab
import time
import pylab
from numba import jit
from numba import float64, int8
print "Initing scene"
def rotate(deg):
for rot in xrange(deg):
mlab.view(azimuth=rot)
print "saving to ./advid/{num:03d}.png".format(num=rot)
mlab.savefig("./advid/{num:03d}.png".format(num=rot))
print "Reading data"
fig = mlab.figure('Viz', size=(1366,768))
#for i,f in enumerate(sys.argv[1:]):
# i=i+74
m,g = readfile_metgas_boxes(sys.argv[1])
print "Drawing metal"
mlab.points3d(m.x, m.y, m.z, mode="point", scale_mode="none",scale_factor=m.d, colormap="black-white" ,color=(1,1,1))
print "Drawing gas"
mlab.points3d(g.x, g.y, g.z, mode="point", scale_mode="none",scale_factor=g.d, colormap="cool",color=(0.,0.,1))
#mlab.show()
#rotate(360)
#mlab.view(0.0, 90.0, 40.432823181152344, [ 20.02327204, 20.03512955, 14.38742304])
#mlab.view(azimuth=0,elevation=90)
# print mlab.view()
#mlab.savefig("./advid/{num:05d}.png".format(num=i))
#mlab.clf()
mlab.show() | detorto/mdvis | src/draw_points.py | Python | mit | 1,207 | [
"Mayavi"
] | d415c92a54156cb7918f297d554e6644ac03d7691dc4dfb6c81e59cfea9e2d61 |
#!/usr/bin/env python
import os
import re
from os import path
from subprocess import check_call
import logging
import tempfile
from contextlib import closing
import multiprocessing
MIN_SCORE = 0.01
#Fitness functions
#All fitnesses need to be > 0 and positive for selection functions to work!
class VinaFitness(object):
"""Calculates the Vina fitness.
This class allows storing all of the config and receptor information
on setting up the instance, to allow the fitness to be calculated by
passing only the list of chromosomes.
This class can also be pickled, in constrast with alternative approach
of returning nested functions.
"""
def __init__(self, receptor_f, config_f, keep_outfile, parallel=False):
self.receptor_f = receptor_f
self.config_f = config_f
self.keep_outfile = keep_outfile
self.parallel = parallel
assert path.exists(receptor_f)
assert path.exists(config_f)
def __call__(self, chromosomes):
fitnesses = []
if not self.parallel:
for chromosome in chromosomes:
f = vina_fitness(chromosome, self.receptor_f,
self.config_f, self.keep_outfile, cpus=1)
fitnesses.append(f)
else:
pool = multiprocessing.Pool(multiprocessing.cpu_count())
arg_supplier = [(chromosome, self.receptor_f,
self.config_f, self.keep_outfile, 1)
for chromosome in chromosomes]
fitnesses = list(pool.map(_starred_vina_fitness, arg_supplier))
return fitnesses
def _starred_vina_fitness(args):
return vina_fitness(*args)
def vina_fitness(chromosome, receptor_f, config_f, keep_outfile=True, cpus=0):
"""Dock a chromosome representation to a target to assess fitness.
Chromosome must have a valid SMILES string as the SMILES property."""
ligand_f = pdbqt_from_smiles(chromosome.smiles, chromosome.idx_name)
try:
fitness = vina_abs_docking_score(ligand_f, receptor_f,
config_f, keep_outfile, cpus)
logging.info("Calculated chromosome fitness: Sequence %s, Fitness %s,"
" Receptor %s"
% (chromosome.seq_as_string, fitness,
path.basename(receptor_f)))
finally:
_silent_delete(ligand_f)
return fitness
def pdbqt_from_smiles(smiles, mol_name):
"""Write an Autodock4 PDBQT file from a SMILES string.
Returns the filename."""
cmdline_babel = False
try:
import pybel
except ImportError:
cmdline_babel = True
mol_pdbname = mol_name + '.pdb'
if not cmdline_babel:
mol = pybel.readstring("smi", smiles)
mol.make3D()
mol.write(format="pdb", filename=mol_pdbname, overwrite=True)
else:
in_smi = '-:%s' % smiles
check_call(['obabel', in_smi, '-O', mol_pdbname, '--gen3d'],
stdout=open(os.devnull), stderr=open(os.devnull))
mol_pdbqtname = mol_pdbname + 'qt'
if not path.exists(mol_pdbqtname):
try:
check_call(['prepare_ligand4.py',
'-l', mol_pdbname, '-o', mol_pdbqtname],
stdout=open(os.devnull), stderr=open(os.devnull))
finally:
os.remove(mol_pdbname)
return mol_pdbqtname
def _silent_delete(f):
try:
os.remove(f)
except OSError:
logging.exception("Error deleting file %s" % f)
pass
def _do_docking(ligand_f, receptor_f, config_f, cpus=0):
""" Runs a Vina docking run.
Returns the output PDBQT file name."""
raw_fname = lambda f: path.splitext(path.basename(f))[0]
outfile_name = "%s_out_%s.pdbqt" % (raw_fname(ligand_f),
raw_fname(receptor_f))
args = ['vina',
'--receptor', receptor_f,
'--ligand', ligand_f,
'--config', config_f,
'--out', outfile_name]
if cpus:
args.extend(['--cpu', str(cpus)])
with closing(tempfile.TemporaryFile()) as output:
check_call(args, stdout=output, stderr=open(os.devnull))
output.seek(0)
random_seed = _parse_vina_stdout(output.read())
return outfile_name, random_seed
def _parse_vina_stdout(vina_output):
pattern = re.compile(r"random seed: (-?\d+)")
match = pattern.search(vina_output)
random_seed = int(match.group(1))
return random_seed
def _parse_vina_outfile(outfile):
"""Returns all docking scores from a Vina output pdbqt file. """
with open(outfile) as raw_f:
scores = []
splitter = re.compile(' +')
for line in raw_f:
if 'REMARK VINA RESULT' in line:
raw_score = splitter.split(line)[3]
scores.append(float(raw_score))
return scores
def vina_abs_docking_score(ligand_f, receptor_f, config_f,
cpus=0, keep_outfile=False):
vina_outfile, random_seed = _do_docking(ligand_f, receptor_f,
config_f, cpus)
try:
docking_score = (_parse_vina_outfile(vina_outfile)[0])
logging.info("Docked ligand %s with random seed %d and"
" raw docking score %f"
% (ligand_f, random_seed, docking_score))
#Make higher scores better, and all scores positive
docking_score = abs(docking_score) if docking_score < 0 else MIN_SCORE
return docking_score
finally:
if not keep_outfile:
_silent_delete(vina_outfile)
| fergaljd/pep_ga | fitness/vina_fitness.py | Python | gpl-2.0 | 5,643 | [
"Pybel"
] | 1ab953b2ee28f67794179fa24fbd0a69ca10a06cd5c1bbde21ed234c7a205272 |
import hyperchamber as hc
import numpy as np
import torch
from .base_distribution import BaseDistribution
from ..gan_component import ValidationException
from torch.distributions import uniform
TINY=1e-12
class UniformDistribution(BaseDistribution):
def __init__(self, gan, config):
BaseDistribution.__init__(self, gan, config)
self.current_channels = config["z"]
self.current_input_size = config["z"]
batch_size = gan.batch_size()
self.shape = [batch_size, self.current_input_size]
if self.config.projections is not None:
self.current_input_size *= len(self.config.projections)
self.current_channels *= len(self.config.projections)
self.next()
def create(self):
pass
def required(self):
return "".split()
def validate(self):
errors = BaseDistribution.validate(self)
#if(self.config.z is not None and int(self.config.z) % 2 != 0):
# errors.append("z must be a multiple of 2 (was %2d)" % self.config.z)
return errors
def lookup(self, projection):
if callable(projection):
return projection
if projection == 'identity':
return identity
if projection == 'sphere':
return sphere
if projection == 'gaussian':
return gaussian
if projection == 'periodic':
return periodic
return self.lookup_function(projection)
def sample(self):
self.const_two = torch.tensor(2.0, device=self.gan.device)
self.const_one = torch.tensor(1.0, device=self.gan.device)
self.z = torch.rand(self.shape, device=self.gan.device) * self.const_two - self.const_one
if self.config.projections is None:
return self.z
projections = []
for projection in self.config.projections:
projections.append(self.lookup(projection)(self.config, self.gan, self.z))
ps = []
for p in projections:
ps.append(self.z)
return torch.cat(ps, -1)
def next(self):
self.instance = self.sample()
return self.instance
def identity(config, gan, net):
return net
def round(config, gan, net):
net = torch.round(net)
return net
def binary(config, gan, net):
net = torch.gt(net, 0)
net = torch.type(net, torch.Float)
return net
def zero(config, gan, net):
return torch.zeros_like(net)
| 255BITS/HyperGAN | hypergan/distributions/uniform_distribution.py | Python | mit | 2,450 | [
"Gaussian"
] | c8dcc10a0f52712fc26353188f53de245ad1588ac34d48466cba80d03ab8a07f |
# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import SparseGP
from ..inference.latent_function_inference import VarDTC
from .. import kern
from .. import util
class SparseGPCoregionalizedRegression(SparseGP):
"""
Sparse Gaussian Process model for heteroscedastic multioutput regression
This is a thin wrapper around the SparseGP class, with a set of sensible defaults
:param X_list: list of input observations corresponding to each output
:type X_list: list of numpy arrays
:param Y_list: list of observed values related to the different noise models
:type Y_list: list of numpy arrays
:param Z_list: list of inducing inputs (optional)
:type Z_list: empty list | list of numpy arrays
:param kernel: a GPy kernel ** Coregionalized, defaults to RBF ** Coregionalized
:type kernel: None | GPy.kernel defaults
:likelihoods_list: a list of likelihoods, defaults to list of Gaussian likelihoods
:type likelihoods_list: None | a list GPy.likelihoods
:param num_inducing: number of inducing inputs, defaults to 10 per output (ignored if Z_list is not empty)
:type num_inducing: integer | list of integers
:param name: model name
:type name: string
:param W_rank: number tuples of the corregionalization parameters 'W' (see coregionalize kernel documentation)
:type W_rank: integer
:param kernel_name: name of the kernel
:type kernel_name: string
"""
def __init__(self, X_list, Y_list, Z_list=[], kernel=None, likelihoods_list=None, num_inducing=10, X_variance=None, name='SGPCR',W_rank=1,kernel_name='coreg'):
#Input and Output
X,Y,self.output_index = util.multioutput.build_XY(X_list,Y_list)
Ny = len(Y_list)
#Kernel
if kernel is None:
kernel = kern.RBF(X.shape[1]-1)
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=W_rank, name=kernel_name)
#Likelihood
likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)
#Inducing inputs list
if len(Z_list):
assert len(Z_list) == Ny, 'Number of outputs do not match length of inducing inputs list.'
else:
if isinstance(num_inducing,np.int):
num_inducing = [num_inducing] * Ny
num_inducing = np.asarray(num_inducing)
assert num_inducing.size == Ny, 'Number of outputs do not match length of inducing inputs list.'
for ni,Xi in zip(num_inducing,X_list):
i = np.random.permutation(Xi.shape[0])[:ni]
Z_list.append(Xi[i].copy())
Z, _, Iz = util.multioutput.build_XY(Z_list)
super(SparseGPCoregionalizedRegression, self).__init__(X, Y, Z, kernel, likelihood, inference_method=VarDTC(), Y_metadata={'output_index':self.output_index})
self['.*inducing'][:,-1].fix()
| SheffieldML/GPy | GPy/models/sparse_gp_coregionalized_regression.py | Python | bsd-3-clause | 3,030 | [
"Gaussian"
] | fca0000996f271742608de7df4d683aa8f473e94d9a9aad0447ea7e8d7584339 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# --- BEGIN_HEADER ---
#
# startstore - [insert a few words of module description on this line]
# Copyright (C) 2003-2009 The MiG Project lead by Brian Vinter
#
# This file is part of MiG.
#
# MiG is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# MiG is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# -- END_HEADER ---
#
# "redirect" to restart
from shared.functionality.restartstore import *
| heromod/migrid | mig/shared/functionality/startstore.py | Python | gpl-2.0 | 1,022 | [
"Brian"
] | 78da60f625b348fe3c85e210c970f8def72b9593fd39f18123ee5ba25efdb66a |
""" Just listing the possible Properties
This module contains list of Properties that can be assigned to users and groups
"""
#: A host property. This property is used::
#: * For a host to forward credentials in a DISET call
TRUSTED_HOST = "TrustedHost"
#: Normal user operations
NORMAL_USER = "NormalUser"
#: CS Administrator - possibility to edit the Configuration Service
CS_ADMINISTRATOR = "CSAdministrator"
#: Job sharing among members of a group
JOB_SHARING = "JobSharing"
#: DIRAC Service Administrator
SERVICE_ADMINISTRATOR = "ServiceAdministrator"
#: Job Administrator can manipulate everybody's jobs
JOB_ADMINISTRATOR = "JobAdministrator"
#: Job Monitor - can get job monitoring information
JOB_MONITOR = "JobMonitor"
#: Accounting Monitor - can see accounting data for all groups
ACCOUNTING_MONITOR = "AccountingMonitor"
#: Private pilot
PILOT = "Pilot"
#: Generic pilot
GENERIC_PILOT = "GenericPilot"
#: Site Manager
SITE_MANAGER = "SiteManager"
#: User, group, VO Registry management
USER_MANAGER = "UserManager"
#: Operator
OPERATOR = "Operator"
#: Allow getting full delegated proxies
FULL_DELEGATION = "FullDelegation"
#: Allow getting only limited proxies (ie. pilots)
LIMITED_DELEGATION = "LimitedDelegation"
#: Allow getting only limited proxies for one self
PRIVATE_LIMITED_DELEGATION = "PrivateLimitedDelegation"
#: Allow managing proxies
PROXY_MANAGEMENT = "ProxyManagement"
#: Allow managing production
PRODUCTION_MANAGEMENT = "ProductionManagement"
#: Allow production request approval on behalf of PPG
PPG_AUTHORITY = "PPGAuthority"
#: Allow Bookkeeping Management
BOOKKEEPING_MANAGEMENT = "BookkeepingManagement"
#: Allow to set notifications and manage alarms
ALARMS_MANAGEMENT = "AlarmsManagement"
#: Allow FC Management - FC root user
FC_MANAGEMENT = "FileCatalogManagement"
#: Allow staging files
STAGE_ALLOWED = "StageAllowed"
#: Allow VMDIRAC Operations via various handlers
VM_RPC_OPERATION = "VmRpcOperation"
| DIRACGrid/DIRAC | src/DIRAC/Core/Security/Properties.py | Python | gpl-3.0 | 1,966 | [
"DIRAC"
] | 16f18959c83e0b01c375e150c3198333b5a91c31a2052e785b75abade1e1e06e |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# --- BEGIN_HEADER ---
#
# statusallexes - [insert a few words of module description on this line]
# Copyright (C) 2003-2009 The MiG Project lead by Brian Vinter
#
# This file is part of MiG.
#
# MiG is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# MiG is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# -- END_HEADER ---
#
import cgi
import cgitb
cgitb.enable()
# call statusexe!
from shared.functionality.statusexe import main
from shared.cgiscriptstub import run_cgi_script
run_cgi_script(main)
| heromod/migrid | mig/cgi-bin/statusallexes.py | Python | gpl-2.0 | 1,129 | [
"Brian"
] | c0f3624ba27b3b85b7f0e46881f21ec722072a39751d5f20050b1c85b25b001f |
#!/usr/bin/env python
#
# Copyright 2016 The AMP HTML Authors. 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.
#
"""A build script which (thus far) works on Ubuntu 14."""
# TODO(powdercloud): Make a gulp file or similar for this. For now
# it's simply split off from the main build.py in the parent
# directory, but this is not an idiomatic use to build a Javascript or
# Polymer project, and unlike for the parent directory there's no
# particular benefit to using Python.
from __future__ import print_function
import logging
import os
import platform
import re
import shutil
import subprocess
import sys
import tempfile
def Die(msg):
"""Prints error and exits with status 1.
Args:
msg: The error message to emit
"""
print(msg, file=sys.stderr)
sys.exit(1)
def GetNodeJsCmd():
"""Ensure Node.js is installed and return the proper command to run."""
logging.info('entering ...')
for cmd in ['node', 'nodejs']:
try:
output = subprocess.check_output([cmd, '--eval', 'console.log("42")'])
if output.strip() == b'42':
logging.info('... done')
return cmd
except (subprocess.CalledProcessError, OSError):
continue
Die('Node.js not found. Try "apt-get install nodejs".')
def CheckPrereqs():
"""Checks that various prerequisites for this script are satisfied."""
logging.info('entering ...')
if platform.system() != 'Linux' and platform.system() != 'Darwin':
Die('Sorry, this script assumes Linux or Mac OS X thus far. '
'Please feel free to edit the source and fix it to your needs.')
# Ensure source files are available.
for f in ['webui.js', 'index.html',
'logo-blue.svg', 'package.json']:
if not os.path.exists(f):
Die('%s not found. Must run in amp_validator source directory.' % f)
def SetupOutDir(out_dir):
"""Sets up a clean output directory.
Args:
out_dir: directory name of the output directory. Must not have slashes,
dots, etc.
"""
logging.info('entering ...')
assert re.match(r'^[a-zA-Z_\-0-9]+$', out_dir), 'bad out_dir: %s' % out_dir
if os.path.exists(out_dir):
subprocess.check_call(['rm', '-rf', out_dir])
os.mkdir(out_dir)
logging.info('... done')
def InstallNodeDependencies():
"""Installs the dependencies using npm install."""
logging.info('entering ...')
# Install the project dependencies specified in package.json into
# node_modules.
logging.info('installing AMP Validator webui dependencies ...')
subprocess.check_call(
['npm', 'install', '--userconfig', '../../../.npmrc'],
stdout=(open(os.devnull, 'wb') if os.environ.get('CI') else sys.stdout))
logging.info('... done')
def CreateWebuiAppengineDist(out_dir):
"""Creates the webui vulcanized directory to deploy to Appengine.
Args:
out_dir: directory name of the output directory. Must not have slashes,
dots, etc.
"""
logging.info('entering ...')
try:
tempdir = tempfile.mkdtemp()
# Merge the contents of webui with the installed node_modules into a
# common root (a temp directory). This lets us use the vulcanize tool.
for entry in os.listdir('.'):
if entry != 'node_modules':
if os.path.isfile(entry):
shutil.copyfile(entry, os.path.join(tempdir, entry))
else:
shutil.copytree(entry, os.path.join(tempdir, entry))
for entry in os.listdir('node_modules'):
if not os.path.isdir('node_modules/' + entry):
continue
elif entry == 'web-animations-js':
shutil.copytree(os.path.join('node_modules', entry),
os.path.join(tempdir, '@polymer', entry))
elif entry != '@polymer':
shutil.copytree(os.path.join('node_modules', entry),
os.path.join(tempdir, entry))
for entry in os.listdir('node_modules/@polymer'):
shutil.copytree(os.path.join('node_modules/@polymer', entry),
os.path.join(tempdir, '@polymer', entry))
vulcanized_index_html = subprocess.check_output([
'node_modules/vulcanize/bin/vulcanize',
'--inline-scripts', '--inline-css',
'-p', tempdir, 'index.html'])
finally:
shutil.rmtree(tempdir)
webui_out = os.path.join(out_dir, 'webui_appengine')
shutil.copytree('.', webui_out, ignore=shutil.ignore_patterns('dist'))
f = open(os.path.join(webui_out, 'index.html'), 'wb')
f.write(vulcanized_index_html)
f.close()
f = open(os.path.join(webui_out, 'legacy.html'), 'wb')
f.write(vulcanized_index_html.replace(b'https://cdn.ampproject.org/v0/validator_wasm.js', b'https://cdn.ampproject.org/v0/validator.js', 1))
f.close()
logging.info('... success')
def Main():
"""The main method, which executes all build steps and runs the tests."""
logging.basicConfig(
format='[[%(filename)s %(funcName)s]] - %(message)s',
level=(logging.ERROR if os.environ.get('CI') else logging.INFO))
GetNodeJsCmd()
CheckPrereqs()
InstallNodeDependencies()
SetupOutDir(out_dir='dist')
CreateWebuiAppengineDist(out_dir='dist')
if __name__ == '__main__':
Main()
| nexxtv/amphtml | validator/js/webui/build.py | Python | apache-2.0 | 5,597 | [
"GULP"
] | 570a7ec570a42c866648fe99fa252e204ba9997b05e6175e3e70e761ccd4d173 |
# 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 openstack.object_store.v1 import account as _account
from openstack.object_store.v1 import container as _container
from openstack.object_store.v1 import obj as _obj
from openstack import proxy
class Proxy(proxy.BaseProxy):
def get_account_metadata(self):
"""Get metatdata for this account
:rtype:
:class:`~openstack.object_store.v1.account.Account`
"""
return self._head(_account.Account)
def set_account_metadata(self, account):
"""Set metatdata for this account.
:param account: Account metadata specified on a
:class:`~openstack.object_store.v1.account.Account` object
to be sent to the server.
:type account:
:class:`~openstack.object_store.v1.account.Account`
:rtype: ``None``
"""
account.update(self.session)
def containers(self, **query):
"""Obtain Container objects for this account.
:param kwargs \*\*query: Optional query parameters to be sent to limit
the resources being returned.
:rtype: A generator of
:class:`~openstack.object_store.v1.container.Container` objects.
"""
return _container.Container.list(self.session, **query)
def get_container_metadata(self, value):
"""Get metatdata for a container
:param value: The value can be the name of a container or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:returns: One :class:`~openstack.object_store.v1.container.Container`
:raises: :class:`~openstack.exceptions.ResourceNotFound`
when no resource can be found.
"""
return self._head(_container.Container, value)
def set_container_metadata(self, container):
"""Set metatdata for a container.
:param container: A container object containing metadata to be set.
:type container:
:class:`~openstack.object_store.v1.container.Container`
:rtype: ``None``
"""
container.create(self.session)
def create_container(self, **attrs):
"""Create a new container from attributes
:param dict attrs: Keyword arguments which will be used to create
a :class:`~openstack.object_store.v1.container.Container`,
comprised of the properties on the Container class.
:returns: The results of container creation
:rtype: :class:`~openstack.object_store.v1.container.Container`
"""
# TODO(brian): s/_container/container once other changes propogate
return self._create(_container.Container, **attrs)
def delete_container(self, value, ignore_missing=True):
"""Delete a container
:param value: The value can be either the name of a container or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:param bool ignore_missing: When set to ``False``
:class:`~openstack.exceptions.ResourceNotFound` will be
raised when the container does not exist.
When set to ``True``, no exception will be set when
attempting to delete a nonexistent server.
:returns: ``None``
"""
# TODO(brian): s/_container/container once other changes propogate
self._delete(_container.Container, value,
ignore_missing=ignore_missing)
def objects(self, container, **query):
"""Return a generator that yields the Container's objects.
:param container: A container object or the name of a container
that you want to retrieve objects from.
:type container:
:class:`~openstack.object_store.v1.container.Container`
:param kwargs \*\*query: Optional query parameters to be sent to limit
the resources being returned.
:rtype: A generator of
:class:`~openstack.object_store.v1.obj.Object` objects.
"""
container = _container.Container.from_id(container)
objs = _obj.Object.list(self.session,
path_args={"container": container.name},
**query)
# TODO(briancurtin): Objects have to know their container at this
# point, otherwise further operations like getting their metadata
# or downloading them is a hassle because the end-user would have
# to maintain both the container and the object separately.
for ob in objs:
ob.container = container.name
yield ob
def _get_container_name(self, value, container):
if isinstance(value, _obj.Object):
if value.container is not None:
return value.container
if container is not None:
container = _container.Container.from_id(container)
return container.name
raise ValueError("container must be specified")
def get_object(self, value, container=None):
"""Get the data associated with an object
:param value: The value can be the ID of an object or a
:class:`~openstack.object_store.v1.obj.Object` instance.
:param container: The value can be the ID of a container or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:returns: The contents of the object. Use the
:func:`~get_object_metadata`
method if you want an object resource.
:raises: :class:`~openstack.exceptions.ResourceNotFound`
when no resource can be found.
"""
container_name = self._get_container_name(value, container)
# TODO(brian): s/_obj/obj once other changes propogate
return self._get(_obj.Object, value,
path_args={"container": container_name})
def save_object(self, obj, path):
"""Save the data contained inside an object to disk.
:param obj: The object to save to disk.
:type obj: :class:`~openstack.object_store.v1.obj.Object`
:param path str: Location to write the object contents.
"""
with open(path, "w") as out:
out.write(self.get_object(obj))
def create_object(self, **attrs):
"""Create a new object from attributes
:param dict attrs: Keyword arguments which will be used to create
a :class:`~openstack.object_store.v1.obj.Object`,
comprised of the properties on the Object class.
**Required**: A `container` argument must be specified,
which is either the ID of a container or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:returns: The results of object creation
:rtype: :class:`~openstack.object_store.v1.container.Container`
"""
container = attrs.pop("container", None)
container_name = self._get_container_name(None, container)
# TODO(brian): s/_container/container once other changes propogate
return self._create(_obj.Object,
path_args={"container": container_name}, **attrs)
def copy_object(self):
"""Copy an object."""
raise NotImplementedError
def delete_object(self, value, ignore_missing=True, container=None):
"""Delete an object
:param value: The value can be either the name of an object or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:param container: The value can be the ID of a container or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:param bool ignore_missing: When set to ``False``
:class:`~openstack.exceptions.ResourceNotFound` will be
raised when the object does not exist.
When set to ``True``, no exception will be set when
attempting to delete a nonexistent server.
:returns: ``None``
"""
container_name = self._get_container_name(value, container)
# TODO(brian): s/_obj/obj once other changes propogate
self._delete(_obj.Object, value, ignore_missing=ignore_missing,
path_args={"container": container_name})
def get_object_metadata(self, value, container=None):
"""Get metatdata for an object
:param value: The value is an
:class:`~openstack.object_store.v1.obj.Object`
instance.
:param container: The value can be the ID of a container or a
:class:`~openstack.object_store.v1.container.Container`
instance.
:returns: One :class:`~openstack.object_store.v1.obj.Object`
:raises: :class:`~openstack.exceptions.ResourceNotFound`
when no resource can be found.
"""
container_name = self._get_container_name(value, container)
return self._head(_obj.Object, value,
path_args={"container": container_name})
def set_object_metadata(self, obj):
"""Set metatdata for an object.
:param obj: The object to set metadata for.
:type obj: :class:`~openstack.object_store.v1.obj.Object`
:rtype: ``None``
"""
obj.create(self.session)
| sjsucohort6/openstack | python/venv/lib/python2.7/site-packages/openstack/object_store/v1/_proxy.py | Python | mit | 10,117 | [
"Brian"
] | 2de35ae42f0a96092b44011c64301b761b68942a1461eb3717ce97b5368bda15 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# freeseer - vga/presentation capture software
#
# Copyright (C) 2014 Free and Open Source Software Learning Centre
# http://fosslc.org
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 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 General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# For support, questions, suggestions or any other inquiries, visit:
# http://wiki.github.com/Freeseer/freeseer/
import pytest
from freeseer.frontend.controller import validate
from freeseer.frontend.controller import recording
from freeseer.frontend.controller.server import HTTPError
class TestValidationApp:
'''
Test cases for validate.
'''
def test_valid_control_recording_data(self):
'''
Tests validating the request form for controlling a recording with valid data
'''
form_data = {'command': 'start'}
validate.validate_form(form_data, recording.form_schema['control_recording'])
@pytest.mark.parametrize('control', [1, 'invalid-command'])
def test_invalid_control_recording_data(self, control):
'''
Tests validating the request form for controlling a recording with invalid data
'''
form_data = {'command': control}
with pytest.raises(HTTPError):
validate.validate_form(form_data, recording.form_schema['control_recording'])
@pytest.mark.parametrize('invalid_form', [None, {'test': 'should fail'}])
def test_invalid_control_recording_data_structure(self, invalid_form):
'''
Tests validating the request form for controlling a recording with an invalid data structure
'''
with pytest.raises(HTTPError):
validate.validate_form(invalid_form, recording.form_schema['control_recording'])
def test_valid_create_recording_data(self):
'''
Tests validating the request form for creating a recording with valid data
'''
form_data = {'filename': 'valid_filename'}
validate.validate_form(form_data, recording.form_schema['create_recording'])
@pytest.mark.parametrize('filename', ['invalid/filename', 1, ''])
def test_invalid_create_recording_data(self, filename):
'''
Tests validating the request form for creating a recording with invalid data
'''
form_data = {'filename': filename}
with pytest.raises(HTTPError):
validate.validate_form(form_data, recording.form_schema['create_recording'])
@pytest.mark.parametrize('invalid_form', [None, {'test': 'should fail'}])
def test_invalid_create_recording_data_structure(self, invalid_form):
'''
Tests validating the request form for controlling a recording with an invalid data structure
'''
with pytest.raises(HTTPError):
validate.validate_form(invalid_form, recording.form_schema['create_recording'])
| Freeseer/freeseer | src/freeseer/tests/frontend/controller/test_validate.py | Python | gpl-3.0 | 3,391 | [
"VisIt"
] | 04b595870dab985632665a7c1bdee900b28d9fd790dae3c602e8c193d3fc81df |
# coding: utf-8
"""
This module defines the last object to be created during the picking,
i.e. the locus.
"""
from typing import Union, Dict, List, Set
import collections
import itertools
import operator
from collections import defaultdict
import pysam
from ..transcripts.pad import pad_transcript
from ..transcripts.transcript import Transcript
from .abstractlocus import Abstractlocus
from ..parsers.GFF import GffLine
from ..scales.assignment.assigner import Assigner
import networkx as nx
import random
class Locus(Abstractlocus):
"""Class that defines the final loci.
It is a child of monosublocus, but it also has the possibility of adding
additional transcripts if they are valid splicing isoforms.
"""
def __init__(self, transcript=None, logger=None, configuration=None, **kwargs):
"""
Constructor class. Like all loci, also Locus is defined starting from a transcript.
:param transcript: the transcript which is used to initialize the Locus
:type transcript: [Transcript|None]
:param logger: the logger instance.
:type logger: None | logging.Logger
"""
self.counter = 0 # simple tag to avoid collisions
Abstractlocus.__init__(self, logger=logger, configuration=configuration, **kwargs)
if transcript is not None:
transcript.attributes["primary"] = True
if transcript.is_coding:
transcript.feature = "mRNA"
else:
transcript.feature = "ncRNA"
self.monoexonic = transcript.monoexonic
Abstractlocus.add_transcript_to_locus(self, transcript)
self.locus_verified_introns = transcript.verified_introns
self.tid = transcript.id
self.logger.debug("Created Locus object with {0}".format(transcript.id))
self.primary_transcript_id = transcript.id
# this must be defined straight away
# A set of the transcript we will ignore during printing
# because they are duplications of the original instance. Done solely to
# get the metrics right.
self._orf_doubles = collections.defaultdict(set)
self.excluded = None
self.parent = None
self.attributes = dict()
self.attributes["is_fragment"] = False
self.metric_lines_store = []
self.__id = None
self.fai = None
self.__finalized = False
self._reference_sources = set(source for source, is_reference in
zip(self.configuration.prepare.files.labels,
self.configuration.prepare.files.reference) if is_reference is True)
self.valid_ccodes = self.configuration.pick.alternative_splicing.valid_ccodes[:]
self.redundant_ccodes = self.configuration.pick.alternative_splicing.redundant_ccodes[:]
if self.perform_padding is True and self.reference_update is True:
self._add_to_alternative_splicing_codes("=")
self._add_to_alternative_splicing_codes("_")
self._add_to_alternative_splicing_codes("n")
def __str__(self, print_cds=True) -> str:
self.feature = self.__name__
assert self.feature != "Monosublocus"
# Hacky fix to make sure that the primary transcript has the attribute
# Set to True in any case.
self.primary_transcript.attributes["primary"] = True
# BF, really just a hack.
for transcript in self.transcripts:
if transcript == self.primary_transcript_id:
continue
self.transcripts[transcript].attributes["primary"] = False
lines = []
self_line = GffLine('')
for attr in ["chrom", 'feature', 'source', 'start', 'end', 'strand']:
setattr(self_line, attr, getattr(self, attr))
self_line.phase, self_line.score = None, self.score
self_line.id = self.id
self_line.name = self.name
self_line.attributes["superlocus"] = self.parent
self_line.attributes.update(self.attributes)
if "is_fragment" in self.attributes and self.attributes["is_fragment"] is False:
del self_line.attributes["is_fragment"]
self_line.attributes["multiexonic"] = (not self.monoexonic)
lines.append(str(self_line))
for tid in self.transcripts:
transcript_instance = self.transcripts[tid]
transcript_instance.source = self.source
transcript_instance.parent = self_line.id
self.logger.debug(self.attributes)
if self.transcripts[tid].is_coding:
self.transcripts[tid].feature = "mRNA"
else:
self.transcripts[tid].feature = "ncRNA"
for attribute in self.attributes:
if attribute not in transcript_instance.attributes:
if attribute == "is_fragment" and self.attributes[attribute] is False:
continue
transcript_instance.attributes[attribute] = self.attributes[attribute]
lines.append(transcript_instance.format(
"gff", with_cds=print_cds,
all_orfs=self.configuration.pick.output_format.report_all_orfs
).rstrip())
return "\n".join(lines)
def __setstate__(self, state):
super(Locus, self).__setstate__(state)
self._not_passing = set(self._not_passing)
def finalize_alternative_splicing(self, _scores=None, check_requirements=True):
""""This method ensures that all the transcripts retained in the locus
are within the score threshold. This is due to the fact that the score
changes depending on the transcript considered together; so that a transcript
that might have scored relatively well on its own will score pretty badly when
brought inside the locus.
Please note that, **when doing a reference update**, reference transcripts that have passed the previous checks
will *always* be retained and will not count for the purposes of determining the maximum number of isoforms.
:param _scores:only for testing. Load directly a score mock-up.
:param check_requirements: boolean switch. If set to False, requirements will not be evaluated.
"""
if self._finalized is True:
self.logger.debug("Locus already finalised, returning")
return
else:
self.logger.debug("Starting to finalise AS for %s", self.id)
if _scores is not None:
self.logger.warning("Loadin external scores: %s", _scores)
self._load_scores(_scores)
else:
self.metrics_calculated = False
self.scores_calculated = False
self.logger.debug("Re-calculating metrics and scores for %s", self.id)
self.filter_and_calculate_scores(check_requirements=check_requirements)
self.logger.debug("Re-calculated metrics and scores for %s", self.id)
max_isoforms = self.configuration.pick.alternative_splicing.max_isoforms
original = dict((tid, self.transcripts[tid].copy()) for tid in self.transcripts)
# *Never* lose the primary transcript
reference_sources = set(source for source, is_reference in zip(
self.configuration.prepare.files.labels,
self.configuration.prepare.files.reference
))
reference_transcripts = dict(
(tid, self.transcripts[tid].is_reference or self.transcripts[tid].original_source in reference_sources)
for tid in self.transcripts)
order = sorted([(tid, self.transcripts[tid].score, self.transcripts[tid]) for tid in self.transcripts
if tid != self.primary_transcript_id and
(reference_transcripts[tid] is False or
self.configuration.pick.run_options.reference_update is False)],
key=operator.itemgetter(1), reverse=True)
threshold = self.configuration.pick.alternative_splicing.min_score_perc * self.primary_transcript.score
score_passing = [_ for _ in order if _[1] >= threshold]
removed = set([_[0] for _ in order if _[1] < threshold])
remainder = score_passing[max_isoforms:]
score_passing = score_passing[:max_isoforms]
[self.remove_transcript_from_locus(tid) for tid in removed]
[self.remove_transcript_from_locus(tup[0]) for tup in remainder]
self.logger.debug("Kept %s transcripts out of %s (threshold: %s)", len(score_passing), len(order), threshold)
iteration = 0
while True:
iteration += 1
self.logger.debug("Starting iteration %s", iteration)
if _scores is None:
self.metrics_calculated = False
self.scores_calculated = False
self.filter_and_calculate_scores(check_requirements=check_requirements)
else:
pass
with_retained = self._remove_retained_introns()
if len(with_retained) > 0:
self.logger.debug("Transcripts with retained introns: %s", ", ".join(list(with_retained)))
else:
self.logger.debug("No transcripts with retained introns found.")
if self.perform_padding is True and len(self.transcripts) > 1:
self.logger.debug("Starting padding procedure for %s", self.id)
failed = self.launch_padding()
if failed:
# Restart the padding procedure
continue
else:
removed.update(self._remove_redundant_after_padding())
self.primary_transcript.attributes.pop("ccode", None)
self.logger.debug("Updated removal set: %s", ", ".join(removed))
missing = len(self.transcripts) - max_isoforms
if missing > 0 and remainder:
__to_add = remainder[:missing]
for tid, score, obj in __to_add:
self.add_transcript_to_locus(obj, check_in_locus=False)
remainder = remainder[missing:]
else:
break
self._mark_padded_transcripts(original)
# Now that we have added the padding ... time to remove redundant alternative splicing events.
self.logger.debug("%s has %d transcripts (%s)", self.id, len(self.transcripts),
", ".join(list(self.transcripts.keys())))
self._finalized = True
return
def _mark_padded_transcripts(self, original):
for tid in self.transcripts:
# For each transcript check if they have been padded
assert tid in original
backup = original[tid]
transcript = self.transcripts[tid]
if (transcript.start, transcript.end) != (backup.start, backup.end):
self.transcripts[tid].attributes["padded"] = True
message = "{transcript.id} is now padded and has now start {transcript.start}, end {transcript.end}"
if (backup.is_coding and ((backup.combined_cds_end != transcript.combined_cds_end) or
(backup.combined_cds_start != transcript.combined_cds_start))):
transcript.attributes["cds_padded"] = True
message += "; CDS moved to {transcript.combined_cds_start}, end {transcript.combined_cds_end}"
elif backup.is_coding:
transcript.attributes["cds_padded"] = False
message += "."
self.logger.info(message.format(**locals()))
def launch_padding(self):
"""Method to launch the padding procedure."""
self.logger.debug("Launched padding for %s", self.id)
failed = False
backup = dict()
for tid in self.transcripts:
backup[tid] = self.transcripts[tid].deepcopy()
# The "templates" are the transcripts that we used to expand the others.
templates = self.pad_transcripts(backup)
# First off, let us update the transcripts.
tid_keys = list(self.transcripts.keys())
for tid in tid_keys:
self.logger.debug("Swapping %s", tid)
self._swap_transcript(backup[tid], self.transcripts[tid])
self.logger.debug("Done padding for %s", self.id)
self.metrics_calculated = False
self.scores_calculated = False
self.filter_and_calculate_scores()
self.logger.debug("Recalculated metrics after padding in %s", self.id)
self._not_passing = set()
self._check_requirements() # Populate self._not_passing with things that fail the requirements
# If we would have to remove the primary transcript we have done something wrong
if self.primary_transcript_id in self._not_passing or len(set.intersection(templates, self._not_passing)) > 0:
self.logger.debug(
"Either the primary or some template transcript has not passed the muster. Removing, restarting.")
if self._not_passing == {self.primary_transcript_id}:
self.logger.info("The primary transcript %s is invalidated by the other transcripts in the locus.\
Leaving only the main transcript in %s.", self.primary_transcript_id, self.id)
self._not_passing = set(self.transcripts.keys()) - {self.primary_transcript_id}
# Remove transcripts *except the primary* that do not pass the muster
for tid in self._not_passing - {self.primary_transcript_id}:
self.remove_transcript_from_locus(tid)
# Restore the *non-failed* transcripts *and* the primary transcript to their original state
for tid in set(backup.keys()) - (self._not_passing - {self.primary_transcript_id}):
self._swap_transcript(self.transcripts[tid], backup[tid])
self.metrics_calculated = False
self.scores_calculated = False
self.filter_and_calculate_scores()
# Signal to the master function that we have to redo the cycle
failed = True
return failed
# Order the transcripts by score. The primary is *not* in this list
order = sorted([(tid, self.transcripts[tid].score) for tid in self.transcripts
if tid != self.primary_transcript_id],
key=operator.itemgetter(1), reverse=True)
self.logger.debug("Transcripts potentially kept in the locus: %s", ",".join([_[0] for _ in order]))
# Now that we are sure that we have not ruined the primary transcript, let us see whether
# we should discard any other transcript.
[self._add_to_alternative_splicing_codes(ccode) for ccode in ("=", "_")]
removed = set()
added = set()
for tid, score in order:
is_valid, ccode, _ = self.is_alternative_splicing(self.transcripts[tid], others=added)
if is_valid:
self.logger.debug("Keeping %s in the locus, ccode: %s", tid, ccode)
self.transcripts[tid].attributes["ccode"] = ccode
added.add(tid)
else:
self.logger.debug("Removing %s from the locus after padding, ccode: %s", tid, ccode)
self.remove_transcript_from_locus(tid)
removed.add(tid)
removed.update(self._remove_retained_introns())
# Now let us check whether we have removed any template transcript.
# If we have, remove the offending ones and restart
if len(set.intersection(set.union(set(templates), {self.primary_transcript_id}), removed)) > 0:
self.logger.debug("Removed: %s; Templates: %s; Primary: %s", ",".join(removed), ",".join(templates),
self.primary_transcript_id)
self.transcripts = backup
[self.remove_transcript_from_locus(tid) for tid in set.intersection(templates, removed)]
self.metrics_calculated = False
self.scores_calculated = False
self.filter_and_calculate_scores()
failed = True
self.logger.debug("Padding failed for %s (removed: %s), restarting", self.id, removed)
return failed
return failed
def _remove_retained_introns(self) -> Set[str]:
"""Method to remove from the locus any transcript that has retained introns and/or their CDS disrupted by
a retained intron event."""
self.logger.debug("Now checking the retained introns for %s", self.id)
removed = set()
while True:
cds_disrupted = set()
retained_introns = set()
to_remove = set()
for tid, transcript in self.transcripts.items():
if tid == self.primary_transcript_id:
continue
# This function will *only* modify the transcript instances, specifically *only* the
# "cds_disrupted_by_ri" and "retained_introns_num" properties
self.find_retained_introns(transcript)
self.transcripts[tid].attributes["retained_intron"] = (transcript.retained_intron_num > 0)
if transcript.retained_intron_num > 0:
retained_introns.add(tid)
assert transcript.cds_disrupted_by_ri is False or transcript.retained_intron_num > 0
if transcript.cds_disrupted_by_ri is True:
cds_disrupted.add(tid)
if max(len(retained_introns), len(cds_disrupted)) == 0:
break
if self.configuration.pick.alternative_splicing.keep_cds_disrupted_by_ri is False:
self.logger.debug("Removing {} because their CDS is disrupted by retained introns".format(
", ".join(list(cds_disrupted))))
to_remove.update(cds_disrupted)
retained_introns -= cds_disrupted
if self.configuration.pick.alternative_splicing.keep_retained_introns is False:
self.logger.debug("Removing {} because they contain retained introns".format(
", ".join(list(retained_introns))))
to_remove.update(retained_introns)
if len(to_remove) > 0:
removed.update(to_remove)
for tid in to_remove:
self.remove_transcript_from_locus(tid)
self.__segmenttree = self._calculate_segment_tree(self.exons, self.introns)
self.metrics_calculated = False
self.scores_calculated = False
self.filter_and_calculate_scores()
else:
break
return removed
def _remove_redundant_after_padding(self):
"""Private method to remove duplicate copies of transcripts after the padding procedure."""
# First thing: calculate the class codes
to_remove = set()
if len(self.transcripts) == 1:
self.logger.debug(f"{self.id} only has one transcript, no redundancy removal needed.")
return to_remove
self.logger.debug("Starting to remove redundant transcripts from %s", self.id)
class_codes = dict()
ichains = collections.defaultdict(list)
for tid in self.transcripts:
if self.transcripts[tid].introns:
key = tuple(sorted(self.transcripts[tid].introns))
else:
key = None
self.logger.debug("Intron key for %s: %s", tid, key)
ichains[key].append(tid)
for ichain in ichains:
for t1, t2 in itertools.combinations(ichains[ichain], 2):
class_codes[(t1, t2)] = Assigner.compare(self[t1], self[t2])[0]
self.logger.debug("Comparing ichain %s, %s vs %s: nF1 %s", ichain, t1, t2, class_codes[(t1, t2)].n_f1)
for couple, comparison in class_codes.items():
if comparison.n_f1[0] == 100:
if self.primary_transcript_id in couple:
removal = [_ for _ in couple if _ != self.primary_transcript_id][0]
elif len([_ for _ in couple if self[_].is_reference]) == 1:
removal = [_ for _ in couple if self[_].is_reference is False][0]
elif self[couple[0]].score != self[couple[1]].score:
removal = sorted([(_, self[_].score) for _ in couple],
key=operator.itemgetter(1), reverse=True)[1][0]
else:
removal = random.choice(sorted(couple))
if removal:
to_remove.add(removal)
self.logger.debug("Removing %s from locus %s because after padding it is redundant with %s",
removal, self.id, (set(couple) - {removal}).pop())
if to_remove:
self.logger.debug("Removing from %s: %s", self.id, ", ".join(to_remove))
for tid in to_remove:
self.remove_transcript_from_locus(tid)
[self._add_to_redundant_splicing_codes(_) for _ in ("=", "_", "n", "c")]
[self._remove_from_alternative_splicing_codes(_) for _ in ("=", "_", "n", "c")]
order = sorted([(tid, self[tid].score) for tid in self if tid != self.primary_transcript_id],
key=operator.itemgetter(1))
others = set(self.transcripts.keys())
for tid, score in order:
if self[tid].is_reference:
continue
is_valid, main_ccode, main_result = self.is_alternative_splicing(self[tid], others=others)
if is_valid is False:
self.logger.debug("Removing %s from %s as it is a redundant splicing isoform after padding.",
tid, self.id)
to_remove.add(tid)
others.remove(tid)
if to_remove:
self.logger.debug("Removing from %s: %s", self.id, ", ".join(to_remove))
for tid in to_remove:
self.remove_transcript_from_locus(tid)
return to_remove
def remove_transcript_from_locus(self, tid: str):
"""Overloading of the AbstractLocus class, in order to ensure that the primary transcript will *not*
be removed."""
if tid == self.primary_transcript_id:
raise KeyError("%s is the primary transcript of %s!" % (tid, self.id))
super().remove_transcript_from_locus(tid)
self._finalized = False
def add_transcript_to_locus(self, transcript: Transcript, check_in_locus=True,
**kwargs):
"""Implementation of the add_transcript_to_locus method.
Before a transcript is added, the class checks that it is a valid splicing isoform
and that we have not exceeded already the maximum number of isoforms for the Locus.
The checks performed are, in order:
#. whether the locus already has the maximum number of acceptable
isoforms ("max_isoforms")
#. (optional) whether all the introns *specific to the transcript when
compared with the primary transcript* are confirmed by external
validation tools (eg Portcullis)
#. Whether the score of the proposed AS event has a score over the
minimum percentage of the primary transcript score (eg if the minimum
percentage is 0.6 and the primary is scored 20, a model with a score
of 11 would be rejected and one with a score of 12 would be accepted)
#. Whether the strand of the candidate is the same as the one of the locus
#. Whether the AS event is classified (ie has a class code) which is acceptable as valid AS
#. Whether the transcript shares enough cDNA with the primary transcript ("min_cdna_overlap")
#. Whether the proposed model has too much UTR
#. (optional) Whether the proposed model has a retained intron compared
to the primary, ie part of its 3' non-coding regions overlaps
one intron of the primary model
#. Whether the proposed model shares enough CDS with the primary model (min_cds_overlap)
:param transcript: the candidate transcript
:type transcript: Transcript
:param check_in_locus: boolean flag to bypass *all* checks. Use with caution!
:type check_in_locus: bool
:param kwargs: optional keyword arguments are ignored.
"""
_ = kwargs
if check_in_locus is False:
Abstractlocus.add_transcript_to_locus(self, transcript, check_in_locus=False)
self.locus_verified_introns.update(transcript.verified_introns)
return
to_be_added = True
if to_be_added and transcript.strand != self.strand:
self.logger.debug("%s not added because it has a different strand from %s (%s vs. %s)",
transcript.id, self.id, transcript.strand, self.strand)
to_be_added = False
reference_pass = False
if self.reference_update is True and \
(transcript.is_reference is True or transcript.original_source in self._reference_sources):
reference_pass = True
if self.configuration.pick.alternative_splicing.only_confirmed_introns is True and reference_pass is False:
to_check = (transcript.introns - transcript.verified_introns) - self.primary_transcript.introns
to_be_added = len(to_check) == 0
if not to_be_added:
self.logger.debug(
"%s not added because it has %d non-confirmed intron%s",
transcript.id,
len(to_check),
"s" * min(1, len(to_check) - 1))
# Add a check similar to what we do for the minimum requirements and the fragments
if to_be_added and self.configuration.scoring.as_requirements:
to_be_added = self._check_as_requirements(transcript, is_reference=reference_pass)
if to_be_added is True:
is_alternative, ccode, _ = self.is_alternative_splicing(transcript)
if is_alternative is False:
self.logger.debug("%s not added because it is not a \
valid splicing isoform. Ccode: %s",
transcript.id, ccode)
to_be_added = False
else:
transcript.attributes["ccode"] = ccode
self.logger.debug("%s is a valid splicing isoform; Ccode: %s", transcript.id, ccode)
if to_be_added is False:
return
self.logger.debug("Keeping %s as a valid alternative isoform for %s",
transcript.id, self.id)
transcript.attributes["primary"] = False
if transcript.is_coding:
transcript.feature = "mRNA"
else:
transcript.feature = "ncRNA"
Abstractlocus.add_transcript_to_locus(self, transcript)
self.locus_verified_introns.update(transcript.verified_introns)
def _check_as_requirements(self, transcript: Transcript, is_reference=False) -> bool:
"""Private method to evaluate a transcript for inclusion in the locus.
This method uses the "as_requirements" section of the configuration file to perform the
evaluation.
Please note that if "check_references" is False and the transcript is marked as reference, this function
will always evaluate to True (ie the transcript is valid).
"""
to_be_added = True
if is_reference is True and self.configuration.pick.run_options.check_references is False:
return True
# TODO where are we going to put the as_requirements?
section = self.configuration.scoring.as_requirements
evaluated = dict()
for key in section.parameters:
name = section.parameters[key].name
value = operator.attrgetter(name)(transcript)
if "external" in key:
value = value[0]
evaluated[key] = self.evaluate(value, section.parameters[key])
# pylint: disable=eval-used
if eval(section.compiled) is False:
self.logger.debug("%s fails the minimum requirements for AS events", transcript.id)
to_be_added = False
return to_be_added
def is_intersecting(self, *args):
"""Not implemented: See the alternative splicing definition functions.
"""
raise NotImplementedError("""Loci do not use this method, but rather assess whether a transcript is a
splicing isoform or not.""")
def is_putative_fragment(self):
"""This method will use the expression in the "not_fragmentary" section
of the configuration to determine whether it is itself a putative fragment."""
if not self.configuration.pick.run_options.check_references and \
any(self.transcripts[tid].is_reference is True for tid in self.transcripts):
return False
current_id = self.id[:]
evaluated = dict()
for key, params in self.configuration.scoring.not_fragmentary.parameters.items():
name = params.name
value = operator.attrgetter(name)(self.primary_transcript)
if "external" in key:
value = value[0]
try:
evaluated[key] = self.evaluate(value, params)
except Exception as err:
self.logger.error(
"""Exception while calculating putative fragments. Key: {}, \
Transcript value: {} (type {}) \
configuration value: {} (type {}).""".format(
key, value, type(value), params,
type(params)
))
self.logger.exception(err)
raise err
if eval(self.configuration.scoring.not_fragmentary.compiled) is True:
self.logger.debug("%s cannot be a fragment according to the definitions, keeping it",
self.id)
fragment = False
else:
self.logger.debug(
"%s could be a fragment according to the definitions, tagging it for analysis",
self.id)
fragment = True
self.id = current_id
assert self.id == current_id
return fragment
def other_is_fragment(self, other):
"""
If the 'other' locus is marked as a potential fragment (see 'is_putative_fragment'), then this function
will check whether the other locus is within the distance and with the correct comparison class code to be
marked as a fragment.
:param other: another Locus to compare against
:type other: Locus
"""
if not isinstance(self, type(other)):
raise TypeError("I can compare only loci.")
if other.primary_transcript_id == self.primary_transcript_id:
self.logger.debug("Self-comparisons are not allowed!")
return False, None
if any(other.transcripts[tid].is_reference is True for tid in other.transcripts.keys()):
self.logger.debug("Locus %s has a reference transcript, hence it will not be discarded", other.id)
return False, None
self.logger.debug("Comparing %s with %s",
self.primary_transcript_id,
other.primary_transcript_id)
result, _ = Assigner.compare(other.primary_transcript,
self.primary_transcript,
strict_strandedness=True)
# We should get rid of the "max_distance" value here.
max_distance = max(0,
min(self.configuration.pick.fragments.max_distance,
self.configuration.pick.clustering.flank))
self.logger.debug("Comparison between {0} (strand {3}) and {1}: class code \"{2}\"".format(
self.primary_transcript.id,
other.primary_transcript.id,
result.ccode[0],
other.strand))
if (result.ccode[0] in self.configuration.pick.fragments.valid_class_codes and
result.distance[0] <= max_distance):
self.logger.debug("{0} is a fragment (ccode {1})".format(
other.primary_transcript.id, result.ccode[0]))
return True, result
return False, None
def calculate_metrics(self, tid: str):
"""
This function will calculate the metrics for a transcript which are relative in nature
i.e. that depend on the other transcripts in the sublocus. Examples include the fraction
of introns or exons in the sublocus, or the number/fraction of retained introns.
:param tid: the name of the transcript to be analysed
:type tid: str
"""
self.logger.debug("Calculating metrics for %s", tid)
self.transcripts[tid].finalize()
if (self.transcripts[tid].number_internal_orfs <= 1 or
self.configuration.pick.output_format.report_all_orfs is False):
super().calculate_metrics(tid)
else:
super().calculate_metrics(tid)
transcript = self.transcripts[tid]
orfs = list(transcript.get_internal_orf_beds())
selected = orfs[transcript.selected_internal_orf_index]
template = transcript.copy()
template.strip_cds()
new_transcript = template.copy()
self.logger.debug("Changing the name of %s to %s", transcript.id,
", ".join([transcript.id + ".orf" + str(_)
for _ in range(1, len(transcript.internal_orfs) + 1)]))
new_transcript.id = "{0}.orf1".format(new_transcript.id)
from ..parsers.bed12 import BED12
assert isinstance(selected, BED12), (type(selected), selected)
new_transcript.load_orfs([selected])
self.transcripts[new_transcript.id] = new_transcript
super().calculate_metrics(new_transcript.id)
if tid not in self._orf_doubles:
self._orf_doubles[tid] = set([])
self._orf_doubles[tid].add(new_transcript.id)
for num, orf in enumerate([_ for _ in orfs if _ != selected]):
new_transcript = template.copy()
assert isinstance(new_transcript, Transcript)
new_transcript.load_orfs([orf])
new_transcript.id = "{0}.orf{1}".format(new_transcript.id, num + 2)
self.transcripts[new_transcript.id] = new_transcript
super().calculate_metrics(new_transcript.id)
self._orf_doubles[tid].add(new_transcript.id)
self.logger.debug("Calculated metrics for {0}".format(tid))
def filter_and_calculate_scores(self, check_requirements=True):
"""
Function to calculate a score for each transcript, given the metrics derived
with the calculate_metrics method and the scoring scheme provided in the JSON configuration.
If any requirements have been specified, all transcripts which do not pass them
will be assigned a score of 0 and subsequently ignored.
Scores are rounded to the nearest integer.
"""
self.get_metrics()
metrics_store = self._metrics.copy()
if len(self._orf_doubles) == 0:
doubled = set()
else:
doubled = set.union(*self._orf_doubles.values())
for key, item in metrics_store.items():
if item["tid"] in doubled:
del self._metrics[key]
self.transcripts.pop(item["tid"], None)
super().filter_and_calculate_scores(check_requirements=check_requirements)
self._metrics = metrics_store
self.logger.debug("Calculated scores for %s, now checking for double IDs", self.id)
for index, item in enumerate(reversed(self.metric_lines_store)):
if item["tid"] in self._orf_doubles:
del self.metric_lines_store[index]
else:
continue
for doubled in self._orf_doubles:
for partial in self._orf_doubles[doubled]:
if partial in self.transcripts:
del self.transcripts[partial]
if partial in self.scores:
del self.scores[partial]
def print_metrics(self):
"""Overloading of the base method as in loci we might want to print data for the double ORFs."""
if self.configuration.pick.output_format.report_all_orfs is False:
yield from super().print_metrics()
else:
self.get_metrics()
ignore = set.union(*self._orf_doubles.values())
for tid, transcript in sorted(self.transcripts.items(), key=operator.itemgetter(1)):
if tid in ignore:
continue
yield self._create_metrics_row(tid, self._metrics[tid], transcript)
founds = []
if tid in self._orf_doubles:
for mtid in self._orf_doubles[tid]:
founds.append(mtid)
yield self._create_metrics_row(mtid, self._metrics[mtid], transcript)
elif transcript.alias in self._orf_doubles:
for mtid in self._orf_doubles[transcript.alias]:
founds.append(mtid)
yield self._create_metrics_row(mtid, self._metrics[mtid], transcript)
def print_scores(self):
"""This method yields dictionary rows that are given to a csv.DictWriter class."""
self.filter_and_calculate_scores()
score_keys = sorted(list(self.configuration.scoring.scoring.keys()) + ["source_score"])
keys = ["tid", "alias", "parent", "score"] + score_keys
for tid in self.scores:
row = dict().fromkeys(keys)
row["tid"] = tid
row["parent"] = self.id
row["alias"] = self.transcripts[tid].alias
if tid in self._not_passing:
row["score"] = 0
else:
row["score"] = round(self.scores[tid]["score"], 2)
for key in score_keys:
assert self.scores[tid][key] != "NA" and self.scores[tid][key] is not None
row[key] = round(self.scores[tid][key], 2)
score_sum = sum(row[key] for key in score_keys)
if tid not in self._not_passing and self.scores[tid]["score"] > 0:
assert round(score_sum, 2) == round(self.scores[tid]["score"], 2), (
score_sum,
self.transcripts[tid].score,
tid)
else:
assert self.scores[tid]["score"] == 0
yield row
def is_alternative_splicing(self, other, others=None):
"""This function defines whether another transcript could be a
putative alternative splice variant of the primary Locus
transcript.
To do so, it compares the candidate against all transcripts in the Locus, and calculates
the class code using scales.Assigner.compare.
:param other: another transcript to compare against
:type other: Transcript
:param others: a set of AS transcripts used to check whether the new transcript should be included.
This allows to avoid creating a whole new locus for testing the padding.
:type others: (None|set)
"""
is_valid = True
valid_ccodes = self.valid_ccodes
redundant_ccodes = self.redundant_ccodes
cds_only = self.configuration.pick.alternative_splicing.cds_only
if other.is_coding and not self.primary_transcript.is_coding:
reason = "{} is coding, and cannot be added to a non-coding locus.".format(other.id)
enough_overlap, overlap_reason = False, reason
main_ccode = "NA"
main_result = None
elif (other.is_coding is False) and (self.primary_transcript.is_coding is True):
reason = "{} is non-coding, and cannot be added to a coding locus.".format(other.id)
enough_overlap, overlap_reason = False, reason
main_ccode = "NA"
main_result = None
else:
if cds_only is True:
self.logger.debug("Checking whether the CDS of %s and %s are overlapping enough",
other.id, self.primary_transcript_id)
main_result, _ = Assigner.compare(other._selected_orf_transcript,
self.primary_transcript._selected_orf_transcript)
enough_overlap, overlap_reason = self._evaluate_transcript_overlap(
other._selected_orf_transcript,
self.primary_transcript._selected_orf_transcript,
min_cdna_overlap=self.configuration.pick.alternative_splicing.min_cdna_overlap,
min_cds_overlap=self.configuration.pick.alternative_splicing.min_cds_overlap,
comparison=main_result,
check_references=self.configuration.pick.run_options.check_references,
fixed_perspective=True)
else:
main_result, _ = Assigner.compare(other,
self.primary_transcript)
enough_overlap, overlap_reason = self._evaluate_transcript_overlap(
other,
self.primary_transcript,
min_cdna_overlap=self.configuration.pick.alternative_splicing.min_cdna_overlap,
min_cds_overlap=self.configuration.pick.alternative_splicing.min_cds_overlap,
comparison=main_result,
check_references=self.configuration.pick.run_options.check_references,
fixed_perspective=True)
self.logger.debug(overlap_reason)
main_ccode = main_result.ccode[0]
if main_ccode not in valid_ccodes:
self.logger.debug("%s is not a valid splicing isoform. Ccode: %s",
other.id,
main_result.ccode[0])
is_valid = False
if not enough_overlap:
self.logger.debug("%s is not a valid splicing isoform. Reason: %s",
other.id, overlap_reason)
is_valid = False
if (is_valid and self.configuration.pick.run_options.check_references is False and other.is_reference is True):
pass
elif is_valid:
if others is None:
others = self.transcripts.keys()
else:
diff = set.difference(set(others), set(self.transcripts.keys()))
assert diff == set(), diff
for tid in iter(tid for tid in others if tid not in (self.primary_transcript_id, other.id)):
candidate = self.transcripts[tid]
if cds_only is True:
result, _ = Assigner.compare(other._selected_orf_transcript, candidate._selected_orf_transcript)
else:
result, _ = Assigner.compare(other, candidate)
if result.ccode[0] in redundant_ccodes:
self.logger.debug("%s is a redundant isoform of %s (ccode %s)",
other.id, candidate.id, result.ccode[0])
is_valid = False
break
if is_valid:
self.logger.debug("%s is a valid splicing isoform of %s (class code %s, overlap: %s)",
other.id, self.id, main_ccode, overlap_reason)
return is_valid, main_ccode, main_result
def pad_transcripts(self, backup=None) -> set:
"""
This method will perform the padding of the transcripts.
In a nutshell:
- First, check which transcripts are compatible at the 5' and 3' end. Assign a "template" for each expansion.
Note that the same transcript might be the template at the 5' but marked as extendable at the 3', or viceversa.
- Call "expand_transcript" on each couple of template-target.
Arguments:
:param backup: optional dictionary of hard-copies of the transcripts to pad. Will be generated on the fly
if None is provided.
"""
if isinstance(self.configuration.reference.genome, pysam.FastaFile):
self.fai = self.configuration.reference.genome
else:
self.fai = pysam.FastaFile(self.configuration.reference.genome)
five_graph = self.define_graph(objects=self.transcripts, inters=self._share_extreme, three_prime=False)
three_graph = self.define_graph(objects=self.transcripts, inters=self._share_extreme, three_prime=True)
self.logger.debug("5' graph: %s", five_graph.edges)
self.logger.debug("3' graph: %s", three_graph.edges)
# TODO: Tie breaks!
__to_modify = self._find_communities_boundaries(five_graph, three_graph)
self.logger.debug("To modify: %s",
dict((tid, [_ if not isinstance(_, Transcript) else _.id for _ in __to_modify[tid]]) for tid in __to_modify)
)
templates = set()
if backup is None:
backup = dict((tid, self.transcripts[tid].deepcopy()) for tid in self.transcripts)
# Now we can do the proper modification
for tid in sorted(__to_modify.keys()):
if __to_modify[tid][0]:
templates.add(__to_modify[tid][0].id)
if __to_modify[tid][1]:
templates.add(__to_modify[tid][1].id)
self.logger.debug("Expanding %s to have start %s (from %s) and end %s (from %s)",
tid, __to_modify[tid][0] if not __to_modify[tid][0] else __to_modify[tid][0].start,
self[tid].start,
__to_modify[tid][1] if not __to_modify[tid][1] else __to_modify[tid][1].end,
self[tid].end)
new_transcript = pad_transcript(backup[tid],
self.transcripts[tid],
__to_modify[tid][0],
__to_modify[tid][1],
self.fai,
self.logger)
if (new_transcript.start == self.transcripts[tid].end) and \
(new_transcript.end == self.transcripts[tid].end):
self.logger.debug("No expansion took place for %s!", tid)
else:
self.logger.debug("Expansion took place for %s!", tid)
self.transcripts[tid] = new_transcript
self.exons = set()
for tid in self:
self.exons.update(self[tid].exons)
return templates
def _find_communities_boundaries(self, five_graph, three_graph) -> Dict[str, List[Union[bool, Transcript]]]:
"""This private method will navigate the 5' and 3' graph to assign a template for expansion to each target.
It returns a dictionary where each transcript in the locus is assigned a bi-tuple - one item is the
template at the 5', the other the template at the 3'.
If no valid template is found, the corresponding item will be the boolean False.
"""
five_found = set()
__to_modify = dict()
while len(five_graph) > 0:
# Find the sinks
sinks = {node for node in five_graph.nodes() if node not in
{edge[0] for edge in five_graph.edges()}}
__putative = defaultdict(list)
for sink in sinks:
for ancestor in nx.ancestors(five_graph, sink):
__putative[ancestor].append((sink, self[sink].score))
for ancestor in __putative:
best = sorted(__putative[ancestor], key=operator.itemgetter(1), reverse=True)[0][0]
self.logger.debug("Putative 5' for %s: %s. Best: %s", ancestor, __putative[ancestor], best)
__to_modify[ancestor] = [self[best], False]
five_found.add(ancestor)
five_graph.remove_nodes_from(set.union(sinks, __putative.keys()))
three_found = set()
while len(three_graph) > 0:
sinks = {node for node in three_graph.nodes() if node not in
{edge[0] for edge in three_graph.edges()}}
__putative = defaultdict(list)
for sink in sinks:
for ancestor in nx.ancestors(three_graph, sink):
__putative[ancestor].append((sink, self[sink].score))
for ancestor in __putative:
best = sorted(__putative[ancestor], key=operator.itemgetter(1), reverse=True)[0][0]
if ancestor in __to_modify:
__to_modify[ancestor][1] = self[best]
else:
__to_modify[ancestor] = [False, self[best]]
three_found.add(ancestor)
self.logger.debug("Putative 3' for %s: %s. Best: %s", ancestor, __putative[ancestor], best)
three_graph.remove_nodes_from(set.union(sinks, __putative.keys()))
self.logger.debug("Communities for modifications: %s",
dict((tid, [_ if not isinstance(_, Transcript) else _.id for _ in __to_modify[tid]])
for tid in __to_modify))
return __to_modify
def define_graph(self, objects: dict, inters=None, three_prime=False) -> nx.DiGraph:
"""Method to determine the internal graph representation to be used to determine padding templates.
:param objects: the dictionary containing the transcripts
:param inters: the "is_intersecting" function to use (must evaluate to boolean). If None is provided, the
method _share_extreme will be used.
:param three_prime: whether to construct the graph for the 5' (False) or 3' (True) ending.
"""
graph = nx.DiGraph()
graph.add_nodes_from(objects.keys())
inters = self._share_extreme if inters is None else inters
if len(objects) >= 2:
if (three_prime is True and self.strand != "-") or (three_prime is False and self.strand == "-"):
reverse = True
else:
reverse = False
order = sorted([(objects[tid].start, objects[tid].end, tid) for tid in objects], reverse=reverse)
for pos in range(len(order) - 1):
obj = order[pos]
for other_obj in order[pos + 1:]:
if obj == other_obj:
continue
elif self.overlap(obj[:2], obj[:2], positive=False, flank=0) == 0:
break
else:
self.logger.debug("Comparing %s to %s (%s')", obj[2], other_obj[2],
"5" if not three_prime else "3")
edge = inters(objects[obj[2]], objects[other_obj[2]], three_prime=three_prime)
if edge:
assert edge[0].id in self
assert edge[1].id in self
graph.add_edge(edge[0].id, edge[1].id)
else:
self.logger.debug("No comparison to be made (objects: %s)", objects)
return graph
def _share_extreme(self, first: Transcript, second: Transcript, three_prime=False):
"""
This function will determine whether two transcripts "overlap" at the 3' or 5' end.
The things to be considered are:
- whether their extremes are within the maximum distance
- whether unifying them would bridge too many splice sites within the locus.
:param first:
:param second:
:return:
"""
if self.strand == "-": # Remember we have to invert on the negative strand!
three_prime = not three_prime
if three_prime:
return self._share_three_prime(first, second)
else:
return self._share_five_prime(first, second)
def _share_five_prime(self, first: Transcript, second: Transcript):
"""
Method to determine whether two transcripts are compatible for expansion at the 5'.
To be compatible:
- their starts must be different
- the number of splicing junctions to add must be at most the parameter "ts_max_splices"
- the total number of bases to be added must be at most "ts_max_distance"
:param first:
:param second:
:return:
"""
reason = None
ts_splices = 0
ts_distance = 0
if second.start == first.start:
self.logger.debug("%s and %s start at the same coordinate. No expanding.", first.id, second.id)
return False
decision = False
first, second = sorted([first, second], key=operator.attrgetter("start"))
if self.overlap((first.start, first.end), (second.start, second.end)) <= 0:
decision = False
reason = "{first.id} and {second.id} are not intersecting each other."
self.logger.debug(reason)
return decision
# Now let us check whether the second falls within an intron
matched = first.segmenttree.find(second.exons[0][0], second.exons[0][1])
self.logger.debug("{second.id} last exon {second.exons[0]} intersects in {first.id}: {matched}".format(
**locals()))
if len(matched) > 0 and (matched[0].value == "intron" or second.exons[0][0] < matched[0].start):
decision = False
reason = "{second.id} first exon ends within an intron of {first.id}".format(**locals())
elif len(matched) > 0:
upstream = [_ for _ in first.find_upstream(second.exons[0][0], second.exons[0][1])
if _.value == "exon" and _ not in matched]
if matched[0][0] < second.start:
if upstream:
ts_splices += 1
ts_distance += second.start - matched[0][0] + 1
for up in upstream:
if up.start == first.start:
ts_splices += 1
else:
ts_splices += 2
ts_distance += up.end - up.start - 1
if reason is None:
decision = (ts_distance <= self.ts_distance) and (ts_splices <= self.ts_max_splices)
if decision:
decision = (second, first)
reason = "{first.id} {doesit} overlap {second.id} (distance {ts_distance} max {self.ts_distance}, splices \
{ts_splices} max {self.ts_max_splices})".format(doesit="does" if decision else "does not", **locals())
self.logger.debug(reason)
return decision
def _share_three_prime(self, first: Transcript, second: Transcript):
"""
Method to determine whether two transcripts are compatible for expansion at the 3'.
To be compatible:
- their starts must be different
- the number of splicing junctions to add must be at most the parameter "ts_max_splices"
- the total number of bases to be added must be at most "ts_max_distance"
:param first:
:param second:
:return:
"""
if second.end == first.end:
self.logger.debug("%s and %s end at the same coordinate. No expanding.", first.id, second.id)
return False
reason = None
ts_splices = 0
ts_distance = 0
decision = False
first, second = sorted([first, second], key=operator.attrgetter("end"), reverse=False)
# Now let us check whether the second falls within an intron
if self.overlap((first.start, first.end), (second.start, second.end)) <= 0:
decision = False
reason = "{first.id} and {second.id} are not intersecting each other."
self.logger.debug(reason)
return decision
matched = second.segmenttree.find(first.exons[-1][0], first.exons[-1][1])
if len(matched) > 0 and (matched[-1].value == "intron" or first.exons[-1][1] > matched[-1].end):
decision = False
reason = "{first.id} last exon ends within an intron of {second.id}".format(**locals())
elif len(matched) > 0:
downstream = [_ for _ in second.find_downstream(first.exons[-1][0], first.exons[-1][1])
if _.value == "exon" and _ not in matched]
if matched[-1][1] > first.end:
if downstream:
ts_splices += 1
ts_distance += matched[-1][1] - first.end + 1
for down in downstream:
if down.end == second.end:
ts_splices += 1
else:
ts_splices += 2
ts_distance += down.end - down.start - 1
if reason is None:
decision = (ts_distance <= self.ts_distance) and (ts_splices <= self.ts_max_splices)
if decision:
decision = (first, second)
reason = "{second.id} {doesit} overlap {first.id} (distance {ts_distance} max \
{self.ts_distance}, splices {ts_splices} max {self.ts_max_splices})".format(
doesit="does" if decision else "does not", **locals())
self.logger.debug(reason)
return decision
@property
def __name__(self):
if len(self.transcripts) == 0:
return "locus"
elif any(transcript.selected_cds_length > 0 for
transcript in self.transcripts.values()):
return "gene"
else:
return "ncRNA_gene"
# pylint: disable=invalid-name
@property
def id(self):
"""
Override of the abstractlocus method.
:rtype str
"""
if self.__id is None:
myid = Abstractlocus.id.fget(self) # @UndefinedVariable
if self.counter > 0:
myid = "{0}.{1}".format(myid, self.counter)
# self.__set_id(myid)
self.__id = myid
return self.__id
# pylint: disable=arguments-differ
@id.setter
def id(self, string):
"""
Override of the original method from AbstractLocus. This override allows to
create proper IDs for the final annotation to be output by Mikado.
:param string:
:return:
"""
self.logger.debug("Setting new ID for %s to %s", self.id, string)
self.__set_id(string)
self.__id = string
def __set_id(self, string):
if string == self.__id:
return
self.logger.debug("Changing the ID of %s to %s", self.__id, string)
primary_id = "{0}.1".format(string)
old_primary = self.primary_transcript.id
self.logger.debug("Changing the ID of the primary transcript of %s from %s to %s", self.__id,
self.primary_transcript.id, primary_id)
self.primary_transcript.attributes["alias"] = self.primary_transcript.id
self.primary_transcript.id = primary_id
self.transcripts[primary_id] = self.primary_transcript
self.primary_transcript_id = primary_id
assert self.transcripts[primary_id].selected_cds_introns == self.transcripts[old_primary].selected_cds_introns
del self.transcripts[old_primary]
self._orf_doubles[primary_id] = set([_.replace(old_primary, primary_id)
for _ in self._orf_doubles.pop(old_primary, set())])
order = sorted([k for k in self.transcripts.keys() if k != primary_id],
key=lambda xtid: self.transcripts[xtid])
mapper = {old_primary: primary_id}
for counter, tid in enumerate(order):
counter += 2
self.transcripts[tid].attributes["alias"] = tid
new_id = "{0}.{1}".format(string, counter)
self.transcripts[tid].id = new_id
self.transcripts[new_id] = self.transcripts.pop(tid)
olds = self._orf_doubles.pop(tid, set())
self._orf_doubles[new_id] = set([_.replace(tid, new_id) for _ in self._orf_doubles.pop(tid, set())])
news = self._orf_doubles.pop(tid, set())
self.logger.debug("Changed the old ORF IDs for %s from %s to %s", tid, olds, news)
assert self._orf_doubles[new_id] is not None
mapper[tid] = new_id
if self.scores_calculated is True:
new_scores = dict()
for tid in mapper:
values = self.scores[tid].copy()
values["tid"] = mapper[tid]
values["alias"] = tid
new_scores[mapper[tid]] = values
self.scores = new_scores
if self.metrics_calculated is True:
new_metrics = dict()
for tid in mapper:
values = self._metrics[tid].copy()
values["tid"] = mapper[tid]
values["alias"] = tid
new_metrics[mapper[tid]] = values
self._metrics = new_metrics
# self.scores_calculated = False
# self.metrics_calculated = False
# pylint: enable=invalid-name,arguments-differ
@property
def is_fragment(self):
"""
:rtype : bool
Flag. It returns the value of self.attributes["is_fragment"]
"""
return self.attributes["is_fragment"]
@is_fragment.setter
def is_fragment(self, val: bool):
"""
Setter for is_fragment. Only boolean values are accepted.
:param val: flag
:type val: bool
"""
if not isinstance(val, bool):
raise ValueError(val)
self.attributes["is_fragment"] = val
@property
def primary_transcript(self):
"""
This property returns the primary transcript of the Locus
(i.e. the one which has been used for creation and which has the highest score).
:rtype : Transcript
"""
return self.transcripts[self.primary_transcript_id]
@property
def purge(self):
"""Overloading of the base property. Loci should never purge."""
return False
@property
def _finalized(self):
return self.__finalized
@_finalized.setter
def _finalized(self, value):
if not isinstance(value, bool):
raise ValueError(value)
self.__finalized = value
@property
def ts_distance(self):
"""Alias for self.configuration.pick.alternative_splicing.ts_distance"""
return self.configuration.pick.alternative_splicing.ts_distance
@property
def ts_max_splices(self):
"""Alias for self.configuration.pick.alternative_splicing.ts_max_splices"""
return self.configuration.pick.alternative_splicing.ts_max_splices
@property
def has_reference_transcript(self):
"""Checks whether any transcript in the locus is marked as reference."""
return any(self.transcripts[transcript].is_reference for transcript in self)
def _add_to_alternative_splicing_codes(self, code):
"""Method to retrieve the currently valid alternative splicing event codes"""
codes = set(self.valid_ccodes)
codes.add(code)
self.valid_ccodes = list(codes)
self._remove_from_redundant_splicing_codes(code)
def _add_to_redundant_splicing_codes(self, code):
"""Method to retrieve the currently valid alternative splicing event codes"""
codes = set(self.redundant_ccodes)
codes.add(code)
self.redundant_ccodes = list(codes)
self._remove_from_alternative_splicing_codes(code)
def _remove_from_alternative_splicing_codes(self, *ccodes):
sub = self.valid_ccodes
for ccode in ccodes:
if ccode in sub:
sub.remove(ccode)
self.valid_ccodes = sub
def _remove_from_redundant_splicing_codes(self, *ccodes):
self.logger.debug("Removing from redundant ccodes: %s. Current: %s", ccodes,
self.redundant_ccodes)
sub = self.redundant_ccodes
sub = [_ for _ in sub if _ not in ccodes]
self.logger.debug("New redundant ccodes: %s", sub)
self.redundant_ccodes = sub
| lucventurini/mikado | Mikado/loci/locus.py | Python | lgpl-3.0 | 64,917 | [
"pysam"
] | 53bd6d920a694e1159550534f4c9ee3f759216ab4165cb559e00a9d9636e2496 |
#!/usr/bin/env python
#
# threat_note v3.0 #
# Developed By: Brian Warehime #
# Defense Point Security (defpoint.com) #
# October 26, 2015 #
#
import argparse
import csv
import hashlib
import io
import random
import re
import time
from libs import circl
from libs import cuckoo
from libs import database
from libs import farsight
from libs import helpers
from libs import investigate
from libs import passivetotal
from libs import shodan
from libs import virustotal
from libs import whoisinfo
from flask import Flask
from flask import flash
from flask import make_response
from flask import redirect
from flask import render_template
from flask import request
from flask import url_for
from flask.ext.login import LoginManager
from flask.ext.login import current_user
from flask.ext.login import login_required
from flask.ext.login import login_user
from flask.ext.login import logout_user
from flask.ext.wtf import Form
from libs import circl
from libs import cuckoo
from libs import farsight
from libs import helpers
from libs import investigate
from libs import passivetotal
from libs import shodan
from libs import virustotal
from libs import whoisinfo
from libs.API import tn_api
from libs.database import db_session
from libs.database import init_db
from libs.models import Indicator
from libs.models import Setting
from libs.models import User
from werkzeug.datastructures import ImmutableMultiDict
from wtforms import PasswordField
from wtforms import StringField
from wtforms.validators import DataRequired
#
# Configuration #
#
app = Flask(__name__)
app.config['SECRET_KEY'] = 'yek_terces'
lm = LoginManager()
lm.init_app(app)
lm.login_view = 'login'
# Setup Database if Necessary
init_db()
app.register_blueprint(tn_api)
class LoginForm(Form):
user = StringField('user', validators=[DataRequired()])
password = PasswordField('password', validators=[DataRequired()])
def get_user(self):
return db_session.query(User).filter_by(user=self.user.data.lower(), password=hashlib.md5(
self.password.data.encode('utf-8')).hexdigest()).first()
class RegisterForm(Form):
user = StringField('user', validators=[DataRequired()])
key = PasswordField('key', validators=[DataRequired()])
email = StringField('email')
#
# Creating routes #
#
@lm.user_loader
def load_user(id):
return db_session.query(User).filter_by(_id=id).first()
@app.route('/register', methods=['GET', 'POST'])
def register():
form = RegisterForm()
if form.validate_on_submit():
user = db_session.query(User).filter_by(user=form.user.data.lower()).first()
if user:
flash('User exists.')
else:
user = User(form.user.data.lower(), form.key.data, form.email.data)
db_session.add(user)
# Set up the settings table when the first user is registered.
if not Setting.query.filter_by(_id=1).first():
settings = Setting('off', 'off', 'off', 'off', 'off', 'off', 'off', 'off', 'off', 'off', '', '', '',
'', '', '', '', '', '', '', '', '')
db_session.add(settings)
# Commit all database changes once they have been completed
db_session.commit()
login_user(user)
if current_user.is_authenticated:
return redirect(url_for('home'))
return render_template('register.html', form=form, title='Register')
@app.route('/login', methods=['GET', 'POST'])
def login():
form = LoginForm()
if form.validate_on_submit():
user = form.get_user()
if not user:
flash('Invalid User or Key.')
else:
login_user(user)
if current_user.is_authenticated:
return redirect(url_for('home'))
return render_template('login.html', form=form, title='Login')
@app.route('/logout')
def logout():
logout_user()
return redirect(url_for('login'))
@app.route('/', methods=['GET'])
@login_required
def home():
try:
counts = Indicator.query.distinct(Indicator._id).count()
types = Indicator.query.group_by(Indicator.type).all()
network = Indicator.query.order_by(Indicator._id).limit(5).all()
campaigns = Indicator.query.group_by(Indicator.campaign).all()
taglist = Indicator.query.distinct(Indicator.tags).all()
# Generate Tag Cloud
tags = set()
for object in taglist:
if object.tags == "":
pass
else:
for tag in object.tags.split(","):
tags.add(tag.strip())
dictcount = {}
dictlist = []
typecount = {}
typelist = []
# Generate Campaign Statistics Graph
for object in campaigns:
c = Indicator.query.filter_by(campaign=object.campaign).count()
if object.campaign == '':
dictcount["category"] = "Unknown"
tempx = (float(c) / float(counts)) * 100
dictcount["value"] = round(tempx, 2)
else:
dictcount["category"] = object.campaign
tempx = (float(c) / float(counts)) * 100
dictcount["value"] = round(tempx, 2)
dictlist.append(dictcount.copy())
# Generate Indicator Type Graph
for t in types:
c = Indicator.query.filter_by(type=t.type).count()
typecount["category"] = t.type
tempx = float(c) / float(counts)
newtemp = tempx * 100
typecount["value"] = round(newtemp, 2)
typelist.append(typecount.copy())
favs = []
# Add Import from Cuckoo button to Dashboard page
settings = Setting.query.filter_by(_id=1).first()
if 'on' in settings.cuckoo:
importsetting = True
else:
importsetting = False
return render_template('dashboard.html', networks=dictlist, network=network, favs=favs, typelist=typelist,
taglist=tags, importsetting=importsetting)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/about', methods=['GET'])
@login_required
def about():
return render_template('about.html')
@app.route('/tags', methods=['GET'])
@login_required
def tags():
try:
# Grab tags
taglist = dict()
rows = Indicator.query.distinct(Indicator.tags).all()
if rows:
for row in rows:
if row.tags:
print row.tags
for tag in row.tags.split(','):
taglist[tag.strip()] = list()
# Match indicators to tags
del rows, row
for tag, indicators in taglist.iteritems():
rows = Indicator.query.filter(Indicator.tags.like('%' + tag + '%')).all()
tmp = {}
for row in rows:
tmp[row.object] = row.type
indicators.append(tmp)
return render_template('tags.html', tags=taglist)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/networks', methods=['GET'])
@login_required
def networks():
try:
# Grab only network indicators
network = Indicator.query.filter(Indicator.type.in_(('IPv4', 'IPv6', 'Domain', 'Network'))).all()
return render_template('networks.html', network=network)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/threatactors', methods=['GET'])
@login_required
def threatactors():
try:
# Grab threat actors
threatactors = Indicator.query.filter(Indicator.type == 'Threat Actor').all()
return render_template('threatactors.html', network=threatactors)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/victims', methods=['GET'])
@login_required
def victims():
try:
# Grab victims
victims = Indicator.query.filter(Indicator.diamondmodel == ('Victim')).all()
return render_template('victims.html', network=victims)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/files', methods=['GET'])
@login_required
def files():
try:
# Grab files/hashes
files = Indicator.query.filter(Indicator.type == ('Hash')).all()
return render_template('files.html', network=files)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/campaigns', methods=['GET'])
@login_required
def campaigns():
try:
# Grab campaigns
campaignents = dict()
rows = Indicator.query.group_by(Indicator.campaign).all()
for c in rows:
if c.campaign == '':
name = 'Unknown'
else:
name = c.campaign
campaignents[name] = list()
# Match indicators to campaigns
for camp, indicators in campaignents.iteritems():
if camp == 'Unknown':
camp = ''
rows = Indicator.query.filter(Indicator.campaign == camp).all()
tmp = {}
for i in rows:
tmp[i.object] = i.type
indicators.append(tmp)
return render_template('campaigns.html', campaignents=campaignents)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/settings', methods=['GET'])
@login_required
def settings():
try:
settings = Setting.query.filter_by(_id=1).first()
user = User.query.filter(User.user == current_user).first
return render_template('settings.html', records=settings, suser=user)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/campaign/<uid>/info', methods=['GET'])
@login_required
def campaignsummary(uid):
try:
http = Indicator.query.filter_by(object=uid).first()
# Run ipwhois or domainwhois based on the type of indicator
if str(http.type) == "IPv4" or str(http.type) == "IPv6" or str(http.type) == "Domain" or \
str(http.type) == "Network":
return redirect(url_for('objectsummary', uid=http.object))
elif str(http.type) == "Hash":
return redirect(url_for('filesobject', uid=http.object))
else:
return redirect(url_for('threatactorobject', uid=http.object))
except Exception as e:
return render_template('error.html', error=e)
@app.route('/newobject', methods=['GET'])
@login_required
def newobj():
try:
currentdate = time.strftime("%Y-%m-%d")
return render_template('newobject.html', currentdate=currentdate)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/insert/object/', methods=['POST'])
@login_required
def newobject():
try:
something = request.form
imd = ImmutableMultiDict(something)
records = helpers.convert(imd)
# Import indicators from Cuckoo for the selected analysis task
if 'type' in records and 'cuckoo' in records['type']:
host_data, dns_data, sha1, firstseen = cuckoo.report_data(records['cuckoo_task_id'])
if host_data and dns_data and sha1 and firstseen:
# Import IP Indicators from Cuckoo Task
for ip in host_data:
ind = Indicator.query.filter_by(object=ip).first()
if ind is None:
indicator = Indicator(ip.strip(), 'IPv4', firstseen, '', 'Infrastructure', records['campaign'],
'Low', '', records['tags'], '')
db_session.add(indicator)
db_session.commit()
# Import Domain Indicators from Cuckoo Task
for dns in dns_data:
ind = Indicator.query.filter_by(object=dns['request']).first()
if ind is None:
indicator = Indicator(dns['request'], 'Domain', firstseen, '', 'Infrastructure',
records['campaign'], 'Low', '', records['tags'], '')
db_session.add(indicator)
db_session.commit()
# Import File/Hash Indicators from Cuckoo Task
ind = Indicator.query.filter_by(object=sha1).first()
if ind is None:
indicator = Indicator(sha1, 'Hash', firstseen, '', 'Capability',
records['campaign'], 'Low', '', records['tags'], '')
db_session.add(indicator)
db_session.commit()
# Redirect to Dashboard after successful import
return redirect(url_for('home'))
else:
errormessage = 'Task is not a file analysis'
return redirect(url_for('import_indicators'))
if 'inputtype' in records:
# Makes sure if you submit an IPv4 indicator, it's an actual IP
# address.
ipregex = re.match(
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}', records['inputobject'])
# Convert the inputobject of IP or Domain to a list for Bulk Add functionality.
records['inputobject'] = records['inputobject'].split(',')
for newobject in records['inputobject']:
if records['inputtype'] == "IPv4":
if ipregex:
object = Indicator.query.filter_by(object=newobject).first()
if object is None:
ipv4_indicator = Indicator(newobject.strip(), records['inputtype'],
records['inputfirstseen'], records['inputlastseen'],
records['diamondmodel'], records['inputcampaign'],
records['confidence'], records['comments'], records['tags'], None)
db_session.add(ipv4_indicator)
db_session.commit()
network = Indicator.query.filter(Indicator.type.in_(
('IPv4', 'IPv6', 'Domain', 'Network'))).all()
else:
errormessage = "Entry already exists in database."
return render_template('newobject.html', errormessage=errormessage,
inputtype=records['inputtype'], inputobject=newobject,
inputfirstseen=records['inputfirstseen'],
inputlastseen=records['inputlastseen'],
inputcampaign=records['inputcampaign'],
comments=records['comments'],
diamondmodel=records['diamondmodel'],
tags=records['tags'])
else:
errormessage = "Not a valid IP Address."
newobject = ', '.join(records['inputobject'])
return render_template('newobject.html', errormessage=errormessage,
inputtype=records['inputtype'],
inputobject=records, inputfirstseen=records['inputfirstseen'],
inputlastseen=records['inputlastseen'],
confidence=records['confidence'], inputcampaign=records['inputcampaign'],
comments=records['comments'], diamondmodel=records['diamondmodel'],
tags=records['tags'])
else:
object = Indicator.query.filter_by(object=newobject).first()
if object is None:
indicator = Indicator(newobject.strip(), records['inputtype'], records['inputfirstseen'],
records['inputlastseen'], records['diamondmodel'], records['inputcampaign'],
records['confidence'], records['comments'], records['tags'], None)
db_session.add(indicator)
db_session.commit()
else:
errormessage = "Entry already exists in database."
return render_template('newobject.html', errormessage=errormessage,
inputtype=records['inputtype'], inputobject=newobject,
inputfirstseen=records['inputfirstseen'],
inputlastseen=records['inputlastseen'],
inputcampaign=records['inputcampaign'],
comments=records['comments'],
diamondmodel=records['diamondmodel'],
tags=records['tags'])
# TODO: Change 'network' to 'object' in HTML templates to standardize on verbiage
if records['inputtype'] == "IPv4" or records['inputtype'] == "Domain" or records['inputtype'] == "Network"\
or records['inputtype'] == "IPv6":
network = Indicator.query.filter(Indicator.type.in_(('IPv4', 'IPv6', 'Domain', 'Network'))).all()
return render_template('networks.html', network=network)
elif records['diamondmodel'] == "Victim":
victims = Indicator.query.filter(Indicator.diamondmodel == ('Victim')).all()
return render_template('victims.html', network=victims)
elif records['inputtype'] == "Hash":
files = Indicator.query.filter(Indicator.type == ('Hash')).all()
return render_template('files.html', network=files)
else:
threatactors = Indicator.query.filter(Indicator.type == ('Threat Actors')).all()
return render_template('threatactors.html', network=threatactors)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/edit/<uid>', methods=['POST', 'GET'])
@login_required
def editobject(uid):
try:
http = Indicator.query.filter_by(object=uid).first()
newdict = helpers.row_to_dict(http)
return render_template('neweditobject.html', entry=newdict)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/delete/network/<uid>', methods=['GET'])
@login_required
def deletenetworkobject(uid):
try:
Indicator.query.filter_by(object=uid).delete()
db_session.commit()
network = Indicator.query.filter(Indicator.type.in_(('IPv4', 'IPv6', 'Domain', 'Network'))).all()
return render_template('networks.html', network=network)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/delete/threatactor/<uid>', methods=['GET'])
@login_required
def deletethreatactorobject(uid):
try:
Indicator.query.filter_by(object=uid).delete()
db_session.commit()
threatactors = Indicator.query.filter_by(type='Threat Actor')
return render_template('threatactors.html', network=threatactors)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/delete/victims/<uid>', methods=['GET'])
@login_required
def deletevictimobject(uid):
try:
Indicator.query.filter_by(object=uid).delete()
db_session.commit()
victims = Indicator.query.filter_by(diamondmodel='Victim')
return render_template('victims.html', network=victims)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/delete/files/<uid>', methods=['GET'])
@login_required
def deletefilesobject(uid):
try:
Indicator.query.filter_by(object=uid).delete()
db_session.commit()
files = Indicator.query.filter_by(type='Hash')
return render_template('victims.html', network=files)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/update/settings/', methods=['POST'])
@login_required
def updatesettings():
try:
something = request.form
imd = ImmutableMultiDict(something)
newdict = helpers.convert(imd)
# Query the first set of settings, could query custom settings for individual users
settings = Setting.query.filter_by(_id=1).first()
# Make sure we're updating the settings instead of overwriting them
if 'threatcrowd' in newdict.keys():
settings.threatcrowd = 'on'
else:
settings.threatcrowd = 'off'
if 'ptinfo' in newdict.keys() and newdict['ptkey'] is not '':
settings.ptinfo = 'on'
else:
settings.ptinfo = 'off'
if 'cuckoo' in newdict.keys():
settings.cuckoo = 'on'
else:
settings.cuckoo = 'off'
if 'vtinfo' in newdict.keys() and newdict['apikey'] is not '':
settings.vtinfo = 'on'
else:
settings.vtinfo = 'off'
if 'vtfile' in newdict.keys() and newdict['apikey'] is not '':
settings.vtfile = 'on'
else:
settings.vtfile = 'off'
if 'circlinfo' in newdict.keys() and newdict['circlusername'] is not '':
settings.circlinfo = 'on'
else:
settings.circlinfo = 'off'
if 'circlssl' in newdict.keys() and newdict['circlusername'] is not '':
settings.circlssl = 'on'
else:
settings.circlssl = 'off'
if 'whoisinfo' in newdict.keys():
settings.whoisinfo = 'on'
else:
settings.whoisinfo = 'off'
if 'farsightinfo' in newdict.keys() and newdict['farsightkey'] is not '':
settings.farsightinfo = 'on'
else:
settings.farsightinfo = 'off'
if 'shodaninfo' in newdict.keys() and newdict['shodankey'] is not '':
settings.shodaninfo = 'on'
else:
settings.shodaninfo = 'off'
if 'odnsinfo' in newdict.keys() and newdict['odnskey'] is not '':
settings.odnsinfo = 'on'
else:
settings.odnsinfo = 'off'
settings.farsightkey = newdict['farsightkey']
settings.apikey = newdict['apikey']
settings.odnskey = newdict['odnskey']
settings.httpproxy = newdict['httpproxy']
settings.httpsproxy = newdict['httpsproxy']
settings.cuckoohost = newdict['cuckoohost']
settings.cuckooapiport = newdict['cuckooapiport']
settings.circlusername = newdict['circlusername']
settings.circlpassword = newdict['circlpassword']
settings.ptkey = newdict['ptkey']
settings.shodankey = newdict['shodankey']
db_session.commit()
settings = Setting.query.first()
return render_template('settings.html', records=settings)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/update/object/', methods=['POST'])
@login_required
def updateobject():
try:
# Updates entry information
something = request.form
imd = ImmutableMultiDict(something)
records = helpers.convert(imd)
# taglist = records['tags'].split(",") - Unused
# indicator = Indicator.query.filter_by(object=records['object']).first() - Unused
try:
Indicator.query.filter_by(object=records['object']).update(records)
except Exception as e:
# SQLAlchemy does not outright support altering tables.
for k, v in records.iteritems():
if Indicator.query.group_by(k).first() is None:
print 'ALTER Table'
# db_session.engine.execute("ALTER TABLE indicators ADD COLUMN " + k + " TEXT DEFAULT ''")
db_session.commit()
# db_session.execute('ALTER TABLE indicators ADD COLUMN')
# con = helpers.db_connection()
# with con:
# cur = con.cursor()
# cur.execute(
# "ALTER TABLE indicators ADD COLUMN " + t + " TEXT DEFAULT ''")
# cur.execute("UPDATE indicators SET " + t + "= '" + records[
# t] + "' WHERE id = '" + records['id'] + "'")
if records['type'] == "IPv4" or records['type'] == "IPv6" or records['type'] == "Domain" or \
records['type'] == "Network":
return redirect(url_for('objectsummary', uid=str(records['object'])))
elif records['type'] == "Hash":
return redirect(url_for('filesobject', uid=str(records['object'])))
elif records['type'] == "Entity":
return redirect(url_for('victimobject', uid=str(records['object'])))
elif records['type'] == "Threat Actor":
return redirect(url_for('threatactorobject', uid=str(records['object'])))
except Exception as e:
return render_template('error.html', error=e)
@app.route('/insert/newfield/', methods=['POST'])
@login_required
def insertnewfield():
try:
something = request.form
imd = ImmutableMultiDict(something)
records = helpers.convert(imd)
newdict = {}
for i in records:
if i == "inputnewfieldname":
newdict[records[i]] = records['inputnewfieldvalue']
elif i == "inputnewfieldvalue":
pass
else:
newdict[i] = records[i]
return render_template('neweditobject.html', entry=newdict)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/network/<uid>/info', methods=['GET'])
@login_required
def objectsummary(uid):
try:
row = Indicator.query.filter_by(object=uid).first()
newdict = helpers.row_to_dict(row)
settings = Setting.query.filter_by(_id=1).first()
taglist = row.tags.split(",")
temprel = {}
if row.relationships:
rellist = row.relationships.split(",")
for rel in rellist:
row = Indicator.query.filter_by(object=rel).first()
temprel[row.object] = row.type
reldata = len(temprel)
jsonvt = ""
whoisdata = ""
odnsdata = ""
circldata = ""
circlssl = ""
ptdata = ""
farsightdata = ""
shodandata = ""
# Run ipwhois or domainwhois based on the type of indicator
if str(row.type) == "IPv4" or str(row.type) == "IPv6":
if settings.vtinfo == "on":
jsonvt = virustotal.vt_ipv4_lookup(str(row.object))
if settings.whoisinfo == "on":
whoisdata = whoisinfo.ipwhois(str(row.object))
if settings.odnsinfo == "on":
odnsdata = investigate.ip_query(str(row.object))
if settings.circlinfo == "on":
circldata = circl.circlquery(str(row.object))
if settings.circlssl == "on":
circlssl = circl.circlssl(str(row.object))
if settings.ptinfo == "on":
ptdata = passivetotal.pt(str(row.object))
if settings.farsightinfo == "on":
farsightdata = farsight.farsightip(str(row.object))
if settings.shodaninfo == "on":
shodandata = shodan.shodan(str(row.object))
elif str(row.type) == "Domain":
if settings.whoisinfo == "on":
whoisdata = whoisinfo.domainwhois(str(row.object))
if settings.vtinfo == "on":
jsonvt = virustotal.vt_domain_lookup(str(row.object))
if settings.odnsinfo == "on":
odnsdata = investigate.domain_categories(str(row.object))
if settings.circlinfo == "on":
circldata = circl.circlquery(str(row.object))
if settings.ptinfo == "on":
ptdata = passivetotal.pt(str(row.object))
if settings.farsightinfo == "on":
farsightdata = farsight.farsightdomain(str(row.object))
if settings.shodaninfo == "on":
shodandata = shodan.shodan(str(row.object))
if settings.whoisinfo == "on":
if str(row.type) == "Domain":
address = str(whoisdata['city']) + ", " + str(whoisdata['country'])
else:
address = str(whoisdata['nets'][0]['city']) + ", " + str(
whoisdata['nets'][0]['country'])
else:
address = "Information about " + str(row.object)
return render_template('networkobject.html', records=newdict, jsonvt=jsonvt, whoisdata=whoisdata,
odnsdata=odnsdata, settingsvars=settings, address=address,
ptdata=ptdata, temprel=temprel, circldata=circldata, circlssl=circlssl, reldata=reldata,
taglist=taglist, farsightdata=farsightdata, shodandata=shodandata)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/threatactors/<uid>/info', methods=['GET'])
@login_required
def threatactorobject(uid):
try:
row = Indicator.query.filter(Indicator.object == uid).first()
newdict = helpers.row_to_dict(row)
temprel = {}
if row.relationships:
rellist = row.relationships.split(",")
for rel in rellist:
reltype = Indicator.query.filter(Indicator.object == rel)
temprel[reltype.object] = reltype.type
reldata = len(temprel)
return render_template('threatactorobject.html', records=newdict, temprel=temprel, reldata=reldata)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/relationships/<uid>', methods=['GET'])
@login_required
def relationships(uid):
try:
row = Indicator.query.filter_by(object=uid).first()
indicators = Indicator.query.all()
if row.relationships:
rellist = row.relationships.split(",")
temprel = {}
for rel in rellist:
reltype = Indicator.query.filter_by(object=rel).first()
temprel[reltype.object] = reltype.type
return render_template('addrelationship.html', records=row, indicators=indicators)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/addrelationship', methods=['GET', 'POST'])
@login_required
def addrelationship():
try:
something = request.form
imd = ImmutableMultiDict(something)
records = helpers.convert(imd)
row = Indicator.query.filter_by(object=records['id']).first()
row.relationships = records['indicator']
db_session.commit()
if records['type'] == "IPv4" or records['type'] == "IPv6" or records['type'] == "Domain" or \
records['type'] == "Network":
return redirect(url_for('objectsummary', uid=str(records['id'])))
elif records['type'] == "Hash":
return redirect(url_for('filesobject', uid=str(records['id'])))
elif records['type'] == "Entity":
return redirect(url_for('victimobject', uid=str(records['id'])))
elif records['type'] == "Threat Actor":
return redirect(url_for('threatactorobject', uid=str(records['id'])))
except Exception as e:
return render_template('error.html', error=e)
@app.route('/apikey', methods=['POST'])
@login_required
def apiroll():
print "Rolling API Key"
try:
print "Time to roll the key!"
user = User.query.filter_by(user=current_user.user.lower()).first()
user.apikey = hashlib.md5("{}{}".format(user, str(random.random())).encode('utf-8')).hexdigest()
db_session.commit()
return redirect(url_for('profile'))
except Exception as e:
return render_template('error.html', error=e)
@app.route('/profile', methods=['GET', 'POST'])
@login_required
def profile():
try:
user = User.query.filter_by(user=current_user.user.lower()).first()
imd = ImmutableMultiDict(request.form)
records = helpers.convert(imd)
if 'currentpw' in records:
if hashlib.md5(records['currentpw'].encode('utf-8')).hexdigest() == user.password:
if records['newpw'] == records['newpwvalidation']:
user.password = hashlib.md5(records['newpw'].encode('utf-8')).hexdigest()
db_session.commit()
errormessage = "Password updated successfully."
return render_template('profile.html', errormessage=errormessage)
else:
errormessage = "New passwords don't match."
return render_template('profile.html', errormessage=errormessage)
else:
errormessage = "Current password is incorrect."
return render_template('profile.html', errormessage=errormessage)
return render_template('profile.html')
except Exception as e:
return render_template('error.html', error=e)
@app.route('/victims/<uid>/info', methods=['GET'])
@login_required
def victimobject(uid):
try:
http = Indicator.query.filter(Indicator.object == uid).first()
newdict = helpers.row_to_dict(http)
settings = Setting.query.filter_by(_id=1).first()
taglist = http.tags.split(",")
temprel = {}
if http.relationships:
rellist = http.relationships.split(",")
for rel in rellist:
reltype = Indicator.query.filter(Indicator.object == rel)
temprel[reltype.object] = reltype.type
reldata = len(temprel)
jsonvt = ""
whoisdata = ""
odnsdata = ""
circldata = ""
circlssl = ""
ptdata = ""
farsightdata = ""
# shodaninfo = ""
# Run ipwhois or domainwhois based on the type of indicator
if str(http.type) == "IPv4" or str(http.type) == "IPv6":
if settings.vtinfo == "on":
jsonvt = virustotal.vt_ipv4_lookup(str(http.object))
if settings.whoisinfo == "on":
whoisdata = whoisinfo.ipwhois(str(http.object))
if settings.odnsinfo == "on":
odnsdata = investigate.ip_query(str(http.object))
if settings.circlinfo == "on":
circldata = circl.circlquery(str(http.object))
if settings.circlssl == "on":
circlssl = circl.circlssl(str(http.object))
if settings.ptinfo == "on":
ptdata = passivetotal.pt(str(http.object))
if settings.farsightinfo == "on":
farsightdata = farsight.farsightip(str(http.object))
elif str(http.type) == "Domain":
if settings.whoisinfo == "on":
whoisdata = whoisinfo.domainwhois(str(http.object))
if settings.vtinfo == "on":
jsonvt = virustotal.vt_domain_lookup(str(http.object))
if settings.odnsinfo == "on":
odnsdata = investigate.domain_categories(
str(http.object))
if settings.circlinfo == "on":
circldata = circl.circlquery(str(http.object))
if settings.ptinfo == "on":
ptdata = passivetotal.pt(str(http.object))
if settings.whoisinfo == "on":
if str(http.type) == "Domain":
address = str(whoisdata['city']) + ", " + str(
whoisdata['country'])
else:
address = str(whoisdata['nets'][0]['city']) + ", " + str(
whoisdata['nets'][0]['country'])
else:
address = "Information about " + str(http.object)
return render_template('victimobject.html', records=newdict, jsonvt=jsonvt, whoisdata=whoisdata,
odnsdata=odnsdata, circldata=circldata, circlssl=circlssl, settingsvars=settings,
address=address, temprel=temprel, reldata=reldata, taglist=taglist, ptdata=ptdata,
farsightdata=farsightdata)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/files/<uid>/info', methods=['GET'])
@login_required
def filesobject(uid):
try:
http = Indicator.query.filter(Indicator.object == uid).first()
newdict = helpers.row_to_dict(http)
settings = Setting.query.filter_by(_id=1).first()
taglist = http.tags.split(",")
temprel = {}
if http.relationships:
rellist = http.relationships.split(",")
for rel in rellist:
reltype = Indicator.query.filter(Indicator.object == rel).first()
temprel[reltype.object] = reltype.type
reldata = len(temprel)
if settings.vtfile == "on":
jsonvt = virustotal.vt_hash_lookup(str(http.object))
else:
jsonvt = ""
return render_template('fileobject.html', records=newdict, settingsvars=settings, address=http.object,
temprel=temprel, reldata=reldata, jsonvt=jsonvt, taglist=taglist)
except Exception as e:
return render_template('error.html', error=e)
@app.route('/import', methods=['GET', 'POST'])
@login_required
def import_indicators():
cuckoo_tasks = cuckoo.get_tasks()
return render_template('import.html', cuckoo_tasks=cuckoo_tasks)
@app.route('/download/<uid>', methods=['GET'])
@login_required
def download(uid):
if uid == 'unknown':
uid = ""
rows = Indicator.query.filter_by(campaign=uid).all()
indlist = []
for i in rows:
indicator = helpers.row_to_dict(i)
for key, value in indicator.iteritems():
if value is None or value == "":
indicator[key] = '-'
indlist.append(indicator)
out_file = io.BytesIO()
fieldnames = indlist[0].keys()
w = csv.DictWriter(out_file, fieldnames=fieldnames)
w.writeheader()
w.writerows(indlist)
response = make_response(out_file.getvalue())
response.headers[
"Content-Disposition"] = "attachment; filename=" + uid + "-campaign.csv"
response.headers["Content-type"] = "text/csv"
return response
@app.teardown_appcontext
def shutdown_session(exception=None):
db_session.remove()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-H', '--host', default="127.0.0.1", help="Specify the host IP address")
parser.add_argument('-p', '--port', default=8888, help="Specify port to listen on")
parser.add_argument('-d', '--debug', default=False, help="Run in debug mode", action="store_true")
parser.add_argument('-db', '--database', help="Path to sqlite database - Not Implemented")
args = parser.parse_args()
if args.database:
# TODO
database.db_file = args.database
init_db()
app.run(host=args.host, port=args.port, debug=args.debug)
| brianwarehime/threat_note | threat_note/threat_note.py | Python | apache-2.0 | 39,764 | [
"Brian"
] | 5a687a25373758069aaded8f835ae03464f45c0f4daecdaea829240f555fe436 |
# MS STRING - NAMD
# -------------------------------------
# The main file for the python script that runs MS string calculations using NAMD
# as the engine.
# Jeremy Tempkin
# 11/6/13
#----------IMPORT------------
import sys
import time
import copy
import numpy as np
from subprocess import *
from fileIO import * # import string configuration parameters
from initSimul import * # input initialization functions
from callNAMD import * # import NAMD subroutines
from stringFunc import * # import functions related to controlling the string
from callCHARMM import * # import CHARMM subroutines
#----------SET SYSTEM PARAMETERS------------
#----------SET UP STRING--------------------
#----------MAIN LOOP------------------------
#----------FILE INPUT------------
# the system parameters are stored in a dictionary, sysParam
stringParams = {}
# read in the string parameters.
err = readStringParams(sys.argv[1], stringParams)
# check for errors in reading the string parameters.
if err == 0:
print "There was an error reading in the system parameters. Exiting now."
sys.exit(0)
# report successful parameter read.
print "The following parameters were read in:"
print stringParams
# check to make sure the stringType was understood.
if stringParams['stringType'] == 'FG' or 'CG' or 'MS':
print "The stringType selected is: " + stringParams['stringType']
else:
print "stringType was not understood. Exiting now."
sys.exit(0)
# now read in the collective varibale definiions.
print "Now reading in collective variable definitions for selected stringType: " + stringParams['stringType']
colvars = readColVarsDef(stringParams, stringParams['scratchdir'] + stringParams['CVFile'])
#print colvars
# get length of colvars.
stringParams['ncvs'] = len(colvars)
print "Collective variables read complete."
print str(stringParams['ncvs']) + " colvars were read."
# read in initial string images from structure input files.
#structure = readPDBstructure("./ionized.pdb")
#print "Initial Structures read in:"
#print structure
#-----------SIMULATION SETUP-------------
# establish dictionary for containing the simulation parameters that are used to set up NAMD runs
SimulParam = {}
initSimulParam(SimulParam)
print "Simulation Parameters initialized."
# set working directory for current image to the scratchdir
stringParams['imagedir'] = stringParams['scratchdir'] + "image"
# now build the batch submission files.
writeBatchFiles(stringParams)
# now write out a collective variable config file.
# this file defines the colvars to NAMD or CHARMM when they are recorded in the release phase.
writeColVarsConfig(colvars, stringParams)
# now, build the colvar data structure and populate for each image.
colvars = [0]*int(stringParams['nimages'])
if stringParams['stringType'] == 'FG' or 'MS':
for i in range(0, int(stringParams['nimages']), 1):
colvars[i] = readColVarsDef(stringParams, stringParams['imagedir'] + str(i) + "/colvars_img" + str(i) + ".restraint")
#print colvars[i]
if stringParams['stringType'] == 'CG':
for i in range(0, int(stringParams['nimages']), 1):
colvars[i] = readColVarsDef(stringParams, stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + ".restraint")
#print colvars[i]
# if running a MS string, initialize the needed colvars data structures
if stringParams['stringType'] == 'MS':
colvars_CG = copy.deepcopy(colvars)
colvars_old_CG = copy.deepcopy(colvars)
colvars_diff = copy.deepcopy(colvars)
colvars_new = copy.deepcopy(colvars)
colvars_CG_new = copy.deepcopy(colvars)
colvars_old = copy.deepcopy(colvars)
# initialize geometric averaging terms.
w_norm = 0.0
w_sum = copy.deepcopy(colvars)
w_sum = zeroColvars(w_sum)
# open up a system log file for the colvars.
if int(stringParams['firstStep']) == 0:
ofile_colvars = open(stringParams['scratchdir'] + "colvars.out", "w")
writeColVarsToFile(colvars, ofile_colvars, 0)
else:
ofile_colvars = open(stringParams['scratchdir'] + "colvars.out", "a")
print "Restart run was requested. Starting at step: " + stringParams['firstStep'] + "."
# read in the previous colvars step locations.
if int(stringParams['firstStep']) > 0:
readRestartColvars(colvars, colvars_old, stringParams['scratchdir'] + "colvars.out")
#---------MAIN-----------
start_time = time.time()
for itr in range(int(stringParams['firstStep']), int(stringParams['niter']), 1):
print "Iteration " + str(itr)
#print "Starting Colvars :", itr
#for i in range(0, len(colvars[0]), 1):
#print colvars[0][i][-1]
#for i in range(0, len(colvars[0]), 1):
#print colvars[0][i][3]
if itr != int(stringParams['firstStep']):
writeColVarsToFile(colvars, ofile_colvars, itr)
if stringParams['stringType'] == 'FG':
#----------FG ITERATION-------------
print "Iter", itr, ": Starting FG iteration."
print "Iter", itr, ": Writing restraint file."
# equilibrate to the target image in steps.
# generate the interpolation.
colvars_interp = genInterp(colvars, colvars_old, int(stringParams['nDragSteps_FG']))
# make a copy of the current colvars location
colvars_old = copyColvars(colvars, colvars_old)
for step in range(0, int(stringParams['nDragSteps_FG']), 1):
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for constrained FG run
writeColVarsRestraint(colvars_interp[step][i], stringParams['restraint'], stringParams['imagedir'] + str(i) + "/colvars_img" + str(i) + ".restraint")
print "Iter", itr, ": Submitting restraint batch step" + str(step) + "."
# call constrained NAMD run
callNAMDConstraint(SimulParam, stringParams)
# now release the image
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for release FG run
writeColVarsRestraint(colvars[i], stringParams['weakRestraint'], stringParams['imagedir'] + str(i) + "/colvars_img" + str(i) + "_weak.restraint")
print "Iter", itr, ": Submitting release batch."
# call unconstrained NAMD run
callNAMD(SimulParam, stringParams)
# now collect the colvar data from the release phase.
for i in range(0, int(stringParams['nimages']), 1):
print "Iter", itr, ": Reading colvar traj " + str(i)
#print "Before Read " + str(i) + ": \n", colvars[i]
colvars[i] = readColVarsTraj(stringParams, colvars[i], stringParams['imagedir'] + str(i) + "/img" + str(i) + ".colvars.traj")
#print " \nAfter read " + str(i) + ": \n", colvars[i]
#----------REPARAMETERIZE STRING VARIABLES---------------
for i in range(0, int(stringParams['reparItr']), 1):
print "Repar Iteration: " + str(i)
smoothString(float(stringParams['kappa']), colvars)
colvars = reparString(colvars)
printRMSD(colvars)
#for i in range(0, len(colvars), 1):
#print "Image ", i
#for j in range(0, len(colvars[i]), 1):
#print colvars_old[i][j][-1], colvars[i][j][-1]
#print "After Repar."
#for i in range(0, len(colvars[0]), 1):
#print colvars[0][i][-1]
#-----------------WRITE COLVARS-------------------------
# save out trajectory files for image steps.
if itr % int(stringParams['backupFreq']) == 0:
saveTrajFiles(stringParams, itr)
elif stringParams['stringType'] == 'CG':
#------------CG ITERATION-----------------
print "Iter", itr, ": Starting CG iteration."
print "Iter", itr, ": Writing restraint file."
# generate the interpolation.
colvars_interp = genInterp(colvars, colvars_old, int(stringParams['nDragSteps_CG']))
# make a copy of the current colvars location
colvars_old = copyColvars(colvars, colvars_old)
for step in range(0, int(stringParams['nDragSteps_CG']), 1):
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for constrained CG run
CHARMM_writeColVarsRestraint(colvars_interp[step][i], stringParams['restraint_CG'], stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + ".restraint")
print "Iter", itr, ": Submitting restraint batch step " + str(step) + "."
# call constrained CHARMM run
callCHARMMConstraint(SimulParam, stringParams)
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for release CG run
CHARMM_writeColVarsRestraint(colvars[i], stringParams['weakRestraint_CG'], stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + "_weak.restraint")
print "Iter", itr, ": Submitting release batch."
# call unconstrained CHARMM run
callCHARMM(SimulParam, stringParams)
# now collect the colvar data from the release phase.
for i in range(0, int(stringParams['nimages']), 1):
#print "Before Read " + str(i) + ": \n", colvars[i]
print "Iter", itr, ": Reading colvar traj " + str(i)
colvars[i] = CHARMM_readColVarsTraj(stringParams, colvars[i], stringParams['imagedir'] + str(i) + "/charmm_img" + str(i) + ".colvars.traj")
#print " \nAfter read " + str(i) + ": \n", colvars[i]
#----------REPARAMETERIZE STRING VARIABLES---------------
for i in range(0, int(stringParams['reparItr']), 1):
print "Repar Iteration: " + str(i)
smoothString(float(stringParams['kappa']), colvars)
colvars = reparString(colvars)
printRMSD(colvars)
#for i in range(0, len(colvars), 1):
#print "Image ", i
#for j in range(0, len(colvars[i]), 1):
#print colvars_old[i][j][-1], colvars[i][j][-1]
#print "After Repar."
#for i in range(0, len(colvars[0]), 1):
#print colvars[0][i][-1]
#-----------------WRITE COLVARS-------------------------
# save out trajectory files for image steps.
if itr % int(stringParams['backupFreq']) == 0:
saveTrajFiles(stringParams, itr)
elif stringParams['stringType'] == 'MS':
# zero out the difference vector
colvars_diff = zeroColvars(colvars_diff)
#----------FG ITERATION-------------
print "Iter", itr, ": Starting FG iteration."
print "Iter", itr, ": Writing restraint file."
# generate the interpolation.
colvars_interp = genInterp(colvars, colvars_old, int(stringParams['nDragSteps_FG']))
# make a copy of the current colvars location
colvars_old = copyColvars(colvars, colvars_old)
colvars_new = copyColvars(colvars, colvars_new)
for step in range(0, int(stringParams['nDragSteps_FG']), 1):
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for constrained FG run
writeColVarsRestraint(colvars_interp[step][i], stringParams['restraint'], stringParams['imagedir'] + str(i) + "/colvars_img" + str(i) + ".restraint")
print "Iter", itr, ": Submitting restraint batch step" + str(step) + "."
# call constrained NAMD run
callNAMDConstraint(SimulParam, stringParams)
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for release FG run
writeColVarsRestraint(colvars[i], stringParams['weakRestraint'], stringParams['imagedir'] + str(i) + "/colvars_img" + str(i) + "_weak.restraint")
print "Iter", itr, ": Submitting release batch."
# call unconstrained NAMD run
callNAMD(SimulParam, stringParams)
# now collect the colvar data from the release phase.
for i in range(0, int(stringParams['nimages']), 1):
#print "Before Read " + str(i) + ": \n", colvars[i]
print "Iter", itr, ": Reading colvar traj " + str(i)
colvars_new[i] = readColVarsTraj(stringParams, colvars_new[i], stringParams['imagedir'] + str(i) + "/img" + str(i) + ".colvars.traj")
#print " \nAfter read " + str(i) + ": \n", colvars[i]
#----------REPARAMETERIZE FG STRING VARIABLES---------------
for i in range(0, int(stringParams['reparItr']), 1):
print "Repar Iteration: " + str(i)
smoothString(float(stringParams['kappa']), colvars_new)
colvars_new = reparString(colvars_new)
printRMSD(colvars_new)
ofile_colvars_FG = open(stringParams['scratchdir'] + "FG_colvars.out", "w")
writeColVarsToFile(colvars_new, ofile_colvars_FG, itr)
ofile_colvars_FG.close()
#------------CG ITERATION-----------------
print "Iter", itr, ": Starting CG iteration."
print "Iter", itr, ": Writing restraint file."
# generate the interpolation
colvars_interp_CG = genInterp(colvars_CG, colvars_old_CG, int(stringParams['nDragSteps_CG']))
# make a copy of the current colvars location
colvars_old_CG = copyColvars(colvars_CG, colvars_old_CG)
colvars_CG_new = copyColvars(colvars_CG, colvars_CG_new)
for step in range(0, int(stringParams['nDragSteps_CG']), 1):
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for constrained CG run
CHARMM_writeColVarsRestraint(colvars_interp_CG[step][i], stringParams['restraint_CG'], stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + ".restraint")
print "Iter", itr, ": Submitting restraint batch step" + str(step) + "."
# call constrained CHARMM run
callCHARMMConstraint(SimulParam, stringParams)
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for release CG run
CHARMM_writeColVarsRestraint(colvars_CG[i], stringParams['weakRestraint_CG'], stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + "_weak.restraint")
print "Iter", itr, ": Submitting release batch."
# call unconstrained CHARMM run
callCHARMM(SimulParam, stringParams)
# now collect the colvar data from the release phase.
for i in range(0, int(stringParams['nimages']), 1):
#print "Before Read " + str(i) + ": \n", colvars[i]
print "Iter", itr, ": Reading colvar traj " + str(i)
colvars_CG_new[i] = CHARMM_readColVarsTraj(stringParams, colvars_CG_new[i], stringParams['imagedir'] + str(i) + "/charmm_img" + str(i) + ".colvars.traj")
#print " \nAfter read " + str(i) + ": \n", colvars[i]
#----------REPARAMETERIZE CG STRING VARIABLES---------------
for i in range(0, int(stringParams['reparItr']), 1):
print "Repar Iteration: " + str(i)
smoothString(float(stringParams['kappa']), colvars_CG_new)
colvars_CG_new = reparString(colvars_CG_new)
printRMSD(colvars_CG_new)
ofile_colvars_CG = open(stringParams['scratchdir'] + "CG_colvars.out", "w")
writeColVarsToFile(colvars_CG_new, ofile_colvars_CG, itr)
ofile_colvars_CG.close()
#------------DETERMINE CORRECTION TERM---------------------
colvars_diff = getDiff(colvars_new, colvars_CG_new, colvars_diff, float(stringParams['Delta']))
# apply geometric averaging.
#for image in range(0, len(colvars_diff), 1):
#for cv in range(0, len(colvars_diff[image]), 1):
#print colvars_diff[image][cv][-1]
colvars_diff, w_sum, w_norm = geomAvg(colvars_diff, stringParams, w_sum, w_norm)
#ofile_colvars_diff = open(stringParams['scratchdir'] + "diff_colvars" + str(itr) + ".out", "w")
#writeColVarsToFile(colvars_diff, ofile_colvars_diff, itr)
#ofile_colvars_diff.close()
ofile_colvars_CG_inner = open(stringParams['scratchdir'] + "CG_colvars_inner.out", "w")
#-------------PROPOGATE INNER LOOP-------------------------
for itr_inner in range(0, int(stringParams['niter_inner']), 1):
writeColVarsToFile(colvars_CG, ofile_colvars_CG_inner, itr_inner)
#------------CG ITERATION-----------------
print "Iter", itr, ": Starting inner loop iteration " + str(itr_inner)
print "Iter", itr, ": Writing restraint file."
# generate the interpolation
colvars_interp_CG = genInterp(colvars_CG, colvars_old_CG, int(stringParams['nDragSteps_CG']))
# make a copy of the current colvars location
colvars_old_CG = copyColvars(colvars_CG, colvars_old_CG)
for step in range(0, int(stringParams['nDragSteps_CG']), 1):
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for constrained CG run
CHARMM_writeColVarsRestraint(colvars_interp_CG[step][i], stringParams['restraint_CG'], stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + ".restraint")
print "Iter", itr, ": Submitting restraint batch step " + str(step) + "."
# call constrained CHARMM run
callCHARMMConstraint(SimulParam, stringParams)
for i in range(0, int(stringParams['nimages']), 1):
# setting input files for release CG run
CHARMM_writeColVarsRestraint(colvars_CG[i], stringParams['weakRestraint_CG'], stringParams['imagedir'] + str(i) + "/charmm_colvars_img" + str(i) + "_weak.restraint")
print "Iter", itr, ": Submitting release batch."
# call unconstrained CHARMM run
callCHARMM(SimulParam, stringParams)
# now collect the colvar data from the release phase.
for i in range(0, int(stringParams['nimages']), 1):
#print "Before Read " + str(i) + ": \n", colvars[i]
print "Iter", itr, ": Reading colvar traj " + str(i)
colvars_CG[i] = CHARMM_readColVarsTraj(stringParams, colvars_CG[i], stringParams['imagedir'] + str(i) + "/charmm_img" + str(i) + ".colvars.traj")
#print " \nAfter read " + str(i) + ": \n", colvars[i]
#----------REPARAMETERIZE CG STRING VARIABLES---------------
for i in range(0, int(stringParams['reparItr']), 1):
print "Repar Iteration: " + str(i)
smoothString(float(stringParams['kappa']), colvars_CG)
colvars_CG = reparString(colvars_CG)
printRMSD(colvars_CG)
#-----------ADD DIFFERENCE TERM----------------------
colvars_CG = addDiff(colvars_CG, colvars_diff, float(stringParams['Delta']))
#-------------END OF INNER LOOP----------------------
ofile_colvars_CG_inner.close()
#--------------SET FG TO CG POSITION-----------------------
colvars = copyColvars(colvars_CG, colvars)
#-----------------WRITE COLVARS-------------------------
# save out trajectory files for image steps.
if itr % int(stringParams['backupFreq']) == 0:
saveTrajFiles(stringParams, itr)
writeColVarsToFile(colvars, ofile_colvars, int(stringParams['niter']))
# close output files.
ofile_colvars.close()
end_time = time.time()
total_time = end_time - start_time
print "The total elapsed time is: " + str(total_time) + " sec."
print "Done."
| jtempkin/enhanced-sampling-toolkit | est/string_method/main.py | Python | mit | 20,185 | [
"CHARMM",
"NAMD"
] | f521bb968623661a92fd93dbc12d25fad040d6c6e081d135a7a41629f88fa732 |
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module defines Entry classes for containing experimental data.
"""
__author__ = "Shyue Ping Ong"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyuep@gmail.com"
__date__ = "Jun 27, 2012"
from monty.json import MSONable
from pymatgen.analysis.phase_diagram import PDEntry
from pymatgen.analysis.thermochemistry import ThermoData
from pymatgen.core.composition import Composition
class ExpEntry(PDEntry, MSONable):
"""
An lightweight ExpEntry object containing experimental data for a
composition for many purposes. Extends a PDEntry so that it can be used for
phase diagram generation and reaction calculation.
Current version works only with solid phases and at 298K. Further
extensions for temperature dependence are planned.
"""
def __init__(self, composition, thermodata, temperature=298):
"""
Args:
composition: Composition of the entry. For flexibility, this can take
the form of all the typical input taken by a Composition, including
a {symbol: amt} dict, a string formula, and others.
thermodata: A sequence of ThermoData associated with the entry.
temperature: A temperature for the entry in Kelvin. Defaults to 298K.
"""
comp = Composition(composition)
self._thermodata = thermodata
found = False
enthalpy = float("inf")
for data in self._thermodata:
if data.type == "fH" and data.value < enthalpy and (data.phaseinfo != "gas" and data.phaseinfo != "liquid"):
enthalpy = data.value
found = True
if not found:
raise ValueError("List of Thermodata does not contain enthalpy " "values.")
self.temperature = temperature
super().__init__(comp, enthalpy)
def __repr__(self):
return "ExpEntry {}, Energy = {:.4f}".format(self.composition.formula, self.energy)
def __str__(self):
return self.__repr__()
@classmethod
def from_dict(cls, d):
"""
:param d: Dict representation.
:return: ExpEntry
"""
thermodata = [ThermoData.from_dict(td) for td in d["thermodata"]]
return cls(d["composition"], thermodata, d["temperature"])
def as_dict(self):
"""
:return: MSONable dict
"""
return {
"@module": self.__class__.__module__,
"@class": self.__class__.__name__,
"thermodata": [td.as_dict() for td in self._thermodata],
"composition": self.composition.as_dict(),
"temperature": self.temperature,
}
| gmatteo/pymatgen | pymatgen/entries/exp_entries.py | Python | mit | 2,827 | [
"pymatgen"
] | 2f3ee86096ff0f2d6018f316a355f2db67fed15ba80ed123f4b6b42e113495b9 |
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
# classifiers
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import BernoulliNB
from sklearn import tree
from sklearn import svm
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.kernel_approximation import RBFSampler
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split
import dill
from sklearn.externals import joblib
from math import sqrt
import os.path
base_path = "/home/angelica/Git/osiris/srp/"
import os
import sys
config_path = base_path + "utilities/"
sys.path.append(os.path.abspath(config_path))
from MyAPI import MyAPI
from utilities import concatenate
import argparse
parser = argparse.ArgumentParser(description='Train')
parser.add_argument('-a', '--algorithm', help='algorithm (knn, one-vs-one, one-vs-rest,gaussian-nb,bernoulli-nb,decision-tree,svm,linear-svm,mlp,radius-neighbor,sgd,kernel-approx)',required=False)
parser.add_argument('-n', '--number', help='number of records',required=True)
parser.add_argument('-b', '--burst', help='burst size',required=False)
parser.add_argument('-c', '--cross_validation', action='store_true',help='enable cross validation',required=False)
parser.add_argument('-p', '--partial_fit',action='store_true',help='enable partial fit',required=False)
parser.add_argument('-t', '--test_set_size',help='test_set_size',required=False)
args = parser.parse_args()
algorithm = "knn"
if args.algorithm is not None:
algorithm = args.algorithm
# cross validation
cv = False
if args.cross_validation:
cv = True
# partial fit
pf = False
if args.partial_fit:
pf = True
burst = 100
if args.burst:
int(args.burst)
nr_test = 250000
if args.test_set_size:
nr_test = int(args.test_set_size)
nr = int(args.number)
classifier = None
parameters = None
# best estimator file
api = MyAPI()
exists_be_file = None
ps = api.get_prediction_steps()
# check whether the cross validation was already run
be_file_bu = base_path + 'data/best-estimator-' + algorithm + '-'
be_file = be_file_bu + str(ps[0]) + ".pkl"
exists_be_file = os.path.exists(be_file)
cv_classifier = []
if exists_be_file is True and cv is False:
for psi in range(0,len(ps)):
cv_classifier.append(joblib.load(be_file_bu + str(ps[psi]) + ".pkl"))
print "exists_be_file " + str(exists_be_file)
elif algorithm == 'knn':
classifier = KNeighborsClassifier(weights='distance',n_neighbors=5)
if cv:
parameters = { 'n_neighbors' : np.arange(3, 8),
'weights' : ['uniform', 'distance'],
'metric' : ['euclidean', 'manhattan', 'chebyshev', 'minkowski'],
'algorithm' : ['auto', 'ball_tree', 'kd_tree'],
# 'leaf_size' : np.arange(30,50)
}
elif algorithm == 'one-vs-one':
classifier = OneVsOneClassifier(LinearSVC(random_state=0))
if cv:
parameters = { 'estimator' : [LinearSVC(random_state=0), svm.LinearSVC()]}
elif algorithm == 'one-vs-rest':
classifier = OneVsRestClassifier(LinearSVC(random_state=0))
if cv:
parameters = { 'estimator' : [LinearSVC(random_state=0), svm.LinearSVC()]}
# gaussian naive bayes
elif algorithm == 'gaussian-nb':
classifier = GaussianNB()
if cv:
parameters = {}
# bernoulli naive bayes
elif algorithm == 'bernoulli-nb':
classifier = BernoulliNB()
if cv:
parameters = { 'alpha' : 0.1 * np.arange(0, 10)}
elif algorithm == 'decision-tree':
classifier = tree.DecisionTreeClassifier()
if cv:
parameters = { 'criterion' : ['gini', 'entropy'],
'splitter' : ['best', 'random']
}
elif algorithm == 'svm':
classifier = svm.SVC(decision_function_shape='ovo')
if cv:
parameters = { 'kernel' : [ 'poly', 'rbf', 'sigmoid'],
'decision_function_shape' : ['ovo', 'ovr', 'None']
}
elif algorithm == 'linear-svm':
classifier = svm.LinearSVC()
if cv:
parameters = { 'loss' : ['hinge', 'squared_hinge']}
elif algorithm == 'mlp':
#classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)
if cv:
parameters = {'alpha' : [1e-5],
'solver' : ['lbfgs', 'sgd', 'adam'],
'activation' : ['identity', 'logistic', 'tanh', 'relu'],
'learning_rate' : ['constant', 'invscaling', 'adaptive']
}
elif algorithm == 'radius-neighbor':
classifier = RadiusNeighborsClassifier(radius=100.0)
if cv:
parameters = { 'weights' : ['uniform', 'distance'],
'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute']
}
elif algorithm == 'sgd' or algorithm == 'kernel-approx':
classifier = SGDClassifier(loss="hinge", penalty="l2")
if cv:
parameters = { 'loss' : ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],
'penalty' : ['none', 'l2', 'l1', 'elasticnet'],
'alpha' : 10.0 ** -np.arange(1, 5)
#'learning_rate' : ['constant', 'optimal', 'invscaling']
}
for psi in range(0,len(ps)):
print ps[psi]
start_index = 1
end_index = start_index + burst
X = []
Y = []
robust_scaler = RobustScaler()
while end_index <= nr:
X_temp, Y_temp = api.get_dataset(psi, start_index=start_index,end_index=end_index, nr=nr)
if len(X_temp) > 0:
X = concatenate(X,X_temp)
Y = concatenate(Y,Y_temp)
start_index = end_index + 1
end_index = start_index + burst - 1
if end_index > nr:
end_index = nr
if start_index > nr:
end_index = nr+1
test_size = 0.20
if pf is True:
test_size = 0.05
#X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=42)
X_test = X[0:nr_test]
Y_test = Y[0:nr_test]
X_train = X[nr_test+1:len(X)]
Y_train = Y[nr_test+1:len(X)]
X_train = robust_scaler.fit_transform(X_train)
# save standard scaler
joblib.dump(robust_scaler, base_path + 'data/rs-' + algorithm + '-' + str(ps[psi]) + '.pkl')
X_test = robust_scaler.transform(X_test)
if algorithm == 'kernel-approx':
rbf_feature = RBFSampler(gamma=1, random_state=1)
X_train = rbf_feature.fit_transform(X_train)
X_test = rbf_feature.fit_transform(X_test)
elif algorithm == 'mlp':
n_output = len(set(Y))
#n_output = 2460
n_input = len(X_train[0]) + 1
n_neurons = int(round(sqrt(n_input*n_output)))
print "N input" , n_input
print "N output" , n_output
print "N neurons", n_neurons
classifier = MLPClassifier(solver='adam', alpha=1e-5,hidden_layer_sizes=(n_input, n_neurons, n_output), random_state=1)
if classifier is not None or exists_be_file is True:
if cv is True:
gs = GridSearchCV(classifier, parameters)
gs.fit(X_train, Y_train)
classifier = gs.best_estimator_
print(gs.best_estimator_)
# save best estimator
joblib.dump(gs.best_estimator_, base_path + 'data/best-estimator-' + algorithm + '-' + str(ps[psi]) + '.pkl')
elif exists_be_file is True:
classifier = cv_classifier[psi]
if pf is True:
classes = []
min_row = 323
max_row = 377
min_col = 99
max_col = 160
for row in range(min_row,max_row+1):
for col in range(min_col,max_col+1):
classes.append(str(row) + "_" + str(col))
nr_training = len(X_train)
train_start = 1
train_stop = burst
while train_stop <= nr_training:
classifier.partial_fit(X_train[train_start:train_stop], Y_train[train_start:train_stop], classes)
train_start = train_stop + 1
train_stop = train_start + burst - 1
if train_stop > nr_training:
train_stop = nr_training
if train_start >= nr_training:
train_stop = nr_training + 1
else:
classifier.fit(X_train, Y_train)
#save classifier
joblib.dump(classifier, base_path + 'data/' + algorithm + '-' + str(ps[psi]) + '.pkl')
# store classes
ordered_y = sorted(set(Y_train))
joblib.dump(ordered_y, base_path + 'data/classes-' + algorithm + '-' + str(ps[psi]) + '.pkl')
accuracy = classifier.score(X_test, Y_test)
Y_pred = classifier.predict(X_test)
out_file = open(base_path + "data/test-" + algorithm + '-' + str(ps[psi]) + '.txt',"w")
out_file.write(str(ps[psi]) + " Precision macro: %1.3f" % precision_score(Y_test, Y_pred,average='macro') + "\n")
out_file.write(str(ps[psi]) + " Precision micro: %1.3f" % precision_score(Y_test, Y_pred,average='micro') + "\n")
out_file.write(str(ps[psi]) + " Precision weighted: %1.3f" % precision_score(Y_test, Y_pred,average='weighted') + "\n")
out_file.write(str(ps[psi]) + " Recall macro: %1.3f" % recall_score(Y_test, Y_pred, average='macro') + "\n")
out_file.write(str(ps[psi]) + " Recall micro: %1.3f" % recall_score(Y_test, Y_pred, average='micro') + "\n")
out_file.write(str(ps[psi]) + " Recall weighted: %1.3f" % recall_score(Y_test, Y_pred, average='weighted') + "\n")
out_file.write("Accuracy: %.3f" % accuracy + "\n")
out_file.close()
| alod83/osiris | srp/train.py | Python | mit | 10,250 | [
"Gaussian"
] | a067ab9b5114e26c73822fdd59292c2f29533875dde0e359fd25ab2ab4c627f8 |
import logging
import urllib
from collections import defaultdict
from lxml import html
from django.conf import settings
from django.core.context_processors import csrf
from django.core.exceptions import PermissionDenied
from django.core.urlresolvers import reverse
from django.contrib.auth.models import User, AnonymousUser
from django.contrib.auth.decorators import login_required
from django.http import Http404, HttpResponse
from django.shortcuts import redirect
from edxmako.shortcuts import render_to_response, render_to_string
from django_future.csrf import ensure_csrf_cookie
from django.views.decorators.cache import cache_control
from django.db import transaction
from markupsafe import escape
import django.utils
from courseware import grades
from courseware.access import has_access
from courseware.courses import (get_courses, get_course_with_access, sort_by_announcement, get_course_info_section,
get_course_by_id, get_course, course_image_url, get_course_about_section, get_courses_by_search)
import courseware.tabs as tabs
from courseware.masquerade import setup_masquerade
from courseware.model_data import FieldDataCache
from .module_render import toc_for_course, get_module_for_descriptor,mobi_toc_for_course
from courseware.models import StudentModule, StudentModuleHistory
from course_modes.models import CourseMode
from student.models import UserTestGroup, CourseEnrollment
from student.views import course_from_id, single_course_reverification_info
from util.cache import cache, cache_if_anonymous
from util.json_request import JsonResponse
from xblock.fragment import Fragment
from xmodule.modulestore import Location
from xmodule.modulestore.django import modulestore, loc_mapper
from xmodule.modulestore.exceptions import InvalidLocationError, ItemNotFoundError, NoPathToItem
from xmodule.modulestore.search import path_to_location
from xmodule.course_module import CourseDescriptor
from xmodule.contentstore.content import StaticContent
import shoppingcart
from microsite_configuration import microsite
log = logging.getLogger("edx.courseware")
template_imports = {'urllib': urllib}
def user_groups(user):
"""
TODO (vshnayder): This is not used. When we have a new plan for groups, adjust appropriately.
"""
if not user.is_authenticated():
return []
# TODO: Rewrite in Django
key = 'user_group_names_{user.id}'.format(user=user)
cache_expiration = 60 * 60 # one hour
# Kill caching on dev machines -- we switch groups a lot
group_names = cache.get(key)
if settings.DEBUG:
group_names = None
if group_names is None:
group_names = [u.name for u in UserTestGroup.objects.filter(users=user)]
cache.set(key, group_names, cache_expiration)
return group_names
#@ensure_csrf_cookie
#@cache_if_anonymous
def courses(request):
"""
Render "find courses" page. The course selection work is done in courseware.courses.
"""
q = request.GET.get('query', '')
courses_aa = get_courses_by_search(request.META.get('HTTP_HOST'))
courses_list = []
if q != "":
for course in courses_aa:
if q in course.org or q in course.id or q in course.display_name_with_default:
courses_list.append(course)
else:
continue
else:
courses_list = courses_aa
courses = sort_by_announcement(courses_list)
return render_to_response("courseware/courses.html", {'courses': courses})
def return_fixed_courses(request, courses, user=AnonymousUser(), action=None):
default_length = 8
course_id = request.GET.get("course_id")
if course_id:
course_id = course_id.replace(".", '/')
try:
index_course = get_course_by_id(course_id)
course_index = (courses.index(index_course) + 1)
except:
course_index = 0
current_list = courses[course_index:]
if len(current_list) > default_length:
current_list = current_list[course_index:(course_index + 8)]
course_list = []
for course in current_list:
try:
course_json = mobi_course_info(request, course, action)
course_json["registered"] = registered_for_course(course, user)
course_list.append(course_json)
except:
continue
return JsonResponse({"count": len(courses), "course-list": course_list})
def courses_list_handler(request, action):
"""
Return courses based on request params
"""
try:
user = request.user
except:
user = AnonymousUser()
if action not in ["homefalls", "all", "hot", "latest", "my", "search", "rolling"]:
return JsonResponse({"success": False, "errmsg": "not support other actions except homefalls all hot latest rolling and my"})
def get_courses_depend_action():
"""
Return courses depend on action
action: [homefalls, hot, lastest, my, search]
homefalls: get all courses
hot: Number of attended people > ?
lastest: News last week
my: I registered
all: like 'homefalls'
"""
courses = get_courses(user, request.META.get('HTTP_HOST'))
courses = sort_by_announcement(courses)
courses_list = []
if action == "latest":
default_count = 20
if len(courses) < default_count:
default_count = len(courses)
courses_list = courses[0:default_count]
elif action == "my":
# filter my registered courses
for course in courses:
if registered_for_course(course, user):
courses_list.append(course)
elif action == "rolling":
default_count = 5
courses_list = courses[0:default_count]
elif action == 'search':
keyword = request.GET.get("keyword")
if keyword:
for c in courses:
print (keyword in c.org or keyword in c.id or keyword in c.display_name_with_default)
if keyword in c.org or keyword in c.id or keyword in c.display_name_with_default:
courses_list.append(c)
else:
courses_list = courses
return courses_list
courses = get_courses_depend_action()
# get_courses_depend_action()
return return_fixed_courses(request, courses, user, action)
def _course_json(course, course_id):
locator = loc_mapper().translate_location(course_id, course.location, published=False, add_entry_if_missing=True)
is_container = course.has_children
result = {
'display_name': course.display_name,
'id': unicode(locator),
'category': course.category,
'is_draft': getattr(course, 'is_draft', False),
'is_container': is_container
}
if is_container:
result['children'] = [_course_json(child, course_id) for child in course.get_children()]
category = result['category']
if result['category'] == 'video':
result[category + '-url'] = "http://www.diandiyun.com/Clip_480_5sec_6mbps_h264.mp4"
elif result['category'] == 'problem':
result[category + '-url'] = "http://music.163.com/"
return result
def mobi_course_info(request, course, action=None):
course_logo = course_image_url(course)
imgurl = course_logo
if action in ["homefalls", "all", "hot", "latest", "my", "search"]:
try:
course_mini_info = course.id.split('/')
asset_location = StaticContent.compute_location(course_mini_info[0], course_mini_info[1], 'mobi-logo-img.jpg')
imgurl = StaticContent.get_url_path_from_location(asset_location)
except:
print "=========================fail load mobi image==============================="
print "We will load this info to log"
return {
"id": course.id.replace('/', '.'),
"name": course.display_name_with_default,
"logo": request.get_host() + course_image_url(course),
"org": course.display_org_with_default,
"course_number": course.display_number_with_default,
"start_date": course.start.strftime("%Y-%m-%d"),
"about": get_course_about_section(course, 'short_description'),
"category": course.category,
"imgurl": request.get_host() + imgurl
}
def _course_info_content(html_parsed):
"""
Constructs the HTML for the course info update, not including the header.
"""
if len(html_parsed) == 1:
# could enforce that update[0].tag == 'h2'
content = html_parsed[0].tail
else:
content = html_parsed[0].tail if html_parsed[0].tail is not None else ""
content += "\n".join([html.tostring(ele) for ele in html_parsed[1:]])
return content
def parse_updates_html_str(html_str):
try:
course_html_parsed = html.fromstring(html_str)
except:
escaped = django.utils.html.eacape(html_str)
course_html_parsed = html.fromstring(escaped)
course_upd_collection = []
if course_html_parsed.tag == 'section':
for index, update in enumerate(course_html_parsed):
if len(update) > 0:
content = _course_info_content(update)
computer_id = len(course_html_parsed) - index
payload = {
"id": computer_id,
"date": update.findtext("h2"),
"content": content
}
course_upd_collection.append(payload)
return {"updates": course_upd_collection}
def mobi_course_action(request, course_id, action):
try:
course_id_bak = course_id.replace('.', '/')
if action in ["updates", "handouts", "structure"]:
course = get_course_with_access(request.user, course_id_bak, 'see_exists')
user = request.user
if not user:
user = AnonymousUser()
registered = registered_for_course(course, user)
if action == "updates" and registered:
course_updates = get_course_info_section(request, course, action)
return JsonResponse(parse_updates_html_str(course_updates))
elif action == "handouts" and registered:
course_handouts = get_course_info_section(request, course, action)
return JsonResponse({"handouts": course_handouts})
elif action == "structure":
return JsonResponse(_course_json(course, course.location.course_id))
else:
raise Exception
else:
course = get_course_with_access(request.user, course_id_bak, 'see_exists')
return JsonResponse(mobi_course_info(request, course))
except:
return JsonResponse({"success": False, "errmsg": "access denied!"})
def render_accordion(request, course, chapter, section, field_data_cache):
"""
Draws navigation bar. Takes current position in accordion as
parameter.
If chapter and section are '' or None, renders a default accordion.
course, chapter, and section are the url_names.
Returns the html string
"""
# grab the table of contents
user = User.objects.prefetch_related("groups").get(id=request.user.id)
request.user = user # keep just one instance of User
toc = toc_for_course(user, request, course, chapter, section, field_data_cache)
context = dict([('toc', toc),
('course_id', course.id),
('csrf', csrf(request)['csrf_token']),
('due_date_display_format', course.due_date_display_format)] + template_imports.items())
return render_to_string('courseware/accordion.html', context)
def get_current_child(xmodule):
"""
Get the xmodule.position's display item of an xmodule that has a position and
children. If xmodule has no position or is out of bounds, return the first child.
Returns None only if there are no children at all.
"""
if not hasattr(xmodule, 'position'):
return None
if xmodule.position is None:
pos = 0
else:
# position is 1-indexed.
pos = xmodule.position - 1
children = xmodule.get_display_items()
if 0 <= pos < len(children):
child = children[pos]
elif len(children) > 0:
# Something is wrong. Default to first child
child = children[0]
else:
child = None
return child
def redirect_to_course_position(course_module):
"""
Return a redirect to the user's current place in the course.
If this is the user's first time, redirects to COURSE/CHAPTER/SECTION.
If this isn't the users's first time, redirects to COURSE/CHAPTER,
and the view will find the current section and display a message
about reusing the stored position.
If there is no current position in the course or chapter, then selects
the first child.
"""
urlargs = {'course_id': course_module.id}
chapter = get_current_child(course_module)
if chapter is None:
# oops. Something bad has happened.
raise Http404("No chapter found when loading current position in course")
urlargs['chapter'] = chapter.url_name
if course_module.position is not None:
return redirect(reverse('courseware_chapter', kwargs=urlargs))
# Relying on default of returning first child
section = get_current_child(chapter)
if section is None:
raise Http404("No section found when loading current position in course")
urlargs['section'] = section.url_name
return redirect(reverse('courseware_section', kwargs=urlargs))
def save_child_position(seq_module, child_name):
"""
child_name: url_name of the child
"""
for position, c in enumerate(seq_module.get_display_items(), start=1):
if c.url_name == child_name:
# Only save if position changed
if position != seq_module.position:
seq_module.position = position
# Save this new position to the underlying KeyValueStore
seq_module.save()
def chat_settings(course, user):
"""
Returns a dict containing the settings required to connect to a
Jabber chat server and room.
"""
domain = getattr(settings, "JABBER_DOMAIN", None)
if domain is None:
log.warning('You must set JABBER_DOMAIN in the settings to '
'enable the chat widget')
return None
return {
'domain': domain,
# Jabber doesn't like slashes, so replace with dashes
'room': "{ID}_class".format(ID=course.id.replace('/', '-')),
'username': "{USER}@{DOMAIN}".format(
USER=user.username, DOMAIN=domain
),
# TODO: clearly this needs to be something other than the username
# should also be something that's not necessarily tied to a
# particular course
'password': "{USER}@{DOMAIN}".format(
USER=user.username, DOMAIN=domain
),
}
@login_required
@ensure_csrf_cookie
@cache_control(no_cache=True, no_store=True, must_revalidate=True)
def index(request, course_id, chapter=None, section=None,
position=None):
"""
Displays courseware accordion and associated content. If course, chapter,
and section are all specified, renders the page, or returns an error if they
are invalid.
If section is not specified, displays the accordion opened to the right chapter.
If neither chapter or section are specified, redirects to user's most recent
chapter, or the first chapter if this is the user's first visit.
Arguments:
- request : HTTP request
- course_id : course id (str: ORG/course/URL_NAME)
- chapter : chapter url_name (str)
- section : section url_name (str)
- position : position in module, eg of <sequential> module (str)
Returns:
- HTTPresponse
"""
user = User.objects.prefetch_related("groups").get(id=request.user.id)
request.user = user # keep just one instance of User
course = get_course_with_access(user, course_id, 'load', depth=2)
staff_access = has_access(user, course, 'staff')
registered = registered_for_course(course, user)
if not registered:
# TODO (vshnayder): do course instructors need to be registered to see course?
log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.url())
return redirect(reverse('about_course', args=[course.id]))
masq = setup_masquerade(request, staff_access)
try:
field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course.id, user, course, depth=2)
course_module = get_module_for_descriptor(user, request, course, field_data_cache, course.id)
if course_module is None:
log.warning(u'If you see this, something went wrong: if we got this'
u' far, should have gotten a course module for this user')
return redirect(reverse('about_course', args=[course.id]))
if chapter is None:
return redirect_to_course_position(course_module)
context = {
'csrf': csrf(request)['csrf_token'],
'accordion': render_accordion(request, course, chapter, section, field_data_cache),
'COURSE_TITLE': course.display_name_with_default,
'course': course,
'init': '',
'fragment': Fragment(),
'staff_access': staff_access,
'masquerade': masq,
'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),
'reverifications': fetch_reverify_banner_info(request, course_id),
}
# Only show the chat if it's enabled by the course and in the
# settings.
show_chat = course.show_chat and settings.FEATURES['ENABLE_CHAT']
if show_chat:
context['chat'] = chat_settings(course, user)
# If we couldn't load the chat settings, then don't show
# the widget in the courseware.
if context['chat'] is None:
show_chat = False
context['show_chat'] = show_chat
chapter_descriptor = course.get_child_by(lambda m: m.url_name == chapter)
if chapter_descriptor is not None:
save_child_position(course_module, chapter)
else:
raise Http404('No chapter descriptor found with name {}'.format(chapter))
chapter_module = course_module.get_child_by(lambda m: m.url_name == chapter)
if chapter_module is None:
# User may be trying to access a chapter that isn't live yet
if masq=='student': # if staff is masquerading as student be kinder, don't 404
log.debug('staff masq as student: no chapter %s' % chapter)
return redirect(reverse('courseware', args=[course.id]))
raise Http404
if section is not None:
section_descriptor = chapter_descriptor.get_child_by(lambda m: m.url_name == section)
if section_descriptor is None:
# Specifically asked-for section doesn't exist
if masq=='student': # if staff is masquerading as student be kinder, don't 404
log.debug('staff masq as student: no section %s' % section)
return redirect(reverse('courseware', args=[course.id]))
raise Http404
# cdodge: this looks silly, but let's refetch the section_descriptor with depth=None
# which will prefetch the children more efficiently than doing a recursive load
section_descriptor = modulestore().get_instance(course.id, section_descriptor.location, depth=None)
# Load all descendants of the section, because we're going to display its
# html, which in general will need all of its children
section_field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course_id, user, section_descriptor, depth=None)
section_module = get_module_for_descriptor(request.user,
request,
section_descriptor,
section_field_data_cache,
course_id,
position
)
if section_module is None:
# User may be trying to be clever and access something
# they don't have access to.
raise Http404
# Save where we are in the chapter
save_child_position(chapter_module, section)
context['fragment'] = section_module.render('student_view')
context['section_title'] = section_descriptor.display_name_with_default
else:
# section is none, so display a message
prev_section = get_current_child(chapter_module)
if prev_section is None:
# Something went wrong -- perhaps this chapter has no sections visible to the user
raise Http404
prev_section_url = reverse('courseware_section', kwargs={'course_id': course_id,
'chapter': chapter_descriptor.url_name,
'section': prev_section.url_name})
context['fragment'] = Fragment(content=render_to_string(
'courseware/welcome-back.html',
{
'course': course,
'chapter_module': chapter_module,
'prev_section': prev_section,
'prev_section_url': prev_section_url
}
))
result = render_to_response('courseware/courseware.html', context)
except Exception as e:
if isinstance(e, Http404):
# let it propagate
raise
# In production, don't want to let a 500 out for any reason
if settings.DEBUG:
raise
else:
log.exception("Error in index view: user={user}, course={course},"
" chapter={chapter} section={section}"
"position={position}".format(
user=user,
course=course,
chapter=chapter,
section=section,
position=position
))
try:
result = render_to_response('courseware/courseware-error.html',
{'staff_access': staff_access,
'course': course})
except:
# Let the exception propagate, relying on global config to at
# at least return a nice error message
log.exception("Error while rendering courseware-error page")
raise
return result
@login_required
@ensure_csrf_cookie
@cache_control(no_cache=True, no_store=True, must_revalidate=True)
def mobi_index(request, course_id, chapter=None, section=None,
position=None):
user = User.objects.prefetch_related("groups").get(id=request.user.id)
request.user = user # keep just one instance of User
course = get_course_with_access(user, course_id, 'load', depth=2)
staff_access = has_access(user, course, 'staff')
registered = registered_for_course(course, user)
if not registered:
# TODO (vshnayder): do course instructors need to be registered to see course?
log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.url())
return redirect(reverse('about_course', args=[course.id]))
masq = setup_masquerade(request, staff_access)
try:
field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course.id, user, course, depth=2)
course_module = get_module_for_descriptor(user, request, course, field_data_cache, course.id)
if course_module is None:
log.warning(u'If you see this, something went wrong: if we got this'
u' far, should have gotten a course module for this user')
return redirect(reverse('about_course', args=[course.id]))
if chapter is None:
return redirect_to_course_position(course_module)
context = {
'csrf': csrf(request)['csrf_token'],
'accordion': render_accordion(request, course, chapter, section, field_data_cache),
'COURSE_TITLE': course.display_name_with_default,
'course': course,
'init': '',
'fragment': Fragment(),
'staff_access': staff_access,
'masquerade': masq,
'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),
'reverifications': fetch_reverify_banner_info(request, course_id),
}
# Only show the chat if it's enabled by the course and in the
# settings.
show_chat = course.show_chat and settings.FEATURES['ENABLE_CHAT']
if show_chat:
context['chat'] = chat_settings(course, user)
# If we couldn't load the chat settings, then don't show
# the widget in the courseware.
if context['chat'] is None:
show_chat = False
context['show_chat'] = show_chat
chapter_descriptor = course.get_child_by(lambda m: m.url_name == chapter)
if chapter_descriptor is not None:
save_child_position(course_module, chapter)
else:
raise Http404('No chapter descriptor found with name {}'.format(chapter))
chapter_module = course_module.get_child_by(lambda m: m.url_name == chapter)
if chapter_module is None:
# User may be trying to access a chapter that isn't live yet
if masq=='student': # if staff is masquerading as student be kinder, don't 404
log.debug('staff masq as student: no chapter %s' % chapter)
return redirect(reverse('courseware', args=[course.id]))
raise Http404
if section is not None:
section_descriptor = chapter_descriptor.get_child_by(lambda m: m.url_name == section)
if section_descriptor is None:
# Specifically asked-for section doesn't exist
if masq=='student': # if staff is masquerading as student be kinder, don't 404
log.debug('staff masq as student: no section %s' % section)
return redirect(reverse('courseware', args=[course.id]))
raise Http404
# cdodge: this looks silly, but let's refetch the section_descriptor with depth=None
# which will prefetch the children more efficiently than doing a recursive load
section_descriptor = modulestore().get_instance(course.id, section_descriptor.location, depth=None)
# Load all descendants of the section, because we're going to display its
# html, which in general will need all of its children
section_field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course_id, user, section_descriptor, depth=None)
section_module = get_module_for_descriptor(request.user,
request,
section_descriptor,
section_field_data_cache,
course_id,
position
)
if section_module is None:
# User may be trying to be clever and access something
# they don't have access to.
raise Http404
# Save where we are in the chapter
save_child_position(chapter_module, section)
context['fragment'] = section_module.render('mobi_student_view')
context['section_title'] = section_descriptor.display_name_with_default
else:
# section is none, so display a message
prev_section = get_current_child(chapter_module)
if prev_section is None:
# Something went wrong -- perhaps this chapter has no sections visible to the user
raise Http404
prev_section_url = reverse('courseware_section', kwargs={'course_id': course_id,
'chapter': chapter_descriptor.url_name,
'section': prev_section.url_name})
context['fragment'] = Fragment(content=render_to_string(
'courseware/welcome-back.html',
{
'course': course,
'chapter_module': chapter_module,
'prev_section': prev_section,
'prev_section_url': prev_section_url
}
))
result = render_to_response('wechat/mobi_courseware.html', context)
except Exception as e:
if isinstance(e, Http404):
# let it propagate
raise
# In production, don't want to let a 500 out for any reason
if settings.DEBUG:
raise
else:
log.exception("Error in index view: user={user}, course={course},"
" chapter={chapter} section={section}"
"position={position}".format(
user=user,
course=course,
chapter=chapter,
section=section,
position=position
))
try:
result = render_to_response('courseware/courseware-error.html',
{'staff_access': staff_access,
'course': course})
except:
# Let the exception propagate, relying on global config to at
# at least return a nice error message
log.exception("Error while rendering courseware-error page")
raise
return result
def mobi_directory(request, course_id):
user = User.objects.prefetch_related("groups").get(id=request.user.id)
request.user = user # keep just one instance of User
course = get_course_with_access(user, course_id, 'load', depth=2)
staff_access = has_access(user, course, 'staff')
registered = registered_for_course(course, user)
if not registered:
# TODO (vshnayder): do course instructors need to be registered to see course?
log.debug(u'User %s tried to view course %s but is not enrolled', user, course.location.url())
return redirect(reverse('about_course', args=[course.id]))
masq = setup_masquerade(request, staff_access)
try:
field_data_cache = FieldDataCache.cache_for_descriptor_descendents(
course.id, user, course, depth=2)
course_module = get_module_for_descriptor(user, request, course, field_data_cache, course.id)
if course_module is None:
log.warning(u'If you see this, something went wrong: if we got this'
u' far, should have gotten a course module for this user')
return redirect(reverse('about_course', args=[course.id]))
context = {
'csrf': csrf(request)['csrf_token'],
'accordion': mobi_render_accordion(request, course),
'COURSE_TITLE': course.display_name_with_default,
'course': course,
'init': '',
'fragment': Fragment(),
'staff_access': staff_access,
'masquerade': masq,
'xqa_server': settings.FEATURES.get('USE_XQA_SERVER', 'http://xqa:server@content-qa.mitx.mit.edu/xqa'),
'reverifications': fetch_reverify_banner_info(request, course_id),
}
result = render_to_response('wechat/mobi_directory.html', context)
except Exception as e:
if isinstance(e, Http404):
# let it propagate
raise
# In production, don't want to let a 500 out for any reason
if settings.DEBUG:
raise
else:
log.exception("Error in index view: user={user}, course={course},".format(
user=user,
course=course,))
try:
result = render_to_response('courseware/courseware-error.html',
{'staff_access': staff_access,
'course': course})
except:
# Let the exception propagate, relying on global config to at
# at least return a nice error message
log.exception("Error while rendering courseware-error page")
raise
return result
def mobi_render_accordion(request, course):
user = User.objects.prefetch_related("groups").get(id=request.user.id)
request.user = user # keep just one instance of User
toc = mobi_toc_for_course(user, request, course)
context = dict([('toc', toc),
('course_id', course.id),
('csrf', csrf(request)['csrf_token']),
('due_date_display_format', course.due_date_display_format)] + template_imports.items())
return render_to_string('wechat/mobi_accordion.html', context)
@ensure_csrf_cookie
def jump_to_id(request, course_id, module_id):
"""
This entry point allows for a shorter version of a jump to where just the id of the element is
passed in. This assumes that id is unique within the course_id namespace
"""
course_location = CourseDescriptor.id_to_location(course_id)
items = modulestore().get_items(
Location('i4x', course_location.org, course_location.course, None, module_id),
course_id=course_id
)
if len(items) == 0:
raise Http404("Could not find id = {0} in course_id = {1}. Referer = {2}".
format(module_id, course_id, request.META.get("HTTP_REFERER", "")))
if len(items) > 1:
log.warning("Multiple items found with id = {0} in course_id = {1}. Referer = {2}. Using first found {3}...".
format(module_id, course_id, request.META.get("HTTP_REFERER", ""), items[0].location.url()))
return jump_to(request, course_id, items[0].location.url())
@ensure_csrf_cookie
def jump_to(request, course_id, location):
"""
Show the page that contains a specific location.
If the location is invalid or not in any class, return a 404.
Otherwise, delegates to the index view to figure out whether this user
has access, and what they should see.
"""
# Complain if the location isn't valid
try:
location = Location(location)
except InvalidLocationError:
raise Http404("Invalid location")
# Complain if there's not data for this location
try:
(course_id, chapter, section, position) = path_to_location(modulestore(), course_id, location)
except ItemNotFoundError:
raise Http404(u"No data at this location: {0}".format(location))
except NoPathToItem:
raise Http404(u"This location is not in any class: {0}".format(location))
# choose the appropriate view (and provide the necessary args) based on the
# args provided by the redirect.
# Rely on index to do all error handling and access control.
if chapter is None:
return redirect('courseware', course_id=course_id)
elif section is None:
return redirect('courseware_chapter', course_id=course_id, chapter=chapter)
elif position is None:
return redirect('courseware_section', course_id=course_id, chapter=chapter, section=section)
else:
return redirect('courseware_position', course_id=course_id, chapter=chapter, section=section, position=position)
@ensure_csrf_cookie
def course_info(request, course_id):
"""
Display the course's info.html, or 404 if there is no such course.
Assumes the course_id is in a valid format.
"""
course = get_course_with_access(request.user, course_id, 'load')
staff_access = has_access(request.user, course, 'staff')
masq = setup_masquerade(request, staff_access) # allow staff to toggle masquerade on info page
reverifications = fetch_reverify_banner_info(request, course_id)
context = {
'request': request,
'course_id': course_id,
'cache': None,
'course': course,
'staff_access': staff_access,
'masquerade': masq,
'reverifications': reverifications,
}
return render_to_response('courseware/info.html', context)
@ensure_csrf_cookie
def static_tab(request, course_id, tab_slug):
"""
Display the courses tab with the given name.
Assumes the course_id is in a valid format.
"""
course = get_course_with_access(request.user, course_id, 'load')
tab = tabs.get_static_tab_by_slug(course, tab_slug)
if tab is None:
raise Http404
contents = tabs.get_static_tab_contents(
request,
course,
tab
)
if contents is None:
raise Http404
staff_access = has_access(request.user, course, 'staff')
return render_to_response('courseware/static_tab.html',
{'course': course,
'tab': tab,
'tab_contents': contents,
'staff_access': staff_access, })
# TODO arjun: remove when custom tabs in place, see courseware/syllabus.py
@ensure_csrf_cookie
def syllabus(request, course_id):
"""
Display the course's syllabus.html, or 404 if there is no such course.
Assumes the course_id is in a valid format.
"""
course = get_course_with_access(request.user, course_id, 'load')
staff_access = has_access(request.user, course, 'staff')
return render_to_response('courseware/syllabus.html', {'course': course,
'staff_access': staff_access, })
def registered_for_course(course, user):
"""
Return True if user is registered for course, else False
"""
if user is None:
return False
if user.is_authenticated():
return CourseEnrollment.is_enrolled(user, course.id)
else:
return False
@ensure_csrf_cookie
@cache_if_anonymous
def course_about(request, course_id):
if microsite.get_value(
'ENABLE_MKTG_SITE',
settings.FEATURES.get('ENABLE_MKTG_SITE', False)
):
raise Http404
course = get_course_with_access(request.user, course_id, 'see_exists')
registered = registered_for_course(course, request.user)
if has_access(request.user, course, 'load'):
course_target = reverse('info', args=[course.id])
else:
course_target = reverse('about_course', args=[course.id])
show_courseware_link = (has_access(request.user, course, 'load') or
settings.FEATURES.get('ENABLE_LMS_MIGRATION'))
# Note: this is a flow for payment for course registration, not the Verified Certificate flow.
registration_price = 0
in_cart = False
reg_then_add_to_cart_link = ""
if (settings.FEATURES.get('ENABLE_SHOPPING_CART') and
settings.FEATURES.get('ENABLE_PAID_COURSE_REGISTRATION')):
registration_price = CourseMode.min_course_price_for_currency(course_id,
settings.PAID_COURSE_REGISTRATION_CURRENCY[0])
if request.user.is_authenticated():
cart = shoppingcart.models.Order.get_cart_for_user(request.user)
in_cart = shoppingcart.models.PaidCourseRegistration.contained_in_order(cart, course_id)
reg_then_add_to_cart_link = "{reg_url}?course_id={course_id}&enrollment_action=add_to_cart".format(
reg_url=reverse('register_user'), course_id=course.id)
# see if we have already filled up all allowed enrollments
is_course_full = CourseEnrollment.is_course_full(course)
return render_to_response('courseware/course_about.html',
{'course': course,
'registered': registered,
'course_target': course_target,
'registration_price': registration_price,
'in_cart': in_cart,
'reg_then_add_to_cart_link': reg_then_add_to_cart_link,
'show_courseware_link': show_courseware_link,
'is_course_full': is_course_full})
@ensure_csrf_cookie
@cache_if_anonymous
def mktg_course_about(request, course_id):
"""
This is the button that gets put into an iframe on the Drupal site
"""
try:
course = get_course_with_access(request.user, course_id, 'see_exists')
except (ValueError, Http404) as e:
# if a course does not exist yet, display a coming
# soon button
return render_to_response(
'courseware/mktg_coming_soon.html', {'course_id': course_id}
)
registered = registered_for_course(course, request.user)
if has_access(request.user, course, 'load'):
course_target = reverse('info', args=[course.id])
else:
course_target = reverse('about_course', args=[course.id])
allow_registration = has_access(request.user, course, 'enroll')
show_courseware_link = (has_access(request.user, course, 'load') or
settings.FEATURES.get('ENABLE_LMS_MIGRATION'))
course_modes = CourseMode.modes_for_course(course.id)
return render_to_response(
'courseware/mktg_course_about.html',
{
'course': course,
'registered': registered,
'allow_registration': allow_registration,
'course_target': course_target,
'show_courseware_link': show_courseware_link,
'course_modes': course_modes,
}
)
@login_required
@cache_control(no_cache=True, no_store=True, must_revalidate=True)
@transaction.commit_manually
def progress(request, course_id, student_id=None):
"""
Wraps "_progress" with the manual_transaction context manager just in case
there are unanticipated errors.
"""
with grades.manual_transaction():
return _progress(request, course_id, student_id)
def _progress(request, course_id, student_id):
"""
Unwrapped version of "progress".
User progress. We show the grade bar and every problem score.
Course staff are allowed to see the progress of students in their class.
"""
course = get_course_with_access(request.user, course_id, 'load', depth=None)
staff_access = has_access(request.user, course, 'staff')
if student_id is None or student_id == request.user.id:
# always allowed to see your own profile
student = request.user
else:
# Requesting access to a different student's profile
if not staff_access:
raise Http404
student = User.objects.get(id=int(student_id))
# NOTE: To make sure impersonation by instructor works, use
# student instead of request.user in the rest of the function.
# The pre-fetching of groups is done to make auth checks not require an
# additional DB lookup (this kills the Progress page in particular).
student = User.objects.prefetch_related("groups").get(id=student.id)
courseware_summary = grades.progress_summary(student, request, course)
grade_summary = grades.grade(student, request, course)
if courseware_summary is None:
#This means the student didn't have access to the course (which the instructor requested)
raise Http404
context = {
'course': course,
'courseware_summary': courseware_summary,
'grade_summary': grade_summary,
'staff_access': staff_access,
'student': student,
'reverifications': fetch_reverify_banner_info(request, course_id)
}
with grades.manual_transaction():
response = render_to_response('courseware/progress.html', context)
return response
def fetch_reverify_banner_info(request, course_id):
"""
Fetches needed context variable to display reverification banner in courseware
"""
reverifications = defaultdict(list)
user = request.user
if not user.id:
return reverifications
enrollment = CourseEnrollment.get_or_create_enrollment(request.user, course_id)
course = course_from_id(course_id)
info = single_course_reverification_info(user, course, enrollment)
if info:
reverifications[info.status].append(info)
return reverifications
@login_required
def submission_history(request, course_id, student_username, location):
"""Render an HTML fragment (meant for inclusion elsewhere) that renders a
history of all state changes made by this user for this problem location.
Right now this only works for problems because that's all
StudentModuleHistory records.
"""
course = get_course_with_access(request.user, course_id, 'load')
staff_access = has_access(request.user, course, 'staff')
# Permission Denied if they don't have staff access and are trying to see
# somebody else's submission history.
if (student_username != request.user.username) and (not staff_access):
raise PermissionDenied
try:
student = User.objects.get(username=student_username)
student_module = StudentModule.objects.get(course_id=course_id,
module_state_key=location,
student_id=student.id)
except User.DoesNotExist:
return HttpResponse(escape("User {0} does not exist.".format(student_username)))
except StudentModule.DoesNotExist:
return HttpResponse(escape("{0} has never accessed problem {1}".format(student_username, location)))
history_entries = StudentModuleHistory.objects.filter(
student_module=student_module
).order_by('-id')
# If no history records exist, let's force a save to get history started.
if not history_entries:
student_module.save()
history_entries = StudentModuleHistory.objects.filter(
student_module=student_module
).order_by('-id')
context = {
'history_entries': history_entries,
'username': student.username,
'location': location,
'course_id': course_id
}
return render_to_response('courseware/submission_history.html', context)
| altsen/diandiyun-platform | lms/djangoapps/wechat/views.py | Python | agpl-3.0 | 47,026 | [
"VisIt"
] | e24c635006ae55b707e6101fc5ce4880c58a7414f63752374a352d527c5c4a2c |
SIMCACHE = {
'a': 0x0cc175b9c0f1b6a831c399e269772661,
'b': 0x92eb5ffee6ae2fec3ad71c777531578f,
'c': 0x4a8a08f09d37b73795649038408b5f33,
'd': 0x8277e0910d750195b448797616e091ad,
'e': 0xe1671797c52e15f763380b45e841ec32,
'f': 0x8fa14cdd754f91cc6554c9e71929cce7,
'g': 0xb2f5ff47436671b6e533d8dc3614845d,
'h': 0x2510c39011c5be704182423e3a695e91,
'i': 0x865c0c0b4ab0e063e5caa3387c1a8741,
'j': 0x363b122c528f54df4a0446b6bab05515,
'k': 0x8ce4b16b22b58894aa86c421e8759df3,
'l': 0x2db95e8e1a9267b7a1188556b2013b33,
'm': 0x6f8f57715090da2632453988d9a1501b,
'n': 0x7b8b965ad4bca0e41ab51de7b31363a1,
'o': 0xd95679752134a2d9eb61dbd7b91c4bcc,
'p': 0x83878c91171338902e0fe0fb97a8c47a,
'q': 0x7694f4a66316e53c8cdd9d9954bd611d,
'r': 0x4b43b0aee35624cd95b910189b3dc231,
's': 0x03c7c0ace395d80182db07ae2c30f034,
't': 0xe358efa489f58062f10dd7316b65649e,
'u': 0x7b774effe4a349c6dd82ad4f4f21d34c,
'v': 0x9e3669d19b675bd57058fd4664205d2a,
'w': 0xf1290186a5d0b1ceab27f4e77c0c5d68,
'x': 0x9dd4e461268c8034f5c8564e155c67a6,
'y': 0x415290769594460e2e485922904f345d,
'z': 0xfbade9e36a3f36d3d676c1b808451dd7,
'aa': 0x4124bc0a9335c27f086f24ba207a4912,
'ba': 0x07159c47ee1b19ae4fb9c40d480856c4,
'ca': 0x5435c69ed3bcc5b2e4d580e393e373d3,
'da': 0x5ca2aa845c8cd5ace6b016841f100d82,
'ea': 0x5b344ac52a0192941b46a8bf252c859c,
'fa': 0x89e6d2b383471fc370d828e552c19e65,
'ga': 0x32d7508fe69220cb40af28441ef746d9,
'ha': 0x925cc8d2953eba624b2bfedf91a91613,
'ia': 0xb40788f566d1124a95169068077031a9,
'ja': 0xa78c5bf69b40d464b954ef76815c6fa0,
'ka': 0x2c68e1d50809e4ae357bcffe1fc99d2a,
'la': 0xc9089f3c9adaf0186f6ffb1ee8d6501c,
'ma': 0xb74df323e3939b563635a2cba7a7afba,
'na': 0x6ec66e124fb93c190e9207b0b82f542a,
'oa': 0xbdcf5fc18e4f01990300ed0d0a306428,
'pa': 0xe529a9cea4a728eb9c5828b13b22844c,
'qa': 0x8264ee52f589f4c0191aa94f87aa1aeb,
'ra': 0xdb26ee047a4c86fbd2fba73503feccb6,
'sa': 0xc12e01f2a13ff5587e1e9e4aedb8242d,
'ta': 0xfec8f2a3f2e808ccb17c4d278b4fa469,
'ua': 0x5269f4d75f5bc75f0f94bab2100a5531,
'va': 0x43b1cc1db7be63d899dd4280f578691a,
'wa': 0xc68c559d956d4ca20f435ed74a6e71e6,
'xa': 0x53e59fface936ea788f7cf51e7b25531,
'ya': 0xd74600e380dbf727f67113fd71669d88,
'za': 0x959848ca10cc8a60da818ac11523dc63,
'ab': 0x187ef4436122d1cc2f40dc2b92f0eba0,
'bb': 0x21ad0bd836b90d08f4cf640b4c298e7c,
'cb': 0xd0d7fdb6977b26929fb68c6083c0b439,
'db': 0xd77d5e503ad1439f585ac494268b351b,
'eb': 0x2fc4ddaa1c378ef0a54726e992c707d4,
'fb': 0x35ce1d4eb0f666cd136987d34f64aedc,
'gb': 0x7885444af42e4b30c518c5be17c8850b,
'hb': 0x774f344a615692604de040918a72b149,
'ib': 0xa132c5cf175a7333483a34f2cd28110b,
'jb': 0xc4a6c07a8a2d7c804a5776d9d039428a,
'kb': 0xba34ea40525a4379add785228e37fe86,
'lb': 0x26403ec6d537fa31f63e294b44831734,
'mb': 0xa9ddcf51419881bdee445181e32ede58,
'nb': 0x9c59153d22d9f7cc021b17b425cc31c5,
'ob': 0x99faee4e1a331a7595932b7c18f9f5f6,
'pb': 0xf09883b57b33d3d33c39bbc8dd3b2be2,
'qb': 0x0d47b8346d57b12a5807c36fb1f14f3c,
'rb': 0x9e3f4f69757d07f6a0d2af4f1f2a1103,
'sb': 0x26148d621ef74844918af182d63976b6,
'tb': 0xe44d967f3e8a44f6a7fee562af4d82f4,
'ub': 0x1c78b486fa89d4f71edbbd0d53d214dc,
'vb': 0x2ab4f27ab1ffdcdad8ed21a965ca62ad,
'wb': 0x264adbc91466b88841687f0d4dd73ea7,
'xb': 0x4f59236a872d3d23fe86871831a2adc8,
'yb': 0x0b8dd1a01a737950a643a578f08f7900,
'zb': 0x78b269619f0723e8037a282f7fe8364b,
'ac': 0xe2075474294983e013ee4dd2201c7a73,
'bc': 0x5360af35bde9ebd8f01f492dc059593c,
'cc': 0xe0323a9039add2978bf5b49550572c7c,
'dc': 0x3212f5f463edb370ff55d3c3a7a15c8f,
'ec': 0x2f53e6f3f2acb041a4e0737e58c45321,
'fc': 0xe05fe30750d3ea262a610d17ebc07019,
'gc': 0xa3973867cdfb643f4b10526c25875928,
'hc': 0x6320c1115d5bc2b6ca615b96be050884,
'ic': 0xf05a225e14ff5e194a8eef0c6990cefb,
'jc': 0xb7adde8a9eec8ce92b5ee0507ce054a4,
'kc': 0x190a4568b24548e0dc8592f61f0a8cd2,
'lc': 0x196accbcf32b0a8e6bef92e1a37d0fc0,
'mc': 0xd6fd0924e324f50669ae0295adf59567,
'nc': 0x1e7342845e24eb3b5b3554490da1c128,
'oc': 0xfb024832e51eb5f6da9113a745f1eb59,
'pc': 0xbc54f4d60f1cec0f9a6cb70e13f2127a,
'qc': 0x9300c96aaec324987ea5ca6e13a02eda,
'rc': 0xff78648be52a4e79513f4e70b266c62a,
'sc': 0xd54185b71f614c30a396ac4bc44d3269,
'tc': 0x5c4fefda27cfe84c3999be13e6b8608a,
'uc': 0xd38af049e086eb7b59102bcf0c93974c,
'vc': 0xc56e52594d4ebe7f6cb2b96c4637b486,
'wc': 0x8399d88e3293cc89cacc1d735af12810,
'xc': 0x831c4baa8a44083a6434b892d573846b,
'yc': 0xa2bf364d91c65964491d6ef7c0a36c46,
'zc': 0x92a870e23eaac7b3c576e91b807f2a60,
'ad': 0x523af537946b79c4f8369ed39ba78605,
'bd': 0xc419b06b4c6579b50ff05adb3b8424f1,
'cd': 0x6865aeb3a9ed28f9a79ec454b259e5d0,
'dd': 0x1aabac6d068eef6a7bad3fdf50a05cc8,
'ed': 0xb5f3729e5418905ad2b21ce186b1c01d,
'fd': 0x36eba1e1e343279857ea7f69a597324e,
'gd': 0xa6be8a33b7c987f4ffb76d9c9805c7eb,
'hd': 0xb25ffa68ad761f8578cc61700c0140ed,
'id': 0xb80bb7740288fda1f201890375a60c8f,
'jd': 0x5a544a1c14fe313c3245c3267a292b8e,
'kd': 0x87221652a79fc3c9b04cde0b335fdd5b,
'ld': 0x946490ad0fdc17b3b899760a445128f0,
'md': 0x793914c9c583d9d86d0f4ed8c521b0c1,
'nd': 0xa69913f66f2cfd4bd3f8ea75954ac476,
'od': 0xafc7e8a98f75755e513d9d5ead888e1d,
'pd': 0xb69466b536f8ce43b6356ec1332e05a4,
'qd': 0x98877902e81c1a83b58eee2ff38dd17b,
'rd': 0xeeec033a2c4d56d7ba16b69358779091,
'sd': 0x6226f7cbe59e99a90b5cef6f94f966fd,
'td': 0x626726e60bd1215f36719a308a25b798,
'ud': 0x3dfe3d7a171097592cb00d6a57b6f845,
'vd': 0x8f7d15a8d28ec153835ef4bfc428d5e4,
'wd': 0xdd3ba2cca7da8526615be73d390527ac,
'xd': 0x7f30eefe5c51e1ae0939dab2051db75f,
'yd': 0xd2b739a728a844acdaa1d75ecdd789d7,
'zd': 0xd6cbb48444bf8cf4e6460eebceaefce1,
'ae': 0xb6bb43df4525b928a105fb5741bddbea,
'be': 0x910955a907e739b81ec8855763108a29,
'ce': 0x9b5394fd929896fb9e90e26b3d18e63d,
'de': 0x5f02f0889301fd7be1ac972c11bf3e7d,
'ee': 0x08a4415e9d594ff960030b921d42b91e,
'fe': 0x2d917f5d1275e96fd75e6352e26b1387,
'ge': 0x0ba64a0dea00947916dfb6a66866e1ca,
'he': 0x6f96cfdfe5ccc627cadf24b41725caa4,
'ie': 0x25400724d7370b0b29c9369d9af3dd21,
'je': 0x79563e90630af3525dff01b6638b0886,
'ke': 0x25bc6654798eb508fa0b6343212a74fe,
'le': 0xd9180594744f870aeefb086982e980bb,
'me': 0xab86a1e1ef70dff97959067b723c5c24,
'ne': 0xd4f917633649a3c47c7ab917fa990146,
'oe': 0x5f8e195948253346669e30c75d02edb9,
'pe': 0xdd07de8561395a7c37f8fcc1752d45e1,
'qe': 0x4d6e7051b02397d7733ea9a222fdb8e0,
're': 0x12eccbdd9b32918131341f38907cbbb5,
'se': 0xefad7abb323e3d4016284c8a6da076a1,
'te': 0x569ef72642be0fadd711d6a468d68ee1,
'ue': 0x31f644026e4c96dee546c228a1894c68,
've': 0xeabb18f0a40c9b3552370c9e1bc1d61e,
'we': 0xff1ccf57e98c817df1efcd9fe44a8aeb,
'xe': 0x956f8a3a1e6c05797e152fc2b2a0729b,
'ye': 0x00c66f1a036bd8f9cb709cb8d925d3d9,
'ze': 0x98b456a0723fa616284a632d9d31821b,
'af': 0xf0357a3f154bc2ffe2bff55055457068,
'bf': 0xc9f9d7dd806cf4122041837a80f47c64,
'cf': 0x4e29342d9904d64e9e25fd3b92558e2f,
'df': 0xeff7d5dba32b4da32d9a67a519434d3f,
'ef': 0xfeb78cc258bdc76867354f01c22dbe43,
'ff': 0x633de4b0c14ca52ea2432a3c8a5c4c31,
'gf': 0xe5bb23797bfea314a3db43d07dbd6a74,
'hf': 0xff4827739b75d73e08490b3380163658,
'if': 0x39c8942e1038872a822c0dc75eedbde3,
'jf': 0xfe0ec43cc4200e777aa2190ace58e7b4,
'kf': 0xd3789a3f91258fcf605452196e19c21c,
'lf': 0x69011ae7d1f3c17284b99c42254aca2d,
'mf': 0xea81aa7df47d74c6737bf98fabf3ff82,
'nf': 0x78d9238c1a217c8bbe8f6c26172fb12d,
'of': 0x8bf8854bebe108183caeb845c7676ae4,
'pf': 0x7287aa2c53d0a440da9db5614937e36f,
'qf': 0x71e8700a3759705df2ea164eb0182e8a,
'rf': 0xbea2f3fe6ec7414cdf0bf233abba7ef0,
'sf': 0x60d31eb37595dd44584be5ef363283e3,
'tf': 0x114d6a415b3d04db792ca7c0da0c7a55,
'uf': 0x9fbcd7dce5e3f334ca61dae6ad57415a,
'vf': 0x6abe33f215bcc63cbd8f92c19e7cd537,
'wf': 0xc1b291cb8522236e421731b509c9fcdd,
'xf': 0xf6440da9c764840fa7cac028c8c6118a,
'yf': 0x18fcd73eae8f118b0bf5f4cf5d1d766e,
'zf': 0x819bdbde26bea3dab1d223bf07d85fef,
'ag': 0x4e42f7dd43ecbfe104de58610557c5ba,
'bg': 0x5523c88dd347d1b7cc617f632b7efdb7,
'cg': 0x6e9cf3eef65da697796cf33f27eb0f57,
'dg': 0x2f7e54fe9de9db73067f562bc22d6eae,
'eg': 0x2a6a84e9e44441afbd75cc19ce28be37,
'fg': 0x3d4044d65abdda407a92991f1300ec97,
'gg': 0x73c18c59a39b18382081ec00bb456d43,
'hg': 0x40fe9ad4949331a12f5f19b477133924,
'ig': 0x4e8e41367997cfe705d62ea80592cbcc,
'jg': 0x1272c19590c3d44ce33ba054edfb9c78,
'kg': 0xebe86682666f2ab3da0843ed3097e4b3,
'lg': 0xa608b9c44912c72db6855ad555397470,
'mg': 0xb351bb9b0af6e4fc678749675c53ad67,
'ng': 0x66e10e9ff65ef479654dde3968d3440d,
'og': 0x5bc3c1d52024c150581c7651627304b7,
'pg': 0x235ec52392b77977539cf78b62e708d3,
'qg': 0x815319a66ff25ad94a0cefe0d5bebfa5,
'rg': 0x0ecb2b966eca6994910caee2947f6679,
'sg': 0x5dae429688af1c521ad87ac394192c6d,
'tg': 0x1f0eb0985870635e62fa2f68a223b173,
'ug': 0x2a0617accf8bb8625c43e2ffeb5b8d5b,
'vg': 0x1815235d384d2912d4668c73298f1e52,
'wg': 0xd876b31399be575129188ea7b662c4f2,
'xg': 0xb30f631559af46272160e9f8d9d88e5f,
'yg': 0x2bd3b10a9bb1ae7613231ed98232f149,
'zg': 0xbdc0439576eaded9b2c516352f1bba45,
'ah': 0x3cf4046014cbdfaa7ea8e6904ab04608,
'bh': 0xc08bba7a0c0386f1551e8474d853ecbf,
'ch': 0xd88fc6edf21ea464d35ff76288b84103,
'dh': 0x700f6fa0edb608ee5cc3cfa63f1c94cc,
'eh': 0x28c494da87ff99969927ac34ba30adbe,
'fh': 0x9226f86eb6b4ec0c78e8b8699a232c62,
'gh': 0x19b19ffc30caef1c9376cd2982992a59,
'hh': 0x5e36941b3d856737e81516acd45edc50,
'ih': 0x95f11b6956a5cd3a7de04c8f2664316e,
'jh': 0x373633ec8af28e5afaf6e5f4fd87469b,
'kh': 0xfa46ec0b4924e8c2194a53ef61b94039,
'lh': 0x8ecc6960abbf70f7a5a70d9bfaae585c,
'mh': 0xed8f5b7e74398143b43a93dc753618ae,
'nh': 0x86e41e046990daf7e850f49eb2d5a64d,
'oh': 0xc7218260ef2b966ab0454e07c55cf4e9,
'ph': 0xda984e42a5899bbdac496ef0cbadcee2,
'qh': 0x498aa78bf9a36a46ea557d907e441597,
'rh': 0x87e4210c7d7e6dbf9a659a8329577bce,
'sh': 0x77cbc257e66302866cf6191754c0c8e3,
'th': 0x1fdc0f893412ce55f0d2811821b84d3b,
'uh': 0xb20b42abce21dcd370986f1b3c2dfa5b,
'vh': 0x0436c815758ca3f3eb3b7be726695e80,
'wh': 0x6148bbf9dee45483f94f992b0b7c7a9d,
'xh': 0xf7c6d340ce08efb15d6240708f05f55f,
'yh': 0x9818af84da6f96579e99ed315f69e0c5,
'zh': 0x0f4dbe309348718e246f4566bbb76602,
'ai': 0x4921c0e2d1f6005abe1f9ec2e2041909,
'bi': 0x99d4fb3db1563c87da2cdfc0158b37c3,
'ci': 0x35ea51baf1fe7f0142ad5f950855dde0,
'di': 0x690382ddccb8abc7367a136262e1978f,
'ei': 0x1ee2225a0118c6a8ff464cf2926cf352,
'fi': 0x75778bf8fde7266d416b0089e7b8b793,
'gi': 0x28dd376c5a44acc92e450ee338260c56,
'hi': 0x49f68a5c8493ec2c0bf489821c21fc3b,
'ii': 0x7e98b8a17c0aad30ba64d47b74e2a6c1,
'ji': 0x78b83774d3a907fbea42783d58a95204,
'ki': 0x988287f7a1eb966ffc4e19bdbdeec7c3,
'li': 0xd70c1e5d44de8a9150eb91ecff563578,
'mi': 0x29bfe372865737fe2bfcfd3618b1da7d,
'ni': 0xe6c151d449e1db05b1ffb5ad5ec656cf,
'oi': 0xa2e63ee01401aaeca78be023dfbb8c59,
'pi': 0x72ab8af56bddab33b269c5964b26620a,
'qi': 0xed5a8409c61b68f9e9abcd349c3513d0,
'ri': 0x08c7b0daa33b1e5e86a230c1801254c9,
'si': 0xac5585d98646d255299c359140537783,
'ti': 0xe00fba6fedfb8a49e13346f7099a23fc,
'ui': 0x7d5c009e4eb8bbc78647caeca308e61b,
'vi': 0x35b36b28916d38b34abddf832e286126,
'wi': 0x993560a792e622201090d67041f7882e,
'xi': 0xd88468fb83a6d5675fcd2bdcb8fa57bf,
'yi': 0x10f9132e586392a6513eeeb303db630c,
'zi': 0x4e4ef151aa152df573ed8771d1423fb4,
'aj': 0x3b6f421e7550395e28e091c5565ac80a,
'bj': 0x39b9df3a0fb3356d11a63e22260e96ab,
'cj': 0x28198b369067e88dab9fefe85484dbf4,
'dj': 0x64ca60972a6ec926d1c4b9d31080c687,
'ej': 0x2c7e13fa20a22154b9c8a0ae2220e18f,
'fj': 0x6fac3ab603bb3fb46e4277786393194c,
'gj': 0xd7bc3c1c6285d1b988bd8ddfc55f75bc,
'hj': 0x1f28e49f34e2406fdb6d6158eebd793b,
'ij': 0x7bed657a775c37c2570786d0cbeefd88,
'jj': 0xbf2bc2545a4a5f5683d9ef3ed0d977e0,
'kj': 0x771f01104d905386a134a676167edccc,
'lj': 0xa25ce144a2d07d0dc3319bf4d9033ccd,
'mj': 0x007de96adfa8b36dc2c8dd268d039129,
'nj': 0x36b1c5be249ad6a533dcfa9654e73396,
'oj': 0x769a605b1c9bdda42486ccc264a18174,
'pj': 0xc983d071bd4bda32ff24c2234d4fc9a4,
'qj': 0x45e0bfcdb33e87e0ac18346037c53b7c,
'rj': 0x56cd07000362b73cbfc6973dcd3aa275,
'sj': 0xb5bf27b2555de44e3df2230080db5a1d,
'tj': 0x456c2e75fe0faa57fd1cfd87117e0963,
'uj': 0x3d2c350ce8caf2783ee09f5f8f9cd5e3,
'vj': 0x8af70c5f31d956a7f0ae021529a11728,
'wj': 0x96f9afe86636d7dc011535bf88125f3a,
'xj': 0xfcd99748cb6720b002105b4410279c93,
'yj': 0x585790f98ff1dd61412aad36654e0371,
'zj': 0xb1d8fcdf6d0db7011c71fc30e7aef4a4,
'ak': 0x17540aef7b8470cc3ea8b2b9046af3b6,
'bk': 0x7e7ec59d1f4b21021577ff562dc3d48b,
'ck': 0xd5a5b3dd1ccb90d30360f0c068fd43fc,
'dk': 0x0ecbf9426bcfbd9a086ded5fc8c4eca8,
'ek': 0x2294886295980ace6bc8edb87cea4367,
'fk': 0xef1cb6e72d149b184cc241037203f60b,
'gk': 0xfd4c92272d5a952adf7aad11c20458e1,
'hk': 0xae4171856a75f7b67d51fc0e1f95902e,
'ik': 0x7bb450ffbcb8b2c73b5f66663190353c,
'jk': 0x051a9911de7b5bbc610b76f4eda834a0,
'kk': 0xdc468c70fb574ebd07287b38d0d0676d,
'lk': 0xd0fa06cd93335c8cae357ffe5cd1c4e9,
'mk': 0x07d935680b6501b2e42fe4baea021389,
'nk': 0x7220d65820839700b6c9ae74f87b48e0,
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'tk': 0xb6717b91c7595cc07f30aa9a784e6390,
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'xk': 0xa2d8fced03cb2e20ef8e1226935c9c92,
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'cl': 0x161747ec4dc9f55f1760195593742232,
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'mm': 0xb3cd915d758008bd19d0f2428fbb354a,
'nm': 0x93122a9e4abcba124d5a7d4beaba3f89,
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'an': 0x18b049cc8d8535787929df716f9f4e68,
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'en': 0x9cfefed8fb9497baa5cd519d7d2bb5d7,
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'in': 0x13b5bfe96f3e2fe411c9f66f4a582adf,
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'kn': 0x8c7e6965b4169689a88b313bbe7450f9,
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'on': 0xed2b5c0139cec8ad2873829dc1117d50,
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'tn': 0xaafb96b2fa8806be307c4496867bad56,
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'fq': 0x62702f7e0146e07253d89a8260e7393b,
'gq': 0xb84a1ed8db3d3bf378f49becdea153b2,
'hq': 0x25906028d04ba0563447f41dccb3c4cf,
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'oq': 0x0055c74f1290ce6b3a53db095bec1fbd,
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'rq': 0xc6d8c86cf807f3f3b38850342d1531b3,
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'tq': 0x2de9a7563091ed8aa3a8926550555790,
'uq': 0x8f554de80cf707b7bf5b758e83b4dd46,
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'dr': 0xb4688aaaaf17fad03225929fe56ad458,
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'ts': 0x4d682ec4eed27c53849758bc13b6e179,
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'dt': 0x3017d911efceb27d1de6a92b70979795,
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'eu': 0x4829322d03d1606fb09ae9af59a271d3,
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'ju': 0xe744f57da9e5a4bb6ec8ba3bc0ad3e4e,
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'tu': 0xb6b4ce6df035dcfaa26f3bc32fb89e6a,
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'wu': 0xd3cb757121f725fe825a1176031a1c14,
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'yu': 0x385d04e7683a033fcc6c6654529eb7e9,
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'bv': 0x121aa3ee4a7d5b1bbbc760fd0c6de79b,
'cv': 0xde3ec0aa2234aa1e3ee275bbc715c6c9,
'dv': 0x80457cf3a7b15afb8f491f8ae06680db,
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'fv': 0x3c77f4029be2e609c22bba665f13b101,
'gv': 0x7dc2e5a1851b162c949b9a7bd58ea968,
'hv': 0x4a5b47e079da271df772b75bb8e5164d,
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'jv': 0x6af057782fede733cab8d3e0bccd8737,
'kv': 0x82d09147453d572bc287f74aad062cfa,
'lv': 0x85d1a9c488d7117ea86291a755e5d43c,
'mv': 0x94d035945b3d82182669c4d3f6daa104,
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'ov': 0xbafd9bfc43549c7e8d21f0ab72bbd0ac,
'pv': 0x99bea2cd698b56b1a3b8c1701bd51c67,
'qv': 0x919e190d3a24095d669e5bb66899abac,
'rv': 0x108bc7b6961e71b2e770387a378cbc10,
'sv': 0x743541121c12a113af807d1582c74bea,
'tv': 0xc9a1fdac6e082dd89e7173244f34d7b3,
'uv': 0x45210da832f9626829457a65e9e7c4d0,
'vv': 0xc4055e3a20b6b3af3d10590ea446ef6c,
'wv': 0xd9215174d890de09724ab4a220378c35,
'xv': 0x22654d2ed4c921c7bceb22ce9f9dc892,
'yv': 0x8dab6f55935a6c4f4defd27af1602905,
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'iha': 0x30bbffd3062e6fc12f248b9eb4fec232,
'jha': 0x2e31a7f0ee77c2a193e9a124a162aded,
'kha': 0x207b40d2b18eacd5f71df1ff5f72eb8e,
'lha': 0xe5f95077d680d8cf3dd22bcea632e99d,
'mha': 0xdac8c9e6ec0efe64569e77030a62afdc,
'nha': 0xbd129b81598dbe63b0cc4f4167f416df,
'oha': 0xb895b0dbc2c36bb4b66b43ed55a6a052,
'pha': 0x1e3e0f1ecc6b5d7c344e7008d5db9a7e,
'qha': 0xd3391ecfd05a4b3b4f8489b6740249f9,
'rha': 0x0ffdc6a47659edae308bd35c138d1daf,
'sha': 0xca794fb2d950acf25c964ecc35f2d7e2,
'tha': 0xc5e5b2a9aee71feb308ca4ecdd626746,
'uha': 0x1a5e2b944676cd39d9d756235eb64685,
'vha': 0xe1f32e016da36967ed6b80b9de173a23,
'wha': 0x2e42ef62ffc1537a004325496b40c926,
'xha': 0x3cb92200974fd627df0379f09a04e0c1,
'yha': 0xd4da83689cb1615eab77a2696bcbb6f7,
'zha': 0xe83f70479b412d38bb02eada7857206c,
'aia': 0x2ce527f2d83a7fb735712a794cc26e68,
'bia': 0xe7662298f922dd9700c9af5363ca0a43,
'cia': 0x1f6fda80636fb763bef93193444b3f36,
'dia': 0x465b1f70b50166b6d05397fca8d600b0,
'eia': 0x9c1dcd6eb099ec8a5da16e9b231e5dcd,
'fia': 0x269bd738e8e6d7683b869ec348c2d6f6,
'gia': 0x64df52a03a4bc8c7a95aa8b29ee436e1,
'hia': 0x2d145e6016a0d7b1c19bc1ad414ec52e,
'iia': 0xe8f13024a522a69da30a686bde7b2d58,
'jia': 0xa6907acf5b337a322193f19b6698c867,
'kia': 0xab99f8f1c87bb63eee8ddc8688ce329f,
'lia': 0x8d84dd7c18bdcb39fbb17ceeea1218cd,
'mia': 0x5102ecd3d47f6561de70979017b87a80,
'nia': 0x04a481486dd84d7c8bfdfc89d38136a6,
'oia': 0xd592abc296e85705cc302018ef2d825d,
'pia': 0x32adf050226995bf2311421ebe0698e0,
'qia': 0x177a662b83f70057d04cd515087235dc,
'ria': 0xd42a9ad09e9778b177d409f5716ac621,
'sia': 0x07af7e75676eab410d1f83937d7afb62,
'tia': 0xe7292d5ba58672ce7f6fc3c0b646ab63,
'uia': 0x0ac523c830311ec879bde6de29892f2d,
'via': 0x14f1f9729a8142e82600dac241e82fe2,
'wia': 0xfab1fc07a47dcae18e4fb42c9818a3a8,
'xia': 0x5919baeb013e60e75e0dbed4d758c924,
'yia': 0x6d2a3195b653b3733332f924dc05e152,
'zia': 0x1c283511f251260283ed04ef8ba7cc30,
'aja': 0xc3b830f9a769b49d3250795223caad4d,
'bja': 0x067417d89f2062c097a013257f2ccfad,
'cja': 0xc335b2ac4ebd1f75f7fb9c13248ba624,
'dja': 0xaa939d032327c8399cf2851d5b0201ed,
'eja': 0xf71924e84caae58443187fb52d13a2b3,
'fja': 0xc54a9ffb595e73efcf71be90ef933010,
'gja': 0x4efcffb38d23496439b3b9137d3fefb1,
'hja': 0x165d3bd8c1ab8dd124bfdd87788de5c6,
'ija': 0x991deb2f730e82bd01e740824e582bbb,
'jja': 0x7c8dc285ae485badc1fbbe5724fd30e2,
'kja': 0xf2da9b062b9592c9d495c7472a6fb353,
'lja': 0x864be8de9f6b7d4869f5ca277ab141aa,
'mja': 0x6514927a1e4398391231ff89bbf29ebf,
'nja': 0x2651ffca2c67ab6e2caa19df3180affb,
'oja': 0x3995e138e4bfad1dc7c5a338b29ac36e,
'pja': 0x987441489448315a28f6ec74ef7d07f2,
'qja': 0x59500454d66cf0d1e09d96be3099def1,
'rja': 0x8be5bfc579d8899f696ff9010c0ebda2,
'sja': 0x2bdef5c1ab8238c2763572541e8e4242,
'tja': 0x082ac35b22127e4c8b014f88d00b4fe0,
'uja': 0x0027c294cb1cf95345a32db4d8f0a6f0,
'vja': 0xa65861bca16cf86cc76d3e29dfea171d,
'wja': 0xd63af2b51b30a86997654bc25b790a1d,
'xja': 0x2c7134ba0514a42ed7a9e3526b271ef1,
'yja': 0x04a40fb978490192d165157812013e0c,
'zja': 0xea850fc5c7783f5d198d76b96643d02c,
'aka': 0x6ac933301a3933c8a22ceebea7000326,
'bka': 0xa490f4603f1c324a2d9df42eccff0308,
'cka': 0x73857270a45511c0544273cd857f7fa1,
'dka': 0xd2dbaaa22bdcd248e204d35988de0eb5,
'eka': 0x79ee82b17dfb837b1be94a6827fa395a,
'fka': 0xfd69d7591e0029c680293e6fac145500,
'gka': 0x22e76792d78232a2c8c17d1887b7aa9d,
'hka': 0x072099bc020246168cbf4a35d1577a66,
'ika': 0x7965c82127bd8517d2495e8efb12702c,
'jka': 0xe8721d0d4aff8f7016b5256fa599a608,
'kka': 0xec7b078a5b8df149983c1210d4a1b1db,
'lka': 0xecc56555a7e9afabd5b5ff2d89e2c566,
'mka': 0x1060ba6003deea144f45d83024e32d5c,
'nka': 0xc4ba6610f5f3b1227a570a14211e7961,
'oka': 0xdcf80b2416ca823e8d807558a9310eb3,
'pka': 0x5645b5fed5b225565098970916e64308,
'qka': 0x411ba092fc2e5fe822b11b6629edc922,
'rka': 0x07e65cb618872a025b26d305f54ca419,
'ska': 0xa4ce5a98a8950c04a3d34a2e2cb8c89f,
'tka': 0x88a8688467442cb6ed8d560b22d35430,
'uka': 0xaa512dae234a1a0e530d0b117861a144,
'vka': 0xa2a92cf644f2b2a112dc44495aaaaacc,
'wka': 0xb6d85ac643c8152e2affa81b6812e0dc,
'xka': 0x6ba426ce4a8ab6bed1647460ed06fa1d,
'yka': 0x2afda183994ab01cf765d2c5e789a3bf,
'zka': 0x235001bbd4d40d94516466f524746aa8,
'ala': 0xe88e6128e26eeff4daf1f5db07372784,
'bla': 0x128ecf542a35ac5270a87dc740918404,
'cla': 0x6aed0145209e439b6b6dc4b365c22c2b,
'dla': 0xe31ef344d4472e8505c7d76e564ba0b9,
'ela': 0x8100240622c5494b0cb9086f15957813,
'fla': 0x61894b21be75260c4964065b1eecec4d,
'gla': 0xf79d4ab46ae52104064d2e12db4c693d,
'hla': 0x6228e72c78f1a20ef3286f3c108be197,
'ila': 0xaafe26449a364e5d6b5db7dc565a9b6a,
'jla': 0x993dbdc52dca0569d202046cdf74a33d,
'kla': 0x7ea7c353b208662f23649d5f4ef33a4b,
'lla': 0x7f3650f4ac0901d47ca7320f070abd5b,
'mla': 0x2c3009dcb3fd706d91dfa18c34c26a59,
'nla': 0xf30fa14283b8150e3fa3bdcccd0e8cb8,
'ola': 0x2fe04e524ba40505a82e03a2819429cc,
'pla': 0xbf688cefd04199b5dff3abe33ceafb28,
'qla': 0xa7a5854b03bae691ac93a4fcdfa294aa,
'rla': 0x845028346281f7429577a349ea768503,
'sla': 0xe3e48b96d966dee1b5cccdbc9d6f6d43,
'tla': 0xe28dfc6b390a6ce981c1bbf47bd481f9,
'ula': 0x43cb6ab4a042b281ac5067a4cfc7ecfb,
'vla': 0xad518e14544446337ca18e4280d10a4b,
'wla': 0xf33224cb1386999b3baa9b061d30fb5f,
'xla': 0x5fbb749495aca62d3021ace190a1d246,
'yla': 0xd1b77e7c9fcb1eef213bb018bf8e3fca,
'zla': 0x932b6435102f6b20578d5cad668b4e5f,
'ama': 0xe292b1d68c8b480c49074ff1b6e266b8,
'bma': 0x8b5a8249e2f9e52091111410b9ed47a3,
'cma': 0xa1f64bc3bec340545184ca0c623e87b8,
'dma': 0x695578cfd3a6c798fefb9d3f475360ba,
'ema': 0x93bdb73b49e88b5ce23da0509da1b8ac,
'fma': 0x8c1b768340dffcaf747fd0acc98f0ce5,
'gma': 0xb4e79da81d560a825aa84272706a14df,
'hma': 0x011e592469e4c74ebb562f6ce07e14da,
'ima': 0x92bfd5bc53a4bdbd7b8c9f8bc660cc14,
'jma': 0xafc5f575731b57ff981a3033dcb512bf,
'kma': 0x4f835cb6af6698d2a8b237aa551a9d2e,
'lma': 0x27fcdc3c74ff7291a0c2f320757eb9d5,
'mma': 0xa9f9d8d4b6db81aed3933664f7352542,
'nma': 0xcbb02a875149b15d815f41d950830627,
'oma': 0xb0891cf08f8898a6ef98113245db8595,
'pma': 0x93d1079ff7f02a00da860903e955eed2,
'qma': 0xd55a846668c619aa4316320efc041429,
'rma': 0xa1eff01a6862aea0d5237eb513a378d3,
'sma': 0xa289fa4252ed5af8e3e9f9bee545c172,
'tma': 0xd6e1ca84308e59169ffc76620d290136,
'uma': 0x18f8891c64e402460381ae66774c94bd,
'vma': 0xc9313f396c87916123195658d36b5666,
'wma': 0x8176c7ce9fff11522b8fec1ecf898c82,
'xma': 0xddec1a6f6c9bbb718b983cbd31805648,
'yma': 0x8b63717f41414625d114d4e3a24c60bb,
'zma': 0x5d6a014b5fa3eb17d321783a287b5ee4,
'ana': 0x276b6c4692e78d4799c12ada515bc3e4,
'bna': 0x6c71f7070bd6c069a04e6d2586167e93,
'cna': 0xad6ea016c8dc36a14dd38e0c34e00153,
'dna': 0x5427e0bf847d7c379a1d2e09a0b45f8a,
'ena': 0x3f265375ee4fb99f2439f3759683b34f,
'fna': 0x708c050046fdce4b72a273c8bbdb5129,
'gna': 0x96dad5d07b7bddf9db9b3028d3eabaff,
'hna': 0xe91ee5d0cc7a921ac694b81803a2b4ef,
'ina': 0xa0fb2daa33c637d078d1d276dd453ea2,
'jna': 0x3a0db73a2ddfb1407f91056cdd8d7901,
'kna': 0x33258a06996ce09e40d31b7f67632d79,
'lna': 0xed2180c7423b7a52afc60c54fe925d2e,
'mna': 0xf0c99833955f0b0b95ce5d606faf2b01,
'nna': 0xf03e861e125cde73d89a30e4b557035a,
'ona': 0x030c76e4f0ed31202379e6df29def1d6,
'pna': 0x346878f486bf0650da4ef183d234f963,
'qna': 0x0aca5e4bf1dcdfb3dcb779be1cd2a71d,
'rna': 0xb320e7dbc758cba6483981eb83c9d7d7,
'sna': 0xbc55acef201a54c7ce2b6ba390600b80,
'tna': 0x48b3942971d1b0c849f62cab16c0acd8,
'una': 0x85758590dcd56f1d170e55f224f36e54,
'vna': 0x941d403e8fad42d6b80b50fe93e00710,
'wna': 0x9684740bc7eb345289edd3f910f58ef8,
'xna': 0xf9af043363f7bb0620ad0bd5b0ccbb3b,
'yna': 0x3b12a7cf550a6c6bf771718ea17465e8,
'zna': 0x60dba8dedc1435a71fba121eb6272652,
'aoa': 0x0eca4c01b672b215d24286d8e4fdcb32,
'boa': 0x8f45bc0c88a6585e27e0cc00a195d936,
'coa': 0xf488bcd7dab5a39e0142f2bd2202232c,
'doa': 0xb8179081fe590f6fc3ca56d8752fc6f8,
'eoa': 0x9452fe2431035cbe8318eb141edef2d7,
'foa': 0x5e0044cac10e3398ba9e0ee859efdbe8,
'goa': 0xe16b5217b914f52573520276064c17fb,
'hoa': 0x9810784ce10b72a1ef2f50bc5fec59ba,
'ioa': 0x4e130dd75e6b4201fac605cc6c995950,
'joa': 0xd015f798339be173f48c84896bd4a355,
'koa': 0x4568514dc71a3ef3d5be2271e9690a70,
'loa': 0x99f6f9353fb42e260a8def4e4e0af2ca,
'moa': 0xa7cc11decf703bbefa04be14514e469e,
'noa': 0x4b96fc21160c8ab5279ac7a3b31e4d49,
'ooa': 0xd2cf24470577806460f4f91e114c4f8e,
'poa': 0x746d2b5777304b256688f7d103be2fdf,
'qoa': 0x886ef608acfa7bf93a930dcd411f08c5,
'roa': 0xe369a11be98d27959eb08fd9feae567e,
'soa': 0xa39d8591483b67fb637bb445f9f4f059,
'toa': 0x937dbb9a0118d36e0a642441a8ba71f7,
'uoa': 0x843ad255f7f28ce9f2e892d957c4c90a,
'voa': 0x74e2f2d6103a5516a4496613d855da65,
'woa': 0x1c75db904e88b36c5e37a380acbffaef,
'xoa': 0x7af1ee4382584b0c081517da6bf450a3,
'yoa': 0x210a87f10da02742af346d566b94d9ea,
'zoa': 0x07bc5279da369d7becd5e8c8b7b7680c,
'apa': 0xe03ae33ee8417ce2c9785274217636e0,
'bpa': 0xbf5a3e6f4cd73cc97e3862d4369bb723,
'cpa': 0xf114427f29d0fa635781f1cc950e138e,
'dpa': 0xc158bdf0ab5c8f438db524ef4afcce23,
'epa': 0xce78245903fb761004056961dda3d017,
'fpa': 0xb43aef660c537b30557b09f6b151a95a,
'gpa': 0x3adb2063b16ca4e9e066ccf897e4a0b1,
'hpa': 0xefb87b1a67e11246e20b6294869903e1,
'ipa': 0xae3272062ae7edb666e8dd33eda728be,
'jpa': 0xf0b4a299c45171493ae3215d69d9b0a6,
'kpa': 0x5dcbbc877e9955e3b29d7ca0baa4c7c4,
'lpa': 0x58abb759e5602eec30e9e8ac773f9bc9,
'mpa': 0x5b4c600517d35939b9922164e8efd1c8,
'npa': 0xa3a6d5d9147cdc16e2f95cd43e79dc89,
'opa': 0xaadb97a59c56040772603c597490d729,
'ppa': 0x8a2a05a31e5a5c8aba63195cb7132fca,
'qpa': 0x86151b642f9822634af2c9b5c8452231,
'rpa': 0x5c0334da46ba28753157144a8ea0cdbd,
'spa': 0xcc91e646ffef196e61f25dce2ada9ae5,
'tpa': 0xcd2408b273207043656ab23fd3c6c02a,
'upa': 0xbf0c6c092b1f9fa932742ee255ae463d,
'vpa': 0xa8f7322ae45832c000a01a321b57c5a6,
'wpa': 0x4e81b6d2a4ce5ef0cd29415e3e6dedd0,
'xpa': 0x0c9d64ab6a7deff485732471d721a73d,
'ypa': 0x3b0612e8dfee506be1510043771fcfe6,
'zpa': 0x3f5431b3f181809b58257ef124183408,
'aqa': 0xd0a483256bbd970137d8e1157293474f,
'bqa': 0xd5514057529b751a2849388077b41ad2,
'cqa': 0x9cc2dec6f91128a3df71561a33551b08,
'dqa': 0xe43c962d317cdc0058ab87c03fa11912,
'eqa': 0xf8338289f2de14ec4e2d7842d40fc84e,
'fqa': 0xc414c52958764e0e8c93ab28a7c848ba,
'gqa': 0x3e40296a811ef4bf983a964651b8d000,
'hqa': 0x6f7675d5328a1fec9752bff328bc03c8,
'iqa': 0xf6c34c4ced51254861caf631b19a64c2,
'jqa': 0x59a4067a2dbfe40179123b2a4c73355d,
'kqa': 0xd5a8617f643d7b3a844d0995308ace7a,
'lqa': 0xbe3436685b64af4869a81065ca440167,
'mqa': 0x5adbe17e432647f7091f6f923e5132bb,
'nqa': 0x9c7e985ef5e66769d90d82205ba457e7,
'oqa': 0xbba14927ce717656eb480c194b6d4495,
'pqa': 0xd74533bd609471244c92f1c5318065b0,
'qqa': 0x054b48c9bd2e8973a3feb8c4145d8897,
'rqa': 0x3c94488a57a14bbcfcabd4adcda1d8e8,
'sqa': 0x9f663cbeb192b2ea864ddc5560fb2e3b,
'tqa': 0x86667d0cd75cfa4126d7ed29eaa4c8ee,
'uqa': 0xfecb2eecfcfe0029af4a64f37c85f99f,
'vqa': 0xa8a268a79175bd44086fa556db60aded,
'wqa': 0x2a76230ba6c042409e5b010fcb5fc14b,
'xqa': 0x229384bb35869e6170194103e66bceb9,
'yqa': 0xca41412a11af34393e2a1ed7c00c48f8,
'zqa': 0x103ccba74d78db69a585a278f76c563c,
'ara': 0x636bfa0fb2716ff876f5e33854cc9648,
'bra': 0x3c6ab8c37ec264689cd0131c7014b99d,
'cra': 0x51f7b4150aece7d0f6d45d8f00b8184e,
'dra': 0xabe401b2190d257afecb3a17551ed5a9,
'era': 0xcf5c0a6a33f6f53bf10be6c527fe5c93,
'fra': 0xffe7470430a737c4ce6dc74bea0155d5,
'gra': 0x4a17b63dd19d308b9c8becf7699b6f22,
'hra': 0xb849ebfa6f27b964d744ea625ac48618,
'ira': 0x3c67080a1a09b022fb9d94e57a75ddad,
'jra': 0x7a573ccf96fc7bbc034dabf9f65618ef,
'kra': 0xd78406cbaeb5be8c2ef9dfca493dc55e,
'lra': 0xe88f7a44ba2cb99334a4ff74c823538f,
'mra': 0x86b546be98d8f58d4dedfb8be7423a19,
'nra': 0x34badf2232d503c520f55e3672f92ade,
'ora': 0xd41fe446faeb9c02909aa61b34736059,
'pra': 0x3c24ca7afbc8766f1acb7d67893ec16d,
'qra': 0x8ed9476ed643ddc7946b2d6c77ed3e25,
'rra': 0x105ca6d0fd6c2c3e136980d3548f16ab,
'sra': 0x861fa3a9748a5e58fc3a651a811b5cf9,
'tra': 0xcfac4e0dc47d98414cf373a2f09d28a1,
'ura': 0x50f3fca4c6134bd001fdfe3159686be9,
'vra': 0xbfb2e83645f8ff382621f512376a992a,
'wra': 0x957ba73c6f87488c744514c4c77bd5f2,
'xra': 0xd923d906582234f7da470514434fab8b,
'yra': 0x848460dd0c4abb93ed8a713952bbe50a,
'zra': 0x87484e2842b4fd7656b7a15a91dfb18e,
'asa': 0x457391c9c82bfdcbb4947278c0401e41,
'bsa': 0xdd43bc4306e23060fd3d63bb661bf837,
'csa': 0xfdcde0ec8cfe1e177670f658ae15721e,
'dsa': 0x5f039b4ef0058a1d652f13d612375a5b,
'esa': 0x80ad0b9fa48a74fe86a9c8ee665d96bb,
'fsa': 0x3c518eeb674c71b30297f072fde7eba5,
'gsa': 0x048a5aea0e0af68ca6782c2ec300a045,
'hsa': 0xf2cda8f6163c8e55da3dbbad2938df8c,
'isa': 0x165a1761634db1e9bd304ea6f3ffcf2b,
'jsa': 0x021e433027c8a6481bccc04d3cada47a,
'ksa': 0xac6e14b0130c885201d6cab30126ec06,
'lsa': 0x0869a9283df1d949c5c529286716e5ae,
'msa': 0xb9342a7d3a63c2b2d444dd9caf437f22,
'nsa': 0x8d2a959e6b154ec9215882b82f28cfcb,
'osa': 0x43f38d003c06cca6687b5991a52787c1,
'psa': 0xd894729d1f8f84d80fd1a92dd6362b44,
'qsa': 0x801311c256e9e5ef4b62bdac2617bd2c,
'rsa': 0xef31070d66440687a73beb6242f298bc,
'ssa': 0x541f98322eaea41f2b2e3d023972f098,
'tsa': 0x49bd32255ea98067c64c478ee9ed7c2b,
'usa': 0xada53304c5b9e4a839615b6e8f908eb6,
'vsa': 0x25495966f19c7964b40d534ec5fd73e1,
'wsa': 0x4bb0a250f62548654e50c3d29c4b6096,
'xsa': 0x7b9d411ce809327df3b08b9f9699f2f0,
'ysa': 0x3e91d1ef3fd99135835654c648d8ae95,
'zsa': 0x56a45d1808724e0c01beec74963ff22d,
'ata': 0x24accbed29ea007663fb3d7e5765f1c7,
'bta': 0x8e184b0ef18895917c660cdd5e2163af,
'cta': 0x06ce7d44db015c845227a378a79eeb60,
'dta': 0x36072180305f072a2e2c7ea96eedf034,
'eta': 0xeba021d91b44a97dec2588bbea58a447,
'fta': 0x19f67cceb7d8d80debb95aff727b7ff7,
'gta': 0xd81c52f524f6faf198b5daa2a865c2df,
'hta': 0x50f87878b588aa7186e5982f5c3ffb59,
'ita': 0x78b0fb7d034c46f13890008e6f36806b,
'jta': 0x4421e65dab4e5ee4a1a5615873a08253,
'kta': 0x44769ad2aea97161f74cca5dd270eee9,
'lta': 0x552f834475513dec120ca22769e1fe28,
'mta': 0xb29f179049415ccddf46387b3b1e7ad9,
'nta': 0xab233411eea3217f6a3179f0759d343d,
'ota': 0xd3a363c7c51059889755aeff86c24889,
'pta': 0x8001188526048445b80af464c1079577,
'qta': 0x9f74f6db6d9eae56b5e51001dc76436b,
'rta': 0xbbf5f25abf74a0e64a6fe2f281c1a30a,
'sta': 0x46f61ffbe2ea4a9a816195416e87d548,
'tta': 0x558db26005bad6c309fde7941dbec51b,
'uta': 0x5c04d53c23e881867ed63658d5388028,
'vta': 0x45fa6604868441858e37aa1661e3b306,
'wta': 0x20a549e584a4295360271c308dbc8446,
'xta': 0xd0d55bb957f2776d563853cb855f9557,
'yta': 0x9570b4b5a6dd818e933b966b52011536,
'zta': 0xd22dda71722d1e1d709d831c8ac8b757,
'aua': 0x9898681adbddbd61a17af9b0e957ccdc,
'bua': 0x01f0f7f2f372105175e9b578c2f33a78,
'cua': 0xc8520774f9240cfe9d240d2ee7b9fb1f,
'dua': 0xa319360336c8cac32102f4dffbee4260,
'eua': 0x4c2124bbb616e04baa88245b0c579623,
'fua': 0x8e083aeab52b9fdb6a73749c0bba77a8,
'gua': 0xc9c1ebed56b2efee7844b4158905d845,
'hua': 0xa4fdbfbe6a68f0a0088c2d98c6447311,
'iua': 0xb92d90ee77f900511b28ce99a99c67b8,
'jua': 0xd0f4162b70411e97970ee64d91024a06,
'kua': 0xa474c976e912b09ee8caa282a41ae4f0,
'lua': 0xf8dbbbdb3b80b4f170a8272978f579eb,
'mua': 0x0990cf4b977cc29a52bfb2e87c4bfc0d,
'nua': 0xc57b65bea3855929e4432575ac8d2c8e,
'oua': 0x46a7988cf6d2b5a1a1cd6df5dbe88e2b,
'pua': 0x040e19b9799a0e7dc34cac0f4faa16e2,
'qua': 0xc210361c9501ab821735f3ee2d684f0e,
'rua': 0x966d0b1f8edb45e914431e833a51516c,
'sua': 0x370e807f0d816af942db37e043c74231,
'tua': 0x550fdf7962b4316c62752607735837fa,
'uua': 0x0661de0b79e0559655cf55dd74cda4e9,
'vua': 0x30ba1794d2e19bbd2e59698ceae24d14,
'wua': 0x865243612481304a317535d35cb728e2,
'xua': 0x48a99e5d161a7225d29f84e100296b32,
'yua': 0xedacd35b04c42c20460cb169b94e0322,
'zua': 0x25aa5de25f0a4449b3e3c1d112222091,
'ava': 0x30bb014e5b15dd9fc656d307c2ac78af,
'bva': 0x2da1da2801dff5ca24c1bcd060330da3,
'cva': 0x3d7c76317dc02619cbf97464f0541e8d,
'dva': 0xc67ed9a09ab136fae610b6a087d82e21,
'eva': 0x14bd76e02198410c078ab65227ea0794,
'fva': 0xaff8c05a10b21815139fb6e06ff94831,
'gva': 0x96d0f363087ac4d233b64eb0c65ef59d,
'hva': 0x4bdce67afe490b1b7cdabdda21b64082,
'iva': 0x1858b78054f5328781ab9c1b3d30b6d3,
'jva': 0xe549c2f06fc6bd44540ebb476d0f2702,
'kva': 0x5b4ff197d0c69f8cfc7570a6e727047a,
'lva': 0x6501b7cb5c6459fd043c781545020721,
'mva': 0x5bb798ce01459c7ed63dccf9f0f3c634,
'nva': 0x282ff72866bf811d13d34bae45dc6f18,
'ova': 0xf81b21fb4e12a0e6917f955a9a305553,
'pva': 0x2a47f5bd299c09cb830fcbb0e276a18b,
'qva': 0x5427bfa07648b322030f16bcb28826e1,
'rva': 0x21bc7d8795addce8085f283cb3b937f0,
'sva': 0x925de71282df10cf92dc5a031ef9f4f9,
'tva': 0x959028536d5f1719355bf034112bfae4,
'uva': 0xd3da5eb0da6870aec2297a935d7159a8,
'vva': 0xbdd7fa0f926bbe4f256837673692e1d8,
'wva': 0xfcf1b116582e65726b7c7b449556b551,
'xva': 0x344a0ca4cfe46e81a8e323cb84194502,
'yva': 0x73ade8a3e6ddae999daa7100fc0deeae,
'zva': 0xd3b120294c368abbf695fabf42c5737f,
'awa': 0x1b5f31288e7d4e053c4c73ec03c3a6b6,
'bwa': 0xa6be17d8a097bb9efd120be10bbc654c,
'cwa': 0x274b535b5fa99888c566818427c176b3,
'dwa': 0xf495be79bad3d692686f63d43283c1f8,
'ewa': 0x14c0f73364d8d9ce0748e89e954e9e26,
'fwa': 0xd24c8913115c4b241f41a5e443953a0a,
'gwa': 0xc0d054f86deab9050e925319010f8145,
'hwa': 0x3b5be10a45365548587831fe685f4a3f,
'iwa': 0x220c9f3e0b71987af0b40b701bdd74c2,
'jwa': 0xb842a5e0961a67986962d618f3a33846,
'kwa': 0x8f72382d96d9e4889cc14e35d423bf44,
'lwa': 0x25eaae2543340293e89d7ffeb2a6fd33,
'mwa': 0x5ff0a0caff6aad6e649b6969d9b0e8bd,
'nwa': 0xb03c812c60e05ae4239297435138b650,
'owa': 0xbf9b62aa00e7986fa75ef400f06d57e5,
'pwa': 0x07ba8a9247a0f91e025d8036a28c3b56,
'qwa': 0x2612ed86ae701fcf52ecf5834710a067,
'rwa': 0x6cdc3974fa46b345a6b1c9d3f43073de,
'swa': 0x3eab44526e5b52fcb8cf8160706e577f,
'twa': 0x4e349e4c4942d66df3db2e44f6f0c560,
'uwa': 0x78f0f32c08873cfdba57f17c855943c0,
'vwa': 0xe7f85016ef1c1ee57ae8f14c5f5321bd,
'wwa': 0xac46eb486151ace237901a3ead8fde11,
'xwa': 0x6d9d1d2a5b533a9e05f0034e70470080,
'ywa': 0x31c448e1d2c20e9576d8727db3d98d27,
'zwa': 0xfa4684f3feffb0d5bcce25b878480baa,
'axa': 0x100ee4be8175bd62d4f954323cff4325,
'bxa': 0xc245474038533addf2c09bd7f8f00b3c,
'cxa': 0x468979cc67f15063596917e91547742e,
'dxa': 0xf2319ae3b312103bb3259ca8242dd16c,
'exa': 0x5de51d3aeb2b0c8c996b31413f45fe38,
'fxa': 0x15df84048b5c2a8f2455472c80026987,
'gxa': 0xc870b29a2d34c4e6fb262a78862e2e7d,
'hxa': 0xf260117bd07290d345cb0206967ce32c,
'ixa': 0xe15356728ecc21cd717610306a87a8a1,
'jxa': 0x7ea958d0fe580c40393e6ab236fde62f,
'kxa': 0x29931e1ddf4ba85edc2fe87ee2bd5c31,
'lxa': 0xb73686e520c36efdb72b3d8836665b81,
'mxa': 0xecabfb8041fa675b8c84ffa0f3aaeb2e,
'nxa': 0xf76146d822f0bda6a54c8f3ba5eb4366,
'oxa': 0x8727daac92eda0f351fc8c8374a8ec75,
'pxa': 0x5e9efa6853eb57320ac980a30f48a628,
'qxa': 0x1276e055ba3e8479f7404ec94ad77792,
'rxa': 0x33bfe376440f076ad77948a8af996013,
'sxa': 0xbfd6b70dd79fa6efa9b7eeaba4834fe6,
'txa': 0xdf3e9e008a21eda12ebd106ef0da0954,
'uxa': 0x6bbc1646ff109a0a3e813d37f1011000,
'vxa': 0x3d3fad0da42f40229042dad2bf7d5a70,
'wxa': 0xdebe4a977c592561ef8997c2a86acfd5,
'xxa': 0xb03481e80c60bbfca84269bfd7a4df27,
'yxa': 0xbd74f429522c7c1481fbba07187efc6b,
'zxa': 0xa30d63aaa0598ff354561a34ad263a58,
'aya': 0x80a095a2da9695ccf95662827e02c5c0,
'bya': 0x9c793ddcef8c296f52e3a0883a2c8d55,
'cya': 0x81a9e5ad086e5d60723a68522a03dfce,
'dya': 0xe1191f70ce6ab83721f45b69806bb106,
'eya': 0xa268b63a775153432e5538c32154fb85,
'fya': 0x842510a4d601fcb6a11c5e4577635590,
'gya': 0x0af69ffa7ff5904afa71b071537e99c3,
'hya': 0x326f6468d8fe0d4a7a7c719810018bcf,
'iya': 0x00e11252db1051387c47521767296b42,
'jya': 0x6d7ba1903b5f55ad5c26de778203077d,
'kya': 0xae1aa4bacba600fb74735af0cb7ce34f,
'lya': 0xdce206cd4b55ca5f42950b60fa81bcc6,
'mya': 0x1762a61f123709ba30f65f09e730accd,
'nya': 0x24e4b3ecb6310d47d38c68da297c5c5b,
'oya': 0xa68e48715df3d2ebbea4121bb047db77,
'pya': 0x1e7091b04b7830bde63399c20c80aa0f,
'qya': 0x8cda308be0a4e4f1e4564e2c440b364d,
'rya': 0xf61f7496bf55c9798ed470b60ff6810c,
'sya': 0xa485a011dd0150fafbbee7648a4caeaf,
'tya': 0xb626b66df044ee336f452bd5d88debb5,
'uya': 0x7bfe538c798a3848872529996c448e36,
'vya': 0x3a5afaf5d67c2547a79f0c517e1191f1,
'wya': 0xf0af4067a570f5403435fd13f83484bf,
'xya': 0x04ea83dcf6d4ed8f870b54e0105285ac,
'yya': 0x392c9c73e889fee3dc7042e72728c1b6,
'zya': 0xe1e61134e95a208f76565f10068d93e5,
'aza': 0x99eb73df38d97583b431742ba8868314,
'bza': 0x496e2b42f9cced75f0e19bf791409a42,
'cza': 0x7cee843ceb0f2bbad16206a2465657a5,
'dza': 0xf3cfd8e7b97e82dad6cca964ddc71804,
'eza': 0x70d9be166c6ca3da7f6364be89aed69f,
'fza': 0xf8150d78da2243d056c416ea3d3052bb,
'gza': 0xa7b9311aa9686ff895eb803e972d7b36,
'hza': 0x4d270c2e986acc4a3e3210a3c6eef367,
'iza': 0x127e633f8c5b2a4f867049116b6bc9ea,
'jza': 0xdc141528467d79c63638f81ddba3ab13,
'kza': 0xf3b84c897bdf17659d075e4b6fffcd49,
'lza': 0x62f037d09f3863ad158f150d7b85692e,
'mza': 0x9701285ebd36b491340283e2ecc3733e,
'nza': 0x46004b45b089b9bf6c8a98ed2089f6ef,
'oza': 0x6231957517f0dee07453546e772c3c9b,
'pza': 0x7ab7ac7858dc326d031f42fce0598dfe,
'qza': 0x239f95307bb1ffac3c8f54b072a9dd48,
'rza': 0xfdb8a8cb7d3eaa2a4d3c1c2b1c8d14c0,
'sza': 0xeeae174735e6f8a579b3b9bc1849ac17,
'tza': 0x1bacb112c654eb43bd5afdba8dbb4614,
'uza': 0xd6a575e1fc91fe47693ddbf7c5c80722,
'vza': 0x146d0b5b0da28c96e624c8051cf3de1a,
'wza': 0x72866a5706675351201832e8bd1b5413,
'xza': 0x89d1572c704da734d890c43e50c12ef5,
'yza': 0x2594475cae3b6722773d4f480057d113,
'zza': 0x6d6d83f2e0f33d4dce3f331e013c53b6,
'aab': 0xe62595ee98b585153dac87ce1ab69c3c,
'bab': 0xd9d7dbddc29177b121a6aa1bb09d15fd,
'cab': 0x16ecfd64586ec6c1ab212762c2c38a90,
'dab': 0x11c6d471fa4e354e62e684d293694202,
'eab': 0x6c03a1381f19bcd8b80eb6e448827882,
'fab': 0x6e3df1e2bccb9e5eea0d1822814ed45f,
'gab': 0x639bee393eecbc62256936a8e64d17b1,
'hab': 0xd2fd0a41a7ae327ed8e2eda4ba27b235,
'iab': 0x6c41bec8a7633a62a2e0fae7a1dc34fa,
'jab': 0xb40157c4e5f49e6490a237102344fea4,
'kab': 0x6458d6f2365858dc27766c47a87cb3b0,
'lab': 0xf9664ea1803311b35f81d07d8c9e072d,
'mab': 0x86ee133db2f0f3158ca63884e9f220c3,
'nab': 0x78b3ea274e332160d26192b0b0cbd513,
'oab': 0x7c609f6d5e0061a6661d6f3be328e470,
'pab': 0x5bbaa32d1ffe54b3350bf0349f558ecd,
'qab': 0x27bcd70b886475295dbd90061bb2f4d6,
'rab': 0xb510c3fca0a1f79d6494f455f9f34d5f,
'sab': 0x2ef4d613a5cc85d9e2217a295b003815,
'tab': 0xe7f8cbd87d347be881cba92dad128518,
'uab': 0x156c08375bdd02e544d2add3d65dd76d,
'vab': 0xe0a1dc2ae6f24c18948ae9a33568483d,
'wab': 0x3d2fdf00d5bd7ac1a0c6496a695d91f9,
'xab': 0x988a4fec8c71f096f8c0e8deeaa0fc65,
'yab': 0x536ebf2acd0fb6c2f73e98b13109fb6f,
'zab': 0x46f6e8fb2d9082a6bff9070b7aee619c,
'abb': 0xea01e5fd8e4d8832825acdd20eac5104,
'bbb': 0x08f8e0260c64418510cefb2b06eee5cd,
'cbb': 0xca1f9e04024c3b64d7ba5fbe95ec130e,
'dbb': 0x5a232a7a00e178db7bdab59258d95cc8,
'ebb': 0x43289cd437c0341064d1d20424e7bf84,
'fbb': 0xc242651fd105071ae5ff57361ea8f099,
'gbb': 0xd36a5575512a3ece2898352e3aa40163,
'hbb': 0xed6c5f555acb134fc48e5ee423ad95c2,
'ibb': 0x61a1e25dd5c27f96825c84e452e268ce,
'jbb': 0x530ff4c7006ca02de7a8b67d338c9b1e,
'kbb': 0x9efb4eeb48f20223f05fb6c3404be778,
'lbb': 0xfa3c3dd92b4aec96192029e435b2d895,
'mbb': 0xe899877691f405507dcd8c078df3daf3,
'nbb': 0x03554e95f75071ea161e6f89ab6b084e,
'obb': 0xb00289b6616fff54bb04f42cfe3a5698,
'pbb': 0x72d8cec7ea0a29973fb23b25d2f80eea,
'qbb': 0x0331cc51332710211aba252af124ce4e,
'rbb': 0x6c8a13a137e995934855c50ff1cdc502,
'sbb': 0xe5933f2aa258ba6a7966fde2446c0ab6,
'tbb': 0x8ef25ebecf5c79e2d7c845aeaf03577c,
'ubb': 0xaaeb1de4a979e1782234a8c21ef2a743,
'vbb': 0x37cb8328fd66d037dc63926f63720a8d,
'wbb': 0x648813bc92454e37faa6fba834a043b3,
'xbb': 0xb5df9300b490b69884132d467f67f89e,
'ybb': 0x610556d89f87d462e290c6631b7bb777,
'zbb': 0x854e425c616b937d7c6fd641516bb806,
'acb': 0x5ff832d9bca8241d653279756f3ccd11,
'bcb': 0x2e176c4d6ea6098643f4c1bd757f7c17,
'ccb': 0xb56be107e02f16a1557fe6b820c07fff,
'dcb': 0xf0e71ceab2a31fa1b197316d2dca3f7c,
'ecb': 0xebccbfa05569eaaeefc7e261dd0bdb26,
'fcb': 0xa1f73568850e480e957b52e8952e8a4e,
'gcb': 0xeb7a455b23e10e8f7158391711637d76,
'hcb': 0x6cc1f15a85d00e665186559776c5eb4a,
'icb': 0xd461253dfbeb618687fc3ede2ec11cd3,
'jcb': 0x5c323639b95e6965b9a0190509df80cf,
'kcb': 0x717c0dd1830c146f960fa480a6ec2382,
'lcb': 0xccc91b7e9a9bfaf5a8dbade148bf0560,
'mcb': 0xdc571867053d4f69c37666db0144d898,
'ncb': 0x31854d10d9ec8bc8a7299a40afb0d884,
'ocb': 0x9286590fe0ee4c876a3dc00d6daff56b,
'pcb': 0xf42a83f45934ad4e2e2628bec3dfede9,
'qcb': 0xe631fac7c40e22f08749c17429998060,
'rcb': 0x94fb192a365267c153785bc570ef3804,
'scb': 0xa58966bcaaa19e5de107026c71fbf3dd,
'tcb': 0x7308cafeb2a7f569c525430b7d69bf6a,
'ucb': 0x67433b103d3ed0c6b582c57c6c8d605f,
'vcb': 0x9500928907ef5926261a712478f43fd0,
'wcb': 0xea8ad41791daf14ab7ad63416b9383d2,
'xcb': 0x1ed8ce83c531de31a6d1e45f8cbbeca8,
'ycb': 0xaeb993e975e47011e39b50e650344a17,
'zcb': 0xabf9eeb4f904d1eb24ddf76fca1f9f84,
'adb': 0x3fdf7ff211eec1dde75bbdba2e8b5bde,
'bdb': 0xec9355bcb8b22be692ec4ec20d7412b7,
'cdb': 0x785b30443fd6e2de160408ddcfcae6fa,
'ddb': 0x8494f38e85ea550d976efcec89ca67fc,
'edb': 0x661f35eb61cbb63a89430a43c3516a25,
'fdb': 0x0520a41422ce63b356f79ca728e06b2a,
'gdb': 0xca3e1c20efd5690f9789a87c66a5047a,
'hdb': 0x87bc3211b56e43dc7ce714d91e3d3f3c,
'idb': 0x43ce5d8f64252650a9c1e739c107b4ce,
'jdb': 0x9137c75b25664c360c272375e684c13c,
'kdb': 0x1ae7fc3ed2cba6d4e162990045401ce0,
'ldb': 0x5c1e1899c650167a34a0e6be6fafbdda,
'mdb': 0x801693b1d56b19d77272b3c6db46a751,
'ndb': 0xe4010cdc95d04c94900802abd1aaefd9,
'odb': 0xe35eedc5b7f4ff5958d7bcfd7bf456d6,
'pdb': 0x32bfb186853488e1a93682f08cf9da62,
'qdb': 0x5c0ae01c102da16c3733dd1bd85be4bd,
'rdb': 0x18e007c81f6b2e2ea02065f78a587bd3,
'sdb': 0x9c51816c474bb23a990ab22eddcd57a1,
'tdb': 0x0c2c8a1b84890b5ddf8ec8b167c70829,
'udb': 0x3fceaa8d9ac464b5ae16b69574ae2ebc,
'vdb': 0xde7f339dcecd3a59790728b51b0bbc80,
'wdb': 0x0722986318ddbc39de1655ef1e7c7baf,
'xdb': 0x0c5bc9675b32699f35e8ad00bb7cc7d3,
'ydb': 0x745203b3968493dc792b621aa411909a,
'zdb': 0x763fbc0d43cf7deae150ebda69cccc72,
'aeb': 0xf5b443d5efd16a1c2902e388f0f1b9eb,
'beb': 0x5be372d4352c9807436913fcd665e652,
'ceb': 0x5d3bb24bd96c8b1e38c187c8039bdc3e,
'deb': 0x38db7ce1861ee11b6a231c764662b68a,
'eeb': 0x92a9c417d9827589956f142a01c4999b,
'feb': 0xd7b85f12bdf36266db695411a654f73f,
'geb': 0xf816a0b97b7ea8f73d61ba72082b7ed6,
'heb': 0x9753485adf7110a5ea2be661ef4463d2,
'ieb': 0xff1539fe125da06a549022d081870a75,
'jeb': 0xff3dad3aa1d860c81e9bfc00f979f890,
'keb': 0xb49cf56daca7180fabbe327c1e5e571d,
'leb': 0x1cf502a6fe4093cace486b1d9be68eba,
'meb': 0x56068e10153ef8bcd4312040a900a39b,
'neb': 0x6dc494f7895b68556766dd8bd6db671d,
'oeb': 0x0a8d4141f6a44e0693f2ff1d44da677d,
'peb': 0x21d60ceb690426a6d34af0afc455a6dd,
'qeb': 0xca837853aaeac2ac6adc629d341ae7d7,
'reb': 0x452ee557f6b2e1de7e059011b6d7ac02,
'seb': 0xf15389363cf66828af65a1d8881e5821,
'teb': 0x3e7579f10b893ca7b98cf2ecfa774c9c,
'ueb': 0x62832e7acfc745fd9d4f29e64e65e3a5,
'veb': 0x731a940bbd16f470d9acf6946c0d7419,
'web': 0x2567a5ec9705eb7ac2c984033e06189d,
'xeb': 0xc708419266be0bedae4dcb1385c37da5,
'yeb': 0xfb41c112ed7a2cb4aac270e1aa85ae95,
'zeb': 0x51a18513814e5ac4799773e0ffc6a3b1,
'afb': 0x9f536174f0d50e0afd5f0e935495f1ef,
'bfb': 0x2099f61f4dbe1f5f764c8ce814d5ee65,
'cfb': 0x18a87474c42cf78430affe62fcfd1907,
'dfb': 0x525cb7384683b0f3e96dc031d7ffbe52,
'efb': 0xac2e1569ede5742bd215a25d133df94a,
'ffb': 0x17449b68737a4924c3e0abc02b1fee5c,
'gfb': 0xb68926d89b43d2dc2b11a11af69ffb40,
'hfb': 0x79fbf531ea7ad1bd955960ce2aa4ed62,
'ifb': 0x6643635ee9b8941a092c1d708d42d147,
'jfb': 0x529587c76ed7c1ef6da702815eaf7a7b,
'kfb': 0x4334c3c63a6e240ec063675fd76ceb64,
'lfb': 0x92618554dd03113e6d4640f5f6960a7e,
'mfb': 0x5a23b5cb0a0c78fce5059ac2c5300203,
'nfb': 0x54f5dec553ef14706a09367aa0cebdd2,
'ofb': 0xf6e813b13c74de318bc2addcca465ba7,
'pfb': 0x4d5363eee79c01dfd9e62ba9e35628d4,
'qfb': 0x75180542698a89dfd94f6d257907e99d,
'rfb': 0x6d9c4f18d7baab721e2505ed2d16c95f,
'sfb': 0x4baa53eaabdc41d95d3bcd0e7bbc9e00,
'tfb': 0xbe2f60aaf604c1135e78fcab62ae286c,
'ufb': 0x88966c145436d540d15e0d5b24cc34f9,
'vfb': 0x29a7e182cba11f4b154e066828b301ec,
'wfb': 0x11bca1205f262971df27a3a61a3e0982,
'xfb': 0xe154b41a7ce6436b87764339a457e303,
'yfb': 0xf4c176f49f2c265ebfd75a90b196d137,
'zfb': 0xe50dc4cfe80c4b2a7c97a28fdbd166b5,
'agb': 0x81dbb2912521f7be92dd1c59acbaabe8,
'bgb': 0x38b7e00f5ccce1bfce8a30d9cad5131d,
'cgb': 0xf25bfe14862a2c36681358bb5f175493,
'dgb': 0x54fd226a01b82a1152e63422011dd372,
'egb': 0xf0537a0f0d77c837f2340a2a87bad68b,
'fgb': 0xd436adeaeb56e877a12226260dede661,
'ggb': 0x3fa69470b134591a5eb1055f9ffc370a,
'hgb': 0xfd09e6f4a38b6706a8313b1c20be6ef7,
'igb': 0x93c535d1ec8718ae407e744a0fa219fd,
'jgb': 0xacbbd94e3c991ca7963f900075827846,
'kgb': 0xb973fd0a932188e9fe400c69b6c9e251,
'lgb': 0xfd48c03eaa6f667804f917b37f89aa30,
'mgb': 0xe759e9c488c8723b68ea06bb653710f1,
'ngb': 0x96f3b285db00fff30a9d13b5a854a484,
'ogb': 0xc72bbd7a58b688ca20ff662277cc4a9b,
'pgb': 0x5f8e18340de5bdb9323a50c308396dac,
'qgb': 0xd884d648d516cf92010e6d515a856467,
'rgb': 0xef70a6546536ccd835479f6cddc0188e,
'sgb': 0x7cad0a832170482243d4fa730891df94,
'tgb': 0xf3c99ce66588c1409e30ad6f285cf6ff,
'ugb': 0xae15fb3d979fe2d4cb1a88f274771c9f,
'vgb': 0xcb69a8474ec770b3f37ac7a3bbc7784c,
'wgb': 0x9c7a11c9b5b330666fb0e705bc484bb2,
'xgb': 0x35149171abd2d09e943c614f7ec497ec,
'ygb': 0x18a2feebcdbfcd8bbb415e00c1eeb167,
'zgb': 0x7a0882f9936336735810bce7abd2318a,
'ahb': 0xb2a49e1cb17f2009e9084f456b32fd26,
'bhb': 0xcf804892a4e29bca8794cbd5de0763ef,
'chb': 0x4ec595bc9758f0912ab381e09f1dc316,
'dhb': 0x00796a2e531674257dbd2e7b585b2d85,
'ehb': 0x2b8d41d9bcbdf9c7c8addee2f1986c6e,
'fhb': 0xf11cbe8c77522e0f10e915ebe8fc9672,
'ghb': 0xcdae1deab2b020069f7b85623de37b1d,
'hhb': 0xdd0f0ca53fb69ec148b25e6efecd9147,
'ihb': 0x9d0aed92469f2d5f48555fe8a44014c4,
'jhb': 0xb2dda5584633b3b563ba808a876f6b21,
'khb': 0xeeac000d9c76ff84b94086173175d0be,
'lhb': 0x63219fe1d371da290a49653a865342dd,
'mhb': 0xbc347ee45c43aa598ae41ff59dc05d1a,
'nhb': 0xa06d983f5e4a80847b4ce3fc94c38707,
'ohb': 0xb685594c6fbcc69ce76d144d67e1ae63,
'phb': 0xbe9c2182066724e4c47c0a9f1d071346,
'qhb': 0xfa902b4cbb1ae8d2f4271a063f9320ee,
'rhb': 0x614ed36f5bb78684aeb3af37882a187d,
'shb': 0x2048f3d022cfc420a189bce8c7c036ef,
'thb': 0x8ddcc3fe22e337df24c4a33f4cb12572,
'uhb': 0x4a3e3fcdc7f4ce9ea556def123092e70,
'vhb': 0x8598f7bcdd5c64fce9f341a2600b64cb,
'whb': 0x6a49da4427a77df9c4534d030ea393f1,
'xhb': 0x5cd9e8fec0b3da3b80dae458f8e7ca80,
'yhb': 0x6babdd4700daabcfe4e28b0013ea9f59,
'zhb': 0x0cf9d7a96ec513dd8e6901cd46d199b9,
'aib': 0x811d6697238feb092ee1332de0558987,
'bib': 0x3222ac28aca85b6a0437ff7907a8a06b,
'cib': 0x7cac1d663f25f868a31bc5bbaac658dd,
'dib': 0xbce0e8190c7756900674b5af659f723c,
'eib': 0xb0df40fdc5f3c1669c8dec97e53ce499,
'fib': 0x5c2debca3440487823ac27f9d7c75c4f,
'gib': 0x23fb72db1458cabd2df5d3c26c433698,
'hib': 0xcc2d8f4eec69d5e55bf47be13b0b499c,
'iib': 0xe50f3ed0fdbd841a8f3b0e0cbe81416f,
'jib': 0xda031fe481a93ac107a95a96462358f9,
'kib': 0x5e6ae87e920222829a027db150dc0629,
'lib': 0xe8acc63b1e238f3255c900eed37254b8,
'mib': 0xb5ec1a88f4e0fee94cb2288dd6e148aa,
'nib': 0x8fb92e021b0bd2f3a5c48c59aa996385,
'oib': 0xd70ab86933a51052c5352096d84e0d85,
'pib': 0x8903d70a4cc1be9f8f126572eb8e5a82,
'qib': 0xeebf4125b3818992ca5abccc3e35771e,
'rib': 0x5de560dfbd3111197969fd3b0e9f0d73,
'sib': 0xa444633d8cf456eeca3138c78f4ae12e,
'tib': 0x060b5f3c5859a1e1d6e9323ff6eeb143,
'uib': 0x07c6b98767324b9ead7cc1fd65436644,
'vib': 0x13bd47c9d1f9111eb63aceba3ce2b4d9,
'wib': 0x071f37dbd5c860f08a8910182708fdcb,
'xib': 0xed232678e9bb19dbaa8263aa25280888,
'yib': 0x065865553fac7af57d1e2c8b9e188726,
'zib': 0x882dd451c44e9aa20a02116669aec04d,
'ajb': 0x862c9944e2f0cd8cf0dbbde2ec0935d5,
'bjb': 0xaec0b5aa834040c150ce37d84e534465,
'cjb': 0x3ddeb3e0a97faa45c08caf3a8ebf64b2,
'djb': 0x6bb6375f029de73c885764af168aa42e,
'ejb': 0x5844a0924955c2f30eccbacb57e0f9f3,
'fjb': 0x29181f6746988e09476ef685a35b3e4e,
'gjb': 0xfe349e15030e362ce1ce8f2921ad143f,
'hjb': 0x707cc4e3b7a4d872fe19ee00797aa7b7,
'ijb': 0x76a760d5542038d26c86e4ac38a0cea4,
'jjb': 0xd254066903f175dafb5101f2cd3201d6,
'kjb': 0x4d7c1e1b16a4d890e4c3c344d9278e76,
'ljb': 0xfbd53658030ff55a3256970f1daf9ee9,
'mjb': 0x0b0ac86432b9a7ca7764c2423f1f494f,
'njb': 0xa6e42ffe204fc96b5dc73f15e70eb930,
'ojb': 0x1764d1354d062c5806d1bbff393455f1,
'pjb': 0x0199cb6ed2d8c3470b54af13c9464d3f,
'qjb': 0x2ab8ca71ba15cbb2bcfb94c7cea028a3,
'rjb': 0x962b79358b568c3f9105abe63530dc8b,
'sjb': 0x9071791b3ac926cffdb32147c75af7d6,
'tjb': 0x019da4dfe52d2dff25084e6713c919ec,
'ujb': 0x027e487c556461eb1ed9cd76b26d5102,
'vjb': 0xc8b58969960f24a451b3794370b45fdd,
'wjb': 0xa4f5805912261ff5b9c4c7ed1b427a83,
'xjb': 0x326e579c4b0cf03220fefbf6f8996d72,
'yjb': 0x7dd9c0bc1db72386fae827f651060854,
'zjb': 0x0da7246847bfd6c2498aa0ebb1bf4a48,
'akb': 0xcb879a4910b8a97da40e96e757d547b7,
'bkb': 0x0533e375b418201d9b9da9e90359b729,
'ckb': 0xfa075a987059a1e8a1f6b8bba8e177b0,
'dkb': 0x0292713f3bd713d65a596de9a449155f,
'ekb': 0xd3448954bdc7bda6f0072e32e3cf0b69,
'fkb': 0x26b351a6fb22ba9658978a4672049551,
'gkb': 0x890a11cedd5a4e7bfa76f9466ec0ffee,
'hkb': 0x2cc193f8e665e62b6829f948efa7c3f5,
'ikb': 0xc864d8e7463fcb6d9edfcd4ad049d3fd,
'jkb': 0x289b493e37f138db551ff62156baffa4,
'kkb': 0xb7ce7616ad0078889c45d6771679bb28,
'lkb': 0x4304de434a9f4f4cadd03a8af90cd8d0,
'mkb': 0x3a69e4a77f356ada9e1dc5f8ddc050d8,
'nkb': 0xdfd2dee5d38df1124f57e4d6e48647c7,
'okb': 0xc993c41c90c642e7eac223fa4e88a2ff,
'pkb': 0x06703e1d663511d5380317a577928805,
'qkb': 0x0e0c0e9213c8545f9b7329d9ad5c4a26,
'rkb': 0x8ac3c0fb799cea94e72ef6f016293954,
'skb': 0x98f58f428d0166d3cf6af2fad52e9a81,
'tkb': 0x71582b1219dfdc772e4499050a6cf4d4,
'ukb': 0x3686600a42081cde982980d670a5e12a,
'vkb': 0xecb57913f7931b5f7dc6b426d6d85163,
'wkb': 0x3dccb0570fe9df71b2f4d3968c0af69f,
'xkb': 0xd2e3de88632cfe181308485a440dc2ce,
'ykb': 0x1be867447da8631922ed9a2e5e66d711,
'zkb': 0xc163f84d3a85f9139e7ca18054ba863a,
'alb': 0x73717aafac1e7701dab0fa02b8936926,
'blb': 0x9eccbda89a0e7b95d8d16095afe2218c,
'clb': 0x4f380256e9b8b48d480d4049ec80003b,
'dlb': 0xf6e19ab7125909e3e3738201f6e3c861,
'elb': 0x44f831531f34241e2685b29a55747957,
'flb': 0xf6bd25c7523008386b3080a251572c6e,
'glb': 0xf9091f3defa83aede49cdf349390d0b3,
'hlb': 0xca7735b527e70831a62f6beeedc6bc98,
'ilb': 0xdfe738c1b4b99804b16c4f3675e779c5,
'jlb': 0xb8e238f58b51e2ca415f0312067360aa,
'klb': 0x2c9ddb2f945f2dd6f4edb0221e17408e,
'llb': 0xa0034567c70b255df7ff4e04046acb08,
'mlb': 0x89d021e682f117a6ff8e6fe859cdcc2d,
'nlb': 0x23ff7a047a16016d16c2996ab16eb0f4,
'olb': 0x450707c2a6f17c6ed39889e20f258661,
'plb': 0x4b4d251a7943a47874a3a06855007c65,
'qlb': 0x704d635a37ec52e615a7f9a3c8925d87,
'rlb': 0x1bf01ac5a2b91d7e52d80668adecb336,
'slb': 0x68117495c543ebf26f6b222b61c441a3,
'tlb': 0x3c77334c30fff8de20116851c36244a8,
'ulb': 0x3816f79d206e1e97037120a56abb4b1b,
'vlb': 0x8317cb563782565525365853248c884c,
'wlb': 0x08271d40b504f62761edd60e215491af,
'xlb': 0x022db96f2ffd89d86912950a81e65e41,
'ylb': 0x2dd1d3c8bd295f53e50c89b03034b8b1,
'zlb': 0x5f9256478845c9dbb59de62d06b0292c,
'amb': 0x4b2b6303956277ddfd7b8ea9cebb31db,
'bmb': 0x349dd279247a964d6e3a9fb1d0e7f30b,
'cmb': 0x4e40f1e5e85c99bdd50c4cff84838347,
'dmb': 0x16352f3da732a6b4cd7f6fcc29c80f81,
'emb': 0x210f1d561352e25f858a1f7ba74bc026,
'fmb': 0x3dc64ca6bc1478b4611a9bf69f91c46d,
'gmb': 0xc16a4ff65bafbe8631a5f01cc127b2d7,
'hmb': 0x1602a50164adec06d04456e24aa4ebd3,
'imb': 0x24c53af423bbfd119dc76b511bbeac41,
'jmb': 0xfefe4c60d912e295e59fd874577ca7f9,
'kmb': 0x1bc20b31d030790fd14ac28acc976491,
'lmb': 0xc349fba1221aa947f3977be278e3429e,
'mmb': 0x28fac7a1ac51976c90016509d97c89ba,
'nmb': 0x9da4fe1d789f1fcb4df20a7bead68115,
'omb': 0x8a3243988bc803427b10af7c0db0fc48,
'pmb': 0x9b4a840d790eadc71b9064c9a843719b,
'qmb': 0x522cddfd2a7889f6ed660e6a562c72cb,
'rmb': 0xa0c4c6f280eb93d3100dac30403057f4,
'smb': 0x79e0f325804dafbdaef73b3b17c0fd8d,
'tmb': 0xa8b687c2e93191d90a6d7b8982ceec4a,
'umb': 0xfa71b968cd9d68525579f0d496ae4533,
'vmb': 0x906ca293ceb84b74a484c663af571a7b,
'wmb': 0xe6ef5588ffc30dc9a057311e386b7f8e,
'xmb': 0xa2807a69700c6e9d988920d2a843efb7,
'ymb': 0xefe8dbbd18fc9f6b20bfbeef0b5a98fb,
'zmb': 0xeccec2b72cb48003a7726a041dcc2475,
'anb': 0xcfecb9a2affcc76e8ac218970040dc14,
'bnb': 0x2589597838b8b4e9a4b99ba59dc4bd3a,
'cnb': 0x318550dbc179b756467214720dff0139,
'dnb': 0x2cfe1d7fb53aa07f7c177fa5d9f7b9bd,
'enb': 0x2d2e14c726db9ef694bed01c7e22a8f3,
'fnb': 0xf541bcb6ec62de093416a7c8de510e84,
'gnb': 0xfa3dda6c258290fcf589eb0e2a2b10a9,
'hnb': 0x83c0009fdf92e9bbbde59df5488ff315,
'inb': 0x2a5e57743fcd4b6492866bba1b96a57e,
'jnb': 0x4d74d70ce6eaf511af492818f0257a28,
'knb': 0xeaa3459992c9a44dea32a9117ff51a7e,
'lnb': 0x8d0ddecc2f73824b75670ee0cc036954,
'mnb': 0x62cd43b670f7e4ec902b5c7acb4d33d5,
'nnb': 0x477347c6ca6a6c12cf23edfd2e6df41a,
'onb': 0x3bdc602bf88c10e009fb76556e2ee8c7,
'pnb': 0x6074b74dff92c68b4424f07fd8d60bee,
'qnb': 0xfbc766d56c868ce7cb2860d2fc96a6b1,
'rnb': 0xa7b2dab44b5ee1981fe92e12229db431,
'snb': 0xde98df916c14108fde2085dfd5f516b6,
'tnb': 0xd1ab69b7b609835d704f43331fc9d9cf,
'unb': 0xa19be82a4af850b606d37980428aa59b,
'vnb': 0x941415520824d85718220f457b96e9ed,
'wnb': 0x9a8eac0cf219d4927d9616c7710abd47,
'xnb': 0x2437649fc17c02408272f9e549f7e102,
'ynb': 0xb9e5368a9a6eeaefb38be47fddac557b,
'znb': 0xaeafec4cfab315da1dcda7ee364999cc,
'aob': 0xbf1127500a1dd1a66e5e8917a800f9ae,
'bob': 0x9f9d51bc70ef21ca5c14f307980a29d8,
'cob': 0x386685f06beecb9f35db2e22da429ec9,
'dob': 0x769c7a78f23b65869550bc1bbf4f52e1,
'eob': 0x11cfc897acaf480b320bb309cb13421d,
'fob': 0x8ba39a9a4a56c44c60b3976813a9eeef,
'gob': 0x4fe3e66b2026c6f3d9514fd8f6cf6668,
'hob': 0x9efa064d89f0fdafb45e2947ddd849a9,
'iob': 0x372d61400abb78f8f236c7611eb8ef53,
'job': 0x9dddd5ce1b1375bc497feeb871842d4b,
'kob': 0x5fbafe260efc5ae849727ab270a41d05,
'lob': 0x3fcf32676bb2478e2758650f43478b44,
'mob': 0x41f02b07e8e203d3260facb55b2f4b1b,
'nob': 0xb12ac9f661b08c941e42e6a3c63a5249,
'oob': 0x50b0dfef4485018bdbcfae068c3247a0,
'pob': 0x8d1ef0c95d0b6721c0afc0ce4a410160,
'qob': 0xf748289df591746ec4fca0ceeac74701,
'rob': 0x760061f6bfde75c29af12f252d4d3345,
'sob': 0xb489de1fd12c3c0ee60dd5ed61d0efe4,
'tob': 0x2c8509340cbd9f67f1bd17c512f67069,
'uob': 0x6e5fed848d16f55b6a2eb0a19745b8eb,
'vob': 0x2023e8bd112ca50d436a83773010a4e2,
'wob': 0x42cfb30b1843bc8b844f186abebd9fcd,
'xob': 0x755b949a55fdc30db0ca54618ccae327,
'yob': 0xd87219e3b44633b77c670885494a1d5d,
'zob': 0x36f3b2748cea3f5714d849889bb4a0c7,
'apb': 0x107c1464a1ab91d4309827c489fb232c,
'bpb': 0x4a0da80cd5f151c580921736d3ff90d6,
'cpb': 0x84c16eb73f88abacf19ae3777a05b1b3,
'dpb': 0x01008f27ef7f98fc01e5d7351ec5cabe,
'epb': 0xd7676e25a5df19987b894569fd17ddab,
'fpb': 0xce2487db2269a7c5d7fd1e1870158533,
'gpb': 0x07eff12de165ebbfda36acc1b5d0e6f5,
'hpb': 0x4cf91c06d98db8f3dd08ed5012e35e9f,
'ipb': 0x521412b378629a548d7e8392212ab4ec,
'jpb': 0x7ef0f8e7eab0db82c4f4f6038252bfe8,
'kpb': 0x7532ba42d90ff517da9499ea0f35c217,
'lpb': 0x14ef452e8076cb930fd5c37a73a18a29,
'mpb': 0xc8203b30af971c6b9163df6f2befa8f5,
'npb': 0x20e20a00dc58ce85373924454eb644fd,
'opb': 0x2073bc31f81582826530d44e02057473,
'ppb': 0x105083a31a53ed471d9b57923e02fbd8,
'qpb': 0x216f2dbd8fb673bd1b50e41e6ad86b48,
'rpb': 0x559c6a4fd979f18f61d19a81aee0b076,
'spb': 0xb1a72c1059dd64cb1399accf2139a309,
'tpb': 0xd12d2c37e8c211f096ea78fd159643ba,
'upb': 0xd4d769dc7edd14036d0d1cd3e1e24c90,
'vpb': 0xb9e70754a3975d4bfcbbab42480046e0,
'wpb': 0x9c0a8b40f6765922fb5eca6fbcf7fc9c,
'xpb': 0xe921ad061fb30a54e5e40d163f687f0a,
'ypb': 0x99bbf526104dc7403f2a6860c0255905,
'zpb': 0x39e9db3154312311b123cd96870c114b,
'aqb': 0x199648dac4e001e48934d7b453faa8ba,
'bqb': 0x7c473bdd6c4f04ee99081c2572bce46f,
'cqb': 0xe6661456b367415054af501c23c85554,
'dqb': 0x167d060b80aa69bb81551234c0786780,
'eqb': 0xea43b72c04cc55a4274b948b11e3b77e,
'fqb': 0x841e5548aeb34189fd8e2609b680fffa,
'gqb': 0x5c5eba4057565560c4a622d0d1d1ec27,
'hqb': 0x88e7ebe9ac16a7e67a2a0a4cbec4b448,
'iqb': 0xe76ac91740273ba54cf0b11cd5262ea9,
'jqb': 0x7d789813825fb62ecb3701de96a5b4d4,
'kqb': 0xffe44cc10adbb889da1ae75c616eb8a9,
'lqb': 0x79aa01b7f59f8afeaa9324396ea02280,
'mqb': 0x673678f9b5559daaa069b1c731b62de0,
'nqb': 0x6bdd13129c5f38306ec779e4e249569f,
'oqb': 0x1c29b9e7814786c51af79661bd7a657e,
'pqb': 0x04e4b80f4ed3c41352a9595614ec7cf3,
'qqb': 0x53412e39bdd68008ba02d5351d567f4b,
'rqb': 0xc207d08c0cfd367730bfba7eb6d6b7bc,
'sqb': 0x788ed58ac67ae60bfed178ce05809a48,
'tqb': 0xad2283b8b2085152edb31bbd0a749f2c,
'uqb': 0x4b8d650809a39cbde467dd48d40805a3,
'vqb': 0x97a4b18a4b1e5d980406a30dab193133,
'wqb': 0x59964e5d540e446cf98bc2af78a2ea58,
'xqb': 0x25bd5d04e7fd809e376b9501131f2ab0,
'yqb': 0xeac70951f59392ef71c2c2c16156b12b,
'zqb': 0x119827b5305502589f627aa4c160557e,
'arb': 0x25ea0ac7e3f16e5f711563c8693651dc,
'brb': 0xb992bdb865efa8b3b7333d0458323f4e,
'crb': 0xc182a8a87c9287b094141cb825472dfe,
'drb': 0x992a415d077a0aa33968e19eab5b3094,
'erb': 0x78282a10139f55d8acbd77d017f47798,
'frb': 0x742aafc3ab75534305eecdfc35a6911e,
'grb': 0xcd941bfdbc8400b8305e3f1e8d3fef73,
'hrb': 0x277717f43cd47a869abe2e494c16b6b0,
'irb': 0x42e1825c7c404d6fe3746d47eba2368c,
'jrb': 0x36f2ddfd15c6a419ad89715677bd374e,
'krb': 0x1acf7f48696df792cfa8ffefd06d7b2e,
'lrb': 0x25f490b37c7c9e8955d68d0f93f8bf76,
'mrb': 0x13eed552a284d37f230c63de3263cf9b,
'nrb': 0xd8b758e7e55a31e2d977ee2e142c608c,
'orb': 0x95cc0182f63c9c72a514566419ece3b2,
'prb': 0x49e1cd72ac222d0ed31831397ac9f338,
'qrb': 0xe1ff01342515b62700ad6726a98248d2,
'rrb': 0x26f450892c31e31c66a0e1caac616a42,
'srb': 0x81437d899972ab88cb8ae0fad8373d5d,
'trb': 0x711f7a034ed19657ae420946cae77d49,
'urb': 0x1f5c7fcfc3e9abdbbc258b4ab29de420,
'vrb': 0x7ed925a30492dee9ca8cec186f4fd953,
'wrb': 0xdc605964e0eb31752fc7c0cc6ba9053f,
'xrb': 0x5f1f4a0ec0edcc3b4ce188d3b7f39882,
'yrb': 0xbb7dc3c4e28a910b238d2547c9e3af34,
'zrb': 0x70138cf8e8e289447e26a3586fd499d6,
'asb': 0xd24cd8df8f41b05ae77d80189644aaee,
'bsb': 0xd318fd5062373ca1b12d05ac9dc0f9a6,
'csb': 0xaf56d1882cfea3759b6e5878f9f4ffa3,
'dsb': 0xf4f6172eb26581952a70d7199bfd2ddb,
'esb': 0xefb03b68f7a0c2a331fc61a491c4b2d7,
'fsb': 0x70ef8f8a06669823a2b00567ad07bba3,
'gsb': 0x161ef5e8cf0791d80a107e5b2fcbf10f,
'hsb': 0xb7cab12b2b81385dd2cccb8ce67e4998,
'isb': 0x321cafdaffa6d8d1de3a674be08f6720,
'jsb': 0xaed32f5fffcfe750aaecdfca61773909,
'ksb': 0xb24176d34261f3e5cd8b3b78bc90072b,
'lsb': 0x58b7eb5ebaa12a0515c0dac315aa2611,
'msb': 0xc4781e1f427c6cb6dddc0662358e3eab,
'nsb': 0x5403dd9de0844bef4266560239d4b2d4,
'osb': 0xc1b5828b1751c223db5c3d0569a57d33,
'psb': 0x7d05afc58dbbb41f9c0ab7b3a0c2ed18,
'qsb': 0xfc73366edc69547735f2bf736a03ed0f,
'rsb': 0xde38c238595c595be17f3057bf9842d1,
'ssb': 0x7998730c62c4a8d0bb98f1bfb41ab56f,
'tsb': 0x11ef24ce7efc2972f71e0584e1be59ae,
'usb': 0x62ed973d963913bb55111a7b8116ace1,
'vsb': 0x9a1aa3e2d997a76a64e7995e33c55597,
'wsb': 0x30477954a8f8c7e62b295c634f2c3d93,
'xsb': 0x446a59bdf5332261eefe7fd9b7718e88,
'ysb': 0x8c2b96bbd195edf7b6e3506aa2e49cc6,
'zsb': 0x1b2435ffa198668d92d48dabf49106e8,
'atb': 0xfceee8e36f95e7c22be2a8c47ce2b93e,
'btb': 0xd3030ec431fdaa27b7ec83dcd3820523,
'ctb': 0xce445102682e75bf533d44e1afc38a90,
'dtb': 0x8e607fc7645189dc4a69ca7189909c3d,
'etb': 0x13769928cf0f70c5da1757bd7912cce8,
'ftb': 0xb63e561d726c1dbd8f4d43b1181b3584,
'gtb': 0x12bef9fe0a47104f6835b8f0e6c3f366,
'htb': 0x53cc5f9c06df19a98e73eccd024670ac,
'itb': 0x1f3e11927c6c05b5344d1a89eb56ee27,
'jtb': 0x56de23691dfcfe774c8f47c4929302dc,
'ktb': 0x4ce0b59f2e08d6dc7e6fc1f9a2c982d7,
'ltb': 0x5b6cca96e9a4df5d3479d7bf81fa3760,
'mtb': 0x178adc1d3b2bb2266c87652e970613ae,
'ntb': 0xfb8a98a6067e7de93a17c7c2e9e3b5aa,
'otb': 0xc4c8aa2a0624d6e67d50e4960f583161,
'ptb': 0x0d2c0ffecd9ffd63c6b34595e97f0eb4,
'qtb': 0x9ece966b7fdc93e9ac735a4e08d3539b,
'rtb': 0xb11260d93de1c1dad5f975b4766311db,
'stb': 0xe2c105408da55d8017215f192b782fd3,
'ttb': 0x5561542342419b28f1e047fa049fcd97,
'utb': 0x12c1fdd074e095be3dbbb82f8b926a08,
'vtb': 0x8ce0bcca416eb81bfcf96c292fcdda2b,
'wtb': 0x5cc59191131d1920c6700a9c9d6e46f0,
'xtb': 0x1ae7ac104a8f392fa56f4c1275aaf0b9,
'ytb': 0x708ba678b2cae37bd648b7bc9681a983,
'ztb': 0x792242a65a5bf774e4a76aefbdc32efe,
'aub': 0x34fffa72be6965817dbc8a58bc640483,
'bub': 0x746b96685b314379bde168cc4e6fec76,
'cub': 0xb8dbeae2cc12034a4021e987f2d8d38a,
'dub': 0xca61bfc4d697d12e7fe636ec755050e4,
'eub': 0xeffa0fd0839a6e60c104a0dd97ab72fa,
'fub': 0x6aa53a22ce1c8c2b9bf903fef3b3cd1f,
'gub': 0xba73632f801ac2c72d78134722f2cb84,
'hub': 0x5261539cab7de0487b6b41415acc7f61,
'iub': 0x697136e61b33ea9438571ae41251dc39,
'jub': 0x0885d87e142c7b77575ba50f9f8c88fb,
'kub': 0xc02fee0c79e128dd53764a2e9fcb7ac6,
'lub': 0x5e5af18321abd413a90b45a6d4cdb578,
'mub': 0xdaa551f036a22f6a1d9396b4d52ba522,
'nub': 0x88dc70776c57f1aa450c1a278b2d9079,
'oub': 0xf0e6790e470e46021152539aa166b2ab,
'pub': 0x3a21cd7317e1445be89999f5d7f62a53,
'qub': 0x4e25e09a7db33cc33e5ba3b009c2ca71,
'rub': 0x8efd23a3fe0ec74453bdd0fadb91b0e3,
'sub': 0x8a68dc3e925eacf92633be230722a140,
'tub': 0x271721b254b162696e14c3f55a6df8e3,
'uub': 0x7975a0a581bf1f8ab03ebb108d5d2551,
'vub': 0x1ff49c94f5e0428eea789a68b08a082b,
'wub': 0xc7a7863eac4a09c89e4d05fd46632d36,
'xub': 0x39389b5b82590599d731d7f6fde55df7,
'yub': 0x125eb344f46bdb316faf29c01cee4f9c,
'zub': 0x896339aa0a31414e80dcf1d6b7d0c2fd,
'avb': 0x5391839e8736a3b8c732bf750cb5a79b,
'bvb': 0x05dee3b8f96e1d70c8732a5de326d666,
'cvb': 0x116fa690d8dd9c3bd7465b59158f995c,
'dvb': 0x60196c13bc1d8ebbf9f687f2f9df47db,
'evb': 0x56f8a07cc544ac8ce35e6707336b3cad,
'fvb': 0xc0878b9e95cf29c5aeccdf980e475435,
'gvb': 0x57518d28b738c103bff1ed18466aa324,
'hvb': 0x56902654b82351d09f7350faa7a7e9de,
'ivb': 0xd0910c2297faee217481e119e0fca5b0,
'jvb': 0x0f226375ce91ffa55099fe1bceae5d6a,
'kvb': 0x7b68ebe8469500f36d4da69451761052,
'lvb': 0x67f250cd642f9652be81840dc5e689ff,
'mvb': 0x1a5af5ae32c40ccd5f98d07f3006f3a8,
'nvb': 0x157b65cc2fe1c07faa76b363552ffb79,
'ovb': 0x54c74190287c86988453fb0dc16daad9,
'pvb': 0x01304c54e32168eef5ad40669296517f,
'qvb': 0xc688c15de6185704bbfc5daf8d65e93b,
'rvb': 0xe1c0d7ec4156a2fbdf290616b7526a3f,
'svb': 0x612cbe62a91cac38c3d85643a3ae5571,
'tvb': 0xe6180d6a007ad544a90b0ed75706acf0,
'uvb': 0x4ff34395195837f049d093702cb85f75,
'vvb': 0x74e1e420da1716ae52ac3ed6655417d0,
'wvb': 0xcd772e577a52a9d202f0ded75587f923,
'xvb': 0x92d2b6ca37af3ce44b985ea0051284f6,
'yvb': 0xc5dc75b5f03425dcd97ccb226fb8a36e,
'zvb': 0xf44dfb5d4ac83c00f566c913197d34ac,
'awb': 0xe5555349c56ff3ecdb8eafb9c860ffea,
'bwb': 0xf9b8d97e48fccca83ed4f3d094601f1c,
'cwb': 0xc7490ef3633c5d387910f3dc020c9cd2,
'dwb': 0xeb01a40b5141f8db3095a27a61abf0d2,
'ewb': 0xf0f53913adaff1e86b3d3fb1d9f066c1,
'fwb': 0x7a1516633a56081ea7b98d0b0f6b733c,
'gwb': 0x9a81e55d028cc819e92b32a2c4de23fe,
'hwb': 0x6ba7f8be784a8f1a2f06e5efdd4b5cb0,
'iwb': 0x1b4a7956dcce9b12774c852244653b12,
'jwb': 0x86a6e296427dbbf4bb45178e0519e8a0,
'kwb': 0x423354803efb3ab609bf02f9537ba5d0,
'lwb': 0x9c82ae4c73da2daeb11845fe7f9c1ab8,
'mwb': 0x443cafa43763ecef31a18198dcc04dc2,
'nwb': 0x48aa1bc42ac6c3a95c70434033b5c0d9,
'owb': 0xdfc3c97be635da73c84fc70374746a79,
'pwb': 0x52f7765e07944974a9c2ac3c554c892f,
'qwb': 0x0630987beae1f40cf1508fa6ae80eca0,
'rwb': 0x9f7aca6b5146aded5276f19c0e72065a,
'swb': 0x3128e23fcce3051e7f9755ea60994fa3,
'twb': 0x56f61016d8108e1835812a20291f7d59,
'uwb': 0x74d6b467cc54aaff7bbbccb78ffd6c7b,
'vwb': 0xe7a2ea58723f5956287143a5c3e38a0b,
'wwb': 0xb96c5e4db7a005dbbabf178437a82dfc,
'xwb': 0x612e82815bd26842b828a9edbb224001,
'ywb': 0x6cdfc743b01e1dc09a1f5cd3673d944b,
'zwb': 0x3bbca8e69ad52e11c12242d144bd6bd6,
'axb': 0xb1a555a659bce81b6a19d06bb011ec31,
'bxb': 0x1c3b80cd890a01b12ed203891e0c12f1,
'cxb': 0xc2a733e4734c91e9d4316a0719208b91,
'dxb': 0xd079fcde65446eb0e26637aa2b537f23,
'exb': 0x3447de6fc448fe2695987b63d3af6aaa,
'fxb': 0x6a44376e9118a3b0abf56602ec19f889,
'gxb': 0x520d7a979a3b1f5cb4a2cb573ce2efcc,
'hxb': 0xca977d393e010e8f3960ecc2a6036867,
'ixb': 0x8fbf1969f1f3c38da424d831d8dfb2bf,
'jxb': 0x2bbcb25ea3cf6cf8d16d97ed50d86fe1,
'kxb': 0x9473bf3279aaaade35e410e4084db49e,
'lxb': 0x670a0541fde5301f53873c625a176b90,
'mxb': 0xb1878f7b186cbfc7be659b048cd0c03c,
'nxb': 0x1c5a6498087d20a4af2c09b58343234a,
'oxb': 0x52fd6035e668085c204e2137c1faa917,
'pxb': 0x3efce9a2bfd7ef73ea7492cd04c288be,
'qxb': 0xdb5ccc728e613d3bdfd2ecdf1f0f2cad,
'rxb': 0x5d573c69be72a45d7a448d4ab99ba7ad,
'sxb': 0x032a91c13c37b6e203be42f90770a534,
'txb': 0xe0ee523bd15aa6cf153c5dae1a89196b,
'uxb': 0x58714925707569bc7c8c977ed0314362,
'vxb': 0xbc29b934f6f8dfd4c9fdd5ec03be9239,
'wxb': 0x540bbabbdc772b1b849f92131089e222,
'xxb': 0xb2b46220d54535eb548c975f37cdf51b,
'yxb': 0x4fed0bf01975be7e3ade866ee9a9c365,
'zxb': 0xe6639c1f878e88af6a7da04bba90a265,
'ayb': 0x706a7a63d98ac7d03191d4888755a831,
'byb': 0x9097e6cd00e2c1d731accef54d98d73c,
'cyb': 0x46db00e989c22948ca6fc74e3a338d4f,
'dyb': 0x91201e7bc625ef1b7d051e80084a4d96,
'eyb': 0x0b89dfb6ec69eb9a8956492f64e910e5,
'fyb': 0x4fdc7bdead8db2faacb751b3408740e9,
'gyb': 0xdd4562e6ebce175762a4bef5d43024b5,
'hyb': 0x3a6b0296dbb50888c6272d1e49281d31,
'iyb': 0xcbea85ba0fa38aac4a2b186acfbfe169,
'jyb': 0x1ee033045bc01edcdca1eaa4e2f244a0,
'kyb': 0xde3256d4a077c98ba408d7930b076514,
'lyb': 0x38d17dd2b5c349531268ae8484112a78,
'myb': 0x6e48dbb6ed6753d9208ab5ddf3c20e97,
'nyb': 0x0af5107be1e00291a0fab985bf1cd39c,
'oyb': 0x6a9fd690f52c57634f9843f597366269,
'pyb': 0x616c7b02d6212e3ac4888109ccba5233,
'qyb': 0xc7f1b3a550387e09cad19ae35723cbe3,
'ryb': 0x698786c1b9d8ce37e69ed6b378b4b77f,
'syb': 0x74926053ad79dca0d0ed8ee93405eddc,
'tyb': 0x7369dfdaf0475533d4d375df8ed75e14,
'uyb': 0x17a9cf670c95cdd4dd3af52bbe18217e,
'vyb': 0xa13031cd600a15a878da73711af2e90c,
'wyb': 0x6f5d386b734fa7baf39bf7a315c5a1a5,
'xyb': 0x5b1a2d40f9ca4d080e3b4a2b30bf30a5,
'yyb': 0xd47ab2150bbd3884b5d2d16347525345,
'zyb': 0xbccd903383d49e20b5fd7fa561dcc431,
'azb': 0x976f58e7e8c9407e347eceeec4bea372,
'bzb': 0x474965207f751b5f89d7a9df78a26960,
'czb': 0x13040a8f2b57639af438d3b921ddc410,
'dzb': 0xd26a5ce20bf64e6b2b2754268e661bf2,
'ezb': 0xe6f39b3c0d25aadceed6883bcc152a27,
'fzb': 0xc919cdb1f227f90273018d855af0e980,
'gzb': 0x15020440accdd94614d81ce0cc0c7a49,
'hzb': 0x98611e4d2b59fd119adb39af4a3e3fca,
'izb': 0x3013ee5530e2b5ec23dcab3841fc3ff0,
'jzb': 0xe24b71fc8e90bd1a489e6538bcecab55,
'kzb': 0x56646bf18c1c9a3bec6185ff396b3d80,
'lzb': 0xaffe2b88e008921c6ebdc6d92e6783df,
'mzb': 0xeaee5d4ffbb3b7d4fd75ac19f7f5ff02,
'nzb': 0x74dc217ad54981009fdf2f6a9beb1310,
'ozb': 0x67c1e0aca492ab74b9f0cc2a2b7fc8d8,
'pzb': 0x7c6e9730a9f6a185a4467d5d998d54ed,
'qzb': 0xafe863cf9cccb598d8817388f4f4b255,
'rzb': 0x71bc76c44acc7d6b977f60090dc866f7,
'szb': 0xf640b9f2c043792e8f54df21582d1f2d,
'tzb': 0xa6335ee9f3a27260e61c90928f8f3ba8,
'uzb': 0x9fd247b0101cd958a1454e9b94e13892,
'vzb': 0x9094e8aac41795b34afcdf090c62e3ea,
'wzb': 0xcaa5eae2ce5ac44a3f8930e6f127b423,
'xzb': 0x54512b1c7414188441aea85d0f9a6f26,
'yzb': 0x0c1ef18e764b41d2e84fb3691f29e7cd,
'zzb': 0xc641c6ec8b80c6c000ad953e8a8a92ed,
'aac': 0xa9ced3dad556814ed46042de696e1849,
'bac': 0x79ec16df80b57696a03bb364410061f3,
'cac': 0x1921f9e1416473a1d0f15d328e837115,
'dac': 0xb75427b031d9ece858e5f9404469c6c9,
'eac': 0x31e0e4c9c2aee79c4bfc58c460f4ddbf,
'fac': 0xd2467530ef7303b14c9988df20bb8ce4,
'gac': 0x45fa43f8effc2fc7b47e915a7d86c2cc,
'hac': 0x3763e42b75ac2b1500a228bf22f4dc5b,
'iac': 0xaed3eced69a81bcc445a6f3215f7e23c,
'jac': 0x2782dba0f455f7e27924d87a4108a02b,
'kac': 0x0208a2e72da71f7d17d6d20080990ad0,
'lac': 0xc490825595c06727da1d1a11497eb89f,
'mac': 0x140c1f12feeb2c52dfbeb2da6066a73a,
'nac': 0xd1de8be1b40a9326cb906e404e2a1367,
'oac': 0xad0cd65bbcd5943577576c8175566f13,
'pac': 0x182a15b93cd323556be21fd4fe8f3a8a,
'qac': 0x4178b35007d7ee2316d17c2579c15ecf,
'rac': 0x04a33dd13425a941a5739880a7526687,
'sac': 0xb5241b4630506e90ecf9d060c28b92c3,
'tac': 0x32390fa731a71f4cdcf6b76a05334545,
'uac': 0xf14c8c4bf1b2bec5d4e18ffec5cb4677,
'vac': 0x5b73533bd5cbdbf81adf0a87d2641e0f,
'wac': 0x76633a53d40be7011a3293bb4ca116f3,
'xac': 0xa8bfc56ff614c327ebca22574ab17391,
'yac': 0xa3392e4d97b0d057a57453dbbad0313d,
'zac': 0x0c22828099b789d62a96fc1f87928f43,
'abc': 0x900150983cd24fb0d6963f7d28e17f72,
'bbc': 0x99be496ab9ad1cd2b9910cecf142235a,
'cbc': 0xccb241b4014cfb8a2ba0da0a943ac9ff,
'dbc': 0x4a429e0b8871ec788a3a171545d959e8,
'ebc': 0x01e40bb5c70ff7f7d1e46cc86d40f654,
'fbc': 0x5edfb283385af10f48a7b0cf91ac82d3,
'gbc': 0xb7d0f4b11386c52485f4e48b56376dab,
'hbc': 0x66bcfee5fb4b8905bba73f5b691dc77a,
'ibc': 0x63c3ae7301647113d04e201e02f67b18,
'jbc': 0x56fb8064ad94d98010dbb10a9b89dec6,
'kbc': 0xc0524e3042ec431850f2fd08ef298024,
'lbc': 0xa09ed67e353a71e57ffa05637dc55a34,
'mbc': 0x9d7fe60f25abdf582afc7cdca238bde8,
'nbc': 0x11d6ff9a17974c9a7a3e1ccd50c92fc0,
'obc': 0xa49adf76667e01bfc999664c544b98e6,
'pbc': 0xf12d5ce5e87a2c8d7fb110c981c94006,
'qbc': 0x45f78ad16bf4471d9407c0a4025d9f01,
'rbc': 0x6a49f67ed8e5e478391b7496381f4047,
'sbc': 0x966bcd74e8482da1569c6b839996c0dd,
'tbc': 0x11c73f5ef965e27ad7f2162c6d862d00,
'ubc': 0xf93fbdaacca359c08836a61778c9d327,
'vbc': 0xa11720ad41e757378e9957b4822cd0b0,
'wbc': 0x631923851297ee00701f440c58dc90cb,
'xbc': 0xc9efacca293ef80d861dd2796de1acb7,
'ybc': 0xbee42646da367915666b3448e208f397,
'zbc': 0xb058389ef0525009e3b54c7fb524eb08,
'acc': 0x1673448ee7064c989d02579c534f6b66,
'bcc': 0xdc9262a469f6f315f74c087a7b3a7f35,
'ccc': 0x9df62e693988eb4e1e1444ece0578579,
'dcc': 0x3b11d4ed537ced20e41a9b8a067f5d85,
'ecc': 0xe0f1a4afdc30e0724bd9be57355de70e,
'fcc': 0xbfca5f5929b01a5119351bc0b1a391ae,
'gcc': 0xe0d511356bd44120af49cc96c9dcf3b3,
'hcc': 0xfadc3c8509db129e10ef4cfccf6efc5d,
'icc': 0xa87c68a857bc5ec5362391a49d3a37a6,
'jcc': 0xc259d6cc99edf5c7bc7ce22c7f87c253,
'kcc': 0xc4ff0662d9c58accf2eac43dc127f7a7,
'lcc': 0xd18f6736f3e3f8a57f06409359a4cdd6,
'mcc': 0xf46f944e351f8b92e40c3308318738a3,
'ncc': 0xb06de212b821659277d610b28ad7357c,
'occ': 0x0f04b700da1d8c0641b4be9bfdd83d20,
'pcc': 0xad1517aafe91ad10d02ca0de7cbd787e,
'qcc': 0x4337db58d9a9ad44f31710aba5ab4f70,
'rcc': 0xa018881f8fefba70a163172087b7f45c,
'scc': 0x9c94594beef2dd07675954d761b8deac,
'tcc': 0xdcbacadf485c141a2b9b0028f2c0b2e1,
'ucc': 0xdf85ad6792af31a47fa90ab34e573308,
'vcc': 0x19f8b291ddc2bbaafe41052e3be4a743,
'wcc': 0x04392afbb50e1ed6f9f7b3b67c05bca3,
'xcc': 0x0417c940b9b4f30aae3d4d703bbae4c2,
'ycc': 0xe821f46be6ee45e3ca80d189f989313b,
'zcc': 0xb9915f5ac809a8c647c29f5c060c647d,
'adc': 0x225e8a3fe20e95f6cd9b9e10bfe5eb69,
'bdc': 0x0a55934d481127bd4c81ac1b76ed45ee,
'cdc': 0x8ae114df2641b681b4fc51ed989f97b2,
'ddc': 0xb66ef205d4bba3390bc51642b3f397d2,
'edc': 0xc630dd16135d2a82b0518e1cd3b5d36f,
'fdc': 0x6a6226f60f9000ed9395aa58f432e8b7,
'gdc': 0xe6044bf1cf0c87918585e613eb02efc5,
'hdc': 0x5b593c651bda77f2b449220326ad4346,
'idc': 0xcca5e614d1755faf382e2c678bd45a19,
'jdc': 0xfada1071eb309cb3b778487f71a41ac1,
'kdc': 0x6414f8dc6468b3497b033a7ab5e705fc,
'ldc': 0x8f83c973a5826b8740cf6b33bd7706b0,
'mdc': 0xf14626796f9fecbc1dd26432e3df5adc,
'ndc': 0xbb4aba0633e187d42afb56ac43d605af,
'odc': 0xfeb79481115cb7938f8ea39708801b70,
'pdc': 0x1e02965bb807a765ba0dbebe1f9b9fe0,
'qdc': 0xa453c13da2bcbbb11573bc7e3b70b845,
'rdc': 0x0181dd937b704caa22ed9c3396e5ff60,
'sdc': 0x77c0a176e54ee5f179b296099d042a97,
'tdc': 0x5225db0d3feff8b7ce6f3c48068644fd,
'udc': 0x152a6dc9e42ac1ccb6fe44e971e9710e,
'vdc': 0x6a1b2de69eeb9a037ea23de6b529394d,
'wdc': 0x6cd566bfa84190ae790cc74243260108,
'xdc': 0xfe1ce5ce34b9195bdb80235ef422341d,
'ydc': 0x7fad35fb5394aced2680edc981d7b589,
'zdc': 0x999fe6a9f7392068ceb6454dd721efbd,
'aec': 0x05b980fd14414110790904f87051f9e9,
'bec': 0xfe9ace81d27c53c205c2ac8a6f1d9259,
'cec': 0x3eb29d792013f8445b89d1c0cb051d4e,
'dec': 0x1feea25ecb958229287f885aebe7c49b,
'eec': 0xfbf70cb49ab268a3ec42a4d17f61390e,
'fec': 0x68e129acd99d4b65789a3c9e07fcbfc0,
'gec': 0x01de343334dd60bdbdda3bd70277a85b,
'hec': 0xd4bf67cc527421da1f2b4c9b4bd74946,
'iec': 0xfef28193e66a52432fbb91b767f8290b,
'jec': 0x853090e7a111dfa6ea2e9dfcf8ee2c24,
'kec': 0xe706be55e3fdb5d36a723752f53639e7,
'lec': 0x0398a2cbcebe784d10360562c28a4b07,
'mec': 0x15d6426563381c7eca0c321abf131401,
'nec': 0x0490b305539f9a2d4fb47a454c3a0dda,
'oec': 0x6b540954e368f9719172706a2ad234a3,
'pec': 0xc3592ad763cac73a15466c0d3c10a77d,
'qec': 0xfb73c17f5099407a3ecb6b988dbd8690,
'rec': 0x0b2c082c00e002a2f571cbe340644239,
'sec': 0x74459ca3cf85a81df90da95ff6e7a207,
'tec': 0x044c3027c1bfd773fc90762db80bf8cc,
'uec': 0x112fd7a3e9ef3b623f74e401785eebc6,
'vec': 0xec99834b54fb5bc3d50f5fe0efb9b93b,
'wec': 0xc8a17d74590dcf86ef4aef8c9f9686e4,
'xec': 0x595e8a03d456392104ad032890e04c2a,
'yec': 0x9aa3299fcf34136f4eb804fc1964ebac,
'zec': 0xa8ce7bd6279f40a86aeaa59b49af4e37,
'afc': 0xccdf76b5fe158066818731fccdf24919,
'bfc': 0xf4f450e119f56ef9134f032558a8f31e,
'cfc': 0xf310cb5807ae5bbd0811d382cea39b22,
'dfc': 0x0b5c4a1eebab6d8468677877384a76c2,
'efc': 0x3af7892a614601fd8632759437984dcd,
'ffc': 0xf668d2179e0d4ce533268be29fc60e75,
'gfc': 0xa67c692ba9da5474f695da06cfbe1f1a,
'hfc': 0x6763b51d831ec04e1a634affbf932d64,
'ifc': 0x86b0534dd52a4094a51869c1e556e1bf,
'jfc': 0xe98a713d524ac7247b4fe2dc4147c829,
'kfc': 0xc3e39bf9f009b801037846853bfa9f2c,
'lfc': 0x186378572ff55a92f6be983cf643f147,
'mfc': 0x3800f845ae86a2e1a26be414d8cadd0f,
'nfc': 0x062993585a465aecb861561818225523,
'ofc': 0x0c400709866ea0a93c9d16bbd9640624,
'pfc': 0x29faa6cb8d44252b5b45b17f964efdd8,
'qfc': 0x42d5b0416d0686b72e492f2a0eed218c,
'rfc': 0x1560a2dff5992750d9748cbda44b4c51,
'sfc': 0xfff64cb6beebfa10fd803f442c969238,
'tfc': 0x6af6ee7cb89ce5139daec1f060d83bd4,
'ufc': 0x0f1e6af3ded265ef957875152c35f14b,
'vfc': 0x4b18e9d16b3ddd90e964c4ba8a0abd5e,
'wfc': 0x810016a5056c4f3f7dd74b3ff02b8f5e,
'xfc': 0x13608fa5c8483c2b0be37273d96f75e6,
'yfc': 0xbeade09abf59c1cce793fbc76fe23295,
'zfc': 0x43d84e44712b09d1636dcaf781d899c9,
'agc': 0xf5004da563fb254400b4aa3b6b010767,
'bgc': 0x0f9e80982566a13dc0c281a4500c4a13,
'cgc': 0x2505897a3c98a69feb2d57c741d8e0da,
'dgc': 0xcd85463c97c8f3b9a5d0638db5d2bbf1,
'egc': 0x25a19104eb5e9420d8044421a204e8b0,
'fgc': 0xb47fd47e84458969a3b7c83f7ef05ef2,
'ggc': 0xbdf39405886c53942cd21ce43d6ba5b5,
'hgc': 0x26bbe78a0f400782a99deb542806cd38,
'igc': 0xbebfde22551e8fcf3557c30c66b27329,
'jgc': 0x2a393bd02483ae3f81c57e39aa350f21,
'kgc': 0xaa51ebd7ec3cc098a6228fd456b523b2,
'lgc': 0xe5b12ea482f8cae4652b10e89101482c,
'mgc': 0x6cb1f90cba489c85caa3c2ee6ebd0ccc,
'ngc': 0xbf00b8253f956e6c91db8bc380b1fb74,
'ogc': 0x1102d439be91e5ed56972fba8bfbbcda,
'pgc': 0x5f16f655a2e735232d6f72ef0d1a6b4c,
'qgc': 0xf258bf626d365348f14b138629b9313e,
'rgc': 0xfbc51880016edcfa2d933c184f9d70e5,
'sgc': 0x35cfffc1ef28ffc003716d57926220e0,
'tgc': 0x7feac949293c3af4cf9668bd6bdb8d33,
'ugc': 0x02aeb8316f736ac0a468160e047055c9,
'vgc': 0x27d25ffc28ede286a8c70d61895e8e85,
'wgc': 0x7381ad8fd29651b2e3be1910bf3b6408,
'xgc': 0x287cf27055fd3b0267ad7d2aec80af35,
'ygc': 0xc0f0ef44ca8b0fb61f09662edfd24b0f,
'zgc': 0x0440c36dfcd903f9698261121fbe5799,
'ahc': 0x4725b21a900349a9474f06bec34734dd,
'bhc': 0x44b239ef48b59b5746401830051029fc,
'chc': 0x354d067913e0c1cd1e28c79c69d341ed,
'dhc': 0xaa61c80631d07ea34d0961912f9f03fe,
'ehc': 0x948ae7a2b2bf4fba139404385a70a70d,
'fhc': 0x1261e7231bbdfae38e1fe9b215f3536f,
'ghc': 0xa7121d4b082d0985cbf21763426e06b4,
'hhc': 0x14d381fb0c091dfef03545d23bedca89,
'ihc': 0xa811374b0843d069fbbafe21cead260e,
'jhc': 0x7d71e02c136d9a2833d1772ca89e4d4e,
'khc': 0x2637f9c7faeb7070faeac89d7196c85a,
'lhc': 0x8716a246719d761013337db8545f476f,
'mhc': 0xdf439a8847da23df70fae346e828d2fd,
'nhc': 0x99178b1f14e44216de4d7433bf375fe7,
'ohc': 0xec339820262a05eb7853db9a2907129f,
'phc': 0x6784074cac49170a43cc0b8e0a3f795d,
'qhc': 0x7e11f63366c6aaccf639ef130529c0af,
'rhc': 0x0039f867cdab87bd35821bd60133c436,
'shc': 0x3c06f208f8e6d493bbb3fae485abc466,
'thc': 0x17c4b36c5f3ffe3cfa347083f98e3e17,
'uhc': 0x2402500adeacc30eb5c5a8a5e2e0ec1f,
'vhc': 0x4b7d82213433888217b20af472304e97,
'whc': 0x1ae28118d7cb9a6a87aec71b22467e74,
'xhc': 0x4da3752d506913c6e253a89b9d3c9af4,
'yhc': 0xe2579830e499366c003a63146db0185d,
'zhc': 0x0a809c2b86e6826fc49c79d3f8777df9,
'aic': 0x487739645cf12002ea61046074842ccc,
'bic': 0x71353177545077b0c97f8c9075f7fc0e,
'cic': 0x87c225cf0ef858082c1e638eae0cce66,
'dic': 0x470dcd7b646bfcf16d7f0e85bd145ac4,
'eic': 0x6201912346f3222a805dd6f9a1afeb73,
'fic': 0xa38d1f85899785dc3567808b2be1190e,
'gic': 0xa2f4a7f5e8424c3df10ae22fbe5b00b6,
'hic': 0x4d7ad23e72ef18d97ef4f28111466156,
'iic': 0xb2611d0346fc09333fb0348624c167a5,
'jic': 0xfcffe00946f19c776b9dfab40dab9140,
'kic': 0x34aa31768eba66cb70229c5636adaf73,
'lic': 0xba066b54c0a283266046638c7431dd51,
'mic': 0x4eea1e5de59fbc61cb3ab480dbbf6a5f,
'nic': 0xe821ba1edb9dc0a445b61d8ce702052a,
'oic': 0x0aadc8bef99505a3e7ace61dcf2c4739,
'pic': 0xed09636a6ea24a292460866afdd7a89a,
'qic': 0xd3de7e62b8b25d48983e167677bdb6dc,
'ric': 0x497b0bd7178e735eef67720b00c9fc96,
'sic': 0x82f5c1c9be89c68344d5c6bcf404c804,
'tic': 0xde79ddb2163733fb03240cebd181aa95,
'uic': 0xf242c846ecd2dd373b135057dfb8ff81,
'vic': 0xd8d3bcfffb223aabf73973451548b12e,
'wic': 0xc045a5a2705a30d5c7fd97229cd5b07c,
'xic': 0x4bc118021c11fb952fe235f8f54f78fc,
'yic': 0x155069cb866744c838fd6c1c6a7685a1,
'zic': 0xd0681e3bc2656897ba77dd9da8079827,
'ajc': 0xd24940945f595d525f591465c0b7ff02,
'bjc': 0x906ab96788a7ec1a26581eb4f29f8b9d,
'cjc': 0x4b1be29509fed7a6b170933b12d49e40,
'djc': 0x1e9582d0218a34b09caef2d8b5a9800b,
'ejc': 0xbce05a9c8d64ffb8ed8548642d87c435,
'fjc': 0x88d51012f69dcbc0c0f86cfc9dca6ef9,
'gjc': 0xa2696e3c59aa901a9a590b276727c2da,
'hjc': 0x5cfc912f305847c7a7e9f6b0736c3b22,
'ijc': 0x34060ac0d871103ebb70cee74dab0913,
'jjc': 0x4118ae8f8e8511ca35827494790c9e5a,
'kjc': 0x89f022f4ef8831a518faa96085a05021,
'ljc': 0x267773ab5a5e82ad3cd8bef6a0115d74,
'mjc': 0x4f398293cdc258b97ca0f31f26df4849,
'njc': 0x6767e0132cb0ca77e5783033e364c0ab,
'ojc': 0xc46b2a18e5954fbcc07d965b612d17ae,
'pjc': 0xc1ada618c44b396364642bbc0f48acaf,
'qjc': 0x68a883e34783cd89d9321735fd0d7313,
'rjc': 0x0404290975b78305db2fd7d71a062c7e,
'sjc': 0x0b5b266a28469a7b52ded76c9a66f018,
'tjc': 0x738678e4adbec542df8505c447feaf50,
'ujc': 0x4e92ec7defb65762c844ac0946af87b8,
'vjc': 0xb82ec1f85aaf7936a122b3d2921fe554,
'wjc': 0xcd70195065f6d836653d77555c481ee3,
'xjc': 0x0f411cb4605191949d9169720d2edc02,
'yjc': 0xbc6a6d029fbea8615fe926de78d584e2,
'zjc': 0x7ff8479977516d0118aea104f537ea86,
'akc': 0xf19d2d0cce480a1d6b3b9347e2f50084,
'bkc': 0xc3562bd47ed0d92b68efbf694c7bd26c,
'ckc': 0x5816371442f0d570fb98bfce21dca39e,
'dkc': 0xb4bd3c0397b492c65d6780aa546abc3c,
'ekc': 0x90bb675689340e03310895f2716c27d6,
'fkc': 0x5aa4eb6ec29e64206c4bbebc14982986,
'gkc': 0xc15c967db740e1c00e643b31bc434e9b,
'hkc': 0xe454899e281d253f2db1dc443fc038c5,
'ikc': 0xf86ace0dc9d7fd09f6bd7a6e12eb831b,
'jkc': 0xd250ba4cb8bbc8cfe518fef9902742e6,
'kkc': 0xbe92d127746dc9a42a7181595678c7de,
'lkc': 0x49f256bd0d24fe778544e93f7704fcac,
'mkc': 0xed51c76e54f471f9245b0670384a8123,
'nkc': 0x1c0fa1dba2431fae4462ef706ac2dfc9,
'okc': 0x6b5b7f4332778cad21b5deaa5fc2d5cb,
'pkc': 0xf03d9e418ee2c11b40bea3923c12a51d,
'qkc': 0x0331380d34a12615e1dee9e6a1817f80,
'rkc': 0xaad73f49f078345726b3686f37165d24,
'skc': 0xb7518df29d644a95ccee1102f2b7145a,
'tkc': 0xc982656c0cbb12befbd5438f81e1e3af,
'ukc': 0xd0389ddcb04f2d7c314ab4b1d291222e,
'vkc': 0xa83b02deeef87584b35486043bc8d4f5,
'wkc': 0xe394c007527d1259b55b4879c053fc1e,
'xkc': 0xe72148cfde4f72b4e63c0f345cbba991,
'ykc': 0xac1d935f05bc072ac5b2f75e7379988a,
'zkc': 0xb553621dbd1966e2c9bfa01961058595,
'alc': 0xb859cebf8edf143b15e6ecbe19c3ced9,
'blc': 0xa0d1b0d348d2997dc238757f6f226551,
'clc': 0xddee12f44ccad98a79e0555d954173e1,
'dlc': 0x95e10614fcf9d33c1d95a17cd02d2303,
'elc': 0x44bbd46906b1edb06389434a1da19320,
'flc': 0x1f0efde51b9c7100f09d0affb24ee8d4,
'glc': 0xb4e56b10a538fe0fafd0f8e145b0f621,
'hlc': 0x927ec3a78c6ebdceeafedb29892fa7c5,
'ilc': 0x67eea2632a01c967653555f98cb84b36,
'jlc': 0xae18a8ad81b87b4d2165ffbec1b5b19d,
'klc': 0xf2e99bd07d1480cb1d7f709d4a839935,
'llc': 0xc1524ae641331d483f7de1d65b5accde,
'mlc': 0x1180d6c0528132e31c0429855d84a7c9,
'nlc': 0x0f05d836c9e80b9558421719491927d7,
'olc': 0x45925a31fa7700a783511efb253fc5f5,
'plc': 0x745e990d9f8ce342b565dd79b184da2c,
'qlc': 0x7fe86819c1d8dd6cf1f85e93b75c4a38,
'rlc': 0x3298c73d6ed6d9390ab4e8a9604d5e14,
'slc': 0x69314f8ffb749a3242ab786e891c4697,
'tlc': 0x017a544acb713ac3b4c46402c94a07fc,
'ulc': 0xd8236151193c0c4c43a234bb9cf48aa4,
'vlc': 0xe9c4eeccab947f986ca992f0ce59df74,
'wlc': 0xc13a8d9fe27f53c265a1139e0c5fc3af,
'xlc': 0xcb830c62d8c5afa02afc9a00d51528b3,
'ylc': 0x7b0df09cd505fb7a8acb9abdbda6d3bb,
'zlc': 0xa56f92671021b4a143b2b670e41b8c59,
'amc': 0x3654b34017508a963ccce2426e91e4cd,
'bmc': 0xc6ad0c4fd2ecf1c208341f364e965709,
'cmc': 0x8010fd0f87d3aaa0ea48aa78948c081e,
'dmc': 0x405fe4391be6c286341a2c4e1ac3429e,
'emc': 0x9bc93c71e5c1dbf0768dd7da3322901b,
'fmc': 0x8ff02d17ac993f52e785de3702e69e21,
'gmc': 0x19b6b9192d17760f89de4817bfae42bd,
'hmc': 0xcf82a9577f8e6fa17ce3ccf4daaf94e9,
'imc': 0x601dd4047a1b7be8c6d9896b80447d5c,
'jmc': 0x2b044c7f54df52545e7ef5717efb2415,
'kmc': 0x9135e112fb07857c3d85803a254b4d21,
'lmc': 0xf26af2dd512680dbcb6463db90985664,
'mmc': 0x1725cfefa41bac1149cf07ce408bbb9a,
'nmc': 0x1f285fa4ce68efd5e697bd8301835c72,
'omc': 0xd30359a56fd53ef610b3cf9ad2176eea,
'pmc': 0x04130da60e65c7800006cd7886515b56,
'qmc': 0xf9cccdc701f9d34017e0a11f11db1bfd,
'rmc': 0x860205b00a059d8d469d933b9d6f6416,
'smc': 0x52c6aaca28a6b621abae2db20e887dd7,
'tmc': 0xcc4774aeecab88f14aef2a30ca324f7a,
'umc': 0x46febf45a49404f23bb7dea5ed6c64f0,
'vmc': 0x22b0614f106e2b7c956c60c8fc0d35c3,
'wmc': 0xe06e46026bc2c7bd5fe1dcb42b564ba7,
'xmc': 0x31746cb42772840fade763a887c1e221,
'ymc': 0x72278030dabf8c487c1730d4a18da290,
'zmc': 0xc9afdf75f954fd191bb68befde003543,
'anc': 0x0b05a0d8cd79e3897c794d8cf89c3207,
'bnc': 0x1e57b7f7e95ba20c99a5887d2172f753,
'cnc': 0x6d77ac81b6104240caaeae436acd8393,
'dnc': 0x7be66dbe15285cadec3550a7dd21e892,
'enc': 0x41bb118a714b344c24603304cf716c96,
'fnc': 0x8fd11dd170fd0262942aea685c02a52a,
'gnc': 0xf17057e33f3e97eb2d2e5eb2b8526f70,
'hnc': 0x36de6cdfb7db25235d9f193157da63cc,
'inc': 0xcf9f3fde7326f1d8e64205f0e07a3695,
'jnc': 0x21cc5012cf5c29fd3eedc2a2f1eaaa87,
'knc': 0xa34cb26c35c3cf9b4f24e4f56449be49,
'lnc': 0x1da42144e4c8d7a4a216c16ad35333fb,
'mnc': 0x06b53047cf294f7207789ff5293ad2dc,
'nnc': 0x5c5ec11d60e9749a9c55a41847276711,
'onc': 0x0f96c74683ea68abf8dde8e2a16f07b7,
'pnc': 0xa249368f2cd1048692453ad95620f4ff,
'qnc': 0xe649bca9adfb387e66caea238d5fbf66,
'rnc': 0x4e8457c1011a1c0b3b1698dcfcb135c1,
'snc': 0x4797d6c9a384b9d252cb39a172693d14,
'tnc': 0xc775cf954921a129a65eb929476de911,
'unc': 0xf56681194ebea036dd1297f1184bf7bd,
'vnc': 0x7ac69cf2e1141a19ecc0355519d41d43,
'wnc': 0xb39a534283e4f72ee017cb463bb820ad,
'xnc': 0x93fec1eac91403ba739426470e766b63,
'ync': 0x32339c25484612271d45a0fad5020bd5,
'znc': 0x182f22a320edbe872bc487ff244bd9de,
'aoc': 0xd07c65135b3478e63420aa39b1b028b9,
'boc': 0x94931e4a3036baeecdacb975e10a8ec6,
'coc': 0x060a8d2168077d5c251e11ea6b7240d7,
'doc': 0x9a09b4dfda82e3e665e31092d1c3ec8d,
'eoc': 0xe2b21aece1b9f853bc86e1b7ae8b4812,
'foc': 0x7926bf1ba1f38670f20d59d8da8f86e1,
'goc': 0x6d4a5d826b43f9417532ad2849c1216e,
'hoc': 0x61b54070e792f37e7749195343cfb67e,
'ioc': 0xe03daa7651c34372b0bf8c442bb00813,
'joc': 0xe7beb058be876baa172388d2012bb69f,
'koc': 0x7a5036a6fc4138573e9b378fc39dddca,
'loc': 0xce04bd8105c135f89b0dfd5d9c1e8c5d,
'moc': 0x1bd86af0360bf008cafd50f83de3d025,
'noc': 0x4a5e687705bc436b698d9c28cffe9e26,
'ooc': 0x64e114f6a70889b0d4778c8ba2548a2c,
'poc': 0x302fac1d6d73cf4fdf2c9919195df864,
'qoc': 0xf5fdc4848dd1c02ae8f4d376d927f55b,
'roc': 0x042dd99eb0ff8653814e445ca0093427,
'soc': 0xc7d5f8495162588e6bb8a9e1975c45e7,
'toc': 0x03d5fa9d21dab14f4f0df57628425f30,
'uoc': 0xabf8412b7c606f8acd8b58968e9b4733,
'voc': 0x1b5fc1c8c4ef246585f7f1dff06e6bf0,
'woc': 0x138014aadcc837d4f473dd0e2333b9f4,
'xoc': 0x4a594de09295819539bd489716b4c76a,
'yoc': 0x8bf25aa12adfdb5479495efa98b339da,
'zoc': 0x501a3a809983cd756b7b65fdd2e1438c,
'apc': 0xcbdeaeb2cf5b812c628b2325bcc4c560,
'bpc': 0xbf7b37e336ce9886add64889821cd674,
'cpc': 0xae24ac2687d344b1435c2bc3e77030b5,
'dpc': 0x4bcdd94dcac1f8d0cc11a08c2aaa6c6d,
'epc': 0xb7ba6a700bdb1fac393630929bad3815,
'fpc': 0xfda3c56bc2774b18d025db6aada9e055,
'gpc': 0xd7b1de01d60a4b3da75369194d2b57c2,
'hpc': 0xec18c168cc9b0e01c6e62427c66f7002,
'ipc': 0x5ac8bf6fc7652745573f0ef24a8ab513,
'jpc': 0x452dff54306495e1b48f832a66623b16,
'kpc': 0xd392b9dce5ec24575df83221f3335b6c,
'lpc': 0xb8f1f66c2655029be78f514661d618d9,
'mpc': 0xfef8195510dfdb75e45f089c9c59490e,
'npc': 0x1579b7fb48b3bdb81d6e6d7f30b734c9,
'opc': 0x762a2206fe0b4151ace403c86a11e479,
'ppc': 0xec3dcae5d6123dcdeec5fee6f696a7c1,
'qpc': 0x1f31772e4a6602dead3f4b3d51e24f13,
'rpc': 0xda0fb2ac892daab4810fb781272173c1,
'spc': 0xcdc8d9bd665a7a390e88b9a5ddcd23c1,
'tpc': 0x2dcea44c952d2606b63431afbe2dbf3d,
'upc': 0x2f9e567efb6a38663162f65eaac2d73b,
'vpc': 0x3918767c49f0151791236f3b99d1ce7c,
'wpc': 0xe48f07f9bb4d6741ba29a33569b335eb,
'xpc': 0xdac294fa08da465d3738255fc128ff20,
'ypc': 0xa8f5d3223ed7e96cd2f8bb91fa04adda,
'zpc': 0x68c2bdc04a32e4836eafabd3dce1bd50,
'aqc': 0x0c0e83b1f5755061315e14022395370d,
'bqc': 0xf6e5f462fd3f5a922cac9ee22da5dcb9,
'cqc': 0x2b84cadea0dd3b81dbec433707ed790b,
'dqc': 0x6659d9e426bc34eb406da120b965ac94,
'eqc': 0x4266f6317eeddf6bdce901ed911c23d2,
'fqc': 0x6af87b93dda331d98e65ebad9558e358,
'gqc': 0x968c5328cf68d2d613b4b65f4836838b,
'hqc': 0xcce9ceb2f4d1d84f137306bd23fa2df0,
'iqc': 0x804c31fad6037ecc2697b2ea7afa7288,
'jqc': 0x6667d3426c509725416074b95031f565,
'kqc': 0x850b48afc7f14f07ead46451afad42e5,
'lqc': 0x1d77fc74a6239c0770bd329d1b3e5722,
'mqc': 0x330385950ae2fd78358a8b328e7e9c67,
'nqc': 0x7caeb9eb7a0c453f13e184419bb7d3ff,
'oqc': 0xb830c14f41a02a2490ced788d07fbc60,
'pqc': 0xccb008a921eafe9a5ef4d4d0cf34d4da,
'qqc': 0xfa83b3fe19d8b3e15dc7bb37c96ab9b3,
'rqc': 0x67b0ac3e3797ed4df66861163d14846b,
'sqc': 0x99cc66031b5a419407ac5c806a553e5f,
'tqc': 0xaa17d94f2029d0b7b4da8fefda2e2671,
'uqc': 0x65b8c1e10974aa7ceb1bae48901a0a61,
'vqc': 0x4031e3a5fb76f0f3201fd2f5071834c8,
'wqc': 0x9808bf8e82f3d7ed5c03e822d1665237,
'xqc': 0xf5c295813cb537ad99034eba8187a522,
'yqc': 0xdb3af70b083f23eb6ea61ed2aeef57b0,
'zqc': 0x4116dd37ac08486cb38552219956aa50,
'arc': 0x909ba4ad2bda46b10aac3c5b7f01abd5,
'brc': 0xa7b4680ffaa097b0b7caae1ea1a4438c,
'crc': 0xf5ad59c5401fea3f2df0703d958fdc97,
'drc': 0xa8f33deb6c41ca27be76f9c264951596,
'erc': 0x5ecaf0d3af3004219bc6b5907d19b6d9,
'frc': 0xf964379c80b1ab8b890cf40876fbd0aa,
'grc': 0x365f939403a7e61514b0f74b6809290f,
'hrc': 0x46b9ab5aa12ee38d44f3475488395634,
'irc': 0xeb15301d463a9ce20497702ac91d1a0e,
'jrc': 0xc2f95c6173a5459f5b94c68b5b7045c5,
'krc': 0xd605072a746a1caef235bccd2aa0eeb6,
'lrc': 0xb38fb1fa9954b59f99616b0a92af400a,
'mrc': 0x1cb9596773d55a2aa7cf5eddc6fbd633,
'nrc': 0x5212324b500df4fc03173fac88b3219a,
'orc': 0x8f5d7a7ba17f8fd9c9fc1edc2770899c,
'prc': 0x5730376dd0033959ffcb14cf25ccdc5c,
'qrc': 0x737625b87f8748eb77f118d968d93472,
'rrc': 0x7f24286285fe0d25399b6152d117d7f1,
'src': 0x25d902c24283ab8cfbac54dfa101ad31,
'trc': 0x500b8afdc35b66099d38010dc4e0efb7,
'urc': 0x520d6c1d66283ee184c675f6807b83fc,
'vrc': 0x68f492f37e310fefedeab20c65329a0b,
'wrc': 0xce65c037472910e26943fdafa0c8a287,
'xrc': 0x989a843ba8d5c9168dfbae3ad32c72d7,
'yrc': 0x8b9f4b370a61e424bc693bbb7ab96292,
'zrc': 0xfdfdc143a522cc00fa1c7ecfa1581318,
'asc': 0x375a52cb87b22005816fe7a418ec6660,
'bsc': 0xb6f6b4efd9391d1fa207d325ff1bbd60,
'csc': 0xee7ddfa19482e219fb5021ec30bd975c,
'dsc': 0x9604ba9427a280db542279d9ed78400b,
'esc': 0x3f0e951cdec5a39685cb08fa6edc6094,
'fsc': 0x26d0e472e93a1a01aee710c6c4d07264,
'gsc': 0xfe12b7693c3903b051f1e37866ff6fa5,
'hsc': 0xa7f9a093d4945aab2be5b0d912193491,
'isc': 0x351a1d2ad68bc401c305b0715268ecd7,
'jsc': 0x1b86f7ff4a01b274d16d4bb38dd57b58,
'ksc': 0x902f66712b8275bafa227211ac47c5d5,
'lsc': 0x6f6c90e968a4adc904152244698d1dda,
'msc': 0xd03669f5c488f298971eab8a42c738a8,
'nsc': 0xc9c0aef4639ce6172054940c78eb5f66,
'osc': 0x3af0e3bbc3d4c00ac8294887eef91280,
'psc': 0x3b94234bd8aa4058536b581bc3171d4c,
'qsc': 0x691b1051c831ed617945132f4e8c7bd3,
'rsc': 0xeecd67b7d03bab4a6d76100bfb9b677e,
'ssc': 0x27aac09307f5a0863d8e1dd8a4e3e76d,
'tsc': 0x3c33894421ec07809347e7b0b7b19359,
'usc': 0x9358ee7c06a9463adfe0c9e8ab6c2257,
'vsc': 0xe7bce78d4d94930210876d3cc24d0476,
'wsc': 0x8abf50cf557512368d7e8384c3513508,
'xsc': 0xceafcfd07637a2a93ffc070e8884b313,
'ysc': 0x842d9ac7b37fc0ad754e76b3ebd7165c,
'zsc': 0x55c8b6341d0a4787523d5b46d7b748c1,
'atc': 0x1317d6b009792b685bbb2811bb858ffc,
'btc': 0x1c762234363a3edc10a2930d330db099,
'ctc': 0x07fe9417379102199a4905bba9447d9f,
'dtc': 0x3a5ece8164238d20f96dbbf5cbabd813,
'etc': 0xe80f17310109447772dca82b45ef35a5,
'ftc': 0x6e861d5a78db23fe5ba1a79ae9431173,
'gtc': 0x3761e7920c97542314b1aa50434f9293,
'htc': 0x4aab4dc9bfbbf4c1f087d9721ffd8582,
'itc': 0x6a67dfc8c2584354e4c8ce9302da7dfb,
'jtc': 0x59456161420b8856ba4d6199d60a6cf0,
'ktc': 0xd4e883eff4abad173c66a78da100b41f,
'ltc': 0x90d9d5d7e72b9f5f385555845ecb8a25,
'mtc': 0xcee1b10636aff1b4bc0dec7c0adbf24e,
'ntc': 0x6758344f9f89d390953c6718f34e9be1,
'otc': 0x76d29a8e75a162799009efe4787ef98c,
'ptc': 0xa32d2d05aa5587ace19f9f5d8f7be793,
'qtc': 0x76de2ac24c6d4baf080824653728b647,
'rtc': 0x68154466f81bfb532cd70f8c71426356,
'stc': 0xe725df0794b817f84db1e813c3512b21,
'ttc': 0x81159ade18889769afc1b7f57c192b4f,
'utc': 0x52d33cb937bbdab234ab1729a0f8225b,
'vtc': 0x2be94139655922dfda4f5146b3d6e9a0,
'wtc': 0x27daa922e196bd91a0fe8cdf922591fa,
'xtc': 0x72e412909a738dd079f71e13c04abbf1,
'ytc': 0x2615b3080568e41d7ba5f6cfd5a5cf98,
'ztc': 0x405450617fc55b98a79696c900c7e3ee,
'auc': 0x889f115d4cb495d9ae44bb311845dd68,
'buc': 0xf1c14e4a39d1b50f0fb7ea6beaabdb6a,
'cuc': 0xf1f4e30acef64bd0d346b050e73289e7,
'duc': 0xf1a3fb2d6b5976af47a5113ce2e03fdb,
'euc': 0x9e3f57c552205bda4ac98e1783bfb49a,
'fuc': 0xbd1e3e31eaf230be358cb83ab0119563,
'guc': 0x42df46041b2ad65fb82fd212d2c8eda2,
'huc': 0xc62508abd312556ee2700a67649a496b,
'iuc': 0xe7501301ee20d8ce33016bbda86f0951,
'juc': 0x20e9794b7ee143b3f66d7126e73ba57e,
'kuc': 0xc7ca4a922350e33c91d7b255d73c721f,
'luc': 0x2e6fdbc573b2975790d504991183519e,
'muc': 0xd39b3a2bd15b5aa12aafc6dee064dc0a,
'nuc': 0xa9fb6efcf3b9a0a32e1f91296034e993,
'ouc': 0x7658b68c34f39081e197f08cf3ab3e77,
'puc': 0x394e7db687c7b5a212541c28fe6ea1fe,
'quc': 0x9c575de139136881d188c0d657764436,
'ruc': 0x21d0e26d0b99b0d483771d5426e8c5aa,
'suc': 0x1e0711f14f527fa4d4aa10f08eb41fee,
'tuc': 0x85b298b2989817dd30458e2bbdd19823,
'uuc': 0xf21d5fa0fbfa49a89d4571224a90ec08,
'vuc': 0x86f2a60db45f263dbc52e7380dadeb6f,
'wuc': 0x75506eb555a9bad409a71c7d298aca90,
'xuc': 0x93eb91f6faaa1f3a7e6e9eef6a7baab1,
'yuc': 0x40c7c32e6796bfbdd1a0da27091d52a6,
'zuc': 0xbe05713f3b2aeaa387ed314c0e1e654b,
'avc': 0x75d22b7a1b5be026653445831b9f0c61,
'bvc': 0x7486fc6c0fba42e3300c2910ac5f87ed,
'cvc': 0x12723ec657afee32d2c4f5bab658d9ad,
'dvc': 0x14ffd92a6cbf5f2f657067df0d5881a6,
'evc': 0xf0eb10e0bfe52080f17e630f10281a4f,
'fvc': 0x7685b0ad84dbbd6781c8781cb125c561,
'gvc': 0x8dc13195eb4dc12a8bb7ed77b84a0f51,
'hvc': 0xd8bb5ec17cb4e92349033fedc3c917c5,
'ivc': 0x62fe2b4bb4eedc1b6b61f89da8c62080,
'jvc': 0x329561adc9d60ce7dfe84ce5293d9f2b,
'kvc': 0x3c0d0c79d44d027e0c74704930b760dd,
'lvc': 0xc77980eeb8a2fd2e78ed27676b862dd3,
'mvc': 0xbf3d6d02569807fa3e6df7de1838dd26,
'nvc': 0x51ec797e647dd66d43a0101ccce411d8,
'ovc': 0x221274872b1dc898aa4e8fe686500ba1,
'pvc': 0x642542e40351edbd731ebad352b31317,
'qvc': 0x88965d2f7c6fda6f18db83d9c68bfe16,
'rvc': 0x0b48bfefcd1dca35589c9a19046fbf94,
'svc': 0x961e38b5146c0b07d078f53dec9b699a,
'tvc': 0x729c37a1d3319ee21991227b1f84c687,
'uvc': 0x81aee7a2b446ac7652ec2132def0cf95,
'vvc': 0xef6f6271e8850285aab462b832d261b2,
'wvc': 0x0e57760807081053face17bb9d7bca5a,
'xvc': 0x85be2c2ca4667528b4efceda87b6230c,
'yvc': 0xdfa22cde8a946a906909ec4d2a8a73b5,
'zvc': 0x89d07dd213c7791c4711d57a4b41fe7a,
'awc': 0x8d9206dce14fc992d18207c04ba10770,
'bwc': 0x7c1d96331c01728643c3c3d1a6a99aef,
'cwc': 0x33a0d2e93e0ad396b7c9374bbbc83a58,
'dwc': 0x8c58838431512b11fb781639bdd6dc01,
'ewc': 0x65ca649900dfc0e5816c287d81b6d55e,
'fwc': 0x382d1621debe3fc01c97de984219c80f,
'gwc': 0x5d3127455c77d2e5c139772837f1ee1c,
'hwc': 0xb1429df9360dc9d3a838fe38dd53c741,
'iwc': 0x0c760aac26fadb53c593bfa94f2c82d4,
'jwc': 0x991dd3cda0962cb55432d76cf03359f9,
'kwc': 0x0a60bab54d04583d7245bb1d4aa53209,
'lwc': 0x1db81bed78eda3b7f87b1b463a51dc20,
'mwc': 0x4c45581afe8acc7c8f550136621c9588,
'nwc': 0xccc0543dc9272f9fffd720fe55f818e4,
'owc': 0x598f8a6c2a5db6e29d86dc800ace4b89,
'pwc': 0xde9eb4f510fd5b7c1314d9f9b37e43f8,
'qwc': 0x06002af4ae19dda3de554c3619b866a9,
'rwc': 0x0895b8b08226828b293cbaab2d9d66ff,
'swc': 0x96aa0444f24af54653301824b0334584,
'twc': 0x975cd70e2de25291acc0efc98a48935e,
'uwc': 0x9c4513c1be05305e882aa75b10ba0665,
'vwc': 0x2357ed490d5ab1285207cd6830b41d98,
'wwc': 0xacaf35c838d072a69d389ebd326157fd,
'xwc': 0x51d82d42eccc852f7a4e71868437d491,
'ywc': 0x18c843721763bfaed298d0d080f05665,
'zwc': 0x0b2909b76958c41f7de3d6a32d2c7760,
'axc': 0xfc994e5c1c67757d0d52e3fab3b7108d,
'bxc': 0xce6ae674dc4e9b7e674d0df31e3f539a,
'cxc': 0x960a94ea9a16df253da3bb4f5545eef8,
'dxc': 0x6863e2f52513a3d71d8391b50150de35,
'exc': 0xd517b2729e14eee23a5cfabb90a4302c,
'fxc': 0x605680c58bfedec16ce665e867248b1d,
'gxc': 0x35229f8612b462e2509bad70e27242a9,
'hxc': 0x057c60340cd14a3ffa2bfb7cfbd7f88d,
'ixc': 0x9fa1d44d7ecc977f5466302f10d98dab,
'jxc': 0xd9d85cd334530d9fc1b174bd1a97dd18,
'kxc': 0xe2819fd981ac0f52db7e7787a6df76fc,
'lxc': 0xbe1b5e5e3a30f5825d7fa207ce95522d,
'mxc': 0xb0165e164b45081796bdd6d4edc3500c,
'nxc': 0x0d4039cfa8a953307c370ccfd8496a4f,
'oxc': 0x26387a7822e19510d0bdef51f4dda24a,
'pxc': 0x5f6bce95a50d8f455710bd929e084c2e,
'qxc': 0x09ce8cc1fc9e636250fda8164998c87d,
'rxc': 0x3bec4b3a04909e338d56981cb40d1126,
'sxc': 0xd31374fb0938daff93a845678874f55c,
'txc': 0xd34475735714adfcb5ec3a2cd61cc6f0,
'uxc': 0x9eb0c5f10c57a29bbf947791b2be6e0e,
'vxc': 0x73fca29f79c29be0aa0a9b416f78b07f,
'wxc': 0x1fd3c4ef39f3723f137fb9f778be8bef,
'xxc': 0x9cb19f353255a6b9b50253a1e6c9c870,
'yxc': 0x34475d2aa46910d5bd542237219fda71,
'zxc': 0x5fa72358f0b4fb4f2c5d7de8c9a41846,
'ayc': 0x2b91d69114c2b3ac1d75ae674f56bdd3,
'byc': 0x9eb6e809cdd682ea0ee156ca8197d9b0,
'cyc': 0xfcb03e26c61229f7a3fc0883b4832072,
'dyc': 0x5e484c5414b9bb52165d85ebb7ca1f89,
'eyc': 0xd98e6d0930ae7af2afe4e1acebbdc9f7,
'fyc': 0x43fd141a872aa7b7ecf7edc672aad4c5,
'gyc': 0xb58228eda77981a3cab2e04a151f7ef9,
'hyc': 0xe264d30611ca1e8d2d859c3e19e9811e,
'iyc': 0x796e67c4eafdf45d0c2e440deff20600,
'jyc': 0x6bac3856fb7d36e55119ad64b21407dd,
'kyc': 0xc5d750de7f03c892d99a58527033cda6,
'lyc': 0xefa664720fac0075674862b40d490830,
'myc': 0x98035231fcaee69b50d4e6e928e41169,
'nyc': 0xc9417a7325cb10e8260e3582392e3846,
'oyc': 0x914ed85453dc5cd781250b9934de4518,
'pyc': 0xe7bf4dd51ae59ca156ef748c0d10089c,
'qyc': 0xd2341b9bedf185dc239f0387966df91f,
'ryc': 0xe41382b259e6d1c57458bfe15cda40f2,
'syc': 0x3b9379ae655db26803d144b64d005a62,
'tyc': 0xccb7e786706e39bb3ec404bbe0c7b9ac,
'uyc': 0x9f4cf3b4df1aecd1604b82c6f9f41e75,
'vyc': 0x3655e2e64d72be43daec1f8a1e5baf45,
'wyc': 0xaec5e388cb6924809ae143e410e32433,
'xyc': 0x722da59d203a5e513897696f6b6d8650,
'yyc': 0xb9d0aa4dadc4e10d5e83b1ad54c8332a,
'zyc': 0xa529a8a1b993705cfdf6f9b48b1baccd,
'azc': 0x356d3da179d387fa3c6e4cc9bb4b394b,
'bzc': 0x6d00e697f7705ab2eb7166fe884433c4,
'czc': 0xe2549172457ea725492eca0d353a18fd,
'dzc': 0x332e6f2623cad3cfe0bc4f7fab19fc5b,
'ezc': 0x2749c4f9f1c70a01d0dfce0da089a119,
'fzc': 0x478b6d3e6dabe4f7e83d776445769160,
'gzc': 0x540c6ab9a0801d5370a1325754c348cb,
'hzc': 0x609998e17d311d9db4023d67218df0a9,
'izc': 0x640b030a6a30e8652b327288dba46383,
'jzc': 0xfdc63db7396a975372347718cc7a3a2a,
'kzc': 0xae770e3c95c9b6a9b3e6cbedcc100183,
'lzc': 0x9d0f18cf32b0a432031c68c5cae3d587,
'mzc': 0xca1a3ed77af3ce12bf5edf653e14d0a4,
'nzc': 0x0bc33f48fa8bb0cab59bad3dd0afee48,
'ozc': 0x8870cdf867230647e30d6ade98e29502,
'pzc': 0x52eaeeb9823b8801c1ac4b7537232d38,
'qzc': 0xc0898a551cbae98d30c25b576512f92d,
'rzc': 0xe9b96efffdc1a5ba3e8af84601a23559,
'szc': 0x31c5b528be93a59e414f8a6b43fbc3dd,
'tzc': 0x39e357349a7c60598661f57b74871ecc,
'uzc': 0x753222936543930c21f3b8a9637189ea,
'vzc': 0x600ee125112eb86bea9b719667b4bf56,
'wzc': 0x85d3682fd5ebc4a600a7200ac7b01741,
'xzc': 0x6d9821a608c387f35760a5f0a9ef5030,
'yzc': 0xad3f1144af25534fc651171b3130656c,
'zzc': 0xbded72d34c9689cfcd895ea6efccb3a7,
'aad': 0xc2f7ab46df3db842040a86a0897a5377,
'bad': 0xbae60998ffe4923b131e3d6e4c19993e,
'cad': 0xb5fde512c76571c8afd6a6089eaaf42a,
'dad': 0xdf3939f11965e7e75dbc046cd9af1c67,
'ead': 0x6f278f85c688b6cf275730b90ec6096c,
'fad': 0x901695fb1e54f4a451ba70a6e45d9d8d,
'gad': 0x5cd1a2c6812a26f4af0bfe8eaf6422d7,
'had': 0xa1e6cd7f9480f01643245e0b648d9fbe,
'iad': 0x79614d156696432da81e72b90d8cfb09,
'jad': 0x9c8c81f0fe707792c45e2538c8d7dfbc,
'kad': 0xa946a7021f0ada61055216ab21dd4160,
'lad': 0x1cf24fb0f4e3cb4a0d5f5ab5ef2d57c3,
'mad': 0x7538ebc37ad0917853e044b9b42bd8a4,
'nad': 0x938a800650dda9e8aba505177c4cc6bd,
'oad': 0xbe1ecb67050e4b31d0ae46cb8b306356,
'pad': 0x4dca00da67c692296690e90c50c96b79,
'qad': 0xd601470f41a1487f4dc2376bd0616da6,
'rad': 0x340f7c2dcaedeae68e4a62c281c7350b,
'sad': 0x49f0bad299687c62334182178bfd75d8,
'tad': 0x3e391ab759390d3f2fb834431a494f58,
'uad': 0xab68dd90bd23234ff613721c17d1cc30,
'vad': 0x1225483c97b36018cab2bea14ab78ea6,
'wad': 0xc389f0f28ae2d2055b0749d13edf426c,
'xad': 0xb3ceaa511753dc06d7d397b89e803ea5,
'yad': 0xcd7ef0d9aa21a51918377a4fb60a1dd6,
'zad': 0xea343e69739e5bea48246c63782cdb95,
'abd': 0x4911e516e5aa21d327512e0c8b197616,
'bbd': 0x4f208c522ae36c3e2f0f96291ba4f8a2,
'cbd': 0xe8c71ae8260892279214998d56e55b8e,
'dbd': 0xc072f66d1530f8c07333fbb03fa7477e,
'ebd': 0x13240f5e323dc07cc6fce5e2641a5ae3,
'fbd': 0x704a9613f075f9dafff1a0146b76558e,
'gbd': 0x5152fac8a7650c9b0a415623bf5bbcca,
'hbd': 0x3601890e93a0a687c7c6cb497eb70dde,
'ibd': 0xe7e1ee8e47cf7bea2ea41cc5eddfbc1b,
'jbd': 0x3580b15cba1c11b1b2f0c4635fa27be7,
'kbd': 0x8c0351d5b7d6c477c14693cc53026b9a,
'lbd': 0xc54243e335e97a760ff4dcdbfe2accd8,
'mbd': 0x7479338d4e6a6508c821fa4f4b621b36,
'nbd': 0x1373d0cb4ac444c16b7b2e6f8f402381,
'obd': 0x937d2d47530fd6d2d5eb1825df0018e9,
'pbd': 0x2ee67b63489f66e377623ed508bab6f2,
'qbd': 0x3f7eb87a46464056cf85eeb3f811f8a6,
'rbd': 0x22f1ba5b5761f52b30eb5311feeb0cee,
'sbd': 0xb98d50c159a938723d8eb8f3039afab2,
'tbd': 0x9a4b91fc26fa67ba8636dbfd29d004f8,
'ubd': 0x877415a294952763559da2a75fa3a96e,
'vbd': 0x20ab488d9b8bc99dbcc909aeddf6e27d,
'wbd': 0x9afe8b48125122403b10c3683f296a69,
'xbd': 0x9c7ffa3dca9fd35aa60684e9e1f5765f,
'ybd': 0x468d33f76cfcd590a77380430d68ce24,
'zbd': 0xc5526a5f8876bf9b00ac3bb6fe577214,
'acd': 0x37029430cfd06ae2a279cc1e2504e7c3,
'bcd': 0xd4b7c284882ca9e208bb65e8abd5f4c8,
'ccd': 0x3becb4d7181c01bf1403c02fdfebe147,
'dcd': 0x7b16783a9d7ce44e12deb7c9a372a027,
'ecd': 0xdf55215a72c9c74bc47076f59b5b2ea2,
'fcd': 0xa9f05953ecfdece37960c0a531627f36,
'gcd': 0x331a5d7e6ea1f9e12bf294b4dc993e64,
'hcd': 0x7b92b307cbeb8741e73b4c155593d49d,
'icd': 0xa63e7e0a7afff98c8da85ee146db0abb,
'jcd': 0xff5bf73010aef4fc80f3c5f3cedcb5f8,
'kcd': 0x471e6742e800217ae15778c2c063495c,
'lcd': 0x6fd473c94d954f6b2d202ff08c362fa0,
'mcd': 0x8322a5e1b691bc6d2d90c7ff444ed772,
'ncd': 0x47a81cd0033c74090c4232ac0c2a123a,
'ocd': 0x1fad7e436a6cdec12cc4dff913e37f81,
'pcd': 0x0f7e4b949f5d5745c3e83fd364275ee4,
'qcd': 0x7a7540196c715647240688ca6e44c134,
'rcd': 0x5e66e01c4295b6114e6651aa3cac6338,
'scd': 0xd8843589f5caccfbc7ff58bd367db8a3,
'tcd': 0xbbf4210aa2483e4ef6232214d0f44e46,
'ucd': 0x1ba26c661254821ed6845231f11873a9,
'vcd': 0x935c34f94bcb203a6a5a2255c3cebffa,
'wcd': 0x56044ca5d657b9882d608eb6d843af7e,
'xcd': 0x65ae50fdfb7f879ac3e1b05b54123ae8,
'ycd': 0x484c14dce5097eb1739490e2129b02d4,
'zcd': 0xe8009c39747a571a096dca0d624cd64b,
'add': 0x34ec78fcc91ffb1e54cd85e4a0924332,
'bdd': 0x61de962f19b684dc9ce24c0fdcdbd0de,
'cdd': 0xf8cccb0199e12f97e1f807872cf7a8aa,
'ddd': 0x77963b7a931377ad4ab5ad6a9cd718aa,
'edd': 0xa8ae67a7d91a310d67c643a73cb9d031,
'fdd': 0x3c5c7ef80bf7defa378f561073d3ec43,
'gdd': 0xaab1f8f539488237e947c8006b4a95e1,
'hdd': 0xdfdf2b9d5b3a77a55769b11d8a0396fe,
'idd': 0x48f4c41602db377162da108a6b26c747,
'jdd': 0x4be8083328539dac11c872cfd041d19c,
'kdd': 0xc9dc96ce6280d0621b7805b067168964,
'ldd': 0x1d147bb8a94f18cb543eeda72189a3ab,
'mdd': 0x2be7d7e09b2268200167ab05e219d46d,
'ndd': 0x267712a0135b511f5cfdb94c8dd409bc,
'odd': 0xa2b6f2a6066ed8700d83335fc50a2b8e,
'pdd': 0xa025f0ecbc7ea235af5bee1ceea8e886,
'qdd': 0xa10354e534fa346f54766f0f8fc980f8,
'rdd': 0x930fcefb1def093ce498ff159a41d18f,
'sdd': 0xe256ee7e3632912a9c92d7b6a0e5da1c,
'tdd': 0x66d26512fa77cc5ff934201903dd7482,
'udd': 0x0c12d6ebd3e8747499ab533400b07e16,
'vdd': 0xcc928348509eee40e33231a524f2b3fc,
'wdd': 0x7918271a41fd47bc935599cd855e9010,
'xdd': 0xe0ec65fcfcf174244bc6201ec441d367,
'ydd': 0xf5b1a3f52a6993e24785086ae42e05fe,
'zdd': 0xc001f20036c6498c479b2de0a7a1c73f,
'aed': 0xe2d24373d6aba4e0ef4ec74a92b172e8,
'bed': 0x001cbc059a402b3be7c99be558eaaf73,
'ced': 0x159ba4a89132f47014da2c413f2ad52a,
'ded': 0xb59d9f9e843dedb09e407849f3396cb1,
'eed': 0xa608a24ed70d4c1103ee78f2da5d69b4,
'fed': 0x02592d82bba02de324f72496b393d16f,
'ged': 0x51072799958f8ae0d7bf4b415116ac47,
'hed': 0x28367df7494bdda353497ac009b0751e,
'ied': 0x181bdbf3fbc46f7b2ec05cc563bbdfd3,
'jed': 0x25f486ef10741fcb9cc5ae85597f9cf8,
'ked': 0x99e53fad809ef372637bddd5cf51d287,
'led': 0x0b98f7bc56c6af56799347f69c1cc770,
'med': 0xde4667d1f44423b565b07a7bb14790fc,
'ned': 0xf68daad189b2fffd0b8cab5e36ec9d96,
'oed': 0xa95548be79e79fa1a9b5e633793d421c,
'ped': 0xd1fa391fe14ab8cc402ebda0587c1573,
'qed': 0xce2717a78ab0e55240a3aa12ba45148a,
'red': 0xbda9643ac6601722a28f238714274da4,
'sed': 0x177544aa797af6f322f8caa5e80e7f24,
'ted': 0x870fa8ee962d90af50c7eaed792b075a,
'ued': 0x69a349f0dfb7ba3ca80d8e1c5041c5df,
'ved': 0x29e0461b02c078c89c7b2ac0b29fbfaf,
'wed': 0x42647ab608c1dbafc2b378c45dc7bd8a,
'xed': 0xd114482c8758b92b9c56c95d18efe926,
'yed': 0x9945b892f55ed7331e54bd8c54a90107,
'zed': 0x89e3eb66497b398d7d2250f0093903c6,
'afd': 0xa5e873b0df0da2a1a057b4b272f88ba6,
'bfd': 0x93f91cf4f964193b8ad5905a4660ea35,
'cfd': 0x81d37ce432deac7aaa9702a64726f047,
'dfd': 0xd926d7bb9ccf46fc04a61bd65d87b9b3,
'efd': 0xc9ddbd1c832b879ddc41cc06135adc80,
'ffd': 0x0c1b3cd3c593655ff1fab8837a79d235,
'gfd': 0xf2a7e899b5af7365d70d252f3fd387dd,
'hfd': 0x14b1e54aa4b2db3aad08df0b505b1b71,
'ifd': 0x3be4b59601b7b2e82d21e1c5c718b88f,
'jfd': 0xbb789f01238ba02379a37e5a0ee4912e,
'kfd': 0x1fbe5bd0197412d6df63530c9f7fff6f,
'lfd': 0x088d559584c8348f90db83ffa425b089,
'mfd': 0x4cea3df6615b683b7faa3b2cd17908ad,
'nfd': 0x6caf8d04636c1659eb0e17f9662196da,
'ofd': 0x6345f55878adf0d9ed7bcd8cb3e77537,
'pfd': 0xd83d3148d759bf796b4f08f5d4167bcf,
'qfd': 0x5360d39d07f1b7d2a3093392377dc03f,
'rfd': 0x01c3224eee3df2e5a57d06e46a7575b4,
'sfd': 0xc15291784c5c9ff1ffee12d66399ad80,
'tfd': 0xa66f314b53836a916f195b42fa27c68c,
'ufd': 0x7d358b09d1cf2f208a8b416a9b6b26f7,
'vfd': 0x4a1ae4264d31385c9620c210f484b39c,
'wfd': 0xc438eedf39f21ddbc723b55b04484857,
'xfd': 0x7256445341a06f1a185bdf3d797136da,
'yfd': 0x22a2bd5ccf213c99883788cd5f03bd79,
'zfd': 0x2178d413e791e0341fedda846b694b53,
'agd': 0xdbb71af0f5d3ed2e5ce4b71db4ec366e,
'bgd': 0xdd6c0577d866e8e5bccffc6a1562e8fd,
'cgd': 0x31b176f5f6deb46651120413600b02b2,
'dgd': 0x281ffabc96bf84c67ca60afa0e9138d9,
'egd': 0xfc3e3a602a80b0edea1896b2d6ea7d4e,
'fgd': 0xc7f176b2c03b7fb1d7eecc43c5cd25c4,
'ggd': 0x07d1cab97172b0ffe143c73d4381ddfd,
'hgd': 0xe57dc53abc14a7891c7a8d9f4703a3f4,
'igd': 0xee472a98470ea343d09937e4b0523e41,
'jgd': 0x7dc4f794b5ad3bdcaa0c011a7b2ad88c,
'kgd': 0xfbb67b6e69eaaad8f3e97cfafbb2ae77,
'lgd': 0xab32101ea2dca53aaa488f2a0dffb792,
'mgd': 0xeace95a6fcbc70f88e4e736275fefeaa,
'ngd': 0xd90cb96fb20b9f4f37fbef59dd9ceff6,
'ogd': 0x043f334c7f494be53a0fd5e6e0af9bca,
'pgd': 0x0a0fd72e02259ebd61995c19584a1826,
'qgd': 0x4fc484b9ef23f12598c3d08fa15b3b97,
'rgd': 0xa1853287db1b5745e319461a1b89f3ac,
'sgd': 0x0f7e401c396b92d3864b22a970fa322a,
'tgd': 0xa9941797c9f430b631aa4cf5dea929de,
'ugd': 0x4cc21d3198e8a6f9136eac1aa216cfb0,
'vgd': 0x8aa1e0d18ae087ed2beb8d801acbae70,
'wgd': 0x75a70039c80d259128070f404652751e,
'xgd': 0x2a0f0452e54ea8368e5b2c73bb209b08,
'ygd': 0xf4167fc6d5844ce2d7c2749f0dd1e524,
'zgd': 0x589d421ca9600f273c35a49db9416621,
'ahd': 0x405e90a8d04644b484f5735aa982799e,
'bhd': 0x5d24e09ece35587d950043c1311598e6,
'chd': 0xce15a41352845eca5da15d80b7c17f64,
'dhd': 0x7f48c8001d3a334157f917c5d2f1cc2c,
'ehd': 0x232c8cb3e2f49a53e817abb232f7340a,
'fhd': 0x215c5a2c32636de5d22c71573ea796b8,
'ghd': 0x573f0c85418608688c0b74f86a630572,
'hhd': 0xa842d30704f61b94c20e66ecc7531c35,
'ihd': 0x6f965d088690a7b8df406f46ea6d320b,
'jhd': 0x41156d2e3d9e1f23669344fd80ad883c,
'khd': 0x6d6e76fb6e165615640ec1e45ad16596,
'lhd': 0xb557ab0619a4c04b3613c63d20abface,
'mhd': 0xd578a4a844d415345f445c021e206325,
'nhd': 0x1b3c4d0c2c3b733970ad1c430ec3881c,
'ohd': 0x58c16a305dec6929e5b1fe5cb5430a8d,
'phd': 0x4295ed0c9cbd0dc7e7476c91e7be83c0,
'qhd': 0xa46dea1a656ed482bbcd480eb7a7fc5d,
'rhd': 0xe7d8abbab93fe3a1b5b83f4358f8055f,
'shd': 0xfca888fc8ae3cd7636925e7538ff91d6,
'thd': 0xd09d33471525bb6a72ddfd9faf3766f4,
'uhd': 0x9b3c495a19bb3fa6aec9c72892d52263,
'vhd': 0x1e0f26c64d8a5e729153e158e736cab6,
'whd': 0x4acf954119d5bf8c0ef5ce07f396defb,
'xhd': 0xc004fd354c9b54a03ff8ee3d2c95bfb9,
'yhd': 0x0915699367cdb1955441ff5aa2b7d0cc,
'zhd': 0xc19ebc9f460ff1bdcabf340d86909936,
'aid': 0xb99eb09924d437dbd445a146af8c210a,
'bid': 0x12879fe24168807c1ef38cd5e580ee3a,
'cid': 0x4b7cc5694dd3a265bac326eaba31266e,
'did': 0xee85b62281ba8c77e8a83721683b5bcc,
'eid': 0x09619678a1403be5dcab79c793f3fa0f,
'fid': 0x6f8605614dde007bcdef82d3cf4a7b8e,
'gid': 0x2d53a8fb7abf5be7f4a3cf4b565cc75c,
'hid': 0x85166c9881acdf2380c617fdff476a22,
'iid': 0xb5210062221c584573e8e554dfcfd026,
'jid': 0x3d4478eb8cae476e97eacd52aa1cca16,
'kid': 0x7de007e43f108e4b54b079f66e4285d8,
'lid': 0xac31868f5f3913b3cd34b2bd3f99fbac,
'mid': 0x22384709d743fe3c6fb0a4b35b2e10a6,
'nid': 0x654432d677a1f0046fd00246a1da4411,
'oid': 0x130f43112bb8a7a7790ebfc08ee9d6af,
'pid': 0x0db3209e1adc6d67be435a81baf9a66e,
'qid': 0xd9871522bb7b0be18c00194cbbbc2c5d,
'rid': 0xfc9004b26dd1e4c402b7872fd40876c8,
'sid': 0xb8c1a3069167247e3503f0daba6c5723,
'tid': 0x97beaa21d4819a1131833b897504ce31,
'uid': 0x9871d3a2c554b27151cacf1422eec048,
'vid': 0xb06d9954793b98cc08c5ed99d67b04a8,
'wid': 0xdb98b762ffd7dd3877eea94a2fc88ead,
'xid': 0x8a388bbbec3f4ba84ca33aac30be8894,
'yid': 0x9e355490ae25e9e3d21da9eaa2826228,
'zid': 0x642a09c4eb7f61896be610d4079f1c80,
'ajd': 0xa4d34be3b6d14ac7a6c8b19b88a65fef,
'bjd': 0x24dd59effe7807db13fe54bbd105bcd2,
'cjd': 0x9e357bc33cfecf281b3e4be4dae3870f,
'djd': 0xb6deb2644be78b53e21f7a4e0c1b3b84,
'ejd': 0x47f4128039408d6c5c0d0b6aa73e9fe2,
'fjd': 0xe30a8257ef313e3996831d16bbb336d6,
'gjd': 0xedf51bc2eca5ed9732417de7679b4144,
'hjd': 0xd928472364a98d5e1b13839145d3836b,
'ijd': 0x780bf0191a17d20d2e612a5aaf566993,
'jjd': 0x5ea6c04a771d7c661bf072ce7e0cce5e,
'kjd': 0xc8b4b490e7c90c01f4ab5b31535a9634,
'ljd': 0x95c1a35027d4f4e9b1c60d35536ccd74,
'mjd': 0x7f7685b39180278dd0fa69f523c7bd24,
'njd': 0x04a372269dd2d0f8f6ba3d2b529d92e9,
'ojd': 0x5c5fefa5d9cdd466efcfa1743bd70274,
'pjd': 0x66a21e4d04276a6d26155334bc71b6d3,
'qjd': 0x94a639556937533c0441ec49758cd45a,
'rjd': 0x6bbb20f71cb5cee8030d21938adafe91,
'sjd': 0xa13e6351a03177c955c281eeec6d3b50,
'tjd': 0x01801b6e76efc165e0a281dea36ef313,
'ujd': 0xf7ef6087b27d98e92f70f1066bf58324,
'vjd': 0xb4527b1b7e86a67190248c1609d37783,
'wjd': 0xa86ba64af6f26d3c5317ef14b7fe91a6,
'xjd': 0x481c5fc1f4390c999350647b22347b00,
'yjd': 0x79ed2f0d6ae72f273076b0da97ba6634,
'zjd': 0x7e57e35fa1507e5f3c0283daef30478f,
'akd': 0xea4c4f469fdaf9f21efef9eb63694cd6,
'bkd': 0x9102ea59edb21c56878f3afcab426fbe,
'ckd': 0xf3de48576c4f3fd4a284d1f8519d0483,
'dkd': 0xad5579c98ffb3fbe2516bba5b7883f56,
'ekd': 0xb1f9e9cce0c7f93e9b68d582ddf55417,
'fkd': 0x673814fb05b9316cce5ba468cea86baa,
'gkd': 0x6def2b2daede12e7f49e692fc87663fa,
'hkd': 0x0325fa41855e00dac2cf92f4b1e8b07a,
'ikd': 0x3414b54f165860eeee9c3c36dd18cfe7,
'jkd': 0x2725beed0d36eb9e33b663d9230f1854,
'kkd': 0x7aa4e2f9aa658e0522306abfe2975ea4,
'lkd': 0x145eea73e8820d7e6987bd750bfcdc7c,
'mkd': 0xe52b3baba6f8b8995e701b32473cb8de,
'nkd': 0xe8249b71c0f431f39cf4f7735c83e59e,
'okd': 0x43e27889d60cd973a3a5da0c342c813e,
'pkd': 0xab2de115318ae40bdd434e193a2f2e7f,
'qkd': 0xbc4d296119a66388f10812b797aefe07,
'rkd': 0x75695a59551968ad91bf6f7592d5c858,
'skd': 0x7f428ca48f27a206b7f0b5fda415dff1,
'tkd': 0x4515db84e56e5ca98928e8928f58a911,
'ukd': 0x4536fb5caff9d4d8a8761bba06874ba3,
'vkd': 0xc9fef3bb2152762e4b52af1070182d27,
'wkd': 0x38b28c5ad2374cf70ab0c6d036b8ceda,
'xkd': 0x167eac235fbd490487b505db79e350fd,
'ykd': 0xdecc1f4d746b186716320cbe59145137,
'zkd': 0x02926b8c2fe8e26a3bc6e810554a2120,
'ald': 0x776833c15e61c1feac12e570742b7258,
'bld': 0x3f641f36cdee11944705fad10214252e,
'cld': 0xc9fd9d8fc94e06f1eb082244e13df21a,
'dld': 0xaca00247070453c27e86f4b3afc8d57f,
'eld': 0xd6ab4b1a2e51c28cb32bfe8982d42259,
'fld': 0x39b7b22d803b1a0270c9c6c6f8a3b4cb,
'gld': 0xba1c00323b37672c66b50f4bf0c708b5,
'hld': 0xb029aa7cc0798e1fb33945e318657ac2,
'ild': 0xa9ed0643e74a7d386f8f465482ec0427,
'jld': 0xb27f9cf6b23d05df60ce6672402ba0fa,
'kld': 0x83b574af169f76df413c25801cf9e7d9,
'lld': 0x050be97050a41e1b5f83c19795b5e33d,
'mld': 0x0f870ace0a4b9665e30aafebc5bc149c,
'nld': 0xb6eb281e772f7b8fc9404fba1fbbb06a,
'old': 0x149603e6c03516362a8da23f624db945,
'pld': 0xb002e213135955ab46a789329edb3479,
'qld': 0x8b8c4500b0e7726af44254ee9f1c9a55,
'rld': 0xe90c8e1edb39b713d0675837a44d40d7,
'sld': 0xa1c5434a35b3c6da1b7a5f3cc41bfc95,
'tld': 0xa311df502ae78951ae4777a2fe90115d,
'uld': 0x1e873cdb905de5b173c0101b7592f396,
'vld': 0x07ae3d0ea0ac067b02ca1002e34f0a51,
'wld': 0x4751946702de608b6b3f2c3e0af29b09,
'xld': 0xec23c10d5e7627fd65185385c0b4d33f,
'yld': 0x930d2a655ef416b220c96edf2dee1086,
'zld': 0x7f42dd45a93fe484c31629590dcba4fc,
'amd': 0x5dc984e2aef527ea2daaeffe646a6a52,
'bmd': 0xd160df71b03287399ad6984a847a07cb,
'cmd': 0xdfff0a7fa1a55c8c1a4966c19f6da452,
'dmd': 0x31e37e94f9d840d2b86ce098172f8b94,
'emd': 0xd70072b5eeebcaa6c7085f94954150b1,
'fmd': 0x884f07a636ab15e780ded8fdd733d779,
'gmd': 0x9f828e54f25510c94b5bbcd38d453949,
'hmd': 0xd5d0809ccb269435e43fa001c0db759e,
'imd': 0x49acdce106ade4fe480ba3753b9bf756,
'jmd': 0x618839d87dcd089d1c1b80c634b953ca,
'kmd': 0x3314401782535bbfaded2826128ee985,
'lmd': 0xca91c1a656499c2ddbb3e602e7084854,
'mmd': 0x2bfd74adce8bb8d8c10364902a2ae19c,
'nmd': 0xa1683440c8981fc93f63eb32c5924085,
'omd': 0x376f24ffc54e13a968392aa9b836aae6,
'pmd': 0xc13b001e849f3cdb4197476d89c2b47f,
'qmd': 0x144da3ea4698e3f60dfbd8a70f91a949,
'rmd': 0x297091d8d3c3c11659798dc2df7c1f21,
'smd': 0x3a096b9fe19fedd4636f217ef0c6e33f,
'tmd': 0x945ad9b340899ec08a54ce62a73a047e,
'umd': 0x404eba79b8ddbe1fdbd432766e2996fe,
'vmd': 0x5f379f200147a2a9101c537edcf83675,
'wmd': 0x18e47f621cae9c76800b10c156cf224f,
'xmd': 0x739440c5d87b2b8095b909f9d2a1bbf9,
'ymd': 0xa88c734dcdda0a2f6f06483e9b96fbee,
'zmd': 0xc0720529eb0f7bd8a031be4cbc9e3b22,
'and': 0xbe5d5d37542d75f93a87094459f76678,
'bnd': 0xb8d375aff4439246bc04eedb9a0f6db9,
'cnd': 0x39865a37b4531b22624a7141bda9d80c,
'dnd': 0xa745f8d7bb5e7c5321dfffcbea1622a9,
'end': 0x7f021a1415b86f2d013b2618fb31ae53,
'fnd': 0xed3f2a92b3f31c5cecb99c9f9bbe2d5c,
'gnd': 0xad363e86b05904f172adb4e3326a0c2a,
'hnd': 0x94c6ee814eb92ad7c9f0270af3f7afcf,
'ind': 0xfcb6f3e6168e7f906b568d9627c1ba52,
'jnd': 0xf4ee6ff8f003166912aa6cb81db847d5,
'knd': 0x07cf9041846583ea0ae5231d5c919256,
'lnd': 0xf30e8ac185556480bef15690e0495daa,
'mnd': 0x470660cf66bf9fb2934065854597ef83,
'nnd': 0xe3df5aea6b2ef20ba65e35811c38a43b,
'ond': 0x69bad6ee370ccd4741dd4dd687e5e889,
'pnd': 0x9640029fe193691f61111f47c0677c80,
'qnd': 0x38ede75dc59eda98265b778a70f40b9d,
'rnd': 0x577c2406e417ed786986e6a6cfd55317,
'snd': 0x87cab54248fb9116e9a0944967396ec6,
'tnd': 0x1907a75c06c191c56d9f30d1aa788761,
'und': 0x5ff2363014e7ff18f687f29900146aab,
'vnd': 0x4a82e4b34623bda13383afd9fe67a980,
'wnd': 0x628a09231290c7cea936fcb0de3d4033,
'xnd': 0x9c73b0e56127b1d3d7bbcd9bfe9343cd,
'ynd': 0x314d01f7a2b40579552d700dbe825abc,
'znd': 0xb8c52321a5acd9d4dee48df191945ff8,
'aod': 0xc1102a1cfd60b3b38a7623b699f22d29,
'bod': 0xd7eb52fe98d0e619b748f5635b02f2ab,
'cod': 0x2d5278b057566a696ccff8d31ae5895b,
'dod': 0xeed346aede1c64193b74d7264a809c3e,
'eod': 0xbfd76452e4a736bb02838ebbcd0c0e51,
'fod': 0x4058af8939883f677515024b455e296d,
'god': 0xa4757d7419ff3b48e92e90596f0e7548,
'hod': 0x17d84f171d54c301fabae1391a125c4e,
'iod': 0x4791b962e9277f8f1305a4ab1c4afc3b,
'jod': 0xe29ed63f9027e188f724423cd630d5e5,
'kod': 0x7a9e2fba2f949c98c0dadbbad7ae09a1,
'lod': 0x4e034d79f6fd38d71ee4b49748485a3e,
'mod': 0xad148a3ca8bd0ef3b48c52454c493ec5,
'nod': 0x8b357529fdf2cb698a7a796696bf12fd,
'ood': 0xffc066720a2201ca45f46d7142fd76af,
'pod': 0xdcc0caa97588ee058c2fcd764e5f919b,
'qod': 0x6323588472670f4d780e67609a36c0b2,
'rod': 0x52c59993d8e149a1d70b65cb08abf692,
'sod': 0x2298b14500a941478efee7887ec2c23a,
'tod': 0xe7b3cdba86ef1a01b7720e21f6aaf6e8,
'uod': 0x169a221bc2a651916c5d981cbdae03ce,
'vod': 0x073a07a55a8ce47f8ed7af52491c732a,
'wod': 0x5b3d279639246b40f48095b5df4b652c,
'xod': 0x795286bc78c609176349dd9a1b7bef98,
'yod': 0x3c3e82866f7e3b4fde3660605f9a515e,
'zod': 0x75eb515de91948d35ddaded470518f9f,
'apd': 0x1e8ae175cb57c6fa526c88790dddec53,
'bpd': 0xcff87aab4d25a39dcd62c3b0e24521f0,
'cpd': 0x4ed3c4b22349dd5a28b271282bdc98f6,
'dpd': 0xabf938dd6f4a811880085030467ca144,
'epd': 0x8dd13709c5dc4da9ce3a0f23ea0efae9,
'fpd': 0xf66fdda11fe02a90de5ef6acb51bb078,
'gpd': 0xf96ce3f78a53ec079e86d45050796ceb,
'hpd': 0xbd3712a8f2b092947ab2b2c220a514ec,
'ipd': 0x513596f90aa9a1a882c45f6a3f2c3e55,
'jpd': 0xb7082308d0a7d54ad11aa9acaae8b7c3,
'kpd': 0x21c1fdc7c5540fe1c6e50a7e2e663242,
'lpd': 0x6d3cdcb17091b57c845310fedeb00a92,
'mpd': 0xc1442c2c6ec7407b0e3ebfc8006dc819,
'npd': 0xe7bb7aa4ffa0c3f89b80ac8d6f189888,
'opd': 0xb9498a1fc311c88af062701a837859a5,
'ppd': 0xa626781ab111dda14eea50343198a182,
'qpd': 0xecd6c377c9a020080a48f9184a0e0139,
'rpd': 0x0d3e549076668cbdc5691466d8540103,
'spd': 0x1e59132c5c434e25e01a39e0e1bbe9f3,
'tpd': 0x5c3536b78da4ec3ba7ee8999c5850b34,
'upd': 0x7da63681b0381c14c36529a45de6b579,
'vpd': 0x79776a695f8b010b59635f30b3937acb,
'wpd': 0xc3a712d1456fe01e4dbdb89a33efbe80,
'xpd': 0x5c91e414f7746515d7ba1df05497898b,
'ypd': 0x45342f17baa3cbbc877c6ce39a7eeeaa,
'zpd': 0x013644feac480de644efd9d6b08d2902,
'aqd': 0x0f254c823f000f1bfd0e3c71c0c7d2cc,
'bqd': 0xe2c2eda4f332810100a24bdbf9673a15,
'cqd': 0xcf4d3265d3d4e019e3ed1010958698d0,
'dqd': 0x989b43a957e69a818ad20d25b9f60e2e,
'eqd': 0x4b995da98761ceb6d78899e9483ad2cf,
'fqd': 0x061ddc4bff7eba948d47873bba678f3f,
'gqd': 0xb2bc3f489a51c6d68da197f77e6e2557,
'hqd': 0x51cd332ace5bce3a4be658028a121ca7,
'iqd': 0x9dab257fe80499f769b05005a3dce959,
'jqd': 0xfc1f88d5a1366522326fbe9e2f329940,
'kqd': 0xa5d444b8e7cf87cd88037a64599ab075,
'lqd': 0x42dc58e9d290c2fbba70fa7e86a2d8f4,
'mqd': 0x668eeaf55af17c01440f12db1277f1b0,
'nqd': 0xf4a73d4824dac309c06d0f5c73e8b3ee,
'oqd': 0x65709d09b211eb5a31109d62fe33c486,
'pqd': 0x37e06e6438f554141b2fb7bb9ba72926,
'qqd': 0x2bbdd6e18903f3405cde0e2318310e6a,
'rqd': 0x7340288f9a61c0c87879ede0d4344a06,
'sqd': 0x43d2018567e1519ca7f459266ad33cfa,
'tqd': 0xc960faa966b4176deb0b445168569b9e,
'uqd': 0xbe3546d6e4c9290770a801796fda7fdd,
'vqd': 0x93f3355c2846f587882eee4e5bea9719,
'wqd': 0xb7e83bbee298f2cc1f7a97d340cf17ff,
'xqd': 0xadc936b0e63cf06c34f1b92cf8aca50b,
'yqd': 0xdffc119b25b43d42a796e03358ead78b,
'zqd': 0x276b7ecf30da85a9220e313a756d4388,
'ard': 0x4ce0bec67fe735f4997426101dd5292b,
'brd': 0xb213e73e577729cd2a09f49ac5e04b29,
'crd': 0xe894240e5f24dea553bbcb0df950021a,
'drd': 0x47b6e15a5eff1cdbfe9e998839779944,
'erd': 0x1c18b56aef5d89ed04adae9ea110e41d,
'frd': 0x2cd6c10d55c39a6f6c3e8ce73545906f,
'grd': 0xc11239dbd8ba7fc29d29523001ac8889,
'hrd': 0x4bf31e6f4b818056eaacb83dff01c9b8,
'ird': 0xc3353de622f2ff8cc0914db57ecee41d,
'jrd': 0xe87269d03a45cb2e8b6f5758cb4226c6,
'krd': 0x7fa0dca5c389ab87052784b4d8a18b83,
'lrd': 0xd3e54172e705d5d39cf9298882c3cdc6,
'mrd': 0xed1fbbef5bfb288aa10218943f58e678,
'nrd': 0x3cb1771de9720115995319127535f29d,
'ord': 0x8bef1cc20ada3bef55fdf132cb2a1cb9,
'prd': 0x23e1691cb0a5e42bd8236b4ea794bf0c,
'qrd': 0xd611e4f98d40c2906d8d6af68e611c40,
'rrd': 0xbd0f17042ff2f6733b4ebb30dfa68f5a,
'srd': 0x7e90f2878fd9ba40debf0a452e95206b,
'trd': 0x63f37e4d9afe47d95d02c7a305631423,
'urd': 0xe63351a13e6564178e4654f96c4b45ed,
'vrd': 0x82613bacaab14cc8fe615b5fdb6fd05c,
'wrd': 0x4943a3960cd0ca75bd24478319729e9e,
'xrd': 0xe3beaa92784c91490a52be488d26b9bd,
'yrd': 0x0a5c99148cd27b14948ce3998991dfae,
'zrd': 0x5e1ec4847dc9f5487f992c945140942a,
'asd': 0x7815696ecbf1c96e6894b779456d330e,
'bsd': 0x759b51eddb89a13c19b41cae5c565648,
'csd': 0xf014b94c35268c600ab22ef3e885b54f,
'dsd': 0x5c7d0c90cf9e0ce560956179e8e82e7d,
'esd': 0x73307549f18fbf8f2b7ab19c0d166609,
'fsd': 0x08c6a51dde006e64aed953b94fd68f0c,
'gsd': 0xaab185a0b33d014157e1519445587d1f,
'hsd': 0xe58d7791d228b7320d603a6ce7a07221,
'isd': 0xfafd23536b04f30eefd0fc45628ed029,
'jsd': 0x0a488f17216d2141214bfcc8e12c2c8a,
'ksd': 0xc6c714c36c19f7a9cbd319ec0f0f4cc8,
'lsd': 0x107569e23396d72fa107d1250903a18a,
'msd': 0xb0f2169aa609c42c1bc96d4aa5da3aea,
'nsd': 0x09daf55148da4f6052c56bea07380109,
'osd': 0xde7a4c26183669164839b27ff8cc6e57,
'psd': 0xf6d7022d02ebecf3d3b12aacad4ef7a3,
'qsd': 0x511e33b4b0fe4bf75aa3bbac63311e5a,
'rsd': 0x0db0f93c6209c605bd74ca612c00ab93,
'ssd': 0xd4576b3b305e1df6f8ef4517ec2f9615,
'tsd': 0x7c59373bfdb38201b9215ff86f0ce6af,
'usd': 0x0cdb5b8ad56b098f43e37b86ce506ea6,
'vsd': 0xc8cfb7e972533537a9a3abd8c387027b,
'wsd': 0xa02065951cc9bf77a167b500bbebb6aa,
'xsd': 0x24788d7b62b899d08b0253802b821938,
'ysd': 0xce9cf8bb4fef8f8a848e884a7734b073,
'zsd': 0x66ac06bd3199070b765c3045d9a0d39d,
'atd': 0x54eb94083f8d672757f663a6985058d6,
'btd': 0xddf13ea22fbbb1ceb4422c7c92202333,
'ctd': 0x981a7673edcc40225ddfc7bfb63097a1,
'dtd': 0x6a87284914054e1ba5d1d288c1b09f43,
'etd': 0x566ee397b0189fd8dda89ef0bfe1e7ce,
'ftd': 0xb06e48a472d47c8068204a8a2a356ff5,
'gtd': 0x0e7870c8deb4d67e5637d2ff28cae780,
'htd': 0x990cf6c570d907177db15fd656c64a13,
'itd': 0xf875a5bfaeb31e0aafdfdb772696a04f,
'jtd': 0x8346d3b87af8c0a9ab16d5d319220b03,
'ktd': 0x008acc124c899612d8c011c830eb335e,
'ltd': 0x5f7da0d5063c15ced5bf76fb97784e1f,
'mtd': 0x3e9e3fafa90f453c8519afb663c6711f,
'ntd': 0x1658f23b58329d8356a33f6ecdff0d0c,
'otd': 0x91e8d45445489154f9a349b91779e701,
'ptd': 0xce6d9e1dc29e7363f338ca0de2b1d75a,
'qtd': 0xee465b5d73e9d4e475e0390a5ed54035,
'rtd': 0x33dca5e853babefba89d1b488e303c49,
'std': 0xd2229cc70e64ae7c07361778573c4e0c,
'ttd': 0x5d076e5c3d34cb8bb08e54a4bb7e223e,
'utd': 0xfa3aa8e57bddca1fe4f6d7ccd8798907,
'vtd': 0x087da57c065a96473b78e507b7e8df40,
'wtd': 0x9162eadbb2d8ad99ae99da665742dea6,
'xtd': 0xb0e8c079c1458dfb93df599b2904c4fd,
'ytd': 0x5042c9100afa8b67ff1e4078556704d5,
'ztd': 0xaab47d9d5cfff03f1015f7277f8d1ebe,
'aud': 0xa1213419a0c503fc2767891f21463751,
'bud': 0x48114f8d489969362afea5a91603fee8,
'cud': 0xba0a0d5dd22b5f55ae830b9e2de0a10a,
'dud': 0x807dfcc396f827846e9631b735c7e808,
'eud': 0x00b6d81808ed39b20afec968da7eb839,
'fud': 0xbeb3a35c125d5367fae987ba11f288a5,
'gud': 0x91e49146121c992aab11a19c77e26cf0,
'hud': 0x8eaab8f57cf980e83393fe2b03efe0c2,
'iud': 0xd49375a738e4b15817277f8ba81f4e71,
'jud': 0xb64ad1bf9f99b49bed77bfbe523109a7,
'kud': 0xbced5064546fec44c870068b705deea7,
'lud': 0xb26989cc34d9a0e3ae33da3263f9cdc2,
'mud': 0xc95e3dd9ec9230450a558cb17be2106d,
'nud': 0xb5d18c548f62b474e3a0cd5a225e9d48,
'oud': 0x4473a4e62e9ded5dfa7044e2c8a6c162,
'pud': 0x8385e6e60a22e18cd347a1f6e4685fb9,
'qud': 0x91db6a8c341e0029a1b55499b78b4a61,
'rud': 0x6a45059d8546feb3343506218bb98c4b,
'sud': 0x57f6fd9c0e6da61c1f802233d83277fc,
'tud': 0xcdc451208246f93fc2d3d19352ef3a61,
'uud': 0xd7598c86bf53b05e71e2af0f3722fc34,
'vud': 0x0e87e020d470f357cff409be11ce05b0,
'wud': 0xc415ca73178cf393317c892ef1f5a5e9,
'xud': 0xc5f01429cac80b7f5a16c7d6b3810bb9,
'yud': 0x080fb9f743333c31bfc1d4b59903f7ea,
'zud': 0x1400bf2efa0aa62c809b6b7534388b3b,
'avd': 0x051b3fc9c5aa2303f1040c53cb8473cc,
'bvd': 0x5aa5aeffa5795ef78735f5a1c9f9d323,
'cvd': 0xa8f02963257448e00262be35c1eb2470,
'dvd': 0x5970bd1634756232824f68fe3b6bb725,
'evd': 0x9ff7172192b7f3dae53f414c0206714e,
'fvd': 0xa1cbe5a370fb4023f70eca41bf81f267,
'gvd': 0x983a81f81e86302ea08675c9a4249fd4,
'hvd': 0x9d961d89b633da3dba6edf48425323eb,
'ivd': 0x0e324e1f14a7c887d9ec0541694a3eb0,
'jvd': 0xfb15ff6297530fd450761ecd1f3f5097,
'kvd': 0xfaadb4f6be22ac3ac4e90ac6ac27bfaa,
'lvd': 0xad88b2dc4ba1e0a3e6aa1260e6ad7bb7,
'mvd': 0x68c307ba22da264268f4a0aee10714f5,
'nvd': 0xcbe8ef9a665c2bccab3991ca0b8ea53c,
'ovd': 0x493be86096f8b3fbfcd06f45a05f03c9,
'pvd': 0x78a84f93d185dcf4c59771263b3f1af3,
'qvd': 0x937726f7b1ca96b47a52f0c43a814fd1,
'rvd': 0x56a0d9da2981ecc1e3397cad699b6013,
'svd': 0x30a81b0abbd4327c72093346cc1bad3a,
'tvd': 0xc29f24f78d5b2c808238005044d35861,
'uvd': 0x10fe8610a3a0e78f92ce8f138c5d6264,
'vvd': 0xcc300ae527ba6151b132a85ca090c779,
'wvd': 0xd0ee2b3836d2e138ed2ce11578aafb96,
'xvd': 0x042849fbddbe48bffc0184089c1a5023,
'yvd': 0x274b695c1909f7acb1af0c3393e1e613,
'zvd': 0x71f944285d848caf7ec13f88b8a2c141,
'awd': 0x1f73402c644002a7ea3c9532e8ba4139,
'bwd': 0xebe43f906e456d36fb9eb4cc25bc30a2,
'cwd': 0x109633366fd0d46d371ede589998abaa,
'dwd': 0x19af0630fd5b74a3f63a2ac9769b613d,
'ewd': 0x4ed15d370ca1ea7836115d87965d22c8,
'fwd': 0x3bedd8336ef442332128e13c1f28bfc4,
'gwd': 0x98345f02e874962aee786280e3857d1b,
'hwd': 0x0a4a455b824b8d9a42059bcc6cfc9590,
'iwd': 0xd8f30279d6a581445a6c0559b80f1846,
'jwd': 0x68325a70465a7e63b18ba1c7895434d6,
'kwd': 0xc9038c2a29cd5383464cdb8930222831,
'lwd': 0xabbbb0bd110d118f77905263a293dfee,
'mwd': 0xf196b9fbda9609f04af04810d67c763d,
'nwd': 0x6075d3d730b8c01ccd9651dfe1b572ce,
'owd': 0xadc861ce4ff3575c509543b1b314cdb4,
'pwd': 0x9003d1df22eb4d3820015070385194c8,
'qwd': 0x02fcdc687ea469de024bc922f68926ef,
'rwd': 0x72f3c338247ea45dc0dc138023ac5edd,
'swd': 0x7287274bc93be739f87c8f8668aa3824,
'twd': 0x7ca71535907a2c199316eb0ea5032d63,
'uwd': 0x71908f89a30f76b5dbbf2ceb1bf59392,
'vwd': 0xadbba6cf8e501054cf753b57dc0d8ffd,
'wwd': 0x02f36950c7dc96375a19462d425c35b9,
'xwd': 0x7b3f08ae05a313f594da3c478184359e,
'ywd': 0x1747959d56e9aee1d81ae31ba3c92d18,
'zwd': 0xd7245c990e20c489fa13832e3ed8066a,
'axd': 0x25d410bfb922a687018c0ed28eb1746b,
'bxd': 0x640f8edd31074956dae7f9550c054c6c,
'cxd': 0xd5bb44dd977b1ebb6ebd2ca3aa6dde5f,
'dxd': 0x279a02b4cea61841880cd4ccb417eac1,
'exd': 0x77ed1ec902536394a9e5a64113a8f957,
'fxd': 0x79715b4b3d995d350689194113978c7b,
'gxd': 0x9cdb200f0a6d8f454c6b4182e338d49a,
'hxd': 0xdf084d2b5bb2e07e3f7e3f4a5d996582,
'ixd': 0xb84690cc79cef6560a72230001d5d7d5,
'jxd': 0xa1e2a0c56ccca12915dfdcd73720b583,
'kxd': 0x4bf6f557bc4b92a02207d3d4752f0fa5,
'lxd': 0x13c784df7899fb6b24e1eb23e96457d1,
'mxd': 0xa91e4192150d1db6f229ae27f99952da,
'nxd': 0x80171f67014b972dc6e344facc7bbcca,
'oxd': 0xf4eabb9add4eb510f7be6ff1e3caba61,
'pxd': 0xa424de99f0d51bb1f9b017ef775a365f,
'qxd': 0xe4d57001da0a93b08c80e928a0cb3e38,
'rxd': 0x38821a619803aed779863ea813e688dc,
'sxd': 0x5de1c09d286475ad7aeca8741ee3d951,
'txd': 0xb3363f2a99bf4231b91d858739d1cebc,
'uxd': 0x38bdc3f9f552de0ad6f61d35068b4a0b,
'vxd': 0x28f3e7dc33b4d97323e672fc94dddca4,
'wxd': 0x2547e73398d848d05fb0022293a3a351,
'xxd': 0x25c04b9b782789c092a38c06cc87632a,
'yxd': 0xfd4df669337625088baa7cac87a7451d,
'zxd': 0x3a126afcf65db8c050ee27e40fffc1a6,
'ayd': 0x8ddf2f18ba8458df7b133dd79d850aaa,
'byd': 0x8bbb60668c2878698ec50a7f4b1d305f,
'cyd': 0xf8830330f3b627ee708e7822a7a5a98d,
'dyd': 0xa67f2ecb44f88a94fe735d3941e83371,
'eyd': 0x07a8a50cb8086e083ad801067c597e58,
'fyd': 0x375bd5ff856cfacadc8a06ce49a67d72,
'gyd': 0x48bc17ee667148a9c5328e609677f9c4,
'hyd': 0xfc46de7cc0e40c908d691e53033c5ea5,
'iyd': 0xbde990571043b373a18fbfd8a723642f,
'jyd': 0x1615177b2e4e7d4d54f7e5a207561d44,
'kyd': 0x1f0f6255434af32b8dd74fb1996123fe,
'lyd': 0xc47c4ea0705aa1d9fa168729565cb301,
'myd': 0x634a3583084e873ccf31d58622707219,
'nyd': 0x3bda85abc4abd8a83a06adccc8365cf1,
'oyd': 0x29722e4fa0805c07f0871d3c72e8cd7d,
'pyd': 0xb33b98e4927e314775a851f38a704900,
'qyd': 0xefc731ab631566150a10b907624ee894,
'ryd': 0xe6a1a875f46a73a023b2605f093fc50e,
'syd': 0x6c85386d229d811772d558365332292a,
'tyd': 0xd883b831d0ea2a3203f7516090e4f7f9,
'uyd': 0x03d4867b477c2cf97f340ea7e19cef0e,
'vyd': 0x6f3de15d0963ffc1330eeb19f159a1bb,
'wyd': 0xe05bf3b6366b75da861070695b512ede,
'xyd': 0x1d6f7ce922ae65bbe01a4130f5b60335,
'yyd': 0xd07e946b0d1d36666f68623a38fd26a7,
'zyd': 0xba157d2ecdcaeac516b27cf02bf45e59,
'azd': 0x75a0360b639e9406d03d17b8e043a206,
'bzd': 0x5942f043903ce41d4e87a4efe6884eea,
'czd': 0x4dd797f6c69c0b9012854d7b08fec48a,
'dzd': 0x0a722e43bd6686d218961493ea962ef7,
'ezd': 0x95e1468ad78c44ed2d844baf5dc34dc2,
'fzd': 0xce6dc0424ad46f69d3916774df31c056,
'gzd': 0xc30be9649728bae6a96eb5d4e9dc32af,
'hzd': 0xd14622d564e2311377d9e4d2d8990ba1,
'izd': 0xc4e9b305c45b41b7679fe17142237cfc,
'jzd': 0x6696616b3710acbfb43b2d70f868633b,
'kzd': 0x09f7ad19ef1c6331e1ce3a4de8ca659d,
'lzd': 0x53a1f8076893b4b9fc2c4293558db5aa,
'mzd': 0x0ea34c791fa9a4c5dd02afff932ea003,
'nzd': 0xc2c991ec115313d968b9bf017f240732,
'ozd': 0x1de6a96b12305c819c358b26de26bb63,
'pzd': 0x6b8741cfb45f8b56a92b6fe242ac440f,
'qzd': 0x7658d426babd41d3295677c901d0ae7f,
'rzd': 0xbde0bd16b786821b0ae6b0fb0b55e6ef,
'szd': 0x4a320bf2b835545481fecf43997d3de6,
'tzd': 0x2eca92d6d2dc53b67edda68fa2ad67c6,
'uzd': 0xfb46641519155779df1d59c456c27484,
'vzd': 0x0d77a3b3d8a49e1c0ab2d32ed36807fe,
'wzd': 0xc018dea23571a1fb6cd9572d53ab1d62,
'xzd': 0x557d028a4bc9be1d5087c7f921f14ba7,
'yzd': 0x5716c6538738743c6f3d76f4f4b63a5b,
'zzd': 0x9518b0e38e521f9f06830311915353b9,
'aae': 0xcf34362ab126ce8338d8991cc1404980,
'bae': 0x8a4fe2eac821cc07db049f1279357195,
'cae': 0x43ef4393816412456a776afb5477cc24,
'dae': 0x39c0b36f38aed3bd87307bb554c74d74,
'eae': 0x5aac058fb4e708d19a77780ed6bd50b1,
'fae': 0x55f8918f76e6173ffa5f2e4c5c87b858,
'gae': 0x42efab72ae050ac8e22ac0ee8508cb9e,
'hae': 0xeef5a1220959b1550c42fe314ad7d5a8,
'iae': 0xe061a0737120a4a29144ecb31694c3da,
'jae': 0xa5759ca3e37c6e6630df12034e06cf05,
'kae': 0xfb6a2c86e4d2d502038589e50541e337,
'lae': 0xb9d7255d6503d87ceebbf475d4cb44de,
'mae': 0xeb4a4a36e4d53916f9979759c3d3b822,
'nae': 0x9b6f9d8259cae55333152c5b186e9e5a,
'oae': 0x655d98d5b0d2d8e8c8a58238e65dc6a2,
'pae': 0x6d2d25cac6ce5b576c4509e535e4d3d4,
'qae': 0xdcd403d8396e822fd6de064b0f79a0ea,
'rae': 0x09764440f6c418cd6421d1cf7d8105dc,
'sae': 0xdabfae3e14243f88c733376f4e6c1a37,
'tae': 0x4752d51bd71f704beec34b798c76ca9e,
'uae': 0x6befa38a9c2d2b9e1889a03cca7ac836,
'vae': 0x35c32b31d2de75cc31a064f0aa1e7faf,
'wae': 0x2b8fccc89c18aff7a1b0179c5833da6f,
'xae': 0xc4ea88100ba6873c2f636b2edad98308,
'yae': 0x03ad61c7168656ce2305e46d9b449ec4,
'zae': 0x3e715d6870661a4fe33a3b1f11965995,
'abe': 0x7888d65a43501d992cc38638b59964d6,
'bbe': 0x33a190996c7d195c4f7726293cbb44b0,
'cbe': 0xff42374122f85b5ecf60c1ad31c10df2,
'dbe': 0xaf127a0b31ade0e5dbd4c8cb4c3094b8,
'ebe': 0x4e6a3bc831604faec0c555c136720b22,
'fbe': 0x3916b3b51b8b4a0a97f12aa77ff25d89,
'gbe': 0x4ebf41c5d40b46af6a1033f0927288da,
'hbe': 0xbbd3ecfb866188ea78988d94bf9809c1,
'ibe': 0x63dd0990d1c2c4693d6d39d6950db134,
'jbe': 0xf4fd335540d202193af240c07ffa29bc,
'kbe': 0x54db205198b7a372f9118b2a67b89702,
'lbe': 0xc4ad93500235bb30db8bfc7d21cb80e9,
'mbe': 0xae88e116cc5eb3d0adc89eaf5039c98c,
'nbe': 0x158aaa7d07e13a609c259e6af760f694,
'obe': 0x59c6f06e471fe1cfaf62afc573fa0bdc,
'pbe': 0x015704ef80eacdde3751702f30f4d980,
'qbe': 0xc2cc1f036de197a49a4841c9d5a5779b,
'rbe': 0x82f87d17d74cf23d29fc49a741e7a935,
'sbe': 0x4c5883452a91db239a3c8c2be4a3bd3a,
'tbe': 0xebf1bd0254e733981928851df08fca08,
'ube': 0xae8e7b1ef9bf9fbd07770b1d60e62d27,
'vbe': 0x63c719c728a65dde8211655b506a2a44,
'wbe': 0x51cc4db1f835d89b97ec354c854f436d,
'xbe': 0x013fa2c3b45c50dec78c716b28730b78,
'ybe': 0x32a18221fd9d74adb58227276cbb1150,
'zbe': 0xb4f580849e8138da04dad7bf6d657cb3,
'ace': 0x360e2ece07507675dced80ba867d6dcd,
'bce': 0x8460edce9cfd317d188e4094a829d268,
'cce': 0x505fb3245746986ec5c2b92d05a3a9f0,
'dce': 0x3a5aed6b06fcfed497ba6585ae456878,
'ece': 0x6f8a28be5f158752eba976d9a69f6abb,
'fce': 0x47229cf2c5684aeba58ce2c461fd6fb2,
'gce': 0x3d8be50d83e259a60aa25d9868dad536,
'hce': 0xc315c38e295e8d86943867b1edddf691,
'ice': 0x7bdff76536f12a7c5ffde207e72cfe3a,
'jce': 0x3d5d08c7143ff3d659b1d290381d9ef3,
'kce': 0x48b7c3cd55afac957c4316e5dade9535,
'lce': 0x69033aa4bb3289e8b378bf8649e9ddef,
'mce': 0xc5f6e3479d45dbb807d46852a6a3b077,
'nce': 0x89a3890134b8eac8fd71e4b1ba7fa890,
'oce': 0x6aa2b79b1fc3984c31af565b51d5e4ad,
'pce': 0xfe4d328e9d1ce517737085f109c54685,
'qce': 0x5a5417aec8549e70390f81ae3c36eec2,
'rce': 0x198717576b4bc32b47474c583ddc712a,
'sce': 0xd97cd982ae7fcefd0e8ba32f2c95ab6f,
'tce': 0x4f13a6287d3e01e333810d9f20f25649,
'uce': 0x006541c356d510a28cb90ebde5d26b24,
'vce': 0x71328fd796c699976be2a1ce429b8b44,
'wce': 0xa9eed887066242deb0759cfba0939c9a,
'xce': 0xa0a535ef35b80a8b52067dbf681994ed,
'yce': 0xc1efdea3f447569cbc3376da0bbb5e66,
'zce': 0xf1963aa09931b5dade50485239cc40bc,
'ade': 0xa562cfa07c2b1213b3a5c99b756fc206,
'bde': 0xbb5be6a04a262551aa6e440d35c70086,
'cde': 0xa256e6b336afdc38c564789c399b516c,
'dde': 0xba00ecaaf5eb69e744692e9f0fded636,
'ede': 0x575350f3778d69173cfd6d265b7844bf,
'fde': 0xb950034705b54888b189ef2a35b5d3b5,
'gde': 0xb76dd29d5f8501a9219a4a122d667b7a,
'hde': 0x28d116b217702a0b367c5795a59068e5,
'ide': 0x1b8bbe06f76b270d848bda6198497dfe,
'jde': 0x1a48c690f490b65020f61e1f9dab06fd,
'kde': 0x186cf28b76f2264e9fea8fcf91cb4f5d,
'lde': 0x20cbcc2cc2344aec3ac82c9fa36d86dc,
'mde': 0x7d166b9ea014098fc61d35cd52efdc06,
'nde': 0xa422413d9ca1bcff6c1b5ba7074d9be3,
'ode': 0x9fa32fa61a3811919b6b7568da03fc1b,
'pde': 0x0107d9692534f70a4911edbcf8c6136b,
'qde': 0xce03bf2b94f67de7ce15db3b21f59a8d,
'rde': 0x81f89484f8cb3ab84d2d843a31fae062,
'sde': 0xb19114175dddde482a8a8064a4e59d0e,
'tde': 0x98e32d5d89405ae7d95fe2622a9307dc,
'ude': 0x6dbd8f08764db032171a6217725b58a5,
'vde': 0x929b00559675fbcd5d16abe66117e0d7,
'wde': 0x4ed3239785e386c6206d62d17e10d653,
'xde': 0x8434974cc6a3719fb3e909f24e71ba4f,
'yde': 0x65d376a6cbcc2399619924496205763b,
'zde': 0x5dc0ecddf56797486f564f62568a3b32,
'aee': 0x9b0a5fa32a0adc5f4f86a750a1c72e98,
'bee': 0x9dfd70fdf15a3cb1ea00d7799ac6651b,
'cee': 0x30f78cd500afd51e75d8351e4418ed9a,
'dee': 0xf31f2f4e88b1f4c29a4542671b247f9b,
'eee': 0xd2f2297d6e829cd3493aa7de4416a18f,
'fee': 0xd4319fefc66c701f24c875afda6360d6,
'gee': 0x47de9de97f04843d5dad246beb6f8a83,
'hee': 0xa6dcd6ccc8a3ca07da9f995919831631,
'iee': 0x5cc3fc04b07599ebe9acc06ead0c7667,
'jee': 0x3591daf03f80ee638643d8e173087e83,
'kee': 0xdb53556dc7a82a91a177ae3effe12015,
'lee': 0xb0f8b49f22c718e9924f5b1165111a67,
'mee': 0x0745dc7435366c036e0b60723f7fa442,
'nee': 0xa011a78a0c8d9e4f0038a5032d7668ab,
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'ihe': 0x3693f1a4c540fa60d6876ca7f8106d3f,
'jhe': 0x3ee5d8b4e628ed11157390764abe78fe,
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'nhe': 0x3f46ec08941b1bd1ff5d43d4eff5a1ff,
'ohe': 0x3ba8254bfbdc902ad3a76678fe8159be,
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'tje': 0x38c3444a8e67c5c8b3c62577ebedef25,
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'kne': 0xcd661a34b2a5030e45d82a5f81994317,
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'nne': 0xe113a38355ed45892a2d3b2d385c48f2,
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'wne': 0x421d1699142ded5c52d63f55e73dc13c,
'xne': 0xb7589a917582b9d052a75c26ebcbe508,
'yne': 0xb88f2928d003b799da3d02da4ca610a9,
'zne': 0x5d2b242c42c25ed5fdf8010bfeed6f1c,
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'jpe': 0x8f2e800e662ba9b620e363447e588f38,
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'ope': 0x52e56600aff9d421d0ec69378e4a61f9,
'ppe': 0xaee715d0bb322766b19aae838ff72520,
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'upe': 0x13d64fa9acea46d870419d2f85f50fed,
'vpe': 0x057e0ace65b2a9e81dc5527a73babf0c,
'wpe': 0x40f42901313b69f6c2ac781daa4d8cda,
'xpe': 0x5bd300bd8864f92c65d8fa6260669aa6,
'ype': 0x4eea6ccf643d5b7f6218395317a4512a,
'zpe': 0x4dab3349c9e3ac5fcf322515f5ac6fda,
'aqe': 0x2ef1db5e5b7123f139c7ed69a86d593b,
'bqe': 0xda818792fbfdbcf732e85c4002cd2fc6,
'cqe': 0x2d5b0d0f1ca9699d712cfe330e6b3b60,
'dqe': 0xac0fc540627eb8ad7471d35df06a94b3,
'eqe': 0xb661fb4e35362bdd6f684d825e5449b7,
'fqe': 0x399866765b688e475d8b2e9668e64485,
'gqe': 0x74398dccc6917d17a1e2b04b7b635817,
'hqe': 0xf6a00b98254c4f9b0bf4f1487edba839,
'iqe': 0xd5e75d0ffed479376bd349a0d20d5d45,
'jqe': 0xa2eaac317d88fd172e4375d8290e7a9d,
'kqe': 0x33ae238c25de6d8861f53d16a1a336a2,
'lqe': 0xc767fc6042ea0c0a1413ab8be9929ff8,
'mqe': 0x43a6c1730655072bde81052bb3567e5e,
'nqe': 0x56d158b45882a9e239581c9274fa2cc1,
'oqe': 0xc4676ba8fdb3aa419ab46db167d26c5f,
'pqe': 0x328fa06c54d5428d8bbbf59742560eaa,
'qqe': 0x66d7039efd94277cb4c202fe73e5935b,
'rqe': 0x47b19d21798a8850199ed317d25f3a5d,
'sqe': 0xcf7f77a92b25393fb3221550f8573f0f,
'tqe': 0x9494c782e1dd5ffac179bf32ddd7db3a,
'uqe': 0x414eac061a2ef6f672216368bca0d2e7,
'vqe': 0x8cce4af2fd5b4f909ed7de01e4bd72ff,
'wqe': 0x3979f7f001b2962787ccc75f394b7689,
'xqe': 0x214879a3ad7a0476ee57066740ec6d9e,
'yqe': 0x98e16757baac548077f29c669af15495,
'zqe': 0xfd731780be3cb75580860b5c53db4f9a,
'are': 0x4015e9ce43edfb0668ddaa973ebc7e87,
'bre': 0x0cd00ec14f1d05d419375d6a37d183a6,
'cre': 0xcaf98268abd13bb8ed384da0313e2dd6,
'dre': 0x0183459a6cb7a31c13f0f1f52ec011d2,
'ere': 0x2bbf803161deb1186defbefb8b4b0903,
'fre': 0x114c1a13ef24e600434a683b9ee9f7f1,
'gre': 0x124c54355f396f0d1b0f653d105340b3,
'hre': 0x9f56a3366094da9772401b9d5612b6e7,
'ire': 0xda3c4ddd569f0853e8e84ca1845e1277,
'jre': 0x2562560f8ce0d1e9b326af39c5b2ca52,
'kre': 0x7506ae33a44816121deca4f7c77c28e6,
'lre': 0x1fa86486ae1d712ef46116858fb930c7,
'mre': 0x632c407f955841f59c0d9716e48ac153,
'nre': 0x9f9da5a7fbffa8287debc5457ccd04cc,
'ore': 0xbe98d2fda00d8768f28b0d464bf8aacd,
'pre': 0x6bf9e70a1f928aba143ef1eebe2720b5,
'qre': 0xb6633d4ee732fe9272a3b767fd5323f1,
'rre': 0x4c449a2bdbc0f879109fe23d1afe11ff,
'sre': 0xb665f2b4ec4803aac65a110a3c57efdc,
'tre': 0x7e764aa6b7529530855f0373606d1886,
'ure': 0x5665d460068887ced6ab04e98aa68063,
'vre': 0xc810d7a0ff78a3c9cee8a615924d08ff,
'wre': 0x80743387e7cd5fb5cc71693b16859e80,
'xre': 0xb2e95fe812f31a21e9eb3a4c9c05c89d,
'yre': 0xa9b7967c23c916189a80dcbacee1ec6b,
'zre': 0x8743fad32b7c2cdd972d949909c773f4,
'ase': 0x8ea3b58eb88f49009e0ebab340d5ba1b,
'bse': 0xb6262d4c716eb973166f7e0d0f86a49c,
'cse': 0x271226cb355bdda491d38bfaf40f675d,
'dse': 0x9c182f9d7860ac7fea5b8fa068f91781,
'ese': 0xe00760db8ed95a90efbbdefa0d167e6a,
'fse': 0x95b5f346d5feef26f7fa4eb17356bb78,
'gse': 0x65bdf1517741ab2f4aef28dfc644d4a8,
'hse': 0xff85fdbd9ba2a43896908930c9048fd5,
'ise': 0x4d643b1bd384922ca968749b93b81db5,
'jse': 0x7e7a5e6225f10f9772b3adff5d27bbcd,
'kse': 0x4b8b79f529f7808a4149e6874c19ce32,
'lse': 0xbb82c12cf764a34a904d96a54ddb9941,
'mse': 0x7f04af006bdeba02616023b6e6c68409,
'nse': 0xd4167c555f400bd43c13835fa08e625b,
'ose': 0x333482b724e4a621465ccc9a939da8dc,
'pse': 0x30ea0a768cc9d95255e241efe585e23a,
'qse': 0xe53c6cc5476befef2f00492a53c0807e,
'rse': 0x8a8429234a5dd01bae9123dd3b2ed0f4,
'sse': 0x64192ca465194480a4621d6905dac5b7,
'tse': 0xbd5a2cb40637623177295aed22db25f9,
'use': 0x5ef76d30bf9232902687324b5bfa0bd2,
'vse': 0x8919568358eaee534648395ab9252980,
'wse': 0xe62a1593df47cd0aefef375643c8d369,
'xse': 0x4c725550fde88e85f2bf4a1e32cc9eec,
'yse': 0xb1ba8f311bfb366b43b95cd1ab98aee0,
'zse': 0xcebd1594c13833728a763bd9da18896e,
'ate': 0x1f0cec14e2c5effcbff50f2feb9495f6,
'bte': 0xfc44fd77a5506a5ad8fbd213bcdfd8c0,
'cte': 0xa619929c1b4ecde777557dc9df8bac30,
'dte': 0x6aa40dfefee41e3ac563444719011f79,
'ete': 0xd52389d3280207c6fa06c9ee6bd5d1c1,
'fte': 0xec7316ae6a0c3af3f8f34f7edf120651,
'gte': 0xb37b2584364bc47a5fcadf2da504e0d3,
'hte': 0xb1ad785736cde0d3a1128f6ad42e1640,
'ite': 0x69922873cb0cd47a84ef3e70b21eaf06,
'jte': 0x4a432488ec5c0fe0348b36efc16d04d4,
'kte': 0x0b1e68265a5406fcc178de022c3f812d,
'lte': 0x099fc18a9d7ef617eeb705cdf7310b40,
'mte': 0xe739b76725f6cda839653676a1118d2c,
'nte': 0x0708f0ba839c2be70fd0a39d8bb6fb03,
'ote': 0xff5d40a52ec2888ac3856394052d63b2,
'pte': 0xbcdae8bc747a067b741ab203c7454b43,
'qte': 0x1f708383753aade6f2a4c7cce99bf962,
'rte': 0xe3fd2afa75b6e2e40021c7054361fbf4,
'ste': 0x84c48d8e8dae6241ec61766c0e44282e,
'tte': 0x0e3efaaad6de051028e5e4dda3e7927d,
'ute': 0xf8b343ba3824a15b186ffb99fb86a12a,
'vte': 0x96bb8f81cea6b7e8f911249114d5ebfa,
'wte': 0xba950bdc5cbb298217cbaa8794ec63dd,
'xte': 0x928072056c94c409b32b4fcebe13f654,
'yte': 0x485b5ce881936c6a2fe268810b168cc0,
'zte': 0x7675eca5432ac5f24e8259f61e6ece97,
'aue': 0xbb650d49e7c38fd4f51fe347cb0855ff,
'bue': 0xdcdbe346fd9c34cd69f1f308fe0e7308,
'cue': 0x042d9bf5b3fde5d6ce05284e8870ad1b,
'due': 0xd6692dd335c3c6b2ad020e2758eed628,
'eue': 0xe886cad43d3432cf687683e5d1a77363,
'fue': 0x209166c2d928efe6d4a2c66e0c31255c,
'gue': 0x944c4afe62b91d13e9b211f0d9105d4b,
'hue': 0x71a6b6f094346a8832df801c8428ea06,
'iue': 0xee151eca2b2708107ad59aeb7ab5ef50,
'jue': 0xd71f30a340bf5207f02bad96401a1275,
'kue': 0x640a3ae9a93298b2784ec762368c8a39,
'lue': 0xdef5c67b41ee9fc5c3b2e27e957812e4,
'mue': 0x17652180f8ef501edc196a83110d6014,
'nue': 0xa263c8011d857d6b8485892de8430b27,
'oue': 0xb9d95fcaa59152ebfdeed7947e13874d,
'pue': 0x7ad081888fc8dcbb541fd25a31a4dcbd,
'que': 0x8cfbcfcd27c86a9ca3648bb0386c654b,
'rue': 0xa7dc5849a233266c71bc62f1d163c34f,
'sue': 0x2e9edf50f2aab9d29f53de966a261cd3,
'tue': 0x29034b2eefb2a581a7c7a11cfc307776,
'uue': 0xbde9732f5c29150a162627fb60b1cc72,
'vue': 0x951c1f757a9f918f0624cd5cb90b890d,
'wue': 0x2619fa44d70d80bc0ec3e48068a16656,
'xue': 0xd63f38a8f0d649e45beb7f133adfba9d,
'yue': 0x338950d9c045e30e626c25e22f2d7776,
'zue': 0xef113efe33020674032e60d82532f94f,
'ave': 0xc8b3f5cc93013adfb0cc205e17b4ba14,
'bve': 0xd55ddeb2977be0734e5e1c3b012547aa,
'cve': 0x1716b5fcbb7121af74efdc153d0166c5,
'dve': 0x39f5f573fa1692bf558d4af3b6590ed7,
'eve': 0xfa6a91ef9baa242de0b354a212e8cf82,
'fve': 0x70c3ca6014afa8a5c1c4781a6dbf5603,
'gve': 0x215d106e4a7275955aaaf3c39bd71388,
'hve': 0x8772bd6d2f745189dbde651e267502ea,
'ive': 0x38ab1afbd102631c8874ed6197ea9ebb,
'jve': 0x2fc49ee2a330a8e5103a8523c462bcbc,
'kve': 0x4ae1cb03024d1341c206c3c14ab9ee88,
'lve': 0xc9afd44e8404c030f5f2fde4ca0a0496,
'mve': 0xba70d0e436cb672de5325dfcc6eaa8ea,
'nve': 0x2c5a714dc3aa0d5b8a3bd504de089aa5,
'ove': 0xc3c7975faa7c1885570d4d8c7441e5f8,
'pve': 0xae4fa14f5710172dbb4004325ad71fb4,
'qve': 0x2ca25a01fe8a87b5f1d070dd59f66e61,
'rve': 0x6ca7a16d5aa37de5d8d20d0be63fe89c,
'sve': 0x6cd801edda713f7926d55a6ce0fa50f6,
'tve': 0xae32a517431fa760f520713e2ddcba5b,
'uve': 0xc9c6fdae2c12bdcd4f914c579dff4458,
'vve': 0x3e9edd08669e04509cec072064f8e32e,
'wve': 0x19f9d3e3c3d7fd741d436d8f7fbe801f,
'xve': 0xbf7ec9a15c2b9e981a5ece68c616131e,
'yve': 0xf30a3af8564519164d6d88b8dd6fd53c,
'zve': 0xb6f477f056a2654ff37e2ac75748e0c2,
'awe': 0x055d8401835cbd280eebce0ad57be90c,
'bwe': 0xaea4943e843235ad090ece9403605868,
'cwe': 0xe7bcc6e091b8d20ed1217032325e74dc,
'dwe': 0xdc189cc59c532e75e957f5dc64f03b82,
'ewe': 0x4382daaece802e7d788053002d0aaee0,
'fwe': 0x883360465df7a0bef51545e79b07147f,
'gwe': 0x96e4435264ebbc238bf2d40aea3684f7,
'hwe': 0x7d285b26385e4e6a1f67ae6fdcfe4337,
'iwe': 0x0a8eab4535c837969d59cff970a08b60,
'jwe': 0x00c65639943b5dfb0989963390baa942,
'kwe': 0x58eb60606042796096c80fbb5abaf8ab,
'lwe': 0x42e56d76aa0dadaee53a32954a71547c,
'mwe': 0x5632357ed38301262c1d06f76ac03f0e,
'nwe': 0xa044cae86176ac54b3c9ae6c38c0aacd,
'owe': 0x92159805cf28ee78e13c41ebbbb1aeb4,
'pwe': 0x4d8f1d4d92428ef4ee2e268c53a13926,
'qwe': 0x76d80224611fc919a5d54f0ff9fba446,
'rwe': 0x7b91e738dcdcd9011b3bea8c2f8c46d8,
'swe': 0x2708c3704f3c6a7b3a9e685289b412aa,
'twe': 0x5187c3cc0c7d65f5b02cb6a27e1f8f7a,
'uwe': 0xc418f34f261efe1473465ade95bfc22c,
'vwe': 0x3ea04c6e1acf97b5be7f9343a36cbbe5,
'wwe': 0x947af30fc5fd1aaf1e0d8899d5d5baee,
'xwe': 0x19d28f85b3fc6d10601d163680bc6949,
'ywe': 0xbd10d969145d21ba87d043aa6f74bc08,
'zwe': 0x895664127427c19e2bc5f52ec6cd413a,
'axe': 0xc89ee4a2c9dcae6581ca53bff9aa4ee5,
'bxe': 0x8990e36dcc319817989da732c90749d5,
'cxe': 0x06c7d853dd2acad3666ac716076c96d4,
'dxe': 0x100c5d0a77b448f869094548f0cbefe3,
'exe': 0x98e83379d45538379c2ac4e47c3be81d,
'fxe': 0x3e50311fffe2bec3c48a85c36b5740f0,
'gxe': 0xf686fb0da4014a1e5235ded234868ce5,
'hxe': 0xca893e14665c31f055a9935cc2f20d09,
'ixe': 0x0c0eca1e3a7fc1cc2eea25bd6e8f2a3b,
'jxe': 0x45142496f5e142c128c8d7d2c4172761,
'kxe': 0x4562f5f1e485286a5777cd10117f8bd5,
'lxe': 0xb1af537b77755d8a5505205c08808f24,
'mxe': 0x9f09116467e69aa87c63ad081c1ccf3d,
'nxe': 0xeb57efe950e8ceb711d79036cb43ecd7,
'oxe': 0x9e5102550e19580a9a6f064fd58b7d21,
'pxe': 0x8615bca832224aabaf5c10d6f1a689f9,
'qxe': 0x6d978b883d9146060166dec9ee825ecb,
'rxe': 0x7859b508f8c95f8410c29851de805916,
'sxe': 0x75e4635b97622e5504161a4a8ba7bea8,
'txe': 0xb59ff839b908382970fb032b175faa2d,
'uxe': 0x542e0fd84827ea277107510e8b30b56d,
'vxe': 0x1029916cf16cebfd7eece000da0a1cc8,
'wxe': 0xe9a7e55ef3812d0f42e79ce5cf26da11,
'xxe': 0x0d0f51ff97b37b54f621e858075e7a05,
'yxe': 0xd470bdf970ca1ff4f13bc938a64dd8c4,
'zxe': 0xb002d7062aecf9ebb9928b7278b2f69c,
'aye': 0x15be96c681f86d5e22721a05dda30a5f,
'bye': 0xbfa99df33b137bc8fb5f5407d7e58da8,
'cye': 0x2c5bb0ed019a5f29a8ae02448303bc9e,
'dye': 0x778c378f86aae5912fc25210fec31466,
'eye': 0xf19bd0844e53369373385609e28dbf84,
'fye': 0xaac34b7f70408f88aa466b5356f58d82,
'gye': 0xfb253ebfe35cc4deee7d8e5690de25a8,
'hye': 0x7984589418d6752169a6a571d8e8b73a,
'iye': 0xc8f2768d98b97982e54a020ee9f33d81,
'jye': 0xe0341431df2546bcf96e4445488ffdc6,
'kye': 0xe205c121d4fc16462f2e132088fb4394,
'lye': 0xa5c91f4db53bb2c563abdf289e5248a9,
'mye': 0x14b6b08ab073037e0b54e390694fc230,
'nye': 0x0e3a1abc3c7f9e12aa86a66a1bbb9e05,
'oye': 0xd683201efbac336550eb619f7cd1de9c,
'pye': 0xb138c9c017413762c8f9537a20705b20,
'qye': 0xe075aacc3440ddd6a057fd6213cfc23c,
'rye': 0x978c7f86526e2d45e0ae34b2d04c3e73,
'sye': 0x0bbc0bb2d673083d2d957ab3f0e72862,
'tye': 0x1734547f065ece81a28f96ffc031306f,
'uye': 0x41b3b100f2c1686edef944ef821679b8,
'vye': 0x9f33f10842c18a89d8965ca7e01f85ea,
'wye': 0xf9240c27ea3894538901461d6fdf7d15,
'xye': 0xfb618604fa38d08b76ae9c73b0e0f589,
'yye': 0x592a3b965bb85ffcdd783956bf830322,
'zye': 0x00beafd3d560ba57ac90bd8ada1206a6,
'aze': 0x0a5b3913cbc9a9092311630e869b4442,
'bze': 0xf3578060421e65cc69b45bb17b6b4aa0,
'cze': 0x8620c96e93ff2c0b31ae59099e1940c3,
'dze': 0x33d93d3b618b24a1f9be97aec29c775e,
'eze': 0xf4420cce8068ddaa4dea3f7fa54df4c3,
'fze': 0xfbb253e5cc8f5b706408c927799cd88d,
'gze': 0xf1560fe7bb08c4be356f863176830d7d,
'hze': 0xc5a29335c4969378722f94e3cf6023c4,
'ize': 0x1bf35d15ff1b12f44af7e28ee43c84ff,
'jze': 0x8cd3a4dbb015f6d5dc01f44491586e87,
'kze': 0x21e3d08bc828521f925fac74f2cc634d,
'lze': 0xb1fdf5c40398ffb230399a478c934213,
'mze': 0x9cb0f8da6d987034b18c71d63584d3aa,
'nze': 0xa3d1f894ef2840da6835bbcc14511ecd,
'oze': 0x3ea963ac22365b15d316766a594d691f,
'pze': 0x67d99061b2c500f0eb8d1fcbf9481b3d,
'qze': 0x79e67e9f01935459075cb37193169b33,
'rze': 0xa5377ba85b7cfd20490bc2a092518e3c,
'sze': 0x12fa895727e2e1769ab8a176ee5367bb,
'tze': 0x9b0383744fbeb293b791ae96810bd64f,
'uze': 0x1fb478ea5dc11722affddca18faa6f5d,
'vze': 0x7470d16104b516643c0e7d05e3ad27a3,
'wze': 0xedd5be3ffbc158fa6f021092343e7b5e,
'xze': 0x91af9fd938b8bde6f4e57f3c705d416f,
'yze': 0xfc43a54f5bb574b42ec1b8e58f5a9a0f,
'zze': 0x5ae1c9dabae55eddb7ac88cab5bbb6ea,
'aaf': 0x3de47a0c26dcbfde469206be4bd55865,
'baf': 0x60710fc39180f03bb8b67a484a969021,
'caf': 0xa041fd74f6e07754fe6b3ba46e53bda2,
'daf': 0xdd982884edf68487cb8ff664b3dfdf12,
'eaf': 0x398e143ef14b4585062ef7a9e2f77457,
'faf': 0x9186844637c7ca38f5f65a804457d2a0,
'gaf': 0x81e60ab8189d3157c4cc0b734c7597d7,
'haf': 0x293e907c016b53e91947a08ca7948568,
'iaf': 0x35a64b9b4deb083ca02790bfefe0a055,
'jaf': 0x1d3c331ef859c185bf35fffaf2fd8f68,
'kaf': 0x52716ae05d0d217bd73c06952bc40c85,
'laf': 0x992b678b98d7c8a821b152b0c26ac2dd,
'maf': 0x949e5a8f3b835593df69a1812251423e,
'naf': 0x2a33a22558b84f226250869393917562,
'oaf': 0xc9360b71fe32e821674121898c01c73d,
'paf': 0xb90a64580ab4862d303597e6491f4380,
'qaf': 0x91873948d7536d3a77425520930feefc,
'raf': 0x0d0ac1d076adb07d8b0e05f06c746da9,
'saf': 0xe8a88bb6f4d420a8517965d25cd54a14,
'taf': 0x63a5ff4002168b5cc9cd595d3b2e18cf,
'uaf': 0x5e3d386b78ba3e70f4ef55b18414473d,
'vaf': 0x8ab217e9a658affdafb82d676ad29fae,
'waf': 0x47342d1bee153385294760bddb8a7f49,
'xaf': 0x51a0b068bbac930f6baeda7c4434b0e7,
'yaf': 0x1c8960de607a5c26c0a775cff5b5f932,
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'hmf': 0x2885caf1e6337e1848d95ec798b39388,
'imf': 0x53c0889d3fe84966ac9a76e97dfa955b,
'jmf': 0x3279e65e8b499758d0988566d1ce2229,
'kmf': 0x5a0c1053725bb789a17452e044541599,
'lmf': 0xe07190eed0ada6a8cde1dbd4cf67ff88,
'mmf': 0xf09231fd001885d7bd37b3feb932b51d,
'nmf': 0xae817d44c4137123fe3006f6ad7ef048,
'omf': 0xbbf09628843622eb9b6f8d592d315c33,
'pmf': 0xb0675c02736b963781310276408a14c1,
'qmf': 0xb5304f22f94a7e025cbf8bbf9f676e98,
'rmf': 0x4e09639452c8d946bc339b23600d7089,
'smf': 0xa6183ffa5f8ca943ff1b53b5644ef114,
'tmf': 0x950661fa652b1ac89c52fae24385623d,
'umf': 0x1fdf0cb526780318e869df064fd2b948,
'vmf': 0x5f5dcb347b84ba966508a66d0f53874b,
'wmf': 0xe45e98a6e628ad018d701e53dd696fb2,
'xmf': 0xbcc534f8f4c5967cf55844b7c0c1eae1,
'ymf': 0xfda159a014b00ca7147e55905cda272a,
'zmf': 0x77fc46fbdd6ffdb5feba9a50332f1efe,
'anf': 0xbce38c246ec89a265e7a7e9118105c1c,
'bnf': 0xda46c6375670bad7016ef1935037cfbe,
'cnf': 0x5a70bc203732dc9108cb922a4fdaaf74,
'dnf': 0xffd93b30364fb8893d5bbb6fdb312666,
'enf': 0x615b921093450ac85866105a6be15b0e,
'fnf': 0x36b755c2bd4095ac49635402b9940dd5,
'gnf': 0x9d2e5bac7c02410a4fc7023519944bf5,
'hnf': 0x00a2632fe4bfb40c8532e852491ac685,
'inf': 0xee7b630995e7a36b6420696989441e2d,
'jnf': 0x01a196309451cd50f08cf684b00972b9,
'knf': 0x07973ab2b09955a531b6b10a3f1c9010,
'lnf': 0xc9f6de4b6609d122bd1b06ff684e6f34,
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'nnf': 0xdaa56a683ded9228481ae0b4f48b6692,
'onf': 0x88149ab8d94aa62ffee7e9fa9250de1b,
'pnf': 0xd0095a9b29beea6a965e2db1f577683b,
'qnf': 0x80fdcae208988ef49f9d596d64534669,
'rnf': 0x949bbfdd5ecf0fbb330b473c61c2b730,
'snf': 0xeda268714c28bff69249c85900fa0214,
'tnf': 0xab9bc7ce433266ca5b4543e538aeed73,
'unf': 0x8c07006c215977af7af14e0ddad43d69,
'vnf': 0xd44d26cfe42cfad9b25836ef75f757c0,
'wnf': 0xf16d1606ef1c44fbbc57bf4c2dbe5d2f,
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'znf': 0x24b767712dbd04563beacd962b9681ce,
'aof': 0xdf4a8b32238c36921a260ed6ab784850,
'bof': 0xc9a3639b57c741dcd0334c28032e4280,
'cof': 0x1160131d2fce7c6fa72fb2dddda90663,
'dof': 0x53ba48f431fe964bb656a05ed16d8e87,
'eof': 0x2e51b1ab42e8a4a67f3445174be5191b,
'fof': 0xc94559fd82bc1345434596643fff3a99,
'gof': 0x8a0f5704d39b9d84d0311b48a1a39cfd,
'hof': 0x64a04398c02c80353502d35f32f12e78,
'iof': 0xdebd2124d8e0c33027f69d688aa37cca,
'jof': 0xf5d872cb69e6c70c6711fbc8d5d37fba,
'kof': 0xc8d9b4056072f6ab7d82e9426e6c490a,
'lof': 0xf9e0910eef74a5744f3f59c9570e539e,
'mof': 0x4c863451e01db034cd73a2554459460f,
'nof': 0xd66a2c5913b630bd176d0967992ccf24,
'oof': 0xb4453d1f9f5386a1846e57a3ec95678f,
'pof': 0x81473c4e044b5380ee37025921c5a58c,
'qof': 0x47b987f8529680b4887a16b956d0d8ad,
'rof': 0x0673de2be314ac7fa71e12deea602709,
'sof': 0xb61f32a2ffdff16751219654fc5ae6e7,
'tof': 0x4392653a13bc19a01a637fc3a6678e18,
'uof': 0x2dbb36ce4d696da3994234fe1d5eceaf,
'vof': 0xf9bb64977d8f864c019741fceb783679,
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'xof': 0xd113b01d608d36d99c6b7eae8cdd61de,
'yof': 0x64509465c18de3eed4b88dccd686cb23,
'zof': 0x1c3f6af549ddbec60d9f84d4378677c6,
'apf': 0xae19ba75a4a3b7e0b7442be6a7742cb9,
'bpf': 0x42026a6790c20091d2c5b9078d3a4346,
'cpf': 0xc7063b84544d04f605c3c7198fda93a5,
'dpf': 0x69dd3a309f644bf7239b2efd8d240b44,
'epf': 0x132765407aeb38b7b5febb28e8f3cb2d,
'fpf': 0x6673b1cdab2693905dd8f02e47665620,
'gpf': 0x272333c8b3b3b32f6bdde3ae900e8789,
'hpf': 0xfa58459947e56fcfd5ac72b255393c85,
'ipf': 0x9e25c769e9355f9f31fb97636e0a4632,
'jpf': 0x1925c096e60d4483126353700e838e8e,
'kpf': 0x42d961f0b7e234e5c2317f12614150fc,
'lpf': 0xe6b8e5098d1621bebf53a21591b69985,
'mpf': 0x4d094e1972f0d645405dc65757b906fd,
'npf': 0x9d45f246b59b1242b9d808a7963cf8eb,
'opf': 0x872d732e7afb7cda1252c58fe1892265,
'ppf': 0xb7a43592912c8e3fb665c0cfa19b8f10,
'qpf': 0x0b698a3f27dd4abe11821ae07a0c3d1b,
'rpf': 0x9402b256963416483517f7a07032aa4d,
'spf': 0x232703760872a744056f9fcc6b5d233e,
'tpf': 0x03916468f594ae2f83c552a26b81f169,
'upf': 0xef8db25d0c3724900b090eb16928f7fb,
'vpf': 0xb86ba08aae61a438c953f940cad1eb07,
'wpf': 0x42fec5bb39a12715eb2038ef2d024f7d,
'xpf': 0xe6d7203bfbaf5b21b66216a4e8591a58,
'ypf': 0xb4ed8ced67f5d079e5a6de3fef0a9d30,
'zpf': 0xf0ffef8c6e110b92049411cfe54cebfa,
'aqf': 0x4a1a90dfd306fabc5686c3e0a318077c,
'bqf': 0x3363bb006665e0a7efc8fd5be2c53bed,
'cqf': 0xb5a81277923b246e03b5e3097bc9d4ac,
'dqf': 0xd919d061908792d46b78b677f13df233,
'eqf': 0x5341f91c6926906e2003fce32cef3fc0,
'fqf': 0xf68c1b9258d9bfd4d8ca849981ea5dc1,
'gqf': 0x894edc8ef3b603b41afe1483026e11dc,
'hqf': 0x8378df9a019d71b4e7511b0ff38f92fc,
'iqf': 0x25f57a6e229a7888b9c52c637635ec1b,
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'kqf': 0xbf7a58379035b5f9bae5a425b6476db3,
'lqf': 0x11ac290e78a90d5c2742fa8218dea1a0,
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'nqf': 0x48b5b7e80b0bad3239e3951637605fbc,
'oqf': 0x31ed0454b67dbbbf6001097bac4f0694,
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'qqf': 0x84091d332ef5cdfce624e60ab0e4397a,
'rqf': 0x185f0f9c36bc867165f95ff972cffaa3,
'sqf': 0x6adf4adac18ec5a53608df5c07a564c9,
'tqf': 0x261d2fe9f17c6aab0bf22ba2800e3eb4,
'uqf': 0x241e75b0dd158099fe3eee1f9e373a92,
'vqf': 0x86879fbd4dbb34d14447471793f5b5be,
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'yqf': 0x917b8f09cc05158d2ca2b79a81a7de81,
'zqf': 0x407ee548f0876bfb6f59e15751ab2878,
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'drf': 0x9edbcd0783b24d58387f0db59bf1f0d3,
'erf': 0x2aa3a5835854786d8310663cf445351c,
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'grf': 0xbde3005de0ae58e737bf4c26553acbba,
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'irf': 0xa16bc57334e791383a1d2b7c6dc6d199,
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'krf': 0x3b86cf22a889775c457ab2f180edcc07,
'lrf': 0xe83be96c0831b8f9ba2fa850872fa84a,
'mrf': 0xca64e8f3e4adfa126fc6806fcbfb522c,
'nrf': 0xdf0893220c993ffbeb28b75726e081f7,
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'qrf': 0x55febb9304de786e7098027413dc27d8,
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'trf': 0x74b128710b3c74c51463d423b44645c8,
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'wrf': 0x071bcbb5451196e214792095b747649e,
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'fsf': 0x951bb1feb19913cf52069601b421bdc1,
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'hsf': 0x84a6f7d08c1847f06b6936d1f5361101,
'isf': 0x7f9da57c427a35652c042c01d5ff0f46,
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'lsf': 0x7ef60e274e251aecfddb80e33c48fdf1,
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'nsf': 0xdd69b4a1513c9de9f46faf24048da1e8,
'osf': 0xe6585105592642023390e3906667fb5f,
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'ssf': 0x1c14e22e616216736c55a3a3b9f508c0,
'tsf': 0x5eff846553db55a9fbfd92e67b4199f9,
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'xsf': 0x84b5897767cfd04342f7d57d9ffd6455,
'ysf': 0x4fc0359029bc3b8aa738770be522c86c,
'zsf': 0xbcb42960eb6482899d7746e439afa8bb,
'atf': 0x8b4ceb24be98af77c13eb481482843a4,
'btf': 0xd86d70495e97987f548d85d2968c2519,
'ctf': 0xa8db1d82db78ed452ba0882fb9554fc9,
'dtf': 0xba9395aaeeb587c383111294a11fdd7c,
'etf': 0xb72792e2b714688f0af5e10041c18e3e,
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'htf': 0x3f5403a27b404a5de42186dfca77eead,
'itf': 0x591f78f8cc4ad1f96deeda4ad64c4f89,
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'ktf': 0xc08c99f17d7e139f1f0bb1445191010e,
'ltf': 0x2bce268f5afff8b822073eb4a9e40231,
'mtf': 0xd37b00dc59109d52d85f39535cd2dc27,
'ntf': 0xfa2b8fd82435327398e4d476d114e57a,
'otf': 0x8dcba57d9b72e4390cbdaa44f580c9df,
'ptf': 0x28a52cb469744f132cd4b49dfcc6d190,
'qtf': 0x730782a7be75cfff38993f6f890d3257,
'rtf': 0x1a4ccad323491979045827f5dae42231,
'stf': 0xb819f2b7c1664f21450f1fd4109906c0,
'ttf': 0x3188a8301b98ccdc7d297e902041bd68,
'utf': 0xa7f38035beaefe30a11d6f601051230f,
'vtf': 0x0e6c962d03a29647a6139630b1bfae6a,
'wtf': 0xaadce520e20c2899f4ced228a79a3083,
'xtf': 0xed27540887ab23fa6c84144888c416ce,
'ytf': 0x61cec9e69711d87da419863e9bd5cf2a,
'ztf': 0xbfa96de7ad7e60805a59121a0a4cf28a,
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'buf': 0xcb7e52b21171fb9a53b498202607f0bd,
'cuf': 0x30c3df590b6cb8b825886746fa2f0ba1,
'duf': 0x45f7f94a2916f9719327b90d70302498,
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'dvf': 0x50fc7087d487932ced47a1160f9349bf,
'evf': 0x49d824daef9f081f2758448ab5918640,
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'hvf': 0xbac1db7ee03487e9db086f50d00bb55a,
'ivf': 0x1c18438c9982876d4a14b504b5de8c00,
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'kvf': 0x91965e166bdf4630cea56f779fc1f72e,
'lvf': 0x1ba3cc41629c7adafa0703c9fcd44033,
'mvf': 0x8b355f3081997ef0fc866d30c2195b75,
'nvf': 0x3d18995b1a0cb22c0a9e5164ca21d0a1,
'ovf': 0xf30c7465ec5a509067d7437d266c93d8,
'pvf': 0xc21b0936373f6f08c480b7eaaab9cf4b,
'qvf': 0x756f59b93560386715280f0ee297abf4,
'rvf': 0xfd8ba8992a3e38fd270f3f5e6ae1a127,
'svf': 0x26b38231c0bd3c315f3c79e1de02354e,
'tvf': 0x1e08c7c5439200bcdd189634a91fd9d5,
'uvf': 0xd998307acb7d4334c20a28ecbdf8b19a,
'vvf': 0x8c33e8d1922e4be1a8b4b32ad6928941,
'wvf': 0x85929ef13a62eb0310940dd0cf3fd87d,
'xvf': 0x7593233967fc79d7199c10a6226e3463,
'yvf': 0x85088c4802603ff5c0d53e3f1b32a356,
'zvf': 0xdb4f7a1210d321826adbdc8811178f4c,
'awf': 0x1a0e464ff4f13755869adae40d07e1f3,
'bwf': 0x6f68aa494a5fe086fd047c6c31bf2803,
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'dwf': 0x324888f00729ced91fd4580dac9d7d08,
'ewf': 0xc953af378ef51531ffc32c398c5edffe,
'fwf': 0x5d47f4d2de7d4910fc297338c7182c22,
'gwf': 0xf32c877e64a541140348ae7f24553bd0,
'hwf': 0xc03223e4c2e74fdac0e5f7469073da26,
'iwf': 0x6e1353127748007f8dd448f9217c831e,
'jwf': 0x39de7031135293ae3f83f2b10983a191,
'kwf': 0xb0828879ed0d449cfffbdfd2f6e4211e,
'lwf': 0x81e5bd772cadec9c5647d19103e9e88c,
'mwf': 0xa95c89b9140b21b6c7d4666d787ff805,
'nwf': 0x24cbe4a40dbbeb94258481335657cccb,
'owf': 0xbe1e3765c29fdf82b10da3c58d513116,
'pwf': 0xd7c4643ea575862587adfd890f24ed1c,
'qwf': 0x493ac5a4c2f9798a52c24fa8166c72a7,
'rwf': 0x69e7672d3ba2bb4464c424180c99dd2b,
'swf': 0xa0dc603e6e626df5c709da881fd1b9a7,
'twf': 0xd23bdf4409ce74974c8870857266bdc6,
'uwf': 0x092151f6b6f0af16e31655988f65443a,
'vwf': 0x52fc7a56a2e3a3e03c053a949a04ec9c,
'wwf': 0x48e673729ce7be929a035dc09ecbcf2e,
'xwf': 0x597b035e34bef2124f8fb9dfb57aa3ac,
'ywf': 0xc81fe4d2058cffded11b5a788cbd8208,
'zwf': 0xd139d19f1b5f4b03c61b53403abbb0c6,
'axf': 0x6428ad89e94e4c00aa1669a7bc44e30c,
'bxf': 0xbb1da88ba651bff6e82c315cb8bdc046,
'cxf': 0x3be3870feca6210f218fc86fa9ab6a4c,
'dxf': 0xfe03a5fedf9541d6c57c16c2b4ed9508,
'exf': 0xbef6ae83235e8be8194d8647a74a1fa0,
'fxf': 0x89bfaa5d6f001fdb07bd9f729fa07bd2,
'gxf': 0xd6912af9cf3a1b0d06146983e5e5f82f,
'hxf': 0x697460fa4f13e7a01221aad809f97d20,
'ixf': 0x52f7a109d3ddd7da7f8a926ce466edbe,
'jxf': 0x961bd52c603eb11041924fc5ef960f88,
'kxf': 0xa08db499fd306fe9dd284be06f21eb5c,
'lxf': 0x9fb788cd5f51b60ad56804cbfdfa987d,
'mxf': 0x3c8cc3fefdef5a6c65eff82a3caf1144,
'nxf': 0x7239480bae59a102a4445aa8630811da,
'oxf': 0xd53e9e343105ade94429774285b6ce26,
'pxf': 0x6262566ff05153c931c8125a3fb6f20c,
'qxf': 0xce85e573321f4f99825e97019f59ccfb,
'rxf': 0x0072ec6587ccd01aa12a9c9f2c46b676,
'sxf': 0xbbd4aef7b4796cda5643e59a3dfe2ef3,
'txf': 0x314939c0c9b63f58a95154cb8bb51a0e,
'uxf': 0x2983338eb5c60a34140ae925029a48cf,
'vxf': 0x1d9fd2b1cecb837d3ca68416607ff058,
'wxf': 0x88926061a8a8103e1986c24b5b574aed,
'xxf': 0xe7d25aad2d9cd6a28c5cc1ef754d8d1a,
'yxf': 0x4de96929a19cc596b206761407936110,
'zxf': 0x2d11e8c0b8dd511a90e0520f18e29bfa,
'ayf': 0x025fc1207b42faa18007e97bc02187b4,
'byf': 0xc38cf74ae7306acf20adcd351343ab59,
'cyf': 0x0813ed120c56667cbc8f844a04bd5a25,
'dyf': 0x607516a05c2e35c56c1a1b1339439d48,
'eyf': 0x1d331510e1ca27ad590e5855b0349fa6,
'fyf': 0x712db57187b764b59004bfa8fcac7bb1,
'gyf': 0x7dcff74ec1c8812f6f9ca48fc780e3e2,
'hyf': 0x5bedece3125418345f3b378d6650ecc8,
'iyf': 0xe9033619962e75cecec7d5188c20fae3,
'jyf': 0xfaae6a47728bab931e79bf74b6a253a6,
'kyf': 0x761c297fbfd69dd9ec9c1f9173be0248,
'lyf': 0x8cec883c5dd4e50431300252f9edb64d,
'myf': 0xc3a25a235678181d19ca4ed881760edc,
'nyf': 0x6f160b149972d2d4e9fbc7591f512051,
'oyf': 0x7c945cb3d1e5d1d7a42eb3b6eeca6db6,
'pyf': 0x545e4471b2eead043fd822883aed179f,
'qyf': 0x7cda62ef9febdba096923e72cb458245,
'ryf': 0xdee4208a855d2dd51f74c30527da7880,
'syf': 0x36d8192e879aa9d47e87e169dc9b69e9,
'tyf': 0x27ec741f75a73244098308be2d052a5c,
'uyf': 0x9eebbf97d3f65d563ff115f826628c81,
'vyf': 0x38749dc548c17c0ff87488e108e1df21,
'wyf': 0x53dfd56cb7ca0ef22ae166e9e1cc06ae,
'xyf': 0x32680ff476e617af7d5017b8fd3a3a16,
'yyf': 0x2c3a5a13006a0c26e9e72c47aaf821d6,
'zyf': 0x00190ea3d40c1c397fc4151b6e27b52d,
'azf': 0x57a4ec1a0182404faa48a7921b4cf90a,
'bzf': 0x09b731846db7a3e87d492b4b8cb18cfb,
'czf': 0x0b6ba65ed650a31114ac983591128379,
'dzf': 0x79a38f6b963bab170d7cd5cf25d2570a,
'ezf': 0x88e9cd5f9d95f206fa4e248045c0cc9e,
'fzf': 0xd42d4425f2971e1f8527127bb8425598,
'gzf': 0x5221433af2dc4a22b5ac1a8c30f5b5d0,
'hzf': 0x48e0264a509f08850a76d6c7f7fc27e1,
'izf': 0x90dbc63fba2640de85f7f87947676f80,
'jzf': 0x3f2b3aeeb2db2deb4b2d699750f8d579,
'kzf': 0x65a3722d53fdc9b0a5f5bbc4b479fd7f,
'lzf': 0xa44fc5cbb123921b0c79d882f1cded3a,
'mzf': 0x58ca1d7bc616d48b0dcabff3e060b875,
'nzf': 0x6b945411ea579030c7ccfe2ee13bc8f3,
'ozf': 0x6172d4df0aed5eb301467ad17da3c842,
'pzf': 0xe03afe74d389c124e1792f1a0355834d,
'qzf': 0xfa19a5bcd172e1346746ab6cf38e40e1,
'rzf': 0xd771ce5a4c5fa81ff70ca35081ee85d2,
'szf': 0x0725a99e81191e2c30e7e9d9301a5f87,
'tzf': 0x6b1c5f2dfc828405850929e845df7963,
'uzf': 0x17454b0265a867a4194d22d237f3317c,
'vzf': 0x01e8d5de4d5b82b211ae40c46f897947,
'wzf': 0xd3495108dc400a473571a4fed25af3f9,
'xzf': 0x9fbb239f9faa3438aeb78f8bee213d17,
'yzf': 0x1f1db6c89f8bcc5ee7db2aeaedf3a0bb,
'zzf': 0x29f7ee15bb2fb02527f23c164f722352,
'aag': 0x32ee8ad114363edfb0b9389f79409245,
'bag': 0x4a82715423d654d61838e81060a4cdf1,
'cag': 0x72db2bbdcb84d7c913a7751cff5fe76d,
'dag': 0xb4683fef34f6bb7234f2603699bd0ded,
'eag': 0x3c9448057945f8b40a7642bf15a47333,
'fag': 0xc592eff5625d551b0c5be656377ff871,
'gag': 0xa5f9a8e4d375eb82ca70f7ab7b08ec7c,
'hag': 0x03f99e79f66b72a8246e610c8eeae66d,
'iag': 0x664eec9e029018782425ba6d09e672c6,
'jag': 0x6d3b29d3effedec81efd70167d6bf670,
'kag': 0xcdc053d387fea277012410a1e2a64a3d,
'lag': 0x74745889a21aee20c4fef05b60268323,
'mag': 0x741f63d12d767bb3fd2b0251ed839499,
'nag': 0x9fdc8b3f3027472d64e26a8e88fa2727,
'oag': 0x11cf751fe2e716e0b5658a1a1a9dfe00,
'pag': 0x77c345c88d5abc96dff43b05f067805d,
'qag': 0xf9504ac07a079a9262483bee972b3361,
'rag': 0x7542dc7e912162d7e7981176ccb41bbd,
'sag': 0x0ed4ad74551735241d31063567fe1812,
'tag': 0xe4d23e841d8e8804190027bce3180fa5,
'uag': 0xcc0dd9e4ac4bde43b0f42e30ca8872f2,
'vag': 0x5e01231573704966c6ed7ea352bda445,
'wag': 0x6f025eaf376c4a6a2ec6a9f08bc2284c,
'xag': 0xd8b2a8141620f9ac2165739a853dbe84,
'yag': 0x1d3f35e249af1b2a00813a96c762a767,
'zag': 0xff1d045f35b400ddc92ff23df27a0844,
'abg': 0x894852696ee75656ba33c03041b1fa7f,
'bbg': 0xea6abe238a5de067d524acdb821b6e02,
'cbg': 0x5593d6214f97f5224215599c7a6bc499,
'dbg': 0xb1da4861a4edf5615ec39f07963ea8db,
'ebg': 0xe6d07530a2dc49c7311c742038c256a9,
'fbg': 0x6f67948f708dd53c2d320da103868405,
'gbg': 0x95b60ee8ac3a8f0a81e08600f8e35313,
'hbg': 0xbc9cf09ccd5e6ec40841348d7b60012c,
'ibg': 0xebf2891d9091c5a1b5b7b20ce3ea77b4,
'jbg': 0x0827c23fc97a1cd24b873a51127c63ba,
'kbg': 0x90369a393bb5683b704290ae37a12c91,
'lbg': 0x3d177a338323be591f10c1b45ff7fd7b,
'mbg': 0xb93776ca54178e43b3a87c6e51d048cc,
'nbg': 0x8d38abc4ee3f1b21e753dde681d5265d,
'obg': 0x6e7c5fbf04accd91f94d85822ff8b4c4,
'pbg': 0x70c23a149199431eb15dadb2794e2aae,
'qbg': 0x6ccaad39cad1c827278a05350283b4e4,
'rbg': 0xc32243ad91d6a404f8174a8815870d90,
'sbg': 0x037b02f21d877dfa5fc5bf220d47471b,
'tbg': 0x30dbf9f1402f22ba624574fb696a5725,
'ubg': 0x9faa29f8f03ec0cec1a6e358f6e81d4b,
'vbg': 0xdea172e4fc1b463ae0f9e457fcbaab06,
'wbg': 0x88ac3c7cf7e9f77feaf7d969346863d0,
'xbg': 0xbaf4cf597accbda6a1666bb02acfe814,
'ybg': 0x0ef180e69f4abe0218f94ae8f3c56b60,
'zbg': 0xfa3ed59c1bb4a135af527ef450b129de,
'acg': 0x5812ab771e5d64e586cacef5aa76a17a,
'bcg': 0x3596b69d068cc15535018bb257b3e1ff,
'ccg': 0x015fb05c2e9b61843249f5b2dcacf94d,
'dcg': 0xfeb1152ea7abd9144ec72c8a49ace4e7,
'ecg': 0xe971346b8fdc81ff919842b9c0fbb79b,
'fcg': 0xee0fbbe9f2b75075b85b43e8700cb2e3,
'gcg': 0x33045cbeee04a3e57791bf0177468bcf,
'hcg': 0xfdd655df85a0a8dd0815df61b2776661,
'icg': 0x5b2eaede449e45891a96ee9188cdc01f,
'jcg': 0x2aecdcf5d93998a77686305d7de9fc51,
'kcg': 0x3848c476ccaefa47fd9974ccc51c1eb5,
'lcg': 0x132527b07cb57cc8958717f2a631276d,
'mcg': 0x09d3b901b15b43dbdd8cf05e050d6569,
'ncg': 0x125791b51fb3d6cc7f09b92041273f01,
'ocg': 0x2f64213176e42c28d3a9f8b3d3131c36,
'pcg': 0x248f9eebcf78ca61cda43bb69002f139,
'qcg': 0xf89932bd92f0ad1b771b1dd35827d81c,
'rcg': 0x17a0f3411968040fe641e02c17d04334,
'scg': 0x82f8408d7aebe00286dc5dcef7d29429,
'tcg': 0xe2d35a19e2bdef86f0b46c6e9ec3dbd2,
'ucg': 0xd5b0a84f25b0d5964ec8f6d93642d4ed,
'vcg': 0x202a06885f99287e6e951175264236b7,
'wcg': 0xbee326c08e72f14e0848febcb68c6f21,
'xcg': 0x64f2220a34aa92357d1f5cdafcb8afcb,
'ycg': 0xeefacee5922a3ca1096eb7a596b79e4b,
'zcg': 0xc3618c31c68dd9315dd343d6dcfaa3bf,
'adg': 0xe6f0cf8118f80275d94d4de3fc63cddf,
'bdg': 0x465cddb2577e3eec1644e04b8713acc9,
'cdg': 0x0addc4416609d628ed133ef1d65f5f48,
'ddg': 0xff9e747b03c22f2be9090b78402130e1,
'edg': 0x7c1e8f60df890199e98e1a4a7ee5cfd9,
'fdg': 0xbf22a1d0acfca4af517e1417a80e92d1,
'gdg': 0x9409135542c79d1ed50c9fde07fa600a,
'hdg': 0x40db79bf823cbedd560c0768773fe44c,
'idg': 0x2f3f6b41c74d0d7d089c883f7d2d33b1,
'jdg': 0xb3b8a5c2b5ed76f235a7e6167b2de0a7,
'kdg': 0x084387d79f1cae0cecd9a8eaccbd23b3,
'ldg': 0x551f4c5602ba9072b52370a5b9a0fdce,
'mdg': 0x1d7831597666abe0f5cbcb5e742006a4,
'ndg': 0xb235fd01d8130026cfcca86a1b206208,
'odg': 0x977c7a0a4554b6071fdf7e33484f3853,
'pdg': 0xcda028f5152b80c486a3badaf25a9aee,
'qdg': 0x8f909c8d456bb2994a9f98aad0831803,
'rdg': 0xb0af4e323ecd83c47875fc67ec029562,
'sdg': 0x4c35abffe1cec5e9b16189fc0ebff34e,
'tdg': 0x857525c211f25e1cce1c4364e4e498be,
'udg': 0xfc45acee94825f0278590c64570c976e,
'vdg': 0x68bf763ac23331b3a6f0ab80d78ce184,
'wdg': 0x348eec6bc2b3aca6947319d0c690b2e7,
'xdg': 0xb996ab4da80a3a97c815f3f44a633288,
'ydg': 0x14dffb5c36051af47cffea8a86aacb92,
'zdg': 0x8221bc7534debe784ae372474a1a7cfb,
'aeg': 0x0bedf9a323ade010294f45fa822ba026,
'beg': 0x63a08f52a29e0f7a1f987f4495164ab0,
'ceg': 0x4bf6bcee7252417629e63e2f672f96eb,
'deg': 0x3e9eb617073a0597ec590d7bd0f2a407,
'eeg': 0x80007f222f88c4460eff8f919afa56d1,
'feg': 0x6e78119512cab6653d150c51045529fb,
'geg': 0xbf3abd3d499e9fcbc4bdf206fdad16d8,
'heg': 0xfad756463d885c8c870d0a7eb28ae29a,
'ieg': 0xa2d891a3470368f3d930e87a13b63f02,
'jeg': 0x4c60de5fc1d2c3ad178b18089792e4dc,
'keg': 0x9734659835d9f5aeababb8d3d2389d94,
'leg': 0x4e1df3d256693afb3630685668820df4,
'meg': 0x35623e2fb12281ddb6d7d5f63c5a29e3,
'neg': 0xf24c2c15b9d03797c6874986a8d19516,
'oeg': 0x3aaa16f534340f33d38cf1ce0f6bebfe,
'peg': 0xac40141674092695d8b98ec0e4a6a838,
'qeg': 0x01920a3262c33492fd245a47703c13fa,
'reg': 0x33c0ee425e2c0efe834afc1aa1e33a4c,
'seg': 0x7931430739acc727db829c12b3915b94,
'teg': 0x7b564a287c93006e8b01456f2c6b8855,
'ueg': 0x29fb6a6438c6ef84869539e4b0fc107e,
'veg': 0xe3605324dc9ab85445908aec0dd4b4e7,
'weg': 0x793805d61d24ed4ade56d8ba1a808dbf,
'xeg': 0x15cbc0459b614f33f3d3b147d5024b21,
'yeg': 0xd17b22bc23ffa3b024788047e5786656,
'zeg': 0x583e0ea46212a69a4c16e9c64711ef28,
'afg': 0xee676ed9ce5bd51b4452ddfbdf962ef7,
'bfg': 0xcd0a03f9e556d3329c99880672f5f691,
'cfg': 0x011134986548f3458aa3e7e2a7fceb8d,
'dfg': 0x38d7355701b6f3760ee49852904319c1,
'efg': 0x7d09898e18511cf7c0c1815d07728d23,
'ffg': 0xaa0c4b3be2968f698aa9999d546e221a,
'gfg': 0x9ef518c8e67ac2ad4e63747a90fd9d5e,
'hfg': 0xb2368cda326f23d1b55edf6d1e49ed04,
'ifg': 0x1c01e2588418ce245acdc161c3dc41ba,
'jfg': 0xfd6976a3f3843c433933c30c21738346,
'kfg': 0x19a22281c2dd785d5c2b695c94fead53,
'lfg': 0xdb019cb11c66098998d629533cb6a9c6,
'mfg': 0xfcc66ac1c0e07a00b56b0dc4c0902567,
'nfg': 0x3b671c4e5270052096ee92a961a24919,
'ofg': 0x1810627b547a253d7b51299521e4e3ff,
'pfg': 0xe5a80ecabedf10df9bc8a066f030958f,
'qfg': 0xf6b98457fd28f2543c6ca7d47d9be75e,
'rfg': 0x77837afb30f8344804e691dfe0c696f4,
'sfg': 0x1d9579ed1ec0d8ba600226647d13de72,
'tfg': 0xd419c7f200c53689a2b4f73b7cff7d0d,
'ufg': 0x6299c2c8bb1b51836da168dd066ca11e,
'vfg': 0xc9a38590a3a7510db06afd19816cb6f8,
'wfg': 0xd3fe265eeaecbaa16eb610fdff281caa,
'xfg': 0x0c84d6a86ac5c4e546b9c8e9cb4fa004,
'yfg': 0x48955f8217e288f32f27536d9f21611f,
'zfg': 0x5451cbc4023c7b3d8af3303608ef661e,
'agg': 0x3e8a6ab1d0e7ed98ef5fad10d14ba41e,
'bgg': 0xd9f527109f6c88a75a401e9ee4c5c337,
'cgg': 0xb2d8dbd40f4c86e98de79de41bbd0e6b,
'dgg': 0x3e157b281fa5cea063d8489179946677,
'egg': 0x0e9312087f58f367d001ec9bae8f325a,
'fgg': 0x7e696161f558ab98fc2e43202516934c,
'ggg': 0xba248c985ace94863880921d8900c53f,
'hgg': 0x2f1cdec1a33b0036795634c4b81aa213,
'igg': 0xff2c7130f5cf4a53cc5fea6aeba26822,
'jgg': 0xdee9b77e4e0398d64abbc97a14d0978a,
'kgg': 0x7ba1d45cf100350934288261ce272f42,
'lgg': 0x02a4d83c7cc27aca2f991f69b34dbc15,
'mgg': 0x63be2953da66a966b3a98d09701ddc48,
'ngg': 0x50167b1ce4902409f9b0fb411b8def87,
'ogg': 0x34da450e959d17db92f6876051d0faac,
'pgg': 0xc57f52eabb1ea228c1cfd30aaec0f5fa,
'qgg': 0x86eda3a7e7f11241792e32271c8597f8,
'rgg': 0x81a2ef85594f2a712ca9762b37df11b1,
'sgg': 0x81386693a89ac041e7701b6dfc7fab98,
'tgg': 0x749a7c735e2f86c0bfe126b4a945a4f4,
'ugg': 0x0748f0993da0a0aed370d8ef28fb8766,
'vgg': 0x2202ef49c3af5c3b6072b0c9dcb9d70c,
'wgg': 0x32e1a88b5b3fe791c945cdc8073d7cb2,
'xgg': 0xea12b57eee4ba9b8cb0bbb8b233b300d,
'ygg': 0x6f3c57c616f6a16b946f420a527a1efb,
'zgg': 0x00df9063955e43aeaee70d29eeb4cf8b,
'ahg': 0x130a5b7f2199284cd58153f0172a505a,
'bhg': 0xe6ecef1744d6e2fa0b3f1c6088fb1153,
'chg': 0x9cf45c66fcb67ddc44b04dafa0a20653,
'dhg': 0x86c73f7f6e21b10c82046e36c930f06a,
'ehg': 0x27ab7a8a758abe9ab596c02778055d98,
'fhg': 0xef48092de84bbabeec72c8e279c165d0,
'ghg': 0xd2b0214c9aced3b346343a19767252cd,
'hhg': 0x51c5c82bfa00bdd4dfaac964bcc12ba8,
'ihg': 0xcd63891496416ec5ab6b3d7dd747083a,
'jhg': 0x3994b0f776913f8934f0a5c633371b2f,
'khg': 0xe7a93972b0fdf2c79213c392e7532944,
'lhg': 0x5faa86d631a6f236d6ce701c1eec2e81,
'mhg': 0x764e3a467fce3f8412b4fac1af9c26a6,
'nhg': 0xf3c555f220b05696e3f87a35f2b4f35d,
'ohg': 0x6c1aa1d33acaa74b0407d9b05010ceae,
'phg': 0x8ffcdc13a622f32da559928207451ced,
'qhg': 0x3ad248f8d3739d4e379e723050c87060,
'rhg': 0x1a72bb191ae848ee3baa640421cfcc6f,
'shg': 0x528e740dfb2b2b9317923c105403fe8c,
'thg': 0x0709256293f5cc32a7fb22d03db6dd28,
'uhg': 0x791ec6bdfe1c3e705b0ecce19143fbba,
'vhg': 0xd1e831a08968c589e477cc992f2ef732,
'whg': 0x8230612276f81887429d757e66ebe9c2,
'xhg': 0x8bde7a6f2e4bfc6833abf6e592e0f8a0,
'yhg': 0x55a3fa06725219407ded13f8ef9c8f75,
'zhg': 0x547d7811807e8775fb0f6da62e8d7e6b,
'aig': 0xf21daefdb424e954c6163d3a4d292fd5,
'big': 0xd861877da56b8b4ceb35c8cbfdf65bb4,
'cig': 0xc918e79a514bda7f2baa124354423eb1,
'dig': 0x1534477b314797aae378d292ec836521,
'eig': 0xd9290c12ced1302f1b30609c54c784ca,
'fig': 0x04d8b0d72264ac23a02fdafcc5fb364e,
'gig': 0x97d6804053f94cbda00ec523d7d6b8a6,
'hig': 0x710ce2839f805ee9758701ecdb78f4c8,
'iig': 0x52e0491052a19730d91df4811fa61e8e,
'jig': 0x7117c7fb44ef75cc158499fcc49eb565,
'kig': 0x041f18d4e2e27cd97f4404c2c67cb362,
'lig': 0x583b6abaf6139cbd83855e21b41e8b11,
'mig': 0x25377d700854d26d447a2fb7be203832,
'nig': 0x6c8de65cfa014cd7cca3ae369de910d6,
'oig': 0x447a715162b5fc7810782d064f5eb09c,
'pig': 0xf74c6af46a78becb2f1bd3f95bbd5858,
'qig': 0x408b2c15a6e5b314d69d7e2decea89b9,
'rig': 0x631cc152ec7c7d64cd88c87adf45a707,
'sig': 0x3311842a74c4736836a29eefbc1ea464,
'tig': 0x87013d025dc55978a525c80e70b79d98,
'uig': 0x1187dff7698d6d824e00fd347f5a21ba,
'vig': 0x770ac15f0aa7c162a8aef7e58f7f2071,
'wig': 0x9251bf426197fb6ce04204856a362d5a,
'xig': 0x688cc4a5716a829024501e407f2bafcf,
'yig': 0x9db29659ad972ae33d5adbf6cf2df963,
'zig': 0x238b8d81fb0e0f03fead5613b5234b74,
'ajg': 0x4463c58f7367d3264816c2ac49824cfa,
'bjg': 0xd1f05aef644a11420637bba1316882a3,
'cjg': 0xe13061c5926bb3b384bd5079111e858e,
'djg': 0x0216dd969277748d3aa2c144a3b6e451,
'ejg': 0xd40a0f3cb7638be3fd4b6b9607edb862,
'fjg': 0xdf58b960f57e05bcd7246a2cf7618c83,
'gjg': 0x6ddaa95729c807e53a3e867a213717bc,
'hjg': 0x0cfe3d553e54920ac33397e6558207de,
'ijg': 0x2f2dc2f539d8bf6223582a79544d5d51,
'jjg': 0x36115d825250ef3eb7484221d8167e7b,
'kjg': 0x4b2cc38e274c4a16b3cb1b724d54ceb2,
'ljg': 0x6be0e4e3dc9df02b308fc8a2b8c9d8aa,
'mjg': 0x4110d9edd9f96a6687bb15c16b1792fd,
'njg': 0xaf574bc6e6185b2185adffc7cf443398,
'ojg': 0x6a6a18879203aa2c344a7b7626fae471,
'pjg': 0x9901d25cfedc6af5730c5e26b70f6d61,
'qjg': 0x4a4828fb8b2d8fc4032b4dd8cbc2ed1d,
'rjg': 0xa7544417a95d25039d43779268c3c35c,
'sjg': 0x0bfb7dc2aa47ccbd833417127f7b4355,
'tjg': 0xde8f4e334f52f8947eb1aeb87f47fb3e,
'ujg': 0x47aae130d5cb54532272b7d72ba2af1b,
'vjg': 0x73982db7fe1e2616c5a4c68146f67e15,
'wjg': 0xdce10e3aee3b84a3b993c49cb8c92c94,
'xjg': 0x2251639094ff8cb5db7f021ca3ba8988,
'yjg': 0x287efee3434098bc2948bf47961972af,
'zjg': 0x9b840e1b18b9961b7d883e9562d9f07d,
'akg': 0xe202c0e9ee37bcd13a4ca05c32a5e56b,
'bkg': 0x7be5273374fe1bc3177ca7eedcb377c9,
'ckg': 0x96bfde462ee877e646fa594347ebc24b,
'dkg': 0xe6b492da244632e2e312fccf71cbda09,
'ekg': 0xb841e58aec2d4f1748fa4dbe5209833a,
'fkg': 0x8bd7ede6e240a5d1ebe8023e534611d2,
'gkg': 0xd175904df8b56c5f5ee1254d1fb62272,
'hkg': 0x933a1b0222da4819cbf0ef1da24d1a92,
'ikg': 0xe16cfe5ec00fcee258a38863e4824f4c,
'jkg': 0x004b77a11cb7992b04f0ad7941f0d465,
'kkg': 0xe886a612330ff894badee1b8b65927f6,
'lkg': 0xd8b9f7fe2b505717bdf2bd0aaafc6124,
'mkg': 0x7478842e1292f643b2607055430be949,
'nkg': 0x2e131cfe01f934f7d57236d98fba39d4,
'okg': 0xbd07e7b4dc068edae56e96355079b275,
'pkg': 0xa46a1bce0b67c45bec8f435bd5390a54,
'qkg': 0x8ecf4c0020d61562df1d0ffbf4d7d439,
'rkg': 0xc7f8f4f97bb82b5b35679274723fdd8a,
'skg': 0x397b2470f446644ff508b68b6abcb225,
'tkg': 0xc131aea1473eaaadefd28338cb9d2113,
'ukg': 0xdced00c57ee3690dac61b9a7a22d28fd,
'vkg': 0xd5cc1a80e0eedf977449cffa70c8ae30,
'wkg': 0x0fb61eb1ece418d9a728bf93634a87dc,
'xkg': 0xb1e5216d1c611fbd0db31daea8478812,
'ykg': 0x7a296c8ec906e5a70094c1248de96d09,
'zkg': 0xf3871627638c05b0d7db0466c99622ae,
'alg': 0xf24233114ac85fb3be6785293636ca46,
'blg': 0x8531d59e7e303e2aa71e72620ad2c5dc,
'clg': 0x1c2402a569dcaa197576687d6bd2b419,
'dlg': 0xfeaea44eebb7a05ae689398a5b6196af,
'elg': 0xd77614076eb9a4352df49a3afc89f9db,
'flg': 0x417fee5685385fed1e02fe4dd6219d00,
'glg': 0x2271629a6a37caf8e67c43007d95b5f5,
'hlg': 0xb485c29a079d711ea9921b72fe069e71,
'ilg': 0x37ea0bbb6083dbb3b768e0efe41dfb0e,
'jlg': 0x7b46e4bb774f669a920c664e39d26db5,
'klg': 0x9718a3e3c33c6ab854775890d1b26947,
'llg': 0xb8ae57911c26ed8313cd09a33f7f43f5,
'mlg': 0x8014defb66f852eee1e8fc2b993270c8,
'nlg': 0x1e0ae2e2adfa537159f75d205bac7984,
'olg': 0x876c803725795ac9540a376ef80ac6e1,
'plg': 0x995b70b0a52c88fa6a3eaadc7ad25ff4,
'qlg': 0xd7d3a6ea8d8c965623dee36824de18a5,
'rlg': 0x7592b989c69da9314920492e9917067e,
'slg': 0xb758142187a5ac0b3c350e3b1f2753ca,
'tlg': 0x603adad5fdb2acbd2014257e5c02a6b0,
'ulg': 0x9dc981b68c66c6d2fe3f14fb61bbd116,
'vlg': 0xffd14f700ef9db82ef673b3ffaf86e58,
'wlg': 0x6cff7ae6a1fb7d8228b995563453cf53,
'xlg': 0x24797b24b3c418ef230f826c05d3724a,
'ylg': 0xe2a9e20f41ca2e65b0369981c582769d,
'zlg': 0x3a0d9153638b7860115c0a8f6ca255bb,
'amg': 0x0b65af7f2357d3fa0ee8d8972d7e0646,
'bmg': 0x12ced2b7254f998a728f5ba70c40921e,
'cmg': 0x80a1a684db979e64f29f4cd9e849c863,
'dmg': 0xa1053f60459c2f20d87ba9a2f484dfd2,
'emg': 0xf1a65da8af3a57957137f4a2864f782b,
'fmg': 0x26482c185fbc648f53bf6df0dd7b2ce1,
'gmg': 0xb98274f8dcdebb2f22757a296771ed1f,
'hmg': 0xa5926d72537c1aa00363aa0133a463db,
'img': 0xb798abe6e1b1318ee36b0dcb3fb9e4d3,
'jmg': 0x02588f3e114edc0cf8640c3502f62a30,
'kmg': 0xb4f6c1ac68a06b5d9908cbbd93c9c101,
'lmg': 0xb9e188b7c5369f1b6d9048f2333e5ca4,
'mmg': 0x7ff5a5bf82d506fe29331e55e652d55e,
'nmg': 0xc42d89d599783590d4c5c69e4701d760,
'omg': 0x28b901e3041d5eddb024f7a581b78f76,
'pmg': 0xddfcdfdd0abb74d2140764153655b7f5,
'qmg': 0x669663bb84eb661e0f34e6252f336620,
'rmg': 0xe2bfc1ea60d3ec32fc2dbd6d86e4a459,
'smg': 0xa825009ae6d3aaf8e8667ab4dd15a830,
'tmg': 0xdd1bbcfcc6a5d18d56b82ccf8346be47,
'umg': 0x83b3d8c330e1d36a583d392df858d7a2,
'vmg': 0x0c4b1a16ac03c3ce195bbe0fc75c8130,
'wmg': 0xbe7d54cfa7664990acaeb91f9b75b9a0,
'xmg': 0xb2b845f3132be0f35e67c898fc3616f5,
'ymg': 0x09408d1c0d9d34d2790fdae5b79dc420,
'zmg': 0xa62dec3640ce6c72bdacc4780510a7ba,
'ang': 0x4fb3b68e84149f91bcfd0102f95d3731,
'bng': 0xcd3b2807b63443bd12bab351742858f1,
'cng': 0x787ca12a72bf9b478944dbfa6431fc51,
'dng': 0xf04d1db8e967f05618bea3202fd51a01,
'eng': 0x74e6a8b111ea7da1a7d0a596f4c35208,
'fng': 0x2498a77f5bb5244f432173fbf065b7ff,
'gng': 0x64fb7d782f2be42feb84fef5d0530776,
'hng': 0x549f39c1c2f1486a4d54b5a9372584af,
'ing': 0xd4408643ccbd7e83d0c54f42e405d618,
'jng': 0x6db661a984683256bce13f71fc8b54ab,
'kng': 0x6d7ce8365b78001cf5fb7ed467b0283f,
'lng': 0x8b8a2268b5d3a89f348d5990b846c29b,
'mng': 0x1d62a19ee745e1b0124db2adb0d626bb,
'nng': 0x97c5eb044a20e6d985154a98a05e4f20,
'ong': 0xddf10d41d5d840f6a7d0079028ddd3d6,
'png': 0xbff139fa05ac583f685a523ab3d110a0,
'qng': 0xaa9f84f69e8e75568f2ef1aa34a1eb82,
'rng': 0xd2d9ce665f66ae49556b47436a514c0f,
'sng': 0x21d761a3914ce64591aaa8f2b43c93ca,
'tng': 0xed25b951b2b54142187a1fd507c45b29,
'ung': 0x365ff37b654785db7350837c3518634c,
'vng': 0x271e8e5b70ee43503d4732aa264431a1,
'wng': 0xf840b19aa78baa0ef25b287c94493824,
'xng': 0x50807050c965dcca8c4a54530130f2b2,
'yng': 0x56457285fb95c4a697804ae46c3072a5,
'zng': 0xf0de28c5fd603655c1fae608e967db22,
'aog': 0xaf69ed57819ec5a8565bbe164001f595,
'bog': 0xe07cd1723fbeb38e9e7485d54b593f64,
'cog': 0x01e33197684afd628ccf82a5ae4fd6ad,
'dog': 0x06d80eb0c50b49a509b49f2424e8c805,
'eog': 0xe95c7b13468496963d6716a40620dff9,
'fog': 0x3811727de5b0ddf6ae30defe2ca4d2c2,
'gog': 0x68f96eefa7b34346670149f370c7af5a,
'hog': 0x0cf04112df2ec4a38fe7356b8613fc5e,
'iog': 0x2b964efd0eb35009bbc25d5bcdf0778b,
'jog': 0xc518790e0463f2b7db28d50df8dd6c4e,
'kog': 0xd464bc738ebbf343810a19f8f7ea04a6,
'log': 0xdc1d71bbb5c4d2a5e936db79ef10c19f,
'mog': 0x15b11cc44d2288cbbad6dca68c3c7e45,
'nog': 0x01104a11188a9cccf59a1270fe3f373d,
'oog': 0x7cfc15e4bb4d17b9871046d06b47f8a6,
'pog': 0x5bb50314c7d970ce6cb07afb583c4c9d,
'qog': 0x77c0f4be49631c8007d8ddf68d4f0d15,
'rog': 0xe324ff9b5d9a7b78b9e166d2c45b319a,
'sog': 0xc85254e55b2ea2fc983c0864a1d941a7,
'tog': 0x5b9ca929004552332e808b9f4fb34e38,
'uog': 0x5a2fe5fad6e4b381ba14fa034a1e6a96,
'vog': 0xd1b1ae66af930188fe02afbb74429602,
'wog': 0x23c6c468a034657e78017f6d19bc3e69,
'xog': 0x776cc2c461d7b073f6f607d95743f9db,
'yog': 0x21b7ec923f812fbdecea653aff7b55ba,
'zog': 0x863f90ee1287cf3ac4e94e2f6ea90fd8,
'apg': 0x6069bd2de2d24d3edb29464e75adc229,
'bpg': 0x53a490adec65204303c6aafea046d2aa,
'cpg': 0x014ed27501ba4df67b59a4fe0529d92f,
'dpg': 0x6623bdf78746d87e7c6c2888a5c70c9f,
'epg': 0x4a80fbca4e5671210885cca75da25679,
'fpg': 0x7960b76754d47ced47d3f1357bd28e6f,
'gpg': 0x013365df0fa8bd11a96aac558f1ea973,
'hpg': 0x089bd744ac90e4ca6bfb7807fc6da539,
'ipg': 0x4d4766ea82ee6b682434b33ec9dd16d1,
'jpg': 0xc36bbd258b7ee694eb987221b2b197b0,
'kpg': 0xd9741b154a1a1e12556039adcb27b27f,
'lpg': 0x042f3b97816f38fc28999a500a3fef78,
'mpg': 0xb4f4f5e3c80cffa0f9cb7debe0bed63f,
'npg': 0x82d776891f5ca2eabf4a214f35af1b5a,
'opg': 0x1390d03d160a9219211ff6cb6aaa2fe4,
'ppg': 0xd7d7478110d44787219e88e86973e7d4,
'qpg': 0xbaba052b3f5556f23b14347def1f10cf,
'rpg': 0x1f29b97f8bcee74a6f3de65c9d0a5938,
'spg': 0xf2c29514140d47d8ce45ad3eda582f1a,
'tpg': 0xb780d54ae3b7982b8a4024d2c22be802,
'upg': 0x452e1e9e7cef5b2ddc1c97a0410d8c2f,
'vpg': 0x88c0a91d6a486a6c2f7a474ef62f0210,
'wpg': 0x6e877975655240c048e27028b57601ea,
'xpg': 0xb35fab23faed0587217f1d1ea1aa7876,
'ypg': 0x3b4fee6ce33ce11cad9e45c135b07fb7,
'zpg': 0x4e69b22a2136603b342abebd2136c01e,
'aqg': 0xd261e39898cff0bf76eadee335ef16b4,
'bqg': 0xa20c16c5fbd6ff8eef057011bf92a336,
'cqg': 0x5efe5848ea3627bc49b73e923dfc1ee7,
'dqg': 0xe3447ff1ddcbbdb129d4c378b6c16a3e,
'eqg': 0x704fe2b592e2b68af409c5870e728786,
'fqg': 0x6268a325973cf1c640c924a8b9e00e3d,
'gqg': 0xe590b8fee5b870af30bdb1b4b11c3dca,
'hqg': 0x006b9cc00c32c5204465978fbdf012a4,
'iqg': 0x84126dfddd34e95e8ecfdadfd1519b1b,
'jqg': 0xdca7eaa995f2813cfb45a00387448921,
'kqg': 0x09c8b9246d137de71f1652f88e2dbc8b,
'lqg': 0x293755e697e7198c3a01bf740354046a,
'mqg': 0x069d0b3904e33af94a26e1f2031b7446,
'nqg': 0x2d9aaa12262ab1ba1447b02165c59cb0,
'oqg': 0xb82419a285715af6c80af32b6986a5a3,
'pqg': 0x34a74351a51e6975a38cf157c09fafa6,
'qqg': 0x03a926fb720927d09a762828c759806d,
'rqg': 0x6426c8c0d45062051d0afc7e9bc330b4,
'sqg': 0x858eb4583c5905a9fac05692b8bef890,
'tqg': 0x3cabe60fa95f2cf2b99fda9c44139938,
'uqg': 0xeb39e9083a4d7230c5cc9a6548a37b26,
'vqg': 0x5bf141d58053d72723ab72372b814975,
'wqg': 0x0abf6269259c63651a3a253afb6664dc,
'xqg': 0x01ccaa87100443802dabbece2fcff7d7,
'yqg': 0xb2730ddd48da00cafedc7a10a48dfb68,
'zqg': 0x09673872a256c81253e6a8884217a652,
'arg': 0x61dd86c2dc75c3f569ec619bd283a33f,
'brg': 0xa4c50154124e3f5d42e4ce41af928ad1,
'crg': 0x62082c0f95cdf3e2296fc0be8b90ec19,
'drg': 0x75fe57ec4a047a300cac5f27223df81a,
'erg': 0x77d9603c1228bf8ad045195418979e65,
'frg': 0xf5455cd4463c6a43e70f2fd8dc3ec51f,
'grg': 0xa5d502e66f116d4bd040613df88e074b,
'hrg': 0xa1ea7596ec0db6cc9c3a3f852aef7546,
'irg': 0x0e9028a1ffccf083b18d331127565357,
'jrg': 0xeff418c3e57a0f69afb25071a7ae9561,
'krg': 0xbe4566317e0b6b6d4776f41cae2a8580,
'lrg': 0x28eb6a46a82ac68d22727c92c031c091,
'mrg': 0x8e3926b45aff8e65eb32e4fc677c1c78,
'nrg': 0xee4782465dacd28618d4a0276383631e,
'org': 0x5a445d710ae24cd276062b0c84850838,
'prg': 0xe73d45437ff69d701ffd982bdccdaafc,
'qrg': 0xf5b7265714bd49893c9f26a1faf027ae,
'rrg': 0xdda76af2974450b722760e636225e1fd,
'srg': 0x9a26f2038425448133e800b4941e79b1,
'trg': 0x90710761341352a9e144d8ad4c92598b,
'urg': 0x3a124aa7a668398efb63ff87331dabbf,
'vrg': 0x0af873e11d5f2f1748273b7c9bc49c1a,
'wrg': 0x91e6b61ad80876a16787d0833aa6c21f,
'xrg': 0x9cd03a797df15e551b72109431460eb5,
'yrg': 0x83396e7eb60983a804a2f3f5d2d2a749,
'zrg': 0xd9bddb3f82f11496f66903e5480f2401,
'asg': 0xb30bc56bd6bc8630d7064a9321f41a9b,
'bsg': 0x6be0c5d46f46c0e756100c24a60c0490,
'csg': 0x4c4a60a5a0d5d69eed35c23714845ace,
'dsg': 0x875ae59131b055497b8a81e046edd9e1,
'esg': 0x689b168f94144e96205b84643760d3bd,
'fsg': 0x112c5ae8f078012e79117f9dc21afe1a,
'gsg': 0xf0508ffda069115565c9eda5385bfc70,
'hsg': 0x94b40c6db280230b4211b06fa04c7be1,
'isg': 0xe2c6517f142fe3a9e64e2a125e6ea602,
'jsg': 0x1ad34c03bf250fb37341ce6802f5a1ea,
'ksg': 0x39eefa3ce2ab388df5443d3e6bf79702,
'lsg': 0x08f7f5baaa0e39a3a8725483bb14ab2f,
'msg': 0x6e2baaf3b97dbeef01c0043275f9a0e7,
'nsg': 0xd58543563a18357e4d141e98c7a936f8,
'osg': 0x1d7fb1d1de5c35ff9efee40541248256,
'psg': 0x268fb4595b079e19467c8feee67a856a,
'qsg': 0xc64b5d01be10ffd660fbf5ac897ad0a9,
'rsg': 0x7133679712c570c1498a11b187933b16,
'ssg': 0x59334e721bcd3101864d09e30f5bd34b,
'tsg': 0x7ab987d5c9a0e0efc94fd1e60e6b1f40,
'usg': 0x77c3a0bc6be31167242711da28ee70d0,
'vsg': 0xbfa68fe69b48bb116180cd0f769d6c78,
'wsg': 0x86a9d6a2bc43c08405d634ccc076e818,
'xsg': 0x6aca7677b7ca637fa161107221ba7e57,
'ysg': 0x559325bd76f320a0252c01bd48844806,
'zsg': 0x0dce93c9042fa9f5abf204fbee16244a,
'atg': 0x2605b1e0c80327b35a007d6a5732d33b,
'btg': 0xcecbba5ce071e7aac9e213c975316ec4,
'ctg': 0x58eef294b54847752782276ffad32967,
'dtg': 0x64cecde47b1d157d9408f699665c1f2d,
'etg': 0x52bf29d05c95ef2eb2d2edebf9b9c547,
'ftg': 0x6999fe197694cae4073650c9541a0b1a,
'gtg': 0xbda08e5056fe7f49c0c02e90522c9697,
'htg': 0x6c4bb686763009ff4d189796f7b85c16,
'itg': 0x85dcfc03e4ef4d36b36330d0a5cb6229,
'jtg': 0x983dfe80a19ba8090ea77195c2e9512c,
'ktg': 0x5c4d2fdc550618c08abf30d896b94d8f,
'ltg': 0x0ed875b7270c3856f7af2f6638afc9ec,
'mtg': 0xd297751fdbc7530d30f5057c16df6c10,
'ntg': 0x4a375a6931428a4a1e92f2527bb7a500,
'otg': 0x4a7d3a468b8eb6ec0acb1960d4b58ed6,
'ptg': 0x1492cba0393c5b1dfc9bd880e2254001,
'qtg': 0x30d48bdae93a1f1294443c7572f5d2d1,
'rtg': 0x1da33616e19f532fca9bf8e3c095d11d,
'stg': 0xc6a5a31f3f1d9e446353e0425ab55c3e,
'ttg': 0x2bb5d5e8f29adbbfd70b5608c6e604fb,
'utg': 0x5211c21fa34f16124db112d48c8207e7,
'vtg': 0xa358f3e2a0b39222fcd5c1a7ba9483b0,
'wtg': 0xc3e234c9d6862f4addbf07f6b56f6236,
'xtg': 0xd2ad2890f5aac59f775fcfcbb88bfd59,
'ytg': 0xa5a50b7fbdbb5373732b55d5555179eb,
'ztg': 0x0ed14d85893384408cf7241cc95902e4,
'aug': 0xb7e53893bcbbe545d281227f12d5c9ee,
'bug': 0xae0e4bdad7b5f67141743366026d2ea5,
'cug': 0xc538e271b5e98687ab7dff00cf59f632,
'dug': 0xfb47e0c94af88b8e489c284f71ab3806,
'eug': 0xeaf63d6c9f5620918534f224fc6c89db,
'fug': 0x5a914febd829e2bf9174005bae0eebdb,
'gug': 0x37f70bed1c27e9492549c54fb36f2ae5,
'hug': 0xaf231c77af76002cdaec813c29a6db09,
'iug': 0xf0a823676cf167e626189307f1bb047f,
'jug': 0xb860d356d91e17ec3fb8e7eec9662fb5,
'kug': 0xb18731ee9d34eab6c644f6c152c8daae,
'lug': 0x9c07fa66f605b5c0e56339d4c9ac2045,
'mug': 0x4c032833c7118276e83ee90998ba47cf,
'nug': 0x6d20ecf2c0f318cdd6d4592f2886b122,
'oug': 0xd97b26beeabf659cad14128e44fbf5a3,
'pug': 0x714ab9fbdad5c5da1b5d34fe1a093b79,
'qug': 0x9859ace87e476c374c4e2dbb044f1d5d,
'rug': 0x717de68e813c2596d41fedd5f5733ccf,
'sug': 0x1dbc90bdaada4a0613566d32ebb2ca41,
'tug': 0xc70d71665cae2a348d1f160924fba735,
'uug': 0xecb46de09a8504b356db80136ec4ba9d,
'vug': 0xb3d872923db4a019574e8decc8abbd0f,
'wug': 0x396d2c1a2e59f673ac470066da791b14,
'xug': 0x022a1d43461281a5f300d74f4af72b45,
'yug': 0x849777ef17449dc49e137e64f56bc5b3,
'zug': 0x268ee2a832014d8fae7cc58a183b3b3d,
'avg': 0xe322d423f075b0ab2daad27011d24909,
'bvg': 0x4f074185fba7c476ec26ac7d862418fb,
'cvg': 0x684280db3f1e3894de5865e9103a066f,
'dvg': 0xb4674c4005d6c9387e90d1f4fc1a0532,
'evg': 0xcf5ba0a372773c013e91ff40d5f41904,
'fvg': 0x221a4353f6caf897caa84af5d56682c0,
'gvg': 0x12ed0d050623a6cd591ba2fe2f4cac3a,
'hvg': 0xe8f1118c50226c3ea18585c33ede1cb3,
'ivg': 0xd69583fef77818a6edf77b7e471afce1,
'jvg': 0x8cce84bc4227cbbfada6d3b9c8be6f88,
'kvg': 0xfc59175e76bc6062de4c18e36869ff97,
'lvg': 0x62e7c5d6098c4b0feedf1303a9b97d86,
'mvg': 0x30374276e4985de17bce5647dc0d0aed,
'nvg': 0x7df8d010c3c0701b305a0198c7e3f387,
'ovg': 0x9d830dcf98382a7cc4a759ac9f559b70,
'pvg': 0x26a0f64ada4ad4d3ced9b1bb5be351d3,
'qvg': 0x7062c10194f5b2c6ab1ad96c9c1114a9,
'rvg': 0x0cb236b006ea0d613bd9a307da0fb894,
'svg': 0xae8eb96df05e788ac39d88948eaf295c,
'tvg': 0xeac1a55021d5e8ff7912bfd5b96220cc,
'uvg': 0xb56eafa83c112c434caefa89ab3c0e85,
'vvg': 0x0a0d4515097a581ae5b405a3c3ee6bd3,
'wvg': 0xfba366d8e4c37de348e18a7ef6f7d46e,
'xvg': 0x250c3eea2c1530d251035eeec2a27eb8,
'yvg': 0xf25d326647847a085643e48dd0dc7494,
'zvg': 0xb34cdf54fcb4c54832e910112005e8ab,
'awg': 0x1f7074e5fa46cd127b22b17a3d2d3254,
'bwg': 0x431202c9ec16f7a4d076dd7f58e40720,
'cwg': 0x0bc1704860b446235080a1944f72534c,
'dwg': 0xc2e424d6b18145c9f53332451fc08853,
'ewg': 0x2619601604c639867c95d816a5e1a1fa,
'fwg': 0x1cb5105001ef368c5933689239efed41,
'gwg': 0x80206c71e9eeb36522629830bbe4037e,
'hwg': 0x6b1baca50d95f5efa12e7491ff16df05,
'iwg': 0x18eca83b921c26915a805b1c1a3429f6,
'jwg': 0xec1f4a047f14fa637849628d3f65d6f3,
'kwg': 0x3eb7d13b9ef60cd41f6cb108927ec038,
'lwg': 0xa49b46334dfcd0b6e7750e90a2dd5c9a,
'mwg': 0x71abdbd177f7e2033cd44aebbb6bcc61,
'nwg': 0xabe97dbb57b6944196ac7eb099a59c89,
'owg': 0x108dab2b34f07358b721cbb004f85a03,
'pwg': 0xcec4702a7d2754c74dfd3c84871089f9,
'qwg': 0x73663b701b9964e12db0b664c30b084b,
'rwg': 0xb600759443d668bef815098f4744d7b3,
'swg': 0x193721fa695368aa13c19bf919f25314,
'twg': 0x0f2f63bddbbf213bc7728bdf865e5ef1,
'uwg': 0x8f75e1a654b10bc363799d021c05da42,
'vwg': 0xb7b78909c7e1b3d34a0c0b7f9614b9af,
'wwg': 0x1a3005a0ce3853530b9e813c166d526a,
'xwg': 0xc396e7bc0fe254098c460a3b1131e3c1,
'ywg': 0x326cd85e5a075ebe49774931cf470507,
'zwg': 0x710ae677b55612f545557dd43bac0ed9,
'axg': 0x91ec71db894050bd23de979437b1eed8,
'bxg': 0x1d79b836994b7b17d35b2a1bb16a66c5,
'cxg': 0xb5d63635159071e83028f0b6a1fb82a0,
'dxg': 0x5d4ea6e196ff9c390e0aa13395f8893e,
'exg': 0x4ee72e6ddf1b70877a1b387ded518b64,
'fxg': 0xea9608934cefb22b75b1d6f5c25882bf,
'gxg': 0x5d41b8090cce51f7621d13f7676903a5,
'hxg': 0x755b3d5f2757012a135ca5728a64bcdf,
'ixg': 0xd5819f02b3749cc5a09da534df24d190,
'jxg': 0xc128719cb6a293376065410044da91f8,
'kxg': 0x5e0a3e6fd5b6c82beb20eb2829d28bf5,
'lxg': 0x93bffb5f7d5677f32abdfc196939a553,
'mxg': 0xdcd4664ba5936fbaaa1f816f8e5f0c66,
'nxg': 0x96e7f0cd5887b98213a0334d6c857aae,
'oxg': 0x974593810b21ebdb1e64f1e725215f8a,
'pxg': 0xd8c5b8d525abd2aecbd07a36674dbedc,
'qxg': 0x8f0f306f4a04974e45c849025aaa5b36,
'rxg': 0x9816ec94e05244696136a64ca47e03e6,
'sxg': 0x2eeb57acf0e38b95d333867bd18e0545,
'txg': 0xf035625a9cf091577a67252b000e2a1c,
'uxg': 0x6121123e3f4277e71dd396421c046e4a,
'vxg': 0xf620de942b36f343d7aab07362d4d450,
'wxg': 0xc439d5455e5ce69401fe8a0a6d5dc29e,
'xxg': 0x2925fbc1590ba86ee0ede3b295986346,
'yxg': 0x1fd153880e2f9d85ffcecf0b48c730fd,
'zxg': 0x0c4341657d9fd439b23a47eeb23f0b7b,
'ayg': 0x36e22f3af50423e1ebd702ec49e401b8,
'byg': 0x515eb0d0dde0363ec385560a1d0dfa64,
'cyg': 0x2bf01b2344d59f1948f61f9b0ab4bfc0,
'dyg': 0x2d899ef3040ec185252e1e1f23ca3dc6,
'eyg': 0xcf6cb2a4a87b3ec0fa976860b6800e63,
'fyg': 0xceb2b31e74322c2363f356d990f3cc95,
'gyg': 0x16621b2cc5e9e42ebbb1fe0cb1a6f866,
'hyg': 0xf8d98d3b397811f73e5869ec5e34707b,
'iyg': 0xa82a70e06b163fbf0d380d015522ce5a,
'jyg': 0x30c0d27817adccec1490576e90e88bb8,
'kyg': 0xfb9672200d25f182028fd2a0037ace3d,
'lyg': 0x6703d4089f6a0cf18915f0c397942f14,
'myg': 0x2621fafbb8b403cd1633de2e6a9cb844,
'nyg': 0xef75b72959eed0cc480ac5a2b2d567bb,
'oyg': 0x3dcd3569c22f070fc59e16ee021c52d2,
'pyg': 0x0d9f46ec43ec3762e05523560f412e5c,
'qyg': 0x8bcad6295d8d4a86ed6f6aa95f47e5c5,
'ryg': 0xccc64ba1a8ff92afe3e8b1fba854e8c2,
'syg': 0x849ade26e1b6da2ed96d00db639a0985,
'tyg': 0x1ec899a16eb648cd2d46e7d2c635329c,
'uyg': 0x5ee30e70d4d6ee42640e62d4715f8ef2,
'vyg': 0xe91f3472b7913b5080924f2207238d04,
'wyg': 0x2c2c9690f9dec26949f2364a87cb3e8f,
'xyg': 0x2617f142530229a903f6700c4a25f9c8,
'yyg': 0x5efe427a4e20b0ab12afe98c3cb50a60,
'zyg': 0xee762fad39f036ea5eb5ef9c15207c10,
'azg': 0x3d74a8a1f157a6ab42cc93bdee4feb7a,
'bzg': 0xfa308c8b606895deb312d5c279d93d5e,
'czg': 0x52a45ffe6a9232e8429f30c546f51d5c,
'dzg': 0x14ef0c11642738af1ad2680aeb036b92,
'ezg': 0x441d2d66c008670ba0ca3be142c24130,
'fzg': 0x65f0527d1f6bcf8071823e2247c7c526,
'gzg': 0xf2faf5878a46358eefa9f99ea723c193,
'hzg': 0x6d3fa4d2e0ab0ad48f85232204abb484,
'izg': 0x9b1e6a0f164ee7cdd555bdc38d4862f5,
'jzg': 0x2a479a5b02ff2617f642eb2c43481e3e,
'kzg': 0xd86a3e27acb47157ed28589eb8f4b95b,
'lzg': 0x7b5a834d39ac40a67cfb829ed11c2e4e,
'mzg': 0x1d7a33ff4aa32f2f2d615e32d3357cb4,
'nzg': 0xc8586220776b32ce73a3c8222a16c54f,
'ozg': 0x73c460d5e0d01071ec33e2f9116b18a6,
'pzg': 0x1b1036bac06e3f3eb5ab2f62d15309ae,
'qzg': 0x3f7486f450f795a291bf106d8bbb0562,
'rzg': 0x937de707e3711f476c99f57938d1ff50,
'szg': 0x8378de26fa5169e6850167b995d08474,
'tzg': 0x601fe17b35aac9d9905c8845aa609d47,
'uzg': 0xfdc8b7fbe33ce87b32772d8db5b6f574,
'vzg': 0xc3121cc6c2cbf1426d902cb1f92382f1,
'wzg': 0x4e76787934a033689628268a86453bdd,
'xzg': 0x4b109803c9c0c922d499ed40f7edb04a,
'yzg': 0x99770108cf1bd16801d5fe3bc451f6a3,
'zzg': 0xaf53fe5704672b3355271d7d14cd70ab,
'aah': 0xead0de869e27edaeb9b64983ba360ca1,
'bah': 0xc56efb94d3943fc77fef3eb949ab540d,
'cah': 0x122a2a1adf096fe4f93287f9da18f664,
'dah': 0xee88e46428656d1af9f31fbb514e7fa9,
'eah': 0x65d4851fc550d45a449e1ef6ce10458c,
'fah': 0xe15c05ebb01125dd997b1d46965ab92e,
'gah': 0xd61c03bec697d8ef464472b0d529fe5f,
'hah': 0x758ed690a5c806c2f75bc02afd6bc395,
'iah': 0x6bed4419141eb28ad4eec6adccb32495,
'jah': 0xf4741177f40a869cc57738e15f0517b6,
'kah': 0xd19e25c649a006d20d67681cecde106b,
'lah': 0xd22eecd293cb20789c88c483f60e4c27,
'mah': 0x9d7b407f0af4e9d2938904a1c7584b15,
'nah': 0xb476dd1f2e29b48fea39d7af0d307101,
'oah': 0xe18b9303ee8f0014b6611fc993d9909e,
'pah': 0xab7136ba08704e4eb4a19169e1a26695,
'qah': 0x8d8c7ac2d47df30e50ae8b5598ae53b1,
'rah': 0x1aa304151c024d2ada8c067c052e51eb,
'sah': 0x6c8063c7409deb9289f71eab7f7a1548,
'tah': 0xd1f1a0cafce848840f83feb1b0979d90,
'uah': 0x3e04933d4bdf6f65ee061d8569589d23,
'vah': 0x1644f1a571de34f6b3597bba304e7c05,
'wah': 0xd43361acaeec0c476b0e0b9000da4e99,
'xah': 0x949a62af465b9c65ec9c3e96dc57b743,
'yah': 0x08ea8248d5344d04bd5393e5e86ecffe,
'zah': 0x3918899ba89da648c35437e477c9ebd4,
'abh': 0x61d4d3b3c416faedb390a5a61d062cb5,
'bbh': 0xfc821f40bf66e6bd1cc62c43015d1a89,
'cbh': 0xeb05199c110e0c15de8e36db31c52eab,
'dbh': 0x1eb267706e34aa7423b280c24efdd775,
'ebh': 0x813cb107c938e9488a7a512afd0a3a20,
'fbh': 0xf8b4d37838b5c028ba382a17d4b76aff,
'gbh': 0xd44e7a48a82a23abf02c6efdeb0a056a,
'hbh': 0x07642703d393c3e84832897e18c4ff2c,
'ibh': 0xb8fbcfff518df04c8a0a21ea877ff684,
'jbh': 0xbdac8cd786a0e73e1796320adb9afd75,
'kbh': 0x682864a5209d100cf868166f16cf176c,
'lbh': 0x2b79cc1a7647c309c5583f70df63f8e1,
'mbh': 0x0d25550695335a47d8b247182f7e2e57,
'nbh': 0xb4777c8ebe076404209bca0e1da28fae,
'obh': 0x7e0a0d3d4ff3e08eb1e0004b88167205,
'pbh': 0x7e62af0ce89cc7a82c71b579c4de60ea,
'qbh': 0x0f64fef1dc58ef3cb921de854b2545eb,
'rbh': 0x13fde8f498e21439969d18235257c357,
'sbh': 0x5e1da5bfb9257b830ec5bc5abf551af5,
'tbh': 0xfe6dea202f96e333336eb19329c25be0,
'ubh': 0x1ec50c0e7138739324f844259c478290,
'vbh': 0x034677c2ee3423f90b1f635f285c8b41,
'wbh': 0xf317e454421a1e606a2c476ebfc534d3,
'xbh': 0x2af6160de4167071f748e4931e550e67,
'ybh': 0xb134467c9b0f1c7ac894f4337b95ea60,
'zbh': 0x9d50022744da15b6bc9970485bc70cd2,
'ach': 0xdd57dcc23a3083551edd85e13c2d5668,
'bch': 0x379541daed6f788a56f24fd11072d332,
'cch': 0x64f5e67ed2b90b1bb9084c7e755bbd7b,
'dch': 0x5292a587f72db34b6711562d587d0fc0,
'ech': 0x1148b082c87927b6cd0507ce8493136f,
'fch': 0xcf9ef824c16b95b5c6e27961d2f1d2b3,
'gch': 0xbc6c6ccb2ce1f0fc4849a2abbf0cc33d,
'hch': 0x862aed5959f6d73e0e2cb3e5ab311ff3,
'ich': 0x3cf49b0960fb2ef0ea03d0b991819630,
'jch': 0x275c216c27200496423bba70a0a871a8,
'kch': 0x289d2da3d15190cb71dbb2f72588108d,
'lch': 0x2252f7e4531c81e4e8ea2a57abda310e,
'mch': 0x040341797a19a29410123669d9ac748c,
'nch': 0x209be0a079e84a36f746d108bc6021c1,
'och': 0x7a49aafcef8b956f542016c86d67ddfd,
'pch': 0x6b6a8f43df41dfb393f929637acb124d,
'qch': 0x0aa0a57330c30d0958430e54b5bf66be,
'rch': 0xc9c0d13ba4e5563b2475736cd0366142,
'sch': 0xc340f4803161a481703d1c8bf74156ee,
'tch': 0x51611d2b16aa0fb18cb815715579f8d1,
'uch': 0xc9f39154f4de3e8cc118eed923731170,
'vch': 0xd910c45ac88695e88174d38c94afd34b,
'wch': 0x62e58b9d541aa4d67b8e5d5dedf18fdd,
'xch': 0x6edb76c0ddc3212ce118b00ed3476434,
'ych': 0xf3a8479fc706bdcaed56ca6d8d4bf613,
'zch': 0x29753f3030aa18135b78e5cfefc73765,
'adh': 0x274bbb158428878924391bf5bd10ab05,
'bdh': 0x8d1e0f3707f6ccae6baea8302a6ac6ab,
'cdh': 0x3bdb542135969379011632021d0e3232,
'ddh': 0x7e07f8d4196adc5bea35dc78dd3e3785,
'edh': 0x0dc722d13fe92f1cae987efd7afb2175,
'fdh': 0x6c7577bfb2d6dc0345f8e00aba028ccb,
'gdh': 0x57c5a9eea786cd47ee17d720420493fa,
'hdh': 0xfc15bfd3bf22181ab4447a4acd393451,
'idh': 0xb1179ffc80d13c0812f905e9c7e285da,
'jdh': 0x23814d16f9a439b500420ad35b09cced,
'kdh': 0x8e9ece3b130181b145acf786b63cd6b3,
'ldh': 0x3f5453e192e572cbc2bed32097c7aaaf,
'mdh': 0x7cea9d84ee274228c7efe8de18592df7,
'ndh': 0xc3c5accec7ed0fc3e8d0a2ef98fbd242,
'odh': 0x7e959acf3393731185379fdb6fd77ed4,
'pdh': 0xea3677a4523ac2397ee9a56bc2ee0760,
'qdh': 0x2d778abb44df769da2a65016ff65111a,
'rdh': 0x789d9fc3328e1f9e5e07b1287e0be5ba,
'sdh': 0xdb8079c8a617cc079a7eae3cdbfff10b,
'tdh': 0xfe22ca94c002d10b71041c5eff3f335c,
'udh': 0x2c30e05a05f8f49e73f6573e78a0b076,
'vdh': 0x3503725bceb54e0e03e5f520f8971201,
'wdh': 0xee66892c3f1f4a7bc6551c3de4ba1183,
'xdh': 0xecc266125c8edd0cd09261012266c141,
'ydh': 0xf2547151dc511fcfbd24ea7f1b28aeea,
'zdh': 0x7b29d141f931c694efa605125f6c82db,
'aeh': 0xcd23b36442cb7a833ce54573a63faea6,
'beh': 0x57aa4ecf9f7a86bd70bf8b743cd79558,
'ceh': 0x975e89cd02486d78cc50bb06678a5969,
'deh': 0xd750abf749618fbe4373515716ded093,
'eeh': 0x9882a9d0fcf0d0e012956e6db92c2436,
'feh': 0xab5af156e6f9b4a7b0e2ab2fd87c4563,
'geh': 0x591f849746d65341494dfa35887d20ad,
'heh': 0xfda71993dbb74d33a8d02806aafd4bba,
'ieh': 0xabb7b14ef453791c81e2efccc2883ad2,
'jeh': 0x9b0137af48408977b9db0b82225e3e8d,
'keh': 0xebf4020dfb15d76f0cdefcd19f2d7586,
'leh': 0x0382738e4320de66bd1ffb3934fbd08b,
'meh': 0x15927d9ab6ea93229b4f22a561664ec1,
'neh': 0x16dd7ce43f31815541cb321b8466fc0b,
'oeh': 0xd6b13c1c93e8a615fe964db8d549b0dd,
'peh': 0x71878d07b05cbef05de30b625a14abb2,
'qeh': 0xfd1b856cacd3906e648f086184b44fbb,
'reh': 0xc33f4cdd3a9a78f313a425b16b5644b1,
'seh': 0x16fc908f3355ab9217245207fa75f2fd,
'teh': 0x38b5a6b9dafc7fc2955561cd08fe1f77,
'ueh': 0x7b438367761a68c0019e36d7e98656e4,
'veh': 0x5816fa40a5327bdf811c5d586351557b,
'weh': 0x0f9c0c46914eb1ff5bdfa31b559ec432,
'xeh': 0x0849467b9702ebe012db187fdc793f7d,
'yeh': 0xa113bd39fd0b16165b6dcb5cf7799a15,
'zeh': 0x84581980574ee895f63d4aaf72b3e6a1,
'afh': 0x96b93113f631af312abb144481ca86db,
'bfh': 0xe205744248a93fdc597df7a9ab16bdac,
'cfh': 0xbda00947b4934dba860f4b80d3149413,
'dfh': 0x7606517b6f62538f9ad5d84832bf0930,
'efh': 0xb8f2e4910063b57a5ef53f45ef6c4e90,
'ffh': 0xaaddac84f4a4de6743d2eecfa4fdf525,
'gfh': 0x580123b1eee6b0f82540e45282730a52,
'hfh': 0x62c841059cdff4129c40cd28b770474d,
'ifh': 0x99e6a78f4cddc93769aed9426186ec38,
'jfh': 0xacfa4e5b560667c7b07b7ec0fb0b87ba,
'kfh': 0xe8b7e892d591c6e36ce67457954214c2,
'lfh': 0xd50138117f09a707f8de68dba359e7c3,
'mfh': 0xd88ac73e733a9079d66497c6f5aeb1fd,
'nfh': 0xec45ecd47fe3937b49b0e119d889b605,
'ofh': 0xa1c3f71b4bf12ffd737ba560a2e8b949,
'pfh': 0x4758be2341af78523e5758945528a098,
'qfh': 0x9942500bf7ec4450d62f18d440727bc9,
'rfh': 0x6802ac8a4e631081e593f505b8c79fac,
'sfh': 0xc0a6e06bf796d9cf53debb6b35cba992,
'tfh': 0x1c46c9d4fca45f547cefb94099815341,
'ufh': 0xe2bc73c66e0599dd4b0c297d54d9ab2b,
'vfh': 0x26f6fd87454d7f3b704418bd02c8836b,
'wfh': 0xe332c3c512c5c16f2256aa90217486bd,
'xfh': 0x41f43061da2d3e7a86fa18512cbb5bfd,
'yfh': 0xacff9906fc8b7f986050718e0e5f8788,
'zfh': 0x857af3b624ea8d6ba1537375d2737536,
'agh': 0x5de08b97cd5cd898e5af0a129df65c49,
'bgh': 0xfb1c455c60b8f24f5d9745605892434c,
'cgh': 0x203f8d7ae7d66732a01eb922e297965e,
'dgh': 0xad3321bbfbd6af8abcf0f0576a5c893f,
'egh': 0x068adb1e4c81daacc95d981a1af2510e,
'fgh': 0x0f98df87c7440c045496f705c7295344,
'ggh': 0x6fca2b0eae7d6e3b9830baad3ef9a55b,
'hgh': 0x025c1e37b069dcd4129f02c842b705af,
'igh': 0x25c39b14f5f5fb71c1e3563d0a89b452,
'jgh': 0x338a47eb5999d866c775c8a801056a37,
'kgh': 0x27f67b803086897f0aeff454a424394b,
'lgh': 0xf4b9aeafe698aeb67a5ca9ecaa6a97f6,
'mgh': 0xd769f3ab30112939f2b4469f8a92804e,
'ngh': 0x0246c5ef4d3bdbe82c63c10c121bcabe,
'ogh': 0x602466be75388ea2a09e6af041b1468f,
'pgh': 0x0db0fa9dc3e5c61eb40c71d157ea9625,
'qgh': 0x1c5fcf17686cb22e607ad87f48ac9756,
'rgh': 0x90782e33e2583c9066119bfd486ee28f,
'sgh': 0xd6baa01bbdc3920d93fa8a2bbf68219b,
'tgh': 0x9f1814ab87687a05806d8d69f3769de6,
'ugh': 0x908b2c4f40d5263ce467a9de2d8c4d7d,
'vgh': 0x26bdfda515daf32afd4fe92f7dd41060,
'wgh': 0xc195e0d4125c6ccb99f2c44265d1d00f,
'xgh': 0x92f2b54f343eb6d000cb129457aa91bb,
'ygh': 0xced4d0dbf4d5540a63c042bf8ea9a129,
'zgh': 0x9e632425614686ec7845a011088f015f,
'ahh': 0xbe97edab9d08c80db8111360d744fa0a,
'bhh': 0x482957ca826bf55b7c4e58f8015c6eb9,
'chh': 0x6e1d1dd19e589f4bf3918ea55fc90192,
'dhh': 0x252c2b34268adb2c09c2cd2ab6f11ead,
'ehh': 0x2071e5eb7fd74592bcfacb3e9ecc4bd1,
'fhh': 0xb45209495e5e2540fe0a8296ea81db9d,
'ghh': 0xb4238aece7397c6651a5193c3baf4055,
'hhh': 0xa3aca2964e72000eea4c56cb341002a4,
'ihh': 0x9352d207270a2ba1176638fdd0aa6909,
'jhh': 0x34581b76f0d54626fc3d0b8404c0c2e9,
'khh': 0x2724d0f0288f1cb50bf30b912cf47507,
'lhh': 0xc3e573d20e72780bf46c5628798300a7,
'mhh': 0xa4310b1057a43a419b440e2814bbe035,
'nhh': 0x02b4bbccdf21b20d232c702a852a7943,
'ohh': 0x51618e45fbfa6459e43d27f9c9ef41aa,
'phh': 0x7fe0be02c5a57dd641102fd88c7fd27a,
'qhh': 0xd5f6603d37d53a2a851a8a11f0a8ed67,
'rhh': 0xf71e3d8eb7439eb13c9c3147e64f83a9,
'shh': 0x471c393a47486f23e6830ce8ec630aa6,
'thh': 0xd11ff37096a87757500e65473b3805b7,
'uhh': 0x023e3e11aeae9849d3cc742f73b512c2,
'vhh': 0x8d9b897f45b9f6babb9a3e48d51dcde5,
'whh': 0x1a4d73b3deeda815a6a4c2e576aa27cc,
'xhh': 0xbbd0887202dc3787f40711a86b7ba42a,
'yhh': 0x41374f08df46e39f45aa10fc07906598,
'zhh': 0xdb2b266cc418a23cfc8c261ada25d56d,
'aih': 0xf434639597f85bb9c214ee8cdcc8c162,
'bih': 0xa9094dce0908f5fd61ac760e4b51f380,
'cih': 0x77f41ee149299425a00e55edceee2ac0,
'dih': 0xf4b2751d8dbb1c1278e3850c7776bc51,
'eih': 0x3e5ef1fa9611043b19f9e10d08b4732f,
'fih': 0x2c5ebd4ff163da893e340b72c6737662,
'gih': 0x8e2f18a12d769b8b2460e7ddb64044e6,
'hih': 0x614b7df69165603557df83f9af9dc02a,
'iih': 0x6455824d6161e3dc518da0280b48925c,
'jih': 0x333fb15fef4ee36cc0a8c7665e18bed1,
'kih': 0x6b26be6b62a48a3e4c5a90fdb33ad428,
'lih': 0x65f289179be002afa0f409b7f2e16662,
'mih': 0xac6b67e417de8b381d5f6eb5d3a425c7,
'nih': 0x9e9e8c76d739212c63eff362e321ba33,
'oih': 0x25ff56e863681b9403bad747b65a22c1,
'pih': 0xdd5ca36c7853e7a189cef218689d74d9,
'qih': 0x109a6f8744356a12ae43180dd3ed8b54,
'rih': 0x58ca2861109a56ba13a1ceebe814874a,
'sih': 0x3d4113b4f0c510f7f581b1643caf3070,
'tih': 0x43532bd0ac4c48663f369c2630bee3db,
'uih': 0x4cfba0ee8d1c1ce2f2c7328481121886,
'vih': 0xf476cc904d3ad684b89e053dc8af3240,
'wih': 0x42267b2203b77055ddecf2f98c52c7ab,
'xih': 0x0dfa361fd6361b2605820b46c6d98191,
'yih': 0x69ba67011018d5171ee1d15c1a312c66,
'zih': 0x315a775acdf57634a16583176fa03117,
'ajh': 0xba71983b507dffaf86a718df1ec91d80,
'bjh': 0x5a10579d2359388c9cb067d5264d5d49,
'cjh': 0xbbceadc3c9cda4fb8ac2c18bfca795fd,
'djh': 0xa765edaff7c2015f9d9ab53d131002ae,
'ejh': 0x7fdeee680f1a134668009c582fe0642d,
'fjh': 0x444aaff69c70f0c299474a911fdb1a89,
'gjh': 0x4e3b63885a0f78a1b471d52750d0cc79,
'hjh': 0x18ef25b59079b7210a0040e48c5e5a5a,
'ijh': 0x0ae8e6b5bf2a07c3acfb1b407de0507b,
'jjh': 0xfd03abf5012ede5805f4882e0fec2b14,
'kjh': 0x0e3e0fee239d849b82b5db788ddcb8e3,
'ljh': 0x67f87c9b32834504b4ddf432055d88bd,
'mjh': 0x197aed2adc7ddebfc6c5969bc75b8641,
'njh': 0xbe5c11d2e6cfe03c3edbee4c848cddfd,
'ojh': 0x762d37780c38046fbf24aa7981135833,
'pjh': 0x23b4027deb57e71f19d38f0547c1593b,
'qjh': 0x70a0d65b568eec41edcff19a19c5a71d,
'rjh': 0x79c646e4df7be12f1fbd11d5b7767a9a,
'sjh': 0x8d99dc2ded8696dbbf87b5af45597cb5,
'tjh': 0x9d7abf1d686c3f6aed534a9fe7d94c21,
'ujh': 0xe7eeafcfd8cf400590905bd1dc472bb7,
'vjh': 0x3d3dbba071ac3049f842e0fa8e00206e,
'wjh': 0xa973b95ca7cb5b564707da9422222c35,
'xjh': 0x95cbf50f925fb343d98926a81cb92001,
'yjh': 0xf97ee432d68b314d7050665ed119cea8,
'zjh': 0x4a59b8e31b436b9c65ad235358e70f84,
'akh': 0x8cab4b3e958f712569f51a9b90c495a7,
'bkh': 0x63aed082966fec61d14e3ae2f2a4ff73,
'ckh': 0x590414e211ac8dcf1c47b1b798681ee6,
'dkh': 0x28691f7dbb41a3c33f45e70f817d6ddc,
'ekh': 0x340b254752698c55ebf3cb21507d2eeb,
'fkh': 0x2c390742dd93ea46481d2b97352b70c8,
'gkh': 0x7e163cae3f118040dec08a8a7b17863f,
'hkh': 0x1a3cc0b221b2b21da3c2dc9135379c52,
'ikh': 0xfe917a1261980668d525583b42528194,
'jkh': 0x2b2baed86b463749b373f3f576760e9e,
'kkh': 0xcd71157a5e3de1a45199be00e9d88208,
'lkh': 0x3808a4e1a81d1ca7fcec161a5ba5219f,
'mkh': 0xbf60685a880fb3a083d93de541a199bc,
'nkh': 0x7cde688e2321edbe4c491d6114b8e975,
'okh': 0x52e87051161b8ae129a0e6f053e6878f,
'pkh': 0xe65ee5192095c53604891e2477611849,
'qkh': 0x3bcf227f7bbf8a722e29c4ce7ab2e39c,
'rkh': 0xaa2104a88d200402e114f9e275dc47a0,
'skh': 0x043439ba83dd276f5fb180e99a1360f7,
'tkh': 0xcaad76e0fa34e51d06622d2e5c1b8f30,
'ukh': 0x8b900cd5f15b786c5d5b98667fd96d0d,
'vkh': 0xb9b910696fa9b552db68a6b7e4bba78d,
'wkh': 0x0a9f07122a1b7e8fb20fb8f003d44200,
'xkh': 0x2ab8961f78675d703f27422541ebb4c5,
'ykh': 0xf504a6940d4655ab14c4458dee483f04,
'zkh': 0x7a80ad5f7fa2f36ebda6866a9cfcb2fd,
'alh': 0xdbada578ffe1c637bc87d85f75a0d3b8,
'blh': 0xef6de87449b13350fbe73a35d268090b,
'clh': 0x0890489a0ed60c3d5ceb18c79eb55a7c,
'dlh': 0x806e53e80337e5dd7edc151657df961a,
'elh': 0xfed942d92e80be76702a20f9f905767c,
'flh': 0xff16eb2d1fd108de26a694afb254ed08,
'glh': 0x0dc94c8810d9de81f86e52e85fc4fb0e,
'hlh': 0xaaa4068bd7e5e20e56970438c296cc73,
'ilh': 0x49b507275774fd1c0cf12983d44a8140,
'jlh': 0x89ad71b0f53da7527c8450b291c99f21,
'klh': 0x535062915a6210ef0394c80449fad618,
'llh': 0xe95d77a22c8b0df3330ab8c4c143e5ff,
'mlh': 0xc4433b6c5759f5d575a8d747f484b832,
'nlh': 0x2d2f0b6a639ea3f6395b9c5627bb1b97,
'olh': 0x2681be5677c3c564820ef9c5beeb8052,
'plh': 0x8d5c0dd1af8dbfd69120fd4882cb3a68,
'qlh': 0x90ea912f65e5fb4cce357dff99e9b175,
'rlh': 0x62f2e1599d53b3d4b4c3e98fdac03225,
'slh': 0x603c1341f5fc3be7a7ff4a33d9eeea49,
'tlh': 0x26bd93071f841eb0d32897bf43bfff07,
'ulh': 0xd06e8bbb88a27007f99a4895766eff19,
'vlh': 0x9de3c9bf7efa9dc868904da05864c924,
'wlh': 0x49ee2ca45ab9f041da28bd02134f187c,
'xlh': 0x5ba0762f95da6709fa7dc9ba0ef9877c,
'ylh': 0x0ead81d68268e2ec6c2a559e67f8a0bd,
'zlh': 0x844b4b3df924b24ef0981db2cd4709e3,
'amh': 0xe44ab71198e40b9f396e512d2c28132e,
'bmh': 0x558e6576c2ddb2056375180a9716da52,
'cmh': 0x8491874e06f471e0dbc42cc8d662907b,
'dmh': 0x04f50bc3bf49ba62b79332901364235d,
'emh': 0xaf403384d89d34e49cfefc35fe11c356,
'fmh': 0x89ddd99aa8db1f154dac979e1b6fe5eb,
'gmh': 0xcf06ce5a29fd512937c042defd941c74,
'hmh': 0xc2a8d80346c3921053faccd823ae6ace,
'imh': 0x7bdacaf5c0aa1a2a75991f8b1f4616f4,
'jmh': 0x6c3f44f542b08208f1b09d5cb4c7feb7,
'kmh': 0xd81466c92818b3a3c1bae9e2a045d42c,
'lmh': 0x78bc33b76c96dd9b74f85db936aacc7f,
'mmh': 0xd2e2ed2a838e6946e86addf8a23fbec5,
'nmh': 0xe2e8c5105d521dc53cea85901e4ebad2,
'omh': 0x6a2aa0d47785d6d4335e2e609b342571,
'pmh': 0x8860a2a2cb5dd6107b1d51769fb9468d,
'qmh': 0xd56543a621b789b6622c6a46393c7938,
'rmh': 0x114c76194fef8812b0e2b2244f768a1c,
'smh': 0xb7c7beb23ae1688521288cdf158a9622,
'tmh': 0xc30c54df31640b94078bd6113e5e01dd,
'umh': 0x94c487f0d7d1fc908e36aa31fc449571,
'vmh': 0x792b1a2e3bcd576a5bdbfb949a3d6ae6,
'wmh': 0x1e289bc155f4a7134f6a14fd15724b8d,
'xmh': 0xff0e3a43c14335a6eddb0019c0379500,
'ymh': 0xc808fa9903693ae1149a741e86c6e6e4,
'zmh': 0x5d8af9d97a8969e5fb05e5c1caa3b499,
'anh': 0x5febda3d0ef90737a60de2fd7c6d7728,
'bnh': 0xf5d032c45bdd7d948fdeab728fb8eff6,
'cnh': 0x6acb792fabd56b43fd8c9ec92a0ee60e,
'dnh': 0x2860b80e9dc6694a3216f5a433ed24d9,
'enh': 0x21aeba8801c0b16512410a1a97ce83d8,
'fnh': 0x05aede577588642bd0aa72a8423e7cee,
'gnh': 0x6d36a53327c58e7a80f857e4e5467ac0,
'hnh': 0x810600818167f4ccdd0e1cd3d93e5e26,
'inh': 0x12bfdac9c3333cd9e4981a7e08ec2293,
'jnh': 0xd1e4e63e457d09901f138368663d1777,
'knh': 0xe6609da52243eeb0f9598fefef05c439,
'lnh': 0xb0d6fbe8a64be6c1f0bbad641e4e6a72,
'mnh': 0xac55454b54fc25f61101a987a71e1b5e,
'nnh': 0x8b7f347cb6853698c985bee6f959cdad,
'onh': 0x803cb0b9dfd50536615f82509d6fe047,
'pnh': 0x05c331e3c44174b34ec3ba520ebc5b87,
'qnh': 0xb426be06d0af6c6b5bfc46b8c92fca17,
'rnh': 0x02d0bbdd9679407514f2aa8aa206cfff,
'snh': 0xc4bf029960a4cbcf6ef65c717700ada0,
'tnh': 0x7a264280f095e4c793a66c7be19d776e,
'unh': 0xa2fffbf435405a98cec152b2a7d7f882,
'vnh': 0xbbb7530b355743c6d8cc1af2ae66cbf7,
'wnh': 0xc6e5a08f693ebd5f5ef41753309811c7,
'xnh': 0xbb217817b727254a45f634b195613f45,
'ynh': 0xb643d4146fed125180c54e79937a05f8,
'znh': 0x65b724c98ac0e8e62ca33a7dab3edde5,
'aoh': 0x844c1df443b3b35cb0072342fef3111f,
'boh': 0xa947d78ba75ca4c58625857289f2a081,
'coh': 0x01df0914b3a321d337c0ccc6f2f7f8a0,
'doh': 0x1ce67b56120a8064b3cf4f6ef39cdfb9,
'eoh': 0x38a846078e877ce49e558dd7290b45e6,
'foh': 0xfe00862f0a7e33ccdfc32ee1dc35da10,
'goh': 0x1a6e877ca6ba3aa609342fca12266a2a,
'hoh': 0xeac63e66d8c4a6f0303f00bc76d0217c,
'ioh': 0x8e86e5cac1521dc02f4cbbbc72522235,
'joh': 0x4fe84c1323098142972c90960547e8a2,
'koh': 0x9b158b2a6044a976ce5aa322fdb514bd,
'loh': 0x3ff1ea09b981d88e7c8752b329a7702e,
'moh': 0x94e510ecc1b1d7a405c0e7aa18db792b,
'noh': 0x0e96619e2594eb6a57684e02ace822d4,
'ooh': 0x00974525f785fd895c88154eada90d9d,
'poh': 0xf6adb607dd78cb98b87035773678d50e,
'qoh': 0xf694bc1d4297e94e9c2b8abf9e2e698e,
'roh': 0xbe7e16e1f31a293e6b3665a80a73c113,
'soh': 0x1739e701ccc9dbf1b485f672203fc81f,
'toh': 0xeaee0cdd2c82e89f15078ff33ca37e46,
'uoh': 0x0d104fcfd7fe3f44ca4cb89eda964cae,
'voh': 0x7286d0939c23ca016053ff71733c8995,
'woh': 0x70e85fd92527d15f8a372b561afab89e,
'xoh': 0x12070d0cf92dca1cca29b26adf43050a,
'yoh': 0x8cb84ede624a697e6dfe6cb914e16979,
'zoh': 0xc716db2f5be562f92dc352a1d4027426,
'aph': 0x140122a0ee7e0a73efaebfadca9a6faf,
'bph': 0x42da3284c02cdb4da3d64b3a79371c8f,
'cph': 0xbd34070fc21f98c144fb996eea89cc92,
'dph': 0xd4c4d571fa61bb56d77ccd20cae2f7c3,
'eph': 0xb2c4bdc9078b3e51ac0e7a7c10902112,
'fph': 0x5f9bbd47a4a35ae9eb4fd6bae2fbf95b,
'gph': 0xc6bf6e93041072f38d3f88d438cd24df,
'hph': 0xeccf049aa36d3f33af63ae3955286577,
'iph': 0xaffae3125d23ab38831134315f762c63,
'jph': 0x1b981df9d0bd7dba07a96b23e2cf18ff,
'kph': 0xcb03b3df901a54807b522f3d84a6590c,
'lph': 0x2cfe9b347ad8873a05417933b3533be1,
'mph': 0x3d5b7ead574023f9f3671d3e953d398b,
'nph': 0xf1ef7af3407e96ea02497b2009be8786,
'oph': 0x68e4dcce79c68b051874cc77d9e94889,
'pph': 0x762f943810d7f01f2b43fafe3c9aa5e4,
'qph': 0x9a59c4b2e0ebc939dad47223206ff2e1,
'rph': 0x22e413b75110d0746f6cd3ad22042e1e,
'sph': 0x5b7579069280fe8db6f7823769b1094c,
'tph': 0xccfb2e8ef4401da786e855e5b7af2e4e,
'uph': 0x40e4c4599ef36aec5ca7b292c215ddad,
'vph': 0x8664883bdfa0ee874ede8a76c7bcb383,
'wph': 0x7d8ae25ea159c454ef205a84010f38f7,
'xph': 0x0c0120c65a65b5cf6ada6d7c6cc0b1d6,
'yph': 0x55dc39e2cf2bcc2b28d6a785f08a0937,
'zph': 0x65abbc7afff08276e39f58c434482e33,
'aqh': 0x117e94bcc81739075f5f2bd6a5acfd1d,
'bqh': 0xba4d8986706d02afdc6db624e360e7d9,
'cqh': 0x599663ba66f1f1b6e1375bcdb3e75ca7,
'dqh': 0xfa6c8231604bfdc9e290d200595eb700,
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'fqh': 0x0ae4ef6d69e18916de8ceefee92c7dec,
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'jbi': 0x215c051ba023f13ec68d0119f48a88ad,
'kbi': 0x89c5a2c5e37c92efd37bacc55ea92ac3,
'lbi': 0xde84eda56ba9ae7dde60dec23b0a5034,
'mbi': 0x3b5cf439e9798e9e87b59be9ba44c73e,
'nbi': 0x4b8bcd8f3038cf9ff3548842d9294d2c,
'obi': 0x8e248757798f5833d3d6da545cb550db,
'pbi': 0x86ed09fdaec06b4e28d4f88b409e7ab0,
'qbi': 0xb38f1046bb64aaacf67da99b66fd37b3,
'rbi': 0x09f553a1dab6bba5c23bd8e577ed13e4,
'sbi': 0xa9fd7932ec51d5daac0ddbc14bc4e642,
'tbi': 0xfa4d09504c196ca8419e14ae8979836c,
'ubi': 0x6c51b6f4f61c76b6811ca72fa7a6f896,
'vbi': 0x6af9a8ac6c0fb4aee85f0992acddb51f,
'wbi': 0xdc06b0f33c2fc68b6ede4a79ba77f74a,
'xbi': 0x3b861dff3334e17aa0b05cf2e30feb17,
'ybi': 0xef2abb0887e1bd2f813a0333a27fe72a,
'zbi': 0xc34e0e02eb881e6728224fbee2b6c96b,
'aci': 0x1d40983150ed9712d42f2495dd8686a5,
'bci': 0xc8cdc80fe3f8a20a79505b36ee9e89d4,
'cci': 0x96757ac010f762fa8fd112c6c7eb5e98,
'dci': 0x711e7569725a4db5ba86ed0d46cdf771,
'eci': 0xe150ca9b89926b05cc3d6bcc7153b1cc,
'fci': 0xeb42c769bfa0d9e082ed41e45156f7ac,
'gci': 0xa21770cc1c0b08a876d402cfdc416ae9,
'hci': 0x702818a8294e9d554acaf62faf0a4563,
'ici': 0x9ee5923fc73ded1b84ba78a17e1282bc,
'jci': 0xc360fe775245342e74a483edc97831fa,
'kci': 0x0604288fc06da0ccba006e51807c6381,
'lci': 0x674768e8c32641ed27b0f45b784cd15f,
'mci': 0x4fd21d225ad051895e74eba01ef62bb9,
'nci': 0x8bf92164bc694e54fc5581fef9d3f480,
'oci': 0xb4f90e114cbaf8d8c8840ea9a0325a31,
'pci': 0x8d829c0a8bc7030417dff221df7e44d0,
'qci': 0x4da623176c45113dcfaafad0446df57c,
'rci': 0xf6e37ff3d460f25c0395db6faec794c6,
'sci': 0xa76f2c01be007dbd8c7fdb01a4ec6115,
'tci': 0x2357303e612515d498b2e976130cb983,
'uci': 0xe208ca42644926ebb0dc4fe7167f25d7,
'vci': 0xb3f4bce375f09646afe13cb07c38d60e,
'wci': 0x8c4549fd26431149142c8ec7a66eeedf,
'xci': 0x085d195d9dbd77d30e6857b759bb4895,
'yci': 0xf2b9cfabb0db5448ea08c094c4ef2653,
'zci': 0x647a52de1f603a32857887fc7787e6d8,
'adi': 0xc46335eb267e2e1cde5b017acb4cd799,
'bdi': 0xd21d8346b6252bc79ca3a6ee8a1ca4f4,
'cdi': 0x71181486a480eb672f9f8e329a7c68af,
'ddi': 0x0848a5c782793767821e1db231bd5d07,
'edi': 0x8457dff5491b024de6b03e30b609f7e8,
'fdi': 0xf088faf32967357cedea2cc194847698,
'gdi': 0x72aae47df19ab4330bdb83110094ebab,
'hdi': 0x76b34c3fc652262fc3104d9a1cf1556a,
'idi': 0x4a75a406b5d5d3383390f561e92a24a2,
'jdi': 0xacbf94a2ca2cc3a256687952056ea537,
'kdi': 0x83fec62db73de683463e356f1f2b33cf,
'ldi': 0x81e0e6ae416537ee2105d9491a7a3ea7,
'mdi': 0xe73716f50b6a127c919198d931d06011,
'ndi': 0x8a366961a76f3165f5e1bd2e4fad4f27,
'odi': 0x95734b47c7b7d7c3be6da6d9aac3a7a1,
'pdi': 0x69a7b2d3a70235d7c3760acd211e17b5,
'qdi': 0xbe50b18b3967d140bb28f628803cc16d,
'rdi': 0xfba662cc36fc65e57623b25288b1e689,
'sdi': 0x0efffbe143d1747cea58f8c3fe1b6f9b,
'tdi': 0xa4b4507ee28be37dc1189257960eef4d,
'udi': 0x98974f9dc037b474c4f7bc3e6914a117,
'vdi': 0x53df8179b6b67a0b75abdb2abaa1fd53,
'wdi': 0x7536f19d4647b62fb8ff424dd5131c25,
'xdi': 0x00c9020588c3734f2b9ff8ff567fee5b,
'ydi': 0xe42bb79e32bfc57bdf9889f21f10d5c3,
'zdi': 0x3dd086b59554fe33c1b8f051475b4b31,
'aei': 0x81c3132c563bd55b447bdff10ddd59b8,
'bei': 0x57fe4b97a911bd674d451164ca62f8bd,
'cei': 0x3a75dc5ce20b51e88fb59a90ce32186b,
'dei': 0x93319a4441fae08309390fd2e8326002,
'eei': 0xc3b62bc44da306e32e847ee68e9767e3,
'fei': 0xa3122aabeefce724a4757528d3439dd0,
'gei': 0xd06554a84241e1cfaadc84cea0a1d43f,
'hei': 0xd5e2a2c9141206704cdace7df654ca7e,
'iei': 0xf67e9538db2a984a43125a9af69a5967,
'jei': 0xe30b0d86488fd53fbbf23eb3e4293b8f,
'kei': 0xfb6ab4c12a9baff730339f00f278bdac,
'lei': 0x688f227b9cad4edeed15f067e04d3764,
'mei': 0x57e80e4a04d61577efc6bcdee853c2c1,
'nei': 0xb630e326b8c2360e887357c2f1cfa2cd,
'oei': 0xc29521c80855a662c9b9b46a783b1a7e,
'pei': 0x9c1dd1900282a69f96c6fb420e957e08,
'qei': 0xca28eec4aa3feb4adc4698d451adec13,
'rei': 0xbea0184aac2ef216c834b3e24a88c38e,
'sei': 0xe8d275af37ce2c474cbd3e65c0bce5c8,
'tei': 0x527bba74c1cb7371e4c7d6405ebd4743,
'uei': 0xfb81f23207217963cd4133ff5fd61506,
'vei': 0x8527eff385be84416fa9409d7239030f,
'wei': 0xd69d8949e163fd84c2a5da50138df308,
'xei': 0xfa9ac3724dd7bb3371bccde4ac04a6c7,
'yei': 0xa7a01329fe60bde7941d8183d258c4bd,
'zei': 0xbd8a96276fe6e20817d3d23d0da414a1,
'afi': 0xfc2f6178abeec3a91654adc3f22419fd,
'bfi': 0x68fea9ba86f5f08d6c1804eaf3e0a5f2,
'cfi': 0xbfd036b1f54c2289831ba9dcc1f595a2,
'dfi': 0xeb835e71488a69d2230d8fe83e86eac5,
'efi': 0xa9585ee26b99b02fd19465902f2fd4d5,
'ffi': 0x4c77a7e0d203b25d1a7b2a27199fdb13,
'gfi': 0x9a3811dec624aaa1f69f52b85320c245,
'hfi': 0xfbf3cfce53dda11a9a4e46dfd6fc9034,
'ifi': 0x06cce67e6d164c6a1bbc38bad20e8edf,
'jfi': 0x7279faef031fe4e5982443d06cc640b2,
'kfi': 0xa3eb2d643047cbdfc0d5861a928d2953,
'lfi': 0x854944cf18b9103d33d229637d669816,
'mfi': 0xd3ed670ec4e8c1c9d9d8bfd0e3c50fe2,
'nfi': 0x84fb2c33ace7523546cbaab5d8570891,
'ofi': 0xd15249b2b3df63eaefab87103f869c08,
'pfi': 0x57c87d8ec1d3ea3442e6477d3c0e9fde,
'qfi': 0x086c8bd9d78dfac316facbe735f45910,
'rfi': 0xb7d877f5d22be19035dcb2f052ccfc30,
'sfi': 0xd2c29450310766ffc33172824dc45c7a,
'tfi': 0xb1af66c7d969a72cf26408b2e7033a43,
'ufi': 0x726da51afacac648bbbc74138ac48175,
'vfi': 0x0611f39a8cb3c1f359169e316ba1f4b0,
'wfi': 0xc91ca906b7d4cc2b051f883cc6ca1407,
'xfi': 0xb0d58babf37dcd22ce1787f10d317fa5,
'yfi': 0x6d6693d2af722c9b38bb629c464627eb,
'zfi': 0x3d0eb9415c9307ed6040418d13095257,
'agi': 0x370790335a0538fc3fc5a7b522b050de,
'bgi': 0xb9334c21c0fcb8f0865c7c9f62e2f4fb,
'cgi': 0x8ddeb2027a8c79b3c46510a6dcef9dda,
'dgi': 0x278a155ebdef8d41b41c9ebe911757bd,
'egi': 0x6d466a5519f78c700659bdd212001a5d,
'fgi': 0x5a646078836733591730df9e04ed296a,
'ggi': 0xc99fb474deb3b6f1d1075926223f7aaf,
'hgi': 0x8ea98a8a427ebac1e93e3f98b6e1a1f3,
'igi': 0xd256a4fae6e77af9a94682a99d4918e5,
'jgi': 0x646d6308dcab212ce84df708192d5003,
'kgi': 0x99e0ae7b365d4a5a1ad494286ee864aa,
'lgi': 0x73cca6f5e215bf207d86f431d799907b,
'mgi': 0x2d8ba2223ae0795eb813db8aa28bf0aa,
'ngi': 0xa1e5b407f811c091c02c25159cd1d4be,
'ogi': 0x4df97a2e1a8878558274b678f98b61cc,
'pgi': 0x31e4f22b0420d188eb4ce1951b45ede2,
'qgi': 0x2a514c93aad5eb3795d300f3949b3387,
'rgi': 0x43c32aefeb5480d98e5df2eead2a4297,
'sgi': 0xf3c4bf7c75c65fbd429f66c8761e6695,
'tgi': 0x0c7e7970ea479fdfaef2da470a298750,
'ugi': 0xf63387536d59c60011ea173793118500,
'vgi': 0x1804fe4a79c65ca97b996241a2ad2680,
'wgi': 0xfcec346bcfbebe663c93536763c940e8,
'xgi': 0x0fe565b4b765e6dc85f8c46f3e505f3b,
'ygi': 0x68dabf6b0bd83d3692a5acbaa9e2f468,
'zgi': 0x424a4d04e9a89c4622212dd036f6c809,
'ahi': 0xeacd5083e881f05b3ae1290c858d8760,
'bhi': 0x8a0c305b83f13c05814794a42e5888c9,
'chi': 0x1e6086b705c7161eeb93a8b249a5ca7c,
'dhi': 0x3bfb36834fe84c43fcfdbebc29b02c4f,
'ehi': 0x5648cb306de33dda04236953958bd8b4,
'fhi': 0x1a739234a387d1f54c945b5c270fc054,
'ghi': 0x826bbc5d0522f5f20a1da4b60fa8c871,
'hhi': 0xd369667754340be7efd18d3fa051da36,
'ihi': 0x48673a0322819eb31f5b57ecbc774bb8,
'jhi': 0xd64f731b01b7b8aa1b2243b1cfd4e368,
'khi': 0xc7c1872c979d3d1b8a9452e3cfd4ec36,
'lhi': 0xb019dd3ac6be3216c275d1e724da6b5f,
'mhi': 0xcb207af06f6b573ba38fd954cfe6ce87,
'nhi': 0x655432a4e858fd392bddf2a2f92c8296,
'ohi': 0x46ea0316f5eb4f3e973dd92e9422578e,
'phi': 0xcb7a24bb7528f934b841b34c3a73e0c7,
'qhi': 0xd3d245f5c06d77702ae4c81a2eb17808,
'rhi': 0x607209d12d384e4f54aa84a12efda4c2,
'shi': 0xcefd573c75ef3e8eee5ae1ffe4243497,
'thi': 0xd9aebf7d5a83db9709fe0af7b92ab73a,
'uhi': 0x4bbae42e7d9203ac79cf69fb8bb2bf90,
'vhi': 0xd637f0d67adf2d75aacba3e6a7966e07,
'whi': 0x8c4529921bbd8e113f4457181645494d,
'xhi': 0xa200ad5863378343710e6fa8999e0bb8,
'yhi': 0x40dade8f80367b6869e21350e5c6b6bd,
'zhi': 0x92710773c4d8cec418e3ee4981dc2753,
'aii': 0x0ed2385b5c7d7cc14b6dc51bfb3902b5,
'bii': 0xd2e4c4af45a3ad7091bc632c7af8dea6,
'cii': 0x97c4adc1ccaee98a4b153075a9166ae0,
'dii': 0x0469a3b6bf2f69a21a00f5924f65758e,
'eii': 0x8bebac8338a20df2b1e2243db142b540,
'fii': 0xdb81a32bd21a7c1005670908f60cc39a,
'gii': 0x2212822ab402af96c5d927c5df66be45,
'hii': 0xf057f5ccb87a310534b9dda3a69a72c4,
'iii': 0x36347412c7d30ae6fde3742bbc4f21b9,
'jii': 0x509b645430caa53aa379704df20088e8,
'kii': 0xc56580590479f830b0c6e2cd6aa1177f,
'lii': 0x31e3d43b8a5dc97bd435dd601fa2359b,
'mii': 0x65474d2d6b43e365751d03be09c0227d,
'nii': 0x428c5fcf495396df04a459e317b70ca2,
'oii': 0xbdb04c9f6f0178b951827410d98a52e7,
'pii': 0x1eae74adfc325f04a8506be8bd11c67a,
'qii': 0x215b45f639b12e96689d2cb75598d00b,
'rii': 0x926a031279be3eaddb1fa012f717e715,
'sii': 0x52257da79a438a3e730368a3c28df84c,
'tii': 0xa135eddb62f5823920f52da6ba80411c,
'uii': 0x3ff01b4f2990f1fa7f31c82fd7c6c9d6,
'vii': 0x44b7ed6c8297b6f269c8b872b0fd6266,
'wii': 0x76b9a6e56f1b628a18e6f51e38c094c8,
'xii': 0x68725afb52c6e8074890a94de7b0c2a0,
'yii': 0xb47ea11a562716ef82316c867286b8a5,
'zii': 0x88604d7381a9e9bdabdf6b53afa38fe4,
'aji': 0x8d045450ae16dc81213a75b725ee2760,
'bji': 0x87f06474031e9010461652275dc0fd8c,
'cji': 0x772773af486ddd962f7b63f008d9ddd6,
'dji': 0x4b1828e1b0684ad5dc85b1ee448f223d,
'eji': 0xa2623f94abd514b46fc15a5624ee404c,
'fji': 0x68bfa6fc93a64bf4e48b6daab883dfb4,
'gji': 0xcbe4183fa2f9ce9ac86b0843a8f2e393,
'hji': 0x76e76cf79d79649f3fb0f6cf004bd464,
'iji': 0xdeab290633293b17ed8eed3a281a7a1b,
'jji': 0x5789e453ea13dff2d5fa108e4bb7ae56,
'kji': 0xc536af635810bd807d14df814ac1b355,
'lji': 0x4c6b768384e79d1b78c10a502a35d63a,
'mji': 0x839d2f69c685976fdfc9a403d0aa9921,
'nji': 0x36919cb4d4f288601896d6b08b5f6d13,
'oji': 0x54b2aa1988fc565e35c6c8155a28d2ec,
'pji': 0x6fdb46ff4c7f85fed9875b4ed0c39a90,
'qji': 0xbff6681a8dda7e0c9e98e0ea117ea17a,
'rji': 0x7ef350b4daffbbe7551c27f2b88e1cb9,
'sji': 0xa884e048779461dd5929b9dfe8687c4b,
'tji': 0x80a653d2d445e345e024b22794c33b7a,
'uji': 0x33efc1b29076ff76df1fbe545de4c2b1,
'vji': 0xe7736fb59ae62ed9002c229ea0b5ef6f,
'wji': 0xd28b5d9e941eec64fbb832e61636212b,
'xji': 0x0ef4bb20540e2a6902194c78e94f71f6,
'yji': 0x7e001b1858f15ee045c401dc8ae5d23e,
'zji': 0x4788dfb6d4a5d478114acc78917961dc,
'aki': 0x6fd394b2f8f7e2438ca7f0a87a6db994,
'bki': 0xcb13c65789b9cab5398732315cecf957,
'cki': 0x67cc38c7f5aacb333ca0040260174d3c,
'dki': 0x8f9c31c09f8ec82c0072ba53935cbeff,
'eki': 0xdaed6ec547a88a5780ace966202b206e,
'fki': 0x5206f46de5813e12a2c368b6c09c524d,
'gki': 0x56a3e5e0e77c1260b2caeb665e95e741,
'hki': 0x480c73d2133cf3c66e53a5bdbbcf20e0,
'iki': 0x71fd94a0d995244544c153158bbbefc5,
'jki': 0x0ab5a9a7da6137c7f97f6fd74c474676,
'kki': 0xe5ae4beb453669f29d4ac3179b935aac,
'lki': 0x90a5fecad10e82f560935ff4a5ad7893,
'mki': 0x13ed8b47b3d9f8be2785e65b3ce13a70,
'nki': 0x470eae148908f52d38e0996cdd9d7f1f,
'oki': 0xe210b2d4726eb89e951f1952be84c02f,
'pki': 0xc1f201102badb1b4d3d863f525969f7e,
'qki': 0xa4a6fd1a770efe98692277ef12c9f546,
'rki': 0xfb754b679c7812f563330a9b712f8e86,
'ski': 0x51d1f7159e9bd983723dfb23cb32e354,
'tki': 0x5922024b8168a84623a86334969f69fa,
'uki': 0xc6de4541b343e324d6a57ecbc2e78503,
'vki': 0xaddd2d272f70fa4407f1fffeb66be505,
'wki': 0x5be8e1f6966971ea09adc81c5d22f5ed,
'xki': 0x7a207998dbbe73226818b4d665ef75d1,
'yki': 0x0489cbcc884351a85f3a0e52844d46c7,
'zki': 0x595791ff2fbed52698237025c41a3ca1,
'ali': 0x86318e52f5ed4801abe1d13d509443de,
'bli': 0x6fbf0c1f4974d0357a20d72ad731af71,
'cli': 0x0898b22730d57afcd394d8e4889ece4a,
'dli': 0x68c06de5bf026747e9ecdca3e054ba06,
'eli': 0x0a57258559de00695ffb0f1d46bba388,
'fli': 0xb6928a3facf4a7bd929ae9cb52e4d158,
'gli': 0xd76386550137da9d8c9bbc0f5d03c9a7,
'hli': 0xe9b37b6ed982700224c9e03edd3b8484,
'ili': 0x358c4c7750d79fd100568536318758ed,
'jli': 0xb2a3176843049962c267cb02436c36d7,
'kli': 0x4adf5a2cf985e84386f32c101252b3d0,
'lli': 0xd7a4bca96ab4988f13c4aa87d62672ed,
'mli': 0xa77cbc2e7eb2157c4c0fba6159a941ec,
'nli': 0x8609b66fc2c66a9017273e170bc3a8e1,
'oli': 0x3db9007f5acd91bf68373c0128dc0724,
'pli': 0x185b2560a6d92d6db632b2185a1242dc,
'qli': 0x8e13988fea68664503bd341418e6d7ea,
'rli': 0x6b0c6b193115532bc5739ba95eed739c,
'sli': 0x60415f6a67d52289aa2524df6f33a1c2,
'tli': 0x30bed0becdea60a2f285dca9d9f7fba4,
'uli': 0xdd55cec2ce59aca4e6647dcfbc90dc27,
'vli': 0x5222bcfa22f0f199bd9e4a3a3f26a4f8,
'wli': 0xef0b707579f27806fa4073c55e03052e,
'xli': 0xfe7297964fffc8b710203e14504e40f8,
'yli': 0x9dea42037ff08782f1269f1bacdb1af2,
'zli': 0x505a0c74cafd9bd13e80361398f49644,
'ami': 0x6c5b7de29192b42ed9cc6c7f635c92e0,
'bmi': 0x810dc6911c825b55eff684098f2beb19,
'cmi': 0xb75a7e51a4e24b030608e776a651505e,
'dmi': 0x5ae0edd7eb973e6932ca4f4896b15924,
'emi': 0x12b41c761b41698d39ef68fdd9429578,
'fmi': 0x5770f6fb915ec678183aec2c35154d46,
'gmi': 0x9ac9b51c907c1a4fea4baed7d215cfc6,
'hmi': 0x7a58f39644b5ec3890274b3f436f38a2,
'imi': 0xf34e082ec6bdde3cd47e7a59a0e5d901,
'jmi': 0x60f561a730a2efa815c57c97b96b609f,
'kmi': 0x82a26dc06f6ad9773bf914eacb02a434,
'lmi': 0x8d4ec25413262f280d2ba0fcbb77385f,
'mmi': 0x4b74d3dbdc2722a2722043105744934c,
'nmi': 0xbcc0f53fbd0c273d18435941509f4eda,
'omi': 0xa1e74e713ea360c66f5d04aa9e0df20d,
'pmi': 0x995f0d99ad310ee6ba78be2ce2e84e3f,
'qmi': 0xedba04a57d96bd03e1052f40942e4f2c,
'rmi': 0x610679d69336ef2251ed058a42c8b420,
'smi': 0xe260daddf3cf6956a87fb55188d6cb8a,
'tmi': 0x1dac77895e8f56fa1a71e7c43ef09d87,
'umi': 0xe84f942d7f93ddc14d24b930d87e3da7,
'vmi': 0x8a8e3def8ea81fcae615714c6bc48824,
'wmi': 0x2360dc7a3b0658bc85dd8e53a235b80f,
'xmi': 0x1f490aabf0793c27d57533eea199c8e6,
'ymi': 0x64b00b8da30279700cb026b3d87dc6ac,
'zmi': 0x0648fbd483dfc486f94b8afa758092bb,
'ani': 0x29d1e2357d7c14db51e885053a58ec67,
'bni': 0x5c199234210e910129d3913b58bdc9d8,
'cni': 0x299c37fb949ef6dacbe22b3a1344d1cb,
'dni': 0xd56f5e97524d5d1ad77ec197ae11dad0,
'eni': 0xe3d96c321f2a71cb81cd7d5f05f1a8d7,
'fni': 0x9a7026a8d7a891d1e209c6b346b57c25,
'gni': 0x8c7e99f2801e0c1362a7bccd5a9d9801,
'hni': 0x300dfaa09d3079dbf9af803a6ae42209,
'ini': 0x4d90362d661461e558408e982aaa49d3,
'jni': 0x9e50b36bb2497496c6398461a2082fcc,
'kni': 0x5842a8c8d94c99e8a8289e86d572ba9a,
'lni': 0x4aa3023bea2ef7a69405fa4886ef39ff,
'mni': 0x98f3021fc3f37e5d9e57a11c2a077c2e,
'nni': 0x37388f3c56188098785ddf70f5a8ab5a,
'oni': 0xcb7f4a5e75e6f7340971c99ea4587999,
'pni': 0xb92623f78d1b691a8f3ecd2ac176ceb4,
'qni': 0xb35a1807bebcf7c2ee812f5f999ca7a3,
'rni': 0x79ffa063479d037ebbfc0f27cdb795c1,
'sni': 0xf70fa2f68bb5022ed9b4cb3a951c8c7b,
'tni': 0xec151da786881e2fc8cd5f735bfab7fd,
'uni': 0xe52805d8344b67b9b3554d45f1c8958f,
'vni': 0x4765b915acd619bf7c183bb72a5ab1f5,
'wni': 0x514ea25d34f1dbfbfe55098d147eb74c,
'xni': 0x796830d94a392819345e8295b3cef08c,
'yni': 0xcb0112a76fb0e02d345b9bd71c72ab9a,
'zni': 0x30a126172f1dd4b797fe97f1edd20efb,
'aoi': 0xe3a76d600fec8fcd0095a48ca3c621e9,
'boi': 0x86a270a74dd36082d129dbc67da00c36,
'coi': 0x49f5087e716922083aeaa0ad7212a54d,
'doi': 0x44cc026eca4028b6bbec35aabc3457ec,
'eoi': 0x31e09536105c77ddc46d6336e53045cb,
'foi': 0x28e85583879c541fb15976565985e749,
'goi': 0x3fb95811188990726c43a431555f0e71,
'hoi': 0x4216455ceebbc3038bd0550c85b6a3bf,
'ioi': 0xa3ff9e07f13dbdeef40534b3d5cc8b39,
'joi': 0x21948c9d95905edba35e1fc3cbd82096,
'koi': 0xce26d78fb3de1e927f115c562d91885a,
'loi': 0x84ab36b2995bb3949db34038a2b24c64,
'moi': 0x8f8ad28dd6debff410e630ae13436709,
'noi': 0x5585e79028e8d00e7a26a68b168739bd,
'ooi': 0xa68f90e39b72bd6e5b792e6d46977eb2,
'poi': 0xd6e1c05c8a81c2ae74c7aedea5ec92c1,
'qoi': 0x5f6f0859a75c19352a287fe3b483ede5,
'roi': 0x4eb2f856e8c3c20f2a0bd9cd45197918,
'soi': 0xae4173ada576e1a595e1ef731aae977f,
'toi': 0x501446ac98afd1e291c2498bb817bbd8,
'uoi': 0xb58d770439d8da70ba9f4f81a965fe07,
'voi': 0x0106085c83556205a5aef641b6eb76a5,
'woi': 0x3d8cf6bf0f86c00fb2031bdef989bf91,
'xoi': 0x7bd00fa2b39356af0b49cee80736324a,
'yoi': 0xbc52a12b76949c90c464336a264cc5da,
'zoi': 0x8092f5690d9caaf3e1ded27491a41d7b,
'api': 0x8a5da52ed126447d359e70c05721a8aa,
'bpi': 0xb06333dc1f08be9c0e8bda010f25ad53,
'cpi': 0xfde6a520e081d136993a48ec29a302c9,
'dpi': 0xa70da940ce76c1217f03376a3ac725fc,
'epi': 0xd0f92a90d5500f1d5c4136966c5c7e63,
'fpi': 0xd366a4f75077f73b18a5d4ebdec30027,
'gpi': 0x00c9749255b322f729aaf5bf63b508cc,
'hpi': 0x7e5bc5a17a00a35f522141db08c554bf,
'ipi': 0x63b695caf8a2838abde06237ee8e3d06,
'jpi': 0x506f16ba9081bc2cf6086ddac3439f41,
'kpi': 0x56e0292e9c1c9e7a704461bb57c5dfaa,
'lpi': 0xbf14ae081392429897f85563fe9272e3,
'mpi': 0x37dc018a7f069ec130d0be0a75c32ffb,
'npi': 0x650832030e204c8edbb7399c7d5075b1,
'opi': 0x6f269e243ba53ab9d4f7ca5b2d7e02d6,
'ppi': 0x405fe60a5db14040114c0983e14cd5e2,
'qpi': 0x9e1e50bf20d8743de187108e52143ffd,
'rpi': 0x2cfe1a659338e52fcd4fc00040ef09a9,
'spi': 0x0b0760e072413088ea8460c3b4f43dbe,
'tpi': 0x215c50ca53808dab7cc59af32814024f,
'upi': 0x98db0b07bba92340ab23fdde110814c1,
'vpi': 0x8187c4eeab08731784630834f28b8f0f,
'wpi': 0x16413692829f83539f2066a07a911a6f,
'xpi': 0x8c7b7d0954cc87ad83e71fb29949882e,
'ypi': 0xd6ee472be4f5cff4613a9400f132a5a6,
'zpi': 0xd93675602a6b89c8359a6e835d9841d9,
'aqi': 0x78b21a804f24074f8103e571472556be,
'bqi': 0x4aebacafa10f434f1aadb4afe832a7f6,
'cqi': 0x8acb31d62c5055f51d2082a628dc284d,
'dqi': 0xa6fe8538fcb66962f6ecc9af23e0fd68,
'eqi': 0x5f13ff067d08c5bedcb9b4a32d78ffff,
'fqi': 0x0daf5a56c436f9a4e445ea84f148d54a,
'gqi': 0x15b61ad6c801cc688c1aebfa6d892333,
'hqi': 0x35644d2ab98193fae5fd83dd42687230,
'iqi': 0x53344e42203fa86601c3ac533c5a4117,
'jqi': 0x37fc6152ee3cc5881a825d9e7e1f9781,
'kqi': 0xe05376cb1016111d3167b81a9180cd8c,
'lqi': 0xa76453d1345d057916d9e1b6e7446b78,
'mqi': 0xaffa5eef38ab9a51cd5b49723f0b884c,
'nqi': 0x05c50f9b4a00af87d4bd68007abcc5fd,
'oqi': 0x33828a73c5113797f09401b501e62649,
'pqi': 0x187278a2d06e067ab0c9febc05a11949,
'qqi': 0x5e79a965f7db2b2a438bb9e6461d6580,
'rqi': 0xb5172d30ed329c32d9e8ef2b70fb9bf7,
'sqi': 0x2368e1a336fc6a04a7632a32645202a0,
'tqi': 0x2c3d982659aca0e60303bdb4f6d5d3b3,
'uqi': 0x9020798dc4f50096e27357153309ef99,
'vqi': 0xa8f9c8aca1f662fac3c25c01805d036b,
'wqi': 0xe54f758cd1c47b2c91379ece31ca5b59,
'xqi': 0xeaad7e83c8617a32523c3fda6ba2e59e,
'yqi': 0x5dddf3d443676b05da6a681c7e0304b2,
'zqi': 0x1970c541ecf147ae456610087b4e8585,
'ari': 0xfc292bd7df071858c2d0f955545673c1,
'bri': 0x11c5bbeea416ba0f55390e306769394d,
'cri': 0x50ee4335a5d9f930e185ae47538fecf7,
'dri': 0x7a2ccce9642fe8539673002dd6660ba2,
'eri': 0x1f5198faff59782cd71dba9588e45697,
'fri': 0xdfc47c8ef18b4689b982979d05cf4cc6,
'gri': 0xbf391b589afbf368925d0f3ab583a5a1,
'hri': 0x074f6c0c30332b90692d65099cb8469e,
'iri': 0x5753d2a2da40d04ad7f3cc7a024b6e90,
'jri': 0x00db5a9c5565e7e59b2ba3598251bdc1,
'kri': 0xee2a7d26d272d8a937a596fdd767f00e,
'lri': 0x4daa919390f2aa4d44a54c43d786474b,
'mri': 0x9249e082ed182bba345912915b180e92,
'nri': 0xf5484d1f47fba004bd87dbbc851bca98,
'ori': 0x16b1c83de8f9518e673838b2d6ea75dc,
'pri': 0xe060bb629c10e1b143614cc1e9ccdc67,
'qri': 0xc45b4ffd6b3fc00bd0ac974104cb40f6,
'rri': 0x2da4ab1728099746affa3959b9d0a2a3,
'sri': 0xd1565ebd8247bbb01472f80e24ad29b6,
'tri': 0xd2cfe69af2d64330670e08efb2c86df7,
'uri': 0x9305b73d359bd06734fee0b3638079e1,
'vri': 0xb8995f6076525ebcd7fe4aaf4c78bad6,
'wri': 0x04127a618e988feecacc1d313c5cba9d,
'xri': 0x8d319a3bc215f4b1bca0457c6b9c2370,
'yri': 0xf38811f30789bedaaa48819b22ef411d,
'zri': 0xba6fec44da12d464c9fd3d305a20629a,
'asi': 0x367f7790e1588cc53aed634c1e9df2d7,
'bsi': 0xda878e5c536f8a3892785d0fcdf3015d,
'csi': 0xa2a20311145545e816805093629f27c6,
'dsi': 0xf1a63a8aa5b5a460477e09e2899e5f55,
'esi': 0x36dbc2a12d8162070240f8d586b6726d,
'fsi': 0xe65b5feab6b8954cf18097183f51b20d,
'gsi': 0xe840a881345d74cef50e2599810a449a,
'hsi': 0x051c27178adea66097c5d78ffaa2d9bf,
'isi': 0xa7a1b335247554be6612f583e32b64cb,
'jsi': 0x22ddf10c7333a08a91f19f94d88520c8,
'ksi': 0xa14c7a85a22d7c8d2d0e35fdb2508059,
'lsi': 0xcabadeb0199406d94efe0489e7092cd7,
'msi': 0x3e4c832809293017aadea2166cd11554,
'nsi': 0xf2f8ad94aba8986f69bb005dfd216fce,
'osi': 0x6428ba22519e37754d6fe98291569fee,
'psi': 0x6115baa419ebbdc15cb267c7bec45d26,
'qsi': 0x624658668274d6c886b74d699becc0a0,
'rsi': 0xa893136449f724af7726ba78208a1c86,
'ssi': 0x3a6edf55dd5b064dc4334ef316e273b8,
'tsi': 0xd895ed90bab5a86173ead3a829e26d10,
'usi': 0x6cead52b796e528b966c2baf719f24f6,
'vsi': 0xc8e7332db70a7246df8220907e90cb44,
'wsi': 0xdc05c7c02cbcca26192bc18bc9f2053c,
'xsi': 0xd9ee1051fec2011c17190e1b8292d687,
'ysi': 0x753d538f8306078b4f6a11fe2fadcee4,
'zsi': 0x43b04cd93c14a0c5e1b222cd5823fa4d,
'ati': 0x6a5bacf3c605dd478c9c119f54d4b30b,
'bti': 0x1aa3205c253c46d8ffb5c312c6cbf891,
'cti': 0xd7f8be03349606bf44d89709b2f1d140,
'dti': 0x294068dab259d028521b171d91d07b3d,
'eti': 0x091443dd922ce3d4eac16a291d787fb0,
'fti': 0x8d80b28fa63fc17e8a45d9a9d0775736,
'gti': 0x72a81f485273eed7147e3e2e474b7341,
'hti': 0xb285ea790b502bc6512beca38cf2547a,
'iti': 0x8ca6342915ac81dd2d3eec49e2098db9,
'jti': 0x2bf94e793158c163af4594fb487b3ec0,
'kti': 0x3048bc99cc83ae6e5eccd4de296cc626,
'lti': 0xa015603ce31fb480dc7366df5987fb0b,
'mti': 0x19a04fafb5f9dd7e606920c3adacf8f0,
'nti': 0xe9c40fa9b52f773cab9ca9a23ad78bc8,
'oti': 0xebce393760a7af1863cbfbbeb9f21c5e,
'pti': 0x13ba46391ffb465d5b857d1ba728a1e6,
'qti': 0xf673131f015b9f4206967223e0ff5072,
'rti': 0x620dc68bbfc5d7654e44817a8d3b2cdf,
'sti': 0xb7aef64988587ef8b7f78424efcff23c,
'tti': 0x6a337523b132fae4b608ee0ce4964e88,
'uti': 0x4d2606237ea94965b5405c99863da39a,
'vti': 0x94991ab32e5b6cac0895e970c861fd4c,
'wti': 0x9452ef5be78f55052ce3ef9f6605ede4,
'xti': 0x87828cf78f7bb46b83d51ade159e7937,
'yti': 0x7f0b5af23c02bc3ee7ef7836c9cc8b68,
'zti': 0xa1226530c017fee8ef7d2cbb534bbc22,
'aui': 0x798450791a62b16e59bf714c34bafe63,
'bui': 0xf8cdbb97841cea138715d7636d1eb76a,
'cui': 0x0b7ce76033a6756b91f5bfb12602e20b,
'dui': 0x972890943b6cbe30175f19ee122669db,
'eui': 0x85be19eb72bb9e44d59e3c0a53e1cfaa,
'fui': 0x2460b253fb566067962c63c284d2ad74,
'gui': 0xc00425797de3c98e7b32e814b54a6ec5,
'hui': 0x917a34072663f9c8beea3b45e8f129c5,
'iui': 0x30822eb8a7af334741f6867927a8c38f,
'jui': 0xb5de674a38a691fb24d04233e8aa498b,
'kui': 0xc5bc101491b42eadfdf6f2c9edca5f23,
'lui': 0x55681f25f2b5ce8a670140d1dff04da5,
'mui': 0xb8a7902b075ede881162dab7fe71d046,
'nui': 0x07660e17b6eda6baff042606cb473aee,
'oui': 0xb2a5abfeef9e36964281a31e17b57c97,
'pui': 0x92b7b667bfeb25df7b5b00661b049d86,
'qui': 0x9d24420082d450cb81caf59e2b173279,
'rui': 0x0eb46665addf43389ae950050f787a45,
'sui': 0x58b829075beb22dd23c93125930a8d53,
'tui': 0xbb07434efc81b1abeeda712ca7f454a6,
'uui': 0x4f0db45be48dddb73d9f104c46abb760,
'vui': 0xc4f531c3970ac07ba1f678764af59cd0,
'wui': 0x5704eba81df8cbebe6ee74ed46cc3a08,
'xui': 0x7292b5df61305ae0084b86391da32cee,
'yui': 0x4eff0335928a2d0e92f38ea9bb56d72b,
'zui': 0x65047b5096d20463ba9c36796cbf7775,
'avi': 0x3fca379b3f0e322b7b7967bfcfb948ad,
'bvi': 0x93c2010ef8993a5d03803fa354931ebc,
'cvi': 0x1e17b811d0ba50cb79df7eff103f8917,
'dvi': 0xdac4eef82d8ac0acdf17f321d36b9f11,
'evi': 0x689635ad79c4a248aa87d21ad4f28422,
'fvi': 0x68fb3ec02b57019b00402799c4dd9114,
'gvi': 0x49c238ec50b39b0d15220e9cab3b7e31,
'hvi': 0xe37f3087dc1887f4d45d80d96e2fdbc5,
'ivi': 0xb52053511c4b2af9a68a0ece64d99556,
'jvi': 0x2a10d039889ad093ed13cd0cf6c94e26,
'kvi': 0x81b5529dce980275b7804a6dda2c41da,
'lvi': 0xe5f5851ac34e2edc21f4027beeab8223,
'mvi': 0x123fe882c7de5e6bdddfd289dbd2c1be,
'nvi': 0xdab23b204e6a399cad07894e430e780f,
'ovi': 0x07bc1df8c6cc6e78c6d0b970db74b5b4,
'pvi': 0x96e0c2de8af6f3574836252e72f42129,
'qvi': 0xf1e1c08eeb9ae7c7897235febfb54f5d,
'rvi': 0xe133c09b4d141a7a3055962f289e3c9b,
'svi': 0xb4d269fab8c35b49a7d29ee4a62823da,
'tvi': 0x1ffe276f95d77bc6c896525718f140d6,
'uvi': 0xeab4b6f7b30cf211a522c6f5fa1f0c93,
'vvi': 0x5b5c1a2a15c0c1e40a5ebde6b29db07a,
'wvi': 0x6271bc655838632ae38dad0dd6eb5b85,
'xvi': 0xb694306290c543aee19aaa25f7092118,
'yvi': 0x8467eac8d2297d43981fb70930ab7b36,
'zvi': 0x5f5ad4db77744a8b8181423025c52d10,
'awi': 0x44583533ffc66cfb93d75578a31cbd18,
'bwi': 0x8cbec0d6848c55e952a470b4ef5ed1d0,
'cwi': 0x46eef08c9d45e7476d36ba36742c21de,
'dwi': 0x7aa2602c588c05a93baf10128861aeb9,
'ewi': 0xa281f4a83f732f9b8243e1af18256679,
'fwi': 0xe9632a304bc1979cfb8f0f7df589e880,
'gwi': 0x496d6d75c3e2992257243073d4f6fcff,
'hwi': 0x6c02cba5240fcec1a756d6d60557a197,
'iwi': 0xd1724a059f200de59aa92cee692b65f6,
'jwi': 0x925fa5ff165ca73644f34734082d7689,
'kwi': 0x6a046a454ba96e18cec94dfc8c5d7c72,
'lwi': 0xf5e64449422b21dcf2bb7672e984e003,
'mwi': 0xc42e49ec8710d9b9d3948dd8a517baa4,
'nwi': 0xd49bb28bea753613dc8ac46800bd7d05,
'owi': 0x55e9a1b1cc2aaf0f5b7417d8098ea4b3,
'pwi': 0x10643c73b0918da6d5d0ee2492dac367,
'qwi': 0x45d9023d4d85e7c584508f63f86b353e,
'rwi': 0xef6645a6226bda56a1e223506ab9f0f5,
'swi': 0xb2bd2574138949516582144c10fa1975,
'twi': 0x82d8f6dc0ff2d8e2729a0e2d9f42de73,
'uwi': 0x6bf84e9e6fd6f545bfd293c7aed0b2c0,
'vwi': 0xd3a43a0a820eeae9f52dc495012a4130,
'wwi': 0x11608c54592648c932e33663970cbc65,
'xwi': 0x946b363debeb6bd12c5cd07909ef28b6,
'ywi': 0x99a92007c43cff7e1e2025a8c76997c3,
'zwi': 0x1cbd97a7145e2f2e13b4295957880d1c,
'axi': 0x47371091a69eef3b0acdd28479bffda6,
'bxi': 0x32593a4a760465b4cb2492df82e116e6,
'cxi': 0xcc0624956739c57d75c75ab775b8d11c,
'dxi': 0xe5a482cc060d06b60ca16fd7d0d9e564,
'exi': 0x41d839018fb193d5d4c3845539450c89,
'fxi': 0x9ee5e87b95cae8e68849f86ca5eac5f8,
'gxi': 0x598c2ec7e07070141bb4b17c5d5cc7a4,
'hxi': 0x4ddd9717d85b38bcc09d5b323b478fda,
'ixi': 0xea39ff7c7647a69d28f56e86ff287860,
'jxi': 0xa64f2dd6f32e7066226f7d9c1234f019,
'kxi': 0x555d2f268c25d2c8487e54ca7b46411c,
'lxi': 0x83fa6201020f6c80cc6e5dc01495bfcb,
'mxi': 0x9be2d8952e3f3f847a25e3570b0ef5d4,
'nxi': 0x86345bb3f7218013e63a00d5dfcc9926,
'oxi': 0xfe773767bb0ee4f4362bfa5b36e29dd3,
'pxi': 0x98781b71a9a42d8581e4267a92f24c56,
'qxi': 0xb64691da2466d7434e34913203d05fac,
'rxi': 0xe1e4b252cbd660b4e109709a96734eff,
'sxi': 0x3b5f227e77f54404747405d68a072082,
'txi': 0x00fe2b2cbdbb28f79c287557dc0dcbb5,
'uxi': 0x292d4b7c565f5aa9b4deddf14d411327,
'vxi': 0xf5547a803f17f21488fa859c3037703b,
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'ifk': 0x37e6348f6d29a8fad081e79c85d3e91a,
'jfk': 0xcf79b13ddf1b3ae7647532b712c1fe5a,
'kfk': 0xb8e59b30ebdc7e960f57b9e317fde9d9,
'lfk': 0x83dfab6610e59db36cb50437ead57a93,
'mfk': 0x5123a50b50374d968fed635e1468ff82,
'nfk': 0x52ad520b60ac4be708cda1652d316bdc,
'ofk': 0x3574284442af5d982d93ef9d2a8a7d72,
'pfk': 0x6be0140257412c6d5e3ccb8d7829ced9,
'qfk': 0xe9b8c30e6c8f4239b1250f4021c3f3f1,
'rfk': 0x90ceba79d803bd43275ae4cecfd4da1b,
'sfk': 0xb7204d2a567d36d12b9de4ad83a82537,
'tfk': 0xb03dbcafc98dacb6556ddb39934d19f2,
'ufk': 0x3f13e2ecc351b5087259cc24214bbbab,
'vfk': 0xfbca48f761b232581c8b7ff6f5e3dcd8,
'wfk': 0x432103a51751cff2a591a9abf9499c0f,
'xfk': 0xfc985dc66e98a07c62db0380c67794b3,
'yfk': 0xb3b83ce0bfc351b8c37b788c6aaa777e,
'zfk': 0xffae13cf295058aa987467a6ece802e1,
'agk': 0x97f44ea6920615ae2cc14f9fe5be7356,
'bgk': 0xe1039c5b1b4cd871d8a75a36e7fc789c,
'cgk': 0xe0aba531044c06f9bf70df99519d9636,
'dgk': 0x363384b4a964e429040b3d7861a5fa32,
'egk': 0x7df1d894a17f1b708041648879665085,
'fgk': 0x511b388b42e6cf192691122ef4f577ec,
'ggk': 0xff605ecdfc69ed9bdd38f44615d680be,
'hgk': 0xc76c87d679d165ce9faa778b32af0b67,
'igk': 0x35579959e0d93f316c972949421dcb39,
'jgk': 0x3c13c56ca28071e38eb7dbd493a1e7b6,
'kgk': 0xf0f3ccfd0ff0ec80a907f1cb155c1d90,
'lgk': 0xb53081841f396b4542699fc0251df680,
'mgk': 0x0e14d45c430806127d2a4a85dd419a85,
'ngk': 0xebe6d9a997cfc111cc46b471c22457a7,
'ogk': 0xcb0ced974477b0244cb3f8b9b0310dbf,
'pgk': 0xa2b5aea7fa54ea9b959dc534d39bf4b1,
'qgk': 0x8392df9c4e32be435d5bf739cbc772f1,
'rgk': 0x48b7096e638a2945617cdc419e213492,
'sgk': 0x4a5376cc8ffdc1baad7fe2d066bc1676,
'tgk': 0xec140fa31f634e5074f17bc3637df0cf,
'ugk': 0xb7c9d515ac378b356c7baa71e78a177a,
'vgk': 0x08910e9c0b8f18e2c2a80f0596fdcde1,
'wgk': 0x57f4a25fad16a27ec561c7c1a338b4cd,
'xgk': 0xe1526a83334d7ea9413daf9cce568149,
'ygk': 0x341ec7c555ca79999dd10912c19d4803,
'zgk': 0xa2c48553e8f3fa67c327c54f8eb4a299,
'ahk': 0x3cfec0c4f384f04f13f71d681dee173a,
'bhk': 0xbc62dbe07e6c1686f17a7b09b124ab23,
'chk': 0xacc52e0f504ecc4eabefad797879545a,
'dhk': 0x65246a8209b7cf86e92e13cbbd636459,
'ehk': 0x6bfeb41ac7062a74c4b40331506d3707,
'fhk': 0x591a6bcbf02603c5c4cbe938fda6ef6a,
'ghk': 0x0e3442d022f04f39dc2456eafe27ada2,
'hhk': 0xe8336216c3c14223ed98f9fb266c4942,
'ihk': 0xee0e3ceac89bacfe4a0aaae5addb8f1e,
'jhk': 0xadbd565f1f901d04525f6e0a35593690,
'khk': 0x581e9b5a60c3ca7c53dee0ea58123ed0,
'lhk': 0xd2a59d796a9d8584be05a259e582153b,
'mhk': 0x7dfa631c6a54ffafc880cff2fdf69958,
'nhk': 0x18cce8ec1fb2982a4e11dd6b1b3efa36,
'ohk': 0x30807916c50262f546d532f26b17871b,
'phk': 0x4df869b28407a97ece27a94c45c5aa5a,
'qhk': 0x9bffed1c9cadd251b83f904465143d9d,
'rhk': 0xa0a7b6f0984453998d85203ee7d44b7e,
'shk': 0xf6105793f7e599b6b335d1793f970613,
'thk': 0xb13f19ff0f8eea59ab5f6fe7a329aad2,
'uhk': 0xb45189cd86356fdcaf4f7d4c6e947188,
'vhk': 0x6de4659459c90eb26d7fc4e7f307055f,
'whk': 0x75a56cc662b445f46421d601cf230213,
'xhk': 0xf46dca1bb12e7bfa707c700aef72f7ae,
'yhk': 0xa3dbe1d83b6caf90701237c674e25e20,
'zhk': 0x45f624fcc83b66bb5a65c9e5e9f01c20,
'aik': 0xd180cffdb59a4e062056e1ac719524b6,
'bik': 0xc5647ca61889fed88864b582df8e678b,
'cik': 0x4efe8550d01620902f4a5b2a79985ebc,
'dik': 0xaccb2994934a6a48d542aac75a18412e,
'eik': 0x5491beba65da2b4b84ac947e8d7eb99f,
'fik': 0xa29530332290da89653790cac7b5f9c8,
'gik': 0x3092af5adf8645a2c0a5d8217c780e2c,
'hik': 0x7f3d45f1af3cac4542349df9e25319ff,
'iik': 0x12f3f9d54a417eddf55c4d5d9f8f2c10,
'jik': 0x49d46ecf58aa33473aff0feedc5a086c,
'kik': 0xe8c5e741752e97483a3cc987f95f910e,
'lik': 0xd2c2b83008dce38013577ef83a101a1b,
'mik': 0x0a845f99b6a57591e9314a5950d31882,
'nik': 0xf64609172efea86a5a6fbae12ab86d33,
'oik': 0xafcfbeab0f0ccd1be81a31ef6993764e,
'pik': 0xdd0541a25a51efc0399fb4fefe396d18,
'qik': 0x558b259b8b96e94e9a2a22d933a197b4,
'rik': 0x496b7e6d1d1eb11c52e5e01947b22b96,
'sik': 0x5f6fdee7ecad4a9eb4e75923a814b7eb,
'tik': 0xa54eba296e7f21fabd54923d9dcbd101,
'uik': 0xfafef8704602048b46065112f96629a2,
'vik': 0x946cc85484c4dd01e2f44fbc88afbc93,
'wik': 0x9888f7833559859dd3ea4cbf253e9470,
'xik': 0xcbe2b4d1c0ae31cb6d27973860e828b4,
'yik': 0xe9f5b3011e5171482ac9003bbb85af70,
'zik': 0x8278a81f880d97b5b6855aa875d23a64,
'ajk': 0xfc361d3bba85e9ffed9c8f476f95846b,
'bjk': 0xae47b8966b1c03100f7cd3b02b8059ca,
'cjk': 0x25b7ade9bb42ba5854528c4a9a9a801a,
'djk': 0x78cd9cc384fbde3ac87db38749c2df51,
'ejk': 0x84024fa52e159efdc36fe09b850d39ed,
'fjk': 0x2e56d129c0d2111e998c698c254ccb4b,
'gjk': 0x4a06c136ccd30f57c52d718f8dbe8534,
'hjk': 0x93f903b0425b684cf8bdbee4951883ed,
'ijk': 0xf6f5478b91ae19f3a736a4caaba879d9,
'jjk': 0xc50c0fca46eb62427a47508a73f01b18,
'kjk': 0xb4b27c9132d1fca14442ceae495a224a,
'ljk': 0xc9c113823eec2aeca2c652adbf99a19a,
'mjk': 0xfc667f7e448522c15e9ea3c0d751dbf4,
'njk': 0x9615070a4260b2bed9abfb7786cb2047,
'ojk': 0x3cadc33a661837636d5da050c7154f83,
'pjk': 0x6de5540ff54024303e76e03193661ac2,
'qjk': 0x7a70406c091c1b4221d6c462d01146bc,
'rjk': 0x3075a3efa187ee9f36cd2de217bb33eb,
'sjk': 0x25e5baf14f7abb5996683ac4efe24c6c,
'tjk': 0xc0a64fa8049e917fc53f07b52fecba41,
'ujk': 0x26f5a144a4db2e905abfe1e8401c039d,
'vjk': 0xbbc7db8e7eff8a81b619e86899d8ae19,
'wjk': 0x81598d756b1a0271e52b15575c52ad31,
'xjk': 0xcca7644eeded27575a816c0eb73ef335,
'yjk': 0xed1a6ba3334290b8e1dfb35b556a5ab2,
'zjk': 0x4545da7e6e43d37e0dd3a9ecf5d08139,
'akk': 0xb610295fea526d7c32a5ab82d74a9063,
'bkk': 0x8d8a02fe57e544106d82b411ca6ccb1b,
'ckk': 0x2e48ec45eb9e358d6391702a165770e1,
'dkk': 0x71af0af38531f868e995152be980181e,
'ekk': 0xd9ba664502d31ea7ff771372f853e037,
'fkk': 0x387ed98407444858ccd660657ea5e86c,
'gkk': 0x29a2363fc1ab737aa94e65aec4e0c475,
'hkk': 0x238fb72d706c6c807fbae1ac72d31d31,
'ikk': 0xb5fca0b63c6347b443180b87696bf5be,
'jkk': 0x1f9454fcad113226a65f78c26071235b,
'kkk': 0xcb42e130d1471239a27fca6228094f0e,
'lkk': 0x30b3db3e65c341b847430e0b88823c1b,
'mkk': 0x7eedb15d71f49962208f4bd9200b7c12,
'nkk': 0x394bb1b52bef52c2c10bfe2f37f8f638,
'okk': 0xd4faa918003c0a68906e88e97486b0ad,
'pkk': 0x59164fab9d03cc63b28f409d928022a0,
'qkk': 0x8983735824a972cefb922b6df0084fbf,
'rkk': 0x50b6e59475b23c83401dba5748973c2d,
'skk': 0x75035f902949b9811e26e8a6c829e9e1,
'tkk': 0xd01e2dd24f3d61e140a6be713b8d876e,
'ukk': 0x7ef132c5e032df8e1187edcac9ce4ebc,
'vkk': 0x1bd13e516414a1f3cbdbc77d2b44ad13,
'wkk': 0x529c8203e21df6b9155001a355cc4fd9,
'xkk': 0x7ce60b13720a34d31d141444b1680393,
'ykk': 0xf90ec6f0d663df4d47b5b3c033105a21,
'zkk': 0x40b79e07eea8e80f6ac49de07e531a13,
'alk': 0xe3e371601bc605814fb0dd1431d966a4,
'blk': 0x9a5ac02c8bf2cc77102a0bd686e2c0a2,
'clk': 0x0a3bc2148686f2b562665c3891507e35,
'dlk': 0xb41b156963943db4606cf3f9ec769668,
'elk': 0xe61dcbf52511a408f91fae3f34aefb91,
'flk': 0x5e1a9c6123108cabd8ff5dbb2b425f6f,
'glk': 0x35fcb83d6c94f0b2b5dc6f7062d18771,
'hlk': 0xff03595584bae58c270d5c41f3ad6ee3,
'ilk': 0x7cda3b3d1627ed9fd078d1fc2f1f23fa,
'jlk': 0xa2bf84aad6c3f75024931d9459b2a2d5,
'klk': 0x1e7ace57c5496b36e00720a9d9a4e639,
'llk': 0x9cac7f43e264788f4f6c847430e64270,
'mlk': 0x8d55cc5497f203ce9814caeeb42dbeff,
'nlk': 0x9870eb552efd1429deb74044f127b0c5,
'olk': 0x3ac99b9ceff3a998afb01c0ddc29b8dc,
'plk': 0xb5cc4dec2284d8ea3fb1d1393707a85b,
'qlk': 0xd494a4f26945258cd6710eac7d6f6df8,
'rlk': 0xb9d0d1321b6fa5bb95a472ca1b59ae05,
'slk': 0x33c14a7f68709f556eb5900555f065b5,
'tlk': 0x4693d86cb83fafc74fbed1b08fc95782,
'ulk': 0xa97421de439218df06b7ebfbd0681265,
'vlk': 0x65383858b763d26238ae7e3e8ef72df8,
'wlk': 0xde289aa1b5bdd0ecb8fc2af3fbdc94fc,
'xlk': 0x17737fd5a2a8a6780ca7746946364719,
'ylk': 0xe0c48940ec67ca9ba458d54a48e7a8ec,
'zlk': 0x5463d9b53b9f469ee180fbe607921a9d,
'amk': 0xd54991b071f6d87c1d965ca265a3a479,
'bmk': 0x890ce1120339f2efd3286b3c9e505489,
'cmk': 0x71158c8195fe350103e92cd653758b2d,
'dmk': 0x26fdb6eafd356c3e4ae0303f5f39e431,
'emk': 0x4843c5aca9f9318253aad325886d397a,
'fmk': 0x5e7557302c7b9c5c5a8bfd82866af15d,
'gmk': 0xd2d037e03ca41ebb9cf415989165470f,
'hmk': 0xb16ec666021790f6b0a6b8911ab09ab4,
'imk': 0x94abbbd8a409d514230b0a793b0f4598,
'jmk': 0x97a014d83734a882f281730807a2c0e2,
'kmk': 0xa9fdc3cdd3359d4bd78c8fff020d2860,
'lmk': 0x2705b305fd867472621e686230a59bf3,
'mmk': 0x3eabaa157b2699930e7ef651cf9e396b,
'nmk': 0x17a9f1951dd035ff768b526ec1fc46a1,
'omk': 0x3d750a9d454399877850152fabc1df75,
'pmk': 0x20f1fb47941e57947c089997d204e0e0,
'qmk': 0x3eea0c504d1a3027bf4cd805790886ad,
'rmk': 0xd6f818942ce1f0d073691c0730621d18,
'smk': 0x3e671ea34dcac32e7e9e7c67ee8cfc0b,
'tmk': 0xfab7b3d7eda3f53796f20142620908d6,
'umk': 0x4d6775fe11a8c85d4aba4572f8cc6b9c,
'vmk': 0x433c9c5b91285dbc8bcf5132241c847b,
'wmk': 0x12db7d6dff6f34ba5d6e7cf3d6e8be73,
'xmk': 0x0aabda16cf40f775fe6a2959a9846819,
'ymk': 0x86a4c8b18168906e7579860480aa380f,
'zmk': 0x54c598d239f448be4bbeebb2b1ab24f7,
'ank': 0x8130465b8ce6e291ed5e85e5b5a97259,
'bnk': 0x2888db4080d8218addcb46e7baa888e5,
'cnk': 0xf42e2a125e8e456368010492857db33b,
'dnk': 0x8381363cab61eb967e4bb45a8d44b7e0,
'enk': 0x8fd98c61181e24874532ecfb2807b3ee,
'fnk': 0x1d87a6ee524c27797fed928639d91f8e,
'gnk': 0x70ecd010452224a92bcf6c9503d8c313,
'hnk': 0x08f72ce1fb8233aa0a79e446dab692ed,
'ink': 0x0a16bc32f55683128983f223de242942,
'jnk': 0xba232614f1c9b9ee0e185c7f671cdcb5,
'knk': 0x12eaffdeb4247101d11b028e00334ecc,
'lnk': 0x87214b3094627e1b9750d464a8a81bd4,
'mnk': 0x38b0ec3296b220bcb0d213e96c23d658,
'nnk': 0xf14488ef120052993e4ac4ab47720642,
'onk': 0x703cfdaf0cd039facefa6026c8d1c2c3,
'pnk': 0xeb265da5429e8ffb129a2ae27192de4e,
'qnk': 0x81d0df685f60b315d2a53c8c451ac672,
'rnk': 0xb27936eae85ab5c2c89baf08b0bfd296,
'snk': 0x0e10d2a4dfce0c27f7361de2544c289e,
'tnk': 0x5c383049a9f5d2b2537425bc8941088a,
'unk': 0x5108c2162963c0091d28956c37fe60d1,
'vnk': 0x57588f711294780b47a298d169feac18,
'wnk': 0xe5eca8d8d1ba4f2ffb3b45ede2ecbf35,
'xnk': 0x50ab72cde6268372eeb07930840faf3d,
'ynk': 0x67e17e81c7555f78852d5027d18ccdee,
'znk': 0x684ba74e74513fee7832566b010c89d8,
'aok': 0x23bac41350be17d854d99df0e09dab31,
'bok': 0x3bec65f6f8000fd631db1300fd420986,
'cok': 0x595aa739fc58403c7c62cc2840d0b7fb,
'dok': 0xed4c137a17b08b2f62b044ffbc078c7e,
'eok': 0xf1d4e72663d3318e7011e5c8932fa9e0,
'fok': 0x65ca5259e6b6142a40d6b2a4dc4aae90,
'gok': 0x9bc27fab7eb5a9be7f564d48473e6769,
'hok': 0xa8288069334801ac61a69131563c7169,
'iok': 0x99d4a78151eb5171a01141cc0aa59ccb,
'jok': 0x4ad44a676546464b2c4b8c9e4529bdf0,
'kok': 0x6ab7ec99b6aa105aeab1acde2019b125,
'lok': 0xf19852e51c1ab671bfd8b9f4a9dc92f9,
'mok': 0xc2d9a2f40369d00d479f573eaa305ce3,
'nok': 0x3ea73b209bb0a90cf6d32619e3ae8fea,
'ook': 0xb615c4b79a1aede83b9ae67104ff4eb5,
'pok': 0xf8013d22cc0ebffec2676c634ddeb4b7,
'qok': 0x7dd0d147fe2c0cf151988bacc1a5c44b,
'rok': 0x12e8fd34a5f5e3b6a21a6610e7c986dc,
'sok': 0x85cc80d763e8f0fe9b36f4c0875c6f8f,
'tok': 0x60ae136e5d49fbdf037fab5f1d805634,
'uok': 0x1b130f0117e833f4547e668e6047cb45,
'vok': 0x96a752ab58e6a44badcdfc33ae8acb0a,
'wok': 0x03486f1a64f37c912295f2073a3d914b,
'xok': 0x835d8fb58a3d7b979aba99a24af44dc0,
'yok': 0x87bb79197626da0c3af8c6755d320e26,
'zok': 0xb6f6527d24031aa306b18d00098a8ab0,
'apk': 0x44b7d352026ef7177875a6cb619d638f,
'bpk': 0x93b4c1909138f8cdb1c3ccfb408ded05,
'cpk': 0xd124cf5b3d7d6f074dedc3b4473ab10b,
'dpk': 0xcd65c14dcb1533b968ca6d6cb45c329f,
'epk': 0xc636fbe76be283becf92a8669e6392a8,
'fpk': 0x406f59e6acfa9f4d19b822ccbcf674d6,
'gpk': 0x847becc53cb7509adb60b6dee42865b0,
'hpk': 0xded887bb83d76775df79d2334e0d8479,
'ipk': 0x532425c602bdcf7e16fe13da429270e0,
'jpk': 0xa0aee25462dcd3db7ecef86e902d9b3c,
'kpk': 0x6b4fdd727a454edaf74f75728fc93be0,
'lpk': 0x0df185985b0f2fae4809b5cc523b1405,
'mpk': 0x9ee8ed09d9c77dbc444414b1c6793747,
'npk': 0xc163ce4f0b3b4040516bf8615ffff96b,
'opk': 0x27e97bd8923daa3964a3dc892e013882,
'ppk': 0x3bb3f4dbc050a34d9c401067d396db13,
'qpk': 0xb2179081813521e25a8418c8186ba8ce,
'rpk': 0x8f54e0cd23bb26110eea6a34d6c613b6,
'spk': 0x72b318ad486f8fd865a73d87f3adae1c,
'tpk': 0xe3db7f83a8ca8433ac2aaa7f3f6b148b,
'upk': 0x0255ad43dbf87a6c01bc2cfbe422f4af,
'vpk': 0xd838a1dd38b4e3cbda38817154a47e07,
'wpk': 0x5de9d8e58a2119cb0d46965a5c365167,
'xpk': 0x49ac8fad6b1d4f44fa24b54275f8aa8f,
'ypk': 0x9be4341156ae894698e510efd362ac3d,
'zpk': 0x5b61099175ddfb5228c53aaeb5a2c291,
'aqk': 0x22b2a9d5a76d28d033a330ccaac0fc8f,
'bqk': 0x968caf106615622e59a7d3fb8f672dc8,
'cqk': 0x05fa54652de90c9ff4f6d130945a46e0,
'dqk': 0x30f59d738f1bd3328a7ed0178c6b1b78,
'eqk': 0xfb3bd40e7e1783df8f5460c850aaa55e,
'fqk': 0xab664285e2b9cd47a1cec9b5ef9d4afa,
'gqk': 0x25b8ff17956588f041c849b19e6d9a8a,
'hqk': 0xeb1c54ff54611660ce8261ef62a39c55,
'iqk': 0xd803e8bb8b8997c3189f76e677321b33,
'jqk': 0x7f8923aad847bf196ebdb8bd2a88f2a7,
'kqk': 0xa2f9155150b185c2b131ef46d0ed6290,
'lqk': 0x6eef7b402e85ab2eabe1ee5d17161dd8,
'mqk': 0x00c725cf8b9fb6f3e9d7849d7c8296ff,
'nqk': 0xfbe9c249e317bac07a37163fcea6d636,
'oqk': 0x303208c182945b916dab39212ce3ee53,
'pqk': 0xec916f4d6e225aa42be50687f1e97b43,
'qqk': 0xcae717a661d7f58902445454b1f71dfd,
'rqk': 0xbd0c614cf6612d570ea7f07c23a19ac4,
'sqk': 0xbc7b85fa0729caee088b23d9bfc18c10,
'tqk': 0xbe0fc411c48d4ecc80a279a4cc625d71,
'uqk': 0x6017b22dee59d8fae2cbfc9ae0fa3001,
'vqk': 0x43884da7b80a2e3392c250c80d99a278,
'wqk': 0xf24b538e7aa64fd381a769bc757317af,
'xqk': 0x54b954b45a3fd7cb172a7585a0fd6f0e,
'yqk': 0x100668a458632c51cd9ee789ced06d58,
'zqk': 0x1442aa3bce1164b3f590c761d3a5a612,
'ark': 0x0d8b93021ea26187591091f3ea26779c,
'brk': 0x986ae684aad2ffd0bb7257deda7882bf,
'crk': 0x18c23d14c7a96e6c2780033f1f12f4b2,
'drk': 0xfa597d2dd559ca9ab9bcccd53a331062,
'erk': 0xde3f3fb1b723886cbca86ec7f54ff7e1,
'frk': 0x8fd199ad7632296c9b475017af696147,
'grk': 0x289762bc08f98e1b1c1521e6a49121d0,
'hrk': 0x59747f5564a5470b3a191f67ca469301,
'irk': 0x41a1c98a932149c5a11e8f8440b5a912,
'jrk': 0x47d9dbdeb4730f067d629b26f7780f39,
'krk': 0xc90c6a0c3862adae4ccc0d874e2cb571,
'lrk': 0xdf6b6bd46a89fc0bf8aa9b6e2a6158f4,
'mrk': 0xdfaa36cca2e5150d63789b4b2e54b285,
'nrk': 0x3b5dd10ce8aa7a4122af052d885c815e,
'ork': 0xd1dc2d1ec5e32ada1fd8de859ccbaa6e,
'prk': 0xa20c0d8fcaa9d371edcfbf2269519399,
'qrk': 0x37541df52ff1fb310df35a0ec7c84306,
'rrk': 0x4442a0ee608fc6058f89112f5209ce82,
'srk': 0xd4b573b3e2275ea535fcc9310ea6b0a3,
'trk': 0xa859cb0544a77c8802d6e610b2abf648,
'urk': 0xb0d6f308291ce45a46deb03f9c59a01c,
'vrk': 0x09e0a7db5133a0faf5274c227327a2bb,
'wrk': 0x9c187f03a9a8274c264a5abbe5441b09,
'xrk': 0x85812251f34782d90456b9a223122770,
'yrk': 0x71900802f6b5f279a1cbb24d4c880e58,
'zrk': 0x4ae791a2ddd8e46bab1ca1246a47d875,
'ask': 0x5ed33f7008771c9d49e3716aeaeca581,
'bsk': 0x6a6b374224ecc268dca1e51322322315,
'csk': 0xea0882721f7f44384ce772375696f9a6,
'dsk': 0x6398a5d89dcecbbcb7ae7e1a7f5bf809,
'esk': 0x9fa6719b9934ff34c0df7ae445b533a7,
'fsk': 0x4387f613b59d8d543b37b5dcd1da5eca,
'gsk': 0xb5660ffcdaa6841bcb87b225154f8bca,
'hsk': 0x1f411f5eb625a6622ebb9b46b162d643,
'isk': 0xe602ed4cda21c8908b80d438bedba1b5,
'jsk': 0x0510d707ce5c3ba9bfa4f054982df983,
'ksk': 0x2ae340ff560506f91998352011f2d47c,
'lsk': 0x5db99666f6f4b64579d38787850f8fc2,
'msk': 0x68b38c3a81772f89eec6834dd51be0f6,
'nsk': 0xc2ba70e06d6046c1f3f4292a8d9e914f,
'osk': 0x7ff180711661c8e7f8941e72ca7ed522,
'psk': 0x797633604dd0ac41f2a7fc6d0be64f22,
'qsk': 0x75991d4ac3b9e66ad91ec19f1d08f2e6,
'rsk': 0x10624fe95ee8f38dee72bc1d97d88e51,
'ssk': 0x1c5edc4bbb547c49570f4dce082f8333,
'tsk': 0xc1e7c5cfd3d06ee8ef28b5c807d50f3b,
'usk': 0xb8c50e75b49b9415b1d4d29c549d10ce,
'vsk': 0x626f936284e7bf1d8873775129e3e01b,
'wsk': 0xa0aec06c0545d4fcc065722addfffef7,
'xsk': 0xabd2efd3466facf963a80fce1cc337fd,
'ysk': 0x958c6b03f7b874c20c91ce2ee7b3a950,
'zsk': 0xcc5c5f9c8a2d0b667ed9780af179bf56,
'atk': 0x82f4b3acc856d7c29eab7983b5b04ebb,
'btk': 0x564f64348efdade903a3b83a985759d0,
'ctk': 0xa6dc341401c6cbd11a5fa7c1d715f0c3,
'dtk': 0xc6fdde9c5780a34de4ef8b9456a12d4e,
'etk': 0xbf5c269dd268552f7d6c2eddf0bb8d1c,
'ftk': 0xd3caea3e7435f345aee2e02cd2438246,
'gtk': 0xed67c347e92be1c45629d20dd3078b8e,
'htk': 0xb2e4866f22e9c93e4a1ce2125e03fc39,
'itk': 0x2f8c45f48a4ad8aa97c7dcd207b97dfd,
'jtk': 0xd2f10795e1b5bf308f65a9f20977e6bd,
'ktk': 0x94fbb879374758d1b0d11e93a12da3f7,
'ltk': 0x1a9419e91d2ced0bf34b39153a8d2b0e,
'mtk': 0xdd78108d2f7283a9534e1de8d74e4c9a,
'ntk': 0xb933ce2550c84139419deebb29f405a5,
'otk': 0x6a9a79ff2713d26058f9ffa79f7d231c,
'ptk': 0xa5e838089bee858c96d3f56fb39a8b10,
'qtk': 0x3ab28a4b3bf86933b43aa3a4904cb533,
'rtk': 0x5a2965be6a5fa63212ad16f5c254f62a,
'stk': 0x4398dcfde97e17c14e5628a8f8520cca,
'ttk': 0xec06ef2af307b480bcba5fb021078d86,
'utk': 0x4fddc644e3fe073e6e6a6f80f2da4124,
'vtk': 0x6cd982faefc3e757818ab9dc2e9470de,
'wtk': 0xc40530b8c91bf74472c37164b78fdb55,
'xtk': 0x18ba2139803e69042a75b0c10909c89b,
'ytk': 0x67babe25a9db5acb1bb0cf136b251de4,
'ztk': 0x4f597420d467e33f6d172acc017a0fdc,
'auk': 0x0a8def4087b7ed061b6123eaceea2c93,
'buk': 0x9c11246b0b9932464bf71fb793c1a9ce,
'cuk': 0x0a8ada1f5d2ea05fc3af10cd808bfa9a,
'duk': 0x02f1d10eeabfcf8c69c682819bc69671,
'euk': 0xdeaa742a153786ca7d7307afffc660d8,
'fuk': 0x69b4d5ce9d769080675275b66a82fdb7,
'guk': 0xd43ce303d8fd94d46e6bffc9122f9e05,
'huk': 0x477d0d21c18056709f69da14724ed721,
'iuk': 0x799e3a706c08a8949dfffc856a3fddeb,
'juk': 0x7eee0bfaef1a055e82206127e5b1380a,
'kuk': 0x6b49b9d1da872b2f6653acb416389993,
'luk': 0x88073210769f9ba0da4cf6b7d6c044f8,
'muk': 0xb60fb7657926287713da4f2861d6db35,
'nuk': 0x5eae959afafa1da32db30f0b73fa7454,
'ouk': 0xbd8eedad3cb016db273ea1de46885e3b,
'puk': 0xaf1d068015addcb0d8cf22bc54103a16,
'quk': 0x73b855d2d5cecfb69eb4f56b9a1930a9,
'ruk': 0x3f638fb11b58df1fa150f1ac6b56c7ea,
'suk': 0x76be879a667b655cec8f70bbf7b6cb99,
'tuk': 0xe535ed5534cb3315a374d5cfa6d1a1ad,
'uuk': 0xb30ae3584851b79c9e3f8ccf12819b28,
'vuk': 0xc3df15753d13460242c23473b5e2f973,
'wuk': 0xf4968a90da4e3ffe5504e110216a54e9,
'xuk': 0x66e901b1f39db656d1a24ff5d74bf737,
'yuk': 0xa4ff15feff1d5a0df38164f1d17b7372,
'zuk': 0xc7929efaba21806b93e2fdadb751becf,
'avk': 0x4a2e9bb5d4cdf0e68d9250cf58c4121c,
'bvk': 0xf37c4752604d891b08a2435dae0f81b4,
'cvk': 0xfc2e17bfffdbcb592202b78509c8e999,
'dvk': 0x4ee387f518c28c0f0c254bf8c47f91fa,
'evk': 0x987048cc0342dd38b4e5f47af4e72a51,
'fvk': 0xa389f9aaf4df1d3632cacdb98ba2cb57,
'gvk': 0x018b5e5881c45c5d9bfc38c4e8433c74,
'hvk': 0xaf19ec6cf4bf736f77372faa0b80143c,
'ivk': 0xd156cb18cc44c2dfc53f0b57144caf4a,
'jvk': 0x5ac6beab89bcdf190e1eeda47f6eea37,
'kvk': 0x722dfbe41861572e7da0417f523ec7d1,
'lvk': 0xf8cb2b55d5177b992e588517a6987b16,
'mvk': 0x8430e791017371e5cfa4ff9b1eef1cb6,
'nvk': 0x44bfc9e9285402029951dd99485c9031,
'ovk': 0x88c648d2bfedff6bc6e9972ba5c0f1a5,
'pvk': 0xf7819e8ed80f54f23975e899473e7000,
'qvk': 0x91177640a5c9442efc59a6725653c449,
'rvk': 0x5350502e94c8583eb6655ddf3910ecf2,
'svk': 0xbd20c563d891fec85a6a56482ee0e53e,
'tvk': 0x255f759e6761158540380ab6920059a6,
'uvk': 0xf28e0ade7d04ead630c8ff2a3d3cc999,
'vvk': 0xadcb6f8ab688c7f4ba7ded7abdb05785,
'wvk': 0x2e831f9965a038272c417085d0bccf65,
'xvk': 0x7a30032adf6a9629a88805533b7f9813,
'yvk': 0x6aa24f7b0da41d0be037aa00d4c0d346,
'zvk': 0x12f805260530442198ec09032c3b0cbc,
'awk': 0x5e4c8dfa9e20567e2655e847f68193b2,
'bwk': 0x553378d25eab01aa1a2df9a59ea3d899,
'cwk': 0x1989870a9753c7b3a9e7d8cc59aeb3a1,
'dwk': 0xc5f6f698547c7ff76c2a308efe7f1cb4,
'ewk': 0xd7e7021d6be5f58a5766f8447e2850bf,
'fwk': 0x33f20f3ba79dd3047194a66fa8572b98,
'gwk': 0x636a1f66e6f8fc7eb444338e751c8bf9,
'hwk': 0xde15e247e71b1558a96514c876da1307,
'iwk': 0x5ed732d935c0241ceb57e83e483dc9ef,
'jwk': 0x2e03179e508d300209ae776c8844af7d,
'kwk': 0x43d68cdbd61fb02d9f7e016469e9df7a,
'lwk': 0xa72efd69707817539965ece58087a6ed,
'mwk': 0xd65df5ed68590701162fc352ddf82b87,
'nwk': 0xe21a8b2b7e1ee95904d1f12649954174,
'owk': 0xfb8820c66a4c476bfd85d91051b6a54a,
'pwk': 0x89f2dd1ca51bd6d83497350badc600f9,
'qwk': 0x00940018835c0763c91717c27ea41169,
'rwk': 0x332061c755a43a3cc51645323274cc70,
'swk': 0x7ae9d6adbd35bd4255d5d4ff76ca78ba,
'twk': 0x5c6615779e90c0b45db5fffbfa4bd688,
'uwk': 0xfab8c278170ec0b10695aa87c2091c36,
'vwk': 0xc3085113370b63ac7c8a20d7aeabb73f,
'wwk': 0xfa33abc3bc6f8ef6e7f19d7a10550428,
'xwk': 0x7090bf515307d046c19e5a99d01a7f6b,
'ywk': 0x75120f75beb4e8fbdab6eff0cb4f2783,
'zwk': 0x3c19cf3a544a76e4160704d8f46fa655,
'axk': 0x82a91ef4377e24b652c6a0a08ce6cdcd,
'bxk': 0x1cf868bda7a6d246b1dff3b8f0275e43,
'cxk': 0xe12f2e6348266d7dc259b4c19af0ad7e,
'dxk': 0x25900ec70e5dcca51ac1028f332dfd43,
'exk': 0x75603104da4a0e937d7852572536abcb,
'fxk': 0x7f197fb042f1de0575e7a43895dffff3,
'gxk': 0xe308bb9ed8e7904b1ae3cd1458475d18,
'hxk': 0x8b750623edd5df86620679ef92e83ad6,
'ixk': 0x93fcef1bd7aeaa8bb840fe5eee7e065e,
'jxk': 0xf4c698de00aa3315ae18f2e4e57acb5e,
'kxk': 0xd09a30aff82b34acff071c41836097b5,
'lxk': 0x4c14dff6314535d0eec4082bde9d6da5,
'mxk': 0xd1aa29f03978784be70a84138647d1e1,
'nxk': 0x7e94a20dc11e87bad56128f2d3eea9c3,
'oxk': 0x4dc93e2eea61424fe2d5e09116ea4fe6,
'pxk': 0x6a2028c15d0bda6efb5764b7de29b11a,
'qxk': 0xee0b84983541f3b29fbb814072d41bb6,
'rxk': 0x40da787a8662ba23384640fe5ab53e2f,
'sxk': 0xa64d0dc96d7c951a61517630db52826a,
'txk': 0x9f4b5a0eb34145102bbbc3d9fbce5a19,
'uxk': 0x7537a6e5f5fc7337d0b4dcb37f5c6102,
'vxk': 0x832b09dec602d8c14d35f54c5179aa00,
'wxk': 0x1d3375b177b28bbf95dab513b01d97b4,
'xxk': 0xcb99c7dd294ab5c25ab4e0d637e059ef,
'yxk': 0xd060cc703e519cc524843ead5cdfbd37,
'zxk': 0xd16ee527b669db5bb27a0db4e728908c,
'ayk': 0x7f36db029aa916ae2551e8f108262261,
'byk': 0x4bfcb22aeef6ddf82fa63c310f7f0ffa,
'cyk': 0xbff20425ae9a14b3327443c85af08391,
'dyk': 0x92ac5a4790525593562bb3b48daa67d6,
'eyk': 0xbac6fbc1ac31ea8b0ba2366be7ddcf51,
'fyk': 0x5987c70444c5c6420d060cfbf125eea1,
'gyk': 0x5ecd6bc5cbc54773db54c033ba7ad878,
'hyk': 0xfd69ee358329f602c634d25e2b105ebc,
'iyk': 0x73c47ec3aa5fd76e4d593a3d090eb96f,
'jyk': 0xb0cc6a88cd443f07e5b21b30faff85c2,
'kyk': 0x11fe8d879419fbcf50776b096c24dcd5,
'lyk': 0xb2cb496b04f4739e5b360d9608c57d95,
'myk': 0x9fbfc6076060af6997c2219503ee235a,
'nyk': 0xaa2257993bd2a7c70fc17f7855679947,
'oyk': 0xe8e621745824a1127ae6396f1f694ea2,
'pyk': 0x22974d7fec3db0ebc05f9ace75fd13c2,
'qyk': 0xa5bdbcd03565a954599889f8e95a63e7,
'ryk': 0x7a713389bab55f5251bd533087329a31,
'syk': 0xb9dfc2d3341369e2565197a07f91fc1e,
'tyk': 0x3f03d372b1ff5d735dfcc2d0a82ad2b3,
'uyk': 0x83145ffb0305aa728a612a6b458fa9c2,
'vyk': 0x50f52f324647859f28277ef5ca14703a,
'wyk': 0x570df7f8551af460290936415a1dee06,
'xyk': 0xbff19096cc85a2627b6ff4e494566ceb,
'yyk': 0x39bd09debc1203d58eb88e528f55e9da,
'zyk': 0xa90157d9f9a8d683152ca521cf1ecfac,
'azk': 0xb40a4dc2995acee7fa1eaf5953414c95,
'bzk': 0x1ddde4cdaf63b0e7fda8ab7e8b85efa5,
'czk': 0x0116932150bf259376239bbf8e52ba82,
'dzk': 0x2845c695c0e087a699c003dff425a0f3,
'ezk': 0x7bce18d7202c2d1dabd3c09638cc282b,
'fzk': 0x69748597d578701eef17eb651adc2b2f,
'gzk': 0xcda17c31e6ca82ac1dfd660af5435624,
'hzk': 0xa1dafb69042b0af1f8a8dd298a9e1779,
'izk': 0x845d749e6b9ed33ca71289d4badc16ac,
'jzk': 0xa4445e578df1ddc52abed8648d2ffb0d,
'kzk': 0xa2be12f9354fdd6be081b8cfeb9bd064,
'lzk': 0x0afd8df4ff83c213ab82b9e0d9d62ef9,
'mzk': 0xcdd6a6041e30e0eaed8dcbcc0250841d,
'nzk': 0x0f837914dbac9b28ef9cdb5cb489cfa2,
'ozk': 0xe5b9ca681067a178861084d6bc67d312,
'pzk': 0x57c6cab14df636f485b68e0046d80a9b,
'qzk': 0x7fe6bf8f2ba21d100cff0178714d1a17,
'rzk': 0x0972b7f55132c19d2ca8f897b06bb3d3,
'szk': 0x005eff7dd9626f0c30f2eb8003ceafc9,
'tzk': 0x23ca4d12759adef769191e0d44be33b1,
'uzk': 0xdadb13760f03b9feffd990cba8400799,
'vzk': 0x581c20d6e56bb14f3de6e09319ea126b,
'wzk': 0x1593d9f22674babdff8894d4ed3712fb,
'xzk': 0x2aec22e60d5c747cc0b563fe61f06958,
'yzk': 0xef1771e3c80a300c3e5a2af948015db6,
'zzk': 0x5de7fbd4186f39f6ccbbd13ae8c16c3f,
'aal': 0xff45e881572ca2c987460932660d320c,
'bal': 0xff9c63f843b11f9c3666fe46caaddea8,
'cal': 0x912b498a2f2f5913c1d0a8c913ac8b01,
'dal': 0x0d2e00d2c8b42eefcd3b0b97be31dcaa,
'eal': 0xa8052cb1da644a54fd5c6741bc608409,
'fal': 0xcd163c8ced276359d763ec1a31301a42,
'gal': 0xd7271b0e449d2ba150b1e9aac7f20776,
'hal': 0xcde9e8dec3ca8de88aa6cf61c1c0252c,
'ial': 0x2d5d16a69c98e0b4717dfb6080b33b9d,
'jal': 0x617b94886b46c84543331ea15fd47855,
'kal': 0xb65cb28b7c2569d90631cef9c8a8c29e,
'lal': 0x551b45c5a2e3acc57690b0469d26f1a3,
'mal': 0x749dfe7c0cd3b291ec96d0bb8924cb46,
'nal': 0x6d0a95eca9d104c167717d9faac320f6,
'oal': 0xa1c41128755c3437cf204bbdbd07906e,
'pal': 0x46d85374284e4224cbad52d4d7882e08,
'qal': 0xcb80dbb9f0710e5a18a2a4c1053fae79,
'ral': 0xec2cbb555d45face1f8dc619edbcc1bb,
'sal': 0x7794fc517590053809f758b7e16d87ed,
'tal': 0xc0079e0d4e801bc94bce6fa2ed9e008c,
'ual': 0x38cabf91fa07b9e9aaa61ae934d1ba36,
'val': 0x3a6d0284e743dc4a9b86f97d6dd1a3bf,
'wal': 0x2f70101cd97aa4896dd9732721ba8e38,
'xal': 0xe4fd228f18e8cc749a68ae6742b222a2,
'yal': 0xa10074b7653c2d7fd8706a2a8189288e,
'zal': 0x55043786413e72364161942d1bf9e836,
'abl': 0xf72a2d493b4ed480917599a1b9d66df9,
'bbl': 0x6664839d97bff7c83755856b34c9051b,
'cbl': 0xa0ac1dcc87f7ea355b59f21bfc1f1b18,
'dbl': 0x365b8373d0bf6dc9e3450af670cc1d35,
'ebl': 0xbfffa156ab2a88dd01ba428990ef4ad0,
'fbl': 0xde064adcd2c64dd894cc2ca2802e3ab5,
'gbl': 0x33f706f3476d267d9caabee1671e2ae4,
'hbl': 0xc6b92dc60e214d56b0466bd2be71df3c,
'ibl': 0x8a2c4d53749afcabb2455b786dda05a6,
'jbl': 0x65268c0e74564f5ffb5d30e500b2aa97,
'kbl': 0x9a0bb6d92fba2eb621634109a3236643,
'lbl': 0x1aa0021a3663ef0c4e3df8dadfaca731,
'mbl': 0xc4e2e803e5d0d35fc59f2a7d7bbb11c5,
'nbl': 0x66eeb3f4df8c2a5221d19c8fd68168f0,
'obl': 0x80ca3dabcfc340705f89cc106f808c6c,
'pbl': 0x25f66d09583995c8fbf858e439e0ab33,
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'xml': 0x0f635d0e0f3874fff8b581c132e6c7a7,
'yml': 0x176a3b23c5e392bb5c047d481938bd7b,
'zml': 0x050f84f1257bd77d105b2ae5e9c71ba1,
'anl': 0x026318392c778b7dc34929a91963911a,
'bnl': 0xc1abd97d40c50d5f1bbce15f3f202e19,
'cnl': 0x91dff62703e0db4de1ab8356e0b65d0e,
'dnl': 0xacfab62edf7b27740d050315cbc326a4,
'enl': 0xaea5aedb5a24e4db6c6fc3d2fea1e044,
'fnl': 0xac6e2db1400d3a14c625c094e29b4551,
'gnl': 0xa61de96b26fe76b13f33f8251ab537b3,
'hnl': 0x3fe7a39ad9289e9614f2846fbec5762d,
'inl': 0xa3526c028d78a9bf51d71b6022be7e2e,
'jnl': 0x761917925f38c1a8652c255cac61d3f3,
'knl': 0xe170d0fb90e0500a2d01838c779131cd,
'lnl': 0x72f70999144ccd011761b341c3d67b4d,
'mnl': 0x036e0b74955355a53662986b3243cc4d,
'nnl': 0x87ea0982e46405bb334cb2534be9e5ef,
'onl': 0x29ffa42da7ceb92431062854065e0cb2,
'pnl': 0xdaddec8bc17b9fa7952d42b9e3e9a89c,
'qnl': 0x623bf8ca99976a2deecdd5999aacad37,
'rnl': 0x021b7bf3ff2076c746a1ed6f90658a06,
'snl': 0x069d8d9e30bd7deadcec1cf0a7bd3115,
'tnl': 0xec16be49c0326fb987b56f70afb9cbea,
'unl': 0xa44117dd4281fe6a889bf47080fdd1f8,
'vnl': 0xe7191f3a16b7a8ef4dfdde87e2c4a148,
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'xnl': 0xc394013dcb5ad6c5d23f7e651e436f84,
'ynl': 0x6697ca910e9af1c36f30dbad0655adad,
'znl': 0xf0a17bcdb87748354a0628e685f53de6,
'aol': 0xa141472065cc26e1ffce0a359ff3c733,
'bol': 0x34f05b62dd43be1e42bcb0acabd0d004,
'col': 0xd89e2ddb530bb8953b290ab0793aecb0,
'dol': 0x4f3909a2092a88c7255b2fff98b119c5,
'eol': 0xd985f63c4c4b313bcc1d5db6c9b79f4b,
'fol': 0x8f9f44fa6c8c562e3d630f384927dc8e,
'gol': 0xb6ab66c58253a0f21931c1def7898859,
'hol': 0xfefb8eedabd821fbeeea6c35c38c1966,
'iol': 0x385464f942b4e33331157569038c93a2,
'jol': 0x5ff059205c430013f5e8a3a2af91c103,
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'lol': 0x9cdfb439c7876e703e307864c9167a15,
'mol': 0x4fcb130eb3b47f0d3916d1cd92a5b4be,
'nol': 0x0c72a0ccc6a4b854f03022f4a791eb42,
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'rol': 0x875b854107b408d2899cce9dff917e70,
'sol': 0x12313a3d28f802e3a22b07d2e01c6dcf,
'tol': 0xd9734757e2e9d7363d18a5d238f419b4,
'uol': 0x8da996e2039ee4e2642f3262bcee744b,
'vol': 0x0acf8be1231e24946c3d10b649fb576f,
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'xol': 0x0296c8052637b4264ac78f4ae5b86bda,
'yol': 0xa5da5ee4bf71440b90dd9082e6724f67,
'zol': 0xbd5ba50569346d524f758683cca3f999,
'apl': 0x508aabef6aa1190d26b88c7174c9a997,
'bpl': 0xf56e4481eedff382cde4ffdf0731f09a,
'cpl': 0x363ccddc87d476ad5f91d9ca39d24df0,
'dpl': 0xbb0dc7247375f56238be7a77ef39a719,
'epl': 0x0435712fc637ca4c4db9ab272b424683,
'fpl': 0x46cc27c92e357e4264273aa8619c08bf,
'gpl': 0xd0664fc488835d9ea73482d71fd954e0,
'hpl': 0x03e73ee7c03a6364b53dc971980fc6e1,
'ipl': 0x58985d66ebff374b87a4c1a38529361a,
'jpl': 0x4243a686b5d8b6441dda1abe3dd07732,
'kpl': 0x361877035a32f3073eb1259ee15286d8,
'lpl': 0x23dd4e72e56cad257834028401d4aa38,
'mpl': 0x6884fda46a82f4aa52c95f8b41221de1,
'npl': 0xc68756ce4fe7be9681aefd4a8dc70c83,
'opl': 0x8a86d60bff0d1799a18ec014bb549d89,
'ppl': 0x5396681eea50ad639ae3c9f8ca17b7d8,
'qpl': 0xb54cfe240335058460ae998ee3b0c245,
'rpl': 0xbd9ae328e27f0832b42b265f5fdaebcd,
'spl': 0xd17d8f0661f8f44dd7dc5110c8825246,
'tpl': 0x4f2afc9c4099ee1f39c9f551123e54bd,
'upl': 0x423993785f4b991f94a99188d042a09d,
'vpl': 0x4d47140a3aad74d69a5c2d0efe049af4,
'wpl': 0xb09197d6f45a6402d5d824ef9ebea0f0,
'xpl': 0xd9cfc38b35148041c698d4a762f0df93,
'ypl': 0xca16274bba5a642a279f4f0518217394,
'zpl': 0x3872679963f9f6f7fc6e1b800e3714ea,
'aql': 0x92fc8968c619e1d21c1b145b2e533fcc,
'bql': 0x5a5d7f3f59df01a3185e60cfab454a8c,
'cql': 0x9a38dff12e3d87ce5fb6436e6592b1c7,
'dql': 0x9e8df9a65d8bee50427b6c255ab6ffe1,
'eql': 0xd1e2b48b196ebcd9a6e9261cd01b2b0e,
'fql': 0x87c2a806a849bd4e4854889d9750b7df,
'gql': 0x1ed485eecfbdbe9d3fa6f81484a2b6f1,
'hql': 0x01d59c705e5148a5f956e27753ca3c1f,
'iql': 0x480d00cf5b4a3a44c024f0dcfd593189,
'jql': 0x0e2b0996082fee448aae2605ebd35948,
'kql': 0xf1d9325cea1305b9febcca156c4447d8,
'lql': 0x63118b951fbde25713fc2dded149eab9,
'mql': 0x66638f80ab055805959fba60070ba3d0,
'nql': 0x5cd7fbb4749d115f49019b4cd1099de6,
'oql': 0x9b15252bad92d4f912b284b9a274a83f,
'pql': 0xf1cbb2e8a13c679f8a3cc8652f1aabc7,
'qql': 0x93c367bd0e12b2f2eb7b69d8e05566be,
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'sql': 0xac5c74b64b4b8352ef2f181affb5ac2a,
'tql': 0x5fd277be39046905ef6348ba89131922,
'uql': 0x0783599267e00fae8888b82ac07b69a1,
'vql': 0xf27d8c68a42da1743d288742d56dedce,
'wql': 0xb41eaa86d547eaf0aec73ca5fcb61742,
'xql': 0xd1643539bf9c041238d893cc693f36c6,
'yql': 0x32ad7a1a0265d596e5545ce907821fe2,
'zql': 0x10aed9da76ba0a92f059beb43911f563,
'arl': 0x15e76f8904e69d0ada402c3d7401518d,
'brl': 0xfe875be3377cc3eadede0e56364f5b06,
'crl': 0x8484727f202108279ec88be93f30da77,
'drl': 0xe9f604cf75267bdae836a39e794390eb,
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'frl': 0x22f4970e67031c3186c884a485e86f50,
'grl': 0xe99064a83fa76232f8b825c07ad08ef2,
'hrl': 0x779562ecdf573e0489bf4b076f5de07c,
'irl': 0x484c5aa99688af4038b3980324f1232c,
'jrl': 0xb8430128676d467023d0261e4ab7e95e,
'krl': 0x1a45921378c81d30b0613cd2fcd3bc7c,
'lrl': 0xb4633974fe4d39179b271bd192fcd295,
'mrl': 0x278262a5b91d8e0cdbb96d96afc9d430,
'nrl': 0x49b0c436d8f1761fd7895fbcd94ea5de,
'orl': 0x4453e44bed02b521fa162f5045fd9e1e,
'prl': 0xb58825cefebdd1dec077a250ee49420e,
'qrl': 0x93b473625d26ceeadd86d75a59169592,
'rrl': 0x2a74daa60cb812ad4455a6f78d43889e,
'srl': 0xec733a11b6acddc2493e3efaa10a995c,
'trl': 0xcaa7ce1bc01e30fc915a687c58e2e69b,
'url': 0x572d4e421e5e6b9bc11d815e8a027112,
'vrl': 0x7277976be9b633599d1437e89686d08b,
'wrl': 0x785cef577f928a7833485a00f2698dff,
'xrl': 0xe84748d608c9c408cca2df90900f534a,
'yrl': 0xab17ded1ce5b6391a015c520ee8cd948,
'zrl': 0xceab3afe8083284e2453bea665ac89a5,
'asl': 0x4ee104fcc6d7fe882291e8e8b1319f49,
'bsl': 0xa942cb04f629d9e8d060f5681d41b630,
'csl': 0x90ff9c27cccd9efba1b868c0cba2a84d,
'dsl': 0x57f62e085cfdf20b39e12de8b1993112,
'esl': 0x55451ac9f139b81c4f44a8a268afcc57,
'fsl': 0xcf0954bcbcceac4ddd38421384c09d0e,
'gsl': 0x070706e904a575542e19aa9b44178c79,
'hsl': 0xb735e250ff5d58e98f3b38fcc92b6080,
'isl': 0x84d1e82cdbcff28e831b565390fcf63c,
'jsl': 0x57357b7ffd3ca935d4b416fd4c57607e,
'ksl': 0x685871a5a8b11fccae063ca7661da785,
'lsl': 0x9f05b617878c89b529edf5b73f69b822,
'msl': 0x8cf205e11d5eb3c998ac34958304c608,
'nsl': 0xffb3cd3a0d0077132e01bdac5cf43561,
'osl': 0x659451ef59031c74416ca1b712833235,
'psl': 0x80fd2454b036a4e39104109bfd09d325,
'qsl': 0x00e7ea205fcc1c526a0f5169879d5f7e,
'rsl': 0x58d6fade8e622f6a5a305c2e7aa6fe9a,
'ssl': 0xf9d5c16a7f42203f8c195432354a3271,
'tsl': 0x65914d806032822d19bf059c0a6adae1,
'usl': 0x176cd2e876a5b2182cbfe43938e4588c,
'vsl': 0x471b6cb4cee7c59bc2c0ddce120a1a80,
'wsl': 0xfb6a6b3d865fcdcc89b421742e70fe43,
'xsl': 0x4204e4de3fc1f8bffa6f77c383d2d6a0,
'ysl': 0x15d9c20d5efba2a9b1615b502e05ebdb,
'zsl': 0xaf99b5504554fc98cf8fc439a5867895,
'atl': 0x3c6b9e80292c67d4878661eb4d8143fa,
'btl': 0x3d2b76f7e038c9d47e139dc241176e4f,
'ctl': 0x612aae0a87469b795c172dee0a3693c3,
'dtl': 0xf01036d2d9d32d843afd7c8cbe07637c,
'etl': 0xfe8927df7669eae62eeb5e5e2b52c6a3,
'ftl': 0x9afcc04a0941bef19d3cec857b25f71f,
'gtl': 0x3f74e9ee63c92de344120dc1995a199c,
'htl': 0x02638ab3896b7a5f3589ed5677b1361b,
'itl': 0xaeedead5e66d972106669bde3b75f5fc,
'jtl': 0x43513542b38658e9bf615e7f910d5c80,
'ktl': 0x3cbe473fc058697fea53a7d20ee7e2ad,
'ltl': 0x7d861c00b4737acb057d10d33ca14da4,
'mtl': 0xb4581065ffc340bd0ee01d093a40d7ce,
'ntl': 0x6d39d71e123cf74acb68549aa236ce7b,
'otl': 0x1a1e2a614acc6f2e5203d0a3ac19f37c,
'ptl': 0x1cef342f426d3d22a14623cb456c3e87,
'qtl': 0x08b744a80f373a000f4c01eb4d2d6432,
'rtl': 0x191b2429e8a8f3fc62c996fd323cdc97,
'stl': 0x7646b54bb4e9ecea20a0ca57e3b2f794,
'ttl': 0xc431a4425bc56080c868435c8d910f83,
'utl': 0x5740fa26067e36fb6a3b53cc293581db,
'vtl': 0x37f453ed876654e1878d890219f6a2dd,
'wtl': 0xb8ca5f459501d7a961ec00bed005bc41,
'xtl': 0x185ef76912def67965cd83c641d3bc82,
'ytl': 0x37a15a6a7978bd6103cd1aa228e14c03,
'ztl': 0x9fc5dd98a6f1cacb0f5fafc34c5a6a48,
'aul': 0x950a5e3732fd173428154f84954a82b7,
'bul': 0x60bfbcdc9570caff12891541ef949658,
'cul': 0x35dbdd6e633566c4160e699a86601ab8,
'dul': 0xdbfc86cc375fdfc85f5acece0103a01d,
'eul': 0x84aaabb0765621e877163e851b0ca99b,
'ful': 0xde7bc591b235b4b2162f12f37660a9b3,
'gul': 0x8fa849068780205d1e50cff86563b53a,
'hul': 0xc8ebf74c1f71e7179f3e1c92074d724c,
'iul': 0xbd6ecd9631304a726ac8321f8790dd58,
'jul': 0xf05c8652de134d5c50729fa1b31d355b,
'kul': 0x212747dc84369b811b3bd8611c78422f,
'lul': 0x2c0b3d4664350f2ff5a2ac539aeacf0e,
'mul': 0x353942263d1bedfbe06b7bfa78226253,
'nul': 0x40a8712b29ac76182ed0c4f632b7d543,
'oul': 0x67e95123ad067c4f47d9f2f96d160f7e,
'pul': 0xd09e4671adbbeb1174fdf3f79b34b436,
'qul': 0xa58e8b3906b93264145912a9e7561cb6,
'rul': 0x50f2644c5a8ad7f508f042f2d7f18271,
'sul': 0x6eef2965e5ad5f10a79ea6dcc2a291e9,
'tul': 0x2417e0501d028f2566cfea42ace55004,
'uul': 0x0bf6a1018b9e49f894d3488213eb7060,
'vul': 0x1f7b4b975491e924484f06985576f5e0,
'wul': 0x82d8c4ab8a35b4942204ed1c382bdcea,
'xul': 0xb1bd11b917ad8b9bbdc7a00ad3317569,
'yul': 0xe306018545c56281306f0cfa8b134fdf,
'zul': 0x1cf440e0df367e8a74becfa88ba0595a,
'avl': 0xbf7ba141f8c200890281893a0dcb4d40,
'bvl': 0x693fe0d3b7788901ca33320965207d65,
'cvl': 0x1fe598306930206029e757fb03ca8fe8,
'dvl': 0xcb9533dc5119ab006794772c07e2aa86,
'evl': 0x700931a2d326e59f6ec436de27bf9f12,
'fvl': 0xbc92c1eda5b9afb784d6ae4f3d32cefa,
'gvl': 0xd2f85407ed21df2f332e7492528a3525,
'hvl': 0x64607d49081f6c1410dae213683208d9,
'ivl': 0xf1da28951ac1130efb77a26f11bab3ca,
'jvl': 0xc5bc7ed3c68f7b9513bea3bdd3ea4072,
'kvl': 0xbb23fd5c96f303bb7c74757245e78bd1,
'lvl': 0x53335b842afc4f52363ec4c89d1c7252,
'mvl': 0xf46392931d723e42ae0a9cccfbfad743,
'nvl': 0x107eee5492953498b2c56e9f292d1344,
'ovl': 0x3057c193ece6e2769c3fcb2d88b48458,
'pvl': 0x8e4eb0b2c764c494527a23ce0d804013,
'qvl': 0xb80613fe2ea612e6faeea04845fd33cd,
'rvl': 0x8939dbf0b9278212f6dc8ef39247ccb2,
'svl': 0x0c25ae3066f1b9f7890ceff405fdcab1,
'tvl': 0xa2b5d7b97312afa9ab49107c6f06fc8f,
'uvl': 0x710e844fbb5e70615ade8df3735702f7,
'vvl': 0x7e2badfb423bc90eb8da49aec7725f0b,
'wvl': 0x47184240b8f59884b2aabf7806e344c1,
'xvl': 0x91a1acba5fed0f3cd426b8a0f1fb1e3e,
'yvl': 0xbde1bedce498f49103341961fe040ec3,
'zvl': 0xc508dc10119bc51d049692a0a4d0afd9,
'awl': 0x7bd2e92a2e1e8b989d52826e33d99d8a,
'bwl': 0x28ef6966beef16fa04abfe6a7bc95ea3,
'cwl': 0x7468ede10e5b87dde9b054eab4d5e0d4,
'dwl': 0xf0cae0d0d05ddc6bceecce4760ef91fc,
'ewl': 0x83d9bd5bb34c95d05f8c98c61d6f8ac4,
'fwl': 0x30c49d54fda5e34e8d8392b910c61bb2,
'gwl': 0xc1a3a4368a8ea5195ba6a49080cc950f,
'hwl': 0xa2b4464c3f7688d1916216b55292878a,
'iwl': 0xd113b8074ee7ae6ffbb46dcb4a9b57dd,
'jwl': 0x23c5549abbf178e7e9ab2f583be2bc67,
'kwl': 0x8d3b1f4435b701cd21d5d2aaf27562d0,
'lwl': 0x43e94ca4cc9804f861ad4c3071fe8a21,
'mwl': 0xf6417c12bab882a752dd1aa0c635aebf,
'nwl': 0x05e7c156c9a469225b7d1f22d7b45979,
'owl': 0x50f8b6c98b0f9a271a562632405ae63d,
'pwl': 0xbb199066a871d4585019e8fa6308fe6b,
'qwl': 0xe1e9aae70cee12b5968787d3d1bb0bc6,
'rwl': 0xf7f53a12cac003bce8909355f4b083ab,
'swl': 0x60dc3bc444722beef0a2c7ecb9e792a4,
'twl': 0x84d0b855715f7a7d2b38c66118a0da55,
'uwl': 0x1cbc6188da0a99dc436d1d607f0681ce,
'vwl': 0x8cfc1402793256f43a4b0061fc22d2ed,
'wwl': 0x003412f0e75b5df9339abd2c23ed84e8,
'xwl': 0xfe8e45beab37cdf18ff7acc3aca59779,
'ywl': 0x7d28cf61e58d498bfc7c13fec364c01c,
'zwl': 0x90a2b68cd536b42b5d34b12382fad15d,
'axl': 0xa562d6cad6b682f0d1b38fc9ad70ae8e,
'bxl': 0x44451f004ae4d51ecff0144b79de0aed,
'cxl': 0x76dd4ef06ba00b107cd804ea94f4bee7,
'dxl': 0xe0c1d44887f319d55261326a41557655,
'exl': 0x8a6a985cca7c3ab055a977356a97cb7a,
'fxl': 0x1b00617de45d72ecca6c59f223b5d28f,
'gxl': 0xd0fa261d5ce8c424c090d0e72089538a,
'hxl': 0x1bcc3dfbc9a36a050ca07b3c72a4810c,
'ixl': 0xc35d7078c4b088481b8e9bf5d6d01efe,
'jxl': 0x838ddf2b74575f929e247c8f81a14ed7,
'kxl': 0xa966f0a7f24fcd769d70746b1875ef60,
'lxl': 0x483d0a6af10b0841dadc2d402a8df483,
'mxl': 0x90d162aa1f38ee74a8a7041bd2201ba4,
'nxl': 0xc641270ae72a3b4f9fd393b7fa8cbdfb,
'oxl': 0xd1444e68f420d1baa9298a280798d5c8,
'pxl': 0xab642ce62f55b2ca05b4697f3bd7b53a,
'qxl': 0x51ab399b7cbacba072dc959eb2cb0412,
'rxl': 0xb7d9f25bfbf5d133810960603abcb539,
'sxl': 0xb43de3f2d210f0052d0c330cbae0ee0c,
'txl': 0x30299f2131ca0ea942fd4de491056744,
'uxl': 0xa899fdb5a20b6aba603d71512e186fd3,
'vxl': 0x8ef4865196b92afc7af6c6c34147b68a,
'wxl': 0x664496c01babe81560ddf81cfcc86170,
'xxl': 0xc2912fe9a6ae9d1f03d142399fab8f4c,
'yxl': 0xbfa6ebc10ef90fb72f1a13416bf88968,
'zxl': 0x91ef4179c8bb9acb60fcf4813c7621eb,
'ayl': 0x064e3ed75c1ef726a71e86d9d4011598,
'byl': 0x0a85c7ae8e0a177f69ef9961d8d1bc06,
'cyl': 0xb415b23d7adf5ec8bd6a3c41f1235187,
'dyl': 0x9e542b7755a6c2f05c3f07e2ddf92aaf,
'eyl': 0xab9d733d2007eee41c85bf65426a687d,
'fyl': 0xcd18e888a847d13761d7a708bd8f884d,
'gyl': 0x75feb7166f148e5f6898adba0fbb6975,
'hyl': 0x4630ddedaffc54b1881cdf30ed0fbc63,
'iyl': 0x6a5d6c1a2e8a7d83ebf74f4e336f8849,
'jyl': 0xecf4b7ebe88f34f60cb041725642f352,
'kyl': 0x248174302ea9f8119080309975d42302,
'lyl': 0x2da1c53d5b69ad46e6c0d4dfcddc2268,
'myl': 0xc700bb8ba713749917404bdc13052599,
'nyl': 0x9618150afa5e725172871681b4291350,
'oyl': 0x5e666a894212880f721e0de155031590,
'pyl': 0x3ca1734a849e44eec59796584b3bdf90,
'qyl': 0x11285f8845bc3dc8119f9bdf6fc76fbe,
'ryl': 0x4d666f4a22d36a2262169ab6c9aae45e,
'syl': 0x0c7e0e3afed9b89ff0fd10b7a5bff0ce,
'tyl': 0x55f5c1c64bf8dc3000b9aa41cbe6b3cd,
'uyl': 0x550fb8072d105094839383a703460ae7,
'vyl': 0x01f8c17425590edc854070275c8fe868,
'wyl': 0x6ca834663e0689f1ba804837cc53f642,
'xyl': 0xc70cd04313ff260a83d6d7990822c032,
'yyl': 0xe3bea3e29bccd4b8d4214482bcacb59b,
'zyl': 0x3a79f9e97bb9c416ceefb40943001e5d,
'azl': 0x60315095d9601a596b33148a359f493b,
'bzl': 0xb67a69a9e5cb257bf3ca6d0fdf43e631,
'czl': 0x3f95cc472930da5329b1e5d44cb6613b,
'dzl': 0x213d66438433704b5af7350fac9bb1d8,
'ezl': 0x3c8cafc3b54624eb305f464199447496,
'fzl': 0xd891f9ae1f5f29c04182aa70d3fbf9b0,
'gzl': 0xdad62f1034b31e5079062a556bb90dc1,
'hzl': 0x31abbf083d9d21e61b88f6b4b6f1262f,
'izl': 0x78aefd273f81bda4e3f2294acc57c138,
'jzl': 0xa2fcee2c6ff28a9e301dedd6aed8d567,
'kzl': 0xf7ce21d3d3743d4e60f6d83b811476d4,
'lzl': 0xed6987e16a531800537a255fc99faed4,
'mzl': 0x0ed7e0e5d1907bf97305b212e6ebf163,
'nzl': 0x5034bc3fd2a153aad8f903a3df44d08a,
'ozl': 0xb57483b5f36e06468bbea5f7f116930f,
'pzl': 0xe4257a23f58a61bcb6852e49ea0d37f2,
'qzl': 0x8de19e44d2ef11d5f756aa1ab6847f9c,
'rzl': 0xf8446be95edd121fbfc27b8fb76a87a0,
'szl': 0xee19e453829326eb7192bf50037b4269,
'tzl': 0x9d989399bb803fa58be2df9d648c001b,
'uzl': 0xa6ef4acbb07de4002edf5c524caa0db8,
'vzl': 0x7a131fc3f519a1d879a8b5b4ec5e1995,
'wzl': 0xbe4646c75cab662c024309fb38497351,
'xzl': 0x4f15aff5ae031fae53f0aef4b3aaf7b7,
'yzl': 0x4fa8d6515821cdbb82631985611f437a,
'zzl': 0x17987070c6f2bd94b4a67d4d4df850eb,
'aam': 0x35c2d90f7c06b623fe763d0a4e5b7ed9,
'bam': 0xe5bea9a864d5b94d76ebdaaf43d66f4d,
'cam': 0xad017e9d6654bd0ccd978958fa1b6da6,
'dam': 0x76ca1ef9eac7ebceeb9267daffd7fe48,
'eam': 0x9b87009f505eff84ae395b040a1ea382,
'fam': 0x6503d1b8c90fd5dea060ae7331e3139f,
'gam': 0x6ac5f51809825cedb31b20d6e3daf631,
'ham': 0x79af0c177db2ee64b7301af6e1d53634,
'iam': 0x0ebc580ae6450fce8762fad1bff32e7b,
'jam': 0x5275cb415e5bc3948e8f2cd492859f26,
'kam': 0xd968a18370429ceee4e7fb0268ec50bf,
'lam': 0x1c6d6ca22cc31cb79e6e1f5277ef06e0,
'mam': 0xb735b0c78e12553e91397a3ff19f8fd1,
'nam': 0x22c78aadb8d25a53ca407fae265a7154,
'oam': 0x09b1fca3f1b52cf6e1978be249a941e6,
'pam': 0x91c0f7100bde719c44790e7df757a1a6,
'qam': 0x795ff54e894c0d11bdae1ac0c0230542,
'ram': 0x4641999a7679fcaef2df0e26d11e3c72,
'sam': 0x332532dcfaa1cbf61e2a266bd723612c,
'tam': 0x3eaf6c984c18d143e1c0a797779afea8,
'uam': 0x3cebb6749dbb104b50b208a5a92b5a70,
'vam': 0xdc19d421670342a37a2114063f29be6e,
'wam': 0x263128cf9107b527122ed0dfba8a8be3,
'xam': 0xc4a8598a784522e018448deddafabdb0,
'yam': 0x806ad1e1a4aa6498a4b7adaed7639293,
'zam': 0x142cb104bb9ea93530eeb1a904e351c2,
'abm': 0xa9253740a0b0976cbda6c4230a76b4e0,
'bbm': 0x14e56e92287a633ddb3ca47d574a241f,
'cbm': 0xed30e91563baba6812e1f386b72ae76a,
'dbm': 0xd43df2649e0ee2d889c9368d88c2e78c,
'ebm': 0x3dc70661cd5ea000608c27661b5c240b,
'fbm': 0xdaa0dd5362a28f26003df81eb617a799,
'gbm': 0xd2bf187f87d107c5d6853021128b5adf,
'hbm': 0xe65e9faeec18c95733e43fb7b188ad85,
'ibm': 0xe757fd4fedc4fe825bb81b1b466a0947,
'jbm': 0xde8ddf86f6aebc85d38050bea6cf71af,
'kbm': 0xb18a7c98e7adb94c12be083a6962a078,
'lbm': 0x0c5c5d6b66f635dddebf4939f276240f,
'mbm': 0x7fa59cc8761d9ca4cc18bb7eb1ce75bc,
'nbm': 0x141daacd408ca680bde2093f3d000d0f,
'obm': 0xdea3e342eaa78eb4735eba9dc76559c3,
'pbm': 0x74c154d4bd8530a0d40fad275ee9bac1,
'qbm': 0xbbe65676ef65288b6f7878b9a343fb9f,
'rbm': 0xf16998c1cf44efc4b8cbb809f782caa3,
'sbm': 0xfc24e58fa3775bae5524f776f9e57ec3,
'tbm': 0xf89fa02b7d644e513115697aa9f7fb34,
'ubm': 0x62b5667f0c4b12946e37934f00361e43,
'vbm': 0x7216e8e74221339189179889b66f6cff,
'wbm': 0x8a65582ad3c3110b930beddba0596a7e,
'xbm': 0x05ade26199700787acb4c09210c841b8,
'ybm': 0x1eb8511872b531070c850fcd1cb10c7b,
'zbm': 0x9093391ce2b609269b83daad9056647d,
'acm': 0x02b55e4f52388520bfe11f959f836e68,
'bcm': 0x83e387d48556c62548b4558e30f7b910,
'ccm': 0xb42f1990c0cee8758b64584877d69b93,
'dcm': 0x84ac41d7c57ea25694b0cb7899a104f5,
'ecm': 0x3fd5ac46a040c95fa5c481167f248542,
'fcm': 0xcbae54a2ed8fbaf23d6ca4bf23471226,
'gcm': 0xef5acdb93989954b1cc5f4e0ed0abbe8,
'hcm': 0xcdc8773b0f1541ecfdbb5465a66f184d,
'icm': 0x17739dffede9324ec5ddb194b5a05527,
'jcm': 0xa26bfec5313adcb959b8a21d43747c5f,
'kcm': 0xfde39075d6371c19452a38aadf08f370,
'lcm': 0x29895a7e60c523c1dd3e85d1ecfff6b0,
'mcm': 0x5b843ed5160086c2d34710c0ef6a1da6,
'ncm': 0x4a35aa35aaa7467ff2ad41ed6806b591,
'ocm': 0x384f8f5b973f8518a1f70fd40d26b310,
'pcm': 0xec5c8103218f27aa6ecfd3f11e1a6b45,
'qcm': 0x68bf09624358e484ec7c4880e70a8ba1,
'rcm': 0xfafa238d6e5a424c180d5fda3ae3dad3,
'scm': 0x7b3a7a628c6e84b2b00d8089edb3b4b1,
'tcm': 0xa005a08d7cc59163d7f4de2b7fdbc147,
'ucm': 0x0331b40c89f63747bcebea3b0fdcd7f7,
'vcm': 0x1792a83be1825d512c0ad13593801266,
'wcm': 0x389917beefe49304693b8c57f185c52d,
'xcm': 0x706a8f6db55a95e9bf7a8f04a5e3e8db,
'ycm': 0x711de35767af2cf7348e501596d3ef0c,
'zcm': 0xb6f4e5e7d8ac17da36e9a0034775321c,
'adm': 0xb09c600fddc573f117449b3723f23d64,
'bdm': 0x528207fb6d011fba827e3f25d37ecaae,
'cdm': 0x16a81ec4aaab8d96c4398d202af5b527,
'ddm': 0x1de16a8e287f7c0461ae2941724e62f4,
'edm': 0x6cb5644bd3cb743ddc6aa7809b2952c3,
'fdm': 0xc820548ac1a1b4680d1fc5fb39033a44,
'gdm': 0x38f0fb45944fc9fc05e75445eb12633e,
'hdm': 0x8f75a903924ae5e87ed48e1d713f5a28,
'idm': 0x679a4004c55e0afdcf4d4bc5b680162a,
'jdm': 0x34171b8ea3c68516dffe90f90a3c5de7,
'kdm': 0xb69da0f2652e4aaab4ab454e084b2fa7,
'ldm': 0xe0a3b28de1b44c62964ff8e4292a1417,
'mdm': 0x5ad2bf82fe2aa1ff3a62ec36d8524104,
'ndm': 0x04b2f0a4ad7772ca864aa569917b2d2d,
'odm': 0x33e753e3579458fb3f46d953a9dbd800,
'pdm': 0xab061d9d4f9bea1cc2de64816d469baf,
'qdm': 0x56b48c5ede4041c23f7eed66d7ff87c0,
'rdm': 0x4625809eb2690f70abd21c4a9aa6b2c7,
'sdm': 0x6d662f965d1e85bb367efaa03594c5a1,
'tdm': 0xbf27c8fbf7e9c3f45dd566d3f8bf822b,
'udm': 0xcdcac6022f9a5d6b5181a5c8b32b9365,
'vdm': 0xa99d19b66a082927e030b940693c3205,
'wdm': 0xda552355df18d706b1cb44aad2699661,
'xdm': 0xe823bce2b299aae89c9aebb963f3360e,
'ydm': 0xbc183a773c80a7b37ece8d6dcb0387f9,
'zdm': 0x5e3ece0d11da8638b21660b52b88ef3e,
'aem': 0x717756119303a0a036b274e28a590a4d,
'bem': 0xd3c654d99bdfaf101e012bfe2810679e,
'cem': 0x67e34ed173dcf2b555855f3408d5e664,
'dem': 0x45bc08e6003540a8698dfabade95cd48,
'eem': 0x8f0346a0216f7913c359f8d167313e06,
'fem': 0x411f822017e8c2a440ad181acfe50efb,
'gem': 0x9855b7f5d316820428a3e475d37ff14e,
'hem': 0x83368f29e6d092aacef9e4b10b0185ab,
'iem': 0xb3325d8a10b7ea3147206722b186ac88,
'jem': 0xb6ff1e02a41535337f28b3d8b18ccde8,
'kem': 0x9f7b0c36272cf9b3608ae0a33b1a4ade,
'lem': 0x45e9ed689390d96ec2334675bc774f1a,
'mem': 0xafc4fc7e48a0710a1dc94ef3e8bc5764,
'nem': 0x87b524b4689c0a64177ed181f9650508,
'oem': 0x21b190e3fd92193f495fc7e7b7c70b3d,
'pem': 0x8921ffd638d5e5fd424e4c197e829486,
'qem': 0x1cbb1ba0d8f91a2db96d0422867bfdae,
'rem': 0x5cadb523cb6909f92350f70f124adfb8,
'sem': 0xbd9a2916f0e054a9361a98a97b8db5dc,
'tem': 0x1ba857050fb952f2cf6ae7c55468ba62,
'uem': 0x057a78b3352279b50d8c8a152bc1eb53,
'vem': 0x048793541e88411b88e0ddae4645a263,
'wem': 0x26a7738967140a58291f7618163c0562,
'xem': 0x4124a442ef3a3dde4a4891808cc9da76,
'yem': 0x310a603028a0c6ff53b7301af9c8fe79,
'zem': 0x739193b14534e482d6df6e986caffc0d,
'afm': 0xe02ac68d6cc2b639edec5b4755d55e8a,
'bfm': 0x215fe43507e4c03a5748252cb7d3c846,
'cfm': 0x2edbcfe7b7fbb8e36ddc7119306c64f2,
'dfm': 0xebca9bcde67fb2a30ebdaab81dfc9adc,
'efm': 0x737bbb143b65fbc8a606d15e7e38bb97,
'ffm': 0x4877d68c4f389a1a16dbf5c4ae5013f8,
'gfm': 0xfa70f83d6376267abf0a3da8a52d3846,
'hfm': 0x3ba9f50932d05bcb3ce561a5fb55f728,
'ifm': 0x0aeb58794c29d040e7da234b563a7574,
'jfm': 0x02131e4c01ee038736280ad5195d1f7c,
'kfm': 0x14dda7e9cc096397e851a3d71465511e,
'lfm': 0x400ad14d9fc8101047cb994ec6dea0be,
'mfm': 0xee50f011c806f8a8420b78057ad2c6d1,
'nfm': 0xe85542c9e731fbff6ed24169ea015e82,
'ofm': 0x6a0fa847ea059e4c27f51bb660eac26e,
'pfm': 0xcb94ab15cf6b81cf813b2ef5a1e76dd6,
'qfm': 0x8d9b054de350106546234a9d053731cf,
'rfm': 0x50167919fd6ff61dcdcc8c57a73cb06a,
'sfm': 0x968e67e4d26c36a6a90813a1001cded2,
'tfm': 0x42718f7a0f709e7bccd60f68c32f2934,
'ufm': 0x04575a437066f545e341cb8bd9d6927f,
'vfm': 0xb8ca5ef27d3379954815273f90d32561,
'wfm': 0xac7d39723388f5f067515b02bd6a2a76,
'xfm': 0x7d07a9594459028078a7d4bfbc67d7a8,
'yfm': 0xc89fed9750c63d7abb33417a2c1b0729,
'zfm': 0x515f5632bfe469129ad339857c2a1c17,
'agm': 0x24092730b4c8763b49d24613a3b39577,
'bgm': 0x6a7975d3dc974fd522610adfc8b1ce38,
'cgm': 0xe7b5f31246e8cb9f6cb24f51055b7633,
'dgm': 0xeb62c9b3f211e864ef49097b917b4c92,
'egm': 0x3b071fd56265d932b3033b26ec7f5de0,
'fgm': 0x1119d4d3dc6683b53617d3423b0276e7,
'ggm': 0xb934915bb8ab1443e182c56ccda0e562,
'hgm': 0x6b5b55308bf14a20869ffcb4cef15bf5,
'igm': 0xddb16b63a2d9d130019c1ef0ad718ec2,
'jgm': 0xdad44b682eabfef18d6974581f0a9cf3,
'kgm': 0xbf59a179c83e6326729576dc1309be7b,
'lgm': 0xf179eddd7acc38486c27325e5cf0025c,
'mgm': 0xe11b0ea2d9dce5e9b25dbae3cbf7b37d,
'ngm': 0x1ff4d60d97c7c4d298d0e2f130d48da3,
'ogm': 0xa8317cc511086eec82bb5543dfb13e63,
'pgm': 0xfc61450d3f31b46b6197c42e6b047f47,
'qgm': 0xc75f293cb0a9489849071e2117a83478,
'rgm': 0x4c3e6a150bcf069e1b9c01eeac44f734,
'sgm': 0x8360785b4bc0e0c6d42fc0e6ad975e3b,
'tgm': 0x8e7b3576e667ac62f55d22f7d9fd23ba,
'ugm': 0x2eb0bf3f359eba85afd7acf5d90e461a,
'vgm': 0xfb0b3c0612ee7ebe40e432fe44815a73,
'wgm': 0x1998c55f23c98ca17bd12a47020a7a8e,
'xgm': 0xb911391d53e2464198bb2f1326afe114,
'ygm': 0xa5ce3951578b339952c5b8f6ef5d14c5,
'zgm': 0x3854149a72d12e55959500f6abc0410a,
'ahm': 0x6ec5dbe0c53114247dd799bafc240c58,
'bhm': 0xf06f29448b3cbe671f6f0c9d716bd86e,
'chm': 0x6e5e4d1b1805aa7866c99c63a26b4886,
'dhm': 0xe659c3fcdc569cd04254d4788426e893,
'ehm': 0x4e1d4fac4b10ced64b509205d1fde0a5,
'fhm': 0x9a4ab5960b2e6d5051ec80f6fc699bda,
'ghm': 0x7cc920348532e7cc63e15024fe5c1a57,
'hhm': 0xb4427b1cbaadb73573aec9542c288552,
'ihm': 0x75a0b9b40f26fcfd635593f22e8b53c3,
'jhm': 0x82dfb88f80303e0d0e2ffdbdc9e82b64,
'khm': 0xff6b09ca20c1013494014346fa031100,
'lhm': 0x7d012fd69545d081459d8faac9291f21,
'mhm': 0x752e2c3e7781c90b889b91dbbb9aafe5,
'nhm': 0x1002cb77b33aa97220dbb66c3647f03c,
'ohm': 0x35464785f1a8901da7d158145ec47417,
'phm': 0x61cfe49699306554470c69319e14a432,
'qhm': 0x3217d347b8df745b3c12fd758bf4dd9e,
'rhm': 0xfa37c97d5b4b7910e0d514d12a0844a2,
'shm': 0x99ebb038380e15bc896c3a17733ab484,
'thm': 0x0d379c25691f1db02fd2dff786ffd19a,
'uhm': 0x5e72c75e893c579d4c3dd3d1e8b22355,
'vhm': 0xe78956424c6e3afaf1b751bfdfcf1b5c,
'whm': 0x4ce31317e5393930a4863ae0ddd5c172,
'xhm': 0xa30b9cf363854f49e7c9323d8a256510,
'yhm': 0x59e13c2327dbb1aaff5904e0653a14b0,
'zhm': 0xaad3b6efbea6ac3d2fa97e14098f8eed,
'aim': 0x4204e3a9e60327e2e86b5877bb856e1d,
'bim': 0xadf1838ff29aae4c44a7f23c6ebffa26,
'cim': 0x2a0a0798dd43023ab30bdd4a777f4225,
'dim': 0x563728df0fdd90631ac1e51258e2857d,
'eim': 0xfcbef5d8ac4993b1562f9f5e15d0ada4,
'fim': 0x3eecdb19eca9c1fd5d7ec39b71617019,
'gim': 0xda3e07e8edb2eaa315e8c7a7af7ad03e,
'him': 0x664d242a7528bf4230386c9ac1a437f8,
'iim': 0xd78c060f6347e344f51a6ca296c8416b,
'jim': 0x5e027396789a18c37aeda616e3d7991b,
'kim': 0xfb1eaf2bd9f2a7013602be235c305e7a,
'lim': 0x499e0d7e3f2f15a72fb4d114388bcb0a,
'mim': 0x8e7f86260c88346052cadd7d68514184,
'nim': 0x51aaf9dbcf1c573b12b329a5668ec05a,
'oim': 0xd96deb3227838e313971c94fa78eeee8,
'pim': 0x8c4db68ff02f4fa64ef6cddb12e69072,
'qim': 0x1ecd320d1c04ff1747b716f8c10fb0b8,
'rim': 0x36dfa7baee39ab2ea3d2e226a5f00c52,
'sim': 0xe9064b74d28acc053231170bb8c858b3,
'tim': 0xb15d47e99831ee63e3f47cf3d4478e9a,
'uim': 0xd1065563a0ca2c814b0b7037445a603f,
'vim': 0xf898198629bb686f1dfc3d1ac8b13506,
'wim': 0x874e1de3766969edaa069f98aa2e6f11,
'xim': 0x009b9849559671be95141fc3a5ec6c55,
'yim': 0xb267562f4175e137ab7ae4b9465e3314,
'zim': 0xdc0a993e43feeb583954acac26da52dd,
'ajm': 0xf4330945803ff0f8b581b5bdbc459ee0,
'bjm': 0xfaba3e98e029c12e6e50b3c442a4afc8,
'cjm': 0x95a601fa7ed200a8d3629ccef938ba4f,
'djm': 0x55e4b2a6201be8555b56827dd6efcac3,
'ejm': 0x7ea396786dca23cd2888510a935988d9,
'fjm': 0x7db0224afa957bf04b573c9346a33882,
'gjm': 0xd9c94ffffffa698accf2ac91ab61e1ca,
'hjm': 0xa2ad0bb0a2cdaead866f1166ad6adb9a,
'ijm': 0xc93d0b30e6bc9b6f4221fcaa4726bf55,
'jjm': 0x4e35703686d8768b080cc7427b68940c,
'kjm': 0x20dc3ccfb75c8c0625c21f3c7066a160,
'ljm': 0x099b8a247543f5b74c59f2612e20d4db,
'mjm': 0x2f52919d096cfcd63b631c2808080854,
'njm': 0xe6b81fc2277cc4ebc2e09f9ec378bf52,
'ojm': 0x239dc3ac00f749525d2acd4d7811c48f,
'pjm': 0xa3c9e7ef3b4329d2077a62394a2ce884,
'qjm': 0xdd61676a1f9431f4cb294774d0dd6403,
'rjm': 0x50432ae983e6cda615525bbb65321b0a,
'sjm': 0xe4d8b0fd1e3bbfa8c38486b9f6aba0b0,
'tjm': 0x0a7a388fb0a892fc4ab055a1691eb90f,
'ujm': 0x977cbeefdf0561f02cfb1720bd3153cc,
'vjm': 0xe92c8bfd7120408346c8f9bf912a52d6,
'wjm': 0xdb945bee77209348db6c1921ae537aa0,
'xjm': 0xc2bd16b65094ae96df8cacc67d8526e9,
'yjm': 0xe09a97391f2f8e1cf98c192c7a1c093e,
'zjm': 0x63c001649527357fde5970c4688b9ee5,
'akm': 0xc6a7d2c304e4c34d720c900db714fa40,
'bkm': 0xad25914c3f825614df4591d22921f970,
'ckm': 0x574e2f69c9e64da5b46ff85d8d12f01d,
'dkm': 0x152110891a7ba3ebb92ff89225afce3d,
'ekm': 0xc4c0ee3a2e237bb7ed539604b18629bb,
'fkm': 0xf628f757dd5ce774425e8a2b40ed8003,
'gkm': 0x97d119e92f570e63d9ef016d6488fb91,
'hkm': 0xf8f782fb6bd0f7cd5082a1bfa1b92ac2,
'ikm': 0x95d411d4555970f98cd4aa0ee41e4c1a,
'jkm': 0x35026e19814e32ec84915896555f72fa,
'kkm': 0x4c4c62c41d067ae024ab441daa6bb2cf,
'lkm': 0xf72f6bc7b9a6a6b3f8ec0a9aec39c097,
'mkm': 0x5465c752d80e308e8efe81d1416a38d1,
'nkm': 0x0ef8bff49446d91676536249e8e0faa9,
'okm': 0xb0bcae18e34a2c88146c298de0a6495e,
'pkm': 0x0eeb040f578112b0406d7cb2fd991d47,
'qkm': 0x1924e8be3761cc3abbb596d464499fed,
'rkm': 0x03830f2f57d0d7b885c3b4dbc2e625f8,
'skm': 0xd7393767a71271956f24cdc608b70688,
'tkm': 0x07df82b0963bd280ffea6f97d07f7e46,
'ukm': 0xe530f0bdad294cb3581855e5f839a8c0,
'vkm': 0xa53535474824a962067016b6592b2f88,
'wkm': 0xf31e337d2c94a2a6c53573498711661d,
'xkm': 0xeb8a2a8b2785160a3312e3810112ca56,
'ykm': 0x8a36b7002d856bf15a500d93ef7e0b1f,
'zkm': 0x77025d12ce6c133a35d0634fc998094c,
'alm': 0xa556e81e187bce6f3718c2fc19a051d9,
'blm': 0x66ca4e6e789bb1adaa03b647781f86c0,
'clm': 0x2506503fad84d714cf17d262244bb709,
'dlm': 0x5e9ea02b8c8f2a518cc3baf511c6a83b,
'elm': 0x9d409b75625b18c4fefcf7486f3f2e2a,
'flm': 0x4233c98bed01420f82087ec799d7077f,
'glm': 0x37cbb7bcf13982de0bd787702c642dbf,
'hlm': 0x95d936f124af05ac385691acf0165549,
'ilm': 0x9eef4856be3cd9a38cf2305172624fe3,
'jlm': 0x24652b8a3406d9c552e90a742dc1a4ad,
'klm': 0x3491f0dc1059a35bb1681b3bd67cb0d5,
'llm': 0xdae1be85dab3650eb56b87c4e3390387,
'mlm': 0xf59ec041772926a34e142c16940cac1b,
'nlm': 0x289cfed0541b56f13fc60af004b8814a,
'olm': 0x417fdd822daffa6a09872ff90a8ae4e2,
'plm': 0xf1f501c2c23fea8dfb0fe1af25b879d1,
'qlm': 0x8698880ae3580267ae09fa2a262fd076,
'rlm': 0xd576cfe2d8b06e5a48bed07eec9cf949,
'slm': 0x75361f30a6a14a05b8bacf8d79803f31,
'tlm': 0x63d266506383ed9b4152479c1777cef9,
'ulm': 0xe90c98c36185bafbff8914c55feb149f,
'vlm': 0x6ad5b816c9f3cbe5103970918155a0c5,
'wlm': 0x4e8fb1745ac38bd452eaf946c55343dc,
'xlm': 0xfac178d584386a5b9471a65c52389ad5,
'ylm': 0xf5f686f10816b5ad17dc9cfd8d947c0e,
'zlm': 0x2df8105a4b2e71b62a205d76dd269ae7,
'amm': 0xce6e4bf499ee49f76d6ce2eb5a3115d6,
'bmm': 0xebe8bdd53b9c99d87dca0e5d36510036,
'cmm': 0x395a1b4f371be78e7078ad92362e1c83,
'dmm': 0x340d70f50a7a4507bc874c8108bb45bc,
'emm': 0x7b44298a77d1a902416de89214c4a96b,
'fmm': 0x45b5b81c8e2420a7aac2ff1c475711f6,
'gmm': 0xbf11722fb797d25e6af3c9e8ecb6a3b9,
'hmm': 0xa5175faf6dc24adc7eda4f9cfc721b47,
'imm': 0xa9c69f2215acca631d4656a30b52db93,
'jmm': 0x6e8c19b0ca409458f47c0bca4630ab11,
'kmm': 0x9332256b50468e6c9c462f45f52600e4,
'lmm': 0x09e2c0eab4e2ef27af2dec0e5fe17c23,
'mmm': 0xc4efd5020cb49b9d3257ffa0fbccc0ae,
'nmm': 0xfd61bea7b915c34d8aafd2a551e0d6c6,
'omm': 0xda582a3883324445f7cb5879c293ed22,
'pmm': 0x7a11e2e651eba657fa3c9737972ac371,
'qmm': 0xb92c83187031cf45f7c7c7efa645733f,
'rmm': 0x5b7a133ab395ae4bcff512b9f77db9c5,
'smm': 0x68007ca905924c65d5b0dd76f91ea63f,
'tmm': 0x1ff4ef142c491de35413426f52f1f096,
'umm': 0x218e09bf3b85f3e71a48dcc56c2d31ee,
'vmm': 0x8e91fa94a50a4248b3a837eed98de4a2,
'wmm': 0xbcfcda141f39cff792d1de4c8fc9bf51,
'xmm': 0xc0c3d6a88dbeebce81a922eea60487b3,
'ymm': 0x767621b29afd2636bec26c57d24c0b2d,
'zmm': 0xb9766159f383526eaa0879c331e953a0,
'anm': 0x4d36bbb707be4bbc97fd28af68c1fbfd,
'bnm': 0xbd93b91d4a5e9a7a5fcd1fad5b9cb999,
'cnm': 0xbf21a14d8b0eb60f8b95ae0df61162b8,
'dnm': 0xb3abcead6332c68d1a39d291bd7788db,
'enm': 0x36ad48fcfb0548fff8271387d883718c,
'fnm': 0xb3e3da0c87d5c425c242968b3cd91a69,
'gnm': 0xe61d5c61e0d448d27234f3527e05bd3a,
'hnm': 0x33bb2e62947fcac530258d5d61596f3f,
'inm': 0x7ca0c3612d976df8a9c929a630806157,
'jnm': 0x1034e7a7fee591dad4cf01bf89ce8fa1,
'knm': 0x556767353523f529a972d77f8d59b172,
'lnm': 0x2d7ed64f26d2574033d2cb4577540564,
'mnm': 0xc3d6ebaec84ae7a23309342eb5d6b225,
'nnm': 0xbf84f722e7d45e7c8bf4f1d2fb440093,
'onm': 0xc9e936db3865680915db9fef0123d8f2,
'pnm': 0x447a1052a85d850a3c7f7d0729b055e1,
'qnm': 0xbf031f9a01b7ab0bbc1b71a071742984,
'rnm': 0xa9daab1fe1a658b04d85bb6a93ceb000,
'snm': 0x49878439e73c29faa6b19ab9a2f00d9e,
'tnm': 0xa651a63a9eb8cb8d9b3b2b43046af321,
'unm': 0x7ea01c6cc4753f4779a2d81441fa5c0d,
'vnm': 0xbde14b7fa51c0513c9d7f6bb628036b1,
'wnm': 0x374b1e3e0225078b92fc9b94aed447bd,
'xnm': 0xf4a0d82ace031c1a84d314980ac82692,
'ynm': 0x449dc3f9e7872696d99c8b8dc66e89b4,
'znm': 0xdd171117ecacc7162e896364ca8353fc,
'aom': 0x8fe64291c0fa7e9be8385dcae9285fec,
'bom': 0xe2e6c938b1ba54909ea0b0952235bfaa,
'com': 0x4d236d9a2d102c5fe6ad1c50da4bec50,
'dom': 0xdd988cfd769c9f7fbd795a0f5da8e751,
'eom': 0x9fe9245f628fd735e094285801ef2c36,
'fom': 0x3b948bd152e89c741213a2b41fd85f67,
'gom': 0xa1cc8d8f4f1cf0ff9bb6753a030fb1b7,
'hom': 0x269c8d954ef79b0c5df6e16f70b70591,
'iom': 0x85df6983e2f4d18dcb097a2a53bd9e40,
'jom': 0xd4778a7f5b6a23a1bbcfe643565cefbd,
'kom': 0x17e65284428221c090fb6624d05f4102,
'lom': 0x8493a4e8e4662fce2ac8e3b103fcefb0,
'mom': 0xbd1d7b0809e4b4ee9ca307aa5308ea6f,
'nom': 0xaee37c30f5d091a495526f636a3527bb,
'oom': 0xb64777a1172ff4005ed6cde9b70d7ca3,
'pom': 0x03806f268e34cf63ef9440ae24cf4580,
'qom': 0xdfb899965e6f2653786146801e32f398,
'rom': 0x5f397a1e588cfe96b4aa4bab7a5b1d44,
'som': 0x8b67fdb219b633dbc3369758651dc9f9,
'tom': 0x34b7da764b21d298ef307d04d8152dc5,
'uom': 0x1ec6fa0eb3a50eef606beaf68608a981,
'vom': 0x9c92413d5f9ccd620e3ea70ee918e001,
'wom': 0x57a124d4485dc56a6b572f8849685950,
'xom': 0xbf4efabe81faadd6b1af5769b6a08216,
'yom': 0x3e699674ab3df46f91393e8c28ce817c,
'zom': 0x189cb858653ad61cb4b64587be45b7fd,
'apm': 0xf7da92dee5768f827533bb5a30d353ef,
'bpm': 0x2dc4e4a6fbeab8a7f828efa9aec7d7ad,
'cpm': 0x4430671a90eb2985b57a0bbf5d6b85fc,
'dpm': 0x49677814b41e9ff6cdc497c85472a4ac,
'epm': 0x2f0345f5824f4cce26201f4b74fb7014,
'fpm': 0xd172fc6c98a3636e4549cf5ce8228ad4,
'gpm': 0xe96e9ae011f66360f6b334de29435a35,
'hpm': 0xf0e0d3695786719982f3f65f78e73103,
'ipm': 0xf2f4e964f79d05675528fb34fe16ff93,
'jpm': 0x6438b9bd2ab3c40a10fac35eea175637,
'kpm': 0x1815c0d9a3e1c713671ad6c9c9dbe9f7,
'lpm': 0xcd0c321f9890a2ca4e4fadeacde8726d,
'mpm': 0x6b9f893f526fef95aae04d527b862084,
'npm': 0xbb30e85411b56df41296726ab445dc8f,
'opm': 0x3f3fad9a74e5ddb24a3e6bf56999cbf6,
'ppm': 0xa06e2d2a6164447fcb3aca27b61afd34,
'qpm': 0x2da2163d205775242611a3099142516b,
'rpm': 0x455385f5d8493917cb83541909b7cfa9,
'spm': 0x51762626b4f785729159fd35eea74deb,
'tpm': 0x6e053d298ab2fe4bc4326c56f4cbe649,
'upm': 0x287aefcf7af1a0d89bc7c914a00804b1,
'vpm': 0x1cb0ef556e82af4c8dbb4a743245058a,
'wpm': 0x681eea918f930a0fd546cf444a35b0e4,
'xpm': 0x2cb45657bda5decf6216a404438f0066,
'ypm': 0xf3efb8fd8d00bf5cae7ddb48fb8aa5ca,
'zpm': 0x634c5776e5b15998cbc4efed23556179,
'aqm': 0x31906db4e9ec18dd62dda18eb8c6d184,
'bqm': 0xad4a8fa5168e48ab02867344784b6782,
'cqm': 0x1bcaba28c815485c2219b71cb4201eff,
'dqm': 0xc4eb03fe23fb9cdc5b8ffe9accef54c2,
'eqm': 0x80303cb519526370aea2d8050092dcdf,
'fqm': 0x848035cf3307f1a4b424103bcbe9f791,
'gqm': 0xf8325d4b66a2662f3e2efd9fa6a38812,
'hqm': 0xeb14e215435170b2a292ee93a6850025,
'iqm': 0x3d19705f99fd92c1160a5b45bc9cd658,
'jqm': 0x3e3f82061e09ed2cf9f421885aa0fbcd,
'kqm': 0x2f87a597648bdee2f574b2ddd0dc29ec,
'lqm': 0xf6579ef776e0f89cfa775efe8fecf388,
'mqm': 0xb6f2c93599586e2f9caaf8cbdcbf1ad7,
'nqm': 0xf8f096c3bb5834b64b492d3afdee3af5,
'oqm': 0xe9aa759da731055db7549694e6b01813,
'pqm': 0x4d4c2b5b46b26ab8b0cf4dc83c2c93c2,
'qqm': 0x7782c00f3f7653827e194be46a6b7a18,
'rqm': 0x14e7754f6d7c27598beadaf3df96bd5c,
'sqm': 0xb056a01c4fb2b56f52dd63a4379f051c,
'tqm': 0xcc5d4ce55c7527b55d520efcca27b125,
'uqm': 0xba9c74bd77464d3e82cdc0df4424c707,
'vqm': 0xbe3931ae78b37944d7d5a389144b460c,
'wqm': 0x3e6a0bde82c59b583739e639e4e3bfe2,
'xqm': 0xe9895f0e928289fafefac9c0275a0419,
'yqm': 0x73b257f843a97516fa4de77052a617b0,
'zqm': 0x4d102f42c70a339a8c9721db9557f022,
'arm': 0xf926b3e222d7afee57071b2256839701,
'brm': 0x8d1d049f5fe5e5a04658e6b381c8bf04,
'crm': 0x1fece0cb254f1f45891bc9ee9fed72a9,
'drm': 0x0abf1b964ca65062ac36a5ff6f3820f8,
'erm': 0xd3e5b482609e714465ac95093e248d8f,
'frm': 0x2609344fa76552a3edb5d46040343c73,
'grm': 0x8cf4d375016adafe33c74bcfd663d32e,
'hrm': 0x0ae6717ed4b10a21cfd627685a748a46,
'irm': 0xab3406497a6c3885adac6bcc254835f8,
'jrm': 0xc24bfa3d880cedda3dd593c15be8f8cd,
'krm': 0xef5166365686c5a10baff9fa791d64bf,
'lrm': 0x5e04e1369aacec4aef9cdb89bb31d9f4,
'mrm': 0x3ea225914905c6f3b650ba84bf06bb38,
'nrm': 0xf23636b296d338ce6723373873e8304a,
'orm': 0x51b0b5a943ae2b04076f7a6cb037afd6,
'prm': 0x5a122789379b0d0733bab57433380e02,
'qrm': 0x8afff7417018b8f351b6ce1fb9f2c1c3,
'rrm': 0x322039e05b7d8660f61ce43f9f0611c8,
'srm': 0x3ee9b850c745f9c89b0faecc885980ca,
'trm': 0x17281b46b834719fbadc7a5d58eafbd4,
'urm': 0x53930ed8095062efd157aab47f0a53ae,
'vrm': 0x41a19412b1c25fb060d5e542051f82ae,
'wrm': 0x81bccac225d6fb06c1f1dd3356ad2a88,
'xrm': 0xd9745edfba03b5fa89ba19f2335218ec,
'yrm': 0x6dc3735bcc27e1d246559a4a232fb1a7,
'zrm': 0x338042c7bb02ff50b92cc10420df2cb9,
'asm': 0x7c5cc52fdb69c634bf9c216c1c2d001b,
'bsm': 0xaa134d9ad2fd2f677ea05c7e63323e9e,
'csm': 0xb4f8b027877dd73de5654b8def971c04,
'dsm': 0x1e78ff5eb56f7b5abd86c9ec674ed981,
'esm': 0x6e0920aaa21c79b11d39c7ee963ca5a2,
'fsm': 0x61715aca53dcd6265ea3e21e58fbb156,
'gsm': 0x0dbecd260563fcb60df7a96236926f0c,
'hsm': 0x75bc08308363144baf3b29af7c580e0b,
'ism': 0x4b84c266f8d8e99113129141357d02e8,
'jsm': 0x541e9cae763f4d68c7b0213caae71489,
'ksm': 0x2e242b44ce0ebe39f93a9d810adfadca,
'lsm': 0x32c0e8a1f06cda80353f0001347a2e2f,
'msm': 0xf58ff891333ec9048109908d5f720903,
'nsm': 0xc3d38f5a4baa830cb019c3836c4c570d,
'osm': 0x3a6f249d97ff4e63d73c5bcba782d885,
'psm': 0x7d56dcb370d296c12a02093a80702403,
'qsm': 0xbbb7c395c9a02fcde0a409780b4eeca9,
'rsm': 0x39d255582e7d35a14ddc3ff096d946fa,
'ssm': 0x078aab7ae8c1a0df339ac8525790d5e1,
'tsm': 0x35f0239b2449d5a7c3098817b85129ec,
'usm': 0x81e61a0cab904f0e620dd3226f7f6582,
'vsm': 0x022f238984fe564364b887c644f9d57c,
'wsm': 0xbdbf54e4869762318a2d86c05409300f,
'xsm': 0xe55a73427976620b632b59969da514a8,
'ysm': 0x9e64e2c2df149d1c6919042c958073ea,
'zsm': 0xfb809197726dc3f4e2cb87cbc4c346f0,
'atm': 0x5d8f94598af1b5110c615f34697fe502,
'btm': 0x0822bfbaf739793e74b46942932621ad,
'ctm': 0x06a92d444f803bcd723da6adc29d4029,
'dtm': 0xe7c0596d00d6d1d17e64d6547cd732cf,
'etm': 0x1c566f8b6267a93141bcf9d7518a3d5f,
'ftm': 0x0ca5aaa5f0c9217e6f45fe1d109c24fa,
'gtm': 0xccdd0722ef36d7096a20a9886ecec09e,
'htm': 0x605c21d571d96c2eba183c08166f5280,
'itm': 0xe0a30ac283dff16147fd7dfb7ee4cec4,
'jtm': 0xc04e12296601713cdcf8c7501494e027,
'ktm': 0xa02c31f256c5f68bcc211dac393fcb49,
'ltm': 0xa2f3e9a3316400d963b04461378df7f5,
'mtm': 0x09321b8d5a296ad4613d6618091ed098,
'ntm': 0xca604076b67989e3973eaa2ec679d022,
'otm': 0xb77ba8e8c4ca36ba7228316bce4b5ebc,
'ptm': 0xfea7d3777160dc607f527d769af42953,
'qtm': 0x24b99ff272a700fdca4dea8c075a68f6,
'rtm': 0xd70aa561de134b4f64f5120e90c7bdad,
'stm': 0x0e0fd492aa6247f68aeacfb1f00ecda8,
'ttm': 0x897d3217598a39a704a14d3579996abf,
'utm': 0xb32d6491ce03dd4e6c877f3bfd9ff07e,
'vtm': 0x416daca287da10f9c19156f9680f31dd,
'wtm': 0xfcba3f5e6a9d9d343bf46ddd371b0dae,
'xtm': 0x6e7baa68eebf7f2880193e3801708c7d,
'ytm': 0x730e21f5fff61d4467c6ebfdf423bcd0,
'ztm': 0xc72996e26fe650603d7b67bda1ab7d2d,
'aum': 0xd4bf664df6e52bc8b95f888d5313de1e,
'bum': 0x62552feb96cabdb632095611d79f0420,
'cum': 0xefde81f569ccb7211e56a522b8b55e5b,
'dum': 0xf36178feffc1db4b9b724cecc7aff581,
'eum': 0xdd73e86c9fe114b320f834d644efdc97,
'fum': 0x448679359e1c1af2c5a5175508713ea8,
'gum': 0x79a7b1f0b4044985f75e8a83533608f2,
'hum': 0x98858152b109f361d6a7324615da8c1f,
'ium': 0xacf216b5b2cbdb26d71a7796986ea910,
'jum': 0xb74c873c157a99278ae689fa9bdafce0,
'kum': 0x514fdcc9dc8f44fdf7ec5328ec2d97ad,
'lum': 0x8fc667f98b294b018dd179dda5a3bb2b,
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'hfn': 0x84fe9cd689a556584a3c0f65b7e6917f,
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'lfn': 0x45835bbf23d26719f1472fdebf47443c,
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'bin': 0xc1111bd512b29e821b120b86446026b8,
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'din': 0xf88639f78d5d6da6dfb8498fd5e763b1,
'ein': 0xffef75ef03094771fb011905bf579fdb,
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'dqn': 0x47099837642dc15b4e9aac0ba143aa7d,
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'oqn': 0x9a77df6d4fded5fa3bcd841ee7e68abc,
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'vqn': 0xed0c375e4cc2fd238864a67385500df1,
'wqn': 0xc285ac2e0d715483b8e3cdf7ba70e221,
'xqn': 0x261a77b4a49b2280187bfb803f53eaa1,
'yqn': 0x2a80df12027cf659887ca6c1869e466e,
'zqn': 0xf01a4c19a3110da469e61ca78421bb87,
'arn': 0x73578a9ef58da464f85f6223b683299a,
'brn': 0x9cc4263531f07d8b95a58813db7adb1c,
'crn': 0xeea926926eaceab1c94e5caff2f9242c,
'drn': 0x1f477ff95bdc7e0455ffaf06b474f4f6,
'ern': 0x45d21e6d284bb45a8b4919f8a3eddae5,
'frn': 0xa49bdd69f0b523998028a5fdedd96600,
'grn': 0xac01483d7b4234aab3aa9d50fde3982a,
'hrn': 0xe2d0c54dad9b3ec7ab1e7d5332daf347,
'irn': 0x5c680c6638580f00ea98d900a771a25b,
'jrn': 0xf14b47a77b35402a872b2aab18dddc75,
'krn': 0xaca0f382cccf451d4df050925bcdbc3a,
'lrn': 0x7d9c7d1747abfac2c3c3ff337712f575,
'mrn': 0x184d484f048611ceafee71fd988efb4a,
'nrn': 0x9c8179d96a554a79c610753c6ca438a7,
'orn': 0xc885be0d3af56b57f4702ea453c999ef,
'prn': 0x3c8362a4683179fef39b9cd1e91787df,
'qrn': 0xa44d2527d73c52c5da9f47837fa658de,
'rrn': 0x67ffdce18398d07ed1e9b7eea6981659,
'srn': 0x5d27cba81c3438772815e58fef3c5ac0,
'trn': 0xb4d11e72de6125a2166a5a66bc0b9072,
'urn': 0x904ecb078524aff16c7ef2e7f8cc4b8c,
'vrn': 0xcfceceb56d8e015af956be2d6f37bbd2,
'wrn': 0xbc16120cc689469b699549608856a110,
'xrn': 0xe062959735d3b519911fc325e0acee10,
'yrn': 0x75bbd87ff69383b8eedc5d5154e048ed,
'zrn': 0xb40d9c2242d872a1966e8d21a9e43bfb,
'asn': 0xc0b1c7977ce2f9890950ba9099afbf99,
'bsn': 0xa65bc173e472eb76a429b0f1b18d27e9,
'csn': 0x893e95afcdf6b9ae131b43f5e3ed025d,
'dsn': 0xda532bf806defa26fdbeee5dd2e0d68f,
'esn': 0x9bf858568cbc659b09ac70a07bc11122,
'fsn': 0x9d61ce4a1685ee8b4020746b752956b6,
'gsn': 0xd85cd0c5294f77837e5d9e2c39ba9d56,
'hsn': 0x01c17306aec8a8dd1772c77e25c57daa,
'isn': 0x4f612977b92f8136c448f4cc86222efe,
'jsn': 0x13106a7fa7e7934825375707b35df5bf,
'ksn': 0xc1e7f7a15094e4f06c2ad833b3e1ed12,
'lsn': 0x89b267753b54dd3686f235ab7793d6d4,
'msn': 0x289963eb2755a22c95b8df711d30e609,
'nsn': 0xbda6faa04180e97ef40aaeab44a1fee1,
'osn': 0x711c2cbf1799b8261a354f35b801ef1b,
'psn': 0xcac464deb2b2bd3dc39218b649769f4b,
'qsn': 0xb7c36d33c2cda710d57f908f6d840209,
'rsn': 0xbecdae1671089315cabd8d429edbfbdb,
'ssn': 0xaf26ea236ece5664803814d7b2f0a04d,
'tsn': 0x57895094b7065dd61f288efa5d9e6105,
'usn': 0xdda3497d64689881f42987be8d45beda,
'vsn': 0xc6e26fc65f1a36b2535441514a1ab128,
'wsn': 0x57d44312047f5f7be000f66286b02cd1,
'xsn': 0xcadd74a5e61b16a575236273551e466d,
'ysn': 0x8523600096774479979ec183d6d2e505,
'zsn': 0x354943969700aaa4e8e9f26dad004d9f,
'atn': 0x44bff383cf69a9928abd7ac8b0186340,
'btn': 0xd4f5c2c94881b8db27f83be1703bfbef,
'ctn': 0x02587745dbd71d978954203d7ec78497,
'dtn': 0xc2167a4bab398128621c130f049ad076,
'etn': 0x0b7361cd4dd0c0d762166a8c36709585,
'ftn': 0x098f270a457f304ff35e5363cf248562,
'gtn': 0xb9cd9b73428cda4a83651ad1658b439c,
'htn': 0xedfc7f6a0fc2644009d10758efec13e0,
'itn': 0xfff0e5fc42e03ea2f8c3f122ed33def4,
'jtn': 0x8f77341b623817b3058446690c70dd6d,
'ktn': 0x4136d79861c2787b991b3988cc5fd5d3,
'ltn': 0x9eaaad7398db84e515c9e077dbf07451,
'mtn': 0x62cb1ac486cd58411e3896aa9554cb9c,
'ntn': 0x3cdbc27b0c9c816de68219d0d73e310d,
'otn': 0xab223f6a502b3b3f18831793682c9ad4,
'ptn': 0x2221cf61477a629b44fdf5760bc94821,
'qtn': 0x34c020012fcb01ff0d796fdee750a2f2,
'rtn': 0xa1ddc56bf1413a90046522963aaf37ab,
'stn': 0x1165dbc83b4b66e5899dd280dcbedf86,
'ttn': 0x828bc84901a9456cc7993734f1972e51,
'utn': 0x419b2c341980aefddd47b961d04833f8,
'vtn': 0xb98ac0baf9d5adcc2dc9c0885c8cdd02,
'wtn': 0xa978fd3c5d9c2b64021e72d2865d7db8,
'xtn': 0x7b588a3c40c5d43be78906c74b9f56a8,
'ytn': 0x4213cb3a26dfdbc940164d3511a29b8f,
'ztn': 0x2317362743a7554f0e110112dfee6e3e,
'aun': 0x17dac098de19f24beb0f6540e0c9f09e,
'bun': 0x93aced76e8e70b113e0162fbe96788a6,
'cun': 0x343dd85de18b3e3fed5494020e601a45,
'dun': 0xb89d9cbe507a01c3b78cba67642f2ba2,
'eun': 0xe8789016e2f38144e314055e49156e79,
'fun': 0x77004ea213d5fc71acf74a8c9c6795fb,
'gun': 0x5161ebb0cce4b7987ba8b6935d60a180,
'hun': 0xfe1b3b54fde5b24bb40f22cdd621f5d0,
'iun': 0xb363ca3b52222ed6c6cc6b1d2df18762,
'jun': 0x6b5843ce9d2d0599c3e3ce6d59c1551f,
'kun': 0x51711d3cb95945007b827cb703fcf398,
'lun': 0x03628ea331120176369891df319619e8,
'mun': 0x6585f290ce92c3de5ff339920330e26f,
'nun': 0x0fd7a44fdd13ad881b07c0446d857a1a,
'oun': 0x83059a5a2ee38c7f6a6dff7d0edafd89,
'pun': 0xe950d3a08eed4467ba0ab10120a83a65,
'qun': 0x8d608533a82b029ea828bb8c0d89727c,
'run': 0xa53108f7543b75adbb34afc035d4cdf6,
'sun': 0xebd556e6dfc99dbed29675ce1c6c68e5,
'tun': 0x842e1d0c0e58780bb0bbad5306b58856,
'uun': 0x923fbf40785ce02b72f85ab1d075c24d,
'vun': 0x19c5a809ce682b3553e913c9fd5007e3,
'wun': 0x95c54a882fe60bcb1ad3ab17f2abe499,
'xun': 0x07b962f39c8b6f561294c1adfc6a3b6f,
'yun': 0x5f1d65f27e370c36dfd845f6dc78b869,
'zun': 0xe439749ff9d63e3d336f057bd23d88b6,
'avn': 0xa9460a7088f0e9d34b930e907f656974,
'bvn': 0xa08b2c27a45a44d273f4252bbe7b7e3b,
'cvn': 0x0a516289150443ca820643c0ea289e3a,
'dvn': 0xe0edb10a84e269826a638cf87f79f414,
'evn': 0xa21fa7c67f26f6d49a20c2c515fb7a59,
'fvn': 0x61f02c0a093c1c270fec39aa14a3140e,
'gvn': 0xd03e4c39d387a74e2c99c8e477d5e182,
'hvn': 0x5472ed1a68b60250b485f333d16315e0,
'ivn': 0x396197fdcc7810cf7b5e56c54f298da5,
'jvn': 0xe2bd4f48ef6667ad18b60c1e8f34deb7,
'kvn': 0x704e06d2cacdcc573faaebfbfdf18f19,
'lvn': 0x0c42e348017394e0248698e6728ff994,
'mvn': 0x67c9500deb0863c5a8faa70b245d939d,
'nvn': 0x3bb8c28ccff4138ef47d3c895849ca3c,
'ovn': 0x85aff5c95ab015afa7267279a52bdd83,
'pvn': 0x573c1393f9ae71a194878ea05b9d5e96,
'qvn': 0x4793450d0491e0a52c49c089215e9f8a,
'rvn': 0x7f16b79669248f9c60a56f597ea71fe5,
'svn': 0xaf04a1aa0e00f476768e6030957aaaee,
'tvn': 0x441a1754ea7709febd9371cbe8e41d93,
'uvn': 0x6a92745c7d2d4cb5d4beb05a18fbac02,
'vvn': 0xd7d65b5e67d5acb21d135aecdfaf396a,
'wvn': 0x2d4898af7c620d930cce5ef06f46cc17,
'xvn': 0xf371b5ea2e4a148464e66ab16886e339,
'yvn': 0x3d7509266988ef8dabf6b4d19afb0755,
'zvn': 0x34656848afc18f372054c3bc81c69779,
'awn': 0xcc6e79391434f01a9ec9b080c127a1b4,
'bwn': 0x6586f3db547859ca624fce18cbd4ec00,
'cwn': 0x42baa0c9b941903688ebf348878373b3,
'dwn': 0x07bad5311d5108d4fbd19b9cae6615a1,
'ewn': 0x07fbb87dc46bbecb3230d0c0a32906a6,
'fwn': 0x0c3fd7a0a32a92191711c6dd5cf72020,
'gwn': 0xf3cd1b71a393128f3e5d6b975bb4c2a1,
'hwn': 0x9aba84e29db37ae826383ac1ed89b1a1,
'iwn': 0x6816a0dba96924ee3ffe9b769ae9ec50,
'jwn': 0x942c77e36ec6309f9ed67db451af1e06,
'kwn': 0x90a271ac8400aa7f57b5e81148322e86,
'lwn': 0x1638dc3609ef47c115058165e38453d8,
'mwn': 0xec7e50bbcabc0e2b625564252c043497,
'nwn': 0xc147c861244b4bc3b1ec5213c25bfc75,
'own': 0xb515e18aa3fbe7d264d7ca5a95ef73e1,
'pwn': 0xe4a25f7b052442a076b02ee9a1818d2e,
'qwn': 0x49b8055a8a231b294565888f2bf20334,
'rwn': 0x03736955985725e152ae7c6fa979555e,
'swn': 0x90a2e8e77b5b13a56f31ada5ebea6de1,
'twn': 0xe84c571d92147b7a299976b4477d04af,
'uwn': 0x4081413e96aef792948f1e53a44c0855,
'vwn': 0x964198cee40b2c3a8f78c1c0c7113039,
'wwn': 0xe98d0fd89b5d30f49a3fa0297cc90882,
'xwn': 0x5d3dc213299866452a5d4bec09fbe345,
'ywn': 0xe653a5a6963c6bc8f5e254bcf788400b,
'zwn': 0x5ad5e7aa5d7dbcb710191e0f96ca9104,
'axn': 0x625fa0bfdb706073072b1d52884d47c6,
'bxn': 0xa245e22ff11b859c05fd9a10158ba375,
'cxn': 0xb48f5346f7a8ffdd57b1ab8be5bd51a0,
'dxn': 0x17558493a4975d129bf5c6b6510861cb,
'exn': 0x36d2389e97b04baa0d4045074d023280,
'fxn': 0x08b51f282d167b13f9630b4e9fd79b9d,
'gxn': 0x0461268ae8e78b98509dd7c47b9d67d1,
'hxn': 0xd50615d35e3d84c6ab5cb3136c34a6ae,
'ixn': 0x6c0718010696856cc017ed08fd37849f,
'jxn': 0xfdc20d9fd23fad6fd2b335855d7f1535,
'kxn': 0xfa487c847c9069adf3315b5914b8ed59,
'lxn': 0x12a25252d2be6ff545d961a48b8034b9,
'mxn': 0xd032e3046bf6c631ea86b8b4bdf2c91f,
'nxn': 0xa5fb28bacb7a089c066a495d30acb421,
'oxn': 0xff288df43c7d984eb600cdc29d91265f,
'pxn': 0xfca9ccaccadcb21ac9f2620861c5c262,
'qxn': 0xd7808126277d04d7bbe3108738fd3fd3,
'rxn': 0xc48124406402926144fc9595ef725e18,
'sxn': 0x7e1b37a8da8df854ef26a8e0cf6317f6,
'txn': 0xe45229a5f4ff5b90103c8eb1b03eac34,
'uxn': 0x554d3fa09758782173cbf2dbad88a9a3,
'vxn': 0xd8a996eb7fd3f7e891108689427e9925,
'wxn': 0x07353b353b2dc1639ca19c5cf048414a,
'xxn': 0xce4ed903a85d90915ff0eaa1bbfd7a96,
'yxn': 0xb63f486162a5791ad43c022f3ee0c897,
'zxn': 0xf3ff20c49fdf31a73dd89ffb9dfa521a,
'ayn': 0x9b559a398b50b2c2f3673613c3a8310a,
'byn': 0xb9534281a23b59ed18a838dbce072a00,
'cyn': 0x5beedb2de26da65b5d3bae7eea82283a,
'dyn': 0xd75ab2ecadfed7b6ea772f152b4fb4dc,
'eyn': 0x27e315101a64318ca08731cfb8f012b4,
'fyn': 0x9854682d71fdb60a819d9188a846f42d,
'gyn': 0xef341f83cc810609c0e071346231d5c3,
'hyn': 0xed7e26b70229a6b55145397a094e9513,
'iyn': 0xa576db05f1eb6faa7d1fede865a20409,
'jyn': 0x2f1857737fb6cff2a293b9da4da50755,
'kyn': 0x5eef2eab7100d7a5092d75039b836640,
'lyn': 0x9ac661074ffe94bdc10e9a29a1676ca0,
'myn': 0x3070910e4d327805abcc9a4c93566a71,
'nyn': 0xc467ad3449242d8bf06ce13acdf81ce3,
'oyn': 0x9983ae9e162e8951d759247663354bea,
'pyn': 0x97fa4f82b5e3f55ea13f7c78d56a024c,
'qyn': 0xaeab884e423b7c0fdf9c26ede803acc9,
'ryn': 0x5b5f70590c15568e20b1f295edbeba7e,
'syn': 0xd726760b0467b77803d6d1f3585deb6e,
'tyn': 0x4f30c3275c2e151fce1f2ec326eb5313,
'uyn': 0x2237f1ca05c83b94a5ec4c9332bb3bfd,
'vyn': 0x23143b25101e230e531c59ee0d50c581,
'wyn': 0x5a4dcbfb755e58a25ece0cd53ffebde0,
'xyn': 0xa743944e5ab343d7dcdab0bf4c065ce2,
'yyn': 0x23063c72e4bc01871069fc229872a594,
'zyn': 0x2416a6208e53ed5246075e76f1d87896,
'azn': 0x484f23b9cec22e55f49f51de5fb44df1,
'bzn': 0xfeea41da49aa2f9b86d0730e3ef26c0b,
'czn': 0xf0e74986038d75bcb094682d5c7a6b3a,
'dzn': 0x79d725f22d7c5b628920d8914305dcd9,
'ezn': 0x39c9535d2dd6d326a58919c91f7746d3,
'fzn': 0x171714bd530e84d9605ea5b407267177,
'gzn': 0x41dfee1995908e5d6487073c9f8dd428,
'hzn': 0xa8b15c8aae4e6e2ce35dc1a8b0cc59de,
'izn': 0x53ee879cbca545183cfb14251d8f9cdb,
'jzn': 0xfa15250665382328d199a52a36664adc,
'kzn': 0xe81e8789f326cc3d9925e5a2de04e50e,
'lzn': 0x5326376ab53cf14e4527e962e2cf4a79,
'mzn': 0xa262ac585e9d1b8d64ab427fbe12ddf7,
'nzn': 0x86080b0f3c8464151ec0d8b0bbffd0f6,
'ozn': 0xf64e2e54c8ca141a12f63c6e08f943d4,
'pzn': 0x9b755673ae13108bf7d5f117df2e527b,
'qzn': 0xb988a68e390d25ad5f74f4da2e3e3aa0,
'rzn': 0xd47bb849309f65f880881261f7f035da,
'szn': 0x37e7cfad10f84d7bcd379bdd8eca604e,
'tzn': 0xf8a04fc807586f3f5b2e56a83a77b12f,
'uzn': 0xa5d3c7916bcb65a4a12ef667b2f6f892,
'vzn': 0xc149fb1359604b1bc3a43eb4fa3b6913,
'wzn': 0xde6d7d977476f8d028d46c0aa36aaa31,
'xzn': 0xfc901d232813c192e5c4999fe38db962,
'yzn': 0x272a2909af26fc57cda65b4b54210198,
'zzn': 0x36b17792b006d78be8e9c2b1495c4d7c,
'aao': 0x2e9aed06ac37f19e27fe0bc7b1e2f6b4,
'bao': 0xb4e1c6620073acda217d807627a78dae,
'cao': 0x18452d47d97eb0f306c59ae38087fcb0,
'dao': 0xf0719ea8e993ccca9ffca5334b96f546,
'eao': 0x02a423994f9378742495fbe1a91bb343,
'fao': 0xae908e52fc41b704a1dfe3f182a3f702,
'gao': 0xf7174f24d005a88597b07aa36d388adc,
'hao': 0x32791e666fef96b588db16200d5fda94,
'iao': 0x364e4cb41e2b66e3de67c11aa73b8c61,
'jao': 0xb07c153de98af7e6ecda7ebf6d1a5e25,
'kao': 0xab2f0f023460c81e7ff570315684cef2,
'lao': 0xa80d6c8b0672a71f1698fe9fdcf48ce5,
'mao': 0x3fe8ebd7f5996651fa46c4aefe24b6af,
'nao': 0x4f876ab4493c98f6e241355c57136259,
'oao': 0xee05d1fc68a03e939d34e3e8916b6e31,
'pao': 0x655ae040aec1d7f78d790580c5ac37ab,
'qao': 0x839487bc5e9ba150d927a5cd3a691f17,
'rao': 0x44978a1316ed2a495d108b47badc18b9,
'sao': 0x8a1624682a9da908e331bfd17ea6d8a2,
'tao': 0x016e73c3c4de5f7c1b1dcf51aceaefc6,
'uao': 0x3f55e57fa340ee56b39dde323ba9209d,
'vao': 0x19854f060062f1e0f511cdd691a4bdd5,
'wao': 0xe58780b50bd8b247d66aa0dfe271e9ba,
'xao': 0x289332daefc57218fe125812466622e2,
'yao': 0x9852f6575c282db621d991fd9ced3cc1,
'zao': 0xb8d6dd03cd9c05342e62fce46510263d,
'abo': 0x1739f7c0a0c6e4792dc494d2e7801208,
'bbo': 0xa6b01fdb6566be44f5d7087e70ee730c,
'cbo': 0x2af9963ee4b47d5a36d078835bfc579a,
'dbo': 0x2d3964b96900117852ca2bf273e53a82,
'ebo': 0x16583432f50078f8532b86c0f458d3ba,
'fbo': 0x66352c28838e1cd07ca142539f213711,
'gbo': 0x7d4bcc2333d7a9b4e7afc272e50bcea0,
'hbo': 0x20590cac6bdbb1d87c1d1587da998b23,
'ibo': 0x71508b992ce8c43d8e723c0eba55186d,
'jbo': 0x2aeec165b5564fbec067befaf685113e,
'kbo': 0xb5fe8e8e5410690e6ec70d3762810451,
'lbo': 0x53646153b33b6f53c41e271c29e3a496,
'mbo': 0x95c969997b9dd2f4da2a2c4b268529bc,
'nbo': 0xc924f9e7ea81db1d94c71821ef039275,
'obo': 0x7f5b75c5bb4879bcede64d1f80bad9ad,
'pbo': 0x3c746f733815e351d3b551af4834ea27,
'qbo': 0x491ac22c8a7426dce5ac2202b6e1de31,
'rbo': 0x24b9c98b011ecef82d0fc37a73c4e386,
'sbo': 0xb5f62f4ea93ce9e41d250024c1ef65f4,
'tbo': 0x6a318d1c95f374d3b95e10a008c15562,
'ubo': 0x86da52f5d1befe78775d17b3567c3b0c,
'vbo': 0x7fc94f349562e914f0f4a040f2e4dcad,
'wbo': 0x8c7ed58b36bb2d0080cd6971eb4f12a6,
'xbo': 0xb26bcf0eb3102aec05a2dae3ecedf7d6,
'ybo': 0x10b8361f2afec510e7b1c27aa13aa58b,
'zbo': 0x53fd3186395e24ff424bd58bdf869511,
'aco': 0x111c03ddf31a2a03d3fa3377ab07eb56,
'bco': 0x29fb6a42d2932db53df39c8684105f79,
'cco': 0x3c0d3c39a68bf9b8553c773b587f8867,
'dco': 0x2a2a29b669ee482ff1460cb5dc84a334,
'eco': 0xe434dd9c7f573fb03924e0c4d3d44d45,
'fco': 0xa0721c988f589e35a7af39a47a1898e8,
'gco': 0x86748529b0e1ceb8f9e65ad5e259b718,
'hco': 0xe9935ca937c809ae406fa3251b1e56ad,
'ico': 0x65f034c0f853471ed478ceb34164523b,
'jco': 0xd4a5c51809dd0043818e42e15668d728,
'kco': 0xf080ffb60ebaa2270fcf5ff02116fa13,
'lco': 0xc912ca8496feeb832af69841d1241b7c,
'mco': 0x04550849567777e0066578865ddc048e,
'nco': 0x4b83647a6a1778bc9c87ceb211dd7e9d,
'oco': 0x9358677d90052f9d703bd10041ac8349,
'pco': 0x6c832797c67549302b62980d2d490729,
'qco': 0x37a2b56c131fb8caccd6ad9abb2690f6,
'rco': 0x9996d7160ead90605f993f6b14f095dc,
'sco': 0x36fea2d69dee02f2e652937af5156efd,
'tco': 0xb6711e605f676b4b7fd249fe55ac1e64,
'uco': 0xe0bf71cec1f86156612cb993585627e7,
'vco': 0xaf20ae28bf1d35589330d7b68c937229,
'wco': 0xa506f6d7081d4bec35993f3abde56b14,
'xco': 0x64dacea92ecd14557bad01b3a7aea796,
'yco': 0x7493646dc5d20d9d920249fde57faa25,
'zco': 0xe4f57ce3e850ddc0ed11a5cdb7a4df93,
'ado': 0x421359a899e6aeb972c11a26fb52ad15,
'bdo': 0x8cb9efb2ba9429ccb928d010e58e21e2,
'cdo': 0xe270be3f83015fc46a9fd7f678e0289b,
'ddo': 0xdf21cc51fb747f57287f6ad92b614785,
'edo': 0xd2d612f72e42577991f4a5936cecbcc0,
'fdo': 0xfe5b78fb1dffd3bbeeb718c07d6af577,
'gdo': 0x667520257228b6b699726d7280e2d76a,
'hdo': 0x1855887359531803275258ef304c7608,
'ido': 0x8a193602d32a8a504465077e16561a92,
'jdo': 0x87aa8bae864632040ed6288846a1d531,
'kdo': 0xd970c4ff926e1d59f8437d485674650f,
'ldo': 0x8d768bba4640bae4a214bf59a047de6b,
'mdo': 0x49e7afe4984f9f94060cf3ed0cbefd6d,
'ndo': 0xa456b778b5db40c6ec2ea0b9dd011f79,
'odo': 0x19b50610c5bdda534173da307f41d5ab,
'pdo': 0xcf66eb5563a602ddc983d03fd906b074,
'qdo': 0x28b0fbf1ccdde518d741293ecc5036a3,
'rdo': 0x70b93b49b07cdb1e318e60ca7547e73e,
'sdo': 0x412b50172b09b39009ec71a14862e873,
'tdo': 0xf7dd607150fbea09c0daa7447d699736,
'udo': 0xe342be854484f3f010c7801174d83be6,
'vdo': 0x0947f2a1758a899c2bb4446bd9d99ec8,
'wdo': 0xc5b14e5b8e64025e8e3681ca976c9343,
'xdo': 0x96f98fb0bebaaf1596bb6cb21aacc0d4,
'ydo': 0xcf8069aa3e4d6a7d823c4e8e92e4b1e5,
'zdo': 0xae00ef5479344424c3656e2e3162592e,
'aeo': 0x17ef1de89a66defbfec45e1b1b56a3c0,
'beo': 0x030723553bff7b61f8edb757befcd5c0,
'ceo': 0x55161575f3e05dfb61145c5d63d67d29,
'deo': 0x94c0eda37f941ed32631d2569f2329df,
'eeo': 0xd995bde7c4c15cad3c04306669211ec7,
'feo': 0x6d5f4bf194187e91e5c83ca8f207b754,
'geo': 0xecc174e3e02c82f34c14fe860bf47ef2,
'heo': 0xbfdb6633ad86a2ba23e1a2b436a844b1,
'ieo': 0xfddfd251a100fc8935ee7fa3aba3362f,
'jeo': 0x4d8d81e3abda21ea855c9663a1027b28,
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'leo': 0x0f759dd1ea6c4c76cedc299039ca4f23,
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'neo': 0xcb59608fced567a14b13a6e5c5c8a1d2,
'oeo': 0x944c3237bfb85f6219bf53909dd35c18,
'peo': 0x7cc68017cd27995482bd04adedd529e1,
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'reo': 0x4234a71a276f53c8574031e537d7ec9e,
'seo': 0x20abaf9d15742fcaca2a79b389919b8c,
'teo': 0xe827aa1ed78e96a113182dce12143f9f,
'ueo': 0xcf0e743d1655efa5d264c160e0e72854,
'veo': 0x77c3ccd511c4e8b1ac2919e3f2ee2bf1,
'weo': 0x625d888c025ed27a81fa44724827d0bd,
'xeo': 0x6c2c4c8788c22c78f974d22cd1e03607,
'yeo': 0x1c11825a7ca69b4a7a2b7b72f96f22c8,
'zeo': 0x1490fdd61234b3be86e09a89ecd9b0e4,
'afo': 0xd8bf830c53bda7d521fb181d50043972,
'bfo': 0x21e8ab48aef705621e6d8fd8594184ce,
'cfo': 0x12d872a3607f9925da8b73df02dbbfbd,
'dfo': 0xaa11485148ff63d232a9850e5c98a965,
'efo': 0x475b9abbf2fa36351ee30c79f440719b,
'ffo': 0x4bdcec2ee7142a086098809266f1c421,
'gfo': 0x6627e4844f9fdbc00e3d62ed24a3ee3b,
'hfo': 0x1bad0ded3d360ca570d3a6c6e4279bc5,
'ifo': 0x513912c8795ea653069133c2cf986009,
'jfo': 0x637ea37bdd5b0eae1ebf3b15e0e43680,
'kfo': 0x2eebab0c614aa58b7267a0a6775c7b35,
'lfo': 0x952a247102335d722684e232e1070cf5,
'mfo': 0xf14df9e163d1f89f48ad4374f5f0475e,
'nfo': 0x47b10a6182651bbf895aa98f996b70cb,
'ofo': 0x0d3112093ec9d0a16e71d5a67142ecf9,
'pfo': 0x9da1185a04b106de7b8f14fc4cd264ae,
'qfo': 0x3191c07552219ef0e798257efda8b253,
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'sfo': 0x7637f1dd77d2721eea2d59a0d3062974,
'tfo': 0x7ea9c296fcb3e0dd6041aabb3cb07d5e,
'ufo': 0x74f6904bb8dede5f8eaddcc20a2a31ce,
'vfo': 0x5d83479af1756e5fd60e6495d154d624,
'wfo': 0xdd050e21c24969033795fa4169932c1f,
'xfo': 0x9841217d5a4f9fe7380383990b667d22,
'yfo': 0x1d5a194abbdb4c3f2f203720c95b2d21,
'zfo': 0xc4dc62357133970aac1ccbb098230dea,
'ago': 0x6d0a578ca2f2c0e792fc923002fccbe3,
'bgo': 0x09bcc7eb9ba3cff5f477d7f3e53f2bca,
'cgo': 0x63e64fb7f6bfcedd6708e1b75126a6fb,
'dgo': 0xcc7c36759cf8c14d8db22e8a10c47feb,
'ego': 0x37349f07c95879abf625e8e7ae56170c,
'fgo': 0x28784a45cd8fd7f41748fa3a5323fd86,
'ggo': 0x1bfdfc95a8d44c2a4fe644ac85bab66d,
'hgo': 0x93dbebb47bb4e5bb48172a68fcf26b2e,
'igo': 0xd225adb4e7bd47bf65846f83634e4420,
'jgo': 0xa43fc443f7f09bf4b268c80207495528,
'kgo': 0x0ff80fce3834a360d50e82a68d79419e,
'lgo': 0xe132f7d4c73fdb84ba5f84bcfdcf90b1,
'mgo': 0xf98b79b5ac5ef914052aac2784ed8eea,
'ngo': 0x503df5ff92a0620d57b99bd878c4444d,
'ogo': 0xc9fbda3b9e7579c58c96c0730f9e64ee,
'pgo': 0xf8f90eb88254b3b2fd028d6c93d60c3d,
'qgo': 0x1b1c6d292068af620c5da011337248e7,
'rgo': 0x3593ddd8252a0272b5f3e5872f2bf51a,
'sgo': 0xdddee644d8ee1448ecc774007ec27769,
'tgo': 0x464538fadf1711494c21a768c14992a9,
'ugo': 0xc648565ecd714357f6c65ebd64a53ae7,
'vgo': 0xb49140d4377f74fc5f4f4d73965aae2d,
'wgo': 0xcf2c667fac54eada30fc90797210a58f,
'xgo': 0x3e6d3c8c7642bd42c7a3f3771b7b6171,
'ygo': 0xe97387a3f58cec271acf3300b13f8f7c,
'zgo': 0xd4db30a18b32a9bdf08702087f92defa,
'aho': 0xcc7bcfbc58c467939c5987584fc882cb,
'bho': 0xc482e7019f58862e2ec878cc33a5f9bb,
'cho': 0xbb97354e4173df9819936fabcfcabecf,
'dho': 0x7b4e15356095efef3407600f08f2a985,
'eho': 0xcfeb3531b0748c56bd8bf0768574cf9d,
'fho': 0xaf9b8425c69f022f3f993d61f7b56e89,
'gho': 0x2a8ab458436ba5dbeee858770b7c7fb4,
'hho': 0xd1abc848989788a69fa04fdba252cb52,
'iho': 0x43677ef786a02e00cfcccfcab1ff1db7,
'jho': 0x1427e904d3da3b984e2cd6e82d97d631,
'kho': 0xa34dd5325917405e6569ed9073effece,
'lho': 0x12acd6e5e9ffbebf64377cd2d9fb1926,
'mho': 0x6dfa39d663b109c4d14efb0ee5739b19,
'nho': 0x136351987899790ffcb77a7455a5b80e,
'oho': 0xa93e122045954df7fcd23442c589728e,
'pho': 0xd159e99a304e29b4a6c9526a4b243a56,
'qho': 0x0c565ca77119f51bf8c9fab83e56e280,
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'tho': 0xdb33f30c28364cd44195ed6105b82c29,
'uho': 0x8093809d4a03f09bf6f74e9fca3ad055,
'vho': 0x98aa4627ea354797fb6d9b46f95630ce,
'who': 0x53d670af9bb16ea82e7ef41ee23ec6df,
'xho': 0x2aaeb4e80bda652e84e42a8765881e52,
'yho': 0x4740c4f8ff79e8c5aea8aa6eccad0035,
'zho': 0x3ab671588c2234489378a52b9b542d31,
'aio': 0x144066ff0da09361c9fe2b8fd900c61f,
'bio': 0xe5ba7590156e333ef9aa4b9616a55921,
'cio': 0x4482571a249db31b3abed938e4567a15,
'dio': 0x27b205035c328b16d8c8329c4b41e87e,
'eio': 0x6a76c3e6f07462642ad175aa4b5043f9,
'fio': 0xf003984af6466a03625ae1b386ad8977,
'gio': 0x2bb55d712c4dcbda95497e811b696352,
'hio': 0x46266719a10cd345b20ad764953b8882,
'iio': 0xa88b77bc0c726aab3ee94f7a6b631d7f,
'jio': 0x3f4121e0b88f5a7cdb33d51d4205420e,
'kio': 0xaa5c7603cb655d47e15e0b477c379abe,
'lio': 0x52845fdb9e405f91e399aa170533be94,
'mio': 0x78c925a3a4b36984d1bcbbb01457eec6,
'nio': 0xd0a5fd04b4b48be7ee56c1eb538d78cb,
'oio': 0x5f9b7d3b5a7e5480051e989994ba9e99,
'pio': 0x50f8129555fdc09e97f6d0268cf4e489,
'qio': 0xfd589e45055532a103f1d014c2337819,
'rio': 0xd5ed38fdbf28bc4e58be142cf5a17cf5,
'sio': 0x40883add63f1fbabc7fe6158f93c1246,
'tio': 0xb71b3e083dd4533ed48421a696890835,
'uio': 0x2e1b53afa9ae8b6c43ebd0cd36b5c616,
'vio': 0x7a476522b745a28405719fa828fb9d08,
'wio': 0x5b12270b2f1c52cb9ae37905880a5a6f,
'xio': 0x5fb0bc4e7d110594272e4ea097d8c28d,
'yio': 0x8878ebb2cddc00171ff93a1ea3d3744b,
'zio': 0x2eceb9799023ce64bfbb0cea4a61fb83,
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'bjo': 0x038e79aac9a324501510d2cf56b206da,
'cjo': 0xef269dc2e084a7da968d4e5df668ee07,
'djo': 0x95303ed3db664d0d3a97e5c1932b5138,
'ejo': 0x7c223a673822e6b30e8da6925660145d,
'fjo': 0x25e4326ea1a868a7d2236fa3ff463402,
'gjo': 0x41616cdba093c1f595ef7b5564becc46,
'hjo': 0x04c3a7a0e2d5436d56a96416bf517cd7,
'ijo': 0xdb84be118c72a6e588d52376c3c7ec02,
'jjo': 0x200c8f526f8e0e186c6c4acd3924b6ee,
'kjo': 0x6048195783d4c80dd8b73b01a3fa253d,
'ljo': 0x152bc16930aee34bb20d825747727baa,
'mjo': 0xbf1f58ca55bb6a554b8fc8839f79f2f2,
'njo': 0x38cdd14e4d6e4df3062688b02ec0b1a7,
'ojo': 0xdfe75bd98c8d113650e101c33fe1a93c,
'pjo': 0x0e023f7fc2c4e726b62cb1b1ad675b28,
'qjo': 0x55167141b8020c78adc5223f2657504e,
'rjo': 0x114e46f290eeea7d7291266666af594d,
'sjo': 0x078da0408f8bd84541ef09352f65af53,
'tjo': 0xad34fd80f426b56ff9d5170c801d0920,
'ujo': 0x5cbe716cf4c59cf4b48e8060211be05b,
'vjo': 0x5327a4dbf82a874c1838104c6d340e36,
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'xjo': 0x03cd6f5b497a89764e93b68d60f23a02,
'yjo': 0xd55902399c0d6c9a12f9884322396c30,
'zjo': 0xaa01bda0c4dc76196891e8b98e2a4823,
'ako': 0x1cd13479e5609d79971c69051158a27f,
'bko': 0xd0476e1f7acbab67a431fff0d1c89c02,
'cko': 0xb2d988b589453487b6c51624394fa0af,
'dko': 0xa587e18b10e55606e1af6979c54d06b4,
'eko': 0xe5ea9b6d71086dfef3a15f726abcc5bf,
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'hko': 0x36480e35639a11cf46f1472e4f8acf85,
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'jko': 0x7a3ca4675948977ee545bb8447dc4fb8,
'kko': 0x3b93015c8e40296af24e5028bb764a90,
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'nko': 0x524ab479d6db7f1278939d884c8df7ac,
'oko': 0x1c1512b89aaaafdc925587db8205381c,
'pko': 0x0c34317ee4e158d07b142491260851ce,
'qko': 0x053d26b6139f036d350f446416db9094,
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'tko': 0x9f7d1f8896c67cc89d8d4c1292fbc901,
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'yko': 0x7845d4ce1fa7c4a01133f61a38977e76,
'zko': 0x877b172c377b9f06f0efeda30204af9c,
'alo': 0x1b2ccf52b54ea2c9468ca24fbe164919,
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'clo': 0x766b4914a2cf28a7523e9ca66b126ddb,
'dlo': 0xa861b5be6a3e9ebd3e39b462901544ee,
'elo': 0xa1d7dfb5eb8d81498eec33bc78f6a58a,
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'mlo': 0x6ec5c762eab34058ae6fe87c33c03330,
'nlo': 0x3f1e09865a26439426f6008e22848f12,
'olo': 0x8c12b8bed6df7665c0d557c30d6a2395,
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'qlo': 0x9e0748932e6870f81b7965ab2319d416,
'rlo': 0xd1bdd3e2cd78c5c728276e19f6c2688b,
'slo': 0x9847ef430e2d9e2edf0633deff643422,
'tlo': 0x638c7f5b58870b16303996cc99ce42e9,
'ulo': 0x5b31115d6aea168b2d0ff5b8156824ed,
'vlo': 0x62c98d505879b4b55fbc01aaa752ca6f,
'wlo': 0xe28b891fe1f5f31b6fbb30a3a0c3d501,
'xlo': 0x45d91d66a63036fea39e364f42a52136,
'ylo': 0x3f4811c88af2015b74f61219aee13af2,
'zlo': 0xa506eacb01125bdb14a83d515ad7cf96,
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'bmo': 0x0c627e29c77502cc3a437c32a315c9ec,
'cmo': 0x99330263c899fa050dc18add519cae39,
'dmo': 0x8b0d3b8f28a96c53b65b5f7e97973540,
'emo': 0x1aa4d26c82cdb31a3f2b58131c585582,
'fmo': 0x8996088844a68060f802af2dd49d9baf,
'gmo': 0xa73ce3a4c9ce4fbe2fadb397e2d6bdd5,
'hmo': 0x443e5821b7a0d3f9b4b716a7554023bb,
'imo': 0x25e47a973e616ca251cb3d068c6b9c19,
'jmo': 0x00dd3e850af1b39f5203d52f0ee96a9b,
'kmo': 0x6af32777f291044b4847c90be14950db,
'lmo': 0xc2d9c8297647370f1f992f2930c4023b,
'mmo': 0xe22901f8c931624d1bea8979be872406,
'nmo': 0x90a0eaee3c3f5cba0ef2e352726f10e5,
'omo': 0x47d3870b12334fd498ff0d95c7421ac2,
'pmo': 0xe9f662f92d4d40288329870c9a597afb,
'qmo': 0x0327035d47e9bc4bdfef1840b3096470,
'rmo': 0xd83f2ef11eaf27a7ae671dfb36d73f31,
'smo': 0x901efdb0bee100f33f3cae3d9bfcb998,
'tmo': 0x75d034ab0d8d8b42487a34f3c6cb29a8,
'umo': 0x4f504e6ae7efeb1790fd0bc6756159f8,
'vmo': 0x0d00b03b8e440e3a9241ef74b1c60c76,
'wmo': 0xed204dd9dbdf93970a84e4d095512022,
'xmo': 0xfbc86676157f8d6097a3d42dbb5ee1c5,
'ymo': 0x01dc97980a2ab28331972e56e34a44a5,
'zmo': 0x323479cb650804f1b19d4512aacecee7,
'ano': 0xbde9dee6f523d6476dcca9cae8caa5f5,
'bno': 0x5de904919c889c8156eed5bff8e0ad35,
'cno': 0x4ba95951fc613d26aeb57a131b5fd304,
'dno': 0xc2679f041d46a4bd890b378459a0661e,
'eno': 0xa825104f1c9120942a7f5d01bb91d3e1,
'fno': 0xaedc7e6d5dfb261a011997c752afe1c0,
'gno': 0xe51a850a368ab4b640116b33d60d3fdb,
'hno': 0x8e41e08467ce55fdfb07e0ccdba9801b,
'ino': 0x8ba744afe510a10886736e9c7bbc496e,
'jno': 0x318e635547261407dfe80be9ebda890f,
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'pyo': 0xf6fa3d0bac9c0a40c13c42ce551b5395,
'qyo': 0xb12db0aab94c296d59073b35468fb8e9,
'ryo': 0x86c40c09feb326e06f4040a5f781229a,
'syo': 0xe6ece7e1836dfe745a2b015fb2da8fc0,
'tyo': 0xd6a4740b2da6818f9d2da4fba80a72bf,
'uyo': 0x91164e326f76f2d8eeb8e5a46a5e33bc,
'vyo': 0x8f700b012cb7b77c969f1a4d41a88deb,
'wyo': 0xd2017f198e7e3796a86e898d6c264b2c,
'xyo': 0xecb1eb3b494c233c5974202c076960a0,
'yyo': 0x8675293d66e1c7620cd9fdb6f5b79bbf,
'zyo': 0xf04ac234da586c07d5b415bd14fa01a6,
'azo': 0x7bd74a0f226d4857206b4c47327c7030,
'bzo': 0x50890867d62dc81ab3bdf105255d4b5c,
'czo': 0x0bf68888df01ccae1f880102510e967f,
'dzo': 0x3cbbd114adc0484a9e7c48f715e296ac,
'ezo': 0x9d63699f0f22e5bd26de8d9723d30eb1,
'fzo': 0xd1603dc0963df18fb4a4fb85c16c2d2d,
'gzo': 0x0d985d07008b1031a501d1ccc91b2d69,
'hzo': 0x506137fa61b7e9eebd030d3db5cd9644,
'izo': 0x4b1aa2a91ab2b46552a3c98183b962c5,
'jzo': 0x9baa9780a4e78d31df986eabc49c2d48,
'kzo': 0xb6bc5a6b71ae828990d16ab1e3bdcd56,
'lzo': 0x2002487b42e97a1ce1fc9134caaebe0c,
'mzo': 0xc3905f4d68fec734fa36849b3a4349df,
'nzo': 0x201b7170038a18eb1fb73fdecfe50b98,
'ozo': 0xf67796c83b70023cd2145f92d4ad01ff,
'pzo': 0x7f51071de4249b60c231f0c17d921f0b,
'qzo': 0xbe16e78961c3e4076c3f0b8185013448,
'rzo': 0xe249bc5f12b7e51a92f18ace0bd9f997,
'szo': 0xf35351cc20620b55a2ff9b8c4e33d20e,
'tzo': 0x270ae5ff8566913422a131dee5613fb7,
'uzo': 0x66d8c73774740833569b56482041ce43,
'vzo': 0x3fed2d251e400c1373c0d5c00bbb81d3,
'wzo': 0x3541c882ba2d2a696e1d4f60f16a3048,
'xzo': 0xd3e810b3349b1e727f6be5d5a4d4fa8e,
'yzo': 0x57fcea9ebb73088f1655c224104c5ca8,
'zzo': 0xfa6465907750ad6eb49b0710512a1c1a,
'aap': 0x6b7850830ec34ccae7cf2179ff8b509e,
'bap': 0x0b259d72a0946cd30ec83efc3a185b3c,
'cap': 0x3d791e43c8d5561e36bd09bacc097517,
'dap': 0x30703582f2f532a305c33d25bf8f40d6,
'eap': 0xfcc3548990c8a1f93958918f01ca7536,
'fap': 0x492d3bbf08ba02368ffb7dd23544ecaa,
'gap': 0xdf9bcbd6578a1e49c06b7ec2874f9e23,
'hap': 0x070210c4d8e072953a1c38e69d8c9491,
'iap': 0xc743f6c69347b64850327e356f9e55f4,
'jap': 0xbf1f178a61616e1a5ffd1c4fc9d23e6f,
'kap': 0x560b51721f3767b514947799cf29820a,
'lap': 0x3a5c9f667568a07a36b0e6bdc378d5b6,
'map': 0x1d78dc8ed51214e518b5114fe24490ae,
'nap': 0x4d3b2b4b063b4e4ae9cc04579c253c99,
'oap': 0xbfc8d5cb8163a46f9a620b2859a82f40,
'pap': 0x4e951936957c783062a399c629ce9a95,
'qap': 0xf59357a377f0dd1daad304759050d56b,
'rap': 0x2e2d7fe5d75b602595df021c7841243b,
'sap': 0x4ef97a5db1519bec8ee8c91d62fcaa08,
'tap': 0x8f98645041aa7d5f53913329c6946a6b,
'uap': 0x33c9bbe3c3da866d747320d5bf204c80,
'vap': 0xef4c77142a02ba9ca17a4d59d6599576,
'wap': 0xca4d8c5af3036c2f6d8f533a054457fd,
'xap': 0xd22a5d41636a0f771dc7c7353c76410c,
'yap': 0x7b10a59d11c752fa1f4f58572c8e9705,
'zap': 0x7068d7a767a114139ca4fe8e6688c4fb,
'abp': 0xda472eab2468230aa83b8c627eed35c9,
'bbp': 0x52466b6d740f6ded52c1ca5b37aceac7,
'cbp': 0x87cd3c0eee9f00c352d165bbe1c1cf4a,
'dbp': 0x06b18ac8deaf14a6416183145c06238c,
'ebp': 0x309a098c21c2fa185cf5b08132d91d22,
'fbp': 0xb484f7c1a50c15c3d2ea3d7b89e5e267,
'gbp': 0x217d117a3dab048919928726fefa93c4,
'hbp': 0x1f388d7c32557558f03eb9b0c2d9a93e,
'ibp': 0xa92d070b53c1656dfa05f2902b5f35db,
'jbp': 0x6109373f50757f8c8d2ca8c8d07840f2,
'kbp': 0x219417b22e3ff314b55cf4c1a406e9e7,
'lbp': 0x47f10b9d69769491363ee49468f8c4e8,
'mbp': 0x83152927692cdd03d2f5aabc1971646c,
'nbp': 0x274b48405aee8489f6a05ef15c0f8ded,
'obp': 0xee9489d31bd650bde05aa7452b52a1eb,
'pbp': 0xb5d85fd3049866bc614228a4a6bb3499,
'qbp': 0x4b90ea834a632b15cbc33f3e173c1c16,
'rbp': 0x5fb4c09ffcf0a2564a2eca001edde421,
'sbp': 0x189f40693751c03b39f3b219b8dc5ed9,
'tbp': 0xb61cb3bed4d1f7da17b240817d4dacef,
'ubp': 0x9e428d55b99630df472b7c6de0a85bbd,
'vbp': 0xe68be015b3cdaa0ad32527415b362426,
'wbp': 0x8aae3659dff946ea375414874ef45e12,
'xbp': 0x745b43e6b36732461af224f549c2e6e9,
'ybp': 0x93de9dc504bbe6f8a509fc141de51d93,
'zbp': 0xb94b51904932dd6434ad0d16749e8d7a,
'acp': 0x1a63625767c3337a241dd501759808f3,
'bcp': 0x021d7ae2408319cc28713236004e707e,
'ccp': 0x4581dcab17ff2256544c5083c3defbed,
'dcp': 0x662cd4634fff88084423c245505f5dee,
'ecp': 0x7e831144e00de73638ae079428d6c803,
'fcp': 0x0f64d0456878c73cbec1ccea842a6f14,
'gcp': 0x50f66ecbc45488ee5826941bfbc50411,
'hcp': 0x463ba1e631ac9ed1c3de0b306afd561e,
'icp': 0x7a8b4dc392bbd09a753a7cd5ba93ca94,
'jcp': 0x006b683484a324d8aa6f37bbd7bbfbbe,
'kcp': 0xe9cec9456cdfe83ac5ce280d5e5cc0e7,
'lcp': 0xf1d72e5909355517b0cd1edb23c446c1,
'mcp': 0x42732a4781317edaa907b98b3c4786cc,
'ncp': 0xecef0fe35e6db01ebd02141d63277735,
'ocp': 0xd686d6b6d69ac3c0e41f349c922556a7,
'pcp': 0x3d27c2e24377377bdd907962a53e13eb,
'qcp': 0x25764aad7995e43f7903e6b1433d73d2,
'rcp': 0xe69ea6657e9b83425816b16002f6497d,
'scp': 0x8a444a43bdf534c0c6338bb7809659b3,
'tcp': 0xe20bb202b1d5537b1415e3263a37ed78,
'ucp': 0xaffcd816bb3a272336e4409414ff10fb,
'vcp': 0xa00c4b6cde53f7520c58686f38a46b09,
'wcp': 0xf28542b77366f8531d86fc00aaae3e8c,
'xcp': 0x430e727eb7fa4c8fdeb29b396f232465,
'ycp': 0x9e897ee90264172abff3cdd6f66785b8,
'zcp': 0xd356a9269128c0bfcc8e368a03299bf6,
'adp': 0xe219be8994730c07d8d6cafdbe9b6468,
'bdp': 0x29a2047bb5908c54034763e93eb92e1c,
'cdp': 0x2c8d34a64e38ade6ba2c7441caf32377,
'ddp': 0x892c70059ae7641e3e9b992abd2916f0,
'edp': 0xed9bdb883dd5d0bf37bd5d208a17fadc,
'fdp': 0x34a7437c7b63d371d229e911df4ab8b4,
'gdp': 0xe785c0883d7a104330e69aee73d4f235,
'hdp': 0x0b11a336692a80307a54d8a37f1c571c,
'idp': 0x5b41dd08733372bb8d68ab7fbe38b9ab,
'jdp': 0x8a20bda8841ac055e8e8d651476b42b1,
'kdp': 0x2b92359122a2f22509f310ca847829ad,
'ldp': 0xd4070a55bdda90f2131a8bbe1c6a3904,
'mdp': 0xaa36dc6e81e2ac7ad03e12fedcb6a2c0,
'ndp': 0x5a67cc3d761b13a9e94ad7f2ef2915be,
'odp': 0xdce46eeb80fc87bfd1881a81f0fc3afe,
'pdp': 0x452af0ea3b1515c56f8f754cde7d2ea4,
'qdp': 0xd1d18a8d0d549d33adc9af20d81fa83b,
'rdp': 0xd68353f7c23514268ef7f41e8a8c6ef2,
'sdp': 0x1d9be44688eefcb9b1360e9811002f99,
'tdp': 0x02096b2a6dc953a2dcb30f42e023f5b7,
'udp': 0x84864c1fe095359bc9c5ac068e24e781,
'vdp': 0x05f001d11301d7a00bae1275ee8492d6,
'wdp': 0x985f91193975e6fac6651f4f2562096c,
'xdp': 0x625faa5676d69cd32894b85f405ea14e,
'ydp': 0x2814e3d8439120d518c54514d42dfe17,
'zdp': 0x8390a8fee083edad3f2dd64180ab19fb,
'aep': 0x473c82e038db2e2e7eb3f7d95c7fbd30,
'bep': 0x919838c9051bea8fdc6aebf00860f764,
'cep': 0x6629091da97f1e713af6b5399eeb9846,
'dep': 0x2254342becceafbd04538e0a38696791,
'eep': 0xa6107c34959da76381fd9dbf03cfe749,
'fep': 0x9714e719adc6f5e447ce2275d505cae8,
'gep': 0x95d54dea76df0370cea278ab6208565a,
'hep': 0x41a081ff431ff42f4424d68416f369ff,
'iep': 0x4347cc1bf6f1250ca348c3df4b9cf0f3,
'jep': 0x453fb01a8a4179e360f8f908afacf751,
'kep': 0x94da4376f0d738db16804f11001b0284,
'lep': 0x08c6e52176ff56f11c5d8b98610c2d1c,
'mep': 0xe0cb4c0129d745ef73efbcea5d05a5c7,
'nep': 0xfba1957daf4381ea376c6b3a5c03fee5,
'oep': 0xee1888018e322918fbfeffb6068c8e3a,
'pep': 0x0c36948d4a0ffc05ddc7b9f2f7918cbd,
'qep': 0x4986574bf647747a1f74cf59fbd4c939,
'rep': 0x75a384057459ae8e69fb9a98a249b4f4,
'sep': 0x314e9e118b3026ce64b768b84a22d816,
'tep': 0xe6893ef750669444cb220cd64888e617,
'uep': 0x07dc64d1f9be36e79ec7eed9119b3e2d,
'vep': 0x7354bf4997fc6ccf8181103f52ea72d3,
'wep': 0xa94c684474f600c1d46b24fd8d16b2a4,
'xep': 0xd0a62c4d4670c3363529497f2c6360dd,
'yep': 0x9348ae7851cf3ba798d9564ef308ec25,
'zep': 0x4ad0cc43e6a5870cac575f93cb84f522,
'afp': 0xf45a1981a993269f904dc188447f840b,
'bfp': 0xcf88aa7ec1cd054346ef160c6cc9e6bc,
'cfp': 0x76285df8cb29c4959b0eabd322c24ffa,
'dfp': 0x173fe346f08f1be6ee139ee82913b43c,
'efp': 0xb439d7a6ea90239cd6ad0a03b17f6b3e,
'ffp': 0x589bdd587cc340652388aa0955bc067f,
'gfp': 0x087431a14c4c073d105df45429ab655d,
'hfp': 0x70b3b7a361005a049d282292b8d83009,
'ifp': 0x4c1a66f5090672d2d9c2ce694f73e20f,
'jfp': 0xdf6533ac9102f56c0c70b2a462d2cc2d,
'kfp': 0x98dd4155c9c8287a5a8e1d92417d0a99,
'lfp': 0x0e51c219713b61c7d4a62d8de4b423e7,
'mfp': 0x0d5cf3ef046e1a7b96483c32a7a67afd,
'nfp': 0x8674a0d522da5850111c4c932607f534,
'ofp': 0xc700cc98c8114ca6d0d7ab4c1ea81acb,
'pfp': 0x634dd08bc9a34bbadacdb1107215f5a9,
'qfp': 0x0163894f4d491cb172c7eadf02151555,
'rfp': 0x393a341469564bef4cde4771e2a43147,
'sfp': 0x75cf98be3d7af2fb6f43e6353ea0afa5,
'tfp': 0x323a1d7961b0e757a61634bd774b8008,
'ufp': 0x3be7d1b2096d2ca466f57938ef9d0bb9,
'vfp': 0xf98b595005cdf57f79068bf36b806b9a,
'wfp': 0x109503a85e82e0af7e2309455ff65d64,
'xfp': 0xf719014569fe4dacd12e6272c00dce24,
'yfp': 0xd4d98a06907deebcfe3e99b2eec07123,
'zfp': 0x7bdb1a82cc9d04b2ce6fceef187db495,
'agp': 0xee8a0c8a0dad4871ff990e2ffbce605a,
'bgp': 0xec50a8f4d28bcb39103379379ddc5185,
'cgp': 0x2d2e8be6f6a764413fc639361245c43b,
'dgp': 0x7a77910cfd8c77ab1b0892a537ec6103,
'egp': 0xeb876289867132d77635ee248ff4ebfb,
'fgp': 0x1d9d59f16e6241dd754393a2c274abc6,
'ggp': 0x93dbd6c4a9d6fc32faefb1efb17454ef,
'hgp': 0xd9daa31ab6b7a3dd6b52c7a95d05cfe4,
'igp': 0x427b420550992db0281716393c4b3b84,
'jgp': 0xddc8f2b30c42909a8d151357a70bcc4b,
'kgp': 0x016c1c01a72eca33c8b7e2fe4182e8ed,
'lgp': 0x93e11164fbf0d867830bf693ed7c0aac,
'mgp': 0x74868d783d5709d604d575273defb78d,
'ngp': 0xcec79bb1e2d940d68810d378bd13d8d4,
'ogp': 0xc69f52e931652f4f60a983ecb2454c58,
'pgp': 0x75fd7686bab25feb3bc711ffbca639e5,
'qgp': 0xc10bc694138e2d7f8d99757e2bc829b7,
'rgp': 0x9e921e69f3a059985d8c176a83208505,
'sgp': 0x0b99c246bcb803eb4afc107419cd79b9,
'tgp': 0x10dffbb81286dca5ac9c3c533cb9530a,
'ugp': 0xf51a1ef55cae2a1f036f5ebaa16f8d26,
'vgp': 0x8558664910af3fdeceacebabc851cc12,
'wgp': 0xf788f30b9264f937450480c912150879,
'xgp': 0x376f8c22edc9ee6a4dfbdd3d83d02831,
'ygp': 0xe4d9c479d5ba8d3913025b32e97cdb4d,
'zgp': 0xc6424680ea7552fadeba23ed3a4ef750,
'ahp': 0x2e2e7521861ea21a1ad28ba9e15d7975,
'bhp': 0x984aa9a9389a170ba3352257ebe8afb1,
'chp': 0x59fcb956ca6369b0e24a225d7e26c795,
'dhp': 0xb22083f7c333a3c4c04a3fc80eec1fcb,
'ehp': 0x9ab575f47af6c11ec339eb8edf9d6302,
'fhp': 0x1ba6cc5627766d812e5d534a929a3de6,
'ghp': 0x33f9a312c47bbe3c02cf881029e57e01,
'hhp': 0xc17e48bec498c302c2211d4f55edbe0d,
'ihp': 0x1875acc4cf6e75cdb379c850a9026f99,
'jhp': 0xec138c07586544035f5e3547eb8cf4b2,
'khp': 0x4e47db7502e792c45e23015a6cbc516a,
'lhp': 0x8508c29faae66d9cfb363ca9cd767094,
'mhp': 0xe09be4b2786fff7fe6688759c85ee635,
'nhp': 0x8625b6b8d1ab1d18bdcb0bf509b33a3b,
'ohp': 0x38d4bde5d626d8f79d2ecb89e3d71527,
'php': 0xe1bfd762321e409cee4ac0b6e841963c,
'qhp': 0xc5c85f669f72cd81d9abb1eaa146a1f7,
'rhp': 0x1101ebc5ce012e432c417176fb67d476,
'shp': 0xd7c1cc7e2340d3a5297ca0231c6105c0,
'thp': 0xa90572e2424a768b3e45e20c63601a49,
'uhp': 0x41b2ee3013e01fcd53ab57f09480cea7,
'vhp': 0x8b02d10c4ad6040529c8eef40fe7311a,
'whp': 0x35b43f65caf0f38daaa9af61658f8c92,
'xhp': 0xa0a8423c6b80ccd6195e5be51b4752cf,
'yhp': 0x35aa96f6752465a07a482989f886b295,
'zhp': 0xb66857686b995b799aa86398bd98ab52,
'aip': 0x4f08f51bf4a8332250797567af889ece,
'bip': 0xd93bea4c204c506cd3825c16020d3f10,
'cip': 0xcd8562d226d37128b84a8bc1aad7ea3a,
'dip': 0xffad3509caa0d87832f811f0f8d211f8,
'eip': 0x19f535a82cbdf2e92a4113c6fbd94eb6,
'fip': 0x7347416ebef5b0ac3c3e6e6778534c42,
'gip': 0x48e157ac9add8406e259b4571096c389,
'hip': 0x997f6b054d31252dfe32ef38f9dddb2a,
'iip': 0x15080aef9becea7697a1e16500b12aa5,
'jip': 0xb2a258136abe0e02277d9151e681a571,
'kip': 0x1ae727a3a8cc819aba24c3d8807e4ccb,
'lip': 0xfd2cfade93518ddd6cff5cb8c6906b17,
'mip': 0x191220d9409d5ac823b6ba4a99f32a12,
'nip': 0xf0b59903e4ba113a2c1ab45592a96ca1,
'oip': 0x90a1b12c7cacbb376a1e8a53f71b3238,
'pip': 0x62ad1c2a46c5298f3e2c95d3babf8d0c,
'qip': 0x533cd35407046c5fb079b56625932a5a,
'rip': 0xb50a2e500294807caa75f3a835796682,
'sip': 0x06b15d3af713e318d123274a98d70bc9,
'tip': 0x6a2139364f96787c8ce1bbb0070b898c,
'uip': 0x6c422acc9ae8c116a52fafdafd7e0b3c,
'vip': 0x232059cb5361a9336ccf1b8c2ba7657a,
'wip': 0xfa687cdfc33f63a773b40c3d403e35e1,
'xip': 0x6b250612c20ccfa9577b994abf75b34e,
'yip': 0x66b6d4c61b23a85b8d375e77104b9e14,
'zip': 0xadcdbd79a8d84175c229b192aadc02f2,
'ajp': 0x91992931553acc688b96a9abf8113b32,
'bjp': 0xab2e79dbba72c3866298b74f1a1c6fa6,
'cjp': 0x531f6f5a24e7ed01f18e05c81c051b9b,
'djp': 0x6d249379f6202e885153ff4267bbdc44,
'ejp': 0x6a723ead6ae3c6ebd420446a2e13d340,
'fjp': 0x386438d05387c376e5da6f22532562e7,
'gjp': 0x2478ac6551bfc76ed8a7d5010c3c76e5,
'hjp': 0xd2e49057ea032030140f8dc8bbf982e9,
'ijp': 0xd607f2a1aeb998ac62c078e6bf534343,
'jjp': 0x4ad7dcaa0b10f29abb2e522f23d86822,
'kjp': 0x70949c9dd5be079b2bfde6e2a1aab88c,
'ljp': 0xafffbdf529a43dc73cbf298af244020e,
'mjp': 0x91566bf33986d88ec33b79b7797e58e2,
'njp': 0xfb8a05cfb51e2610791e9f726bf22d05,
'ojp': 0x5bce8abe1ffde80b22e0c2a4cd2a0a9f,
'pjp': 0x74e3e0f4d124343a55fd3575a312eb09,
'qjp': 0x9250a70b4f8c2fd8654c6c137980ac41,
'rjp': 0xfbf8a0d1ffb67905067d4eca0c55f8e8,
'sjp': 0x66508a94fba75e6f06b28b715a3a0c36,
'tjp': 0x3ebee4932d2795bea1e9898bb7371ef9,
'ujp': 0x4d82290a649a51f83f17a2c9bffd66c7,
'vjp': 0x24051bdd85f7f62ba3bb5f5b1ff56716,
'wjp': 0x679147bdf36b41502cac9c0ad71b44e3,
'xjp': 0xbc115cb788e4cfbec64a3bdb0702e77d,
'yjp': 0x7cd18337125856e548d4c6120938240f,
'zjp': 0xcb4273c4aaa94175d070e3fc521c19ba,
'akp': 0xe8d659e66614dc6e5671a2e1245fdc40,
'bkp': 0x417f2c1dd0517807528fc5360d7904a4,
'ckp': 0x309dca723e76432144125342d92df350,
'dkp': 0xe6c2ee4aac968c19f61eeaab09401374,
'ekp': 0x043853f440bd330be5d97ea20e4ad571,
'fkp': 0x1de5e4a67c73b1a72598d9f477d8b5c1,
'gkp': 0x2f6a32e4f642a01db4ac26c91d6a8169,
'hkp': 0x6aea95ce4cee5ec77b1f2f69af17e178,
'ikp': 0x9dd62874e29ef4a0c3108a0d59e0fe0c,
'jkp': 0x530cf1449f4f8ae3e023142a32c526f4,
'kkp': 0x4c1e8f51338356f9ed2faf098eee974f,
'lkp': 0x5a4755242d62baed1fd9f828b7a68c2b,
'mkp': 0x39d40eacf1a803b072dc739dd4750a49,
'nkp': 0xbc3facf1d681a947a9bd04fc25d9c1f5,
'okp': 0x35513c93644d7b47d8407231f6ee95eb,
'pkp': 0x35737bfd0b0686ae955771b88f67e1a4,
'qkp': 0x12f1e10e497a848e4ea3416d90c36c3b,
'rkp': 0x48e39eee5a2e780540d9c10f0e1d40de,
'skp': 0x4cdcf78bd2c4185ad6a0b8de3101b042,
'tkp': 0x17442fffff96a11233d6bdb6a92f55c5,
'ukp': 0xb466e2faaccec15dfe9116f04f0fd2de,
'vkp': 0x19f5a472be5d0aca03df06ed85ae5947,
'wkp': 0x99f180b066e8668d26f37c7f49178a49,
'xkp': 0xf575ac8f8132ca19a8cee9c6bb01138e,
'ykp': 0x1c33c06d9aac849850cdaac297a1b60b,
'zkp': 0x6f9e70e3eb392edd41c7a2e169b0616c,
'alp': 0x786957b81c3db271a0b7b88ae2d5c982,
'blp': 0x41129a4824eaa3f50ec0bbd04c337128,
'clp': 0x1746c6e2409525c3ffbb01d7f15688e8,
'dlp': 0x2ca7beda17b071de9a70502e24f00161,
'elp': 0x04c43a59a7f35a59c2ef082d69627781,
'flp': 0xf1f150df60f354291e8321b9b395e406,
'glp': 0xc0861a8908e543488c7698646cffec2b,
'hlp': 0xf37ad8785734b582f65f918cbd28bb24,
'ilp': 0x8bc3b4d83fe8fe05f95d8b27428ee17e,
'jlp': 0xeae1a8e0d882027b9c4d451fc9b84ffb,
'klp': 0xd0cfced9c33e6a5e31e7f71519af0009,
'llp': 0xe2043a154e0252c34d9073e751457b00,
'mlp': 0x6a20507c5f69d5b8867fca69fe848540,
'nlp': 0x4c5adbed16b4c9d16698f71cca4218cb,
'olp': 0x55d42df47f28600b9220bacd3cdedc5e,
'plp': 0x95ed136f1ff343e42d36fba7b010746f,
'qlp': 0x7aa64f3dc87fbf23f34c4864472319b1,
'rlp': 0x577db1a0d959f5c84621d779926a9161,
'slp': 0x32f09e43994fd9f0bb3684f9d3de8d9e,
'tlp': 0xf666ad4dc5d95bac7f35964e7d56e41e,
'ulp': 0x728996853f295d0206575099f828878b,
'vlp': 0x242d819d6f818357c5df7b348875c0e6,
'wlp': 0x7239a1029062514ced2b3e0c499f2d10,
'xlp': 0x40f6c58c93c8da42f3879a8b4b312161,
'ylp': 0xcb1273cb7fd6f0ed2295588daf69691e,
'zlp': 0x19d209fe1f4895978a62509d21a87afa,
'amp': 0x162cacf0d591b432412c89437dea28e0,
'bmp': 0x96a75bdbf222c58cabde08ebe2172c9f,
'cmp': 0x31b4e550aa5fd883246f9b9ceae82483,
'dmp': 0x62568609a56c624866dba97854be05cf,
'emp': 0xac8be4aee61f5f6e21b8c5afffb52939,
'fmp': 0x5e596594042576209f90cfbda5422f5a,
'gmp': 0xfa57b71f67e2bb2e55303b33f8358751,
'hmp': 0x6d87e0c7efbeb99ac6bdf9ec93ec4c24,
'imp': 0x72bb0a20dbae84efcc752cb287357197,
'jmp': 0x0ef6cf926f2f394b0cc8e11be8022625,
'kmp': 0xf9c1fad79f38e43455afb85a4a50b8c9,
'lmp': 0x01bc906993658be8be9a09519fd9bec1,
'mmp': 0x6d531219a85503136ced0612b6208835,
'nmp': 0xe43313a48eeed4619fe7b1608a68345f,
'omp': 0x5e4c0ecb1ca74c29f5973c5c738173dc,
'pmp': 0x458d8aab000e42e82040abeadd25172c,
'qmp': 0x66274c4d2d5c398e9afd286714310d55,
'rmp': 0xefecdfae536bae52e683435e553ad2fc,
'smp': 0xb3de29d2072b317fc5f8c87a5fbd9564,
'tmp': 0xfa816edb83e95bf0c8da580bdfd491ef,
'ump': 0x5d0c214266e4f0f90b328969fc40073a,
'vmp': 0xbb8d78987230b5319da7baab05de714e,
'wmp': 0x60f5d7ba8796b5e9259d8654989ec754,
'xmp': 0xbfae74dd35a05ffe9addd44269e75dfb,
'ymp': 0x9599528a82078da91b05c270ea80c770,
'zmp': 0x4b9e47c477a094f8deb8f59cd5c364e4,
'anp': 0x78b79ca363ea2fe10912fdc62577f8fc,
'bnp': 0x9cdaaed716080cf038f191fb40196f76,
'cnp': 0x87a501444f19e68f4f39514a2f4ca30e,
'dnp': 0xf860d4775a5622fd5a1243df80803cb5,
'enp': 0xf64667ec624e7db2da4094eb36b330b6,
'fnp': 0xab498a6016e13d49555152fce6d7c726,
'gnp': 0x2c2d413b8c4df7c3aabd615bb406874e,
'hnp': 0xed6be5e9e5bcd52dfa935af71a49a67d,
'inp': 0x2c6f00854b4702a9da9a18e5fcfdf279,
'jnp': 0x462e2df02b23c0f4fc4a36f22407b2fe,
'knp': 0xecd7da5cbb58fa26df78fc9f438b5d4b,
'lnp': 0x940bb6f30551adda12ddaac64f8645a7,
'mnp': 0xbdaad796945a5a1d01b2ee794ae33d3d,
'nnp': 0x34441179b7dc4bd2aede891bd8ce903a,
'onp': 0xd29a65bfcca6cc851d0b6e9e614bf041,
'pnp': 0xdd659348c317942ce6b00831656a1703,
'qnp': 0x23e3817f89a5d4ae7ecb4de4eea424f8,
'rnp': 0x52320cbd0bef06bdd02d882b49b7abfa,
'snp': 0xd90db89c5669f052e986c7f6bca7f878,
'tnp': 0x2e2cb3fa476370df822f27bee84980ec,
'unp': 0x3073f2f63d0fdbba09ab904f78dbb940,
'vnp': 0xd51ebb70752bb5363ec00a5781f6cf1a,
'wnp': 0x52cade030d65445fd0eb1d8d18ecfefd,
'xnp': 0x802f88a56c96cddc34cd3352d1166d83,
'ynp': 0x96170c1a764d76976b0fe41ed70edc50,
'znp': 0x5502891909079b9d6a5e1c283fb98c40,
'aop': 0xde58c033284230595c1b6faebef80768,
'bop': 0x8379ded97b868b2760ced47e3f69a5c4,
'cop': 0xaa3ef5b6ebb78d184429cab1ec2ab521,
'dop': 0x7385cd258dcaad1cade7b738ff3144b9,
'eop': 0x8373443e533b80c2a8801f6b3344c562,
'fop': 0x7ac9b3199784fd93c13244a06cce31ee,
'gop': 0x1c6b57c9025453964e72c38be0f20873,
'hop': 0x5f67b2845b51a17a7751f0d7fd460e70,
'iop': 0x9fbfb220e03aa76d424088e43314b0d0,
'jop': 0xcef5d3ee85edb4d695c189c857380cb0,
'kop': 0xc7b3ac3e458121536d7911bf4c47fa88,
'lop': 0x53c9332629ac50edbf2d2bbf7fa83f7e,
'mop': 0xb7208774faea1d155062bdb773b5f221,
'nop': 0xa571d969c8661fb0342afd08e571dfa0,
'oop': 0x403a96cff2a323f74bfb1c16992895be,
'pop': 0xb21afc54fb48d153c19101658f4a2a48,
'qop': 0x050ad68c27a56f6fd44a6ec857ef877b,
'rop': 0x08cac5c2739f613c963b0550c07e6d31,
'sop': 0x3f71916d46f94f5ee51bcee817ad8fae,
'top': 0xb28354b543375bfa94dabaeda722927f,
'uop': 0x6a6cda5e53e68520465f32d0fc0ce38b,
'vop': 0xfc892cf66d8623e6a4ad9c6184926e46,
'wop': 0xd43c6cb609bb37935f47215474db2c46,
'xop': 0xecd50e7bea60e8c153d4591217da6ecc,
'yop': 0x1216068a02a4f291332802f42d23987f,
'zop': 0x9ab2e2da8b7a2e8eb0837913e14bd796,
'app': 0xd2a57dc1d883fd21fb9951699df71cc7,
'bpp': 0x0c68c2daa0334704116676287d54c2ae,
'cpp': 0x2c5f2cba9af7f92986d3de1e753f3faa,
'dpp': 0xe4ba495fee46c6a3c3107a519636ef2d,
'epp': 0x1c28d87300e5a0c965e8254f1820b14e,
'fpp': 0x1cf1318db42376e7530367c760025f5e,
'gpp': 0x1320264564c97ff3a090752ba4df4064,
'hpp': 0x3c842ac24d3a886dd95fa2b7dc90aacd,
'ipp': 0x0d94687af4d9232aebe28598ac3ffc3b,
'jpp': 0x39e30eac46faf1fedcbfdd279ea6d2c6,
'kpp': 0xf6190952570edce4211cbe9503cb2707,
'lpp': 0xd4cbf60909942c7e9410233637f061a6,
'mpp': 0xc0605d29f676dd1cdb0a94dca8cb0e06,
'npp': 0xf5d065c9fde93d07b20c4398aa4ae0a7,
'opp': 0x3ad056c91cd3698877680b18756af52a,
'ppp': 0xf27f6f1c7c5cbf4e3e192e0a47b85300,
'qpp': 0x2b6453c261612a1017a288c2a5d6f79a,
'rpp': 0x7bb99ffc3b66f581d7613506ac9cd920,
'spp': 0x5566919ceb387560157c4fddbf8314c7,
'tpp': 0x6da5eedc4c64343127dc6174c885041e,
'upp': 0xd56cbca2617c2dd7bf068f4cb423eee8,
'vpp': 0x21f66e7dd81ae29064c26b66d9b3e967,
'wpp': 0xb385ee7b0927fd5299dde2ef18546b36,
'xpp': 0xde30f4ed64a614089b1ecb0a6f039ac3,
'ypp': 0xe31e1cd693159fc458cb7ca71f4d4af5,
'zpp': 0x090d5dcf598ffacac71b5e67eb69406c,
'aqp': 0xf6c8fce8ec5f2541efb6083759832928,
'bqp': 0x5b5dfe60b78ca0be3a2a16d85d4dae0b,
'cqp': 0xd6acc9f1ba4b28b91ea9de99a0ee53d7,
'dqp': 0x323c05f3370d4490f9ded9d6425fa3f9,
'eqp': 0x5507696533520233050d68b5af3022b6,
'fqp': 0xd17aa433ee959de738d339c7f3ad2f97,
'gqp': 0x9039159fe242b32af3263c31a502c8f9,
'hqp': 0x948be45c6d58d4f9bbcc5e6b2b6fdb62,
'iqp': 0x5402ef8bef0446869440259ec13b80c8,
'jqp': 0x7a00c54bdd5545aee2142933e2b5f845,
'kqp': 0x1b00f6f91c6b0596103c17854a5aa9f3,
'lqp': 0xf68fd2ac10043697a454ccd67dffaaef,
'mqp': 0x1d645c0aab99ae70646e2ed37b130131,
'nqp': 0x5471870b6921439d429db6ab00d32bf5,
'oqp': 0x8b653e3cf70ea78af3e837a79e435514,
'pqp': 0xe3264f3f1fe95dbf44b45bd8a33cd17a,
'qqp': 0xd0bfc6412b21c099ebea3966c99399a7,
'rqp': 0xad79d05d0dde12f71bbb3552f775aae1,
'sqp': 0x3a6e6cdf5c8792716dfe9b3f226a769c,
'tqp': 0x90edb893dfbd3c03136e022f65e17800,
'uqp': 0x8edc2f3ac89df9b47687bc9160d5e82c,
'vqp': 0xfef73b36afd80f74156a89b79ac14665,
'wqp': 0x3a7e100444505cab3a5a13a90efbd862,
'xqp': 0xd20b1394efec9bf4ff21436e3c5fa860,
'yqp': 0x7d61d5327be70a6c6b1503794f685b37,
'zqp': 0x151d735e317e326c6a9a4c1bd0218ff9,
'arp': 0x2e0ccdd846ffd60116ddb796dc8409ae,
'brp': 0x13d3ad9095846cb3dd5ae42bc283a7a2,
'crp': 0xa7939d043567ee90a9f1febb314e563d,
'drp': 0xd7fa3035763943194c712ce9d95e3c4f,
'erp': 0xdef6d90e829e50c63f98c387daecd138,
'frp': 0x40b6d5dcd2295641ee77cf0cd8e9b144,
'grp': 0xaae3c71672121c2397f9f81ff2eb7659,
'hrp': 0xecca84ac88a9a70ab5bcfffbd8913d15,
'irp': 0xa2a4743367f6a5235aeeaa113eb58c5e,
'jrp': 0xf0c9a442ef67e81b3a1340ae95700eb3,
'krp': 0x17d7a40f313560d4d9a1189a91e45ab9,
'lrp': 0x55fe2d9f44d15839815b651b599f7da9,
'mrp': 0x412ed2e9aeba86664aa62a87e296a1b5,
'nrp': 0x35f71b6968296592b283199ff47eb64d,
'orp': 0x802c33b34b545e5a3bf683bd81d96ba0,
'prp': 0xb01c6fd4b56d65a5a737ecdf107489c9,
'qrp': 0x616cd522f1098345f50d9faca5df73be,
'rrp': 0xf0ed791c0fc0a2f5b856a13c88f2882c,
'srp': 0x62a165d59f8daf42e2df5f3c5aed8a2f,
'trp': 0x3678a3f8e003e7635b0b76a9d832cf63,
'urp': 0xb90223d5a6d6691429ff1fb16bcefbc1,
'vrp': 0x9406f7c83b10ac45f811ec2193d30f22,
'wrp': 0xbb2ae1996c21bebfdd49d47440c714f7,
'xrp': 0x295635e63aa72d18e14800198e3c447b,
'yrp': 0x64bfc677e07d98a0700df63c05c35504,
'zrp': 0xd69817e7c136f594f3866517edde13a7,
'asp': 0x9451810adcc13a25e610332880cc447a,
'bsp': 0x3c247ce3b763bfa0225f15c27bbbc328,
'csp': 0x3c2f5decde47940c8baf3b80dea449bd,
'dsp': 0xa9bfcbd77aed59d33d77710050ce42d6,
'esp': 0x4878a0c647b31c5074ae45586fefd018,
'fsp': 0x933e301a1b25760b896a5eadfbcde902,
'gsp': 0x309a42df34d2fd16979b368fea304d0b,
'hsp': 0xc435a9810009800cafef7dce3b7844a6,
'isp': 0x6950d1d89a0a96715e5a350129e90346,
'jsp': 0xec407cce6b649daa8e320157e5763976,
'ksp': 0xa3d65f2e6273b9874a9d045b31ba1845,
'lsp': 0xf3b3567de9e676a3a56db74f06664ac1,
'msp': 0x9e5236b0197a19704ab82b7bd13ff745,
'nsp': 0x9518e0694a21e0b9ef3ec36461cb5b5a,
'osp': 0x3a8e55937df0d596fca0943b924306c9,
'psp': 0x95d52d58778762c70ae3328bb07d8a3d,
'qsp': 0x8731b2971a008d082da40ca2c87a6136,
'rsp': 0x451e86cb8c690e064ceb3078192d8615,
'ssp': 0x97c9c694d99f729e1a48940d0b386a9b,
'tsp': 0x902bcfe577b6644c4a7024c28fc3ec94,
'usp': 0x75581170ffc0cc5ae2d7c2823fe21d6a,
'vsp': 0x31f613078471b3d039afb53a8846ce89,
'wsp': 0x5fcb3686f6a0befe36ee537d3fddbfb6,
'xsp': 0x4c5c859a3e5649a0aad443d1c8975e94,
'ysp': 0x09831b380c21b73918f3d2437e0988e4,
'zsp': 0x7719b40816742c1e564e7e22863119bb,
'atp': 0xddcf3f1d229411acd59df0073c29b45a,
'btp': 0x5137658346f64b3ba16c2e68193b396c,
'ctp': 0xc4aed387427f99fb297dd833379861b5,
'dtp': 0xf0b5ba9b8a365c0a8709d9016f865d95,
'etp': 0x5060bc256b49055830cb6ee2c22c436e,
'ftp': 0xff104b2dfab9fe8c0676587292a636d3,
'gtp': 0xfed55d430b475d5d6785387b161568cb,
'htp': 0x1cd979309246dcf03c76739887ba0aec,
'itp': 0x04330147d2af6e87dc0a91a4a90fdaf8,
'jtp': 0x5890235e6db09de19c87f6119cd55dff,
'ktp': 0x92849aeec004d64f3cdd984a81100ee7,
'ltp': 0x626016a29dbc62b0c903f6fafc775790,
'mtp': 0x5884bf0e6464833e0550ebb13e0578e3,
'ntp': 0xc09633a606c51a161b16f8bf38f5ebb1,
'otp': 0xd2270e7120a93c8b0a6a34760e654c7d,
'ptp': 0xbe19d835ae8962460c01703fd05ad9ad,
'qtp': 0x8109df850e199b6083212f0f060c8b16,
'rtp': 0xc766e112de79c244fdaed0ed34ca5e12,
'stp': 0x1514ef4c38ef3b21bf970755926aadde,
'ttp': 0x673711496e1acf40122b825d1c9d34cb,
'utp': 0x6545700ab0c5f892423b7a3b78bc3612,
'vtp': 0xb110860129263a32910deb6ab48bbb6f,
'wtp': 0x058520caa81c8e451bc6ff547386a446,
'xtp': 0x85c4e874572ea3a6de4caeff32bb6628,
'ytp': 0x668d5b63e451b5c73254e70bdb2b2e2b,
'ztp': 0xcae01c3f6bf83c60ec2898aaeaabe31b,
'aup': 0x5a62a5b8e1ee3a635d87c8b355ba2f3f,
'bup': 0x07672cabc9d5903ebba85d9851ffbb12,
'cup': 0xf3f58ee455ae41da2ad5de06bf55e8de,
'dup': 0x0e9f1e8e40bb79e800b0cc9433830cf4,
'eup': 0xf54f9ef1d93eb0a89cac76effec9ac25,
'fup': 0x86de25aba2c1318e6f508ed298072ee9,
'gup': 0x88ee566ec2b9b735358dc1586cb3397c,
'hup': 0x2ae5050db8876c01c093d5cfb519f1d0,
'iup': 0x10ca7d460e9bb0cea03b9d65032e6279,
'jup': 0x0a127746975c831cadafe67a7ef74221,
'kup': 0x96b6e384cad40d608ede8815f8cc5b33,
'lup': 0xd2ae99ca69374f7f660a496ab609b99a,
'mup': 0x909a565ff642e7b829acf0174110bf80,
'nup': 0xbb30bf8998422a7b62aa8a751a185b9f,
'oup': 0xcb533ba44ee319dea6ab1f95487de83b,
'pup': 0x59f6af98d759634f8597238fb0b86854,
'qup': 0x8bb44d1f2a50fc1bb7b3da41c63df10e,
'rup': 0xcf49de8dce0882063532bfe93fe34a29,
'sup': 0x2eeecd72c567401e6988624b179d0b14,
'tup': 0xb2b31595141e73148c92f89e087d35d5,
'uup': 0x2f4df692ba18673330cfefea5f7661d9,
'vup': 0xa9383b13cdee2fdd79d88410660e93be,
'wup': 0x16ea71e1069897399d76e4caa0234163,
'xup': 0xf617aa2217636f610b8e505f06346bd7,
'yup': 0x8690303dcae84177bf642ba30b76393f,
'zup': 0x167d8771360bf94f31d351777fe97df8,
'avp': 0x349433344be987d594aa70f188dec534,
'bvp': 0xeb83f13c287d64603462b964813444f1,
'cvp': 0x9679a0f417e4a4c26f95db8f1939f197,
'dvp': 0x1aeb891abbe8677ab4e014831bca77ee,
'evp': 0x08759776f1fdd84d8ed60ed5dc662535,
'fvp': 0x05bbd0081b4d0a06e00661b04238805e,
'gvp': 0x1307e99f3fd135e2433da80b791b74d7,
'hvp': 0x0611e02c34289010783b0eae0a48fc12,
'ivp': 0xb61140724079bd738819daf2d45fbce3,
'jvp': 0x16e9cfb78808526901f9befc5bb268b4,
'kvp': 0x2b69e2768e9b23fb6665f58560853916,
'lvp': 0x26f1e77a85c920221ee73f21571f54c8,
'mvp': 0x4d8673e0ed492955a486e0e4c5ff0646,
'nvp': 0x0c7cf445966fbf9295e8286d51af4712,
'ovp': 0xe3e6083cd5e8de5744db9d3593efd13c,
'pvp': 0xd81d2324fce5918da1183d427d81e3bf,
'qvp': 0xc317a65d97a1bcf9cf14c16451a2828e,
'rvp': 0x37eefbda778cb519809627a063c99da7,
'svp': 0x30283fd7dcac1f4cba559b5093ce8a3a,
'tvp': 0xec0c8d02a41aaba2e01c8133db91ca1c,
'uvp': 0x9cb857f998504c396dcc75d1f2e71311,
'vvp': 0x4e76873149b5faf0bddafb0b9ef929a6,
'wvp': 0x7e564b1e4d2c8f14c87a3c0e7ed4f9d8,
'xvp': 0x1e2d4ba26d41c0f7ac96afacc55beefc,
'yvp': 0xbea6a5c28194e104af0e150e6c5bc883,
'zvp': 0x1a577406e53622c4b2fcf4f23953dde6,
'awp': 0x83c40978009dd8a1a89d73028be27a5b,
'bwp': 0xa30957698f9ae01d785c6946eab79330,
'cwp': 0x87fabd90c8ce7beae91b471c00c97b5e,
'dwp': 0xc390bc28b43ddff7ff8b6fbc09078528,
'ewp': 0x5bf259c3d99eed992a65cd03901f32e4,
'fwp': 0x26aa928b2ee893ee7721f35f8e2bde58,
'gwp': 0x920fe71ee48fc7a0159652ec1781d119,
'hwp': 0x238052d0194f8e71d2b7040b716fe0a1,
'iwp': 0xe7ae04ba6b1ed27ea811236e518ea94e,
'jwp': 0x349f19e133c1d8b668eefe064faf7853,
'kwp': 0xfd6370676118da9c4a2bfaaef47f8ea3,
'lwp': 0x510abfb8a391a5c6c9a81a285cc077e7,
'mwp': 0xe8cfc01b37b8e9f27af09ffae08eae36,
'nwp': 0x5cd8766bb4367e9d32565e9809e43b5c,
'owp': 0x2e623a8be42e9a1663c66ac562316872,
'pwp': 0x978d82f18a26b0eea6c67138cd408e85,
'qwp': 0x3f70a947bbb41a7f555d3812a2f63540,
'rwp': 0x65e3df6194b3eddcb92405aede40c649,
'swp': 0xa9ec6fc4217038a6f91294b8e5ed9933,
'twp': 0x486b65e303535f389971acd73a4d2c61,
'uwp': 0xd0836d7d5dbada368e56915f6b3fbaf4,
'vwp': 0x9ae70189270faeb4aa42096bd9ff95ff,
'wwp': 0x239c73a6e8df31e4c47f0e073b608120,
'xwp': 0x43c0f1b0784862f3f3998ec294fc05e2,
'ywp': 0x06a9a6253270011817eca4ed77be1398,
'zwp': 0xe6feb44502c0330d296dde0741122977,
'axp': 0x1fa52dc6c9311addfac0c6a1a4ff51a0,
'bxp': 0x9007d545df9e942a645fc201c9d9595c,
'cxp': 0x1d3ab10d2d5e250151f6aaf31db53fb3,
'dxp': 0xde89089b3f28523876061b4a1f8c9f69,
'exp': 0xb0ab0254bd58eb87eaee3172ba49fefb,
'fxp': 0xd7bc68e9ee18d3c497441b598e42789c,
'gxp': 0xee1bc0688750cd9a96802766d3641b26,
'hxp': 0xb82684e6f455dc682a65a786bb1ed05c,
'ixp': 0x94fbf8da1bf2a39842019c960c02ceb3,
'jxp': 0xe7308f74034b0651a93d6c9c11caf34a,
'kxp': 0xbf49a18bc9c63c9e6b1b0c2565920993,
'lxp': 0x6ae96a1f4df95ecd47ef0553e7b2fffa,
'mxp': 0x7e4cadee5c0c6c74852d7aaae20e978a,
'nxp': 0x2f8676637c8e450fe91d162ece5e41f4,
'oxp': 0xfb2a34b5295ec624345055ebe5fb7016,
'pxp': 0x7d84649242d2621272cd3159df8006ee,
'qxp': 0xcb8e8acde50654884884ee62cc11ec09,
'rxp': 0x95445552a4a412f50cbad8abf74171af,
'sxp': 0xa445ecaf347eb52c2cf966ffd3e50659,
'txp': 0x00aaee999a7a337a9202292ff26aa90e,
'uxp': 0x3df672b4b2a94298e47ba9ab1dec2b68,
'vxp': 0xed32e9c065ffd35d5ee70582e7f7fab2,
'wxp': 0x8bd8012759a8912d021eb2d1b91a4a66,
'xxp': 0xc2227bc88157cc2647139d7d3cfcfbe9,
'yxp': 0xf62890f297e8ba28489d1c8a0629c630,
'zxp': 0x344d009495fe76c502352b6fb348ef53,
'ayp': 0x96333100ab42d131e13321f70ed2346b,
'byp': 0xba8f6ef3bd7b7cd9ceaa781519bb6ae2,
'cyp': 0xf45169741bfce8b1b2882da4a7fbb5eb,
'dyp': 0x260bd0a2e95b19f4235ad84ed060d9d0,
'eyp': 0x9e5d9b1f7fa4a8d0d06c796f22916972,
'fyp': 0x6d3bb4865e3fcb97ad0cc6a4a89bdb06,
'gyp': 0xf8466a35e4bf55255c99120cabd1d5a4,
'hyp': 0xd5ba23194ed27cadbaae786de805f40b,
'iyp': 0x89a6174b7fa388378dd6d0f7ae4f09a6,
'jyp': 0x140a94509e9dc9401c8cf456863b44dc,
'kyp': 0x5455bcc38fb2a5515db34938deeb0799,
'lyp': 0x9ac5c2f0a81fbf1408c6a90bc6bc4c1d,
'myp': 0x3e8555846a7a6d8e9bacd6bbbc1710d4,
'nyp': 0x480a45ad83c2c12112a7a943364144aa,
'oyp': 0xe29937e288f315b4f97733d6679ec81e,
'pyp': 0x3b1b11474a0e9f2e28b5b9f4ecc1c092,
'qyp': 0x557c9246028b2b315e811a8ceb7691bf,
'ryp': 0x9b6f0aedc005e0c286b27637aa46723f,
'syp': 0xf19fe81cc92b1efcd1dd6a097ab17b05,
'typ': 0x51477bf023258e57bb334bb8f07e3664,
'uyp': 0xcaf05a235e8811ab554bb42cca51dddd,
'vyp': 0x87cb014c2d9dd08bb3bff84f5353274c,
'wyp': 0x2b3311e6af741e71f2dbe90df9f9c1da,
'xyp': 0xf3e1448e811a9db2b7b08fb95f983592,
'yyp': 0xe6e49c625c89ac128b962631a840ca49,
'zyp': 0x0878dafe2374adc39fe3ed25059a31de,
'azp': 0xb489be0dc1c45c44390e990b728046f2,
'bzp': 0x9416772ae64db34008664b9b838c975d,
'czp': 0xaa4e7d05526a568e057b69f9ffd6fc61,
'dzp': 0xd4788bec76ff7f274ef1a5b44b3c8dab,
'ezp': 0x88f5a2f0d1351b4dfaa5f509b4e0b5db,
'fzp': 0xe914459c55188d10371c78c894051507,
'gzp': 0x43267a6876f29e67f27995201e547a44,
'hzp': 0xa847284517297bbf056a8fd81fed0296,
'izp': 0x61a28c0d101505c1988598cb6614dbeb,
'jzp': 0x2602782797bd22105ca0c2b3e6e39849,
'kzp': 0x0b78bb0cae88b12aca917bd9918e7afa,
'lzp': 0xf68ac95bafbb929143cfb3406f94fa54,
'mzp': 0x8c6da308854f895f01b45b44a34261b0,
'nzp': 0x70cc5dc23b478f7036c509e9d87977a3,
'ozp': 0x4e28f8b239088620e20ef85fdd8f7fcc,
'pzp': 0x19e01f9aeaa4e9939a28dd25f1aa29d5,
'qzp': 0x429e0ae6edc6474718a8a67cd81d7fc8,
'rzp': 0x30f50b5d6169479276d877b7fcecedd1,
'szp': 0x772dd5b0190c0ab683d672e1e438f496,
'tzp': 0xdb9d5a084383957081df8b0c9f00ef59,
'uzp': 0xec8c479114d7dbe337af73b81b1cb35b,
'vzp': 0xa88033ad9c13df0635e4756b1927b80d,
'wzp': 0x198bb8f9be82ba8d1349b0374a12932e,
'xzp': 0x06903ab17e833f33b256281c381a23b9,
'yzp': 0xc25a1809b3399650ed4699691b4ab714,
'zzp': 0x7ec0d93966f46b1e1a8c59a906800634,
'aaq': 0x69bf1b5d2c24150535f6c80e5933e43f,
'baq': 0xb47951d522316fdd8811b23fc9c2f583,
'caq': 0xf4f51d53b47ded5c4b59d34cb1989341,
'daq': 0x91c095b62c3914e69235cb5337a4fd49,
'eaq': 0x4657a4aa46695a6db45a021c5a9b320e,
'faq': 0xda75cd652254fce37e953d7f261f132d,
'gaq': 0x439ee447b35c1c23262aeff42a38372a,
'haq': 0x0170159c366e2c96fb7ae73300630be5,
'iaq': 0x32219ebaf870e04c06847e5e9aa7ce56,
'jaq': 0x82d0d02decc40be462249205d5373816,
'kaq': 0x329e0fe1b98026f0b14ac71b370fd3ce,
'laq': 0x87b7110c9a93826b50f500b0b2c9077a,
'maq': 0x262a223e4fed4f13e42e9c37a608ca43,
'naq': 0x7170e5a4afe370e1b0ed9159c13978ab,
'oaq': 0x1a64b6851409c35f069daa8bf1707600,
'paq': 0x981e5fff06f26c103c199601d06a3749,
'qaq': 0x563ba72ccf2593689f6d215b93eb48b3,
'raq': 0xf5000d14faba23ef5f1b5bab4860e860,
'saq': 0x1526c671a36654503d711b997796f034,
'taq': 0x60f462ff5e1f1d5a092ea1b9ada4333c,
'uaq': 0x5b696af15114a0dc52d5b748d3bb1546,
'vaq': 0xad15ddee31bf46195914b63e5323f2af,
'waq': 0x4adbcc8ecb8b273455311b2414d85543,
'xaq': 0x74d49eef29ef96952a766ecbe9ef3997,
'yaq': 0x038bfcd4977f44af727dc1a03c054352,
'zaq': 0x9e3895cedfa93fc7d6f63cb00ad91d1b,
'abq': 0x039410e18a02ea5692db20b5d5fb17be,
'bbq': 0xe01fe28f6cd12003e2298024321d97d7,
'cbq': 0xa1d066aece6a04638308d85a3b73d665,
'dbq': 0xc2159e8620ab81f839f26b021eca1bc2,
'ebq': 0xe045abfa3793de085f161f6e15bf1c8c,
'fbq': 0xe4e53b1d3215914b048ddf560430a5ea,
'gbq': 0x7e6021d9236189c47fb2d681f8f5dd0c,
'hbq': 0x98c0ba023396348fc6fe2e866365b1c7,
'ibq': 0xe70c379f2a2cbcc214d0946d6e6959c6,
'jbq': 0x67b5d83f1728606a7d76d4a891f625f2,
'kbq': 0xda6bf528ad2d643ec3d6227c838f620d,
'lbq': 0x3e3514845a31c2f2d267abdbc5235811,
'mbq': 0xf4ec45525c656126b663a55563581c4b,
'nbq': 0x3e059870c2b89b760cb521678e5ae7a3,
'obq': 0x9b9c2770bf27d23a95a0753a476c669c,
'pbq': 0x11c7184b9bde874d2405bc0d60a00d38,
'qbq': 0x316d136e04d6147466428663329335f8,
'rbq': 0x9c92c67a42ff4cb9f48f096dba2fdcea,
'sbq': 0x99d2041789211871e52166e10db585ce,
'tbq': 0x4993dfed28d6d20813d9c76fcb3dc942,
'ubq': 0x0aaeb6bf9ebec24cc098729cb18acdc0,
'vbq': 0x05b7c3036114f5644aa0b2a87b4de32a,
'wbq': 0xb5f7e38cedbc2260bf70a02ff4af58f4,
'xbq': 0x18f7faae41232f122744dba950c27911,
'ybq': 0x8d687281238e12cae09ba8b24e2a0cbe,
'zbq': 0x3fa1cfb589240e522a794ae2f460a53f,
'acq': 0x1d8f641a947b3f8771d6eedc9c7f6988,
'bcq': 0xfd76c4079f65a804151557c566679cae,
'ccq': 0x4715c8c39a49faf1625aa9a65a5f2244,
'dcq': 0xa0e9fe2c6fc37811c45a72831cd9e2d1,
'ecq': 0x1d5c0d6cbac5109130c51aa8defe27e6,
'fcq': 0x3aa6a65e6aebcd1601d8235528b1e60c,
'gcq': 0x26101cf606c2caca71073d3dd887afe9,
'hcq': 0xf4fbb8fe0aa976a24d5659265f7e0a9f,
'icq': 0x2765d621af8a58b78b4d528bd5ef7f6b,
'jcq': 0x6d69fd53b088a9d92cf32d51cfbec4e7,
'kcq': 0x5808097bde9a7c2bbfa519dbded98d83,
'lcq': 0x687cfc40f25be25465488a23423230ea,
'mcq': 0xa898f6f2038233dfb2f66e2b16b7242e,
'ncq': 0xdbaa1a291887fff225c932c794ff9c6c,
'ocq': 0x7e1b99424b186ac602cbe91256e42ed4,
'pcq': 0x62dabf3c3edc9816b3e2edba1b75943f,
'qcq': 0x44d5a95348aa82aaaf84c6db6260b00a,
'rcq': 0xed2ff68e2a7ecb3539fc312839a586ad,
'scq': 0x1f233f9859a982b42034dd17a45f264e,
'tcq': 0x7fd852a001523e2df2edc9a52aa02012,
'ucq': 0x3db5467ddf3546f13f5c962a18c62c54,
'vcq': 0x34077472e2b99446df79c1aaddc0be92,
'wcq': 0x2637febbf40f41e88c2af96525cfe274,
'xcq': 0x9738d61dcd0800b5c54f6eb5c46902e0,
'ycq': 0xeeef412772809b5047d492400c3ac2b2,
'zcq': 0x90ecc4a6017d226a69637e5b980e0578,
'adq': 0xd18b61be2cff59949eb5276e771a3fb2,
'bdq': 0x02e50bda16930d6afdc0b66bf89818d9,
'cdq': 0xf8aa1907eeb27f5bc55c724ef21f32ef,
'ddq': 0xe9168909a5e6406a67a40ee817d032d8,
'edq': 0xeaf7ae0743fd15eb56854b1338c36ddf,
'fdq': 0x09eaa876428a5172965d80feab9e1b16,
'gdq': 0x0969d5016c3f1b06818c82b7bf46b5d5,
'hdq': 0x53a0b123dbf48a124f60d95b1e3e37c5,
'idq': 0x8f7c934daa202a9515bbc554a71bd64d,
'jdq': 0xc7e6b216dbca63f7b8003d38df3bc8df,
'kdq': 0x0e75ed62ad2413c358c02b590f56c045,
'ldq': 0xdf365c4b1b515bae9ade2921455dc278,
'mdq': 0xe1216bd7a777ba082ab8711f16968f26,
'ndq': 0xe55f4560ed36a2766c7bc5667d6baa57,
'odq': 0x2c06f7bb3f683dded3d505167e1c0bed,
'pdq': 0xf28935486e030a740ec1bbe0e22b6f1e,
'qdq': 0x0f6b42d7acd2faa40944e4b7a7c7daf5,
'rdq': 0x5e471e1e47511d8f35bd19b9e514586b,
'sdq': 0x597114bacce7448d456b25845178da3d,
'tdq': 0x41c85a62d32f06111295018926a3a6ab,
'udq': 0xe1d187e5e46cf223c1b03d60d0b504ed,
'vdq': 0x6935c312fb3ea66049a874331e2cda30,
'wdq': 0x516932c89b8283d44479cc00aed39748,
'xdq': 0x064eeef6336915d16a2f152c7f1afe99,
'ydq': 0xfc98a2557ad44f40e51300328ffea8b5,
'zdq': 0x876016c8e27732ec55bdc7de033d5bec,
'aeq': 0x81922c6e7c4bdebebd7be669d60b7a59,
'beq': 0x6e00bc895a8369c5a5f779aacbbbb50c,
'ceq': 0x23aa16d703616a5f65bf5a990d799aa4,
'deq': 0xbbc5b7d123b9daf14f120776a06326d0,
'eeq': 0xe81d183b6de1ea62e8e967f08639f44c,
'feq': 0x8864f09730e919b859c6f85e597cdd64,
'geq': 0x42d01130c31a6db1a25b5c8314cf9fc7,
'heq': 0x970108fad41319d3a3bbdf100ad413ab,
'ieq': 0xdc9d76fce9fdae8379a5554e6d727a46,
'jeq': 0xb55e48b02362f8608c2793543031b365,
'keq': 0xa427e3a3c14e0389c84fc207eb0809bf,
'leq': 0xe8742a3606fa66940a24d4453fd5883c,
'meq': 0x290924fadb756e95dd37611499a00f7c,
'neq': 0xbc68613fd1fb63c55cd63311464adfbf,
'oeq': 0xe7599c0c4f7835ef687fd12ffc53603a,
'peq': 0xf2c2b4dad3fe01fecaaefc872fe8a1ae,
'qeq': 0xdf95126c4d938271d6faaee2d6cc0804,
'req': 0xab4d0a658aef644a039b90c2067b45c0,
'seq': 0xe068c2de26d760f20cf10afc4b87ef0f,
'teq': 0x54f94000f583d2fb6ad3a5babade393d,
'ueq': 0x1c4fdfc0117ae90efb4de8b615414e88,
'veq': 0x102c55b0cca93e4ba0ce420a1cc18121,
'weq': 0x1d095b4d29ad942b992b9f39d7ed0483,
'xeq': 0xa9822efd6688df45eb34e6e2645e3fdb,
'yeq': 0x8d8f7953d0635f3bc95a1594c5821d80,
'zeq': 0xac95be695372adb7b2c8ef873f350c81,
'afq': 0x14675ff6689e41e33bb01382ba685249,
'bfq': 0x08e74443361ae9146762b56a2bf15c25,
'cfq': 0x841bef2f190cf60c6c44c3fcd014313b,
'dfq': 0x2ae4f6b34e3cd40a78e090b831dc109c,
'efq': 0x2323128f0bd54519c92b267a2d95d921,
'ffq': 0x218f212256cd47ec859dd9e8913b0f36,
'gfq': 0x029ebc1540e3463a60dbcb0ef9463fbd,
'hfq': 0x16f25f448414c8b853ac189d298c08c8,
'ifq': 0x3b20c15e2cbe0833c814637ceb2ae2ba,
'jfq': 0x287596d559d46083f134e66af387841d,
'kfq': 0xa43b4b915e1c22055a31004f47ba29be,
'lfq': 0xc94cc707675548503b9134b858c6695e,
'mfq': 0xbbdaceca82859a61e6daf0513fae7edf,
'nfq': 0xf802bb26fe7a3bb7f2fe23963623eccb,
'ofq': 0x1629fd8819057587cbdcb47ee435f05a,
'pfq': 0x71e7bfe562580c24e42478e03f2c1171,
'qfq': 0xedbe2172b7f2289d31bbbf6245f56f60,
'rfq': 0x9eef3f14be8b87bb633a5a66d6a2feda,
'sfq': 0xe42b3950cc4bb0bde76cabc5eca3a87b,
'tfq': 0xa339d3b09090abfa4df5399e7fc59325,
'ufq': 0x69f92a66822a4b296f59949207e0ac1b,
'vfq': 0xc9ca35b325c3ed012edc71f18e4ba110,
'wfq': 0x9a561caf5aefe9ed71f7897a1ffb0a1e,
'xfq': 0xbdcdee380f9dcf4287319f4186269ca4,
'yfq': 0x972f28b2eebb36d0a77a923b1973f3ed,
'zfq': 0x10e2b172d0e30f8e68c8e926d96a7218,
'agq': 0x76b14772d08c09b75764b57cd7892354,
'bgq': 0xa947e9a115b523f3c14b76448ffcc066,
'cgq': 0xf6aff1d695ae232e68306d8b57ee41c9,
'dgq': 0x9311a5ca8a1fd9a7939c543fddf747d3,
'egq': 0xda0efa693887b002b14f195fb4761bcf,
'fgq': 0x92591f455b83cc9ae98b7099d18df0c9,
'ggq': 0xe2d390a36f9c8f14062fda0e62a787ec,
'hgq': 0x4ecce4bb4edcd96e63c6bb7d2bf6e8b7,
'igq': 0xa68296eddf8d3c65d199e18fb88cfef0,
'jgq': 0xaba346e30a2b233f86f910672647de2e,
'kgq': 0x520eec5c672511040edc04d4468e9501,
'lgq': 0xf5dd1c24a6ae9c2907d7889045cc6304,
'mgq': 0x44062572f9507a2c960284ff8aaf56cd,
'ngq': 0xc4d64b1b700cf5a1b4525ded16b27694,
'ogq': 0xdbd1ba5dcc6152a2af424054b00c4996,
'pgq': 0x22e928d36dc35d16fcae1bf2760ee4e6,
'qgq': 0xe0674af0ec5cb33b11761c8069847e39,
'rgq': 0x37f88e379c9b1aa83ee2786c87831975,
'sgq': 0xda5c7f883723301d0796740f8bf55995,
'tgq': 0x1673f8fd0f582c53db3bcdc7d6db28fc,
'ugq': 0x6c671bcc2d7d8b992c46e6a4bd109ee3,
'vgq': 0x9b5edc71948abbfe67216a7c6177934e,
'wgq': 0xb3e112f287216f71c5632689260df0f2,
'xgq': 0x6c4b176ca4a37d5b48a1961f03c1ac62,
'ygq': 0x69f09d8974fc879fbe50fcddf514ee17,
'zgq': 0xec4113f75f221373ec942694cfe03ec7,
'ahq': 0x3f1c0e25bc905b1d53cf1a4e6bdaec98,
'bhq': 0xe7956c284929079c7d0c7293d087c50a,
'chq': 0x6d5f2d0430dc040d6bebca6614042017,
'dhq': 0x745cccebbcac587410cbb657bd49ca8a,
'ehq': 0xaeca8ebc20aa6ac812a3f8c845b651dd,
'fhq': 0xff7a7425e7c3cbf8a8d82c38cc63ceb1,
'ghq': 0x01826dc432538e16fd8ea4a61d0306dc,
'hhq': 0x9e8ca68bf7de888a350681d2f060c18e,
'ihq': 0xb6534bf36582d6ed38b46561f781b903,
'jhq': 0x6ff90adb020d1c9a96da1dc8a2a94b5b,
'khq': 0xcd62fc821bf8eb01bed5310423068344,
'lhq': 0x27d17a2199aae2ee0e8e49b07f5e43bd,
'mhq': 0x5ed4409e69f3b3b23bed09894331a783,
'nhq': 0x717eebe4d0c84f73c3a9134eb1ccf26e,
'ohq': 0xec8ae2b20c074e97cc6945b8e788e032,
'phq': 0x6a61700f00c0096f2b862091eb618f5f,
'qhq': 0x1f747ace4d6699b51f2eceec042e9e07,
'rhq': 0xf547e39bdf86472de2342c0c3c01b9f9,
'shq': 0x53f598dd60d2efa2e9a098a702d3503e,
'thq': 0x9daafcae5a4d2a57ab3a140784e42d72,
'uhq': 0x19b387d20097558b9557ceb94762c9e7,
'vhq': 0x00ba87c8715f5d115265ddf0f17a4545,
'whq': 0xbed56fa8272f3b972b60bec5ac02010f,
'xhq': 0x6a9b2d2e4915d71643129ceb9399b5ab,
'yhq': 0xb6bdc8dcddeacece09d5697145d373d9,
'zhq': 0x18b0c4c764d07a101a25e3f51ce482d9,
'aiq': 0xbfdc8e99b0f7700e040dec7273668a5e,
'biq': 0xb7021c8a7b53a14e26a484447647738b,
'ciq': 0x71ed972e5bcb5da7c26284c94a5d11df,
'diq': 0xb4f069d381da01436e732806111e1fbd,
'eiq': 0xa01f46bc3bd88cc65671dda3f22a1366,
'fiq': 0x19127f4320da9ab81e70929165f7ee9b,
'giq': 0x6c62a877821126cb2e17b9c18c53be2e,
'hiq': 0xd28a05318f36976945560eab95536d8f,
'iiq': 0xc4799903257edcf6826541c04f13981a,
'jiq': 0xbca191074ebb1313bfb78100e68cc2a7,
'kiq': 0x61ef47bfdf2700477c139616ef34b8aa,
'liq': 0x217b2b6b845e65cf3be0dc0e7707c8e3,
'miq': 0x94e22a0a241ef1215e1ac3590bc8c321,
'niq': 0x3fa0ca0f183120c91131ab7f480ffb90,
'oiq': 0x4513f95d61395de1d18bbaffb25189fd,
'piq': 0xf28184b096915566bc84313f88acd489,
'qiq': 0x5d1032ad8098a4ce31943c3a92c32a28,
'riq': 0x28b4559e8fce9d6ca963ffe3b644e815,
'siq': 0x1ac0aea57a18d096dca32ad1fbf147e0,
'tiq': 0xa3e87a40f39b66e3cef6abf02702910a,
'uiq': 0x0de7f1204f3e53db2e4a14ee896a2322,
'viq': 0xd1d116b6a67816db6dc9493814d935f7,
'wiq': 0xabb3b33bedc00bf9b87faef51f100ead,
'xiq': 0x8f5c96e2b3a7506eecb1595baae3d82d,
'yiq': 0x46d60b78c79d683d7407588d92c2802c,
'ziq': 0x90b7f63686c6e902712e0e1fd7d8db3b,
'ajq': 0x4dff9226faad24d0e194c20228cd4ce8,
'bjq': 0x5c04f58b260c713495e0736235927b0c,
'cjq': 0xee35e0172ce4341b847610cf132cbf28,
'djq': 0xa319079bf39d1710d4fb78071e68f5cc,
'ejq': 0x4eb34ab1a8224c34ab44c71465ce79dc,
'fjq': 0x7503d256e3565f6c0cd8ced82ff0dfdd,
'gjq': 0xf91659296f5cb7d328311f3c40d5258c,
'hjq': 0xa3873b4afab737de6523673f8e989841,
'ijq': 0xdc5e477302dff650e8b71c598ab9680e,
'jjq': 0x30c085c0404636afb7e8b8f8a6b3e040,
'kjq': 0x533fe1b069474cbd1cc79f1afcd33593,
'ljq': 0xdc2870b1dfdf0fd2c6fecf13d3de0a68,
'mjq': 0x236176af179c692684e83e73ae6118c4,
'njq': 0xa4643a23b98487e65fa6e7795293f31f,
'ojq': 0xa9f2da57f6cb05734244a5f7041543ca,
'pjq': 0x65cedd1e0c62ac8f62907f4c26347e87,
'qjq': 0x64043c634080735082fa5b62a7b10326,
'rjq': 0x7bd8ef1c0935882fb852ed1fb730f88e,
'sjq': 0x274350c6ac6141a4f8c0d8daf2f610c6,
'tjq': 0x7906989e85cbc80207fd0db4b16806f6,
'ujq': 0xfbf322120c5388a5426a889332c3496f,
'vjq': 0x6f94580c739efd9c17c8f6fcddadae09,
'wjq': 0x18e9c855004a552b0d8328bddfb160ea,
'xjq': 0x763b8c047da62a2699b620f8c696b9d0,
'yjq': 0xf60549fb3187b1c91ca4ac9dcbdb2f93,
'zjq': 0x24b0f8a1eed83972e86329a2405cd336,
'akq': 0x5e42d16c6585f5ade100478eaad07ff9,
'bkq': 0xfa3f9e7acc0c4d7abd28347d6520e637,
'ckq': 0x497085678962fb49eb2a6624bcdbd9dc,
'dkq': 0x49df1fbcb7bcac6c595c0ed59ef8a329,
'ekq': 0x32fa0ace16da2792efe462524bf06444,
'fkq': 0x79ce9d5c36c179c67588b3d919db55db,
'gkq': 0x2ad8eb00d900a6cd42538a31c17759be,
'hkq': 0xdbec7061d935ea0427ac309df8392efd,
'ikq': 0xf93eb767d62c9c817859d59a1b0d78c9,
'jkq': 0x82e94595c1d01bfaec167a7e72174660,
'kkq': 0x286673e96d3b2921b39e08b6d8e7f414,
'lkq': 0x056a80f96f504a4fdedd40544656ee97,
'mkq': 0x7210e733c3f3b28be8bf7ea33327d9de,
'nkq': 0x32ab4be41f3f965a585e19d33df87b3b,
'okq': 0x0e8a838b37c9b3abd9cb6cd6bb9773d9,
'pkq': 0x4f1f32c46f0a22928a2d5bb715798b2d,
'qkq': 0x1515df0bc96a822dd2077f8b131b853a,
'rkq': 0x353cc3414855de767a71dbe995e2516e,
'skq': 0x5f45ea4636ca5bdfae43e309e630b7f6,
'tkq': 0xa895d8b03bf724573078ed5ce6df103c,
'ukq': 0xdd5c600057ecc5483da5ed5c1bd1ed3b,
'vkq': 0x9cce25856acb7c63e24e383a02d1ece1,
'wkq': 0x6228dd4856f1af320d1f3a340eb4eb43,
'xkq': 0x6bd37d382fed677c4e90117903d54dcd,
'ykq': 0xd5772e80bfcbad5179f7a9b50c5e82f2,
'zkq': 0x818c1af660d82bcb02c104907751d982,
'alq': 0xefbaa15c9b42fa22cc8d382ced74816f,
'blq': 0x777c818cc479e28994a2390d47ec09b0,
'clq': 0xbb7d2333898b71bce22f19b16888882a,
'dlq': 0x07bb489d09494a9d4071f5ffa6223d45,
'elq': 0x45b4ca7816f486beec3b30ced9dfdbb4,
'flq': 0x954f41945ffcf869fa55a6d6d921e4dc,
'glq': 0x96bf5f740b7abec6a1223f76554def0a,
'hlq': 0x9b0a35331e10c1605ceb1d46d1e5b135,
'ilq': 0xffc9892faebe5d2fecba6c8d620f7ea6,
'jlq': 0xb0c79c8719111a796f2fab07ed1561d7,
'klq': 0xade0721065f344170587ae5a0cfaa286,
'llq': 0xf0e442afbfd16596d6f566399f490444,
'mlq': 0x236a6f4569b6650bf5540cce451b91c1,
'nlq': 0x52b6162778cf25236c0e21c4165a6460,
'olq': 0x54580cbff4af4bf0433b2f470b112ca3,
'plq': 0x3ece904cbcb922fc0c874e5822d96f9e,
'qlq': 0x60cc8664d8e6854b7716f04d5ad2b9d8,
'rlq': 0xd4ca7f308bd8411d87c1ee18d3b3e633,
'slq': 0x1f977b0c38caad0ffc7829c0f8a9cd9f,
'tlq': 0xa41337c5f65da0d8fa139e81317d1b18,
'ulq': 0x04f812e23f91a9fe229061d71a80e0bf,
'vlq': 0xf1b78c9608a71507561b18ea904e0db8,
'wlq': 0x39da3e10f68f1ffe2b97d7f6fac34cc9,
'xlq': 0xfe31a4284adc208e9d49acdc882fe98b,
'ylq': 0x05c2e308499bf3be5edee130a9e1083e,
'zlq': 0xf797f1af386dbcc74bff292d19616914,
'amq': 0xb49dbd22d04cceac1514abf107d1177d,
'bmq': 0xa0ad2e3e4d1a1cf1ed1bfbbbdd69df6d,
'cmq': 0x673cd2647242b91d4d5df49a991d8498,
'dmq': 0x89ff602b84b79fc8ca011648eacf206b,
'emq': 0xef3cacaf440434063e5e8ff2fbca1d07,
'fmq': 0x6c6577af5434621fcfe2c7c0cc49d037,
'gmq': 0xa6660452306ad2ea69cbe822c7a6d8bc,
'hmq': 0x18742162632a2be39ac4d8fb10d7e1ac,
'imq': 0xc2396da12f73f7883c35f53c8d01072f,
'jmq': 0x427917ea04e912a67000eaed9fac564f,
'kmq': 0x7ee503880a3e21dc25b5998a19bee88b,
'lmq': 0x080b08c67fa1b5426be4ff612b0a36f2,
'mmq': 0x97434c3154bf727998ab131197094c6f,
'nmq': 0xbb877477545a9525d0c77ec2267675c9,
'omq': 0xb10e545b27e91acb9facf8fe49364189,
'pmq': 0x8e9d3f7e452c5105ea7eb84245f931ef,
'qmq': 0x5294f1f3ad4c51ec1604f94bb9baed55,
'rmq': 0xea48b7f3742e15096d03d14c0cb23bdb,
'smq': 0x40f01c613ecde32ad753fbf93bcf419b,
'tmq': 0x17ff026d024e1c7c9e4cbd4b0f0c94f6,
'umq': 0x31dcd032b8bddfe6548f6c8f5caaf576,
'vmq': 0x43a322af681005a84be952bd847b5805,
'wmq': 0xdc2d713ea0ddfbce2468305810e7d004,
'xmq': 0xb60088c65d0afcdb5a79c56aee07ca9e,
'ymq': 0x0e36f6e3611a581e00520a85cab23014,
'zmq': 0x70e19aa7b15c7994b8ad4fd86fec067a,
'anq': 0xfb3fcfd0f0b72bac64b805da06c7c8d6,
'bnq': 0xdfafc6a98dee0c7b64501548ec64b581,
'cnq': 0x1c2ad8bee4376eb38f7190c1f7d033c6,
'dnq': 0x1a4fb6af24b968353c7c4f6a420ea5e7,
'enq': 0x98dbe91ed4cb65817c3a61e6dd2b1a77,
'fnq': 0xf732eb41d175a2e7c2ed7d20d1792046,
'gnq': 0x7a3c7a31deb19e098f0827dec47f7020,
'hnq': 0xce2e3c033c72674953bf9874d607aca7,
'inq': 0xdfd99e70c070eddf005b926af7afd6e4,
'jnq': 0xb4516cf95769c510ca88d7952246b4f7,
'knq': 0xaea051570f30919b7fd1e1e4cc9116be,
'lnq': 0x088d664b72c8a7e3d7ae3cb92f8a3912,
'mnq': 0x18011a2d3e9911596b1d71165c688699,
'nnq': 0xad41d8158ca74850d1696f9bf30bdf9a,
'onq': 0x1483575cdffbaf61a2d08bf72f0db072,
'pnq': 0x65186990cb9f98c9fe4701285ae4c93a,
'qnq': 0x8b862c73020e2405ebed5d8b58d9a5a0,
'rnq': 0x8e4a75d28c28577f5a80a98bc275eac4,
'snq': 0x321a41f176db2c74ddc92beeca33161a,
'tnq': 0x2ce8bcbba0f4fe109af34a16fc86b001,
'unq': 0xbb00334c46b6c19f93337c6bb3c37596,
'vnq': 0xfc0e8a06dc1393a9668f8188b1b19c7c,
'wnq': 0xcd26944b36949563ad0bd06cab65b20e,
'xnq': 0x1b001316d1e1b2669237299cfb6a2f7a,
'ynq': 0x4644151ab2cbf659ba2ec18726889a00,
'znq': 0x3910ee7d8102eaa71cb8195e5062add3,
'aoq': 0x572bea7d1f6108b1562fc4242eec2de0,
'boq': 0x1305951b029daa9c563351d2a9eab7f1,
'coq': 0x666c5e16f4a89bc5a2f926b3d2e9da2e,
'doq': 0xb07cacfe484597c4bd846637ac4318e1,
'eoq': 0x52e22cfb25214f7f9b0b90033ceabb37,
'foq': 0x4ed2a2f19ba26559f2102399f5343fcc,
'goq': 0x0b8e238c731e8d0f453d81e2eba80985,
'hoq': 0x6baa26c77f6a0ef05f8cbe2b69257447,
'ioq': 0x01c3f3a1cb7041459ece08ef2359c714,
'joq': 0xdcd21da723c79bde808e531f31f838b5,
'koq': 0x5a9191f285c7e004dd31055a14eff45f,
'loq': 0x67ab168d78dcbce8697262f34de6c8da,
'moq': 0x7a5856190cb6fb0489d93e1002328992,
'noq': 0xdf2a61dcd5db4e439b98d8fa28347884,
'ooq': 0xfb1963491e0eea2bdf6cb6d3344f7473,
'poq': 0xb1fe54b8ed0b4c729af1b4bcf5b45943,
'qoq': 0xbaaed3a892b90b96b1a7e4e8aba85d7d,
'roq': 0x2c4685ad2d33f6a40e15fcda4a51351d,
'soq': 0xf0d932cf6e2ea9a40671f750214397cc,
'toq': 0x6c60e02542ccb296e11d2abc564abd4a,
'uoq': 0xe4e6d006a0521dac90daca456f043d40,
'voq': 0xa04395836423b1518353d49c8d9eb7e7,
'woq': 0xa2b571f94ecb59b48c68e0a6ca0eef3d,
'xoq': 0xdbd732b4ebec55ae4fc60126c8c2941d,
'yoq': 0x96ef69a441325c69d649b77baa533cfd,
'zoq': 0xfc35023170854145666f5bcd92abce2b,
'apq': 0x559e2c90c211a96b609c9d1af6aa878c,
'bpq': 0x074bf126e6dea6d1a7703a533416b55a,
'cpq': 0xbae8e0e96d0f012bf12aa3ea218c9636,
'dpq': 0x39e037e4f9586fd367447ecb36c3c637,
'epq': 0x544a6afa4ac2240fc740a24c9ac5dd7c,
'fpq': 0xabb5dd08bac680261dafa4c78af6af7e,
'gpq': 0x45514c1b4c5889614840148b675234cc,
'hpq': 0x914f63bde37419b46679de701af45b18,
'ipq': 0x711804ef5fae417179ff243414e8de85,
'jpq': 0xd55f4a4074fb1aa37f94d80a1fed8447,
'kpq': 0x50bc05547e0327f1bb0e1dca0ff5351a,
'lpq': 0xc8962bb6463ffcf8cb9cffa8366d70f1,
'mpq': 0x1fe7eb197a9fadc2688f2fff31ef26f0,
'npq': 0x2de8c5cbaedaf6720b2a1007ba5f0ac3,
'opq': 0x479068cd1993969ebdec39f6cdb08e61,
'ppq': 0x9f11c10b5e21488df16be85ab0a78cd4,
'qpq': 0xd569af064e3563bffd9556d313454613,
'rpq': 0xd17c005f6ba5794f017259cce015c544,
'spq': 0x2df75afc91c620ebe23ffd1abad17cd1,
'tpq': 0x251e0a886d70a434fb330f7833e83d7a,
'upq': 0x2ee8e9a8eb3585e6505facb0dbc07938,
'vpq': 0x58e22650fa041e31212e5402071f51ad,
'wpq': 0xb39edd0d49bcb064707bb0a8538f265e,
'xpq': 0x1996b45e1ba95f0c522656fa1ce6f0cf,
'ypq': 0xf131cfeb85632a6e01e40003b5583c60,
'zpq': 0x1844f87975024a4b9f405ce209cb6b66,
'aqq': 0x5c29c73a81f8a55036eeaddc155cf02c,
'bqq': 0x608af9559f09eec0ad1509de47759443,
'cqq': 0x8c6fe6437b203b7687e1ddfdff1e6f4d,
'dqq': 0x63cd09f02f6cdfa27ecb6529468ab848,
'eqq': 0xfc71ba6ad695a7130c5a44cc5e0d8c21,
'fqq': 0xb89971da87b54e0a02bd9f203f066683,
'gqq': 0x76e206dbbc676b540e4dacf9ac69990f,
'hqq': 0x9f7e8d75cf96f801790f16839b7c642e,
'iqq': 0x033f15795ac71684e3f7332adf3afde1,
'jqq': 0x3f560d6904245ce3befde5a2a0b0df30,
'kqq': 0x69effc945bcf3e4bd19a73a0b7e64bba,
'lqq': 0x60f8ac0818d050a292ffa9615280e55a,
'mqq': 0x8454317345ff2ab168bfe954f2ea9d3d,
'nqq': 0x4f3fa7e959789812dec6528a7e14cd39,
'oqq': 0x41137a99b49a0be3b6f32e39edb7d41f,
'pqq': 0x12b989888ce9ade561e7412bca16c3ca,
'qqq': 0xb2ca678b4c936f905fb82f2733f5297f,
'rqq': 0x2f7aea4c9c1cecdf5ba8bbbfd57e7b80,
'sqq': 0xc3b2e0d5d6d668736df58838fbb3caa2,
'tqq': 0xc150ad55c100e7345bbbf22bad94de32,
'uqq': 0x32729008b6da6ee7bb3a0fbbab4fff59,
'vqq': 0x67673c1eed02db1669818ced90fb0ce3,
'wqq': 0x6811e4f88eb5ee02275feba49c774584,
'xqq': 0xb71fd2cd6fd8063840f0e27645ab1981,
'yqq': 0xe10f3fd0681f430c266244c738b10be3,
'zqq': 0xbd8c9bd2f2fa87914fba54fb622feb52,
'arq': 0x984205cbc1164164190a64eee175bf67,
'brq': 0x3842bc4a3ca1df198b89288f3ebc3ea7,
'crq': 0xcc500c485c095baa790cba51fc8079f9,
'drq': 0x4cab1c6a9c54cf13a3036260528d8c7a,
'erq': 0x4bd1a07d59e1dbf29eba8cceddb00f37,
'frq': 0xd1103521468423cafc056738668101aa,
'grq': 0x055a3d8bcb0a13701f8bb4e9bfd03f40,
'hrq': 0x199d8023b5f4f19b3bf13588a2b83b34,
'irq': 0x0246c2251217c97899bc8c89f5126045,
'jrq': 0xb75e5bc3defaf94a4405a5e80dddd1bc,
'krq': 0x535a10f1ac9c3a1e7819e6b2a23e6fc1,
'lrq': 0x3dd5f93092b3d30b2774e7947b3a8a93,
'mrq': 0xfed86d76f8faeb4ff6e022315aa8251c,
'nrq': 0x3b3e362c26a202746aef751d0cc82f83,
'orq': 0xf02bd6ea26a2285da2ea17a25c0bf827,
'prq': 0x579758451927356eb488aa91c35eefbe,
'qrq': 0x5e0920a14534bbd3d6584720b437bda9,
'rrq': 0xf5154462d6d9d8609fd31cce86888d61,
'srq': 0x2c3bd8014a8611165eb364790fc3dbbc,
'trq': 0x49702711ca1dd264da673c1e4a22aacd,
'urq': 0x3ed37134af557cda9c9f67145e6e61b0,
'vrq': 0x9835da46945e44b7ef65f14e39bffc73,
'wrq': 0x608f1ad03f121328994db9bce29fc9a7,
'xrq': 0x2a3ae0de5c980ed7945212fe9192a3c8,
'yrq': 0xa07923efd64b7b1816a46f68d56ded73,
'zrq': 0x3de82c39d7caf14cba202bc5f370a551,
'asq': 0x1cc18548883588e18b98e37b633b2d4d,
'bsq': 0x0ad1a8d7dee5a89461ed52a717f66e41,
'csq': 0x849eee2a601c3bad3d13eedc864c906c,
'dsq': 0xfe37defe4f8d4c738bbdda259ef79b19,
'esq': 0xb13640df13ae738448e7aa35be753459,
'fsq': 0x94c8fcb2aa649900e47d4733ac48f600,
'gsq': 0x31fcda91106d46f29f9ea389417561ce,
'hsq': 0x454db35cb34c037051a0adab17b4c27d,
'isq': 0x8751724c9a35da98229f2347284f9cfc,
'jsq': 0x989bd55ba17c57a87cb22dc24d1f15a5,
'ksq': 0x09c4af4a3592e3fd590c21629fad4e84,
'lsq': 0xcb2cae3e631e60d0918af478955f90c9,
'msq': 0x7041206c30b85805b414d992b9005833,
'nsq': 0x03664a1bb274312f2ec762e27d891162,
'osq': 0xb6e881b3debe79ac3d0d5d6b2b4173b2,
'psq': 0x8d04d9a7abe8640dd4313ffc61dd2398,
'qsq': 0x1eb382d402fef490ef2d60c68130eae2,
'rsq': 0xc60d63c12ea1bc22eeb5acf20527d580,
'ssq': 0xadc82fa9655e7e9ed06723535035cfe5,
'tsq': 0xdd9f5377cdc8c242e2f5653dd38b3578,
'usq': 0xf2c601ccf8fa10a9c4c3c3ee298a633e,
'vsq': 0x7e978a220b825cd36735e6ebad65a38f,
'wsq': 0x69b819b24572381974328f9ccb0ff94d,
'xsq': 0xd5fdfd7f4b6382f776ca28bd44ae1c53,
'ysq': 0x72ab94c6091fd35f86a4d05353231da1,
'zsq': 0x495643bff155f54d3642ede411523edd,
'atq': 0xf811ffa9462834c435fe67e7e6b0f8d5,
'btq': 0x78b52597091eba0d0148445b87f10aae,
'ctq': 0xef73c4826c8e276255a18bb72e3df535,
'dtq': 0x9989acc06dd9cca90520f060c449ed9f,
'etq': 0xc6d6313d4f53a2ee2dfa8091e26667ed,
'ftq': 0xccf0c402003edfaa3ae765a96d7c00c0,
'gtq': 0xe72a9ca59c2ca0595123a1b8f2763eaf,
'htq': 0xa0aeefa4dbcd8a7322177adf5fda2933,
'itq': 0x12fae8f0aefb71ed8fb36a64b7a7c7ab,
'jtq': 0x8de524d7f5dd7689abd5660d3de2106f,
'ktq': 0x6ed3c31f312b2170ca23f3bff25339b9,
'ltq': 0x59fa3511c81754dcd15f641655d2167b,
'mtq': 0xeae145c78d2c5cea89501704f29569d6,
'ntq': 0x949c2b03ab5c7172947ea53ac6a10008,
'otq': 0x20b664ccb6ed77e1f833a2226b77078d,
'ptq': 0x6be7d7c85b5551e7409aa0ac9d635049,
'qtq': 0xeac72de8cfe835de74d0b53f47b383c7,
'rtq': 0x3d307fbfc89a2ba3340c76f969c8d21e,
'stq': 0x74feccb5017b027340f6ce93fbe45644,
'ttq': 0x486bd12e45d7319546dba211789e58d0,
'utq': 0x62900bfa96def18fc90d20bc46441373,
'vtq': 0x34a22cfba30ed978321344a0bbc0861f,
'wtq': 0x10696fc4edd87bfbd1fed6f1161de6fe,
'xtq': 0x384f91bb0b7ddd10d4b31591d4c03925,
'ytq': 0x25dd142ae032f2dc7bf58b22d67f86a1,
'ztq': 0xf6fc7b5cdb6ae8aba55d1584b8729dea,
'auq': 0x5a20c071603b7cecd5cecfde1a224181,
'buq': 0xc13329f56b9e195d72d444e79122b55d,
'cuq': 0x8868eae1a01e5cfb22714b13f34aa8a4,
'duq': 0xf58a315bb5515dbdbab568d674b21946,
'euq': 0xe81a619c17291809d00d87da19657fc6,
'fuq': 0x8121a5aaa8209660e759c46eb54788f5,
'guq': 0x9b69a91ab96c5f2aa3fb485ac7e436a6,
'huq': 0xa161521c25f04113464d306d0ce26e40,
'iuq': 0x3926f242e7d77f42af08c402a9f42b19,
'juq': 0x1b39c5ff0fef5485931bf9a6019bd2d3,
'kuq': 0x0292a3970af1aa50451eb312782c756e,
'luq': 0x94bce5751038404f1b1ec8094a1e19a2,
'muq': 0xb534245b08f97a74ada4ad1dbfafdb09,
'nuq': 0x30a4353f70811b0b5ef3e8c164e36105,
'ouq': 0x98585fcdd6ce65e27d3c4e0a60bb36e0,
'puq': 0xc0ffbb8634e9a6067a98bd580ae0da8e,
'quq': 0x52a8a22af080121df711a0664a3e699d,
'ruq': 0x6d8d2fc55fb15103354dc1f03384d359,
'suq': 0xe957ba0795f88410181916f6b2d9095b,
'tuq': 0xb03f5925b94438e84cd862dc8eb44345,
'uuq': 0xde9ae0c5c0117bfe5dd402cb0cdafaee,
'vuq': 0x3e69aed9c973e963136bf3ffaef769dc,
'wuq': 0xb8947871f34bd026a8edb14531668704,
'xuq': 0xc86d7c0ebf2de5d325534f77215add01,
'yuq': 0x27ecab9e7106b16cff4f2b8ed2da6f37,
'zuq': 0x6d94dc6a4ee4b0f1067729d55a20082c,
'avq': 0xe0070b8dcaa584760725f3bd3a77d7c6,
'bvq': 0xa8be4d91384a96fc7cd79f37ca1f8cda,
'cvq': 0xb8f75fe3de4018536c7e1b4ce4eef38d,
'dvq': 0xdecc1d5568cb3c272ac5da82d18b3bfd,
'evq': 0x5b551764af20fd3e72660593588cc3b4,
'fvq': 0xcfb63a8e294729924a0f947822037660,
'gvq': 0x42c050f143db2e7e5f2b919d639960cc,
'hvq': 0x7208941c435319bfd5e0387a32444a2b,
'ivq': 0x1acd0ac15697df9a713501c3e737fd08,
'jvq': 0x13f85598a7c01385568f9b3deb7fcf74,
'kvq': 0x3111151190a2e4406fc5113ab907b96e,
'lvq': 0x9feade9833803543bdda09fab4884368,
'mvq': 0xf31958d9932d4c5560eb054b7034e89f,
'nvq': 0x2d1424b223491f690eb5c455803eb47b,
'ovq': 0x0fdf3fd0389c033d63818f007c345385,
'pvq': 0x2c3b665e8c587214694aa05c63394e77,
'qvq': 0x3354ef0a4b118f6f03724b482db08b28,
'rvq': 0x601d0b91e3adbba837976f5553cae460,
'svq': 0x8bc00883f552e38051424286a2bd7aef,
'tvq': 0x02d3e766034266af2b3cb22b245d98b1,
'uvq': 0x21bdb30a618b150ffc7aac60867a46dc,
'vvq': 0x6a66cc0176b2e5fedbd23e8d6ada5e11,
'wvq': 0xa39617ad644e04fd7ed1ad1249edb8e1,
'xvq': 0xbf573269bdbb0c45f964305208a76293,
'yvq': 0xb684991813d5b70602dedd59a80fa144,
'zvq': 0x393851fc4a3e2b8076f3e28112d1a750,
'awq': 0xb72c2e5a9144e63a094292c8070b4829,
'bwq': 0xc2c99a73bf22cd8c33e9df5970e489c2,
'cwq': 0x9b5f57cfb28b301058a30adaadafa8d7,
'dwq': 0xcac2c01db4faf27bdce5b3cb8f8c5f6d,
'ewq': 0x4d1ea1367acf0560c6716dd076a84d7f,
'fwq': 0x3bfd6fbee6a9b644cb35832335ce3aa0,
'gwq': 0x65bd3e96dba27691f8c33f149c5e5e83,
'hwq': 0xde9cba0e3f4604b0cf50fb67cfc9b748,
'iwq': 0x501889041cc17a8cf2bfb9f2bbbd2b27,
'jwq': 0xae5130b2e58528c9a917345b3828263c,
'kwq': 0x7aecf141564a45c1aaa10e1309a71e6d,
'lwq': 0xe2938aed6e1d405cd23718d9fb5f1e07,
'mwq': 0x06523e4a9a666687d334e41dab27cef4,
'nwq': 0xa90bbbb017a748e0775ed52c6de6eaec,
'owq': 0xf819158ec343d99fa4de95e7eba869b0,
'pwq': 0x02bebe42c9c9d764337451b7a8598237,
'qwq': 0xa078b88157431887516448c823118d83,
'rwq': 0x6882abe33d707231789b8c2458f32082,
'swq': 0x2392e13c1765b365a24717cd0db6b035,
'twq': 0x7a1b1627fbd44c31d1108f57a29a58db,
'uwq': 0xa635709b29f37a921edba269b6e81ea8,
'vwq': 0xf8bd42827266ac95f2747bf75358de37,
'wwq': 0x7dc6f5e1c139479e5bc2afb259e7964a,
'xwq': 0xa28673f249d1ecbd1d9e9c82f6673b26,
'ywq': 0x166724e6e41a58b578fd81919368b63b,
'zwq': 0x47ccc82abac13739393a94ddb1abfce7,
'axq': 0x7b01c9a929b344d86a227013b77c948c,
'bxq': 0xb3ae6e3182de8c5b3e7bb681e54223c3,
'cxq': 0xfad0f2a68dcc1aee15301cd4810c2fac,
'dxq': 0xf8ee510baabb3c33cd04dd39e41587a0,
'exq': 0x5f251d47d427b94686ba7e35857d820c,
'fxq': 0x3ede1cfcc5a0e97350685fc746ca1420,
'gxq': 0x28923c7c615d2b00dbe12eee194e0541,
'hxq': 0x375d9ac60f86e93f8172bad115de6afa,
'ixq': 0x4da01c4b904c1f4ea7767e8444c2270b,
'jxq': 0xbe2cdf2953946189577c97cbd6b002e3,
'kxq': 0x0ce164f18c8989aebd09dedb6bcf553c,
'lxq': 0x60e57b520f3c52694a88611a7653f079,
'mxq': 0xe8b7f5d07197272468b3beacb74242c1,
'nxq': 0xb3af7eb1844098b3f3ac7c18afc5caa9,
'oxq': 0x36cbafeddee769d13e9fbcf0dc2914b1,
'pxq': 0xc4ca0f50f59e700282ec90b99ef4253b,
'qxq': 0x3d7f6fe6cdb41e1496da1e0fee395b67,
'rxq': 0x5a3f119fb2bb34537b9b196c4288ca92,
'sxq': 0x58b627ab1c9dce6b19f32303d2d6f22a,
'txq': 0xa9c132f04fa8287b3bff8ec4c537f7b0,
'uxq': 0x9423051dc3730d6e439dbb4714112b96,
'vxq': 0x7dd29191ab86cbc723616ae646d2d402,
'wxq': 0xdd78b5f11755d1aab0ebd2c42f9867e2,
'xxq': 0x5cca6754e5c158a770be7264916aef59,
'yxq': 0x2ae0bd2232621c339b4e9f6d382d3231,
'zxq': 0x9f5f640fb9768c57eefba475f8899242,
'ayq': 0xa630bbb8293e74e8b54006ba67b73ac9,
'byq': 0x145a2ba9c214038e9c28270bcf0d4dc9,
'cyq': 0x24e089d29e2437e484fa166ebb2d6bab,
'dyq': 0x7411ad30e63f08ff23616da0dcbdea3f,
'eyq': 0x9a3e81c5d824ecb8d8c445713dcec1c7,
'fyq': 0x2c20d94fd5311d3acb9e3eb828d7e9f3,
'gyq': 0x1c46a800e900fef5cc3232c443936e8e,
'hyq': 0x3eb0e8fb4dc4df71929dd728eeee546e,
'iyq': 0x800815a8c5403f9d7ab004add7a7e9db,
'jyq': 0xf6eaf97fb9b8e9e6ddce57b67694b352,
'kyq': 0x10ceedeee7f7261d25a96359c3e62190,
'lyq': 0xbaa66ac74de93b1318597b4073911740,
'myq': 0xd9f54880cb67f0fcad0b6c679c1e6ccb,
'nyq': 0xfc77b2414b1feb3cb9e28a635a96f12c,
'oyq': 0x0f1bc1cd1286b67680d0c10e5ec01a6b,
'pyq': 0x28552e70982a9cf623d181c4c97545c6,
'qyq': 0x6f8491b14b46a9cf80e9ca793827ebaa,
'ryq': 0x321292cc2149095ae20cc96b440d2e58,
'syq': 0x6b47c8e201106831a794e3484e10795f,
'tyq': 0x7975867fbd4763efe7947e292f6163e0,
'uyq': 0xe19bb59ce8f7ce943f95f95107ad694b,
'vyq': 0xa4cba37bf4f804bca715b3bd25d86a29,
'wyq': 0xdbb4a18d482c44dfaa9bab620e5747d5,
'xyq': 0x7ed6365ba26aed89979be27d6fd1586c,
'yyq': 0x7948be85b53b4fb418a1be145263250f,
'zyq': 0xfe98a94d2bfcbfee0d6b2632ed116fc9,
'azq': 0x60860a098a5db70fe46ea5fd34fd3e1b,
'bzq': 0x8ea3f1b85bdc46138d15d699fc8a60f1,
'czq': 0x75b9fcf9685afe44ec2798baa4e5b25c,
'dzq': 0x4249ff8905e8730fbfd712b19ed18ec8,
'ezq': 0x288eef2ebbe16c9aeae0a1ac6e74e3c6,
'fzq': 0xcb734d8cdc6ff5040c711648a417cf3a,
'gzq': 0x3b329242a3d2498d313f71128f708f8a,
'hzq': 0xa1f422ab3eb31059c22a4a3750560a03,
'izq': 0xb7519a0dd99b02100bca16f0ddd5743b,
'jzq': 0xaef4af9a59c7af737e5b2ebe4615e88a,
'kzq': 0x5537998bb4e00689c0a870dd1bbf5598,
'lzq': 0xe7323ffbdae2c49b1b52583b87b635ea,
'mzq': 0x098a95947e4486023cb83d76f0f70d5d,
'nzq': 0xc64b526434ff1cc75916cfd5313746d9,
'ozq': 0xa88adea8562f6541503de52a05ff2dc2,
'pzq': 0x0d982885893cc6ed33088f6b973e0bec,
'qzq': 0x2049783276f3bff62174b0aad1a0fd5b,
'rzq': 0x981fd9cadc00a2ba1844115971e5710c,
'szq': 0x442a36ccc45001169472dd902807c726,
'tzq': 0x5237035cefaa1174b5298db46254c3e9,
'uzq': 0xc4b8ce1b67d38aa98345f108f9f529ca,
'vzq': 0xeee4c730edb7c655bdb185040dfc3a81,
'wzq': 0x175d4020fe13e47d38baecb4713fb04f,
'xzq': 0x341a16c8a49e0376850e1fc259ec0e89,
'yzq': 0x7667c46828a340b3df237c37050e8f29,
'zzq': 0xdcfdde909acb47b9d4ed618e8642e3ee,
'aar': 0xbfd2e2d0b502df8e078e1ae470a839b4,
'bar': 0x37b51d194a7513e45b56f6524f2d51f2,
'car': 0xe6d96502596d7e7887b76646c5f615d9,
'dar': 0xac638a13498ffe51c65a6ae0bf7089fd,
'ear': 0xd8ff99616fc9df8a54a32eec86ba8109,
'far': 0x17d965747c701d6ffb406cad0f4265dc,
'gar': 0x4618c2b26e9e222b2cf19471e1f2af8c,
'har': 0x7efd1210b31b96d372a4d1975a6941f2,
'iar': 0x792a3194462bce7c44b400b97f187a97,
'jar': 0x68995fcbf432492d15484d04a9d2ac40,
'kar': 0xaa8ae3b340c34010e4500a0d6294dc2c,
'lar': 0x540497198b6b1f33e890e10f9742bb34,
'mar': 0x5fa9db2e335ef69a4eeb9fe7974d61f4,
'nar': 0xc0c669570f0dd6c896db86e0447802e8,
'oar': 0x82d8a8be8cfb6713683bae3c160e3007,
'par': 0xd018268506e2868537a478629b59e7c1,
'qar': 0xa76b31036e209b2c627b993e5d3fd361,
'rar': 0xbf0cda1e8b92bf9c134c0a8291e320e7,
'sar': 0x74101aa14a1d883badc954c4f462e365,
'tar': 0xdb5570bf61464b46e2bde31ed61a7dc6,
'uar': 0x355cb440d65f09747230e8852b19aa4a,
'var': 0xb2145aac704ce76dbe1ac7adac535b23,
'war': 0x4ca9d3dcd2b6843e62d75eb191887cf2,
'xar': 0x51d5522baf939027bccb1ff79c154c3a,
'yar': 0x883452d07c625e5dbbcdaaa47f0aa92d,
'zar': 0xb24d4be77066cb0bf70247b7d9176ebb,
'abr': 0xfc1e82b9a18586b50eb40c3867a61bc9,
'bbr': 0x756fe2b423fddedb2960a247031e4cb1,
'cbr': 0xa8186307f5bbc554b722505f23cfc81e,
'dbr': 0x63ba4b6a2bfae4e7fb1e754d8efc9037,
'ebr': 0x071005a947a6520eab5f9512f31dc298,
'fbr': 0x06db955b5913c431ec5ec30c67d78cf5,
'gbr': 0xec8989f78fab481d87f43ecacca55aa1,
'hbr': 0x575f041b5e3cd0a6affcc809d79da8d1,
'ibr': 0x0b005f99fa635b476164be20dfbaf7ac,
'jbr': 0x0a64f50d49e9499f24e481ed2fc9453c,
'kbr': 0x4bda4216e1e679d58153b0538c698612,
'lbr': 0x698b740a7075bada0ad7f541c3964e44,
'mbr': 0xe4c5a79f94c6b300ef484a417e3ccfb6,
'nbr': 0x1fb5c4179c7734f9c7e47c8f5ccac5a3,
'obr': 0xda924fe5d7d8072dbf838ddfbebee4df,
'pbr': 0xcdc3ae6882ab6133d9448100253b6da3,
'qbr': 0xa5456767b954caf16ac9299e58eff1f4,
'rbr': 0xc25b2f79797c0db6d0f79524b6636d95,
'sbr': 0xc0b5d39cf3fc35ac592b4aad14b30c0e,
'tbr': 0xceaab931e75d8a7dbbbd4e340e8ba152,
'ubr': 0x479abe7acf4a510702aced4961dc8edf,
'vbr': 0x8934a03621a3cba7b120185ea4d9aa25,
'wbr': 0xb48f3ea6e4f4f4285f69918ead79ca1f,
'xbr': 0x819b5f27575cf5eae94864c030ebc192,
'ybr': 0xa611baf08754586a0bd4ef3e571bf2d2,
'zbr': 0x58bcba11f0d663c88255e8c2ce5a7e2d,
'acr': 0x9f8878742e956027afe7786868dfb506,
'bcr': 0x19c5284143464e91fdec74a078005500,
'ccr': 0xb48ec279411f7dbbb68393c61a9724d9,
'dcr': 0xcad62314330b0a7a106aec2b9b6d9411,
'ecr': 0xde7db04805a33606a40b897578543648,
'fcr': 0x02de45aaa805622b2f8ef9b317826b53,
'gcr': 0xb634e75f655dff77257ee6acab07b747,
'hcr': 0x7328ffdb9159544ded1089b798408ae7,
'icr': 0x58f4ad4b11a5dee20fb6a94abf5f9310,
'jcr': 0x6426afaaa7efafa557f04664322e5c18,
'kcr': 0x14fbd6d2351cb39f0168de7287d0a887,
'lcr': 0x0c9bcffee34c2bf9fd9b83acfe7aebbd,
'mcr': 0x42ef4c9cd5923ada0be5833c43f1d2e4,
'ncr': 0xc9c2956ce7fe96adeebf39a5311ed2d3,
'ocr': 0xe937d7a161458fb314ccbbce94150e22,
'pcr': 0xce5256bc9919dfe0fc41de42b12f1d3a,
'qcr': 0x22674fbd11be5883b59ae7d8a95c2c16,
'rcr': 0x247b8bef1e534a6ecd9c4b87041a0e8a,
'scr': 0x456727ac6d97e749f87ec95009617bd2,
'tcr': 0xd4b2e776fe3413156a72ce8dee43f310,
'ucr': 0x6cffd99a02e79e9dbed43e787512635c,
'vcr': 0xc957434d5046bcbcd48c62a49459b910,
'wcr': 0x95d9945a7e9888958aaf73d06051bee9,
'xcr': 0x0839c4b601782ac26addf1aac4e1d69c,
'ycr': 0xc7eef6711a75f2e6709d5b42ac9410dd,
'zcr': 0x022ed30a8d1a67d798efc92c3e88e716,
'adr': 0xde7c54d99428fb909bef013577070f0d,
'bdr': 0x539e3b7376faced9f29c3ebb813ab622,
'cdr': 0x30491931d0d510243046864dbc71a8c9,
'ddr': 0xb9ce391e970ede27e9eb0c3f6ef3f274,
'edr': 0x02b7e6d9887d34c42d9204e727b91068,
'fdr': 0x343df2065675b9092473771511110b02,
'gdr': 0xfff7c7603b50cc39962ecf42277c0d0d,
'hdr': 0x4ccdcbc7ec60819cfb8bca1c20862b69,
'idr': 0x38d2ddea2861d3515818bacc51818adb,
'jdr': 0xb2b449e81749a987436c0cffa4482602,
'kdr': 0xcb58e6788fc9a69c674bb00f98bffc14,
'ldr': 0xb4a298122adee74c760c97a7ea6e5eec,
'mdr': 0xf9824ba4a0c1053d3c448d752236fe4f,
'ndr': 0xd74e97d017d1d868c59d07f022613670,
'odr': 0x1490b5fc3f21959eb7e43c7355ac5104,
'pdr': 0x9149348a18459bbe18bcab66b06f1967,
'qdr': 0xecb02cfa6713be936376873193eeffb5,
'rdr': 0xfea7c91cbcd83e04bb761416601724c0,
'sdr': 0x8220b4ee3616cf3d58d7f73a118d4a63,
'tdr': 0x059caf18c820a78f87205e66126904e9,
'udr': 0x1144ea8379574ca04550be4e7fe940a3,
'vdr': 0x20a7ec1e4c28e748928ffa135ca55fc7,
'wdr': 0x5f32665da5abc99a675befd7c703fe7a,
'xdr': 0x05e46a892c9f038a22dddb6b2877477c,
'ydr': 0xccd5f4b1e51e34979838851a8d4f17a0,
'zdr': 0x9315de2a6bf976864192cae61821ec31,
'aer': 0xd194f6194fc458544482bbb8f0b74c6b,
'ber': 0xc9fedf43460997594c3cd56c47cdd6ee,
'cer': 0xe23ca1e4e92fdc29b4d4af7d4d33263b,
'der': 0x6bd48b1e57856137037bfee4dec8d57f,
'eer': 0xf38aeb65a88f50a2373643a82158c6dc,
'fer': 0x90eb8760c187a2097884ed4c9ffbb6a4,
'ger': 0x98302eb9727009d08199b25b7b72b1cb,
'her': 0xa43b4caa46fb39a1d48b89e25f0e79df,
'ier': 0xa078fb91566687fc624c0b92485f923a,
'jer': 0x8f192fcf63386fa01b7ba156f3022e00,
'ker': 0x4e27f2226785e9abbe046fc592668860,
'ler': 0xa2c74dd68dbd8410791021e4827b407b,
'mer': 0x2af9ef9aaa6d32873e51431d5cdd3dfb,
'ner': 0xda1a38f3a450132623ef0a9c2d06b986,
'oer': 0x5b755730fd9df79c6ac4b541fee23847,
'per': 0xfe3838c7c11aa406dd956566e17360d5,
'qer': 0x1441cfd34ae99b50910c2ccc38444910,
'rer': 0xd66c264e1dbd73e6111d3ffc70908e8e,
'ser': 0x9df1c16e82351bd6abc74d230391cd1e,
'ter': 0x322650e1328739dbca646008305dd95e,
'uer': 0xd27603272c722655e3af9d416c3b0a0f,
'ver': 0x0812f14f43315611dd0ef462515c9d00,
'wer': 0x22c276a05aa7c90566ae2175bcc2a9b0,
'xer': 0xfaa709c5035aea00f9efb278f2ad5df0,
'yer': 0x9e4105d172e0c26fad98471f9ffcadf6,
'zer': 0xd674a71e054c2c7a6761c366e6eb73c4,
'afr': 0x748ced3dd3add8223b8f2e73f28ef722,
'bfr': 0xda45d024f091e6b309904e140e86affc,
'cfr': 0xbe94ccea53a936073b0df815aac8e6f9,
'dfr': 0xab127ff4625f98cc88ff0e0f1f911e64,
'efr': 0xc6e285907444ace4568ae3dfaaac78da,
'ffr': 0x22a8dc42ff1026051367f4a76bf896d0,
'gfr': 0x27d0d0a8d55bac261789260651e4468b,
'hfr': 0x4a87d9276892006dc60088411826a0fe,
'ifr': 0x3bd29e58a2ed049848d7803c85ff3cb1,
'jfr': 0x34226a0fd95abd0f6524b83caf27d974,
'kfr': 0x517e8d4a5eb590eea4ad5e9bb0e426ac,
'lfr': 0xa93a9f17ab21df2028e893c7a7e1b4a2,
'mfr': 0x54f901ce508a34bb2394e34ac2371812,
'nfr': 0x02defad7ec9c3ce14c3f67ee260072f6,
'ofr': 0x05de0817ff61137e8fe729080a456caf,
'pfr': 0x2e1f2991678cbdba01d0412a95049341,
'qfr': 0x209206407178379f290f5d34546142d4,
'rfr': 0x32918320e34145e38401038af541b0e9,
'sfr': 0x3ae71d9805bbfe325bb5787310e6a9a2,
'tfr': 0xdc2b400be79cc0b9776dcf9dd5d6e923,
'ufr': 0xf898df88d59f88053d651fd53bcc2a7a,
'vfr': 0x4f8ee9648840401ef4f13fe88435ddc6,
'wfr': 0xb5ee44d7316af452747f43409a1857f0,
'xfr': 0x52d8d5f634814a99828c9416d4e94316,
'yfr': 0x27dd0adc9522cdff5117133d6520a4a0,
'zfr': 0x6d1f7d4d1ffc302eb0d50df1c9df8526,
'agr': 0x348395d346e03ba2c56db7827d319b66,
'bgr': 0x84746ad530563de3445efec60aa7ff59,
'cgr': 0x2fbecbcc914e8e57f3cbaf15f8eb5a68,
'dgr': 0x244f7dd8d19dfa57095d58a315f79da9,
'egr': 0xf6fad9ad138f09b0ae79cad34b690900,
'fgr': 0xc6c3722a19e41c75fedfe8223bf4bd0e,
'ggr': 0x9c426349882d858347b4d810160dd15a,
'hgr': 0xf276a5ad8b3f0f4321e37e98371f31e5,
'igr': 0x3741ad0b124bbb41dcafcd9af3ec3476,
'jgr': 0x2829a0eb6cbfaf2228f40d743ae727de,
'kgr': 0x18509498322fd3b87f552dda588ed316,
'lgr': 0x1d399c5af7d0188ea2ba0d63946a7d9a,
'mgr': 0x37bd0d3935b47be2ab57bcf91b57f499,
'ngr': 0x2bc12977899899e17656a3fb4488f4c8,
'ogr': 0x7f2ad88ce7f815d24b4f5efe82da83a2,
'pgr': 0x6d00df506eed78175933b5ecde287c64,
'qgr': 0x512f49a60edde240bfcedd4aabf41fdc,
'rgr': 0x5828bb6f63885517f2710fe2371a9af6,
'sgr': 0xe46a1f5dbb9a45681f26992fc8754a49,
'tgr': 0x2379ec87d135459f0424f394c00c3812,
'ugr': 0x4ac6f8b642eccc9f22fc07cd4f22bf6e,
'vgr': 0x0dcda7111ea59769ac201a1d36d1303a,
'wgr': 0x7630cdd2c7188efec7384ab7354826bf,
'xgr': 0x05e049a66f147cdcb9215140366f2671,
'ygr': 0x4af33efc68a3c6fbe757f4b36666aa06,
'zgr': 0x47a3e18c87d71db19d3d7857a1052462,
'ahr': 0x35b605f929209fc6cba65789d1f6b61c,
'bhr': 0x4c26c97ab3405728e4518b11e6d235c1,
'chr': 0x599e41d8cd8cf1ea79e494df54ede29a,
'dhr': 0x89c62730876da4a266c46af21ec60364,
'ehr': 0x77eae7aaebf39fd0c8bef84e58b37cfd,
'fhr': 0x1ec75a8bbc76c5b6b37491e613d2acc9,
'ghr': 0xcd8e67ef15114f06a34d85ae70a7b860,
'hhr': 0x37d93e509ad9810f189677578d0125df,
'ihr': 0xda6ac101b05b6974d66e6485b7a34629,
'jhr': 0xb5dfd1677d909a8d086eac33004c991e,
'khr': 0x2ac6714e58958808d9abf62a182d7089,
'lhr': 0x3996643de967b80174e48fb45d7227b1,
'mhr': 0x143d650435967dcee49ae7e90ed19c6b,
'nhr': 0x25ac7375c1fd64eca8dd8cf309071c0d,
'ohr': 0x3143c01b76f4ecb9b29992f0685da52d,
'phr': 0xb266e47eb657161825cded6cc0dd5730,
'qhr': 0xbba36748a201c49bb3123b5c6b34add9,
'rhr': 0x981e8cc039b33d06dc4916178f50c50d,
'shr': 0x82cfb068052c46d36d9050ce1fcbaf2e,
'thr': 0xbb629dea7af8bce50ce94bffdc515405,
'uhr': 0x1da1b478b4eab71c98527b678e201fd4,
'vhr': 0xb6be0fb133de3084a8d453bd4e63e724,
'whr': 0x85418f6b7584d930dc63f39a4a6b7dd2,
'xhr': 0x4d1ad43b240cb75aa65040538d578676,
'yhr': 0x5861a28226bb4bac95737de9f004896f,
'zhr': 0xfb386bed8e3fbc8d1b3b2662fd847ad1,
'air': 0x3c4588116394d2164657875b7430907e,
'bir': 0xd706870ecb8bee9c7a64f7ad569e4da4,
'cir': 0x200a0788aebd9afa51a778331cb0d3c8,
'dir': 0x736007832d2167baaae763fd3a3f3cf1,
'eir': 0x72339ce1db2d58404ce9afd0cc8ff60f,
'fir': 0x1846c75923656922409346559bd15573,
'gir': 0xe589d341207cfa0210eb13e751b4656a,
'hir': 0x083c40ebbfe7920961e91425b6ac88c3,
'iir': 0x0bad7c2fd94d05c5536bc4f60af52440,
'jir': 0xe0bd88befa21c1fd487fe5b4e9f720cd,
'kir': 0x002e1a6e54da86cabc77fbb474c2df49,
'lir': 0x9e318174d24c1286817e269bf78a8c97,
'mir': 0xbc2e8b2ac93b02432d1816f80e26e7e8,
'nir': 0xb4c0de9803d05ee007366029d2a7cf62,
'oir': 0x21d6008fb9eddacc6e1c1b864f241174,
'pir': 0x4bfc20abc0e6830ce15fdc1a1315b8a3,
'qir': 0x1bb0e2f5a3bef493b749cddce29edf78,
'rir': 0xaeec645249be520c2b20c9d4b33912ee,
'sir': 0xdcff57c9a964f83fbf81cc75ec2e413a,
'tir': 0xbadb9bf8204a093c7a05ba7223f74043,
'uir': 0xe94c8db6a50df5f5f5cc9848ba8a817c,
'vir': 0x590f35821fbed7b2ab58a9dbaf36c42d,
'wir': 0x4c38b6b1835c6934dc866ef3dd858bc6,
'xir': 0xa37f030dce4ee733f1588a9bf10d4d0c,
'yir': 0x190195655a7739dc62be263c9fbd83d9,
'zir': 0x0940af0f2bdc6f646f5086aafbc18902,
'ajr': 0xfe170624f5852b6af7c787c1c707f643,
'bjr': 0xaec8097f41d57dd488e4a05f06db218d,
'cjr': 0x56b1b839743fd578b4d231dadb8d42d2,
'djr': 0x4b3e2f9b772fc45fc84d6c9913ddc83b,
'ejr': 0x1f2e841105f1a361d79cfd8e27b09dc5,
'fjr': 0x6f9c09d414c3a407dca9ae213a4bf7bb,
'gjr': 0x3fe7f943d223875d1d43427d95e65f01,
'hjr': 0xa6b770867f2a3513ce8cf087b880ebe6,
'ijr': 0x65c36ef63f0f285327d4a64666f1e17f,
'jjr': 0x536f148897cfa657aa974fdfc2b5ca69,
'kjr': 0x69e2e9b6faa43ce63a2ecee941ecc641,
'ljr': 0x1b51157b2b77ebeafc2c922dd5140b3e,
'mjr': 0xa0101d6066c1661ef038e46d6d89f226,
'njr': 0xf06cb16a159df99b671901e9875173f5,
'ojr': 0x6d51c9ce237af3f95df86877291ef44a,
'pjr': 0x1c6b9de42bfda32395ac47576ed4bf1c,
'qjr': 0x4b12c1644d118913cdbff6b37d06d9da,
'rjr': 0x437730c9e953566461037b29ca0b36cb,
'sjr': 0x17e32b1208cfc33351815ed609f6ed37,
'tjr': 0x32d4b91adbe3e0fde2b4494dd3db0531,
'ujr': 0xd24838f07341bd97596793a7bc0972d7,
'vjr': 0x1490602b22192efb5f7ca4f616296afc,
'wjr': 0xf69425d556eca2d90b82ecc4140f1753,
'xjr': 0xa4642cc420326d9e5e2f7f6e303a3c48,
'yjr': 0xd0b049666f187ac01cf272e95fe58743,
'zjr': 0xeb377433ca92d4eaf196ed541bd68963,
'akr': 0xf3ef9a0aa89a5100b6657b35bfd46058,
'bkr': 0xc016ad73552ab217f5a23b097c85fdbc,
'ckr': 0x10c65700a81ee22a83f1123f825f6ba9,
'dkr': 0x0320b7ad63d6e187f5f284db81176ed8,
'ekr': 0x46e5ce9a61a9d376bb6fe6318aa6b244,
'fkr': 0x66b62c1886683b0e66a20ae3715e26ea,
'gkr': 0xf80afd4f93613d205ba7b0fea3ae9be9,
'hkr': 0x2cb0985f22e099d18e0bd1a876d48019,
'ikr': 0x42210909312af60263cc21fa15009ce0,
'jkr': 0x4425fbc226e5962b9546dcfb92e9407e,
'kkr': 0x18b38bcbe603c320a88ecd4c31c19f21,
'lkr': 0x361fc895dc934f47c9257d8050ebccee,
'mkr': 0xa497866f9116bbf1b1e82ff5fae38e56,
'nkr': 0x0222d9813bd07e03eab4925f3388a3f4,
'okr': 0xfe49515bf8a5cffba1ef9abc28665a35,
'pkr': 0x9e2947713878cdaa68d84834299f84bb,
'qkr': 0x26c5146efa300b0384dcc1273c6aebf3,
'rkr': 0x7ba64906c960daa650e15d172a7cd45d,
'skr': 0x154211326b13dba32f059317703da7ed,
'tkr': 0x28127c71350953278626e0a78794ca39,
'ukr': 0x6eecdc762106c8f35f52e510dd061c29,
'vkr': 0x591e38da580c917eb4a5075d3c6574a5,
'wkr': 0xc5fec74f87a35b2eec68a5b5476d9698,
'xkr': 0xa75db4ba27967da94d3ddc3c3675bb9e,
'ykr': 0xdf4631ed5e55a2996febfbacac75f0c9,
'zkr': 0x65878519ff10efffd56da3e1dbab1427,
'alr': 0x2d7d8ae5a2303c46d1f1f5d75d715c75,
'blr': 0x73e588ba736daa50923d71ddb7539ae8,
'clr': 0x9f46c8f0b36988c8437f877fa293c656,
'dlr': 0x846fcfae6e840199389ec8fde0358605,
'elr': 0x4bbae18312fb55d3c9b7c6588db43391,
'flr': 0xd7a58e30ae87f756761af7e7f4ac9567,
'glr': 0xf45182c4ab2ec56dcfef65e83e92f76a,
'hlr': 0x8a76319249af3810e6def07b414d1451,
'ilr': 0x7ffaa0cdd21964333ecd1e7e4344cf5c,
'jlr': 0xe1d0ec734dc911362c095db5f236a45b,
'klr': 0x6c9ec47c8f022ffb4e739a55f2373e83,
'llr': 0xba9cda3ea441763fc9b01087bded7059,
'mlr': 0xfac131120365e920d07c1ef7f062c7b0,
'nlr': 0xe1e35397829fee3b6a0a7f0f803b287f,
'olr': 0x721651002db5a818d0a6db2cc2901352,
'plr': 0xd4d035ad06562e2b49349edec9ce4d4f,
'qlr': 0x0285056771725cae4081819b6f8465ec,
'rlr': 0xae9a9782142d8e4116e85a2358c36b67,
'slr': 0x31913ec79bc997f01d13c30a2dfebedd,
'tlr': 0x8d52bf87a239352ba7cc0aba1035e393,
'ulr': 0x78fc34deedfeb1782c7b927db6b721ab,
'vlr': 0x9dfc551749f7c1be20c7a1dd34abd3c7,
'wlr': 0x99468af92ec17f830a9ee9bb6291ddd7,
'xlr': 0x6a6139b3c687dc65d508e2f87d2790cc,
'ylr': 0x1303253d65ffb917e4733779069b5776,
'zlr': 0x33571126176a203ef529df725059f36e,
'amr': 0xc06e016f63751d7af476fd754d97a533,
'bmr': 0xa47840407546f86bd1ee65155dc8ffc5,
'cmr': 0x827fac6a4d016d53b6874573cf37fc05,
'dmr': 0xddd8f73a9e0e4da6b3c9f73f3afe395c,
'emr': 0x98b84a80080b49716cdf31b29e11dbb4,
'fmr': 0x5c6458a50473404467a896ee5c853db2,
'gmr': 0x23ac21ab56d4a5b10e0f69b87dfc65f8,
'hmr': 0x98c3bc175de5dd9d486a621c6b6c2b63,
'imr': 0xcf5ed1f35ec8f5264e84b4f63cd708f8,
'jmr': 0x676a4cf46754b50a09c3c7a16a38c0aa,
'kmr': 0x28d6e41249d3eb09bd96618e9413fea8,
'lmr': 0x6f9247eb06cc4a7c1c762555945b1aaf,
'mmr': 0xaa29d6984108c685bd651011d6733c00,
'nmr': 0x3acc04fb854f7a377e9d3656df8e99f1,
'omr': 0x75a9a00b6ba7ef05a03855ff132733bb,
'pmr': 0x2baba74f3ab8c4f508ce57085c17cd62,
'qmr': 0xc4aae6909e9e53bb18b060838ebeef6c,
'rmr': 0x466c8c1fb4fcaa4398b3347fb36f74f0,
'smr': 0xee522e665d33bb6813a26be8a17de10e,
'tmr': 0x434b15c6c9f02cc71c98ba09af71f226,
'umr': 0x4f12f0ee41618a30b76368993b13d671,
'vmr': 0x4a64b1714b324eda3928be5f063f7b59,
'wmr': 0xc1c4044002cb54231f3d54a577599831,
'xmr': 0x28b3e55de5db0e07c3d7be806c17792b,
'ymr': 0x3cd7a820c9f9307957da7bdf4a21e6f1,
'zmr': 0x04aaebdbb6405678e0d1f5d93a02ab78,
'anr': 0x457528c43fabd74e212db2ed61101075,
'bnr': 0xfa7ba49d6a3a109504b8f7da5201542f,
'cnr': 0xd44e7033210ef6026d594960f8d12004,
'dnr': 0x2defb250c866d504e7776fa50d3bc29b,
'enr': 0xe047091b58efe957732be9bfb2b398ce,
'fnr': 0xc1607e393783fee4415fcab0c6e38a67,
'gnr': 0xb3a3072739064bac5f3b2bff0c106666,
'hnr': 0x038d7aff60bdc0fcddf73a656680b90c,
'inr': 0x61a59a5d966b00c6dad60bc09139ea63,
'jnr': 0x23191b271d2a651092d5ab71ca91081f,
'knr': 0x5db4495973672e7dc74070bcae3f992e,
'lnr': 0xfbf972e23b48a1f02642d860c230df71,
'mnr': 0xedc6f280ded497627b916535f4f6a587,
'nnr': 0xa942fa888b55121bf54c695b8c0d6ab5,
'onr': 0x04d3cb12d2d736a0ec26561cd1bcc7ee,
'pnr': 0x4da17b3f8983e6d85192cc485673048d,
'qnr': 0x35ead6dcdc7ce4a4473069857ca0693d,
'rnr': 0x39c15847a4508dc3132cfd6f8fc66263,
'snr': 0x04b492492c1847d54aad2bbbe84815ed,
'tnr': 0xefdbbf9d9f20cdf89f19d5cc9171b33b,
'unr': 0x9af870b687c9c080740c108296069cae,
'vnr': 0xdea76678d73a48ac127d5367f823c561,
'wnr': 0xe84b06e62d0fbf25ee1511cd7ffdb819,
'xnr': 0x4aa6bd4c4ca439be37cee482ec5fe589,
'ynr': 0x1ddbe06a1dc972646e0c0fe9117285e4,
'znr': 0x8c59613761863c6df1cedc0414424c74,
'aor': 0x6c56991cf499681b796ff0ce684c2a82,
'bor': 0x597e592d65a2f3814020cbdf7e9e522a,
'cor': 0xcebd65946444e5cd3e861a2dbf69e221,
'dor': 0x5d35ee739e0ef7fed39be900453f8957,
'eor': 0x5c08bb4976931502a7c91a119f73b34d,
'for': 0xd55669822f1a8cf72ec1911e462a54eb,
'gor': 0xb34fc2170d0ce2f7a979c8ff6a09f41f,
'hor': 0xa0a98f69512d093ead0bb45684f0db29,
'ior': 0xa0481da2a13483f6977d411b34cddf57,
'jor': 0x9b1e2f5b48647011e1c88ef057f236d6,
'kor': 0x9c285fc4f99ab149f8a6eb1884c7c215,
'lor': 0xd8ce24ae7dd4b1c9e167ea9b40693300,
'mor': 0x01738775a7541d8943768635f81cb715,
'nor': 0x57c7d11cd49333e3f722204c63016da9,
'oor': 0x4aba38bc952244a4220fa7d66c5c635e,
'por': 0x7cf41ff971d626b865524717448c298a,
'qor': 0x2713e329e78da0aecde97fb831d7fdd3,
'ror': 0x993a7ae7a863813cf95028b50708e222,
'sor': 0xe8488294d71f5dcc4a8b57d40dce60e5,
'tor': 0x7c0b1b361a964c9f5a2c89bec5740412,
'uor': 0xea544ab926ae6a86622194a49691a7a5,
'vor': 0x71807f6a7f44d0a733a8b3f6588b8380,
'wor': 0xdf732b2ad1e8728d2defe7ac2729dbf9,
'xor': 0xa392960421913165197845f34bf5d1a8,
'yor': 0xc40e633ae47c85e78c46a9030ef356f3,
'zor': 0xcbef2a7ece933c79251e532523cc4fe9,
'apr': 0xcf7a2216034ddbc62bb1b1b3d70d1272,
'bpr': 0xa4a7c54f01f1fbebeb1f08789ec935ab,
'cpr': 0x293a5b816a2fade15a3c7b02d660198b,
'dpr': 0x6c0efbc4aa07f0e16339ac026af31949,
'epr': 0xdf6ce113e1c804856e5d4201c7a76554,
'fpr': 0xc8966768a54c044b66d1bad39c4b9c4f,
'gpr': 0xebad1f7d044f8df769b5586528791087,
'hpr': 0xd2ec6e250d819f262b769e31afbf8cd7,
'ipr': 0xb4bc6f75868a971276084971b1cb79b7,
'jpr': 0xea70a4879aa9b84342ccf21e52ed07ec,
'kpr': 0x5a9668f6b9a169bcce8c018a616a738e,
'lpr': 0x6db6c23450f8f6dafe05759f08aa6a98,
'mpr': 0x9bfd3f6affd43a85562cf3430c157175,
'npr': 0xbeb454c55754e2e94ad97fee19eb2540,
'opr': 0x05b8e35f93bc745e61dd7e2cd1f57880,
'ppr': 0x60ea78fa63ac6f820c0b0e23f1266639,
'qpr': 0x6635f5664ed2b64db8ccb89165fd578c,
'rpr': 0x050c9d8444b3439eaff87a40031b7008,
'spr': 0xdc308a0713062375862df6c556c43bc0,
'tpr': 0xb126f477dbe77d2be3a1073e74aa8a60,
'upr': 0x5353cc4dc024d5175392ad06c1db0e9c,
'vpr': 0xcd2908531e1054fe29f9309860ab4e9a,
'wpr': 0x4a5354b760bac2b17870a7eb47dfc46a,
'xpr': 0x196e09ce7d855bd30d81fcb7a8d29e8e,
'ypr': 0xb47f430952e8cec7e80537fe1a1e8ab2,
'zpr': 0xcae2c166cd6f1094ef1609cae3721759,
'aqr': 0x3f56250cdff4f965dc378af6dce5e6b8,
'bqr': 0x849e13a3697ea7a3bea41025aa64abab,
'cqr': 0x3d4956c502feaa64d4f2edef7b4a2e5e,
'dqr': 0xdf777780564b19b33557cbfae71e9545,
'eqr': 0x04776873f53e4ce5ccb4f66744eccf74,
'fqr': 0xf6d72a7ea45c5b6af9a3600e6ca16ce0,
'gqr': 0xa3c226b00e0aed3f5c2cfd7efd0bad6c,
'hqr': 0x1865dc26497a0db651665efa62990c21,
'iqr': 0x5df77897921a1ffe7e4b5efed6249905,
'jqr': 0x1072f1cd354174047e8954a121b3e36f,
'kqr': 0x16a55a9899af427468885d50c0703c78,
'lqr': 0x351ee7448108a724992c28714dbbf435,
'mqr': 0x0eaf8f3896b6141ce6c39189ef3859e2,
'nqr': 0x1ee4856a2035d84c3720bcece1396cce,
'oqr': 0xb4f6f9661a4a694aefde792d1a32787d,
'pqr': 0xf734fd4ff1214de59bab601aa34030d2,
'qqr': 0xe5aab83f05d34ae913e3d673e41c3002,
'rqr': 0xaf63d48f1f56527ba808a07411d14abf,
'sqr': 0xa30d171c23d637d14193b0dd57277b47,
'tqr': 0x01730249d51a809ac9e855e97da1c902,
'uqr': 0x53ce8c123a8d7d75e92073708c071c88,
'vqr': 0x526c1af3222e8e2bdd777e9696bf8ae2,
'wqr': 0x81f5fdba6e8c4a8085dad255de0664d5,
'xqr': 0x69a44a3a6ec744258d44306c467d15eb,
'yqr': 0xd974b711c1660e95a47cf62274284bda,
'zqr': 0x912a34a9e7471085046ae5340bf4cb6e,
'arr': 0x47c80780ab608cc046f2a6e6f071feb6,
'brr': 0x14db43821fb74030ac6e8bdf662646d5,
'crr': 0xa397cb6d0aa28863073463c037db46ff,
'drr': 0x5dab2126db06b1b0a6d0b1521ef30062,
'err': 0x56bd7107802ebe56c6918992f0608ec6,
'frr': 0xc3ebe36dafbb69eecbf47c119e3657fd,
'grr': 0xfeeb6e14161bc62635a8edf1c4b6b733,
'hrr': 0x71b7b337e2987e91fec8e799fe2841ef,
'irr': 0x110ec26faddbbee599d68849ffcbcf57,
'jrr': 0x1f8c82f29cf9fe14ecaaef2f4e90e44a,
'krr': 0xe83b6a275e41a2a7fdc0a1260e8d7c70,
'lrr': 0x12584befd0c22f75060db04e214fd7ab,
'mrr': 0x8097371a20cffb6fdadc8b9a798f9ec5,
'nrr': 0xd139a2fd0b43cb7c42a1138caf55d787,
'orr': 0x3945cab5b0d2acdcbb8eab2a33314507,
'prr': 0xfbf5f9f4586965c71a5f00cd69d60d32,
'qrr': 0xbab31368b47acede599d353dcecaf741,
'rrr': 0x44f437ced647ec3f40fa0841041871cd,
'srr': 0x0a21c1c5fb97a4b5cd5b70b721efe335,
'trr': 0xa971658e3a70c1f39e66c3b07efc5872,
'urr': 0xa17f96ffa9d588c0f30e04ef8f37390e,
'vrr': 0xc14665635a9d588bcc1259eceeb233f5,
'wrr': 0x9acfd39cc0124ac0c31a40470f274631,
'xrr': 0x21f8d90bb58cc939ad42df17184cd7a6,
'yrr': 0x458b4464cc8bab308e7f16b9c9a739e8,
'zrr': 0xb784ac5a92d32c8ac0840e7999702318,
'asr': 0xc5aee1e49695056c0c9c1da749bc2a8e,
'bsr': 0xd279fe86a16d1a064c3019efca5ef1d9,
'csr': 0x1f8c50db95e9ead5645e32f8df5baa7b,
'dsr': 0x021eb1191fb292fc788dae94acca7e50,
'esr': 0x631e1f6e8339acb74ee135ee18166b4c,
'fsr': 0xd0ce808bdfb4bfcec7a4ba919815edf6,
'gsr': 0x4693311894dada0a7c5e6f44f4995f80,
'hsr': 0x53332ce44ef999b1c13b3bcdd2cf67ef,
'isr': 0xba8eaeec35b54c67fea9bb1645a489a8,
'jsr': 0x521206c4bddbf24162cbc476b2cd3fab,
'ksr': 0xadbacf703347268cd6355570cdd665f8,
'lsr': 0x971ea0ac060f20c995a5a3871d91deaf,
'msr': 0x38b33779833838a98c2a241ce465fb07,
'nsr': 0xb8c8c992cf2feca2c83b861b37a9499c,
'osr': 0x382f4002b65ad5e76f761fddc7896e7f,
'psr': 0x40b725832600963a389b434066532f4b,
'qsr': 0xf5f520e6bdf7af62e55e3ca22f6611fd,
'rsr': 0x44d32fb571ad39745c4fe3b68cf0ce6c,
'ssr': 0x9486b0c37ac7d674f865a8d4a3209fe6,
'tsr': 0x5b740b2cc2e06e0f26f5ca022b100fd4,
'usr': 0x0a744893951e0d1706ff74a7afccf561,
'vsr': 0xd9b85018edd8b275ba628f848ad17575,
'wsr': 0xdc5ec7d259e98fb597ccdc1dd895b0f6,
'xsr': 0x8f31f60f5547d5dddc9720989d4eac09,
'ysr': 0x1e7aa0ac658a816d907be1998c963b3a,
'zsr': 0x9b99670225ef2d59e08724f6fc5b4e01,
'atr': 0x71b73dd6741a66d75ca45935b83622e6,
'btr': 0x0a47700e8b6b816c73959fe21f8ace04,
'ctr': 0x30f33f93a3c7974a1b5fcb33b6302242,
'dtr': 0xc411ec3fbe63b02c622f135613672f82,
'etr': 0x97b33391f2d3ddd3cfb3e85abd0b8c2a,
'ftr': 0x17fa66897cfe41e6426ec71330a220fa,
'gtr': 0x9b2dc2637646e5f7a0ca63a935c31d2a,
'htr': 0x0bf71cf94786a514a4e132474c394c2f,
'itr': 0x62fce98555002a61a6da4d29476b0a9f,
'jtr': 0x86617a4c43c85ebbd95f1bb56c4ec810,
'ktr': 0xd3f4263a4c026e0f047e1b6a895fb853,
'ltr': 0xc39eaab018e2e16320836a210cab2617,
'mtr': 0xf80a6c5780922a877062945f3fe22753,
'ntr': 0xac8e0c9833cf4e961cdb6bd42b8a19eb,
'otr': 0xfce5590c6fb2dddc508ecc33c0043b1e,
'ptr': 0x4d9ad2b37053671b594b237bd061b3f2,
'qtr': 0xe2154ecd475a12864622644e5b25f0cd,
'rtr': 0x5fe8937e6fadb3c23c597828aef6fcbb,
'str': 0x341be97d9aff90c9978347f66f945b77,
'ttr': 0xda1ca8f34cf9fa260557c3847aa16ec1,
'utr': 0xa4d9c9124d10730d396834871f7808e6,
'vtr': 0x5892625e1777c599538ac87266ba74b1,
'wtr': 0x8a17fd4df5e36fb25574b8ac920a91f9,
'xtr': 0xc4ed48d875cdd6d30e6f95d484caaf6a,
'ytr': 0x1358a3cee90e0142f43b71059081d6d5,
'ztr': 0xf1b5c85a74101d751deeec43b7717f6b,
'aur': 0x1f179e3c0b4bece3f50a7ba552b71ca4,
'bur': 0x5332c994a9ffe7d2d59e8e6e4a732216,
'cur': 0xb5fddf1ef601e74db01c1425561a38cb,
'dur': 0x8ca1954a9d263e1cab513ed4f44c897e,
'eur': 0xc528434192ac3f126e95be730d14a145,
'fur': 0x40bc2c3326dcc0c9ea95f303f2165aa0,
'gur': 0xaebc771649d88eca437ddcd3e122478b,
'hur': 0xe828cb861ad28580b8dea6e3a325bdca,
'iur': 0x7f092d7f4a6fed6131cf9d3c30e073d5,
'jur': 0x467d8f4cfd2eb6fd24a13990df1614fa,
'kur': 0x7663d8c57a4fe7d83db6bbc1bff52586,
'lur': 0x7f0b65d03fb2ce8b47d354a4f474c5c2,
'mur': 0xdf9301d912973424a2dcd97792b1b6ff,
'nur': 0xb55178b011bfb206965f2638d0f87047,
'our': 0x162e31afc5ade88a04d3f428e97e8f46,
'pur': 0x8fc789ca71a8700c00acb230a58928bb,
'qur': 0x360998388cfe77951b33fcc2add455d4,
'rur': 0x8c31b7bb884eaf566834c0004e272fa9,
'sur': 0xa587b16e0a2c4a9c61feee6486c3a6c5,
'tur': 0x832e21f9da1ad55895637d00686fdb42,
'uur': 0x7991ef6f186729b8c4b428bf67610bde,
'vur': 0xacf1333999c78685220cc56cb20ee816,
'wur': 0xabd73323468ed86b333a7a67b6a63094,
'xur': 0x4c6d031fefd9923792eafbb18d176bb3,
'yur': 0x0daa0d1935f4033d015c90da3de1b10a,
'zur': 0xcb4d14cc8190d64b70ab60bc23a812da,
'avr': 0xbd0253b598188adb30ec60068d9219e1,
'bvr': 0xad800bd8f68c045a2db8624bacf395e3,
'cvr': 0xf11942778dfdd62deac66383770b07af,
'dvr': 0x293730af74185bc2bb0c0d77ac61548a,
'evr': 0xd519ba76eca3732241937701c304bfa4,
'fvr': 0x65e8836b51f71f13b539dad7f84b02a1,
'gvr': 0x18800cd4744faf24e7011f8b4a382efb,
'hvr': 0xa607e491785cb0b32b28c208721edf23,
'ivr': 0xed1b188032894e21cf2e035879ba055e,
'jvr': 0x4973cf1465aad770e41d24c01ca807d5,
'kvr': 0x00e1749bcdb8c6ab5d9656dd61939daa,
'lvr': 0xf6dc1ca6e047a512316ad6ff43128128,
'mvr': 0x4f0e1ac61791fa56ba78d22be00326cd,
'nvr': 0xbdf0b38928b8b7898f6a931fd506f9ac,
'ovr': 0x9f279f4eba01da6077f3d835558e9f29,
'pvr': 0x77218dc23f414949275c899fc31a24bb,
'qvr': 0x783e68f883135ca502c21aff8e805edf,
'rvr': 0x0364a05366edf872fa5c32a7502a5916,
'svr': 0x99d32fff00fbafb0a853260d7e9ed5a9,
'tvr': 0xff66925c3023a2e991a96c92e4ce4bb7,
'uvr': 0xf0889f7af3b7b78ebead5ca60f2b8666,
'vvr': 0xf7ad45d8954e7241a1703884c974db9f,
'wvr': 0x5d6636b48699354de92aaae201b2f27a,
'xvr': 0xaea8fdd3757e7139034bdd8650f2d8da,
'yvr': 0x1e01177a47e95fa589ddca086df228eb,
'zvr': 0xbf32b2cc51dd670cf80d86e98e885d0f,
'awr': 0xe5df30b242311b7d0621c7e77e5ef0a0,
'bwr': 0xf1e5fc3ea7db0cffde6aaadf2c0e9766,
'cwr': 0x5000fc9b5ccecef0eae1845cc07337f9,
'dwr': 0x593ea464044b46a9d195a10509fdf0f3,
'ewr': 0xb643f013cb9f3ccc9b44c0bd1ebbc669,
'fwr': 0x1ee21061af34ffb5e40d8eca11aa53c8,
'gwr': 0x3b923bcd05ca10c318e6718c1b1baf31,
'hwr': 0x0ce30daff692ee814bc4345f5a24d7e9,
'iwr': 0xd30e9c4be115b51f444d532bd2d06df0,
'jwr': 0x3bb5e2814afb244e95fc895283472ac6,
'kwr': 0x0c2679a11399f50aa02239e5ec20c93a,
'lwr': 0x2f19b65e920075b0f180e6c2fbbe58b0,
'mwr': 0x7ed558701a7af73b22603873865b8766,
'nwr': 0x9402f2eb60c3104372312f4ff722ca71,
'owr': 0x9695c6afbcf10b8e3e28fef3767530bb,
'pwr': 0x128d2ab4b71b0aaca5d6cdd9defcc306,
'qwr': 0xe9e77eb539683225ffac89da119f1c2e,
'rwr': 0x2edd78b3167483e04561faf8b7af370c,
'swr': 0xb1dab74548399bb04198cb2fde8909cb,
'twr': 0x66423724860ce175a01aa38f63fc1d59,
'uwr': 0x517c34fd7cff38958c80df697500cba2,
'vwr': 0xdd40bebddb24c03120386dac67d80922,
'wwr': 0x3e0408cb8f13f3316a5246d4c2f04850,
'xwr': 0x573edc1b0a66bdb80c816560e634116b,
'ywr': 0x2bb753f1ab4c66d75c5b3e7eb018a994,
'zwr': 0x46044d65aed64c15a141b8f9b9bd94d1,
'axr': 0xf2f784017c3a4c8d49f8e4855603e85a,
'bxr': 0x61e1db17223cc0e25cc636aa8e8d1405,
'cxr': 0x03d184e16d45eba4de9616863612dd9f,
'dxr': 0x0f1737de5629bf816957285070c409e6,
'exr': 0x8fc5e5597263242327211c5f858c4ba8,
'fxr': 0x117e0da0d03966dea49fb4877e5e2014,
'gxr': 0x76b97265d078bf48da8622cde24158bd,
'hxr': 0x050fff4acff372636942c28484c6f0f6,
'ixr': 0x0d8fa5528296378451931b2b236f5340,
'jxr': 0x32033fdd8bd7b74f871baaba4a55b3ec,
'kxr': 0xcd21d3bee5631eb0880b9c67c09d8ce7,
'lxr': 0xee115aa980cd40e9489af2a5567e1802,
'mxr': 0xdd0d1b950de2ebc40f048d289bb7ae92,
'nxr': 0xd4a63f00dbbe893bd951a0216edf338b,
'oxr': 0x186d09969b6b9219518532ca4037986f,
'pxr': 0xa03629e4b71e4ce0ed7c168397030288,
'qxr': 0x463ad7b92076c7252bc92d7272160a4a,
'rxr': 0xde7fa945db307daab4c5194f32e4a70f,
'sxr': 0x23e30901588cfcb05598a43b1e46f061,
'txr': 0xb618ec2fa0492c6476691a2a53cdcfa9,
'uxr': 0x075adc7cc967edc110ef024d5964bf0a,
'vxr': 0x88270f971690e2954aa0dfae7643791e,
'wxr': 0xa5e79413b2f624ed801315dc5564c7a0,
'xxr': 0x2bd793dddba8b2bdb1160b2a245c7543,
'yxr': 0x6000a18f186b994d3f5987a4caf96887,
'zxr': 0x501cef808575f83f83e363d5e666742a,
'ayr': 0xc00bad408d9254d2b3280933d1b3b382,
'byr': 0xd688fa514c61a513b6fb6dcdd6c32e65,
'cyr': 0xd88b06c0f35225ef7f763b5a5739f5aa,
'dyr': 0xa0062b1153beb0b3bb3cb726497cb479,
'eyr': 0x30731d4a1e57a3ea421c41cdd1f5e4fd,
'fyr': 0x1f94822a09ce8014444b8a40a6e2405b,
'gyr': 0x9929e03b19a91a549bf2277ca2115d9f,
'hyr': 0x38193afabb1a87728c6e8ce7aa5299b8,
'iyr': 0x0b9dacdf2c34359f16ffc3b0b57338d2,
'jyr': 0x7c78bd568663be8f34099fe02ecb9cb3,
'kyr': 0x498b7b9f8f22e525967abc9adb82f5c9,
'lyr': 0x46e5bf3dfc21bf6d6ab1368ae6121902,
'myr': 0x4bac9d1d3eadba19447c548628b9b2a0,
'nyr': 0x8fdb02d2d1759b4a6b6d0458602e5f2f,
'oyr': 0xfe31ac7991c8e722614a06bd6f7d52a6,
'pyr': 0x17c785697635530049e493a4cc5a7c52,
'qyr': 0xe7b8fb2c1d6dd6ec4ca690cafff49199,
'ryr': 0xf0a9ef32ac022d4590fd516270cd30fe,
'syr': 0xdf79e22fa3cc733f121bf9e275da4630,
'tyr': 0x6d75262aae0a21c3efab696c125232d0,
'uyr': 0xa8597f533a539bdf3f00382bae0ad9f9,
'vyr': 0x063b7ea5946f185cad573404ea5a0b92,
'wyr': 0xfb60d30292512e065f6bbf10f797efec,
'xyr': 0x0abfbf97e5cdbbd02d4cbc5d55831bb1,
'yyr': 0x191d8c87e3c1589fc143147eeebc6a10,
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'azr': 0x79aa9ef11e7bcebd49c6837a16c89859,
'bzr': 0x08ec04e750c70dfa9ddf9a5e7d85fb01,
'czr': 0x09d106e7630e7f30074274bf9a9ffc39,
'dzr': 0x07bea5a1a254cf92cdf6e17cc9dcaad0,
'ezr': 0xe07063113519d1c9e89788de47b955f3,
'fzr': 0xdf15781e53c0f322d0d6ae85ec5db0c1,
'gzr': 0xe807eece6e62cfc9ec5d239f89c35ecc,
'hzr': 0x3215efbbb3aba61c94786400bf601102,
'izr': 0x3d0b0bbbd2cfb315ebf4032935da4c15,
'jzr': 0x84869835db8d42540d7011750674c58e,
'kzr': 0x7cf5f7076d73f747993cba02bcda0dc0,
'lzr': 0xd374ceb7e0de18c11b0ddff6ff5b5f32,
'mzr': 0x2b5da8891907893cd36bd952e773995c,
'nzr': 0x09da86077857ba1553437ce54fa23903,
'ozr': 0x26822f329a9dd5bfcc8e66244bbc83f3,
'pzr': 0x9bbb099be9e3675bba7f7dd3b170b32c,
'qzr': 0x6928c9661589e09bfa09d67a795d9239,
'rzr': 0x79492b3d4863883e122633ab9fa71065,
'szr': 0x4305679aafd0c464bdde78b985a09b20,
'tzr': 0x7ee24da8b83e2740f30a476fba02f82c,
'uzr': 0xa85abc141bc45a8d9003bb109b9a2cad,
'vzr': 0xcedac9d5a9e10cbac5c50d73c618ad2b,
'wzr': 0xa365bf8ff21ba7dcabf5a0de1585661b,
'xzr': 0x29a9c4b3a3201b67e5386127fb6739cc,
'yzr': 0x8fe5654bbb600fcea62de3723d606980,
'zzr': 0x2bb34611d6ce2374bbd7410dab2553d9,
'aas': 0x37f31694ce2528a64cfacc73c612ef06,
'bas': 0xf6e660ced42e946f69a41cc473d923cc,
'cas': 0xf90721c90de9bd9ef516bea0b184fd30,
'das': 0x2a6571da26602a67be14ea8c5ab82349,
'eas': 0xf58c3ba6e10aa6581680e62eb5381d20,
'fas': 0xc5341e883d09ced169abfac23dc13abc,
'gas': 0xfee74eac0728a1bb5ff7d4666f8c4a88,
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'kas': 0xd7b3bbbc0a472166030089695af7d5cb,
'las': 0x1102b0bde793717c4382ffd6894155fc,
'mas': 0xf022b8970604d7f4472189cb21df2c20,
'nas': 0xb9d04cd47823420fa061075ba05345de,
'oas': 0xdc767f6508fb8059865c83b08c3ae7db,
'pas': 0xcd0acfe085eeb0f874391fb9b8009bed,
'qas': 0x5668eca6cce28dbd64d888303dd7fb60,
'ras': 0x1c28067048213d48f168efc6d6685406,
'sas': 0xa8a64cef262a04de4872b68b63ab7cd8,
'tas': 0x0f1778c82ad1715aef78be8d9da4ee04,
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'yas': 0xe89f5c212146679abbfce5ceba3e975e,
'zas': 0x9c8cafccffe06d16454788b80aee4ea3,
'abs': 0xf9ac6b05beccb0fc5837b6a7fef4c1d3,
'bbs': 0x28aa17dc7c163e7e7cf88d0c556a729b,
'cbs': 0x8e113caded25c50d7d81dc64aa07bb0d,
'dbs': 0x85afaab5f3b6a638269e33d12da2fedf,
'ebs': 0x5adfcda6fac4906e45b800d34eb90fa1,
'fbs': 0x0c676dd1e9dd719c36bef5fbc7562df0,
'gbs': 0xc7c16ad0a608fde08aa24cdfce1a062d,
'hbs': 0xb202bfd02bac7afbd0398cf17a070496,
'ibs': 0xa94b16f8e50cc76b01353efe02df0b3c,
'jbs': 0x6e12cc6f2e411e7ee51bc633f05bcbbe,
'kbs': 0xe5b6dfb866b1dfa5d4b41beb0151f9b6,
'lbs': 0x826a1d1b3ddd23e4879fc8e767af1fcd,
'mbs': 0xd41671114a78f1fbc51f5e0077ef7f68,
'nbs': 0x3e36d6268180496c5a7a52eaa58d2ca6,
'obs': 0xdbabb977cec6af84577d101d6053b0b1,
'pbs': 0xf13594e09cbba8986323e3cf52d2a445,
'qbs': 0x07838ed5bf41684827ed636966ef5acf,
'rbs': 0x58ee6d7920593d3b04d173d68717b778,
'sbs': 0xc3cb8a08cd526b96ae0f7f24a7cf65cb,
'tbs': 0x9f0f0a28dccf5dfd1976ed455fd0f4f4,
'ubs': 0xa51fe3d58d97650115803090779d17f5,
'vbs': 0xb3720bcc7c681c1356f77ba9761fc022,
'wbs': 0x703b12873eb6b9cedd72ffce8a2ff6df,
'xbs': 0x3c69c1afaddcc6cda5337afda42ce933,
'ybs': 0x3a9a9fe8483bf1f0b0a9a1b2ebc8db29,
'zbs': 0xe2444647ccf12bc19bc3e25205f5f6dd,
'acs': 0x77c1fdd80c91593ff851e71ae3865fcb,
'bcs': 0xca129d9bf2875916863bb25324a23a5d,
'ccs': 0x1537c7ebb7ff5e0c5bb5f436df96f65a,
'dcs': 0x1f3604112472f3385058ed324bc4aa83,
'ecs': 0x2eb2930111864beeb409e946751215b1,
'fcs': 0xeaa18a15c1915c2884627ff0ed97889b,
'gcs': 0xc9fe99b9b26a69ece268aba6e41eda08,
'hcs': 0x9a54d2441871f41b5077dccc27bd07eb,
'ics': 0x669dbcecfbb91195b183fceab6920a7a,
'jcs': 0xeeacf15502fc386e7a413c810dc223fd,
'kcs': 0x8a89e888be5e7c90128b4616a5150aaf,
'lcs': 0x9351c00f73660c0a9076d185506e355f,
'mcs': 0xba3f25b5c85a8f70298423afce9b205d,
'ncs': 0x020a8cfa922969a0b9035333f45988c3,
'ocs': 0xd45dc8b654be6538135eebe03ebc05ed,
'pcs': 0x3ecf2642759a04bd3616642bd56bed59,
'qcs': 0x855f4471c5bd9fb01e7f56bfc0d7f48b,
'rcs': 0x2979619335f3ae305cf14fa9d7113800,
'scs': 0x83401f0f0dec1447d0f5197df673521c,
'tcs': 0x1e9451afa8de4ec7272087866c07ad12,
'ucs': 0xe82ee578bc06305f46ed464346e02c07,
'vcs': 0xd6bfe3ce1bf5714887f4ffbb7b94feab,
'wcs': 0x72d06cb904796af7f578e71087140ac0,
'xcs': 0x33d567ce02a95e1114c6bb147b3e46a1,
'ycs': 0x0b6beb9fc639004e8e43b3f245f27c0b,
'zcs': 0xcdb26828f1d4940e6b9e15b4575f0a31,
'ads': 0x2deb000b57bfac9d72c14d4ed967b572,
'bds': 0xdf634a9910dd999c92fbead38dc1f018,
'cds': 0x83b861efd4ce3bb438dd07435c9e7044,
'dds': 0x016998dad6c68fea05b9df7f6f56fb96,
'eds': 0xb972e58b43af4730e439efaa6f14cb79,
'fds': 0x838ece1033bf7c7468e873e79ba2a3ec,
'gds': 0x202567a75aef2e66a3ebf2366bff048f,
'hds': 0x920f0dca8e292c6b8b1605195e346d1a,
'ids': 0xbf516925bb37a8544c8ee19a24e15c05,
'jds': 0x4eeb2780435784d22dab8d629d9e892d,
'kds': 0x24cd40e9644f98e7e02848578ff2ec00,
'lds': 0xacc801aaf80d6f8f8e8b542102ea9e78,
'mds': 0x60a114c91c41983174b484e188856fb3,
'nds': 0x7bc4fc082fcbb303343765a065237b9b,
'ods': 0xef1817547dffab0331a27d6733b49538,
'pds': 0xf5382f3dcfb3c8aa96a19a353f3679db,
'qds': 0xbf89063f2beaa557a3fc25f4f39ce410,
'rds': 0x1d374ed2a6bcf601d7bfd4fc3dfd3b5d,
'sds': 0x82d5984c2a2ad4c62caf1dd073b1c91c,
'tds': 0x3b84f8cfd144e7211160037ebacbf0e7,
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'vds': 0x51888604cf507f0d151b78db9eed4e30,
'wds': 0x46f209867fc3eabc5a8db98f07262f86,
'xds': 0xf86c6a7b1180f03dbcd64c496266c5cd,
'yds': 0x99d595fdf0ff4d0a478fe5c2c8817051,
'zds': 0x316afb4a28734231af52f7e98fff161c,
'aes': 0x6d3c5993ca017d0ff169b425d3193f02,
'bes': 0xaf2289331f3d2c843730ac459969dfab,
'ces': 0x9d89e27badedeba14a6e13bce87c9957,
'des': 0x0ecee728bf87a4c1a02883004044dcd5,
'ees': 0x0092fa0ac96bde20ffdd7a69a3c39b76,
'fes': 0xb70c708d14a092f38dbd0448e3bcb3d8,
'ges': 0x02f9155d0dbadc5809c512a2967a9577,
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'ies': 0x3144690b4e4fb793c36647a34d5be42d,
'jes': 0x2f937532f5c1e55f340a07a2c2e7af08,
'kes': 0xed593c51f411c0f6ecbbb6c6a844ff18,
'les': 0x3f816a388569d5a30f20546196aaabd6,
'mes': 0xd2db8a610f8c7c0785d2d92a6e8c450e,
'nes': 0x1d7b2eac3b0ae5d9f772a801186d6c71,
'oes': 0x67ba2ac5679d1277bdd702a6fdb14fb3,
'pes': 0xbb4e1313cdd8219fc70b0faab9115eb3,
'qes': 0x2494eadca4ab0c2cf407f875f7cbc293,
'res': 0x9b207167e5381c47682c6b4f58a623fb,
'ses': 0xf2b32bda85a5a4a613eb47fb01c57ce3,
'tes': 0x28b662d883b6d76fd96e4ddc5e9ba780,
'ues': 0xdb44ff009e860fd8b9f740e978193004,
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'xes': 0xd86ff92350b51f660a8fb4a2519cb821,
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'afs': 0x47e8b9940d67c57e6b6870083f8ce025,
'bfs': 0x52d14db65fee1ab916713f3f5f4030f5,
'cfs': 0x7b2f1bf38b87d32470c4557c7ff02e75,
'dfs': 0x4ec503be252d765ea37621a629afdaa6,
'efs': 0x21bf745da611cadf93e64da3c3e1c8a5,
'ffs': 0x3b3eda5569bbfb7f93d465529f5a3db0,
'gfs': 0x2bab854b30d12e5c22bf329fd4a4e0c7,
'hfs': 0xb907ddeefeca25a50427dc9a1488afe5,
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'jfs': 0x21cbd81ec38ce8db13ac6780dc770a8a,
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'mfs': 0xddb18a8f8767a4666019e0ca7802f234,
'nfs': 0x2521ef5d58fc027d3121662b7d8f9ac2,
'ofs': 0x59c76fc4bc89caf6f28b97e9e2296e10,
'pfs': 0x83e4971fa15938ebf91e527b2c015c51,
'qfs': 0xbb8d7ceee11154497e8ac0bb49e6f738,
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'sfs': 0x4e55831e3a1585ef273394be01618028,
'tfs': 0x58c7e3173a83184fe920cdd1203c3dcf,
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'bgs': 0xa3d39e1323013c2ef9013d96161eb645,
'cgs': 0x8f30526caf7218250c2549e996ac2083,
'dgs': 0xccaedd915adea52be54d30d0a8c5a57f,
'egs': 0x45c85b6138c3b273586313f5a8c7414d,
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'hgs': 0xfbdc0cf1df1e40748cbdd1483b53a05a,
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'jgs': 0xc20308ebe0cb983281e50af4728db84d,
'kgs': 0x8475ab1a7ddb040822e508512c58bc5e,
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'mgs': 0x8f0c7cb61ff113f553b01d302349363d,
'ngs': 0xb823f61a0c70250d4d198172c403da87,
'ogs': 0xc08ff8a395104a88d09eec4396031ebf,
'pgs': 0x908ea47162d020873e19f62a8278d2d0,
'qgs': 0xd66904186fc4de4fe7c7806ab6e1cdae,
'rgs': 0xa6d3e8851646f709b50321d47957239a,
'sgs': 0x8c7f69400162033743692a155810be2c,
'tgs': 0xb8e0e9cce4f6ba9e35f9a40b439530cb,
'ugs': 0x7a8f8f5efc3abc7a75c1760ae8eac16c,
'vgs': 0xda936c17a03aa194ac2fe522310394d4,
'wgs': 0xd677c28717c6fb0257896b57861167d4,
'xgs': 0x30716e4a631dac4e2da783f4f7e99f97,
'ygs': 0x40aa19d9fba1cff1ba587e008e6745a1,
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'ahs': 0xb2654b918d1623652d4084375611bdb6,
'bhs': 0xa07e6a4ddb31b9003063d516ccb784d1,
'chs': 0xc84434f6af0e2f52f6d25d65af31b97e,
'dhs': 0xd4d71aa58fdb9e8d6409d414626d63b0,
'ehs': 0x12d51609e67d27fae743c3813a3e62c6,
'fhs': 0x6114a2fdd96b56c50815f0501291f955,
'ghs': 0xe6a0c1183fef7d755eae58ae933b6c4b,
'hhs': 0x6f6bcb904e80c47007fa72478954286e,
'ihs': 0x1253ebb331f1b1d7d6947b481b4fc84f,
'jhs': 0x08f5d57d67012c8ea2bc889d6c1b5f8d,
'khs': 0x066272fc807a838c3b3455996acb9b79,
'lhs': 0xe48b7bf1a447021da85214b43f51fd4e,
'mhs': 0x0357a7592c4734a8b1e12bc48e29e9e9,
'nhs': 0xc0076f112011cc677dd4d1f4439c6c66,
'ohs': 0x7fe6fca526492e7ff57f0a8f0f1c75d1,
'phs': 0x5eba2d67f4fbb33c0876604053cd91d5,
'qhs': 0x4bf906ba6141e5a85b0f08a175c6a661,
'rhs': 0x83ff9f9e3dd7561d3dd91204cf546b7e,
'shs': 0x8ce4f98878b0c302cb3de0dcd27d8bc8,
'ths': 0xcf3cb0790e6da72fe989532a8e63973e,
'uhs': 0xb05b0df3e73beca4c415c4d3084d2039,
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'whs': 0x64ee1bed1d92e122bdc87e0999d0ddfb,
'xhs': 0x9a539709cafc1efc9ef05838be468a28,
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'zhs': 0x63c1d6fb7c4e68ddbda14682596f6497,
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'dis': 0x4cdf5a25d4673bfc4546ca7843071f65,
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'fis': 0x37ab815c056b5c5f600f6ac93e486a78,
'gis': 0xb12c645e186f93509b1c0c36552ec334,
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'iis': 0xc7c9b5abc39fc757817e8a49a1e269ae,
'jis': 0x288aae6924fb949c0aa401b3ed2d3156,
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'tis': 0x12662f0b3e3b7454907bff5ac8b985fa,
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'bjs': 0x9033e6dbb2ec0a96b1e228ea9a010edc,
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'ijs': 0x7843608ec0025eed385e94d38e8f721f,
'jjs': 0x772b49cab4ec618b1efdbaaedcc4b962,
'kjs': 0xb23d57db04dbce778139990aa8c423af,
'ljs': 0xb9713d4d4dac7d3a669e2ad9f6902583,
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'njs': 0x8558ad6d5b17747544254721263234bf,
'ojs': 0xe6dc3d12efda69731159aa27268daf8d,
'pjs': 0x0d13199c64d04f59d9e56a68f1844d09,
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'sjs': 0xa1d2e8381323da392803e751bbcf5461,
'tjs': 0xe6194f2d55347a86f6825a1c8862f36c,
'ujs': 0xc3650c1f8d63aa2716e409df8fdb0faf,
'vjs': 0xfe38d6275db13475b93ea034c49688f8,
'wjs': 0x577bc8cb629dda6cad85c86042d34751,
'xjs': 0x23ef2dc2e76a6154e20643262b0414da,
'yjs': 0x0c88373bbdb84c6cd47cc98226b18273,
'zjs': 0xb7e1a547f24fb8db578ae7743c866209,
'aks': 0xa25d2a4d3f57a81cf51d5ce4b8f9c62a,
'bks': 0x2a0ce6fd63f10057ce68f1efe41c6126,
'cks': 0x8bf72f3753f3fdaf383b6b8c3142dce8,
'dks': 0xd36802a6f32b7e5edc5fa3cf3b81c581,
'eks': 0xdca66d38fd916317687e1390a420c3fc,
'fks': 0x9181ab10cc2003eaa38dd19a0603ab2d,
'gks': 0x55b52b7e07c36d97f9aa202d99f11338,
'hks': 0x28a141a5a1b7431578dfd61c9ba9b140,
'iks': 0xf9a0493ae853245f113c5ef3ec62bc77,
'jks': 0x210072a7be2cc5e793fb63b8e5e101c0,
'kks': 0xf7d7d192cda02d01e06a6216bba31486,
'lks': 0x004806ec9c0e2b008cb781a7c77809c8,
'mks': 0x173e46b1192ee4a8072c150139b997ac,
'nks': 0x1f0c281a1508da2c1f19165ad4a592fb,
'oks': 0x8d2cc35e70deca8505ee61f089a46ec4,
'pks': 0x80346f9fcfe9c5fb57ef5f56bf1168e6,
'qks': 0xacb268fa4c9078061aeaea21bb99daed,
'rks': 0x5bbcfe97466b10a3c7efb7ebba9e5196,
'sks': 0xcd5706ad15b95f97f59434ff8ed3c976,
'tks': 0x83cf231b1533457ade9d693cb67afa5c,
'uks': 0x92be88071bdee6de932b86e5f160f8d3,
'vks': 0x3115d3ef2314988051598aa96ddd1961,
'wks': 0x4ed6db01dd21186f0050084d96e6aeba,
'xks': 0xffec2b0882724cb742c75798c2753692,
'yks': 0xaeddcb067f81d404afa5ae780f1201d1,
'zks': 0x4fc25b8cd9fd31918f5d640802f1716a,
'als': 0xc3955cf318f60d815066daa73a622297,
'bls': 0xd5ff4b9a8e9d0f5e15c521bbd2b98188,
'cls': 0x8a0496f6a6397581367536557e603826,
'dls': 0x22986c5e8be8e3489851ec5d7848e7b3,
'els': 0x9c98948d313ccdac4c41cf803e329784,
'fls': 0x540fb4a8a362f8138d445f1498f50fa2,
'gls': 0xca6efd547a7b6d9885402b9544f941d5,
'hls': 0x6b517d4134797cf29f129240def46bba,
'ils': 0xf86d495aee2542639ff7ca231ede6fc3,
'jls': 0x5356bc33dbbf12c32ac9848fdf09a2db,
'kls': 0x487d77a9ee7563de48a3890c6bcbb7b3,
'lls': 0xcc862772bd3a95d82d2edda0c0a82d1a,
'mls': 0xa8a548d2607b07b390d9b7992cebfb8a,
'nls': 0xc0f870209d7a47fb7637050b0faef81c,
'ols': 0x566ac2c59178ec053b61e41e86fa8575,
'pls': 0xd89a8633204d02f952c89b8245f2287e,
'qls': 0xc53395c476745439fa01bde6ab0db0ab,
'rls': 0xee0a18ca596d33ec7ee2a45dc8ef5b02,
'sls': 0x77539a26fb5c4d0af80d43a295bb6053,
'tls': 0xa60aeea3d4cdbb5049fc37164644bb34,
'uls': 0x35cd1dfe100f7768dfef1417863589b4,
'vls': 0xc8fd5d0bb8661a163aea0351143f5a4c,
'wls': 0xf006f1548ae0a58e1586955659328785,
'xls': 0x018e2a13e58c535167ed37cf44316005,
'yls': 0xb246fea497c6448ec6cbf35c31b861a1,
'zls': 0xc7bdf8dddfea3010b7634caaa0e82a17,
'ams': 0xc5be1e9291812bc094b77f72f382787d,
'bms': 0x71baa0b71725058671c6e86ca7f06181,
'cms': 0x570d99e4c85914470d914170d1e95144,
'dms': 0xa6e05afdf596a8e7fd8df7479d1919dc,
'ems': 0x52a19f46017561a8e70a4ca9dfde6340,
'fms': 0x3b7d026482c3baf3d24bab884e1ebe46,
'gms': 0xc1abb5469e3da9c4c90e62b396c94e28,
'hms': 0x67b75781b18ab757d39edfb13cf90113,
'ims': 0x27f2dd02ab51ba5d5a9ff7fc5537a09a,
'jms': 0x62e337a10fb349c1bfc3151c7ac12670,
'kms': 0x4951a865fa68d758b42a9cef50ae168a,
'lms': 0xd27cfd44d40d37576bbdfefa8ef30556,
'mms': 0xd55977c223a588fdc11aff690a068b03,
'nms': 0xfed436f003a8f4697a779be5864dcc7d,
'oms': 0x9ffdb33ef5802146e555cf9e1082e314,
'pms': 0x80e9fc22001cd7b0808ebebb9aee76d6,
'qms': 0x9549267e5420e0c07c977ea9dd9db463,
'rms': 0xba431d163238dc15bc32c6a9ad77ca92,
'sms': 0x18b43c6a536a8fe1362f7a3887936be6,
'tms': 0x77ac309c0e78ae7e7321bba06c1bebab,
'ums': 0x76b9257c673c9e071ec36c96ad74ad8a,
'vms': 0xf64831e719dfe3e624db2daa17085cb1,
'wms': 0xde715d4979bbe0a778bb9a23267dce51,
'xms': 0x7556dde610d2d96355ed1ea2615b32b8,
'yms': 0xde9457247fb222d232958ff3965ba221,
'zms': 0xc2799f3c2ba4d061b95c0d3a23dea721,
'ans': 0x1298cbdc3eacd6eed0690cdad2626813,
'bns': 0x7f5d070f243dad6536d9454c5d33d57d,
'cns': 0x3395f0fcd8ea7f6dd119bee2b612b8a3,
'dns': 0xb3bf60b851ebaeb2768b01a32e2ef32f,
'ens': 0x56abe4a6a2dc9e41a20485227bacbace,
'fns': 0x1b5b2818fd59c0a04b4f43ea9ec75030,
'gns': 0x8ea26b7614330f392f101770aa6905ea,
'hns': 0x9082e3bc8ae5ae104acc093af93b8de1,
'ins': 0xcdf1e220d89c2dcd2e000c3d105bf93e,
'jns': 0x509354ed7d9c41c1a7814cd3e8e716fc,
'kns': 0xed7b25e54bfc9a4f8e51d9c4c004cfd8,
'lns': 0x5ae9c555e008b084f9425dba2b12a226,
'mns': 0x11b080d07a42355a374e830a4a0dc392,
'nns': 0x9e0256d8370c7db3e0ce3af5d7186455,
'ons': 0xf034787ff1936f7ff3d29be4fcb21878,
'pns': 0x331a737881b15d5764dba9effc6f5e66,
'qns': 0xa84d9bc604265adff121937ae57974cc,
'rns': 0x737c655bcb3253b86c590d51a6e3ed68,
'sns': 0x6002c6713f40e8a35d365605542e72b0,
'tns': 0x33393b48ae9594bd56b4e8db6a8923c4,
'uns': 0x07525332b1617934911c9fbadb3a304e,
'vns': 0x1557c9eebf17b8fc56da59756791b636,
'wns': 0xe45ac55de617525b2479f83fdf0df331,
'xns': 0x78afd53531a08ec5f2952ecca16c2e95,
'yns': 0x9aa7a62e059a28e0042c163fe3822cb5,
'zns': 0x0a34cae869fb829f34dbc0e9af18ab1a,
'aos': 0xcf037072a1491f0ffa23a37296b93df5,
'bos': 0x15fc4a53992beba40ae91e5244e79dff,
'cos': 0x4d00d79b6733c9cc066584a02ed03410,
'dos': 0x0196f6c4f97df3f48d570c23e46501ae,
'eos': 0x21db4a5fea0f4dedb0aa90007de5c3c3,
'fos': 0xe571b045697d6c923b7e4487896bc777,
'gos': 0x2d96956360c2dbbb860c0b71c01032a0,
'hos': 0x6f639541ab1b6fca4e23c65899e12684,
'ios': 0x9e304d4e8df1b74cfa009913198428ab,
'jos': 0x6f7f3b0fb100c0b0fb21a0287cd72f7b,
'kos': 0xdeae50f3368d21ca1eabb74a7799a95d,
'los': 0xe581aea4ac62865539aeb1931cf0ff61,
'mos': 0xbf4f534a44db066c08e2cd9a0dd7df0f,
'nos': 0x757aa4701447ed532346bb8ec941390a,
'oos': 0x2f09cf6a0c7823b6aade31761b07ab12,
'pos': 0x5e0bdcbddccca4d66d74ba8c1cee1a68,
'qos': 0xc514e03e4dbd058201c2afcb4044fadd,
'ros': 0xd36b548c4c7a2369249fb4cc0e2eba46,
'sos': 0x431e077067178a6dd061f1e2ab20209d,
'tos': 0x227f82daef18fd97b0ed9538ce418761,
'uos': 0xb3dc71dae1cc14b85b47e9097c65615c,
'vos': 0xaa3807c708efd3305ef2325948dfe82a,
'wos': 0xe0c3d1e1da19d9101ae5c50f0c74c27e,
'xos': 0xd7bcdd04f7466252d8ae8971ebe6f8bb,
'yos': 0xfd58de9a9e5906e3ea4682bf1a2da8aa,
'zos': 0x3897e7ec31433d7210e7c782e6f36f01,
'aps': 0x6524fbb44e3d8660f20ee545d9480ddb,
'bps': 0xecf6fbc4690b6ecd2245d8db1602bda7,
'cps': 0x510c5684a4a8a792eadfb55bc9744983,
'dps': 0x3f0f45ddef4fbb6fadc33f5f2e161d72,
'eps': 0xce8e4a184e2e534c09d6bf6ae773ca4a,
'fps': 0x05934928102b17827b8f03ed60c3e6e0,
'gps': 0x11648e4e66e7ed6a86cb7f1d0cf604fe,
'hps': 0xaa59dee700607ccc8def3ae41ff9ec23,
'ips': 0xde56be8fa19339d679efa6232455f342,
'jps': 0x949258fe0c21b4643bcc81491c088988,
'kps': 0x75e6cf979e7c200b899474e7f9b2077e,
'lps': 0xc569107d3dda2369ebe895cafd6aa772,
'mps': 0x7c1dc91629afef3369fc77b119a2e24d,
'nps': 0x53253ed09fe22fe66c8a4ff78619a636,
'ops': 0xe847897826ceb8346eb5141f8c23436a,
'pps': 0x98aa876e2d8a8296926f8c9782d44f40,
'qps': 0x8423b0c52428ca1082ef7b5c1f3d8011,
'rps': 0x651cb023e8a7c22b217106f3145522d3,
'sps': 0x7031b6518a99ce96a9f2b5991fc4955e,
'tps': 0x339a57d16284b3a9c5adbcb66b85e99a,
'ups': 0x7b0746dabfaed032913530c495453f0f,
'vps': 0x9bb97f529b65ccd7ddabaee3d26fcce1,
'wps': 0x80db15125f01725d141584ad704cab47,
'xps': 0x5e370ed72f11412c763dbeb9a8e53e1a,
'yps': 0x824b7c531591af853d310b1b028107fe,
'zps': 0x5c284a074767998e9708c3656d41a91c,
'aqs': 0x05f38245190a568c7317edd5f354721f,
'bqs': 0x26febc83bbb5c1efef55febed6a36654,
'cqs': 0x7d729e5db1170929650cf534f45e9919,
'dqs': 0x7f84d6a8fcbafa0ae493e2d0c1e1bd70,
'eqs': 0x5b4391d9ee3615e5afc50cb2e5c6889f,
'fqs': 0xc8f240d95f6d94d34400700a31e016c6,
'gqs': 0xc6ff630c527b9652250b5bfc9b7c1149,
'hqs': 0x9336e64680c0e11f2ebd398bf23abd8f,
'iqs': 0x5713377fa26568ebec686469a544e528,
'jqs': 0xc4afb85f2c4f1d922c68f19e009a593c,
'kqs': 0x4db2c842f369b59f84245010d417344f,
'lqs': 0x8be45b7086d1bfa8a88abb7d4112ce25,
'mqs': 0x5db3f8420b53fc377a6bc5e28e8b9385,
'nqs': 0xea4af2f0ac7c90843529cc5b7ddb4eef,
'oqs': 0x0aeb6e5f2e7ba8654f702eadeacb345b,
'pqs': 0x93e7955eca1bfb712ecc9293479248df,
'qqs': 0xfa87d2d66521c64841a6f292d744acc4,
'rqs': 0x7b09dd7dc71d7a6339049538baa2e024,
'sqs': 0x252a8156d87a671bfeb32a02f200406f,
'tqs': 0xbf1e3da731dcf7f85a053d0550a25391,
'uqs': 0xa57a9956a4b498c684a4c388d720f4ce,
'vqs': 0xe64b1b09768bd3cc80b38b4a7e003f0d,
'wqs': 0x12afb6d4ceedc2ab1b94e3c0415b125d,
'xqs': 0xa95385e61f6d057ae67ed2872ea5482c,
'yqs': 0x4e0a24089588ce91feffa8b63150b63f,
'zqs': 0x4b52b342e7e82c2c886a1211dc0f41cc,
'ars': 0xedb18509e4a0c9173b432b83121805cd,
'brs': 0xca01ba434cb157dced11b9f0ba552096,
'crs': 0x29fa19e764c47fa898f697bf7bb940db,
'drs': 0x2a3b4bb3d8bee59009b8da70135c9637,
'ers': 0xc67fd61e3b7d35f07e3ca92c8a84a5ab,
'frs': 0x8d9ffc6dc6df2e5732fded8fec33e5c0,
'grs': 0xa1ca50b49044afbeeed4b5e31dbece6c,
'hrs': 0xc760237f74bcc7e3f90ad956086edb66,
'irs': 0x604571a3be2c02bb9e48d9284fded237,
'jrs': 0xc5f463701e07238d500611d7ee42ea47,
'krs': 0xcc4aaa1245234d2de974b392f8b4d880,
'lrs': 0x7862e5f4b4bb31408eca50803961b332,
'mrs': 0xadc285704d36eea9d4a23748ac0ff936,
'nrs': 0xc8a42edc6bc3e9499e1ae393e8fd5a65,
'ors': 0x70b6401e3bc944ef019df3cc8f783564,
'prs': 0x7a51b166dfe235dfec2901a24ed2f671,
'qrs': 0x8375d12f7e75c78f422e6df7b65c3ed6,
'rrs': 0x6cd0472c6d4b6820fc7d7891ede7a544,
'srs': 0xfd2a389677248c3d839524df77a9e73c,
'trs': 0x16e0fa9487f8be38583cb0dffde1e955,
'urs': 0x134edc12dcc61911b084b3e06e4ab16d,
'vrs': 0x30967e36448a9cc4a7b13b48a35ba90a,
'wrs': 0xbf114c476511e1a46b08fc05b3fbaf45,
'xrs': 0xe8eb8411a4c5a77693e3658af4958eef,
'yrs': 0xdd54b79040542ce0f6691e1a1945827a,
'zrs': 0x417e70d702356cc48bab2a1ce8e194f0,
'ass': 0x964d72e72d053d501f2949969849b96c,
'bss': 0xf71487b7aa051b7c1531ea4e0d0963de,
'css': 0xc7a628cba22e28eb17b5f5c6ae2a266a,
'dss': 0x07304e56c452be73ad2b51a4647d0300,
'ess': 0x38107303966b78ae457d3b5af233a9b6,
'fss': 0xc7b904c76d64eae332d14b7c9afdaff9,
'gss': 0x024743cf5c8d4c372dc312c74aaba64c,
'hss': 0xf771eb6211d0f2afd59376c3af8f786a,
'iss': 0x32d72713167aa8cc88de917e16b79890,
'jss': 0x64a55d578f856d258dc345b094a2a2b3,
'kss': 0x931b7714f8e92ee8fb4265b5d68aa5f6,
'lss': 0x4ee4ea3d0591f335b48c1637917bbcbd,
'mss': 0xe5148401a38978b6117a6b09df52d916,
'nss': 0x4d6c337a3e6b95ddea8fb109f7c0ad19,
'oss': 0x3696441120a402f793a704766540e69e,
'pss': 0x1a57e0bcdeb551acf8b8aac651b5c6ee,
'qss': 0x879eab7b1e226d729c4a452fdf657ab2,
'rss': 0x8bb856027f758e85ddf2085c98ae2908,
'sss': 0x9f6e6800cfae7749eb6c486619254b9c,
'tss': 0xc449c5ffafa1c27c6a60c62a5be3ef21,
'uss': 0xb4adcb50f1ee7b54194f82d866331b31,
'vss': 0xb4b2ce5fd627b3ad6c902c2c0693523e,
'wss': 0xebc308f979e135f40483eec4b35feea1,
'xss': 0x2c71e977eccffb1cfb7c6cc22e0e7595,
'yss': 0x4b8ee6d4825e50b3319945f0b5ae9ca5,
'zss': 0xaad1db35f0b1aa1072936d9977fa3cb5,
'ats': 0xe203a9bc993f9067e54e2a36034d38ea,
'bts': 0xc3ea886e7d47f5c49a7d092fadf0c03b,
'cts': 0x961ed8239bc1b87dbf4d7ba27cfef9ba,
'dts': 0xda358a1216cf11bd724c0aa17441a5d2,
'ets': 0x68d5d1f1f90a79bfcc1a02a14f58e17f,
'fts': 0x25f54e7fabba09fa3b75cd4a29235ecd,
'gts': 0xafe18ed979afdfc8b7f0f6e657481f2c,
'hts': 0xbb4901446d6398dc9e444f35e2dba7ed,
'its': 0x584826e70485130530f7f01a973d5637,
'jts': 0x50c3c0d172521fa2e607579dc047fa97,
'kts': 0x173a89986ca2440c40eb92481db7e6e3,
'lts': 0xa371c48b884aa5799cc4e15a625d2be3,
'mts': 0xd7a14e30636d52e10b35d10c02578019,
'nts': 0x5c96e4f24ce6e234e6bd4df066748030,
'ots': 0x3b4622d6ae72798660938088626ef22b,
'pts': 0x8c9a70dc1e604715e475d87430ba602d,
'qts': 0xfec5cae834722dde2a100aa236dbe34a,
'rts': 0x9321de6b7ef6b1afa10d8fd803fbd437,
'sts': 0xb846077409dc6a1778d9fc7e313fd53b,
'tts': 0x72ecb0838c7b4ffba137d25212333414,
'uts': 0x695d878bda76c75ee101da0497b7ad93,
'vts': 0x2dda8284800eb328b87b7161c94104e2,
'wts': 0x662456d60ff2d15d8bc3a6ec17b27255,
'xts': 0x8ef0fcc21eb2eede584c39d6ea11aa75,
'yts': 0x023241f327c5b72c52d14fe74e9f1bcc,
'zts': 0x1da03ca753a1a6b0dd1288d09dc1bd52,
'aus': 0xfc8baa6879e639926be3916810962e13,
'bus': 0xd20f83ee6933aa1ea047fe5cbd9c1fd5,
'cus': 0x5ed3341e31fc13fb3bb013723b9c14be,
'dus': 0x98e653972c27a447e3a67946067f5ae0,
'eus': 0xeef2b6cec1c08d36f23c100c58b71647,
'fus': 0xdd128d4ee5873d0d4cb822e94871336d,
'gus': 0x84a26c4612a7f9958174ee6552625282,
'hus': 0xee4b742d5fc2159c78cd15cef9d238d1,
'ius': 0x689c4f6017e042e16a6315622d4fc11d,
'jus': 0x6ecd178dec5ed191b3d7911eabe7b75e,
'kus': 0xc1c17bb4222efbc946772909c33c370f,
'lus': 0x90cb16c5c9989fe4a0a05c2df0ec5523,
'mus': 0xd62ec24d065e424dd816ce7828f62584,
'nus': 0xb84d8185d9fc5d64de366cc8a06d8ef1,
'ous': 0xd56d05986d5d329974c87a115f4f81f1,
'pus': 0x4d4ba904b38d85f8001cd15526c82a56,
'qus': 0x39afcf3048cb78f35f19c15f0b30b8c8,
'rus': 0xa04202c712aa415f47dbacb817a60397,
'sus': 0x1158f5f8d240a731d28068742adea0fd,
'tus': 0xcd61363a23cbbc0606ec6f2725b1224d,
'uus': 0xa6e8c26fdaeff2c0230f864ccbc610d2,
'vus': 0x59a0595a9505de6afcddec15ffa149cf,
'wus': 0xe57975a3de048070cbd23de65ac5776b,
'xus': 0x7406043fa0b4f5a418237a69731bcae0,
'yus': 0xefb6e5a9e90a1126301802ee0b3f11b8,
'zus': 0x8c3de64e1452e7f81a3bbf99046c07a0,
'avs': 0xd788a5499d320b05602a6a0805c81403,
'bvs': 0xa12583b16148c3246e9c0f35ad08b852,
'cvs': 0x72f1a850d966375fa159121c7c8b09a1,
'dvs': 0xb85feeb4cf07470148789581f37ac988,
'evs': 0xd1c994cea20642120b8ccb13bf716471,
'fvs': 0xecaa86117f9713c1d28412675cad61ad,
'gvs': 0x4bc188199dcedec73419afeace796232,
'hvs': 0x4f33bc98c4bb797aed8304af2f80ba2b,
'ivs': 0x873852e38ceabd55753ff7992c2a1ccd,
'jvs': 0xd8c8397be0eb69b390706072033c854b,
'kvs': 0x6dcd454ae712b667e0f2fa2f04f7b99d,
'lvs': 0x804df3e8847f00722630d36840188506,
'mvs': 0x68ad985e21342c4d442c0320b7e6025d,
'nvs': 0x0f1edc61cfd92bb31d2d61eeaf480207,
'ovs': 0x99415f0b9a2ae6d7290f1add23e3e4f6,
'pvs': 0x8da66bd15fddda7df4d02c12421a2d7f,
'qvs': 0x2c9057d2bd473f944c796558e908d715,
'rvs': 0x42016874b6a93f7700fba1bfe1f7674b,
'svs': 0x68fad0f65f870566534d1d5ac0442596,
'tvs': 0xf3d320408efc5ab66630b9ffc6c6cf2b,
'uvs': 0x04b78a7985bab2a4f7725b46fd204fdd,
'vvs': 0xa1892eb3e1f268599f6b370dc714becd,
'wvs': 0x1c20e20a6b917f71446abdaee225a7c0,
'xvs': 0x76a935c47bb47ef0c963ad8f92b1d837,
'yvs': 0xb65505fa8d70c80318638e4faa9e7c79,
'zvs': 0x76c4bae87cb1e95607c37148d4339654,
'aws': 0xac68bbf921d953d1cfab916cb6120864,
'bws': 0x416cc041babb74d461e4c2690e1e536b,
'cws': 0x23abe62a446fc05ce0a6c810f4045308,
'dws': 0x898e82c94d7703e96f7ea83b31e0ac2b,
'ews': 0xb1e38c354c61f5100c18333b70255c0c,
'fws': 0x398bb4752a2b9a362871cf89249ca133,
'gws': 0x562ee01fdee0fe4d087546f7be1d3929,
'hws': 0x39babc4eda447a5308b60d1ce70bc6e7,
'iws': 0x8ec2374d232744ab1483941fdaf1dca5,
'jws': 0x62e9fb11526872de8fdc91e512673b66,
'kws': 0x4947e26b15c977d269f9f85e167e5871,
'lws': 0x53405b60239bc84097da5a94c0c79233,
'mws': 0x980e673db875092c7d33775b92bdc2ca,
'nws': 0x88c4dca8b14125e475c06f45c66b3990,
'ows': 0x4bd2c1ff3b95e7effa8bc72975fdfd68,
'pws': 0xf9ba7d6af07eecbbccd33510304aba1b,
'qws': 0x548b8a698396d01a24b4a85518075673,
'rws': 0x106f6952596ed3938f8cc92089d6c30f,
'sws': 0xdccac6c2a05156cf3279028f88fafe74,
'tws': 0x85b791c3a202d46a7f5982922ed6d3d6,
'uws': 0xf4b31e210b83185d7ffe2f7d483b2def,
'vws': 0x8eadee186be9fa5eacc6b885ce55fba5,
'wws': 0xc3a2aa77c8d8be2979beb15c96853d38,
'xws': 0x3bb145f8a6e0970c917da74058fe71e3,
'yws': 0x091ba0055e5dfe397667dd56b5982aaf,
'zws': 0x9b3416b56c3d63ec1758d76e5ab0a231,
'axs': 0xdf70c99bf26d19858d254e0be0e1812a,
'bxs': 0x02ea9ade434d0bc22f5f3b664790541c,
'cxs': 0x5622dc892acb274e1dd28e18aabece26,
'dxs': 0xffe819f9f281b896ef8a734aa9103cb2,
'exs': 0xbc5c47b2bca00caa34a928db51d36119,
'fxs': 0x2a6d5f243dc4ba5f79bc7c7d1164b8fe,
'gxs': 0x510a3a39d5bb8fdc5bf2fe542c6c6f6c,
'hxs': 0x3d680d299358410d6cb1f865cce9821f,
'ixs': 0x6c400ee9ee7fcfcccde4fab2c4d3d10d,
'jxs': 0x2b55a058c78fbdf07df0f4047cdd4141,
'kxs': 0xb1b6fcd70384fd32e4b5600086631d98,
'lxs': 0x6dde18b9439c4ae7f7ea18191a60e192,
'mxs': 0x510f47cf182cc6c796f93a0d00e532c2,
'nxs': 0x8434e40da3d8aed467fdfc65da3f41d3,
'oxs': 0xd6089445ce7bee6658559ace3b0c1671,
'pxs': 0xd165e6ff7930087a39a66e9788bad92c,
'qxs': 0x243fbac8eea05d67c7e868d542d08f21,
'rxs': 0x361f6d19ce7007eeeadd21be9ac41ebb,
'sxs': 0x484c687aca38100463a763fc4c53d3a0,
'txs': 0x5ba4ea1aff45c3a8db09f2b8f3539218,
'uxs': 0xf151d568c77fbf43dcaeaf0b9a9e984c,
'vxs': 0xdf40f49ad62a33e33c751560e9341599,
'wxs': 0x8a5d516026d1a29c0d95d3d95d079d6c,
'xxs': 0xfb7b7da256a1cb6145c3347d1a77c832,
'yxs': 0x0bfca60016123804fafcbc8cfce83e91,
'zxs': 0x68f43f44054de89e6ee5ff03adb26542,
'ays': 0x17ea46ab121b0f57bbd930afa2ef38ce,
'bys': 0x6205bd1e57ab19a6f63a6d57da0e87e5,
'cys': 0xf9c516e42fdbabfeac4c0b6d3e32b04b,
'dys': 0xa851e45c1daa1d762f2500f9d0ab0700,
'eys': 0xe732d745e4da1f2f68565ec64e652ee7,
'fys': 0x6314df16084c18e60bf8099e987d6652,
'gys': 0x66580a1fe9674a5ac280c5c1b4bc1dc3,
'hys': 0x1de0f669d7a4a366083cf44aa7c531fe,
'iys': 0x6ba746cea48cc6ccc4927a974296e45f,
'jys': 0xdbd89e2c65db519bc5a9e1761ed7cfa9,
'kys': 0x0a47484cf8a49fd036e1acf41c7619f7,
'lys': 0x8653e6b3b07f55366c57d2148fea12ad,
'mys': 0xc798cf2f72704a7ef75ae09db3687de7,
'nys': 0xf7f107aa447e58448fa6b00372659fce,
'oys': 0xbba0b41160c3f8a3c5179a900b20e071,
'pys': 0x28e50223db5c0cad71c56bf0c480d945,
'qys': 0xf8b2d979b89cc1443a08a7e852291601,
'rys': 0x998585e549db262db7bc3c77945cf9ce,
'sys': 0x36bcbb801f5052739af8220c6ea51434,
'tys': 0x595980803175ae40fc8c801a77476747,
'uys': 0x3618edcd267f4836f4eec0222bc714d9,
'vys': 0xd515db7305c8b22e6a3d9c9a27344c52,
'wys': 0xac723a3af67d7921b264df1ecc59cd7f,
'xys': 0x25eef40b18348c42d271a0c1090bf9ec,
'yys': 0xe5c561c0ebc6c9e81db062120bc22176,
'zys': 0x85558627188caaa2138099aa53d4ff5a,
'azs': 0xa86390de0dfcf805e4672ac6857c1277,
'bzs': 0x01950d1058c5a4a540c7da28dfa03105,
'czs': 0xd090bdfa565d061a6e59f8e444486d71,
'dzs': 0x89a94ea2ea6278c9a9e7ce0694fc87bb,
'ezs': 0x015175c4db7a66221a5dd89fbbcf9b4b,
'fzs': 0x945775b23ed2273f134fe367d2f46717,
'gzs': 0xb6cd69f5380863535dd0a8dd4fef58da,
'hzs': 0x9963793b0f70cd2af5549e1d9f3cd052,
'izs': 0x2ce198d7a761dba470fdbfc46b560428,
'jzs': 0xb2da5cc56186204c959bc0c9f5b9596c,
'kzs': 0x9a0e2732fdc7e4a92189a09b0c0f3dbc,
'lzs': 0x85f32fc59a55e5144d8245340def0116,
'mzs': 0x3a1336648188224304194d762fb75132,
'nzs': 0xd93ea2dfc7e494b9eedf846c404e433e,
'ozs': 0x5b5fe936064814179eabe18c4fa9712b,
'pzs': 0x931ebcb675b3f812e432f90416c7d25a,
'qzs': 0x0aa0fc704f563e0b592ed176c25c9813,
'rzs': 0xd728621325f55163526ff665bbf65519,
'szs': 0x1cd24b635348736ef24cbbae0db6faf5,
'tzs': 0x86fb9662aa9114866fdfc30a8616c313,
'uzs': 0xc0ad6d3c4c1046727c80d0f70decb037,
'vzs': 0x693480d95357ca0276b936a324985b0a,
'wzs': 0x041bed082da040a4bf38bddbc2600aa0,
'xzs': 0x2f200ef7bb1abee2bd8b9863fc990bf8,
'yzs': 0x64bc9ce8f93eac87911c82a01df567e9,
'zzs': 0x70721871b78b35556d4a3979e65906ed,
'aat': 0xfc484e54710fe8d1ddecf6faf910e202,
'bat': 0x5f3f4681121b460e3304a1887f42f1c3,
'cat': 0xd077f244def8a70e5ea758bd8352fcd8,
'dat': 0xe34d514f7db5c8aac72a7c8191a09617,
'eat': 0x9a97eda6d89f5c9e84560468a0679707,
'fat': 0x0d8dc086e16e3ac48f05d555994da7d7,
'gat': 0xba3549d034e186e45d4ce000487741df,
'hat': 0x46b5e59b2fd342bf8fee10c561958725,
'iat': 0x555d469d4f308efa4c4151b4934e32f7,
'jat': 0xb28c197774bc8c9501d3d6352e679e00,
'kat': 0xb146a357c57fddd450f6b5c446108672,
'lat': 0x4073d41bb178273881fbc975e1af6118,
'mat': 0x4a258d930b7d3409982d727ddbb4ba88,
'nat': 0xd933df149c62be04ea54d3a9bfb0372c,
'oat': 0xfa15da3e120212962dacb650ee67980d,
'pat': 0x7852341745c93238222a65a910d1dcc5,
'qat': 0x5152d8db45a6cedb98308f888bb8fd76,
'rat': 0xe756f6aafafdef4debdb5e49bcbd3f11,
'sat': 0x53e8254b3222a33f42b5a6b3d156056c,
'tat': 0x872e384a07f7ea1e05fc742f5be6008b,
'uat': 0x0f804f9408ed7aedc080058ebc1d5b18,
'vat': 0xd04a4a2c8b7ac4a76c18da1a927e7aea,
'wat': 0x611c59af56268df5534fef5bc3c37b1d,
'xat': 0x8769abe0e4c7e76132a451ed48041d09,
'yat': 0x284e1a1e8aaaaa7b9e6bcf555c17a69f,
'zat': 0x241a7ddeffd13fd8f0d2e973c52c57a7,
'abt': 0x7ac686d555992ba37ca0f975aa06ac74,
'bbt': 0x72bbaa53b3296b3821e5225e8f7e9327,
'cbt': 0x7638e20f13e9fed7eee4a576059bbf86,
'dbt': 0xb6ccd40d2285deac430714c82f73e033,
'ebt': 0x30cb6763a8a6ddd1c4306a4bdc2a121e,
'fbt': 0x9d144dc245c8f7cc4f9681babf1703e8,
'gbt': 0xd28e52d1c918252a4bd35bc4ef59bd23,
'hbt': 0x783b85ab7aab9a3200db2aacbc54192d,
'ibt': 0x39d3e4357807214720180d95e3ea80f0,
'jbt': 0x9275381c5e1f4b3a9f5e04d5da0ec755,
'kbt': 0xda8b02aa4f00dc7eae04377bc4a636a2,
'lbt': 0xa99e12a65114d9936a213d57688d5974,
'mbt': 0x0e96e0cd79f42046228d231f0d772746,
'nbt': 0x16103ab618dac97a8dcb7c31353e1716,
'obt': 0x33bfc0a7990e08c5f7a41ff950fd8078,
'pbt': 0x106a10188109b25136e6bf511fa8dcaa,
'qbt': 0x96e52f3a6ffb35d1ed569ffeb15a7eae,
'rbt': 0xe6312567c1c5ab98ae23b089715d997d,
'sbt': 0xeadc1bacb17aea28d29fefc19868b5e1,
'tbt': 0x2ff7dc56fa2ee9726a67782e2c6b442a,
'ubt': 0x09362bb0d95fbbce0555dbf10a7b7754,
'vbt': 0xb6d5259034c687bb1421bdd6f3922623,
'wbt': 0xab159ce3cf8b0be3e2294742085de248,
'xbt': 0x66d666d33e1ec00ab77bff9a533cdc30,
'ybt': 0xf57b5362c2a4275ef23ae64f95a40e1b,
'zbt': 0x10c1593a52ba83ee501592b3e8a1e569,
'act': 0x316c9c3ed45a83ee318b1f859d9b8b79,
'bct': 0xbeb2320f00aecbb437ab14156caef338,
'cct': 0xd4c9fa26976902c7c4cc2e3326e7e07a,
'dct': 0xc2479e80583273b2466807d87b214c55,
'ect': 0xb3116a7bcc1c58745d7730dbeabfe653,
'fct': 0x3421f5eb10975742a75d3f42e1a9e848,
'gct': 0x90ccc88964ea225803b03491dea359a7,
'hct': 0x1234fa03f415956a18d248bb3471ba0b,
'ict': 0xa7304e71e071e089d5edf94ecca2c3c7,
'jct': 0x6ddae37df634a8a3ec2bc625a4a5f458,
'kct': 0xb3a62bf700481ecc4700e5913d2e4361,
'lct': 0xcc753cbe55f2b4e2228e5c668ed2bc68,
'mct': 0xe4c7d6d100b4a12588847e6b2101fbe4,
'nct': 0x130d6165fbf2872cfbe14c667d5a0c4b,
'oct': 0x1cdbdf89f2d1bd3dd9e698b2b320cb86,
'pct': 0x973333fd6d3ab217522f3785d5d3e6ea,
'qct': 0xc5c462483aa9cb6d54e06e22ccdc8c5d,
'rct': 0x2650801e79db80f9dac78d532e718415,
'sct': 0x04ed5e26fb5bf48948de7b14fcfb8c81,
'tct': 0x039101a793e5708579036ce933ae2766,
'uct': 0xe47163e3ea4b4dd7a31568b7eb3c0838,
'vct': 0x7ed6cc10b2b9ef57dadbe3579f2d866b,
'wct': 0x01ac064d4b2921a21a39f513175529a9,
'xct': 0x0db9774b86aa5a219a0939cdd5c5aa08,
'yct': 0x2c5e5304b550076ef3a66d8e4d3812f8,
'zct': 0xf4e2e47d44d64366dfdcd0e31a462a54,
'adt': 0x018c0257ca7d132785ef77637a7e98c4,
'bdt': 0x0098bba99b2eedd9ae77e7d563b28f3a,
'cdt': 0xa7a1072f6fd63598c9972e6db057ce00,
'ddt': 0xaa37a3d1f575827ce549144f253f84f8,
'edt': 0x7d7955f02d5608ccb748069bd729c519,
'fdt': 0x35c34be51b1fb62b33823e4c8985a4fb,
'gdt': 0x0bef679c27a1c77dd1f1dfd3c591894b,
'hdt': 0x2ad202b4999db3676097a15e7a8d1982,
'idt': 0x34bef5f7d97d280abfd86e52d013fbea,
'jdt': 0xc6ee7a8e76e5b2f213741ab8e92ed938,
'kdt': 0xa084f3dd58f1023654a7b69dd4c1e9b6,
'ldt': 0x0f17a594524a3488c7f8a691b7f9a800,
'mdt': 0x7b381369f7d8628000d4e609432c5c7e,
'ndt': 0x8484cb2ef6af8a910db021e5fd4ff2cd,
'odt': 0x221fb7895674c954d8e1af19c3febe95,
'pdt': 0x1620c86eb4be6eeca543ac90760b04b7,
'qdt': 0xfa0484f11e9926f4e7e8ce7c6c78412a,
'rdt': 0x53051481fab60b6a6735f9dc3624c2d1,
'sdt': 0xca536f6151a7bf74d9b969b61c8f9412,
'tdt': 0x9de46e96e347231fff03e4d807f7a922,
'udt': 0xdf03a7cefdd848a26593ba8981c4d3d3,
'vdt': 0xe1e22cbf37732c37b42d0927dd571298,
'wdt': 0xfc487b58a6194c1631bfabe793c97e6b,
'xdt': 0xf13ed1bb4ec274fc9abe315d2459082a,
'ydt': 0x570928fe8663c789e34f298190120c99,
'zdt': 0x0678144464852bba10aa2eddf3783f0a,
'aet': 0x75165652f5b4322d4c618e0ab3a6c49f,
'bet': 0xbbe991a1b594ed7d73b4a0f43c407745,
'cet': 0x2d9140411b361d2a492367d3069fd678,
'det': 0x5b22539a9d8246393e3e09787f03dd46,
'eet': 0xba62fd6169414cfb7c2d7e4b896ef184,
'fet': 0x27e9eb286aa03230973989bd4ba197e0,
'get': 0xb5eda0a74558a342cf659187f06f746f,
'het': 0xc66133177cd4d5050a02ff2bb947475c,
'iet': 0x9df034031306e4b14ee97b5675e83b44,
'jet': 0x564f60a2dd82ea24bfa3f2f615348f7c,
'ket': 0x83f989dd820bb3683ef6ff6b2bc7fd68,
'let': 0xe1686078d1b60d351da5a87543a2a663,
'met': 0xc4c4909eaa4fc41a39f0fa5c96f30cd6,
'net': 0x40fa73c9d0083043c6576dd2b40511e4,
'oet': 0x822aa94b45262f1bc286cf33e9e664aa,
'pet': 0x6c43c0a88fbf0f44ba944d00524e45c3,
'qet': 0x6a14d0b5928330fa099a3df0f4b085e4,
'ret': 0x2cb9df9898e55fd0ad829dc202ddbd1c,
'set': 0xcdaeeeba9b4a4c5ebf042c0215a7bb0e,
'tet': 0x129cc883df47f2c03616cc0010ed30c5,
'uet': 0x49f5b4da5f1e84c44a2b6831fb5e596e,
'vet': 0x2fe585b232995699edf68a537f91d31d,
'wet': 0x9ff49c6a11b0ed72fa190e9b55ec4f6d,
'xet': 0xd8ba29a7e705e9a8e9b13558ed50db57,
'yet': 0xb8284650440a8e32b5189e1bcb3e94d8,
'zet': 0x18c11e60d1ed2e67520dfa33acf7e3ea,
'aft': 0x87784c5f687a8e202cc7cb71b5ba7381,
'bft': 0xcda2762f16e369d664ee7348813bb04a,
'cft': 0x0e205d766779bb943585731ea51cfa54,
'dft': 0xc555ae2c9a58e4d5845515455fd4ee8d,
'eft': 0xcaa3416ed8680836c498d62f05f7da48,
'fft': 0x9473db81d7af66f5e2a8319424834877,
'gft': 0xb6481cd189a68e34e5aeadb484532fe1,
'hft': 0xaf399ee75d34a5038a79d8d510c0e0af,
'ift': 0xe514adfb55756cf232d029065004ef75,
'jft': 0xbfc52fcd07ca1cb618f65e3f0c2bd41c,
'kft': 0x20b9950a3334104c60288cc32e4420ae,
'lft': 0xcaf7cc51e5d933c36ff32726b525acdf,
'mft': 0xcf9cefec9fe949410174bbe6ce6ee566,
'nft': 0x7401714a8393a2af87c8be3e173183a3,
'oft': 0x53e8e820ace8d675ad8a9572f434baa5,
'pft': 0x42068325089cb151d38358f24861f4ef,
'qft': 0x88d4da8d1518b856d02fa530ddb80724,
'rft': 0xeaf0bc693dc21d1a7bc3f48be231e9fb,
'sft': 0x6da9027d9879515082a45831e969415d,
'tft': 0x3a945be25af08a8ba26677bece997569,
'uft': 0x0c3d90a13d20ea94e93eefe2d260a192,
'vft': 0xa35d7bbb240b578980367d2bfc0bf7b1,
'wft': 0x4620cc8252ffe1ce9ba795cb341eff75,
'xft': 0x82435ee1bb39490eadfa9cb0e126951e,
'yft': 0x6293879e905cd6504386f5bbc8c61926,
'zft': 0xb0929ee370032b06893d18f5f08a6c00,
'agt': 0xa2922ec60c2c56d3f2e0beb5cab1d6e0,
'bgt': 0xa93e8d03e221da79dba13102ccfafd23,
'cgt': 0xdccfb54edd35bba8307f41d4142e1fad,
'dgt': 0x8c6315048d64a36b21d450d63650deb9,
'egt': 0x8595e6733a55bcee2ee863a53d652603,
'fgt': 0x9e76638dff0ecc29c7137e563f4b10c5,
'ggt': 0xff8ea425d3e258fa7138863bf64ffa82,
'hgt': 0x94d1b2cc770c23e6727b0c4071004168,
'igt': 0x05d377f8051d7ec841c88e6b231a1b27,
'jgt': 0xfe02de679c1eb64620041678470ab05c,
'kgt': 0xe087c4339d7f65063471b599e0471a93,
'lgt': 0xacbc33202f57e5d261b36acc9aeb91cf,
'mgt': 0xf747668bbfad36f9fa48d8e31b026846,
'ngt': 0xf56148a2719f5902eaf6f15f7d96f7df,
'ogt': 0x857c6674a8be6658c6e9a1eff2ae44ab,
'pgt': 0xc85422e48c356b7c9fef85f6384f7114,
'qgt': 0x8bc9c1beabf3fe92773e1246af3657c8,
'rgt': 0x0d000fbb4c10a447bd54c452d6f24384,
'sgt': 0x1ec1d0cd6f4a399c948f847d63a10c9e,
'tgt': 0x058de92451763e137316df3a36c24b7a,
'ugt': 0x46e4f6f9feb1d86bba749d5f30aa4e33,
'vgt': 0x68d09a1f111fa50588feb036685f241a,
'wgt': 0xce020a96e767776724bcca6aa05d07cd,
'xgt': 0xc00e693457e18ac070c8ad23cb1250c8,
'ygt': 0xe33f023eca35a4843e1a40d0527ba3e8,
'zgt': 0xe9bd7b2b676ebb440db65643fb841d2a,
'aht': 0xadc0ef2877a51be4a441780a6535e8b5,
'bht': 0x3ea53dd7919fd4c58650bbf13485831b,
'cht': 0x7d1cf34ccafd0e26b00bb21cd8cce647,
'dht': 0xb06531b17f9586226d526fff54527c8c,
'eht': 0x39b23b8ff24d27aad5f34b68a60ce3da,
'fht': 0xce0536d4a09bd690b6bf3d97e4cd9631,
'ght': 0x15bda1664d8a3e8e78f8a054254728ce,
'hht': 0x7b2270335426de7fcaf0822086e226e0,
'iht': 0x16cc3a64eea40d4025ef48333b4ca79b,
'jht': 0x9778ad4dcfcae0d8510f93645a57876c,
'kht': 0x76656a564ad0b747adf6230a3e5d4271,
'lht': 0xdc339219139348e2d1f8eafc3a01b015,
'mht': 0x74f3215b67fd2333ec276f6b8d2c6c01,
'nht': 0x6c827367ae4ff3a29242c85b420064d4,
'oht': 0x60334a961a96f932c2dc6de60c5f0bf2,
'pht': 0x74f592ef463954b4a756552fbe2870e9,
'qht': 0x0d9e19e0adfe059ac490b3868cc7b693,
'rht': 0x66178eaf7202c7afeb4753adb20e6e0e,
'sht': 0xef63c2cebe365f81f899327a2f044988,
'tht': 0x5549219ed402706bc6972e8bc830bdef,
'uht': 0x8e5cf84b4852b8d33488ff0e6e2d2583,
'vht': 0x57e49e761fc682e67d892535b8792e20,
'wht': 0xb3da3b9f0f8591eefdd1839944e9ac12,
'xht': 0xa1bbc30a4d9bfd5d7f6a8dbbf5d13d18,
'yht': 0xd4ef4399baab48b48af109baff033854,
'zht': 0x1bae87db3b0d24372000b0126e007cc6,
'ait': 0xc08be1f2b04de914268d602786d04531,
'bit': 0xf67169dfbf72c4ca285e9ee12e3e9ac5,
'cit': 0x29ac42eae102c3a959a282836a9ea832,
'dit': 0x1ea8cf293ef069b55b38613eb7a1071e,
'eit': 0xb9a606302084e3bf1dcef8eac7ff7b60,
'fit': 0x1977c9daa1d67de51a4651abdb160c09,
'git': 0xba9f11ecc3497d9993b933fdc2bd61e5,
'hit': 0xf09328d495fa622700aabe5707edf00b,
'iit': 0x8fa985e47a9d6f1bd3bbb75427442f6b,
'jit': 0x3b6ed253881516f91c2cfc9ac246c406,
'kit': 0xffa044f5d18974a3264c23a5e1e06217,
'lit': 0xef28f869b241b00b879922832b14da10,
'mit': 0x72e4fb8f76b9782b79a91e549325bc6a,
'nit': 0x916e2e1eb19b39b28a13416974202bbc,
'oit': 0x83d3a49a637067949b283caf9e98e5ef,
'pit': 0x721f99927350450e076a57d4c74221e1,
'qit': 0x7113096922776f0e770e9c3eeb726c03,
'rit': 0x080045a7526717bb2ad9b5e7ca28dd13,
'sit': 0x87d4eeb7dec7686410748d174c0e0a11,
'tit': 0x46f4b5abc1f9a4fa0eca0520517e5730,
'uit': 0x91b40cd81fb2a51014aa79bcac852a5b,
'vit': 0xa1a1c4dbebfbcb0569e5142c2f9c8de2,
'wit': 0x26f6bd393df766642c4e6215573c6059,
'xit': 0x0914589dea7deae99c73d3a00d0e42e6,
'yit': 0x622c66105ea77117224419fe811d09d1,
'zit': 0x9a7171c0a2a4b205ec90d824c5d635e0,
'ajt': 0x3ef7fbdded23d84bd56eef6048da485c,
'bjt': 0x487c70e53cb7de3f09fe4195be43fab5,
'cjt': 0x66754f4b92b2308707a301f72934c356,
'djt': 0x26f588e9861af433da4017c6052abcf3,
'ejt': 0x924ded73f0f7fb2c22c7606c3146d2d3,
'fjt': 0x51b35f0dbae0f72dc4dce4425ab03503,
'gjt': 0xe805fa162761a8fdcb44c3d969b565bd,
'hjt': 0x49b25f3514b418a21d8c8cd6645fc8e3,
'ijt': 0xf98b69116f09d235e4b8e100a6b373ef,
'jjt': 0x8df0acbab8651ee5e4e21eeb9709f138,
'kjt': 0x55a6101d8ecb59ec52b629ccacb56fa4,
'ljt': 0xea408adfc93bb6bcb058252d19d5afb8,
'mjt': 0xb0c4fecc272dd135ade5ef75e9120ebe,
'njt': 0x574cafb35c55f110b70c7b9fe1d16e94,
'ojt': 0xdacb2608981c68303ca3c13ef01bbca9,
'pjt': 0xb209830afb6ee785a2eafdfeedc7d314,
'qjt': 0xe41d6fc42b64c903298d357d769fbdaa,
'rjt': 0x9b791347070f793ec17c8dcda2b26ec8,
'sjt': 0xc66fa3775be3626720c2c63b5fbc3fdf,
'tjt': 0x95454ac5819f875bfebf61bca2702c5f,
'ujt': 0x0c184f7be3a62be8167440f0a2efe3a6,
'vjt': 0x69380565ea9c2a90601b5b60f17dcfb2,
'wjt': 0xc9b93bce6fff599fb147efe91636f515,
'xjt': 0xc4704a294cbf6a6b5ddc973dc7bf9969,
'yjt': 0xb512f10c85f46d4e98a1631f5bf58690,
'zjt': 0x38e3d2887e81f8caf5007f94b9a4e65d,
'akt': 0xfe0d872c4ea3e7d9f07488fc82841cc8,
'bkt': 0x3f00289d115df58802100220d0ffc351,
'ckt': 0xb14b5740f0bf5e966f9379ff89dc479f,
'dkt': 0x10332ba37475a62ff2a10c1d80575a3e,
'ekt': 0x3d962abec697aec6b122945e00548841,
'fkt': 0xbf9db2fbb790f4b1d5a68c90588f8c1b,
'gkt': 0x2778c57b6c7bdd4d66ad79684b8f362b,
'hkt': 0x726f3488cf75df63596b4062b141d08e,
'ikt': 0x91151abff74713e847ab89e316acc061,
'jkt': 0xa936a0058e0963ff14134e1d47dc8107,
'kkt': 0x909191416b510ce894ae1c5ec9951c9c,
'lkt': 0xd77cde6a7871520172673b5f650323be,
'mkt': 0x1eb0fa9e4ae6caeb56fd88cf422d6be4,
'nkt': 0x09737ebc4c0c9ee99945f0124d813215,
'okt': 0xa599236cb06af6f23c3907dd8fff8087,
'pkt': 0x9fd225aed9b8e1092008b6a18da358a3,
'qkt': 0x1a8f65e859cce05efda7f22daf8bb6f4,
'rkt': 0x10547896b5ec87e535107872d6279528,
'skt': 0x6e5ed3c1ba656eed7316c413df612bf2,
'tkt': 0x7b57f31bea0ae2e9c8e2985a285b922d,
'ukt': 0x78faf69f3c753c1417a9f6d904a15d70,
'vkt': 0x8531ecd4e8c5619a058a102550a3e9f5,
'wkt': 0x20d55abcdaa5681082b755b652a8ab68,
'xkt': 0x16a82bbb0b83f1f7092e64f855cdb4a9,
'ykt': 0x4fd737de3016dc45a2055e270e81d13d,
'zkt': 0xe92ad3b9b6881e0014daee2f5ca53fa8,
'alt': 0x34823136d0dd91d0f5d22db740f7679c,
'blt': 0x63ea8d8c31c06f36545b9de5bb2e3563,
'clt': 0x42e7ef3662a4459c9c98f897c1ffee4e,
'dlt': 0x8a41ddcd329749af8218828b17253c34,
'elt': 0xa34cc4df9efbde613123ec83659261f5,
'flt': 0xa77d267d7c75b97b497e18d447f8b67e,
'glt': 0xc9d80f7fc666d72c6c3abeac240f8ac6,
'hlt': 0x4f4000d921968b9665f52e88333007da,
'ilt': 0x81732273c9057c4b4f211dd629cad9c5,
'jlt': 0x0f3f98f8d4a30e74126311e332e8fc01,
'klt': 0x826f5f947350a8b09215177602e8168a,
'llt': 0xa5931013ac153142771455ff516272e1,
'mlt': 0x4d9254acfdc372ccf15d9df40336bebc,
'nlt': 0x171f1d786291ab46c2b40a81f66ad938,
'olt': 0x837832088ed019aa6f738121b9b693af,
'plt': 0x60443f67c6816f5e64f33e62827883cf,
'qlt': 0x2af62e937404dc6d159c4e2730b305d1,
'rlt': 0x005faa431f4ed13a53b9fbfab52148b2,
'slt': 0x926435b4224ee910519100e761c58ba3,
'tlt': 0xf8f16c9efd71804f0c2a27dc5111a9b9,
'ult': 0x3c19c5dd9d58ca90ab5654b8a519b179,
'vlt': 0x4c8251eab114b6fd75012a16109d01b5,
'wlt': 0x0078c41e3723d2a5976593d2c9b4b23b,
'xlt': 0x1ba4cc134ddc62a8a24bd4f4322d3b21,
'ylt': 0x154aa24c513047e85adc69d76e588b7c,
'zlt': 0x9e8290ba07d184f2c002c784404f86e0,
'amt': 0x8def24c6f2087f4f9b2e04c3d6d559b7,
'bmt': 0x4479459e822346f1d9db6301092026ef,
'cmt': 0x4d32cbb621520add76c87c914b538b5e,
'dmt': 0xf22c8d4b7318fcb85563faba1507ce96,
'emt': 0x17df5675c2ee9097f8f604167fa89f7d,
'fmt': 0x7631454338ff70b1a6b1262f5f36beac,
'gmt': 0x3cb8eb17ba602a2951b9d2c11f1de623,
'hmt': 0xcffacbddac2b362eaa241551bf705dcd,
'imt': 0x162c15a41e2f00002d718030952f991f,
'jmt': 0x4356a35b6aa848963720d397c9cadb9e,
'kmt': 0x2e3252ce6e6465b286f46245d94b0153,
'lmt': 0x593fa33952b888e342b6ea8797ade4b3,
'mmt': 0x82915a4d88af6b86b4d6cb42705ffcb6,
'nmt': 0xf02b72cab4825557d7d297eed46c7294,
'omt': 0x0470be162633dea40c14135fbe91def4,
'pmt': 0x6ac84c6a0057f36df671f9d47b915902,
'qmt': 0xd240dfdfd6f081db2329d059a753cc88,
'rmt': 0x9405ff73d46075a0a69acfc422546504,
'smt': 0xd8cc653b02f7897915cdd2ee65540ac0,
'tmt': 0x049b1aef075344e6bf565af8365fe80f,
'umt': 0x718a056e741edc84150411c119fd9ebf,
'vmt': 0x00e6169f29fb18b8b005327e82788cbf,
'wmt': 0xb5aaa055318011e29cc4b0dd8946ec61,
'xmt': 0x44964dcd69d30b071691d0afc9ffb075,
'ymt': 0xc9a0830760b20ec004d8e32c4ff62dc0,
'zmt': 0x9aef758529a2ee63e1a3f7c106cb84e2,
'ant': 0x63b07e828bf016e976ff95d6ee07a105,
'bnt': 0x3490e0f05ae6902a1f12d2031bf493f9,
'cnt': 0x2817f701d5e1a1181e657251363295fd,
'dnt': 0x7dc861e501db7b21de378a1ce5fa17fb,
'ent': 0x645ec79f22bec5efe970061d395cf7c4,
'fnt': 0x0412dcbb70ad3f33805cdf25a24a1d58,
'gnt': 0xa5e669e9a3073f1384994f3b02576748,
'hnt': 0x520b9124fcbc46b4313ef4fefd9d2c23,
'int': 0xfa7153f7ed1cb6c0fcf2ffb2fac21748,
'jnt': 0x8f94d7b22fe16a0580f7bbac7d15060c,
'knt': 0x4f39175e74f3048686c7e2780fd675c6,
'lnt': 0x8d5b8cd0ffbb5b79862c0fe6634117d9,
'mnt': 0x9cd78390881d564d549c61bfa2dc2229,
'nnt': 0xa508fb1b5037213c2bb0d9a5243706d0,
'ont': 0x82637188694a3aa89d6cf201c08f1a33,
'pnt': 0x94a55296231abc15bf694cc638fcc4c6,
'qnt': 0xe93b949eb7b647d779d11b4c06fed225,
'rnt': 0x6510714ebb57e32bed27f19cf52e8837,
'snt': 0x1682776e775d1fa84d30d360e26b5837,
'tnt': 0x6a7ef638f3cacf258370a83d3dc741ff,
'unt': 0x8daa34c8e8e1cf098158f4ef2699598e,
'vnt': 0x2d9c29fd0d814dd1f82a042e69b04a53,
'wnt': 0xe6286bd0e355236fc0699eaa11b124af,
'xnt': 0xd2e062fcc9ff3fc11e455e424925b131,
'ynt': 0xc18cde2de82f7b1d32726bda78b88870,
'znt': 0x9cb5a1265b9853bdb5d7947b7d10807c,
'aot': 0xe717cfe9d1ca2a2245d838567640a326,
'bot': 0xfabcaa97871555b68aa095335975e613,
'cot': 0x97223fab7b0d4c64c07e6e004c602302,
'dot': 0x69eb76c88557a8211cbfc9beda5fc062,
'eot': 0x8cc16517b421262382c5cf28e0ed9a4b,
'fot': 0x365e3451c49bd083b7b9ebb3115b8ac8,
'got': 0x4a1a27296188c273f6733ead4fe4eff6,
'hot': 0x27369b3bf4483e8dcfd85ba9a39a947f,
'iot': 0x97c209ca505f2958db30a42135afab5c,
'jot': 0x20ea15fb4a4715222f7cf635247ef0e8,
'kot': 0xa986d9ee785f7b5fdd68bb5b86ee70e0,
'lot': 0x209f2308333b9973de9b4ea31d526b89,
'mot': 0xde81459305398c88048a05a620fb4717,
'not': 0xd529e941509eb9e9b9cfaeae1fe7ca23,
'oot': 0xbb34bdb533b492a62429dd0541d70c6f,
'pot': 0xecbfde1394f2dfe02d50081d8a35dba6,
'qot': 0x0a806bfc38d9f9e991896205f962b744,
'rot': 0x0d3d238b089a67e34e39b5abf80db19b,
'sot': 0xcf3c295cd4c898b870f681dd410af750,
'tot': 0x0b080119cbf1138edfa9132471e1a661,
'uot': 0x3bd88f208f6b538fae0f054a4636214c,
'vot': 0xe2bc7371e35ef379536332596becfd3e,
'wot': 0xb8032aea7cb0afae02c9ea8300f32399,
'xot': 0x3c6a5c896324fcc535baae6aa75a133b,
'yot': 0xd74582a6c3b2b8ae27a8b4cc0e76950d,
'zot': 0xc0a860f6159b8ea3fb49dcf531cf64bd,
'apt': 0x583f72a833c7dfd63c03edba3776247a,
'bpt': 0x980c07ea3b8d4fcbd75fdf93a87132f5,
'cpt': 0xdb03db5fcac3e42e9a44b54048ed563b,
'dpt': 0xae308b836b6dac98d08a719c7c53f8e3,
'ept': 0x9faeaf641865e634df7b65336a5c02e7,
'fpt': 0xe42d2ffb9097703605c32e83ea1ac20a,
'gpt': 0xaeebd1980c6662678028fc899ba441eb,
'hpt': 0xa1cf62324ea980ab05fee7b1d3147d7c,
'ipt': 0xd7a9da82f067c68ab93a0531e6957616,
'jpt': 0x1d41b874a5b9ffbd234227561fe33919,
'kpt': 0x7fc07040c19450ddcc07d80928134f11,
'lpt': 0xd490fce86517a384816375dacdbe0a2f,
'mpt': 0xac35308fff7abada50397bb69d5658e2,
'npt': 0x8c6a12a9390ec7590a6726a5a29fa4c8,
'opt': 0x4d29ea5f748c4cfda37434ce76a5e788,
'ppt': 0x8a267223a8635f5e7348cd0d01c3bb3c,
'qpt': 0x97db9bcd41d5fdd1ff6b068c8e8a453e,
'rpt': 0xdcaaf5237b6c18f979b4383fb42d3be3,
'spt': 0x86986dbb64d3f73c82718f1c91280b30,
'tpt': 0x13a5254387e4ab06d03bdbd2391a7efe,
'upt': 0x6a76df9dddac232e6a086ba74d53cfcb,
'vpt': 0x048aaa98e514b18982634b1bf6163fba,
'wpt': 0x58ca9dfa6ef708e90c9e664f9f41497e,
'xpt': 0x49f85c27ad6c7bbe7e38afc7bdb23377,
'ypt': 0x36de3f1dbf9a6e92ee65c170afae41ab,
'zpt': 0x198d0e215d3ff3ad68077b6c891c4189,
'aqt': 0xfe0acb89b27e96b18b0f2bb046f4b25f,
'bqt': 0x558f9faa033e9768f07a9c94a1918fce,
'cqt': 0xb9dd929f68b505e6be1d2eb87f47feb0,
'dqt': 0x1b0fca90fa43073d1a4b89a8f6e3875b,
'eqt': 0x5d801282d504a17b782b8c567e9ce971,
'fqt': 0x706087e1476351ed0e301e26d7e37d00,
'gqt': 0x78a05d45df1613609332c9a6f54f4417,
'hqt': 0x519534ec6a70b6594e856a182a572092,
'iqt': 0x895c34aab92ab39d89a69f08c927dca7,
'jqt': 0x8bebad8a7e4ead60f7d87d2757d7bda8,
'kqt': 0x65226eb3e025d4a7ce491be8ed0b645c,
'lqt': 0x1353482772422c42a1e7ac57f5839bed,
'mqt': 0x469bb8391630403e5c8e99ab64c50085,
'nqt': 0x7dc3e464107ad4a108e98ac6add68513,
'oqt': 0x88ee9394b000865d04c9d9f61f9111db,
'pqt': 0x6377268767f065492a3d9fa70639178e,
'qqt': 0x10dadbc3e6c50149303bfd820049e7fa,
'rqt': 0x9b46ba9b4adb1ef41f717dbcb7403807,
'sqt': 0x3e01524af96412c5e94275a0ec763580,
'tqt': 0x4050c2e3e3a08b9495ab36379b943c26,
'uqt': 0x051e1b3349d1c285741f0c9f38e35ad6,
'vqt': 0x52cc5d2cd98148cc95792cf740c62abb,
'wqt': 0xd32ea444a094889b10e2fbea4f5882a6,
'xqt': 0x009492d6f3919001b9014ca200393f21,
'yqt': 0x97f291d2ddae50773aa9e3bb10c3a1e4,
'zqt': 0xe3888f9ac8d9f1a127b4de84c85bfe4c,
'art': 0x2c5f64ab07ccb3e410aa97fc09687cc3,
'brt': 0x6f58f1c6e1c23b9883b863c676e249b7,
'crt': 0xb9265da4fe5f418a67f87e005cfed644,
'drt': 0x577a59598ca3b7faa0447dffeaff70a2,
'ert': 0xe3e84538a1b02b1cc11bf71fe3169958,
'frt': 0x16146527ae8976a41be672b2d8f1f6ea,
'grt': 0x537daa262cc758ee7ede412f3d73b2d8,
'hrt': 0xfbb3bad6a2ff77484af45d9da2ea501f,
'irt': 0x5b330d0812d96bb765e6b2eaecc58ff2,
'jrt': 0x6e2b209098094a927bbdae5543bdd07b,
'krt': 0x0ad1854d0bda8933d65166c4825eceba,
'lrt': 0x9e3efe0add3f706053b91cb81a4f48c7,
'mrt': 0x823d7bddd42716365c1a7713c4e20d8f,
'nrt': 0xa38ddc8d29a8252ac3bedc8f55ac059a,
'ort': 0x62527ce88d6fb17a11416229434fc887,
'prt': 0x06202b87153d246b5acbf8ed52301f6e,
'qrt': 0xd3f9d1b53e1dfa6b91ef40466ee99335,
'rrt': 0x90b96938746e2807481a9d64bc80cad1,
'srt': 0xdaefffb93e6c3be7136ba40edae4f2f1,
'trt': 0x30403986d25b7469318fff15ba08d6f6,
'urt': 0x938b601cea7ffc3d5279315fe13cb37e,
'vrt': 0x3180c4a3923265d8db6f9f1f4393c43d,
'wrt': 0xe335843aefef60d53a220841789a8887,
'xrt': 0x86c4fa0e42772735cdf97c398d352f2f,
'yrt': 0xe8e898e70e954707adbbebcf2c9973a5,
'zrt': 0x856bdd469fa3c802c0d00c3248844bd3,
'ast': 0x7c3a6c0f7beaa0d19955f0ed6fa2df6b,
'bst': 0x37482d08ba742837abbab57e40d7d5b9,
'cst': 0x2b9ed9314c9b75f085fcf7356337d30b,
'dst': 0x28e3d688a3c077b887921cea3fb1dbc7,
'est': 0x1c52bdae8bad70e82da799843bb4e831,
'fst': 0x3bd77778e6ff1c054e94a0cab12706c2,
'gst': 0x5f00708ad522f5847f584dfb8b50d611,
'hst': 0x36928fac51bfb096466390c7e989c3ea,
'ist': 0xe5a93371cfc7eab4a88221dd1f6c1a3c,
'jst': 0x61c0249db7a5e88feb04302c6c5d443c,
'kst': 0x2b75d61c4d4e844423945c34846aae35,
'lst': 0x6448ada3307e145ae5b2cc7bb2412662,
'mst': 0xa88ad3a75f88bbe93b64a7c55ade7a5d,
'nst': 0xb26c698cb35f2b5a9a6caceac7a83fdd,
'ost': 0xc00687fa6a20757e9048706f1465221f,
'pst': 0x67a063aecbd1fc4701ba184b92f695b0,
'qst': 0x8d8171ff1d9a8553e85d7da9b32a2aa1,
'rst': 0xd674dfcd8b4db6762bcb3667316d3bb9,
'sst': 0x51251df01484cc547f4d59bd93cd7512,
'tst': 0x9301c2a72c0f099d0313099f1cd54799,
'ust': 0x359045a3b7d0d1b035e19dcd3af5f948,
'vst': 0x7995a8ddd4cdbd8f120056cf1d167141,
'wst': 0xf1a9606104d2d32ec809d3fafde84694,
'xst': 0x1d5ea6c186020334915df7f8c2da5c11,
'yst': 0xb7ffaac16d2ac9e31f8a0cdeed6c948a,
'zst': 0xad5d4999682bc0b7d3684ec61905805f,
'att': 0x65db43f237832cbf0f299bd8f2bcf2ac,
'btt': 0xaa443757d111757165de7c1f991eb244,
'ctt': 0x6587cf8fa59d3c1140d6198955211d96,
'dtt': 0x167313fa37efe8d7e1f1916936fc89a3,
'ett': 0xafd809d729d54297c84efe986c56c22e,
'ftt': 0x8a15fb6fc7418fdfe922cf98ae57f171,
'gtt': 0x219c5d2c3124872bf731b3520cc3757f,
'htt': 0xd7bb3ab3115058ecbc7b0ce22221fd1f,
'itt': 0x94fff2216f5a387cfbeff2f1a7331cd4,
'jtt': 0xfaf0c32105716ba1ecc49bb834e8de34,
'ktt': 0x21037326de57ce777879634492148af2,
'ltt': 0xc1318c81c572bd3a19c41e93aa06eea9,
'mtt': 0x40e9bd78f2be6d5a4cfd392e10194b82,
'ntt': 0xccb3669c87b2d028539237c4554e3c0f,
'ott': 0x92f1711381180e9aaf1075b548544929,
'ptt': 0xdfe1ca0b53261f0ce13f17f354bf3698,
'qtt': 0x2e2b8374327f20f8543ad258ffe0c067,
'rtt': 0x2907765013f63b7f1f0316ab22edfc86,
'stt': 0x7588b80b93864348b103ab67c6e4b2b7,
'ttt': 0x9990775155c3518a0d7917f7780b24aa,
'utt': 0xdb68ed5b8a3f19eba78872457d2aa8f2,
'vtt': 0x7cf3862758afdea62ef699006c609097,
'wtt': 0xfe0768d6d455816d9185dd94f564309e,
'xtt': 0x1104c56e1cbc65c3a49b101623ab8eb0,
'ytt': 0x1d16ef871483ee99f3b62105a986b3c5,
'ztt': 0x28c6222ad463269808bd2810d34ad846,
'aut': 0x131d5a3daa39b73050f40a62372ee44e,
'but': 0x37598dad8f8805ce708ba8c4f67ce367,
'cut': 0xfe47aa7c733c490d36e80508d5dc4019,
'dut': 0xe747ce5172826f10f5880082e2b72729,
'eut': 0x808e822405561c05252ba37d71455548,
'fut': 0xf13f5ebf1198cffee32ff1ea9769e382,
'gut': 0xbd4ca3a838ce3972af46b6e2d85985f2,
'hut': 0xa11c65233b2ae3700c9133dbf9623429,
'iut': 0xb890b207381df06b12c82200068952e6,
'jut': 0x6c3a069907ca9724cf5c71a92f3e2195,
'kut': 0xc758810f4721f3c31dc44a881830aaca,
'lut': 0xb56efdaa67baedcc830a453ecd15b37f,
'mut': 0xd7304e43ecdabb4efa0636d51408ca80,
'nut': 0x18fc9c6baeabe5f323c9dab371901e31,
'out': 0xc68271a63ddbc431c307beb7d2918275,
'put': 0x8e13ffc9fd9d6a6761231a764bdf106b,
'qut': 0xddc07cc4218e37757e4bdadb6fcf399a,
'rut': 0x915a6a53e87cde98efa46d557a4625b9,
'sut': 0xb54d6ede5a9e895fa2ccc3b29e9de18d,
'tut': 0x77d8c068b77e5abd62ba3f873706079c,
'uut': 0x056fac2a7acd5d87f6214c1f156a4a93,
'vut': 0x7016b4494826ffe4d8603f5c2ff03ef0,
'wut': 0xc268120ce3918b1264fe2c05143b5c4b,
'xut': 0xc6e4d3795a24d391258b76addb182d23,
'yut': 0xed7d52625b305006a0c012e2e92bebe4,
'zut': 0xa8cd54ec3d9cdf563b4c3558ee28e4ee,
'avt': 0x235ba497c4dd101bcefce1a4f29a9e24,
'bvt': 0xe7f2d9f8decb1e6da93206e54e505a62,
'cvt': 0x0a8844b8c0c2bcad58a6b516656c8db0,
'dvt': 0xc4137a0a1cf3d44391a742ec11b318c6,
'evt': 0xf043cbe7b5ca8034d57c5286f4d1fee3,
'fvt': 0x6312a551a4d99fcfc21f3314d93d87ba,
'gvt': 0x8fecbc7204c268dbf9e32d70da22b742,
'hvt': 0xb3a1ac98cfce29720ecbdd60067193f4,
'ivt': 0xc29a094ab5fae28478d85dfcb29b9aed,
'jvt': 0x9ad3c79d68cb75e89efe2a0829b7b8a3,
'kvt': 0x5ad477c05c80e0a429228fd08ee2a909,
'lvt': 0x3b2b5c9b70189c85d37773be6addf4a5,
'mvt': 0x2fe69d1442a908dfd62735edf206911f,
'nvt': 0xf3a47d6a686e542580d2e3310a14288d,
'ovt': 0x604173fdc3ea2b51586c4f085bc99609,
'pvt': 0x4b35c3ea636a9eb0116c0cd108410053,
'qvt': 0xcd8bf4cafabf3aa316f347f7147bb580,
'rvt': 0x7f2f209f1d9b9eab7ffea4497a35bbbf,
'svt': 0x944147994f55d7e035d2317682c82391,
'tvt': 0x86ba81dea51e6f11b2e937d93144b346,
'uvt': 0x4437a20903623b8c0f528f48ed8ade3b,
'vvt': 0xcec577c173e4ecefecd5ac64ef0f03fa,
'wvt': 0x1a9d62f5a35429e26b92249a8863d097,
'xvt': 0xbd47c1170006aeb7560af87254605913,
'yvt': 0x9844c4891bde8a699b9d8257c5f07884,
'zvt': 0x9dac4201987ed78b1df5f425c05f3fa9,
'awt': 0xc8e1fe27d18a1f14bdfec450801cdfe8,
'bwt': 0x27476e35f67b63fefefb2bd5b0fa7e9c,
'cwt': 0x28f023e5ac93b1f59680012d195ffdac,
'dwt': 0xeb8fae66ad4fb1dab255c5a9dda908a2,
'ewt': 0xa415db1a3553dbcf4f8dd97389c447ea,
'fwt': 0x72a81d133342c19203ac38c2809a4aec,
'gwt': 0xd362c55c18ddf8da607de9d90bf32790,
'hwt': 0x63ce26ab8eeaab411ede72a91080f9b4,
'iwt': 0x47279dfb75164f8bd351c22fcf31ae54,
'jwt': 0xcfd61b8a7397fa7c10b2ae548f5bfaef,
'kwt': 0x1a11ced2ad024b1cd071ec8f4372dcc7,
'lwt': 0x0390972ad0cab49f44b99192e4def9f6,
'mwt': 0xb546592949dc3ad0af7318080a8f00f3,
'nwt': 0x0493074db6b9484f0453704e59e0793f,
'owt': 0xae38d30c0851e92a01c796426ba80315,
'pwt': 0x78c1c0a2d2f4e7c8de39feba83ff725f,
'qwt': 0x75c5b02864bb5004865f81c6b8e8d68f,
'rwt': 0xfa0740e88b51054f23051b151772a6cf,
'swt': 0x7a4a8ab9389c3abb3bbdc0a2359721c1,
'twt': 0x2e9087b02957f5c29d14a8fd492cbeb3,
'uwt': 0x9b5afdf3d47395c98015c8cae7241201,
'vwt': 0xa7946150114b20fc649ac0722b167b2b,
'wwt': 0xf6d90d9e7ecf357044dfc439ef0602b1,
'xwt': 0xe7cea04cf3a6e36310fb7264dff4f370,
'ywt': 0x86a0b85e4c1924b486cdbee60a1e416c,
'zwt': 0x605b2b8900a9eab7fbeceb20bd05bdcd,
'axt': 0x3474fa07b4b9328f9f5efaa11fa4b0bc,
'bxt': 0x50a71f6bdc092293a724944f19c65968,
'cxt': 0x1b4e19518f4680508e9cbe52f68142b9,
'dxt': 0xe294d5649f17f342c19a45322e363fd5,
'ext': 0xabf77184f55403d75b9d51d79162a7ca,
'fxt': 0xd1db0fb69ff0442f245f781a64644218,
'gxt': 0xe5a6f507538f093e8b48d258e54beae9,
'hxt': 0x8f730c864734fbb7d0dc821bd7b29610,
'ixt': 0xf31f340c5f33d433a63dfaf5d2b8d398,
'jxt': 0xa6fd6b44ad6c7ee54b01928943090e54,
'kxt': 0xd556b2870989378ad67245f5269cae84,
'lxt': 0x4a0495c5a28bec50db82b2cfb5552b43,
'mxt': 0x8d73ce3712b114a6ca411303e19a231f,
'nxt': 0x07635d95f92116adcf0e12fd96ffba82,
'oxt': 0xa1f7932887b1cd826defc7dabb669905,
'pxt': 0xdca1e04f33b05ad0314d7969fe66eaa0,
'qxt': 0x13cda85e8aa1f0bb6ca266b48f220da5,
'rxt': 0x258f3b37aa3c03c4cca3416b26a5267e,
'sxt': 0x7b94964c00d41926a1d1b5de3f0408bd,
'txt': 0xc7824f3d4d5f7b2f22d034758c1e9454,
'uxt': 0xf036382503c7542480841919d7f5d47d,
'vxt': 0x8d85452d88f97df04490cbbe4e765a8f,
'wxt': 0x31aa1b74de685d3e992b8b20b29277a6,
'xxt': 0x99b9199698e156b5718508f7b03f2681,
'yxt': 0x854d4094c8b5149568483c91cce753f5,
'zxt': 0xb8cd14afce21675f4432b9d2b3b8595d,
'ayt': 0x04303944fba5c762a719062b8b484550,
'byt': 0x4e41dbce1f409634d6f1fe845c477499,
'cyt': 0x9c9c9cc90ea8d068d24aa45b11423c51,
'dyt': 0xa363e54c35340a7e8715c7957313c6b8,
'eyt': 0xebf4c5c40d73f65fb5bf27e02d19f453,
'fyt': 0x86946648fb3158db2a7392b2037afd35,
'gyt': 0xcd365b5625f7fb5430d84065066aa58b,
'hyt': 0xa2fa10dcd29e45b9c9a8f5168c7cb1d9,
'iyt': 0x6c4b0d9d96b8cba9d5cc8ea2d8f85b84,
'jyt': 0x62978e90527e5bf1a2910ba747155b6a,
'kyt': 0xb662c2f6a114b00579e5d45df52726e2,
'lyt': 0xccdff8365b15485ac1947044666dd88a,
'myt': 0xd05f285ddc99f57480177b78a759aa6c,
'nyt': 0xbed05837f6bdb672d2b857b338a044e9,
'oyt': 0x4d79845122dd122e6d4506363f32f1d6,
'pyt': 0x353f6bd012e3598dfa12c7334b2a5e13,
'qyt': 0xa1314cac5bc90c57571aa88a49dcdeb2,
'ryt': 0xd11324339f91d770765044e18068f359,
'syt': 0xbe55869059f0856dc25eb767ee93439b,
'tyt': 0x70eaa4c4c54f602749276c75f50e2278,
'uyt': 0x355069779c3420831bd25514ad739ed6,
'vyt': 0x05e44b38eb3d73a6dfd7ad89d6149ab4,
'wyt': 0xaea45cda8380434d220ddf2b66078406,
'xyt': 0x669559915cee30287ab7fabb3d246fb0,
'yyt': 0x1b7adba5ef3a2bddeee1a5aee0f97a60,
'zyt': 0x2813faa8715ed7668c9d01eaa1668049,
'azt': 0x205a47b2f41db365f7fac871f78be6e5,
'bzt': 0xc0c0ebba166026998339052386f0a795,
'czt': 0x1b5891787bc89e661595913c2fa0d9dc,
'dzt': 0xeb95ebd1acae7f07f27e10163084b0d2,
'ezt': 0xaae72891b8ff504a9793e7d9b7b66737,
'fzt': 0x58342e2bf0ec17c35c01babe9eac212b,
'gzt': 0x0db08a2382dc53a2a6f989433c0518e8,
'hzt': 0x2fdfa95196f026b8965378bf9aa51dba,
'izt': 0x5e9b4a1632a317044c5070c32c160f89,
'jzt': 0xac6685693cc9294d29bd2674d0f45821,
'kzt': 0xd3aabbc8a7b68b359869d4c9df4d86e4,
'lzt': 0xf5b69da4b47c990caaadd0f2b0ef98d9,
'mzt': 0x82e82b4703aaafff131883279a11b265,
'nzt': 0x0b1fd71a2be96dd50a6f8daf707c3ac3,
'ozt': 0x043a06a4a9288284d5107a0aaae91839,
'pzt': 0x4cd738ebc0a1305998b9619ad36a68d8,
'qzt': 0x71317e3313a1e148fbb2f5102e5141b3,
'rzt': 0x2ef9f2623374e26446c02252619446e2,
'szt': 0xe60b0844ad2fa52ef02cbac51c445cf8,
'tzt': 0x7640a5382feb6a06faa330ad910e1222,
'uzt': 0x60c74c0268aa3fa250fbd061989d3542,
'vzt': 0xa698fc79b32ed3fd5c379d208af86238,
'wzt': 0x89a2d0ecba8b5fc5c7ee9367c42a8213,
'xzt': 0x50aacbbdb8f499b608ba912c5cac4b82,
'yzt': 0x784d1ad6abd892b07c80a605556ddd10,
'zzt': 0xc3386b46b8ea1c913218cf5c495c5b98,
'aau': 0xea2e5c30cfa4e8508e1439fc0159c986,
'bau': 0x5debc828f54260d0243115148649ca80,
'cau': 0x486ec17aef822a40aeb41f74a8b56ee4,
'dau': 0x3bf2893d7de64871d7d1f2e4d7017c2f,
'eau': 0xd7ebc8bdcc77816f7c12054d257ba82c,
'fau': 0xa84a996fbddd53f5fac65b17cc4eb77f,
'gau': 0x9284e30bfa7d58f2e157ef28d9c82253,
'hau': 0xa23ed18c6f9425dc306fc002e5c2046e,
'iau': 0xa2517ede2b3a3f531d2e02f7e708e8f1,
'jau': 0xb3f3d45ff24d51bc0688c70d430a4607,
'kau': 0xc4efdd2e1da74076e57f58f8d4b35667,
'lau': 0x6869ba1b3d33a374546fd83498c1548a,
'mau': 0xe3d0cd173a3342f1d2119cc5bce5d1d4,
'nau': 0x4d118fd4ce53d84b80a065ed1f020727,
'oau': 0xc681b3412b866621914f65ae9485caab,
'pau': 0x0ea58701b84295bdd11c5b05426c6c3f,
'qau': 0x754fd75ff1c08008c2dcadfc684ed044,
'rau': 0x5787de089122c2f70dccedcf50daeab9,
'sau': 0x2405c79d70f52098b0647f79e96616d8,
'tau': 0x4580c2740ab6d9222ef06d7c6865583e,
'uau': 0xeedf20a7d6fbf79f2f2f64f525f38bc1,
'vau': 0x900b7f7eea4859d9675db6c3dccbda19,
'wau': 0x565048e16b8c72dbbe81902e20f223d3,
'xau': 0x0411c78ace2b67bc443f8e4a16d4811e,
'yau': 0xe25d84524e150fbf30bb661de9768e82,
'zau': 0xde017c0b02a5afbe89f6757afe805934,
'abu': 0x34d71b97c471931334f385b4e7b7379c,
'bbu': 0x6d11291869bf393bf0f9f0326d55cb80,
'cbu': 0xd138070508b63f519aa554e5668dca97,
'dbu': 0xe68767ea79ef0c42d8c4653b39374eda,
'ebu': 0xb2c058befff605b2f7581ffad2d9a1f8,
'fbu': 0x295b4fe087323e5cc5e310e584dd0365,
'gbu': 0x83d706e26da9516517e06dc9a7316aa0,
'hbu': 0xec34e3ddb39fa86abf919bd677795429,
'ibu': 0xbd4c70d40f7fa5421cfeefbd66b7fff6,
'jbu': 0xa41c8884c6cad57f479f5d9f94c09e0f,
'kbu': 0xce03478d3fbadb890da997fdf58c5f7c,
'lbu': 0xae539005626c61629db198e4d324a722,
'mbu': 0x19135ac4d2cba76f6d1d0a2fd9abea9d,
'nbu': 0x1ea8d536bc5de1083de2db5041c61592,
'obu': 0x5d451c9b257133853756e4da029375ec,
'pbu': 0xd39164cb4be9a27d3a40ca9d9d46b2fd,
'qbu': 0x36946a75cc63bc77c5f15c564efe5828,
'rbu': 0x87ba7c90bbe16762e524a1b2f51f52ef,
'sbu': 0x7598f602f628456b034590c15cc7a3b4,
'tbu': 0xc43221b12ac4410fcc2c0b43630e03a8,
'ubu': 0x562bdb388c042b36a24d8c6572267da2,
'vbu': 0x8e8834199f10b50ec16675e7aebc6100,
'wbu': 0x9bd8fa9d39917912100ca2a69fb51a67,
'xbu': 0xbe4f6707552bdf234ac9dcee2cfc5b2b,
'ybu': 0xf002ebcc0256c90ab5c0f896285b61bf,
'zbu': 0xa73d4ade8d8c957ef45426e6ca0fe3ee,
'acu': 0x3457f04bb829d77657b1d92117774b27,
'bcu': 0xa67ca00169727490f6c225033469d1b9,
'ccu': 0x1e0178ad6c8b559bb7f054e98aaf97c0,
'dcu': 0x9f8cc62c3640bf6eb115b4c78bb22a3f,
'ecu': 0x203567c84e78a74b2544d99b37ed1af4,
'fcu': 0x5416014b83907c4544d85aa839c8fde6,
'gcu': 0xcfc9f3ba74e43d432443d374fb176175,
'hcu': 0x8970fdd332b5e345afa285f1723799d3,
'icu': 0xf8eaf860ef6bf14b6a52fdf35a5190fd,
'jcu': 0xd33d1a8f31a57273e179fc6387d6e6b8,
'kcu': 0x3e3719992817c8440403061120d2347f,
'lcu': 0x0f0e0e0751f5f4ba36f708de33b68930,
'mcu': 0x9727479252e200330c1e9cc9a3fea96e,
'ncu': 0x268fab97a775719b80cf160524b4e9ba,
'ocu': 0x61a53ef301fe5c76d8b3a4152e182b7d,
'pcu': 0x127ddf1f6cfc65b4e98463b8f76cf654,
'qcu': 0xffc5d930cd3e2e0f79456bda2c4c596f,
'rcu': 0xa492c3e144c08d87b42581da14f7d867,
'scu': 0x84923d2a7452a78149e0778628d88324,
'tcu': 0x5a759b1da6828086e69e8b88846923ea,
'ucu': 0x3bf642c0dcc3df2d2b99d97b1b712891,
'vcu': 0x32ac29b482232594e518f20318a7fc2e,
'wcu': 0x6b48764c6e3fedfadff3c738197bdcd6,
'xcu': 0xe02b2cbaf115b9a82db66abef5bfacf8,
'ycu': 0x4eadee40e87e2222c490c04e1c85b443,
'zcu': 0xd8dd8014b4c73718daa6057f45c67b5a,
'adu': 0x814b9c1a48f94893e1f2ca9f7b83c17f,
'bdu': 0x2c1ac245d9b01fc9c158d6d61d2e20f9,
'cdu': 0x0495faf87cf0ebc18906c9ec050118a7,
'ddu': 0xbae9a57b2ace3897255dcfeaa60a54ab,
'edu': 0x379ef4bd50c30e261ccfb18dfc626d9f,
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'jou': 0xa2d536596a91ef09a9116bd91f664b08,
'kou': 0x71566d43bd45fd932fe256fee2f2ea78,
'lou': 0x298de5aba134234d98b8e17f2d16b2df,
'mou': 0x361228d0a65bd2355b029b2fe0aad7c6,
'nou': 0x8b75615d8a4116d8f385e51ed8c55329,
'oou': 0xd5ad000b6930cfdfbc2512f79379a0d3,
'pou': 0xeb232b363238f52fa21f8bf7d57aab43,
'qou': 0x91d6f3753cef8e9de8afe378e4c2d956,
'rou': 0xf25823a28798e1806aa60585cce3dabe,
'sou': 0x3bcce67a20e937f5a85a85e01fc37652,
'tou': 0xe382290672264cfe4daa0df317ca10c0,
'uou': 0x55562f7a7d9a57390d78f207201c60c3,
'vou': 0x5db06414e265179b281c1f3f857e71ad,
'wou': 0x87642b222bb50054c23c23f155815b2f,
'xou': 0xbe33701b98a196fd09f700ed5d5cdb3f,
'you': 0x639bae9ac6b3e1a84cebb7b403297b79,
'zou': 0xb1bd49922e29a4955f40570a461722b4,
'apu': 0xb34daa835853f71d4d1bb8ba3c8dff8d,
'bpu': 0xc79e2d1c16c3af744685016d2ba721e3,
'cpu': 0xd9747e2da342bdb995f6389533ad1a3d,
'dpu': 0x225734fb10a045efb6e591ef55b939a7,
'epu': 0x2ee8a8c71ac28f239374fa441cf51875,
'fpu': 0x0075ab5617d7f0d01c017e92e8a46e22,
'gpu': 0x0aa0be2a866411d9ff03515227454947,
'hpu': 0x046de168c8136cef9ddfa580642efa15,
'ipu': 0xf9f5db212d8c814b045e79ef4cc02bf0,
'jpu': 0xfa431de24e4af63e92b560fbb4c99d34,
'kpu': 0x46a7033a2bc3074cbbb7bb76a2e2731f,
'lpu': 0xcd721f657be19f9caf05e0c6617ecdf2,
'mpu': 0x8a97a2266a2751fb4967e9ccc4f50157,
'npu': 0xeff0dd6626ffa2e3059e0becac0f879e,
'opu': 0x5985c96ec14b608ad0861b44d6fa93af,
'ppu': 0x4d36cf6ca8eee2b718d195690566a863,
'qpu': 0x44e6de41c702b1359a75919e98580d48,
'rpu': 0x90c80c40a182a47693b232b126507806,
'spu': 0xe77ac59d083c3122535d02ac0d6ae14f,
'tpu': 0x0dea849e9fe91b89b411440f4cc0ae0f,
'upu': 0x6b4f60efd4cedbb94f20043f936a3d97,
'vpu': 0xbc6e911fb2e345a0ffeba29f0f9c3290,
'wpu': 0xc664656535f7c4ff651010c896691074,
'xpu': 0x7d9bf46d1a069d1a3d07e4f538cd40bc,
'ypu': 0xac3b036e78c1aa90a7ad0e4789ede0bc,
'zpu': 0xd6700dff227a0ee802bb2dd957655199,
'aqu': 0x236e9069c47bd24e81d9e189bf43c8e9,
'bqu': 0xc4c8a99ab8ec09a139235a83d85f5ac1,
'cqu': 0x3edfcfdee824f52b75b9eb821563d950,
'dqu': 0xfeeff2bbf1d382d997e11f33f9ca25b0,
'equ': 0xb49d75de8b355a6d857fa2b655f35f7c,
'fqu': 0x0d023df03883583e9e2340191466f3e0,
'gqu': 0xd44215c9deef606807c0a9e6bbd3fa48,
'hqu': 0x861622ff1fda12d2024044796cce0226,
'iqu': 0x56d7f9ebb9e49c4e3a6499a87844fbc7,
'jqu': 0xc91ed5feae3521872defba5855e3f4c0,
'kqu': 0x761da09f15000934de953d4d190d1e90,
'lqu': 0x1f49ceedc90b467477a0b7e82770a5b1,
'mqu': 0xa21577f6c46b09adb01e71407d4a83a9,
'nqu': 0x5c9aa12422fde95f8be4875907c39453,
'oqu': 0x5945e3225fc843b76c5bdc40c866eeb1,
'pqu': 0x639fa7522d890b6e4a07bf6c1ea0df9f,
'qqu': 0xff573bb701dd71ecf091274e4cc3e415,
'rqu': 0xd22e2980d69a4f0038facdb1ea85638d,
'squ': 0xb8a656a1c7b91be7e82d42d9fa683d70,
'tqu': 0xcd123fb70cf802430c2157da9973514d,
'uqu': 0xe36f960b89dc29a7c711a477a3d6a73b,
'vqu': 0xdc6f631616ed5eea96af8bb46f16f271,
'wqu': 0x4203c17c7e911207c6ce3ae87ad26371,
'xqu': 0x2febbd99fb81bd2d693266268798fb16,
'yqu': 0x458bbf2f51894e95a92e9f737ba45048,
'zqu': 0x4e72d00b51fdfb093e581a7b167523de,
'aru': 0xe95f77c464f303a7a830b98c145a0711,
'bru': 0xf9dd4ecba5e25999ab63a9aa968c939d,
'cru': 0x9694716ae724b72b366b5adeaad3db71,
'dru': 0x28a0f3b13d9956c87668053c5f1f1cd8,
'eru': 0x914f3bd79426b4b2b90e7093ea07dcb3,
'fru': 0x8d276b234bb8ebe920aab27a756f1cd9,
'gru': 0x14c486a4f0894fb7437d405b730d9e5b,
'hru': 0x29fa3d9b0eccb85bde41910d4ead727a,
'iru': 0x52e60fcd94ecfb8cc5749909f2be6646,
'jru': 0x9eb57734eacaaed2b98f740963baccaa,
'kru': 0x31460057760e7b1aa8d384b8b6247193,
'lru': 0xdba3799464b52d8c7ce5446e998d2bd0,
'mru': 0xef516e0accc8fb34f78e88fda58cb054,
'nru': 0xf8332734a659f4938bf7aa23b535a45f,
'oru': 0x023f623a72ade7c7e004be7022b8749e,
'pru': 0x3a91997270269de6c5d31b38dfc7b75c,
'qru': 0x2071c534422bddbccc4e0b839e949a59,
'rru': 0x8680e920011cc424098d3a63cc5fe28a,
'sru': 0x8ddfc8b15cef0cc1e8a85243721b3f10,
'tru': 0xf8354de67d79a99e880000c5f3cae57d,
'uru': 0x4345ba9176bb651551250fc2385c7a8d,
'vru': 0xada0af7a5d4c8a805760e0bfdc715ab3,
'wru': 0x45af5d7604476ce01a80e8f9fe16ca2e,
'xru': 0x18e3afe68cb081b98982c9d17735ce5b,
'yru': 0x5630ffdf474c43978d764516eca96d0a,
'zru': 0x379115ba055fcccdb41ca116846b019d,
'asu': 0x102a6ed6587b5b8cb4ebbe972864690b,
'bsu': 0xd3cb6bf698afb8a53eb32ab40efd519a,
'csu': 0x8b56e81c6c8506ef166c3a3b1278287c,
'dsu': 0x4ac4921f4a8befb624715e22261abc73,
'esu': 0x8eb21c0833737d9b4ff70e17a2f4dd2c,
'fsu': 0x190aeb66f1328e93195a70e36f3431c5,
'gsu': 0xf67c73f47358bfedb2203e15649e865b,
'hsu': 0x726ee131c7efc6414a505bac8233806f,
'isu': 0xe0ae12e0d4949fa42439e78469e5f7ce,
'jsu': 0xdb5028f021fcad70f40ec4d3abcab1bc,
'ksu': 0x05bb00e51a0d4af49dc456b9bdf08c7e,
'lsu': 0x5a972c4722b097d69ffc5c7a3cd89e7c,
'msu': 0xa87ddbe9f55d9617142aed3d7b0f7bd6,
'nsu': 0xb4c90ed043af1a95460a1483dca85bd0,
'osu': 0xa0485de5d3213f4bc5c38e2d302bd169,
'psu': 0xdb98332ac69a5ddc4a496619f6c97b80,
'qsu': 0x7ac13c8bb1aac5f64fd0018383cd08f7,
'rsu': 0xdfdb73449fe2b136ac55f7341b197376,
'ssu': 0x3c14a20c7ca6776a1eec3dcf98c049e6,
'tsu': 0x52ebf4f9694107b25c3b56dc723c2df5,
'usu': 0xcc25dddbb8e44fbd804322fd50d2620f,
'vsu': 0xa6bb98395e1729e86a10c0b673e3f69a,
'wsu': 0x9d93bda7e012181b978d26faf4f04748,
'xsu': 0xe8a0bded2c467f4be8a408cab7153952,
'ysu': 0x2a6d47acaa6a67048660166c5b746279,
'zsu': 0xcc59c34e9e7ab58f05790e3d9c511a93,
'atu': 0x90b50d63a26b9688ed8c62e5882fa758,
'btu': 0xf079d3b2708028aa7f7c72ca4c3a5af5,
'ctu': 0xef70f70e8c5b05d33ae08c5c9d43dd02,
'dtu': 0x2ce66bc8fd710d2edbd9834236dd7083,
'etu': 0x30cb132e323a5b4ea923ae211aeabf33,
'ftu': 0x37a3947a51de04e73a436f849fc1edf8,
'gtu': 0x4c300fd33c9f214dfbeb0f4a3ba0b283,
'htu': 0xdc56164df23fdc45c764691a498672f5,
'itu': 0x6b79a69eb7d09788acb81710c0ac5ac7,
'jtu': 0x0c227e3c693347b1d4fc087be073284f,
'ktu': 0x2e18bc3df6490504a467d30c1541bdfb,
'ltu': 0xf979373634ee4fb95937b68742ff12b1,
'mtu': 0x661a504c6c4cd163fe4d67836577b926,
'ntu': 0x76f5e5a7d1d59eb488c949a16930d7c6,
'otu': 0xdd8e0f7c154754ca75130449c09850f2,
'ptu': 0x0ec243ed443b13edb53c78afc70dd1e7,
'qtu': 0xbc98737e251592628e37da4d2254e29b,
'rtu': 0x32130ddfee945c98e6869159b36a7b7c,
'stu': 0xbdd5af62d46f0222f61908a1cff92f16,
'ttu': 0x3620ecfcb3e8c2f963ee567d1d7555ce,
'utu': 0x037b2d56dd15afa9e43a0de04eaf2c85,
'vtu': 0xba2602bf7fd7b50d98443d622b97c97f,
'wtu': 0xb5eb898bf6c707fdb3166673c896f495,
'xtu': 0x6dd133e58f6647d7d576f082d716cc00,
'ytu': 0xa00fce33b6a9d13778a9f360cbb0fe10,
'ztu': 0x635d4ac55e937592abfefa391c4c167d,
'auu': 0xae92a43f23986a91079e731927ac03db,
'buu': 0x452ae46e84f26d6bc20c073458ecee1b,
'cuu': 0x0f7e3a6ef9cfb99caacc869abed4c6a4,
'duu': 0x946f1890dda2e1e3e807e0d12aaa1da1,
'euu': 0x0ef8dea03ac3f873c6efb29916b0e84d,
'fuu': 0x321e318b253b6ebd88750f1c50e6882a,
'guu': 0xd34ee26ccaa38b43019bc74cc0647626,
'huu': 0x6c21e540a289955ab18ca498ec5735b1,
'iuu': 0xd3ec665d18d43ff621f4eeed5f474acb,
'juu': 0x78cf7e048de48d11ee1444520e44bbf2,
'kuu': 0x82d2934b974f475760420ddc79d47eb2,
'luu': 0xb082b04c7a891a7f67d2400c2688fa06,
'muu': 0xbc10bac5767cf0be78c89e48bf4e2cb6,
'nuu': 0xfceee3403b3f47e643d0b27d13fa9591,
'ouu': 0x5c39abf98f6bf3ee0a85695ec3bce52d,
'puu': 0x3a2dd34a6b994c032201fcf325e5062b,
'quu': 0x2719e3e66fce440025aa0a9e5964daa6,
'ruu': 0xc974cb282325c51d0db5f07e31e2fe72,
'suu': 0xbe1210d8fc1cd60c79abe0b68a036660,
'tuu': 0xccf711d49fa4b9067e997fc35bf49c56,
'uuu': 0xc70fd4260c9eb90bc0ba9d047c068eb8,
'vuu': 0x4ea5679b5925e8f4cca916a35ada0176,
'wuu': 0x80c68767984f424905f8aef3a1ea0c2f,
'xuu': 0x365cc03bbbb728965946eb1228346c2c,
'yuu': 0xac065740bfb1a2339fecfec667c33de6,
'zuu': 0x28e1105612cbddf7becb1a56863b8e66,
'avu': 0x900ad1698886421654dfc380cc8f6c9a,
'bvu': 0xd14aebc66a3970174112f8315dae54b8,
'cvu': 0x68e8d338953224294f39f03fa1e98b0a,
'dvu': 0x209d7869b9d59dbf589654cc156fd711,
'evu': 0x1729c1b5b6b0d02352f2f77b5206eb79,
'fvu': 0xf181e5b4f9f4d33b565f1d9cba862230,
'gvu': 0x19a515c211ac512dfc2e9847b294acb5,
'hvu': 0x084cff4c4e5f95c47430d564de0dd118,
'ivu': 0xb953a0e38cdc66c6119ed3a7bb466427,
'jvu': 0xb3d558a76e8046fc0bf1ab7656bcbca4,
'kvu': 0x8fdbc593f4a4628ebf88903416fb7421,
'lvu': 0xed2d3dbaf1b386c494131aff3e6c5473,
'mvu': 0xf37068b13b672fa7e8cff0d70a57e789,
'nvu': 0x2b08b877cdeb5f4e52326a7ce2ff5f81,
'ovu': 0x42a20566ca105b6540e434f56d8793d4,
'pvu': 0x8a5d625a4184592eb090319376c7a29f,
'qvu': 0x8639b9efc2ae34395b82277ea53f0572,
'rvu': 0x869f5258b57e58bfdf77df0424ae4c1b,
'svu': 0x0b2d182efae23710a3f4f438e1744306,
'tvu': 0x2a80f3e2c44a617e971be2ab45b82192,
'uvu': 0x2f8f72e7037fef6bca87d58c8c756e19,
'vvu': 0xf3a27e5e622f3f1abb0ee3b25e3ab628,
'wvu': 0x315eeedce8b7747552509d3347a912da,
'xvu': 0xe45b070a18ded6eae2d022e9e18346ab,
'yvu': 0xc50c212d9d781261929fffe4ecd590c0,
'zvu': 0xc08b0a159a0a2a996df9552d60cf73e2,
'awu': 0xaa807c7f7b5864522861e9bc6c0d9eeb,
'bwu': 0xa6a9afcb221828bc54cdb023bcdf52b8,
'cwu': 0xe7e36e56c1533b42a4ceb17910564f75,
'dwu': 0x39c93328c5697b5a5d06a9a8302dae1c,
'ewu': 0x6351676416a157a7ba098aa0662d9496,
'fwu': 0xdb1e8f09f8227789b0693de8728603f4,
'gwu': 0x91b106a801d5172e3bd07f589ddbd5d9,
'hwu': 0x092dfa27f7c8e4c62f589ac0276423e4,
'iwu': 0xd6af8048c16c63c6f4b4dfe14541b19b,
'jwu': 0x2bd74077adb297b428aac96565b16ff5,
'kwu': 0xbd1519f4255813980a080b479a74947a,
'lwu': 0x07ef0e7d74ce96a39dca90f580d95ee1,
'mwu': 0x404858f155badabef5367dc5db710acc,
'nwu': 0xc97c4c229908409a76dbe97092b3c9bc,
'owu': 0x4516bfb26441a912f0a6c3834af2cf37,
'pwu': 0xb350798a23d7544eb353a6f8dca231c2,
'qwu': 0x3fa7f9c2f7f0044186032fd148fc558a,
'rwu': 0x9097ef8244b76ac77abac843fff29fa0,
'swu': 0xf836c3a69b195d3d314906cf6f05639d,
'twu': 0xdd1426f0c78d0c045e2e1c219b0274de,
'uwu': 0x174a3f4fa44c7bb22b3b6429cb4ea44c,
'vwu': 0x76c65f679576409724dbba48965ad9fc,
'wwu': 0x6a8e45824a2567a466e07c70285ce09d,
'xwu': 0x70c1767e65932d08a89bc3fbf346fd37,
'ywu': 0x873885c446cb58bbcc08c3d3b6af3c25,
'zwu': 0x61825a084ba8b28882eac66df0347170,
'axu': 0x2e12a29aba31455901efcaa74089c03e,
'bxu': 0x38315f4db78dc04d1002864801e3454e,
'cxu': 0x32cca9ae78b345a424c1554f81b9fa5b,
'dxu': 0x46701df6a6e81d0e5b3d5b83d4fdc371,
'exu': 0xca4ec8d189b15bda1b58dca05225b6f3,
'fxu': 0xb16fc7ee30a7aa3f5d904b2457e1023a,
'gxu': 0x701db9944dbef0644da035d813c36d95,
'hxu': 0x47bde65a0612d052fbf8eae1a0423e41,
'ixu': 0x15fe5440dd98b6f9d5ee9382e88fe5ae,
'jxu': 0x48aa69b0af95be96ee71cd0fc613bdc6,
'kxu': 0xa1858ed7482c13572b31452f55f647ff,
'lxu': 0xeeda5f276a5ff9297be96d919d0b3caf,
'mxu': 0xd18a20c8e16e5b889eed2cc142a67dba,
'nxu': 0xf391c31f8b964dacb77eaae32fa413d2,
'oxu': 0x0327e7ca8f48509670266dd6c2cd8aed,
'pxu': 0xe1dff15a930d43b131e921907cd54ed4,
'qxu': 0xa8419edda8ba9e2d88fbdb2c69e9bb69,
'rxu': 0xe02f2e9907f3ffe07bb3881dee8397d3,
'sxu': 0xe7c8c6eefc445f0e98be56db52f95457,
'txu': 0x71156d352b737970b7a771a04f6c2680,
'uxu': 0x6a10312fc647f56b298c83a428c1d03a,
'vxu': 0x1fdbab226ffa252e708a52832f5f02e4,
'wxu': 0xbd82c4d557f634b7401bcdbfeba70227,
'xxu': 0x31bcd4c330321bb2f2de93e6f1744d20,
'yxu': 0x4a60eeb31b165d9f2127b126fc0c6ccb,
'zxu': 0x8ec70e2e3ea3b07e87038ad0aeb8ee63,
'ayu': 0x29c65f781a1068a41f735e1b092546de,
'byu': 0x8bb6b8970b6e44a4838ddc1e269a388c,
'cyu': 0x4f5b858ccb24ecb9dd4e92f8f77a2fd0,
'dyu': 0xbccea1611c647307bea5f8259f0fbd2b,
'eyu': 0x8886e2abfa09410b98576e6f8ff1c4ee,
'fyu': 0xe632a7560d374c80ef34e0e8890dfc9e,
'gyu': 0x5aa0c8a0cc071752cb5b9989e6684355,
'hyu': 0xd3d76d7ca6a6fbfb22ad7574f065cacf,
'iyu': 0x488501e7f2f475d7b7befb77265ca0c8,
'jyu': 0x615a4515491fb1f860edcca25bf39a2c,
'kyu': 0x2b45dd79684b41a595b5543904f1574a,
'lyu': 0x8aa04834fe8682bbfc646b7c176a37e5,
'myu': 0xaac81b5c0cf7bf4b5842e3908a709502,
'nyu': 0xc5aa99c68366bdc1dab51099a785998c,
'oyu': 0x04c2b503cc191be41c93b06a9fd088a2,
'pyu': 0x796bb5ec3e7b8becfabe48c1923c5b07,
'qyu': 0x522fd20bb7451a705a380b1c3baa42a1,
'ryu': 0xd4cfab1b518d245bc1fc8db52b6d8ddc,
'syu': 0xc0a8ce9d3994d1dfe091ace6206e9e90,
'tyu': 0xaf27bab84283536c346b97ced4bc5c58,
'uyu': 0x544f77a211b7c8f6343e821067c9317d,
'vyu': 0x1a212dcb4b576d9955f1e6bedf99c250,
'wyu': 0xe1411a9602d4571d98b1f85aeb3a3e75,
'xyu': 0x35f5dba43b471cd2ad28fd68e4825e2b,
'yyu': 0xf1d12a2bf3667a8bf984a183fe65c7d7,
'zyu': 0x20c15b9494dc448585036556a819b5e7,
'azu': 0x692842daf7ed9c5e4413b083f3a1bfc5,
'bzu': 0x79ab86229ae44f3e10d260742814a385,
'czu': 0xb560adc2e2f8bff11e62428576eac8e9,
'dzu': 0x67e997e9b968cdbb986aaf4059940b75,
'ezu': 0xe28801fda7ac1c80b32b4c9d3df3d250,
'fzu': 0x2e3a47ce3385e3104fb1855d87b00584,
'gzu': 0x2ce7800c018248ef0ffb08dfca9a8617,
'hzu': 0xb9edfda0637b7e4046867dadbeaa5336,
'izu': 0x8dfc7ab6ea4f38aa91d69cef5f02ca0e,
'jzu': 0x68a1c96f37d5cdb820b4aa8c862d7729,
'kzu': 0x195bc3751eef941b5acc50304d9d151b,
'lzu': 0x9d09263a6cdc139a2c86b4262939ba40,
'mzu': 0x3646147ff6369ba7cf49a64caf59304b,
'nzu': 0x5c8cdd8bdc302d0fd11a236822e16082,
'ozu': 0xd99065932d8321abf4e0328b955c91d8,
'pzu': 0x3bf8b1beeeb26c995dfe9fefd9ffa073,
'qzu': 0x6f0d4105fe7981219307643cfebb893e,
'rzu': 0x9fac6de9aed068e91fd946d542809aea,
'szu': 0xdb62b7383f3ae98726e9d17f5c2f1650,
'tzu': 0xd95e4410070c4817c026259e6d1d86dc,
'uzu': 0xaba76bf45de3cc848ee68a69d1b5b5d4,
'vzu': 0x5a39c7d82b14449a0e64d7232d073877,
'wzu': 0x7dabb06815458dda514529e500961a16,
'xzu': 0xcd7720b4369ee699e68eee1ae52fbc9c,
'yzu': 0x194892632f5630567da615a6fdb9dc03,
'zzu': 0x176ae987396327f628c05c2cbc0e79eb,
'aav': 0x370af5302c6a2e643cf2d172adcc9e38,
'bav': 0x4e1c42a503fe15b6ced230aee6abf0d8,
'cav': 0xa292bf94dcf09dd8d19021758704fd75,
'dav': 0x676ac6f0f8dc64239691d8052409a54c,
'eav': 0x6c4663aecfaa6122bfb80d2dbf2b7f87,
'fav': 0xebf7e62abaa2db8e332e3e4662658308,
'gav': 0xa1a1258234836336f4e7006b92fa5577,
'hav': 0xe6bbf8106395d2ae3f6304dbc3ebe161,
'iav': 0xd12ac2b6f070ac1a7ad23ac7c814a027,
'jav': 0x2181d277b2d06e83699912fd8b56f061,
'kav': 0x686569379343cd3df1eabd9b967a14b2,
'lav': 0xbe3eb647beebb8602b5f93a7030bf591,
'mav': 0x9efd395356ee9d52f66fd6022510f246,
'nav': 0xd72e5ee9b367833956ed9f88a960c686,
'oav': 0xdf12f19ad0c81375f3efd7a3f58f1f6b,
'pav': 0xb0bef02616cfef7b5d16896a04be26b1,
'qav': 0xb0a9fe30fd0f43c899c4db07b7d23d31,
'rav': 0xc555a8cdf623ff526c8cebe9fdf8cd9d,
'sav': 0xcb20cb3deebe08865c976143b319efc5,
'tav': 0x670615d7175935a9b7ddd3c7c22cabfb,
'uav': 0x30484b94ebd1fac41701ebeb678f4e84,
'vav': 0xc96c080c0a3e230168b59267dd7cd706,
'wav': 0x4bda4933646d3ce6ceedcdb1e5f982d8,
'xav': 0x7b5fbb2361fb6d1465c8df5757e1f53c,
'yav': 0xfd4bbd923acc240034678573938b01c0,
'zav': 0x1066bd423eaa2b148af192dc18c3f279,
'abv': 0xeec582628e4be2232fe88127090e5da2,
'bbv': 0x029c2aa336dcc9c402e693086e5fb0ca,
'cbv': 0x600f745fb86661835acbb7244fbe3e53,
'dbv': 0xd308cbfce7e270e5b41a84d84385645f,
'ebv': 0x1a38c728407ff141c6b51fc174759ed4,
'fbv': 0x664aceb04052c09fcd0bdc07a57b8157,
'gbv': 0x9f80ebe06011b37da7be3f25e155dd6e,
'hbv': 0xbf6d0451d40f1c470e6d603b5c2ad8cb,
'ibv': 0x49f2da76a772defd9c5521ff30f4efb1,
'jbv': 0xa8adbf2aaf0ea014b4a788d0138fc505,
'kbv': 0x93f905480524e6c252184e495dc49254,
'lbv': 0xb90b4946e5b5ff2f14bf711eda8fde91,
'mbv': 0xc6d51a350fa3186a41d8de832eaebacd,
'nbv': 0x0fde9b5d39abc84397749bc7532e2ae3,
'obv': 0x28c80e9e3e3b2a4c675f633b683f43ac,
'pbv': 0xb54891ba951af0fbf7db1ce217a8e802,
'qbv': 0x04e55da750c30f327b0b1f6cf29e623e,
'rbv': 0x89b90bb694186e2889ec6bc893627a51,
'sbv': 0x8ed289bb52b64ad907b14195d61689dc,
'tbv': 0x8c390a7baf620eadffee48cf7ee61aba,
'ubv': 0x917e130384de781771f890c23c5d37b3,
'vbv': 0xbdef841028db3b57a722a356fea4d7b0,
'wbv': 0x060f99a12520865294a23613d4a6ba2e,
'xbv': 0x23104af3b655c3f68ba7d66ac977960b,
'ybv': 0xc5c1a56cf83edaf02f1501e4a9a4c6b4,
'zbv': 0x9932936338bbd8d75e5a7cd78fa2035b,
'acv': 0x5dc343db9ca692e7ebe70b20bed22833,
'bcv': 0xaab26cd1276d7ffdd5066e1b3010a183,
'ccv': 0xe97752fff31c71ec88a0e6ca761fa623,
'dcv': 0x9475f02f2512f5a191583404c6531161,
'ecv': 0x4676d869f88a0b183a866f575201f93d,
'fcv': 0xa0564436e2213b96133f914195f9943e,
'gcv': 0x501df69458b6d7d2ad8cc8e13e84bc56,
'hcv': 0xc7726f4cb163a448790c88eb0483dcb3,
'icv': 0xe6116ca0b5600da29395df42d04a71ec,
'jcv': 0x19fd6903cc75cb303d1d95a49619ada6,
'kcv': 0x1dd83a36e51d9afee8e869ffb218b92a,
'lcv': 0x23701d6633e25bc085aec39f97b48941,
'mcv': 0x1c67ffb497a141188315526c9259b1b0,
'ncv': 0x76c817fde5a6d6678bb3205fe9375444,
'ocv': 0x021549912df8cbbaba93ae8941e09fb7,
'pcv': 0x62198d22d71c6bd732926546095b0459,
'qcv': 0xdd84f8c3b293ff0fbe99bde56bbbb88e,
'rcv': 0x18602198230612d60e163e023c2411f3,
'scv': 0x4ad4f418c4318efd7bd7acf71ea4d054,
'tcv': 0xbfc51774cee00161be2bf890e3252271,
'ucv': 0x98ed947639ae5b855368319a083c2f70,
'vcv': 0x631996e5a547675f6a95e8b8d62479c4,
'wcv': 0x13d4bbc545a52d95bf43911f4100ac1a,
'xcv': 0x146b65fd2004858b1c615bc8cf8b8a5b,
'ycv': 0x521dc5dd1448ed4da9305d0c33f44679,
'zcv': 0xa854ff8ad64880b99192ff6417a669b5,
'adv': 0x4acedbc1e89475eeeabf43c3088ed4a0,
'bdv': 0x8d4a7faa1115472e03eec4eda398e604,
'cdv': 0x1160c23e9274116850c7f0a8d6c8a3d9,
'ddv': 0x5d963a7bfdc032af4852f72f57455cf9,
'edv': 0xcb50179ebd60c94a29770c652a848765,
'fdv': 0x66b61db15ea325243be69c9a139dedb7,
'gdv': 0x3a3fee1a77df5f956b34fcb359734616,
'hdv': 0x8f0bf49bd67ec8974294b56c2d6348cb,
'idv': 0xee0cf53dfd7c7fbb5322b2f9bf9726b2,
'jdv': 0x8613a32882541c4efd9fd2cf8081ae69,
'kdv': 0x9fae63de1dadbc0b2543222235efa4d1,
'ldv': 0xe44c18bac95f541a74dce84b7373d8cb,
'mdv': 0xa4a4b90c8383284b792283ac658c1c05,
'ndv': 0x729d3519d2a190c1c26f88a2d11edad1,
'odv': 0x506278b964ee00f975c8f704cd16b576,
'pdv': 0xedd3c66af5af3d2d6ca87efa36cda4e8,
'qdv': 0x57ee9bd4e942a92c7e01408be8ccc002,
'rdv': 0xfb1858562c9068403739106aa11ffe0f,
'sdv': 0x7e6f8380a1f1a94a9a520856707ff004,
'tdv': 0x100d59fe9778bf05a3072091f553b63f,
'udv': 0x440c31146f26cc9fe27cb61d688c2557,
'vdv': 0x34f5159100913af62e54704b8f3dd1a9,
'wdv': 0x6372974317421ca09b6d8a8f84f8ed83,
'xdv': 0xa314441fe8ea5f3b0d360afec4c6d235,
'ydv': 0x21db280cac374b8908af584a776df435,
'zdv': 0x2e2ee4035cbbd976e94261b6c6c1f70b,
'aev': 0xd810e525444975748d8eedc09cee31a1,
'bev': 0xfca84d592e2e7655a9dc7afd28d4fbae,
'cev': 0x1debd8d8147aa4a5021e823a7799fc65,
'dev': 0xe77989ed21758e78331b20e477fc5582,
'eev': 0x59384aafe22f7b79dd215be59d6e43e4,
'fev': 0xc617f4d32274be227085738537d3c224,
'gev': 0x0ceb697eaa18962b40f8da9a5aa63496,
'hev': 0x66d8898d3c49ff827a02559e6f97db8d,
'iev': 0x3f7e667a94c7434daa21a029a9e45969,
'jev': 0x27884368ddc7ad67547f071ce8f4ec9e,
'kev': 0x3b812175a3bbca97b079857d20ee12f1,
'lev': 0x86af1c2fd820797c1b2b39c09ade58b6,
'mev': 0x3671768d0593382cd6cf23552e08db2d,
'nev': 0xe61943d0eab5c5df9e8d39990808b4b2,
'oev': 0xe1bfba10fdc04b20d6b9f3fbf35b7b3a,
'pev': 0xc2ab82e8b3db202e4e432888143fce5c,
'qev': 0x27d9494488ee52f9fdde184a71911b2c,
'rev': 0x48cd7517d21176f980daa5502d9efb31,
'sev': 0x74ae88178f23d39281113deece3efa90,
'tev': 0x3830bf15a3270274304b07e1c301c7b3,
'uev': 0xdc3c326ab27cc51bdef7d589f5693049,
'vev': 0xc78a4b0b1de166d645a0f319a5183c65,
'wev': 0x3754c3ba0a7e1f887f5ae6303b906a50,
'xev': 0x5b0829d687e51dfd2f2531437beba60b,
'yev': 0xac217442d817a1e186cef69bd3557f6c,
'zev': 0xc2a44694de97346149332b1b0392c892,
'afv': 0x9e9616ef0ad7846e6f30f9db46e299fe,
'bfv': 0x9c1217c663d36f77e1984c43328d037c,
'cfv': 0xaeb3d41cb49238fb13d4eab3473cb9a8,
'dfv': 0x357be52f82b87b4b58449cfe591b4ae0,
'efv': 0xd7abd5bde78935d4ef542ed2c873fd00,
'ffv': 0x06d2cf7ffea5b2a6d37031d3492397b5,
'gfv': 0x608afa65817453c3ae7a69c7850e3861,
'hfv': 0xf2a7dd937d235385c8b2400130786d5a,
'ifv': 0xd81ee83ae20008e6c9662d9d77a39d40,
'jfv': 0x649d90a815a30ec46e1a1f3e47cbce64,
'kfv': 0x300f0b37013017c5ad1015e83dd6e70d,
'lfv': 0xddbadf7e6e8c7bb55b0d6f6b9c605895,
'mfv': 0xa76186c32df6a14e3976fe459fa1fcbf,
'nfv': 0x0ca857a5cd5311cc6654f29fad074ca4,
'ofv': 0x542faf3691703aad6a16e95b711ff809,
'pfv': 0xfee45cff2992d9b2a38d7e45f50e300f,
'qfv': 0x33abe4052c6ed7d258dd8a554139ecef,
'rfv': 0x116e055b63d8698142614628d179f5f9,
'sfv': 0xa8d50e2e1c81bf9b4ae217bde15ba733,
'tfv': 0xd5e1d18de873cd777433419cc3125b13,
'ufv': 0xf7953918f62680912cd9c3b034df887b,
'vfv': 0x8aeeb1175ab5ff245dae3ff5690e26e8,
'wfv': 0xbaae6caa928772161016b8d1c1b9bce3,
'xfv': 0x7b20cfa5537629168de26d11f330b702,
'yfv': 0x54453b56925b30aa95fe10992a444ab3,
'zfv': 0xd01cda0c491760ddfd5ad5050733c3d3,
'agv': 0x4e7a17a46bbf09a94af971efe37a8340,
'bgv': 0x2e222cfba36e832597112846699e7a18,
'cgv': 0x8351f8765ec782ce42869889fca6ba49,
'dgv': 0x13a9e350f8777af120c3690c6aeff204,
'egv': 0x2869f629292536d4891855f6929b3656,
'fgv': 0x8a3e27d45bcfdce996d9c672aee85bcd,
'ggv': 0x93a91eedbde2fd32bce21aef42e6f3b0,
'hgv': 0x814b9c2ee0ac2ee0612dd11ca3840240,
'igv': 0xaac37832ca4eb603a9d8a130c5148a82,
'jgv': 0x5835aa9a4d6cdc300236c93cf23e86e2,
'kgv': 0x7878d315b7a0c54223bd407e2b529b9c,
'lgv': 0x5464c493cde33679ea59ab286060d8b3,
'mgv': 0xf97a17498298cbac888ec45f0bda8359,
'ngv': 0x4907e648aa72b65c6c4da9a4083e246f,
'ogv': 0x0d4bac4f9bfdad9a2cc9bad9d2eadfd1,
'pgv': 0x87837d8cb0130f17a5435fa8eb32361e,
'qgv': 0xcbbce8168c468e98b9cae8606c0e040b,
'rgv': 0xea740752359bad331daf44a49e1f2576,
'sgv': 0x4278935350235a142ec466f8525f84c2,
'tgv': 0xe375b5ffec26dd1a6b55fbfc2c00546c,
'ugv': 0x98037e34712702eabdb01f02090f0285,
'vgv': 0xf1a74a2f3a1616710d7177d721ecfb0c,
'wgv': 0xf1dd5d424b4cb3b99b426eb71b995348,
'xgv': 0x72a780f081b236c159ac3d6090dfbffe,
'ygv': 0xa032324c1fec474de5c10cc8e45a34cb,
'zgv': 0x6098b7056dc8367ac0131cb6a8070dcd,
'ahv': 0xb2089cddfd024804ad7c6164403a3a49,
'bhv': 0x8d539a225dd0ea051fa0b0c3a33d24a9,
'chv': 0x422072369359d3bf7dbe00c5a92d5291,
'dhv': 0xf20e0696ef3a53dc94d76a7f9582322a,
'ehv': 0x0e5afc246320073ac9490e3e0392e9c4,
'fhv': 0xb84cf344e7c5172a3d1768c7d8a36cac,
'ghv': 0xc5adefdfa9bdb82e85b9eff589afb707,
'hhv': 0x1cf3975580316c4638ebaf52c4bd468e,
'ihv': 0x96c1784071eaca8269aed77c8eaac8bd,
'jhv': 0x18f9b0d20de2f0bed753eece09150292,
'khv': 0x61a87d479e4e0e471e85226810324513,
'lhv': 0x87f48083de66842a12bd77128f46ce5d,
'mhv': 0x11bf590f6bc4818da23b8565c278f2bd,
'nhv': 0x1212bfb601fcf8e11b197cea2f0f81d4,
'ohv': 0xb15a5c88c144641d9a35ffc8b1778dca,
'phv': 0xeb1068c31179a9f72ba22e4af5e02249,
'qhv': 0x6756c62ead972657966c5b7cdd73f266,
'rhv': 0x1f617b6a211b33a202470c8d0636b666,
'shv': 0x85e074b6e6a348c07cd5a5dd3a28c7be,
'thv': 0xe145fa575a279ff767b1010e2adade85,
'uhv': 0xdaacbd62e9467b71140b26fa6cc31704,
'vhv': 0x01275c8d0794e788a1b9d966a88f6b41,
'whv': 0x9032e2e05aaad3de6ac51d59568f819e,
'xhv': 0x9a4602883c2c6180c3ff9a2afc76060d,
'yhv': 0xe62d4df40d1c5020013a670a7166246b,
'zhv': 0xc05df2f2ab047ff3cd22676d1c0340c4,
'aiv': 0xfce9ab2ca76a6b6a43fe61ff8b6250c8,
'biv': 0x44505f892b1d9b3ef277f3814eaf2355,
'civ': 0x65f0a1f5a2e4ed1b1276dd03aa618e74,
'div': 0x38696558dc98494c08d951c052900a2a,
'eiv': 0x5ecdf97a006db984865ef2799b8dc3ed,
'fiv': 0x3926d53baa0b16bcb755c0dba7854720,
'giv': 0xdde76bd39bcd27bc8c3d0e60a79cd100,
'hiv': 0x32ab39bcfd7517b22e842c42c074d897,
'iiv': 0xe693eac1d8301b89ef4043253decbc46,
'jiv': 0x898c2bfbd94a26c251b9948c1c2183d6,
'kiv': 0x5c1ed888957b0ddc9c161d98b35b8224,
'liv': 0x8d2182a7c1ba1205b94af8e273d0798b,
'miv': 0xa0d93b018346b028076c31bdb022ad82,
'niv': 0xd5d32ee2a54924d048b60d5a92702141,
'oiv': 0xa36a01229feb7dce0be9ee4241def9cb,
'piv': 0x155815fa8fdb4e3326e55f81ab4a3f5a,
'qiv': 0x7146d90d3e3a31ad7a4b6de49a2dbdd3,
'riv': 0xba62a8a95a8856efde735108a634a574,
'siv': 0x7b68742c36b75259702f1d732b528d2b,
'tiv': 0xae85c3bb143330c15b92d9212091511c,
'uiv': 0x05c34cd1f38531ea40184fb5f4b216d3,
'viv': 0x3b6fd1b3ca4936df4452f53f55f7959c,
'wiv': 0x6026848f149d0669a0715642d25958d4,
'xiv': 0x68cfb3fc398195d0127375545ee99f11,
'yiv': 0x45073a6fde24e0948f2d5cb6317b7c5f,
'ziv': 0xb3a186d2049e264976f85ab40b463732,
'ajv': 0xfd8ca743ed4abc6e6aaaa4622772fe96,
'bjv': 0x7032ba31640348d2bcea0696935cc6af,
'cjv': 0x7922f5684c6ea036d0ee19fd0252d574,
'djv': 0xe30eda2b100ff52738da0a7c12815196,
'ejv': 0xb324daf2bef15dc2a02ee6d61817f076,
'fjv': 0x859db440eae56c09f831880637885e04,
'gjv': 0x9298c17c69f2017aff3b7663cf3920ae,
'hjv': 0x22bd705d62805ab9c1b41e4293e9911c,
'ijv': 0x80122ff6cb228f9b9126c96c391d1f48,
'jjv': 0xc9625afc26eba8b8ce93351602861989,
'kjv': 0x441d215b24a7d1dc87d6df98f90e4ef5,
'ljv': 0xe567c9eb99c04906d2406e4c5972af6c,
'mjv': 0x2c44491c3dafd1eb2311290faf1c63e2,
'njv': 0xb8401c1f7cfe2eac0e07caa687694f04,
'ojv': 0x6a5af66b5f4d4761ca0294d46fcb028e,
'pjv': 0xb607db01b11ee558fda6f0291ca60d2e,
'qjv': 0x2d19ffdffef2b2636d1add252a155f32,
'rjv': 0x05d8404bc2aa23e020538da0857e0613,
'sjv': 0x0a1297ef10116118bf670f22b0965594,
'tjv': 0x7973ebfb48205c895ff389405967d38c,
'ujv': 0x09c6eea2e068ed8846e6763e5c09e3c0,
'vjv': 0x52854a70da878c1af085adcc2ac04898,
'wjv': 0xf2d0ab8abd401b9d6e25df76dcca3aca,
'xjv': 0x5c488b1518246ed9109ccd2cf8b3ead7,
'yjv': 0x59908c8feded37d422322ebfe683cbb8,
'zjv': 0xe1af56433a5203a21daf00b6244d9614,
'akv': 0xca2681520e896ae8e7d3cf3071150ddb,
'bkv': 0xf21e6757345d1c623792dd32fef34742,
'ckv': 0x46d27e1d5289573b0b1a2d34a4e6bb01,
'dkv': 0x6aa7b1e57bed59aa20fd986caf3aeb6b,
'ekv': 0xf6e0ab8a7b64610af6338ab4a0532e3e,
'fkv': 0x8df85e897b01b1313642b8047253767f,
'gkv': 0x3e43ec85c9b1d760f7ee4ef388843c94,
'hkv': 0x33ff3fa4744db564807b99dbc4a3d012,
'ikv': 0x35f125085639e2c0ae894c046e4179fb,
'jkv': 0x30f8fb990cf3dab46e60785c2973d8ed,
'kkv': 0x385d6b17bd0555a2a36b01865e1b7017,
'lkv': 0x3daa75544cb7c04d76fdc09cb5f69c1f,
'mkv': 0x8a122aef5f7fc4b4154b9127cb39ec14,
'nkv': 0x4d879d77cec6dcfe5b3d396b019b73d4,
'okv': 0x9977b996e87bf4332e5ab80dc3643f52,
'pkv': 0xa3c12c4a9e6fa907690e277d057697d0,
'qkv': 0x8aab0601021012d54aa142ea254898f6,
'rkv': 0xd2ca3d881d2c38aff4a947bed461409d,
'skv': 0xf6ca0cbff053677183a9aeeed858b475,
'tkv': 0x695083663ff518b375541c42e2918ee0,
'ukv': 0x192062d31fa8e480f6a578401bf3b0a9,
'vkv': 0x899e4b0a64e028abd3a27d8dcaecbb8f,
'wkv': 0xe2175045e8f872641f8375111061abca,
'xkv': 0xc1db068d73e7b16ee2e76817f43a757a,
'ykv': 0x924c0cc7348ce3acebdcc8a3e4dc4253,
'zkv': 0x4d60993b286b00ebf403004a82407414,
'alv': 0x985992a229efeb82cbac3990f237194f,
'blv': 0x3692c74a6ad64ac04f0f53b92894b59c,
'clv': 0x806deb5a285e769a9471fa8e73393eab,
'dlv': 0x738d3706ec0fa1f9b29d702b33a8c13d,
'elv': 0x40ad0496f6ff64221c805d50992bbf8a,
'flv': 0xa7bb76826558fbee47b11103c0fcd35c,
'glv': 0x46a51499e33a96e66e31a2b45b99c43a,
'hlv': 0xb2d7e18f1245b0c2036c7e206cabf124,
'ilv': 0x7fb2b8a373c324ac2ec2add97a92bcf1,
'jlv': 0x74a4d406ba3cb377c73cafaf2b38864b,
'klv': 0x70978e2cad3e164ed4e873c6762789ae,
'llv': 0xa1cbd0e8d2357a66243fe06e9c4e392f,
'mlv': 0x688bc81e3911a4c4d1723b10f8e0703d,
'nlv': 0x2d4b22c642b3eaffed7f0badf5939cf6,
'olv': 0x6bfd6f7abb115fbcc9ecb4c732158730,
'plv': 0x09ea8e8d8f29b176bc1842100d5d9f57,
'qlv': 0xcd793c2790f33b31ea1356374455c625,
'rlv': 0xa82a2869905762703e2a5a0049837a8c,
'slv': 0x06a9ad3bb6121f438d91f827a3ac1f90,
'tlv': 0xff5911dec548243b4df2a6f299d45939,
'ulv': 0x736ce7333896aa8dd99135380bee62ea,
'vlv': 0x5e4961ceedde07d6071ac2ba16d26419,
'wlv': 0x3a1042b324281476d6e9d98e6aa0b6ef,
'xlv': 0x97f4e5467ab13a3866b90c58e0fd8f5a,
'ylv': 0x8a12b253efafa3875621d0981881fca8,
'zlv': 0x71887cc0ace49c0fe1ff82c8cc1d0045,
'amv': 0x433bdafd135d0f4014bb258dadadc570,
'bmv': 0xa7d944aa0bfd1e5c39a6a8c67171c39c,
'cmv': 0x8ff861424673afa182b5d000fe1555be,
'dmv': 0x48562433ea508fb417158cee45759d12,
'emv': 0xc0a73c056b1b098ffa56cc53aeb96cdc,
'fmv': 0xecc7a94d2f92d75b162ffe0c4c62e197,
'gmv': 0xa0889b54a5816dbbc17e5f20c4729169,
'hmv': 0xd1b662f9ec3fddf1607dc797ffdbd07b,
'imv': 0x7444f935466a360e525ebfe798971f75,
'jmv': 0x5778e2726b11b159c5a88f65afcfb4c0,
'kmv': 0x39c1915b03acef83d00e7bcd4d8bc117,
'lmv': 0xb8b6a325acb95ffbe3c72c786b90ccf8,
'mmv': 0x0ff4646677d8611a6bf1270e4cc72615,
'nmv': 0xf47424e7cef19b02e47a8fa29c87ec7e,
'omv': 0x252debe03bba4ee71a08997bb8d2cd42,
'pmv': 0xd0e3bc6886f20ddbf4420ab17a0ec357,
'qmv': 0x46baf27cf4d1864ad30872bf9202f2b4,
'rmv': 0x9cd31366e088329d33d1351866165252,
'smv': 0xbea71c4f97b3dd65efd3ee696adfeda3,
'tmv': 0xe967d6b37c5113dc66170963fe7e54f0,
'umv': 0xef860a244eaa21c3b6b0caf576009daa,
'vmv': 0x6d0965c8490bd4000e63bf0b78eec4d8,
'wmv': 0x27a58e77af88013192d4701019bbc9d9,
'xmv': 0xe78e0ff88f3e90f2cae3f73a277eee6f,
'ymv': 0x0c922cff6d5ba7173f99c54eea4d159f,
'zmv': 0x924de29f6147cd6e154f9b5ac24653a6,
'anv': 0xdf67ac282cc10d9bd080453af9747785,
'bnv': 0x11714ee92eaf3956cd5e460e181f8b52,
'cnv': 0x7a2c43149ca6af7e99acc82fd6ef73c6,
'dnv': 0x72b53bbc83fa76a5be310b77a80f19f0,
'env': 0xff035a1dd7655da15295fa5fa89362a7,
'fnv': 0xd5f51a6b3d05a2651e100f6a985ae930,
'gnv': 0x5b8598bed42b271cb8ec62c4bdd4f3af,
'hnv': 0x7a4e104c280234ea3b98bec792aa06a8,
'inv': 0x545f7f577c93318b34476e9999931731,
'jnv': 0x2a75079a0a1db8cc7e75b5b5d95fa371,
'knv': 0x7d613c196198ffcd9319be9b582f1535,
'lnv': 0xfc8b67ee50335a2f65e8ad5d3930a0fd,
'mnv': 0xd446f531b34ab8b4feb72d6ad76a3d06,
'nnv': 0x7381b0ba2fc86bd89164f623813da61b,
'onv': 0xea794e3c2c81af0b548dab1bb525233e,
'pnv': 0x4ea16acee0908d1f3554a179c3758edb,
'qnv': 0xd9298a6d4eba41ef98940a1442a5e61d,
'rnv': 0xc96230ebc85cf4154b6809e1898060e7,
'snv': 0xf357f93ce27f2d59d30fd2e745715c3d,
'tnv': 0x7a5ec5507d6a98ed81d266ffaf898e69,
'unv': 0xda1d0ab163310bbc0a98cccb65ad4ac1,
'vnv': 0xee37cb596f23673eeb864dd66ba42d7b,
'wnv': 0xf60bf3f0a455d5ca5389aac579b1b640,
'xnv': 0xd51d874163c467815376756e26123f98,
'ynv': 0x8c94bbf92d2e2775683d0359ddbd7d08,
'znv': 0x33411a67a7cbec11b637a9e5a47ec6a8,
'aov': 0xadb866419ba0cfac94d323aef20cb30b,
'bov': 0x1de152bc6668cb170b2de6fa12794e12,
'cov': 0x0b1b986bb265e86a359376d3f9ce8f22,
'dov': 0xe445465dea6f975a8a7315f7f3a52539,
'eov': 0x827243b999b277bc10937e4b7055c54a,
'fov': 0x0a9ad70b57e8cef7739121df324bc47a,
'gov': 0x552360015f23b99978d5f1a6bd2b9c7d,
'hov': 0x3502b4cfd8d32e461aefbbc5d6c61d8c,
'iov': 0x3ff10eeee3b1839bf6f6a2bdcabd4e6c,
'jov': 0x3c3a776920f9ec570e8422ba204a544a,
'kov': 0xa9e888a7a1585f349ffd77cc99bb2996,
'lov': 0x75e0088227d62d4fed87b7d44a2ae7a5,
'mov': 0x96a4a0e6e9ac88756a0fcba989d0a844,
'nov': 0xb27862005f4e30a31911588fecb381ce,
'oov': 0x2a2f0984becae9348a7e7bbd171759b2,
'pov': 0x26f005fa864d50d584590486c0dbedaa,
'qov': 0x0e73185d55a85ff6e361df2463eba0d1,
'rov': 0x759e948cff231a74f25831cf053c7c5d,
'sov': 0xc86a878805931c0e62badad2837500cb,
'tov': 0x50b88c8a09d3d178430b1513950cc814,
'uov': 0x510d41178bccbb97246f90a9d5ccd965,
'vov': 0x8454973d2940158a33e20ac12c15c02b,
'wov': 0x160882e4110145a319a8fe7ef8d1cf64,
'xov': 0x5267bc9b7cc9596f7b7e57ce3d63832f,
'yov': 0x451fa5a7d6b64d81bc84d88748a02894,
'zov': 0xcf2533716fe70a7bf7e3a08487451488,
'apv': 0x24c556da8d01da4e3b3c3de9ca61ea4a,
'bpv': 0xbd0cc4154a6754e28893de2998f50ce2,
'cpv': 0x8cf30182188efeb0631246f917f93f14,
'dpv': 0x47959044351694e6a5bf4d4b132579bb,
'epv': 0xe264a6a92fbf06e402d73c2fe5f95cf5,
'fpv': 0x2b57103cab66edfcc05b362c4c6bcd08,
'gpv': 0x4299fe53a9ebe74d9557e88977699ef5,
'hpv': 0x7373394127944fff3dd78df59c745c8f,
'ipv': 0xd6e3193d19271e88c08a57319f37db15,
'jpv': 0xc3bc88761e70b7270751c2e110f6ccc2,
'kpv': 0x271e0f23731e31264b5ced055943b572,
'lpv': 0xba3cf11f01ad5f048a95d6626d0a5d3e,
'mpv': 0xa50ebbf3b00db049f8437d8323b088c1,
'npv': 0x59879eabe96b11dd938393add67d2a32,
'opv': 0xe249de1cdc8750847cc34d81c3c8a240,
'ppv': 0xf3b02e594bee897d494f09f374dfb1a9,
'qpv': 0x58a7949a13cc2462a80f3f40d269b8c8,
'rpv': 0xf6c3fcbd15baf94d6b6e70d2aab0588c,
'spv': 0xf4984324c6673ce07aafac15600af26e,
'tpv': 0xcfb218d748e6dcbfea4f9b2ca6849338,
'upv': 0xb8b9dea083c05db9d3e40247c50eb620,
'vpv': 0x7204f5863d59d90337f81b0e0c8df7ab,
'wpv': 0xbec5b4a5b01514f55b37f5ed3ade81b5,
'xpv': 0xa52ed5f14a61cb015bfd08a04ca230ba,
'ypv': 0xadedc7cd52e20205a5ddd09f7ef776eb,
'zpv': 0xff718d9f71c395900da823f01e77fbc2,
'aqv': 0xa65fa7e3a3f55d14bd8c7b845ff0c0ce,
'bqv': 0x4d34ab0580aebdeddce5a723afd4313a,
'cqv': 0xe2ad4b0cabb3573f0b3d4343545f6328,
'dqv': 0xa1d22161624bf73399db7d62a0da2d3d,
'eqv': 0xe12cec23a22e1b35e926f1c706aa1c0a,
'fqv': 0xb2c91c1caa74dec1d9026c238c20bae8,
'gqv': 0x366fbf76669e0a653e16bee785b29f96,
'hqv': 0x8602d3c21506a19830901be9bafd8d1f,
'iqv': 0x66b572e710ad7dc81392024b16bb76fe,
'jqv': 0xe4885fbead13aa03d6f38086fe9d8e0a,
'kqv': 0x9e770be08d2fc7ca6a5d904a2040ef1f,
'lqv': 0xfc4a09b67ba088a5129ecd7c28edd09d,
'mqv': 0x830d80da7317e4edcd8ae555cca74918,
'nqv': 0x4d1a902a37809dfbe7554160aa7271bc,
'oqv': 0x9288b69d53ddd139c738d394e479d428,
'pqv': 0x32235ac5159c5e323315ea6bc13a07fb,
'qqv': 0x4eb152d0b33da93ce2d34a3e018449e8,
'rqv': 0x5c4c91d9b084741a9016fad82ae155de,
'sqv': 0x3d88cf6426c09dfb67e91521521c0c0d,
'tqv': 0xa5c073d2145cfe6005ce29905dc6d67e,
'uqv': 0x4e1d63e2f6913b548c107e586628fccc,
'vqv': 0xff563cfaf154ab228edba1924abd70da,
'wqv': 0x98415c6e906720b918de1953451aa67e,
'xqv': 0x67b23acd78e23752938232ff14120c90,
'yqv': 0x88a80bb5aea02ecb995bb1c8a661eeda,
'zqv': 0xc88dd8ec904c4df5a72e0cc3050357e1,
'arv': 0x0969a1eab115c1b287221629bed8f0df,
'brv': 0xb2b06497a561f8967e903e43c06a253e,
'crv': 0x25f302e1f606294a6a3c731200f86bcf,
'drv': 0x96f08195ab7495519480a196743a0c0d,
'erv': 0xf097bd097592cfb7304c4a495a84e5c1,
'frv': 0x18ed0be048e711b5e6713a8c562c04bf,
'grv': 0x7807d8bebff77687c2f18770c9783e50,
'hrv': 0x12f367dfb34df8ddc20d530e29b6c89c,
'irv': 0xdd7ce858460f53b473af56bc149b62ef,
'jrv': 0x351ed2bc0c2ebe810187603ae3edbe31,
'krv': 0x008ef38abd7841577acdebc4424ce1d5,
'lrv': 0x6e39fc182cde48f018c30db6144125e4,
'mrv': 0x36e6889dbcb152f682343617d82dfd00,
'nrv': 0x9000ab4d07acc4d4305035c47c30e9d4,
'orv': 0x89c1b42f0895eca4a812e676b7faf0ab,
'prv': 0x6f683603084df0a15496d735362929df,
'qrv': 0x22b55477dc640e3c3eb46bf62b6be64e,
'rrv': 0x989fbe8f0de90264bbf860547bc5e006,
'srv': 0x254d290cd568731c37ba45103c16c22d,
'trv': 0x9709f63f970338d12cf00458eeeaf24c,
'urv': 0xb3f655e15ee20aaebe4a1a5afd4c5f62,
'vrv': 0x8f7f26389c9ceac4e1fad147ef8b3b61,
'wrv': 0x4f9226ba4c5011f9f7dec18e77e72af1,
'xrv': 0x703ff0c5e9865bdc4ab265df6507625b,
'yrv': 0x284af711fce02acbea4eec70f7ebdea9,
'zrv': 0x80dedea52ed5f43d7acd5fb7f708f42e,
'asv': 0x275258a5f50081f8ec031e727bde98b6,
'bsv': 0x71cbb877826aa98bbca84e6a59b8e147,
'csv': 0x628cb5675ff524f3e719b7aa2e88fe3f,
'dsv': 0xaf7018bce3402d595aa0d0a2799da15b,
'esv': 0x15e46c79400bb6c62d97d8c4b75e07ec,
'fsv': 0xb33deb7ad23009240fcadade0f9f0912,
'gsv': 0x79613fb8e7ea792c36d318bb4b2ac641,
'hsv': 0x587e6a7556ea454af6a44bc6c021dba7,
'isv': 0xda5523a64cceb1d95d889f2e424e5d82,
'jsv': 0x24bedd45b74c442ee9131bb0434dcfea,
'ksv': 0xf4529f4d4fa1c763247d09e25f80ff58,
'lsv': 0x5e22a39ccf920b270a88def51d3be172,
'msv': 0x7f0a8265f16d8141ea97020e218219a4,
'nsv': 0xb86de4c5592d68c6a47a8d3e708dc70e,
'osv': 0x7f791f89aca4f442da5d968f1d01eedb,
'psv': 0xb5e74fe2a6a3409510b3b7ca649b569e,
'qsv': 0xf77e2b3bfd908c2352f3a5b39d50c596,
'rsv': 0xe4731d626c4452a365b40a3a846f1e45,
'ssv': 0x8e628f5d270e1805ff7aab960565b62d,
'tsv': 0x5b61aa1a0a205f2e970c2d7f6889398b,
'usv': 0x0688c8d567ca9897c9c3e738e4cc6159,
'vsv': 0xc8d148a8865003b2e1fc5365c4ef23ef,
'wsv': 0xd5b03b7a9c5388e912f11ea4eb6c4001,
'xsv': 0x6db25a931acd0cd0427ceb4c84ab9f67,
'ysv': 0x816b76fa096523dc9625197efb5b5474,
'zsv': 0x6d988bb979f39f05317faeb8f277e19a,
'atv': 0xa0e011bf980afc83ad884218c1e86ff7,
'btv': 0xd8655228ec57a752bcaf08e31982bea3,
'ctv': 0xc29af3b84996f6c09069a44971e9e980,
'dtv': 0x53da17a30bd0d3c946a41c86cb5873f1,
'etv': 0xac58713fda0b07d6401d35f1dbfa3187,
'ftv': 0xef4af395a3adbe2993b4b62aa7c332f0,
'gtv': 0x5d7b7671d668e72883dc0bf87d0c0620,
'htv': 0xb7be8e32859d845f7b7043c47f43ae8b,
'itv': 0x4d3c78b14533469da225cf7dd52888a1,
'jtv': 0xc61ed732b4f9bafe14de9fe1e6eba245,
'ktv': 0x93ee2cc07694a81f35db8bf5602cf079,
'ltv': 0x2c19c83e9728f5270e1f77aaa51fa262,
'mtv': 0x88eaac40b27c1b47df9a3f7b7d71e3e3,
'ntv': 0xe3d4300b49c8c901e9339174215d7580,
'otv': 0xad670749d4e4a54fd0d03d475272b07f,
'ptv': 0xdad15a2641c94e3fb83e0c13d7de51e5,
'qtv': 0xb4818a191c7cc474d3395e51d969fa39,
'rtv': 0x9fe7c84b8253e88099d47237e0b7367c,
'stv': 0x83edb09ff31e7366cb623731b679376e,
'ttv': 0xc10681f5d8719688d607e0be89aea3aa,
'utv': 0x7df9191deebe61f6ff9b063ba81dbc1b,
'vtv': 0x7a0f24809c234ea78d972b1f336ac1b9,
'wtv': 0x48b356a4a4012789e42062cabc9e38d2,
'xtv': 0xf3677a93d505ca46b990d2dc880c7a46,
'ytv': 0x5e2e5f68916c8313cb5ea54fd3337f6d,
'ztv': 0x0e6254be0902e99d8dc00d0c4042f5a7,
'auv': 0xc799d15de7ddf24def4a051fdd5ea5e8,
'buv': 0x9d2a046345b1347dbe5e6fa6a81adc97,
'cuv': 0xef95b58b12d82437a04657eafc1fe30f,
'duv': 0x189e0220e45e747481b9ee09bb1faf5a,
'euv': 0x9ae18ddad03f0687f7d86a58104ed852,
'fuv': 0xb173b46680972db57e1d396fd8007ffe,
'guv': 0x2586cd2c71402d7fc99d4c8cb87aa44f,
'huv': 0x76bda5e23aa11a83c6d1888e3def8373,
'iuv': 0xae5024ac818a13b21fc859cf8aa0be63,
'juv': 0x61f95b0b3daff12d80bbb73804042dbd,
'kuv': 0x326bf90c2b1092305c16a083caf183e0,
'luv': 0x83cc23319cec76faa60676417850cae9,
'muv': 0x4ee4d12db567f2bacc3cfd7c2dc9851c,
'nuv': 0x3a309df9fc2506d6508bcd136d1ffedf,
'ouv': 0x5f8e0043dbfdb1986125591f4be5514f,
'puv': 0xdf91d05a7953d4864dc00b98896175d8,
'quv': 0xdcdb70ac415f92a8d41b2e0ca67b9ea9,
'ruv': 0xca111c6dff9003e862ad4fa7d4884311,
'suv': 0xc0276be2e060fea8d3c26d0159c6e920,
'tuv': 0xc5a4dd427c7d73be0413ee1f50aa73dc,
'uuv': 0xdcc019c9c8968f138c449032ee535d51,
'vuv': 0xa509b38eca2d1cf14887b6aceeadf491,
'wuv': 0x0cc10e3905f58b6109fe94a22dccf8f0,
'xuv': 0xd81e065807197836362bd54e7c9a0f1d,
'yuv': 0x0f6895d5d358b563d1b1e9f8c7437acd,
'zuv': 0x6470f91050c5685f8d5be1a642c84ada,
'avv': 0x067026f0c6e2164684867a79f48dc9fb,
'bvv': 0xb0286f5263eed551c51c930d3098b3f3,
'cvv': 0x05f59f175b8961c00305e4ee7c88f9f2,
'dvv': 0x2f63aae372b2fa7b160ca6066f75b2d4,
'evv': 0x920703ed2a555a09b5684fb7cd74e19e,
'fvv': 0x615a61f69b9c5340db527c692b03eb43,
'gvv': 0xf03d891b134e28ddff86710284046906,
'hvv': 0x0791d958455108bca30c9c668fc4855e,
'ivv': 0x037236ad7fe4b614150dab5171c698b2,
'jvv': 0xd0b3ee3f570e869f0a49634c99a4a422,
'kvv': 0x32c29d1031673081687bee6843e9b64c,
'lvv': 0x679e97a2b822d875a592e128e94c9fff,
'mvv': 0xc483793acdafad74d160a51cdfd1f1f9,
'nvv': 0x8da68732922445efbdfe87dad102aeeb,
'ovv': 0x6cdbe985a51b013ddef95ed7dacd2068,
'pvv': 0x23b3c1ea8d96c1e72809c9c8bd55ce00,
'qvv': 0x38a1106ea66853cc6c639e4ee2869e0b,
'rvv': 0xea5ab042ab51a16fd4c0f89d383e8731,
'svv': 0x2c222631e39521f5633dc16465023b6c,
'tvv': 0xc0e770d5a7981cd03ee806bbf949e8e9,
'uvv': 0x5406ca1136504342b1dfbc986970a1b5,
'vvv': 0x4786f3282f04de5b5c7317c490c6d922,
'wvv': 0x78f630d243b9c184b10dc755c7cc41ee,
'xvv': 0xb5c0055f1c8350a0753cf6823d539d66,
'yvv': 0xe106aad4c4eb01117607cdf9c3c53758,
'zvv': 0x63304b2a3ddf9b71d63658df71014f76,
'awv': 0x295cb1d23336b2d98e068508029a9da9,
'bwv': 0x228f42c4d8bfbf1ade80ea28b8f8116e,
'cwv': 0xa690948d98663a140948e54a201f604e,
'dwv': 0x7a204dac5b31b842fd13d3935316a803,
'ewv': 0x687a6070a677e6961c4f7705c3224a3e,
'fwv': 0xd942e3e0c94f74f79c84b799697dbe48,
'gwv': 0xe268be3da0760855988533cd1527cd3f,
'hwv': 0x463af22e1d34e266982936fc2ed9eeb8,
'iwv': 0xbadd959b123173408a4bbe862fd69ce5,
'jwv': 0xe2aef3538f28fbc1b24d27f5c5074953,
'kwv': 0x026fe60443c8256f8d43fac09fbc40eb,
'lwv': 0xd2024cc84cc4164259741c27f976cd58,
'mwv': 0xf63e67d32a1109a3423a5ed4f480c934,
'nwv': 0x97e23b4aac1ce314c84a948e69d929bc,
'owv': 0x900cd2f4bdcd005064e382be8d0a8891,
'pwv': 0xde45d94363888927a83b8e8649ab546e,
'qwv': 0xfa02d1f43aaca9e3e23c905827dc1a3d,
'rwv': 0x64f12464fda68e098e02489355fef0d7,
'swv': 0x5d17e47e6c3bb90e65701cf782769ea6,
'twv': 0x8184a5fe3bd3a16d072cfc6bcceab2cf,
'uwv': 0x00ccf19fe3c3af0746c7f8ed80ecd212,
'vwv': 0x53b57a1853bdfe11d538fe2d4d325dfc,
'wwv': 0x79de068f92881db78f78b1948e426227,
'xwv': 0x01246b4c11ae0ad3a4adf1478d7c53e4,
'ywv': 0x72361c262093c319f8f6c0a61d5bae89,
'zwv': 0xbd7e352f4c2d81be18dd89b36195e7be,
'axv': 0xa9884bb3d1b3c179f929357cac34ffe1,
'bxv': 0x5fa36cbdf782c15c99430f72f171d0cf,
'cxv': 0x9616ce3f4183c5e7241e7a9bb3a192d0,
'dxv': 0x1696bfebb3a6a66bfdeb4a7ffdfc73fb,
'exv': 0x0b2c2f1e241e8a0338cd3bb59abc3d3c,
'fxv': 0x9abb76fd2eed1abffb989564460e1bb6,
'gxv': 0x661894d5ef4878bde5e278ef6f610900,
'hxv': 0xf968b7d4e4be9de740cf4eb755ce944c,
'ixv': 0x8257d0138f886f64db2cce346fc6ea0c,
'jxv': 0x17f78f07383a9e08e7082d782818a29c,
'kxv': 0xa0852a30026e866d8829b73c63e80b43,
'lxv': 0x003037be47a3ddac5c4472d799d95121,
'mxv': 0x75efe531fe788a12252e5647bbfbea9a,
'nxv': 0x046ab44c052e6657be73cc78e927e3ca,
'oxv': 0x08a99a43ff4d0b6b1887d8457688838b,
'pxv': 0x1d1ce76b52ce017de9ec7d05b0be3f49,
'qxv': 0x5297da5f38c52c5cbd329b010e34bd37,
'rxv': 0x6fa37cb391c0ea306230bfe929bf2681,
'sxv': 0x8dbb8d4fba30df33c17c15896eb7ce22,
'txv': 0x1f4f15aa75e0669a83a8c0baa7bf9a58,
'uxv': 0xc3ad5d43e167f6646f03a6b5dc2bec49,
'vxv': 0xc3c4bea45401fa4db7191114693d0aa5,
'wxv': 0x1fad0e4bfb59d3c0e8229ede1f6d26b3,
'xxv': 0xe172832bb48bdbd4864bb90b4cc91d9a,
'yxv': 0x6a5985087a4bb0547f8f228e06dd2b38,
'zxv': 0xafff79410b50fe209fec76899b162c37,
'ayv': 0x6f64f26c939ec3097c5a307e539e7154,
'byv': 0xc4690a81e51b9420b34315cca31f2aaa,
'cyv': 0x6cf3859095ace3354ae54e524bc0143f,
'dyv': 0xd768c4639008cda80e5350aabb9e7c5a,
'eyv': 0x46c1eb672897fdf131f328fcd8aaf94c,
'fyv': 0x27d52c749e83485d06faa91f92968d13,
'gyv': 0x67ab64f5349e510eaaa72b6d432ade4b,
'hyv': 0xef7756700a5bda8d79033f2f3347100c,
'iyv': 0x3ddaa1b572b6ea0612fecafaf031a456,
'jyv': 0xa5c963214ecddce62a882e285268856c,
'kyv': 0x5a237621fea328187c41e4ac84062c24,
'lyv': 0x0f0b585210a1b28d77f3dbd3b548baf9,
'myv': 0x78b30e8bc8554a4884747db8bdd3b816,
'nyv': 0x72bfb91d16fd62e9dffc8f52cc129f05,
'oyv': 0x7d78bc4849d09f17502448ca7963b40a,
'pyv': 0x3fc9a5380bd82e8064748362dc5cf111,
'qyv': 0xdf7ad452df89cfa1be57b4b26d5635c6,
'ryv': 0x1172622ce0576c1bd7cf8f410384f5b5,
'syv': 0x3e725ced914d22a551c910257c8f174d,
'tyv': 0x18bf74e59c59dcee91e6a64e1258bd9b,
'uyv': 0x6909f7a0eaaf014a17991c7975399b60,
'vyv': 0x8f24d96188b06a9bd4003a6414ec3340,
'wyv': 0x371e8ca08426debbcd3a3fba516d3771,
'xyv': 0x796b9e64f6a49827b3e01346a0da428a,
'yyv': 0xf2c150319b648fd08de57f580a8be96e,
'zyv': 0xb9c0f1a5d397abfab62784397cfbc43f,
'azv': 0xcefd9ce79621d74699b57a992726b491,
'bzv': 0x60980a4be6b8765ae5650a2ae007a5c0,
'czv': 0xf9cf78daece6cb449a3a0d21c6be5e04,
'dzv': 0x4e6ec8e0b0216f8d3ec4bfccaa6e90b0,
'ezv': 0x75ac48e2fedbb0405ce9acfbc1fc6000,
'fzv': 0xf00f25b6addf7e625f07a7520a2c054f,
'gzv': 0xd0d0a4e73c4527282f69acd109b6ee3b,
'hzv': 0xee2596ce056687f405dca97319bf0b8f,
'izv': 0x7348f03e1288a10cc66fe106621db9b6,
'jzv': 0x1906e0c7c53671a11c3c637b1d7097a7,
'kzv': 0x3d17895853fafc58c8fb61772845e703,
'lzv': 0x0dc9d53a93bdd4f42abee0f30cfb74c8,
'mzv': 0x78b4f9f05614441beb77f924b92c2f7e,
'nzv': 0xe5935f3145b82875585f6b6081584ac9,
'ozv': 0x8dc648c4693d05e3969e5ebbfbfd8055,
'pzv': 0xc8fe39c26aca47c59c9cacbaad9ece8e,
'qzv': 0xb3fba8a799c9edb035261f9191809d09,
'rzv': 0x00a2f2f369aadf8c5b427714c2afc8ef,
'szv': 0xbee7ef18381f7183f3e7e1fc9bcee825,
'tzv': 0x2d2cdf859a99b5ef4afe9d0195e0ae3e,
'uzv': 0x73683edcded6920b8f3e7c5f06cab509,
'vzv': 0x6bdfb9ee4076a54e68d856348e927c33,
'wzv': 0x1b3bf8e34737dde54cb6c7dc9e1a93e9,
'xzv': 0x18ae27ca25542112fe6db9bd566fcc79,
'yzv': 0xefed7a0138e530e6253af55c3c8ab117,
'zzv': 0x161fcfab3ec9ce9bc438318de0aee3d7,
'aaw': 0x40618960c732ac68a7b5cf574a759ecc,
'baw': 0x5b676fbcbef570511f68feb777b62c96,
'caw': 0xbb0e26c7b06992fd1fe8214f485286a5,
'daw': 0xdfbf6f079b428caa9410f95b2ef79bdd,
'eaw': 0x534d3de568ee4c2fa4b87d082fcce234,
'faw': 0xf3b392b91c4328916e84f050830c8e6c,
'gaw': 0x77aa7ef0259067a7086be2c7e10f4790,
'haw': 0x5a49a647820fcbb4021e4278ffe74ffb,
'iaw': 0xb47f442202b470d639f4aa012f703d25,
'jaw': 0x272f3da8ea6283df73187baf02345637,
'kaw': 0x9ce357fac7e32b396c7b6350b6dfa7d4,
'law': 0x829a56cc8ffa56209e3a10b80d0bbdf8,
'maw': 0x2d2c33440fe945b67f7625131bc5b161,
'naw': 0x92669441d9510ef235ebddb3be2a8626,
'oaw': 0x98d3b259f126f5f61902cae82f0c89cb,
'paw': 0xd8d3aedd4b5d0ce0131600eaadc48dcb,
'qaw': 0xf2835b13e3db32ba2d12df97cc014037,
'raw': 0xbdd166af3a63f7be696dd17a218a6ffb,
'saw': 0xa07a8f8eab60eaafe24af1016a02fbf3,
'taw': 0xa149081d8030fa48f48bffee99c21b0a,
'uaw': 0x972c3db622fab3ef5a2aab637d9d45c6,
'vaw': 0x86a76549b332de18a53c84889258d6e2,
'waw': 0x7dd87e1bac147f619208ad97426ef9df,
'xaw': 0x15ed6fb9db608318e078c1a29d2175e5,
'yaw': 0x92dda6448f3b313c16d01c8f17cdd6a3,
'zaw': 0x5ee77659d2b94f7c3ae493aa2ccdddee,
'abw': 0x8fe07056d5ef1e6af7e9be9ca365fc4f,
'bbw': 0x823ef2e6030957343803e071bc0e8f0d,
'cbw': 0xa7175bfce36da67f40da75427439d638,
'dbw': 0xd1e18ddfbf85c067f05be387480ee5c3,
'ebw': 0x581d78388c86625ad872e436468ac90e,
'fbw': 0xd144d8fbc3d90d5c7185477bf34a9b06,
'gbw': 0xa3e75bd37959fbdc75112a63588976b9,
'hbw': 0xb0499aa8f0bb75d2dcf815de1d1f1614,
'ibw': 0x19ac23d254bfd7520b4e22aee823a8b8,
'jbw': 0xb533989c54e0c261ad92f3f4a94e5533,
'kbw': 0x0346c8d6175af60be31ff1ae4ec26bcc,
'lbw': 0x934ec2509e1e618c5b779a91c9d22cf9,
'mbw': 0xafcea21a1767d28b41b1a932b060c607,
'nbw': 0x1de07a179c391c0a0a3d23016b558391,
'obw': 0x19c2d7d91ed1ccc25531856e66c42bdc,
'pbw': 0x09d288b706a49462289af355ccea0cdf,
'qbw': 0xebc92802135ae474d8ee6e33e9a70581,
'rbw': 0x413c40dc3d0166be0452eb451696c83c,
'sbw': 0xc3584683649930cbbbed5d7a4de07cc3,
'tbw': 0x3bfc487ec558f286f42df37238d8d6bd,
'ubw': 0xdceab205ad7cb872152b6545b254a229,
'vbw': 0x24ba9e3be1f86b5ec63ecf7291c49a96,
'wbw': 0x413c0f19320f604144ef189b803950da,
'xbw': 0x5e4cb32575bddc6488ce61b22ce6f44b,
'ybw': 0xb24f5b85108aeaee7f55b6995e277083,
'zbw': 0x1999bb7e3d51064fa33c55607b5c939b,
'acw': 0x06ba74590a61b6073bd73be19049a00b,
'bcw': 0xc99fa13828bc8cb857c19056f967490f,
'ccw': 0xd5e5cf0370b9d10e06756dbb1806e763,
'dcw': 0x624133ce9cba7c5d74672070990532b7,
'ecw': 0xeba67d038353456346fd8f0a2c6aa7ba,
'fcw': 0xa6fda85cc34eb070dea2a1be39063067,
'gcw': 0xf5597b8fd99b62bbcc29ab47854edcb3,
'hcw': 0x14f46bab70bff947c19b566f60bfb7cb,
'icw': 0x4a91c71ac3da9237376368b19ee8eea7,
'jcw': 0x968ada64dd5242f9b6e60ff6b89e7644,
'kcw': 0xc48f5daf373f87e5e3d7b08d9f9a8215,
'lcw': 0xa1dc6e64cb6ecedc99c3388af49d64d4,
'mcw': 0x58c09bcc6a736a715b3afb63f349d0a0,
'ncw': 0xbc4eee572ad38f8a5c2534e0b2533100,
'ocw': 0xc08501e2be48a9318db339a5a86e963e,
'pcw': 0xe425a90e3a9291dde6447ffaa7bb9472,
'qcw': 0xbf8c5f8f5cd1497fb9e6a819a37eac47,
'rcw': 0xd214fb97d5a4cb10a8682136c1ee46fb,
'scw': 0x7a933c7b91d1083c2f18c8acbeb4c5fb,
'tcw': 0xe8c34469f6a974f8c1ef4afdf59b55c8,
'ucw': 0x6f6621d6436fdd23671fd9dd9a53e3e6,
'vcw': 0x819991f8b6348d281ffecae29e16cc54,
'wcw': 0xb6c235909d7aaf46b579aae8da648af4,
'xcw': 0x3769d01318cfbf48be4bb0f3bde4bd55,
'ycw': 0x2c08ab48bc5b804b9235e5bebac74d85,
'zcw': 0x5af6814c150d71e4d9a2fa5d26c0f667,
'adw': 0xd1b46720b256ff6a478a806acc251047,
'bdw': 0xcadf54049be2d757105d745321480ab6,
'cdw': 0xf262be313467a4ce3ec02c8ee6535ffb,
'ddw': 0x3aa56b5882f28159b323b41ca6917fa4,
'edw': 0x151bef5624d2839482b1d021f9eddb83,
'fdw': 0xe444854cea1af56e4dd19ccf02677629,
'gdw': 0x2fb8039b83bfa0e432a80feb302bb700,
'hdw': 0x7f9ecf9352443254121021599f88b567,
'idw': 0x2f1acf712c5822318b01275448b3f058,
'jdw': 0xa4017d6322482403bc409f0533b0f84b,
'kdw': 0x411e8d42e8b9c5c97f9137929df7aa80,
'ldw': 0x23df29b5eb874d79e88adb948fe71124,
'mdw': 0xcf35f5031a0f4c135f050fc1d148d3ad,
'ndw': 0xabdbd42ab1edfab3048f043eade4c0d4,
'odw': 0x07b0cbb00cbe69061217b12df5eb104e,
'pdw': 0xa04c507a807f8bdd7543a397a71b0ac4,
'qdw': 0xda2755f208eb79fb2973465ab0d71bfd,
'rdw': 0x4559adfc323e82f49c24a5496859abc4,
'sdw': 0xe6b1d85b6b7cc0ef1959d94f0cc3e2d8,
'tdw': 0x7f9c81ab2d49d0fdc38fcb94a1fe98b3,
'udw': 0x2f585bc144649ecbd2dbf5e4107797ab,
'vdw': 0x1c0de9bd43ae302260a0d0997d2ed075,
'wdw': 0x9d03f4c2dae07ef9153d4b31328c110d,
'xdw': 0xcc9d1dfe18fd74d677bb122de768663a,
'ydw': 0xe1b56aa59c7c67da4f4ea647c4a00713,
'zdw': 0x6f43f6b19cf54b5ce90c4e9b2ac42f67,
'aew': 0x0e60dec2ca8cc1bf1f99088d61e4d9a3,
'bew': 0x779f1ada394f9185cef93a0af9506dbb,
'cew': 0x6ba813f51471206a2d1a8288e5a125d5,
'dew': 0x1801bc89e752077204c92b3dd9f9f62d,
'eew': 0x81f4ad03b62421561eab7f9295601aa6,
'few': 0x7c3daa31f887c333291d5cf04e541db5,
'gew': 0x76c7f65881bcc6f33a10cd894e79c2bd,
'hew': 0xe6e41a1ae2a7f7f67245863f2e0cc878,
'iew': 0x96cbc374222d5e18ac3c523774eedddf,
'jew': 0x0505c8383fd19e8fd720a777738a57a1,
'kew': 0xd113a6b12a72d9f37b78d1f254de6cf2,
'lew': 0x09562b589307511fd158dd516e583ac9,
'mew': 0x4b673ef9d9869d2661a128b4d115f5fc,
'new': 0x22af645d1859cb5ca6da0c484f1f37ea,
'oew': 0xb68f1b8d02222f88a5a545a66fd31ea7,
'pew': 0x08db7753f1701612b8524b87f1177518,
'qew': 0x466bd066eaea252f4853611938852cfc,
'rew': 0xb37c8dd8b83fce9b7239550976117d7b,
'sew': 0x48519f7557e1b0f05c65a0455dc64fb1,
'tew': 0x5b06fdaed4d25b346b65c59d42d9e4df,
'uew': 0x6ad8842d614eaea7160450e1ca226006,
'vew': 0x6fd6b4802d878103b22e519bec3a722b,
'wew': 0x3847820138564525205299f1f444c5ec,
'xew': 0x3a308fa7c259648bf508bb444be59599,
'yew': 0x24c56ae68137903725d16a0c67965a68,
'zew': 0xcdbecba26b7e90271ae37bebccb1c586,
'afw': 0x8b08f67e52084cfb3ed9d81588fd966b,
'bfw': 0x5cd1e52052505f14ddba45782f0049e2,
'cfw': 0x0b88134e3881a422fbba84e16ce7f021,
'dfw': 0x3a63ee463554269952ed5200ff36ba77,
'efw': 0x6ce22b387783a421a2d13d5dce477b48,
'ffw': 0x39af34ca22078e972611b21fd5ae0834,
'gfw': 0xfcc647f68de5a5f8e3aaf6dc3e76da7b,
'hfw': 0x6744ab25915337667431fe838184b4d9,
'ifw': 0x52e481b202a7f8928c72b11b3401da79,
'jfw': 0x0de880d51affa149c57de7abcd25ef85,
'kfw': 0x85f933844890e20f1513313814687689,
'lfw': 0x598d3faa1ccc7bce900c7bc0eb7ce51d,
'mfw': 0xa89d168e9ac50fb36328d5a8de6ef164,
'nfw': 0x42f23d33e8fbc9b8e59649e84e945b98,
'ofw': 0x04d7406c1105fd37bae14bec0bfbb3e1,
'pfw': 0xfa6bb2e26a0ccfef79a2df429956092b,
'qfw': 0x0e6f0eb3457a486f0e0c4d40be01a3ad,
'rfw': 0x1925706104145ecf52dd0ce36f603787,
'sfw': 0x4b5c3f3fc5fac1527f906b29f5ae340d,
'tfw': 0xaf4a335ed97c70fe408eefb33bc9c204,
'ufw': 0x43a8c9840722971d7bc00f689aaba839,
'vfw': 0xcb01810ab57bcf66384514e780a4e3db,
'wfw': 0x1b0a09e29a394014949b21db3aa14f44,
'xfw': 0x7ac932a4ae2bf7948ddea565066118a6,
'yfw': 0xc4b614286f958779c107087bcc07a61c,
'zfw': 0x9d5525690691a83806eb012ecb8ff097,
'agw': 0x698f7a896e1a902919f1b4d6cf60f894,
'bgw': 0x2da2dea17e18faabbcdaa0bc5127a755,
'cgw': 0x7b5fffa37a5d7399efed0aae2766dd5b,
'dgw': 0xcaa2c83924f85954b2701ba3b9777c82,
'egw': 0x6f5808953093eb5b6e77ed59e73368c6,
'fgw': 0x33c674a20d2a473a5a981b43f6dca344,
'ggw': 0xbb4d63bce78a8400d32b22d9782e57be,
'hgw': 0x0d844991be6cbacd3c088c2b6966eb13,
'igw': 0xb9a27ba5d63341d8ce736acf7091c0eb,
'jgw': 0xb710e19c69cc2fd07bdc2ecbe491f013,
'kgw': 0xe6dc0c4602cef88eaaa93d07ef930fc3,
'lgw': 0x03f79b3ef0376c1fa40d0e5c94f9ec44,
'mgw': 0x62a4dfbbb4de5c046cb446ebba0439ee,
'ngw': 0x18bc46ea906c5e95246bbb19e89e9a2c,
'ogw': 0x3f6a3b96384c5ea02a5bf7c5d55fc2e6,
'pgw': 0x0834a333659946af21f6e607f4cc8479,
'qgw': 0xeab76ddcb2044273cbbebd8bc1f82c7a,
'rgw': 0x18a677ec6194e7a14c366c67113f85e8,
'sgw': 0x7f077fd9780dbf3666a11781ea262e60,
'tgw': 0x9df8ab1d28727d0f640b1517d5e49c6f,
'ugw': 0x327704f2a792173d8fe896e6f6ac8d1a,
'vgw': 0x653ff9403da2d3d06f006109ab09c52a,
'wgw': 0x3dc2a78a9648330c062cb819ca5432c7,
'xgw': 0x280b846a2d191a76dbba1e54daafbe8d,
'ygw': 0x59f45d54651c465b98f68cb683e32336,
'zgw': 0x9ea9c44a5b92051ac2488e9aebc07a43,
'ahw': 0x239cdb881d55a19c6fbbc086f86e4bcc,
'bhw': 0x6adcff9bb6c324d349dfd67c82e1e832,
'chw': 0xf5b84b40469cbcfaa4cd7183856ecfaf,
'dhw': 0xdb75a3877d61df1a6cb85e0af3254de2,
'ehw': 0x86a2ba5fd795ea0e7250928e04f97df4,
'fhw': 0x6bfdbeca6999109928337ef6636ec28c,
'ghw': 0xd9a4a2f868deb8150cb9d711dc3cc9e6,
'hhw': 0x3b518414c8109f940922e44bc4e1a4ce,
'ihw': 0x928b9270a33bd4446b89bf50ec597784,
'jhw': 0xff2e7b04a947d394c5e052e22692bbbd,
'khw': 0xf68cea2a49cc80159f5d4f8e00f31e35,
'lhw': 0xb9a18c36770541586817f3c4de118b9d,
'mhw': 0xba210afe3f02f05e1ffbbeab26ceef8b,
'nhw': 0xf62643d4eb337378d2b2c1e90f09b793,
'ohw': 0x7d1862d416dbae7ebb8019e1ca70da23,
'phw': 0x4fddfef0324a0baff6cad34fcb5403a8,
'qhw': 0xcec414197739819e61f875b8304dad18,
'rhw': 0xe4ad856731c1b986c99da3474f31559f,
'shw': 0x0263d0d9af0bdb2b2637454dc727022e,
'thw': 0x13537517db0f89e5fbc60b177640a437,
'uhw': 0xd99b5b27e516db11c15151621721ad3d,
'vhw': 0x6abf051aec6d4751b189bc17299b5693,
'whw': 0x31cc6d20f2b49e25ed56eef37a4871ec,
'xhw': 0x4164c07000f11b2e1d14a8b91e377e2d,
'yhw': 0x923defd0cc776bef78663b2f991f978b,
'zhw': 0xcae0e7fa9ab5739e03afe830ef0e6535,
'aiw': 0xb699259e658fd46d6a8a0233448cda4b,
'biw': 0xb095f94c40a51d105766c33344d7af73,
'ciw': 0x28c328b109720ec2c2d4a931377dbace,
'diw': 0x279c94b505626602042e701f75222c3b,
'eiw': 0xa84177a3a930ea0f1e51170b24c85e6d,
'fiw': 0x75af3670feb250716dcaaa14f40aab94,
'giw': 0x9daa63cb1687dccd21f913b3f56daf9e,
'hiw': 0x2b4b3f61c60303db21cf2d5f77f5f944,
'iiw': 0x4fbe49be988adf8095181ca7aeb4952f,
'jiw': 0xb331cdbcac62b6cc4bf5c0c702c983ed,
'kiw': 0xdf5e9b26ad759fc90c579af4b91e8733,
'liw': 0x395ca8e1baa4d206949809711457a9a6,
'miw': 0xb9049b87140c97cbe2fa70786c70f7cf,
'niw': 0x032828e503aea60bf1c60838f7bacb8b,
'oiw': 0x3a8cb832bca6da72735b742014560a5b,
'piw': 0xa0af4cb370fdd305767e06f4b798870a,
'qiw': 0x8332197511e844102266246bd368122d,
'riw': 0xaa6e65a9a33737d13e265572ee6fb79b,
'siw': 0x2740ff8426180708839a48d42b15a7dd,
'tiw': 0x1f4ef2b0011e8537ccfc089c921bb643,
'uiw': 0xed1c12a6d9e95579867f3e6f96b1d275,
'viw': 0x657095cd7a4e67bc1822ffee9882c766,
'wiw': 0x329cfe5146c03a56147a0521fc9a014c,
'xiw': 0x5c993fdb16f9daac95a70944fa3da0a0,
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'ziw': 0x286014348d72389de4c93a06f7c62cda,
'ajw': 0xaec34046930190bdeecd2e3d4ec1aa37,
'bjw': 0xb6bb2be28bb33f3a1122b159f239d2ab,
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'ejw': 0xf700129980233ae6342c0b7ab5c35a9e,
'fjw': 0x278eebe16ef42b3c3e456a0d53a09c5a,
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'jjw': 0xa8be27ec3d0cae48e5868dc9c798610b,
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'ljw': 0x055b3c3b2672015a14cefab16264d31c,
'mjw': 0xa25e32c96dae24244f159a19464da7e1,
'njw': 0x3d90e44ca7d875a570cfb41dcdfc6f09,
'ojw': 0x86e8337706589e2599330237d0b20358,
'pjw': 0x30ee6da815f673d5e7d46264312d83f0,
'qjw': 0x69ce331cbc83208197749757161559ba,
'rjw': 0x68b4ac2e681195aa861b23d0ed7a388b,
'sjw': 0x8d269ba47634f0aff4fdd7c9d87dd808,
'tjw': 0x7cc37be15993f69450e7498584d127d1,
'ujw': 0x682b53c8552ec0d1f83e44e2dfb56504,
'vjw': 0xe8d82bdfb8e4045c952cbd3fb6349046,
'wjw': 0x566390159abb136aa0b984e8ed39a620,
'xjw': 0x367aec57ad250d11deb6486e74e425d7,
'yjw': 0x28f008e37ddf3675d5e3cf4a5f23ab45,
'zjw': 0x51e10bdbf0ecb7f46aef1a7476ccc250,
'akw': 0x4d53f23b03cb47d189b1886b0475bc37,
'bkw': 0x4a4710479d99df5602f7968933d9653b,
'ckw': 0xbbb4b38004869f86a753f937cc26dce8,
'dkw': 0x0d9e2868717c5a8ee8a66951fb5f5d76,
'ekw': 0xe7b0f284a4525b9d1d39a8d3cf055be8,
'fkw': 0x63a02fdb5709fd5a37cd6d3d9b006535,
'gkw': 0x8d8b1480d8149a2cbee65776b0ac0974,
'hkw': 0x92251762a4d475ee95af9a349abb3dbe,
'ikw': 0x0567cc2dd35ecac386e42efe388bf4fc,
'jkw': 0x444ca0ed9f714f1aeeeefafbcf17d37a,
'kkw': 0xd0498b14cc182ce00fa7ceefdee3510e,
'lkw': 0x666b8ebd93b23b67710523c66a812dea,
'mkw': 0xb49c2a25091d7a2dc445e0748b05f44a,
'nkw': 0x2644ffce411556d843400f5a0e34fc97,
'okw': 0x4f305e1b9cc5e07a2e3f3f35f911461a,
'pkw': 0x3cae96074a8a354cb8ca5aa72167b90b,
'qkw': 0x77f9bea1d126d5780be1460c7a86b8f5,
'rkw': 0xc5b44508d7a3c05c3c1edb8bf633d374,
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'tkw': 0xc1e9eed508a1dca77973418434e0df09,
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'vkw': 0x56cbe24ec5d47f47aece4fe400e96b04,
'wkw': 0x246a85de7f4bd902f11631d214173c0c,
'xkw': 0x4d22b8eaa4985b633d84ebcd7f5930f5,
'ykw': 0x847cc5e21d1105090fe30759e139e871,
'zkw': 0x32bb80a7de09b632e16db661c662066b,
'alw': 0x07948a7072a14fcdf4245170d594f356,
'blw': 0xa59dd2af3bf0f82029eb63521027672f,
'clw': 0xe184e530ba65b987a85ef4dce13f3dee,
'dlw': 0x94eafd1d8b59ff3a991e9f226b023a29,
'elw': 0xc309521498d2074c1e456c814442bb68,
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'glw': 0x353cd609d9870b03de864eb73c3ee913,
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'mlw': 0x13be1cc047e046a5761f3b5f5a57826f,
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'olw': 0x4656f81a579f0400ddf0fb11f43238bd,
'plw': 0x789a1affd3f0c6ad168a3c49feaac8a1,
'qlw': 0x1ff33748eb01f2913951d2d69b75928d,
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'tlw': 0xcb3d0ce16662b8468d35bf2664e095ba,
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'xlw': 0xd2b5d3637749f6c9d654cfc303000854,
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'zlw': 0x6208bc99891b9e4320813c727f962549,
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'cmw': 0xdf05c0a274091592e1a5b8c6e7fba954,
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'emw': 0x8176dfcfdee1aef1aaa60b52c298b1a3,
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'gmw': 0x8b83e0f1cfd0d896920b1cc6969024ef,
'hmw': 0x7286f811bf83d0cdd68148f00558a06a,
'imw': 0x4fff2838f627379c2ca60a7d206287fd,
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'nmw': 0xeac221c240a3a90c496e0535dffec858,
'omw': 0xbafa0c53cc6fcdf04d2813af832659e2,
'pmw': 0xa856b51e195143ee94bd336a0a6dabd0,
'qmw': 0xab9d54ca1f04edfec3d64cc084dcb2aa,
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'tmw': 0x47be921d9bd6480645c54800fd3ef528,
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'vmw': 0xafc0cec4f72bb7f268176a997207da9c,
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'zmw': 0xddc7256889fd96339160318036414a1e,
'anw': 0x027c1e5c6cb634c8b7a284a314ba23d8,
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'cnw': 0xc0d0b9e7950120d4b0ed185883b16f71,
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'enw': 0xac56699b2a5254914100cf7313192529,
'fnw': 0xc6e2b102e2fc343423357fb458e37400,
'gnw': 0x32dc3cfd1b4d9c33daa5ee54be2c644a,
'hnw': 0xecddcb6952f0ed8c608d8f2c481c9bc2,
'inw': 0x19a4bddbd7236bd066bb1c3dd8cb6cab,
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'knw': 0x959005d3e6b49550c8dc882766170be0,
'lnw': 0x2c3673864097ffa10e07be6584656e4e,
'mnw': 0xa90364f2e445df4b74247c4185f6c5f9,
'nnw': 0xffe1916f1889e4233dea59f44897f66c,
'onw': 0x5d7bdb95668e42073bfec699ad500e98,
'pnw': 0xdae2e509bee5240f883ccbfed3b54eb5,
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'rnw': 0xdd84c7eb321c43d7a7599caddcb5c595,
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'tnw': 0xd188c38daf0ff3539f30165837c57a5f,
'unw': 0xbc5540f67799f9fae6dcade25dcd7ac2,
'vnw': 0xe584b399d597334f4b3514ba3f6fe2c9,
'wnw': 0x812995188a50fa0e156915f6c786e5f5,
'xnw': 0xbb9350f8cad23b37d83552ebbd4d0f19,
'ynw': 0x05082c1251c2ab6956e8d1395cd4ac2c,
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'aow': 0x70f4c329c3e4aa37eeb62a5461955699,
'bow': 0x1822266030c65a00f35ba3836cd61158,
'cow': 0x81566e986cf8cc685a05ac5b634af7f8,
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'eow': 0x5786279758c9064fe43a17aafa78f80e,
'fow': 0x2cc2feb15a247bfbe31f76d3a5fd6096,
'gow': 0xd51b2bf666420e87ab91d08ef07f2e08,
'how': 0xdb88a0257c220dbfdd2e40f6152d6a8d,
'iow': 0x3a42db1e3f0bcc290681fd1dc4958b6a,
'jow': 0xe34678c80868038ff7d2d26e2e5dbfc3,
'kow': 0xa9d489591e78210fbd5190249af8725e,
'low': 0x53cced8d281a1a0ace3cb6594daaa4f7,
'mow': 0xc135d68899c826ced7c49bc76aa94890,
'now': 0x97bc592b27a9ada2d9a4bb418ed0ebed,
'oow': 0xaa17058e91a30b37eb104fefbdac0083,
'pow': 0x30d7e0494351def45591fccb21d3510b,
'qow': 0x6acf943e2718659a8a7e3b0b0b3a2537,
'row': 0xf1965a857bc285d26fe22023aa5ab50d,
'sow': 0xd525706d6f19ea00e7d7a4574f7f5e3f,
'tow': 0xba1fa7e7999dd72ff18d0e6d5d6e6c93,
'uow': 0x835a53a2046404d0614c8baba430d429,
'vow': 0x1b36a396c06bbcecdd2740c9716bb331,
'wow': 0xbcedc450f8481e89b1445069acdc3dd9,
'xow': 0x4af326f218bea71da1c18cab731fb13b,
'yow': 0x7ca1b044172f8347e4927c4fba43e7f0,
'zow': 0x33653230d637b1f40932585db0d21c94,
'apw': 0x0beee8164a92f9fe12c408468b4c9330,
'bpw': 0xb4a33bee3337726c86b51d03c4c846d1,
'cpw': 0xeb1b24702ab3af352eae9a67ef953b9c,
'dpw': 0x6c921336b6912406a062d3ad95e94843,
'epw': 0xd08aec141d473ca2d7161de34adee514,
'fpw': 0x1bece3ac37239f83fee97981e7368633,
'gpw': 0xf27b2e56f097d124c7e69ecdc42f95a5,
'hpw': 0xf9c06cecffb243f3e499e8c7d5123db1,
'ipw': 0x6a6dd801e20ae4f34ec964ecbf76d562,
'jpw': 0x9203f0e7b6a1f79dc700ce934d8d5fdb,
'kpw': 0x1b1f9c1719f4d2907364befe40f10948,
'lpw': 0x0076215514891b38fdd03ef512907176,
'mpw': 0xc3e57e97f4c290780d0b9f7a20942870,
'npw': 0x4998522bdca59f31a0840d7ee9260e63,
'opw': 0xfd2b55386a7e7d4c353627b9e662a3ad,
'ppw': 0xa9da227e8e0f1857751e833d99de07e9,
'qpw': 0x0d107b5fc5cb42327db91cf9b7eebbb4,
'rpw': 0x3ddaaad69e8212003ed323d670ac47dd,
'spw': 0xb8eb3759fdbc886370a1b550e39d1190,
'tpw': 0xff9dc7b49f1580d2da7e19f703521881,
'upw': 0xbb5f57abb358171ffd2686d0a155a94b,
'vpw': 0x601368d32dbe678d2cc8331c2c1631e4,
'wpw': 0x2e45a4f932206795497f45edd3218e80,
'xpw': 0x8a3597f82b305ba653a48e99bef29805,
'ypw': 0xda49964ae5907cb3df42734661fd10e7,
'zpw': 0xc4fd2bdd6c6f3a7897ebdfdb47edbe63,
'aqw': 0xf15779c65bf7141196d01ae83f19ac83,
'bqw': 0x5cf9a545d72d1955e9da09c7fb94fde9,
'cqw': 0x6b0624c14a4871b35dc64877d06dc498,
'dqw': 0xc7e7bebd73a4eb2dd8ebaa018b141019,
'eqw': 0x6bdcbb606161c47eab0615ff6e13313f,
'fqw': 0x8837ad1dae74f7a2906e19280d166255,
'gqw': 0x25e13b441dcb27cc6614123853132c24,
'hqw': 0x8911a402714beb3180a9d396eedfeef3,
'iqw': 0x368ebd944aca831155ff176203e3566d,
'jqw': 0xedcc2fc7c4062a1b8f44e42922f28993,
'kqw': 0xf8db32fef41d89f3302973d023b37e07,
'lqw': 0x06076fd0e89d55aec71d3788c424e1b1,
'mqw': 0xabfab50f2e08d6d4cfb353338c9f56a2,
'nqw': 0xd033a8c60578ce5650840512fe9400e5,
'oqw': 0x8337a8670c11785fbcfd73a4ca85a897,
'pqw': 0x2b2ae5cf3a2a224699462eb8f6b9c842,
'qqw': 0x2327286446091a9cab0ecf56b7d196f4,
'rqw': 0xe973a11711a11e0dd62ef6b640eaa498,
'sqw': 0x84b6de7a7151f25f1f26211ac059b4bd,
'tqw': 0x65dbee4581840474dfc6c1779633446e,
'uqw': 0x613fb3cb5e92a7ae60638d9a78f84470,
'vqw': 0xc46e224feb33a5fcdb2f031b9a595be2,
'wqw': 0xec6de7d1c993a8e7fb0cb0948356e703,
'xqw': 0x7e675a2473199c67d5665fc9517b4faa,
'yqw': 0xf29548a1ad160be855f9fbaf6016a132,
'zqw': 0xb40a2b005da5f4f9188c817be51d39ab,
'arw': 0x6b366f3742ee0c85432245dd5f45688a,
'brw': 0x81fcd3a7427ea13b04abc3c06209d476,
'crw': 0xebcc8bac51a9125349ef4fd40a03a058,
'drw': 0xf5709af03c9b28dc263042e62d15b506,
'erw': 0xa893f8f514feb300f3283947f20b253a,
'frw': 0x8f79b00ab448dc769d122b6ab77896f3,
'grw': 0xaac907a23bcfc468a3ba30959a772aa5,
'hrw': 0xa09c2006939c62dad81b39cdb96ee8bf,
'irw': 0xb5d91885fc42ba03aaebba49ef6e7513,
'jrw': 0xbf3302d057c10c59c6d4cee9270c6892,
'krw': 0xa3d723f149082126eb678ac13980f8ad,
'lrw': 0x2a4577c76588402123943d8ce5c72d62,
'mrw': 0x4ed2211cec0955e0dc71827a501558d4,
'nrw': 0x92ca884a680bba38e75a59000b8c5fdb,
'orw': 0x779243c8c5097c6920d52b79d31f0d4b,
'prw': 0xac46aab16f0a9424fe445fabe468a6b1,
'qrw': 0x175b902c0a9f5af2224bf4097561acfa,
'rrw': 0x1bd392fdd9bbcd3c20187ffbc32b0dcb,
'srw': 0x8518de53b8b91ad4523a3a7c510a13e0,
'trw': 0x5ff7405aed6ee160acb384287a45b298,
'urw': 0xcf3dbbd678db09f839e2e6cb31792ba2,
'vrw': 0xb080047706609e0e6a20046ef7008bf1,
'wrw': 0xd69dc8c061f6093da232e19c85e6b5f2,
'xrw': 0xbaeb8916914032e13f7c99fd42d04042,
'yrw': 0x4c8dfcc0e80133f5c1bbf72ace2a4d61,
'zrw': 0xc888f3dd6a385becf18970f14086b692,
'asw': 0xdd0ee4aa584f539800e1443fdf250d38,
'bsw': 0x3ff10c9b3f975a38e927c654e60d25bd,
'csw': 0x7da518a55e2d9c4c217a921aea54d45c,
'dsw': 0x2f4aa615ea15b3b3756a6082968bd103,
'esw': 0x0ad73f25374ed0e5ee7ad713030642bc,
'fsw': 0x37dbe588ad5d6d0ea03e609c8fa7a98b,
'gsw': 0x2e6caa66096f1d4bed0fe21ee5468fe2,
'hsw': 0x260364ad96a4618f14199aad78710295,
'isw': 0x42b0876ab3ba2f83356436a24cf80600,
'jsw': 0xbec83dea67aa407f58e5869df68f952e,
'ksw': 0x858d95ff49fe15c5255ea0999c607f15,
'lsw': 0x45629f7a7509bf9f743bf0a0004b3ee4,
'msw': 0x3d0272e1f71768b309817c93e9a59c70,
'nsw': 0xc3768fe04ce22117d45f8d401c628de3,
'osw': 0x94c0462d05e46dfe8d15e86dc429b6f4,
'psw': 0x7bb483729b5a8e26f73e1831cde5b842,
'qsw': 0x7c79746d7be0e8ab87136aa0f7069df8,
'rsw': 0x6ad83d7697fc8bccf6baadc7f77253ca,
'ssw': 0xfc11f35fe2b9f3609799a7f0abb4f30e,
'tsw': 0x9d0225dec53603fad8ab1246627d2da0,
'usw': 0x2cc2c74685f65f453bf6a6df1fc560df,
'vsw': 0x0500742f383e18cfd3c0fdd3a049409d,
'wsw': 0x1fe06a09833dd0eaf46927e9a406a1c5,
'xsw': 0xc6b2a21e35cd83836c2906273cd823ce,
'ysw': 0x46f97d63f32a67ffb83d983b22aaee21,
'zsw': 0xbed372cc00b0cf686847016882ca53b1,
'atw': 0xd0a263288fcf1d42ff956f457f3fcc01,
'btw': 0xa1f26721ed609e446e95f43a51951b49,
'ctw': 0x7c0b8b75c27cd5860e1ba09312e8351e,
'dtw': 0x6562a22d7bdaf9fa7e0e6c850fe9e18a,
'etw': 0x9e73b5f9aca3bcfcc13f110a91f553b6,
'ftw': 0x830266bc8848e9e0b62835f6516d9327,
'gtw': 0xa6aa2dee95afccb06f85436affd3f586,
'htw': 0x0fe63a2be16c17289a5405fafc0f9b7b,
'itw': 0x57aa68400cd56444e4117b74ba9e94e9,
'jtw': 0xec6bfd88596eb8712d707415f439efcf,
'ktw': 0x97201321153ccb19a792b400eece7275,
'ltw': 0x1bd6c5f0d437582415cd8ca55ec2126e,
'mtw': 0xfb09877df11005e0904f84f5c0c1dedc,
'ntw': 0xc08272a164451b59155217bbf691e92a,
'otw': 0xa5df22d4964a538c1531f12352824bf0,
'ptw': 0x631e10fb56715eb9d7f484aa1510d485,
'qtw': 0xba2268dae2ff4e0b832c695456fb1ac9,
'rtw': 0x5ffeb6c0e55ee6b6f27d8788fd052f77,
'stw': 0xc3fc1d219b0ef8de1abfdfeabc98ec82,
'ttw': 0xfff3fa8549d01bc6ac23ca9fc77088d4,
'utw': 0x1e0fb8c4772fb546e997b851bc4afa66,
'vtw': 0xa398517a0eb8d1c48f009e5e6e9d3a59,
'wtw': 0x15cdee57398ba71d861076d38eee91e1,
'xtw': 0x1976952cb57330e9bf2094308c6e4faf,
'ytw': 0xdaf293263b1d923664ad8061a71a5d13,
'ztw': 0x6b7cb26b9203ae8f8c9c84495c1699c4,
'auw': 0x725a68991f9d6af8777b726fe8d2ad4f,
'buw': 0xf32eee73bb6a429d2178cd03a1a8b8fd,
'cuw': 0x96813564d7708478564f4bddbcc42cbd,
'duw': 0x68b80bdfae7cde1ae4bc97973fe5c3e7,
'euw': 0xb1406c2e9e50aa8962bb578d915be0f7,
'fuw': 0xc299be4a029f82262c396a67e13036a6,
'guw': 0xe54bbb8741d55358e5860639bb69dab2,
'huw': 0xc7a7d82ebd55d81bcf09d2160bf11358,
'iuw': 0x1797f28fee8554211806fd04601f442e,
'juw': 0x1a03096d349affd7f8ba9d9b35a92f63,
'kuw': 0x3574aff270ef4c919b43dccacf7e7070,
'luw': 0x09a9a8b864baa423793ddbe5ad27d1d2,
'muw': 0xdd62eedcf65f552221ed06e9c4a55738,
'nuw': 0x1cb6ecf47d175b4055a768fee5f76e97,
'ouw': 0x54d4f91054f348ac0369e804ec9e7d48,
'puw': 0x607705336de4a582a2544ee5a1c12c11,
'quw': 0x5a326ad0f5332ad9256eaafb95d9ea27,
'ruw': 0xe2da7783eab9f9a21d60bda23908d60e,
'suw': 0xcddd8e3013021bee72212d58030b6ede,
'tuw': 0x1584dd753e5555dc732ca2f8292ab7a0,
'uuw': 0x3cd1d0d8ed949cf9094f07293081d162,
'vuw': 0xcb5c7872cb398351244880e46a1351f8,
'wuw': 0x566e464e5e3dde68655c3b8f1e9f5e09,
'xuw': 0xafe2753211f186f81ba842b01c4b481f,
'yuw': 0x2e5e039b79c1778be3c8b0e78c766552,
'zuw': 0x73da2763d8070c9161abb1af5680debb,
'avw': 0x4a07df7d16253bcdb03104f7ee66c2be,
'bvw': 0x11d7f10830a3cc6c643de677962d0f71,
'cvw': 0x25d81324ff2a3ac0b5395d6dd2ca3573,
'dvw': 0x0a4e205dbc8ba4634168756e30770e29,
'evw': 0xd2953c23b362c08ee3ea4670ce6f4af4,
'fvw': 0x1774ab3b64756296340d35bbf72bcf81,
'gvw': 0x847fdadfb812a6895b3dba2b37dbe197,
'hvw': 0xdf157b4bde57c8d654b6d9051d5408f1,
'ivw': 0xec989ef3f3eded2f3b914ff03bcf6fad,
'jvw': 0x230f0cac46a8789d8a37082535acb4a3,
'kvw': 0x8de390d8f4f44c627164453ee4e5050c,
'lvw': 0x6dad0253a997c10bd2a07060816fdbb3,
'mvw': 0x80c5524eb3bfeed0fb04b256adfa99c9,
'nvw': 0x4c623dd0725257918870c12445336fe7,
'ovw': 0x36d3518d698522bdebdb405657026085,
'pvw': 0xb2809eeb41db52020cb25fc1e8738deb,
'qvw': 0x9692905576dce63747e1c1fe4b9d55c0,
'rvw': 0xa5eaeef867cd7f07985c929812f1c1bb,
'svw': 0x46460edc0af788d1b401f4590b3c9c22,
'tvw': 0xf5f7d1a9066b8387930e39569e712a40,
'uvw': 0x53f21197fd88556f12d066faea1684e9,
'vvw': 0x8910d8f1598532f64e156b8c68941e9f,
'wvw': 0x15c07d0841fe07ef236cbc40290989bd,
'xvw': 0x16f1c51e405f7a5142ad997ad06196b2,
'yvw': 0x8997fd4928293aa4ade93699b3b11aca,
'zvw': 0x4fa18eb5e362bca0a00019515ff6e4cd,
'aww': 0x31d5c3221900f07120d3cfd1b2961ded,
'bww': 0xd3d69c7500b00ba1b96518d1347ffca4,
'cww': 0xf666ba62c4fa321c0d91598eae0796b9,
'dww': 0x9a1edacbd8ce551077530c7c1099cbd1,
'eww': 0xc69c4f782cb4d2cfb046fd1038802efe,
'fww': 0x77fd7d3af917dff1e51bc5eee966ff52,
'gww': 0x13a2967c7e1f18785c839e5c8c0c9c71,
'hww': 0xf0ee226443c92644bd871ff19a6ec4b1,
'iww': 0xf21677aaeb24c9fcf4b0ce877843066f,
'jww': 0x83faa89c7c6fe1661edfbb4135853b15,
'kww': 0x30e0bb29e8f1760694f6732632c14d88,
'lww': 0xf32c784dea99459143adebdf4d2f4b7f,
'mww': 0x6d9d1047b329628d927659f7a0a68df3,
'nww': 0x5fbc24308cdb3d9f28a263f01f27ade9,
'oww': 0x47f3e0d9f9ba7f1bfb745acd50a1b37f,
'pww': 0xed0518100ede5b9f9938eadf2f608e8d,
'qww': 0x870e856360391980a51958b91f12cb3d,
'rww': 0x2035f1be6e06c2f27780732435a90217,
'sww': 0xe0141b91c05a0a3628ad1100d3b81270,
'tww': 0xfc89965d47de0e136dc0d29e7e19c2ba,
'uww': 0xc3b6777d3666a3ad00be4130437e4954,
'vww': 0x300e193ec1ac5dd2a4f4e93c88605cf4,
'www': 0x4eae35f1b35977a00ebd8086c259d4c9,
'xww': 0x885c1720445f323fa7d453e42972d9c2,
'yww': 0xaf12312420b4a4546bc981ccd50a10ac,
'zww': 0x5c26fb625f320f7a011d87f34dc1ec54,
'axw': 0x128c17bfb82f27ac4f27e6f165924264,
'bxw': 0x3c641700a855f8a7cd5e4aba78bf8112,
'cxw': 0x4ad91395f37cb7f760d9f8413f31fe01,
'dxw': 0xf0080e9f5a75f8c376d6e51294465d64,
'exw': 0x4f2d2e812829f1b2d1b5056c2fd5bd4a,
'fxw': 0x657c96a87c2c20e69ae61d623c3e5612,
'gxw': 0x9efafdfe32c34684237d9c0e85688082,
'hxw': 0x9a467a5505368da0c81ca19a6b774b6c,
'ixw': 0x612aa317c0972e3cb93804a898f060ff,
'jxw': 0x2599f13fe7966b8616b46c0d221a8e79,
'kxw': 0x7661913d97a6509803c27ebf57babc09,
'lxw': 0x275939945caa6f44d32a2bdb9687ca47,
'mxw': 0x02c3ac996efb4872b235aeea4c87d8db,
'nxw': 0xb7276aeb6faaf882c4a96c691f50d236,
'oxw': 0x3fcab0d19c79fe37cb1724aca7ca0bff,
'pxw': 0x97324e70943d8682e7454dd8cc63557f,
'qxw': 0x01acd4ffe1bfc4946fc7637b045e76e6,
'rxw': 0x6a5d4b726e15a9df41f528512b53f483,
'sxw': 0x5b09607bbe69339146676a2e3f696419,
'txw': 0xfba6913ae91d481fe060ccdd56eef881,
'uxw': 0x795b698217c14b3e2d0a74e364c742cf,
'vxw': 0x978f8dc4297d50bff82dbe197f1fbc90,
'wxw': 0xfd05bdd45e875e3b28606bc215d5498b,
'xxw': 0xbeca3389582d0f65ce6e536c053619ff,
'yxw': 0x6f76e9bf0e259b2b70da8f9be1037b0d,
'zxw': 0xaf52a37ed4f916399826961fd303bd48,
'ayw': 0x0a5c57c87e28a2641062c8e09f40b46e,
'byw': 0x3ce420b0aecfa257b7d5771b31f52b11,
'cyw': 0x5b70b128c9b1898779fcddc66e9fb741,
'dyw': 0x669852fce132d7b74c2c5d5d9c665db9,
'eyw': 0x0b5db25b33f2cca48556123209b429d2,
'fyw': 0x71ec810069902f5fdc7cbb21bac94ced,
'gyw': 0x73509eabb28c3730e29e4825fe6a1841,
'hyw': 0x8d13c34804bb4ffc4ea2dc9e07d77587,
'iyw': 0xdc31e6e9f6b7ee9763fe4f73ea11a0ce,
'jyw': 0x9f274a3d864fa11191d8de138ce17eb0,
'kyw': 0x15f9253eaa3883a2106eaa0cd1ec9427,
'lyw': 0x6eb4f98d02c88add53d3758db8572664,
'myw': 0x01a25b6ff809017b7e836d1a51f583ed,
'nyw': 0xe2141ff78c1cf79a9c954eebafc4ec36,
'oyw': 0xf8ce587b14fc251d6d6dd45f7490f5f4,
'pyw': 0x8aacb909229c86549afae3f2de961f14,
'qyw': 0xd0a37d2e50a8a06d26b861062c7c949b,
'ryw': 0xa1c8867cf6746d57083fc10764a9ffca,
'syw': 0xb83bca76c4fd6d3ba5964edeb300df6c,
'tyw': 0x748b80d33fe65f4822aaa27406b52e5c,
'uyw': 0x14d71679db47843a4de7d7eb17ca8275,
'vyw': 0xaf30dfaa072dec8d9cb27d1fc16da27c,
'wyw': 0x89c7582101cba2d96efade948d05af39,
'xyw': 0x42a09bdddc63daafe2ecb2859f5ff220,
'yyw': 0xd2c3646b52177902a841ac159812b290,
'zyw': 0x4e17c2506889741a7bae860b59247012,
'azw': 0x479c97be867a909fb7a92b8a0fd65bd3,
'bzw': 0x66742fe94a00b8a8236fa0d96c0300b2,
'czw': 0xffadd5bf845956663e1f71a6674de855,
'dzw': 0x0134ad6b9a4726a6aeef0870f11c81b0,
'ezw': 0x8b7d60954fd4dc86b8fb9ed0247b19a7,
'fzw': 0x42c900e7cdf4c85830453ee9614b7d54,
'gzw': 0x83b13903922f5ca811448d4704a76749,
'hzw': 0x02e0056f5ac6ab93d91a438e13d17d57,
'izw': 0x1d90660674611175e3737b65519d4455,
'jzw': 0xe44fa55484fc4d30c76b4851fc6ab111,
'kzw': 0x9e17def625ffb9c7c125dc1c08804f82,
'lzw': 0xf91c0edc018cccab7e524c099990550d,
'mzw': 0x6aa5590d1e35198ab1badbdec503a8d0,
'nzw': 0x0e1b4a29fd964928ad76037899ad4995,
'ozw': 0xad3a9fdcc61bf5a3af69d7b581236f55,
'pzw': 0xaf877b1427eb981eea0eb5683007c0b0,
'qzw': 0x6ef067cb122675aac237c31eca7a4e7c,
'rzw': 0xe0071fe7c126ec28865be60e6947000c,
'szw': 0xcf6d6203441e8813b74c7e5a2eaa7047,
'tzw': 0x09b31d2d59f00393f9af5b2336bbbd8f,
'uzw': 0x900614e99c1163d9ba37d6b43db2cc12,
'vzw': 0xc8601d6f98d02d1e03e93c946ad9c420,
'wzw': 0xb91f7b5679833f44750e8b450c2e8002,
'xzw': 0x14f4a0e6461ec581054179de999d37cd,
'yzw': 0x0372bd336c1ece9b994907eeca7d1e2d,
'zzw': 0xfdea199d71b110a3533e55b6f25dee46,
'aax': 0xde0a3e55528a048a853b0856bf59a1e7,
'bax': 0xe5b942a9860bce4e877e039db9b31336,
'cax': 0xb769eb6fd71d1a23c502d8b5879f4cf7,
'dax': 0xcddc72c908b895a8121569f0af360509,
'eax': 0xd1b52ef1e322a9f557e7544ae1385990,
'fax': 0x236c3b7f761221f195b428aca2f06c4b,
'gax': 0x2cfa0dbf24934744ee107be938bbb505,
'hax': 0x758e3940a50685dc33436f9268628b52,
'iax': 0xbde9257989832772c4b934758f27bb23,
'jax': 0x7e65a9b554bbc9817aa049ce38c84a72,
'kax': 0x81accd3d953c19567b0f72a107ad6ee4,
'lax': 0xcef8605189c6f354b710d90610fcff74,
'max': 0x2ffe4e77325d9a7152f7086ea7aa5114,
'nax': 0x5171fcbf42bcab65758aae7e9218e28b,
'oax': 0xc4637dd769de5941a4b639c4d53303a4,
'pax': 0x991df245b7aa31f83f359cdb0a32e242,
'qax': 0xd18a2709431135f41eab9828d90c4bb6,
'rax': 0x0b1c108d8b9c46d402a502c5b6b9261c,
'sax': 0x6e7646f19214b0417cfd9c5b174d754d,
'tax': 0x06565e5611f23fdf8cc43e5077b92b54,
'uax': 0x9bfee621ac54fc944be50b4ca90af8cf,
'vax': 0x0091725dc148360935f7dc6a321cbc70,
'wax': 0x6f96cbe1e77384f429dfce5f6072b105,
'xax': 0xc5fc29b612cfaf450274490c1b9372a7,
'yax': 0xd4e601e58a1c47bd6da3275e88d4355b,
'zax': 0x5f26ba5f14b065940bc9c117f0099df2,
'abx': 0xffc810508b973a0cc3fba9474a856ba7,
'bbx': 0x28eaf61354ceb121c3d70b56a4b5b248,
'cbx': 0x66f7c3a8b73e1d5e52cb7131b356840b,
'dbx': 0xe031030b566da82eede3ddc2ce2dd12b,
'ebx': 0x940911b0fd0fbce250de05f6348035a4,
'fbx': 0x7e6b605a0aea67d9a78043a0fcc1d2a4,
'gbx': 0xfa6d89aac48c588f7de677cf22515883,
'hbx': 0x1c3ba6915720608c7f79bc2835aa120e,
'ibx': 0xcf0a46f06d877f7305a802fdfe69b7c4,
'jbx': 0x242c3a9aed1859410980a2c293e0c66f,
'kbx': 0xd8249e4fbd47d9abb005fd9e50d4f39e,
'lbx': 0x6c479755af7c2edee30f4970901b8955,
'mbx': 0xda61c0476c0d8ab5436adff24e3b0dbc,
'nbx': 0xcee235c7facfbf29c65b2cf0b469c637,
'obx': 0xbebe09112fa39f76d853beeabae29cbb,
'pbx': 0x569f7a090087f9e7071a247a24ea0809,
'qbx': 0x4946c495d322efe4395f24618e12dd30,
'rbx': 0x004fabe7cfcb250d5662eb53b16293a2,
'sbx': 0xe8d01ef91da56d14780dcec52df225b2,
'tbx': 0xd7f3db172a60feb16aa4c611b95e8100,
'ubx': 0xcf696d4223239d0a1face7057057b094,
'vbx': 0xb6cfa12c807098ca97e3fba6aedec7c5,
'wbx': 0x5bf219a23362052ef7777396a7890e10,
'xbx': 0xdb44d0105e7a9e779c127d0f23c07821,
'ybx': 0x9943099a35496dfd792cbb33f6f9ba90,
'zbx': 0x4592356b8beafeeb2d25020cc0fe8d96,
'acx': 0x0f329c662d82f3ba0aa383c8e7e9cc92,
'bcx': 0xefb77ba090a477ae312070e5639f6e7e,
'ccx': 0x8a0bdbd84559fcb34e1aa8fb6f52378c,
'dcx': 0x7e7ea41f9dbde9db253641fa1afa32f2,
'ecx': 0x8a6db67b3d979ffadc6b517b64239b6d,
'fcx': 0x3f604dc3a2da1ffc7be935e3738454a2,
'gcx': 0x30c4267c214e9f137cb51cd52a136d93,
'hcx': 0xb904dafa4accad904b83dce1092bd369,
'icx': 0xd761acf21947c2bbb32cbe7456ac9ae6,
'jcx': 0x423cf2ca48308e49084d1fa6a13649a1,
'kcx': 0xb1b2fe1aad5251169dda21a461b14f0b,
'lcx': 0x7bd79dd168489a880e06888392217001,
'mcx': 0xdbe802663997c005410bbbca900f6cf0,
'ncx': 0xb23188e0e4bbfd2e1727eea1fe647ce7,
'ocx': 0xaa0e6dff9f2a63b0a01a22ca3fe7e6dc,
'pcx': 0xc5de03784d3023ee376276e55cd9353e,
'qcx': 0xdbbde511ac125ed32f9ec842bcf72113,
'rcx': 0x690ac99ca47423f97ec67e7cbfe15a1b,
'scx': 0xeb205fa121f0409bb86356ee84ba5f15,
'tcx': 0x98ddde66ca35468178835270933b7c4c,
'ucx': 0xed4f29c0d201b44babe6d082decbdf30,
'vcx': 0xa13c0a4090cf2fc9504438b5a31cbf73,
'wcx': 0xfd4a6a31d3e20f9068d74fcd0ac1c97a,
'xcx': 0x4ffc8a0cd651e1ce774c3e8fa7441b76,
'ycx': 0x5e6b2488b95f272b88a8f19a235f84fc,
'zcx': 0xac0881ac3355c807bfb2a197d9c77535,
'adx': 0x63ea7cd92a728838bb57180b9e1d0dd7,
'bdx': 0xcfa37dd8e4f7ba0dc948abdd2ab9c0bf,
'cdx': 0xa2e1687722d8fb1cc5da48eb50d16c12,
'ddx': 0x49d3650727f25f1831b88d97028cf8b4,
'edx': 0x8ec6a6b8d19d2b1b48870e22c91d9144,
'fdx': 0x30d21cc84e1be664238561dfc2cfc001,
'gdx': 0x7da10de0f41d6612aca62e98520dc15a,
'hdx': 0xf1dd49eab870764bcf9fa9f023b0a3c6,
'idx': 0x7f9bec28bc8902d45d905788d7aa59a1,
'jdx': 0x6f6496f7b349f42ddf75ce3065ed4c5a,
'kdx': 0xb6559ab63eb1e4d3dfcca6bc52ee2571,
'ldx': 0xf4225582c728b58fafec87c3a69c44f8,
'mdx': 0x93c3b60a8b8c46fe78ba27927b2280e7,
'ndx': 0xf3505f318b7249deb4db70fd1d60a697,
'odx': 0xb7b04354b4188ad2eb631458567c1f81,
'pdx': 0xcc3e77224aa2eca7df1bbc0264d70a34,
'qdx': 0xbf1f293badb73b7b492803859c73064c,
'rdx': 0x3cfac727c3b7a5feb2f9d11ee4fe4a70,
'sdx': 0x40ec194fe96529b811f12690860b6ddd,
'tdx': 0x7e9e051c9dae3eeb9539e6ef8c64b402,
'udx': 0xad8f377733a82deada64d02c974f291a,
'vdx': 0x203624d945e03961805bfc7a3e7681a2,
'wdx': 0xd2da5a205c44a8dc4942309f85ca1eab,
'xdx': 0xee632c464502eeac84646ef37ace30d6,
'ydx': 0x63bd5fa08361af6493e9b5ddb0666752,
'zdx': 0x0324bfc92aaf1d07808fc6151dac1c5f,
'aex': 0xdbf6fd985c90aaaed37765657b03574a,
'bex': 0xc0b6f45f0cb4ee285a37d522cda20ef2,
'cex': 0x4fccab82e74b42247e5fcf309a258a14,
'dex': 0x5dad4065c3dc25a179279b2dc4449d63,
'eex': 0x4349deefd26d15ef587a008b44612453,
'fex': 0x43304f2f9cad5f4c9552df0196b6c753,
'gex': 0xb1567c934e6195d14a5514d855460e84,
'hex': 0xb8d1b43eae73587ba56baef574709ecb,
'iex': 0x8b8188830f8b3fc0cd4692225ed68c1b,
'jex': 0x5c282f868ca77469e5c4d741118c2f5a,
'kex': 0x17cc05dc329ed081b3c26ae60527a809,
'lex': 0xb067b3d3054d8868c950e1946300a3f4,
'mex': 0xc06ea190f2cfc59429caf097d12eec57,
'nex': 0x1dd300a7572d1455f6d45db299ceceb2,
'oex': 0x1741cfc425a91887ebb978c46a9d44d4,
'pex': 0x14cb3fb7ea14d09e6f348c8ee0862ec7,
'qex': 0xb266cb38950fb6d34caf41fec04d33f3,
'rex': 0x6b4023d367b91c97f19597c4069337d3,
'sex': 0x3c3662bcb661d6de679c636744c66b62,
'tex': 0x5df398e7946db5ded47290cbb43c5028,
'uex': 0xb6aa1741d1fec2f7f7fa25f2db1f9e99,
'vex': 0x89e3150706fadb5be2ac7ecd81fdaa16,
'wex': 0x1f054ce96c157e8bfe18943c7fd79485,
'xex': 0x6a069767b54bfece9f6c73aa8a2169c5,
'yex': 0x0ebeb62447a3aa739a0d474009c69dc5,
'zex': 0xe94e9251873eb82f47a8f0e1f11b6ce3,
'afx': 0x86711417aebcbfd14b876f3072cf1473,
'bfx': 0xfd9c11375f8575c5c02787cf923d7b2a,
'cfx': 0x1e7da7d0f1ca24c899585ae4af53fa0e,
'dfx': 0x14a0a7f33f29e46f0238c18d528772ac,
'efx': 0x5fae2f52560bc53ae1730fbf7330b009,
'ffx': 0xcee949b1c3f16e2bfeb746783777f305,
'gfx': 0x18152f27f9dcedfef27a65bb162eda05,
'hfx': 0x957d85aabf2b99e4e8349d881afe8b72,
'ifx': 0xb8798e41559859c58a1e8b8f45ea137c,
'jfx': 0x609d96daa54d2df3a93dc695ce9cd5b8,
'kfx': 0x4db47bf9e1ea71f814f2fdf825b7b686,
'lfx': 0xf51139b6d327d2a72a97c1cbf39e1c34,
'mfx': 0xe22136ffb80e606562f08739bc7fc9f4,
'nfx': 0xe3fa3672c0c138a6635084134f28f95e,
'ofx': 0x720f1cd7646b34fd5fe1be215c380a29,
'pfx': 0xc2b7ba617ad9f72c5bcb699a76656df7,
'qfx': 0xab446dfe2fba64561f9b9123a3c47abf,
'rfx': 0xd5633cc6d074a9c8f7385c5d7b8f0b0e,
'sfx': 0x07df0d82d1a0accbc1e35392109bfd48,
'tfx': 0x56ac3598fe365f62e4adf531eab56eae,
'ufx': 0x1b3b97a739e27ce7e6bb170ba82068fa,
'vfx': 0x5a9abd1d18a507a0268f1266d57512de,
'wfx': 0x3d3ce95b5f628eba9599c1c68acbf6f2,
'xfx': 0x283602a5f37ff865b919e93859da26d6,
'yfx': 0x57fa9167d358c043696c5cac978f03be,
'zfx': 0x116b6e681578a714b8bb3abae492045f,
'agx': 0xf91338b2ab615b9e5c0ff5bba373ed67,
'bgx': 0xd199139dd4fc85cbe0f52311d7faeb62,
'cgx': 0x0187331cdb304bc6ee3010327b7f26d0,
'dgx': 0x4edb74738452607e6b5f9199c5d28898,
'egx': 0x7c149426fda0d4e17ebf5169c6f242a7,
'fgx': 0x6fe1c510b446fca9672120aec62cfcd6,
'ggx': 0xb5e29396c69754d1c9772d344b691218,
'hgx': 0xa48c907d946aaa208077635b3ed3dc2f,
'igx': 0xafc5b3c875a4218d523108a445eeabc3,
'jgx': 0x7a4bd860b4d1148e1bf1f223d34eed94,
'kgx': 0x0201b3853db9215092666d1a6927d111,
'lgx': 0x57109ce04908c4cb38fb4214f523cb5b,
'mgx': 0x20bc70df035c3c0d0f4a444406a9ce13,
'ngx': 0xdcc2753ffa270f576957f426a0e944dd,
'ogx': 0xa3e5d11efe760af6afeee80eb5769ecc,
'pgx': 0x14b099ecec78f05c4cd5674365f74ddd,
'qgx': 0xb02e0ba893ee557c2927a2d8f8f96a8d,
'rgx': 0xa4597575732905133f8563ec41addd20,
'sgx': 0xbadc8315a2ee426f148f3711037a0e05,
'tgx': 0x2da790253022bd05cddda6d06bdc1e60,
'ugx': 0xb38193b5a01ac110a224b88c409dc812,
'vgx': 0x9013510f996934ad626e5839d825ef61,
'wgx': 0x4e9c392afe2ae35365b448cd18a95767,
'xgx': 0x3722d573cf67e8d079cac616d4809857,
'ygx': 0x69f74ea61d0ba79986c2c0441a926740,
'zgx': 0xf4b1a451629847da73ec65aae93294a3,
'ahx': 0xb9e8a91b217a8874525ed0e02adb5d4f,
'bhx': 0xc7cd21a6e942e0cc89140524c287c209,
'chx': 0x10f09471a412152a70c1a8b128bbd0a3,
'dhx': 0xa25ff226d2804e5c84d3c7adfe969e4f,
'ehx': 0x1bb2831f34a0de1e722d1c55993586a9,
'fhx': 0x7faa4b238e1880b5317b7c4db0ad5267,
'ghx': 0x5459ebfcfbf441380a42a12494721ccd,
'hhx': 0x594bdedec6e3de7a8831aa27d9e5e250,
'ihx': 0x9e954a6716ec1369de5941da5513de71,
'jhx': 0x30411aca5a59d8a56472849e62be84fd,
'khx': 0xc713dcf47507ff76ec22914e983c641f,
'lhx': 0xc7a23cae2d51cfdbd0c3d950f3d9c7c6,
'mhx': 0x6e3c263638e312ead798812f3009d8a5,
'nhx': 0xad27e3b66ecaf9fb582a78d5c9fae063,
'ohx': 0x1cb492bedfb041c5f842e3b7f9cde395,
'phx': 0xb06988c85a3d06d23cc236b528a3a7a0,
'qhx': 0x3d443dfda4901b29da7600034763e496,
'rhx': 0x7a1b476e9fdddb2d95201c420b3cc4ac,
'shx': 0x14610b8fc55922efb9cd2688beacc675,
'thx': 0x5d3586e9b819072f0182941d79d8e36b,
'uhx': 0x18fb115c099d1a401513210228d326d5,
'vhx': 0xce344800f19d59c21dc9bee31cf03cf4,
'whx': 0xf9cbfaf95c9a31942336daff48d979c0,
'xhx': 0xb479743920a618303d39a4df7e6bdfae,
'yhx': 0xccace0fa28a698369daf165e2477df50,
'zhx': 0xcc6545e94c76b38b1251c8c411763cb7,
'aix': 0xcdbce5fca5918eed20ded6147ba8526c,
'bix': 0x7759bd47e41cb517c4dd943e67d2be89,
'cix': 0xe17733c140f2e9fe3c561f3219649c91,
'dix': 0x569c818b1e5d5800a1b834580b0ebb22,
'eix': 0x877665ce6faf2a55f530ea2b885bb65a,
'fix': 0x8ab87d4fb3600cc53fc0c5c958a250b1,
'gix': 0xf93ee85ddc145e5593d555d4f0e16877,
'hix': 0xbd4ae3202ba4bf976703d262768cb116,
'iix': 0xf171683b7d999c1ad1598da5eee0f70a,
'jix': 0xc6f0693fb38025bd1bebe941954a08b6,
'kix': 0xe6ca88bac78bcf9de5362d50d6a20662,
'lix': 0x0d3d316b0c0f2bc0241b7065fa3b44e8,
'mix': 0xda386e629171b833abc1e85955664bf4,
'nix': 0x393b7e38038f12c5fb4c92e81a1b5a01,
'oix': 0x18a60b6e2ca8a7787b55547f07d61992,
'pix': 0x744b41f0dccd32ebf5d525bc1c64af5a,
'qix': 0x1a8dba0e84b07d9100b47b280923f8b1,
'rix': 0x94ef29bdd1454cfea2e27a4aa14741d9,
'six': 0xf52b5e449a2303c031a0c3a1109360bf,
'tix': 0x19e4341e8f693996bb968c476554b01b,
'uix': 0xc4125c883b92c16ae868ca9276c97232,
'vix': 0x8fd452081c17e6d01fd4c249fd22e5bd,
'wix': 0xc3307fe0411018a602b5203dacfce695,
'xix': 0xdeecbfa00b6b28500a8a2b0d2cd484f2,
'yix': 0x23a7681b526e4e6d8b5224fda63c2e3c,
'zix': 0xe5c908de0be3af8c2838513d19799831,
'ajx': 0xe3f30bbdf2a31a06976edf8d27fdbff7,
'bjx': 0xcabb3cfd36c4e17a19ac723520c6b671,
'cjx': 0x89f943f1795339d0dc717f4e49f71646,
'djx': 0x6ea8fc37e779dc36ace761ddef3d6c95,
'ejx': 0xda0563d0ca87bf43d6705b9701c320cb,
'fjx': 0xb7349bf20be5d6d39a01d8e215a32495,
'gjx': 0xb31d04de80cab36ac7c606efbc5afe62,
'hjx': 0xffd529f6b20aff2291bf876c68433e78,
'ijx': 0x534b4c1e78a52b8858f96330379a8bff,
'jjx': 0xce683c8698735eefa02cbc1e67708696,
'kjx': 0xdae1a3eea8990f4e204feb2d8e5d1275,
'ljx': 0xf0b5acd24bf41fe5d0ce9ff222ccbb53,
'mjx': 0x5fe29e2da48a4fa8d93390652df3a5b2,
'njx': 0x4102fd9d7e1f985a9def8feb905931e0,
'ojx': 0x290b2c7e8073190c2b80078c57c02a7e,
'pjx': 0x2eb7fd23f344a6dee939540195d24256,
'qjx': 0xc04390b1671f392593047bad53ceae69,
'rjx': 0xf5e4c631bb18c392aa82da8128a9760f,
'sjx': 0x551ac0a0c6cc20e2b3faf4e3521163c6,
'tjx': 0x7c41ac04fe0bbb33ddf18dcb4eb39927,
'ujx': 0x9f014be4df3fba58c8cb2060842d4a07,
'vjx': 0x78491f22566720ed81d0ec17591bec1b,
'wjx': 0x4e716b1f2782f74c728a855f917385c7,
'xjx': 0x745aed3e4b47ca0e22db614335b10d07,
'yjx': 0x063f0ac49b172a6e2e92eb10e4960b1b,
'zjx': 0xfeaafd83b528d7b8f802becdb48ba00b,
'akx': 0xcadcbdaa856cf777187142fd3be89a49,
'bkx': 0x47522147dd1b35df3de88cd60df923a4,
'ckx': 0x3706a1fbfa7602d3b43da6d00edc64fa,
'dkx': 0x3932e6b4f58913af642999edf0a013fe,
'ekx': 0xfb07bcd64b53be7394628183c2c83b9d,
'fkx': 0x0f95005512ff3d3ae152fc3b377c4fe4,
'gkx': 0xa48bef8db68b297d71936ae7500da10c,
'hkx': 0x3e1400fac8092119d2ab9e3f791bf8d6,
'ikx': 0x0478c6f3174e29a2af62135f6d30c49e,
'jkx': 0x0025b8426c826faba2bf187723e73a6c,
'kkx': 0x9a4206432771c7edb13535619050d795,
'lkx': 0x24e7f4dbc29942a79b857571efcc8f40,
'mkx': 0xeae732cc846988cdfd4df477834f6728,
'nkx': 0x033c086739bfad8bc047c0751f376765,
'okx': 0x4038008ddb48406a4a142053c9c4da16,
'pkx': 0x0a89756ae084bdc8b752f98bc6edbf53,
'qkx': 0x34a0fe065004f88474c6157f08181fe7,
'rkx': 0x9b462ca7da89153d0106bffea35b4fa1,
'skx': 0x5aa88e693142eb569f796fc7b08af302,
'tkx': 0x786890882f5dd0d8414edfdbe2f086e0,
'ukx': 0xa61510352c12fc782d177bd791696dd8,
'vkx': 0x5e0746e29d0299151f7594e2122a3123,
'wkx': 0xbc32291f5589e1d4a5af6beac8ff8d9b,
'xkx': 0xd3e5f987249c8b17f7e5ad6ead3e0842,
'ykx': 0xefdf28041456aafb0629df55ff58b158,
'zkx': 0xd5f0b9cdf710280f7193fff6f92a0d79,
'alx': 0x2db814ac68e0caafaa38b0b066f8f2a5,
'blx': 0x1edc5f3c3f790a96f63995cc8205fb27,
'clx': 0xf99519d76941552d74812382a1cc0713,
'dlx': 0xecebe14c1b33bbc53678bc7a8f2ea9ca,
'elx': 0x5a6fcaf6eb1c2c9f23d060750c46b23e,
'flx': 0x38c82cbcf85b0ec02fb25df7e4423965,
'glx': 0x559b96c1a5750569b68edcb20b9a5417,
'hlx': 0x8412cda386adc51674f0866412687bb4,
'ilx': 0x9a4fd56d1ee7d445d16cfcb4c16b0e0d,
'jlx': 0x19266b3202e66e40338948895ddd993d,
'klx': 0xfed697aa7b0a2984ff9865335fbda28f,
'llx': 0xf4c1e1e4cf6578c2b7c5fc35628b789b,
'mlx': 0x873610b17f46c832819aeb4af1426fbe,
'nlx': 0x2d50caf7115ec1050917ba663646d446,
'olx': 0xd194561ba6304d365957b00fb5de16ec,
'plx': 0x379bb1c46b664b711b6117683b5c32c0,
'qlx': 0x1fab5b0e3f9e0e4ae21d1fe4b01a247e,
'rlx': 0x6650ccd8a7917e7d192777d86c28ed76,
'slx': 0x6c72c8d7a781ef1cb90ccf33edf1963e,
'tlx': 0xd210ea223271f748be896dbce3e601f8,
'ulx': 0x6fd3afc64c5a33c18f4ed5340b8f9c4d,
'vlx': 0x213aab395a4669b464c046435979f40a,
'wlx': 0xeb37d6a162e1ec09b546dcf911e166b5,
'xlx': 0xbc5c5de5d5cbe9780143545b49686d63,
'ylx': 0xe28c496c3bb8de9bd55af8b544b62951,
'zlx': 0xc2bd438c10bd1cf450a9710016dce28d,
'amx': 0x37cd751e0e5ad34a93b37acf47435426,
'bmx': 0x93ece6b83bf3a781259edc0f8338ac4d,
'cmx': 0x8e16d3332f617b0b48a5ad8a7d4ae306,
'dmx': 0xbb972e95232d6094269d52fa66abae30,
'emx': 0x73ac748912ee97fa3be0fd1d4811ef74,
'fmx': 0x5d00c7492712fe9163b3de551587a6b4,
'gmx': 0xb11a3f4d01a89bfa21d0fad8a392f683,
'hmx': 0x6298095e199328615ee78b6331d681b6,
'imx': 0x0f10d265bf201b14a4ccd2ddd50dfc7b,
'jmx': 0x83fd5054f6bb72a738c9669731181a1d,
'kmx': 0xb74d6c4d32c93c8623a7dfa921014ed0,
'lmx': 0xdcc77169f507c60b9e778c37a2738560,
'mmx': 0xc1cdb8620588857c5b18f05191f45bb2,
'nmx': 0x22f039194158b8821d6229d0a7c3afd7,
'omx': 0x502394555cac957859f700ccffa39e1e,
'pmx': 0x7a6da9e5c6a257dada5185814b3d5978,
'qmx': 0x6a85137ec8f3fea6bf0f408c77a94999,
'rmx': 0x8b5ec07f1dd3868272e0e0ec45343e32,
'smx': 0x067978902b11982fb6ded827a5caa47a,
'tmx': 0xd73fbca9f19a294db16d18e225c61472,
'umx': 0x0f8077ba3b3054f9d20eddfce7a9168e,
'vmx': 0x6c096a5ac5041d94699db9c11917cc22,
'wmx': 0x5a1a279e253f2da96c2d4df9e48b5c68,
'xmx': 0xd3542ece75d3114721fa3a34198aae2a,
'ymx': 0xfccde061d79169ae0affc68141cf1bf0,
'zmx': 0x94ebb9858b5c1671ae697cea296b3ecb,
'anx': 0xa34b6578e0f9396852827ded4cc21b1d,
'bnx': 0x89bb4dd75faf414a317212f2a00dcb18,
'cnx': 0x7013e0b26ea43fcf2193909d8c6a9e87,
'dnx': 0x8fef6501c4b5114a870105baed31bc5b,
'enx': 0x7a86dd525f15d69cde4e3d8354d1b8ba,
'fnx': 0x075d2973aabcbe7437fad553fb72f16b,
'gnx': 0x5b64247a8ed1dd6a696c71ed119d4d68,
'hnx': 0xc1909ceaa7e4edfaa71e3738eaa0f553,
'inx': 0x6a8264af3edab5bfe59650facfac8e51,
'jnx': 0xe04cf9e884737172abd13a7d36f776aa,
'knx': 0x39d7487b88a9c4c4ef5c51c8c369cda0,
'lnx': 0x354317d0ca91ae2cd9042b8d9bcbcbac,
'mnx': 0x778321f03da0b9f15768c76c2603697d,
'nnx': 0x9a7d1d0aab21d53aa90fbf45e4bceae5,
'onx': 0x417d7380cdc843fae81495bb81f3526a,
'pnx': 0x8033bed4dcbc598750d36396d4666435,
'qnx': 0x8e1b9c930f43b466908802929201de04,
'rnx': 0xba436b844c3acd6747b97e9d3c0ecbbb,
'snx': 0xf7f0e1769ec57246d791d09b466b15cf,
'tnx': 0xaaae4c7bac4f7c975bd722cfc5c8a6d7,
'unx': 0xca99327a0b6703dd48809610b649023e,
'vnx': 0xaddd4817507a1ebef29ec2c17c992800,
'wnx': 0x6b723911adb349ebaa143b7ad00a2fa5,
'xnx': 0x2277d7539971bc63e14677884d5e5a2f,
'ynx': 0xfb7e34673421c70513c0a05178418a43,
'znx': 0x457d1acf0e517c253bd89e1b4a87928c,
'aox': 0x0658e5c62c93d1cfd2697eea9a3166ee,
'box': 0x34be958a921e43d813a2075297d8e862,
'cox': 0x45641e46f614125065559617b3efc5a2,
'dox': 0x171994ae268f7cd4f6eec68883d9c553,
'eox': 0x70503f102c5329ae794c2a47a36bdfbb,
'fox': 0x2b95d1f09b8b66c5c43622a4d9ec9a04,
'gox': 0xa81f9017f446952845f45d037646949d,
'hox': 0x79de317fa67a3e0726bbe2fbeadbe187,
'iox': 0xd2131d3e0aace0b349881db8669c843d,
'jox': 0xe60ecb57d890e870d544bd8d72ebdaab,
'kox': 0x39071ac019da873c22024c761ccdaaf1,
'lox': 0x54508291c59a2adb825c143f61b32f53,
'mox': 0xabd376c762478f4dd16f2142a73f2b62,
'nox': 0xa3d96aa454aacce406c1174dd38dfaea,
'oox': 0x091eb8bd65d65cbe28e131e18280ccb3,
'pox': 0xc2f92de37c193c71fbe860a599d46d6f,
'qox': 0x7922f8dcc78f72981c815b465d863c9b,
'rox': 0xd5d4172de318d6dc6c1ca52c8dcab618,
'sox': 0x792e5dce80b4067446351fc2f8f6d81d,
'tox': 0xf6372f469877925b02cf2f7d56b1efcf,
'uox': 0xd7f1945a38044a1968e9fd2a070cbf01,
'vox': 0xcd9a346bd6528176ced3ddbe105ec4d6,
'wox': 0xf6ea029ae38e766f0fd921290a28e568,
'xox': 0xda3dea58809093500b05ebd5b8463d90,
'yox': 0x645f2033d9cd703d4989df092d4ab550,
'zox': 0xe948bfa73ec1d928acb06dd38e92d157,
'apx': 0x91c7961fb393b168237a0a232b5c6f72,
'bpx': 0xf4bef6778c076d5fc137d8dc0c2ac782,
'cpx': 0x18daf02e2b22e37361830dfc2e2aef26,
'dpx': 0x5b0e5a0e1f5a6255f4f968bf4c075cd9,
'epx': 0x06ced17194d00bdb607ebcf008f2562e,
'fpx': 0x9b3eb193279bf669883408220a30442d,
'gpx': 0x9cf2423c9a86fc539b8e30b87a68a6e2,
'hpx': 0xbdd765a67fd5ae0e45fa61ab7b81b1cf,
'ipx': 0x37024af54abf6d35e2ad49a214854e65,
'jpx': 0x00c7a3e60eb5a07b2e9ddb91dfe4a89d,
'kpx': 0x3f1ac89e9172d6c15a6b87eab8234f8e,
'lpx': 0x72d53d38d4ad1798c74032008d077d5b,
'mpx': 0x501abcb9c7b818654ca219a4bcc9681b,
'npx': 0x0e27ffafe58f9c3de275946cc07d05c6,
'opx': 0xc0db13e7b2397f57ab886b5e3a0343ed,
'ppx': 0x7859e00fea28b85d0bf2a8d496db6e20,
'qpx': 0x40bda277f609fa917d28e7bf54483fa2,
'rpx': 0xdc2957cb0694f718f90aa0724c8123a8,
'spx': 0x60b301e15dffaf3f3630a5dc6d44fa2b,
'tpx': 0x43c108cadd9f8c135833f0edb0f9cd8e,
'upx': 0xc993811f4c917910dbf008283e969c09,
'vpx': 0x84ff6063a88f1185a7aa6adb51500286,
'wpx': 0x23cc412617492641fcdec98f6de11e5d,
'xpx': 0x3296cb915fd3db6df1a911b2e111f613,
'ypx': 0x1d04389137d39b1f3bf8c61e7de4491a,
'zpx': 0x6a8023fc96b239eac4d01444f9ee4334,
'aqx': 0x11215fe97be703951f3eae7f5bdb9ced,
'bqx': 0xd1b645c2ccd6417939235719f2e2858c,
'cqx': 0x7b07bd5ac4b92170c8a0eac0a8689861,
'dqx': 0xcba6c55713638a21280700a29a3560ea,
'eqx': 0xb87f65375aafd7a34cb4fc2b07c1d596,
'fqx': 0x6ec6945d13ac7024d9ff3bf3f4cc131e,
'gqx': 0xffd091f41a8af580e137eb8d737761f5,
'hqx': 0xb758850c75aae41d39bd3545cce687e1,
'iqx': 0xcbee8824304ec930172bdad63d0fd864,
'jqx': 0x48b63d44cad582a9708a64fd2735e30e,
'kqx': 0x472b1b5ff6871cec575b00087355203a,
'lqx': 0x1ebeb2a2f2ba8acfc3d27ac38524ec1b,
'mqx': 0x6075fc94d6519d097de323d7cac142d7,
'nqx': 0x513727cde2da0f4580ceebbc7df92549,
'oqx': 0x38f4539ac3569828e248a9f286592334,
'pqx': 0xa5a1854c41714e903d38aa739a5dcf97,
'qqx': 0x166fdba79f6f11ce5337371cb3fd4ea3,
'rqx': 0xae0cfb83acef2780473813e9b315950b,
'sqx': 0xb69609fc8181bed2ee8e3eba38413f41,
'tqx': 0x0c3d1230a33ae014214c9ca9aa95be15,
'uqx': 0xcee56fb691f12862be759f5d5fb9e9d0,
'vqx': 0x71f37bcfaa6617c4b6f9b73fc9f4db34,
'wqx': 0xac5ea3caa81d4cb934b2359a4ab9fba8,
'xqx': 0x6855de861d04d86563cb1497d7ef9708,
'yqx': 0xdc38d8f5ab2e2d58461c45845fd8f7a3,
'zqx': 0xe4c344fa8a130d2c199bceb967574c8e,
'arx': 0x8985c1d64f80ced7c310f668021976ca,
'brx': 0x3b956ce5f79daa52f192e0f3618b8e2e,
'crx': 0xd157ab90c1ff50493509ac8e72e27bf1,
'drx': 0xe64607f5e3656df8ba54dd700a8e8814,
'erx': 0x3aed1ec43c3e58274fa10e7bad3bf2cb,
'frx': 0x0d55a4019b90bf28360e0f15ab8c4c49,
'grx': 0x0d56cde6af3ed1d0251c15692a9a056a,
'hrx': 0x7a7372f71d838aa4b777ccd82bb9ba9b,
'irx': 0x255231992eece9e8d568c4d44d5f055e,
'jrx': 0x3cbb145354438ded10f0e10d0acc4e27,
'krx': 0xb62fe84ef4748914e564fcab8e9ff7bf,
'lrx': 0x57cc80319fdd01d42819a8e1e25e024e,
'mrx': 0xb1a0f5dd9fa30bd75c606ad054193855,
'nrx': 0xfabfe7ecc91a4a13818c0c81df6c1987,
'orx': 0x470af03c0538b5ffee7367eadf387868,
'prx': 0xf1205d26ae74eb7ebcd012fd2decfc92,
'qrx': 0xefce5d9ee4a25f9fbd1a8883e9fd4b41,
'rrx': 0xaf774aa70fb9a35051fbba315d0fbbad,
'srx': 0x3658d56fa7574b517fc08f71c8d7bbe5,
'trx': 0x40b665bd17f6959c63f6eca1a670cc21,
'urx': 0x2ddd8528bb2d27100c52421f2ed57b3a,
'vrx': 0xd394454aa12bcf966e2a345a13ae2ec8,
'wrx': 0xe87fbe7cdce7f8880b09f60cb43aef8e,
'xrx': 0x2a833a94311be2e37438d938900c4bcd,
'yrx': 0x5d20f2384c7f45d1d79a6fb864c8a754,
'zrx': 0x3bb850e33bad594e141f8542dee0ea7c,
'asx': 0x5765fafd258a5a1e87c0582a67862675,
'bsx': 0xa292d4f2973aef95723bffe87e5aafb9,
'csx': 0x75d1f0ed88d3d6c477b482fc159bf842,
'dsx': 0x522a5b309239eff866a02d89a86f8605,
'esx': 0x24fb40ee80bf1a74d98f92735ff55a19,
'fsx': 0xfea0543b0b4b9d4c240c6219de0db41c,
'gsx': 0x4efa7b6383bfe54e32f7af11b512819a,
'hsx': 0xdb91e961158112f56b9009c601f12e70,
'isx': 0xe680baa452ff5e720ba7e5b2b68508b0,
'jsx': 0x44ad1c2b2704e2710a857a54471f6125,
'ksx': 0x2d8c6c38b48a7f25a2c4ab8dc8d2de44,
'lsx': 0x03f6d3b315ca27d3a8f3859e3fb7fb01,
'msx': 0x203128b0796857258e0a44f5a59756a2,
'nsx': 0x2ebb9f0996bc7146d7cc004c7d8c20a0,
'osx': 0x8e4f88e9d6ec0106ccc73b39d369c3e8,
'psx': 0x839768c4abcf693cc09e4f4291869571,
'qsx': 0x63a77021ce2c11562e4cb7bc0d5875ab,
'rsx': 0x1b1745a437f817e954c0d2e9ca0c89ad,
'ssx': 0xac7616f7a04546cb52430908df2df4a7,
'tsx': 0x35e966ba83d5c48b2533aeaf5f39d7e5,
'usx': 0x23a3719e74cffd9d2cbad6137d9e0a1e,
'vsx': 0xe110347e6ceee912d8394bd74f01ddc2,
'wsx': 0xaf83f787e8911dea9b3bf677746ebac9,
'xsx': 0x178533e7f670efae6fa6497703b1426e,
'ysx': 0x075ab69db8cc3c35bbfea9b58e41403a,
'zsx': 0x7dd10e48efe9c17ce1ae0048587338cb,
'atx': 0xc9bc44d290788dcd5bfb1c52578c09b6,
'btx': 0x045fef463ad46008a8444730a41dbbab,
'ctx': 0xecacffffc22141f3c1c9cf77ddf0308d,
'dtx': 0xd30b44e1278e989f6ac88d11556ed6ed,
'etx': 0x17ebcdc3e55a6bd07653a4056e88f61e,
'ftx': 0x6aecac5e4af18f283d09b56e3d5dc5b8,
'gtx': 0x13e2062c0a9665d1736c3a948f94049c,
'htx': 0x3282e2ec8bf7565b0a305112b627ed16,
'itx': 0x02c090cce63b04b00603a089f8e99c8e,
'jtx': 0xce29d5eb0118e0f89a6196b0d9ba5d30,
'ktx': 0x0e18355b5eca3c17c230ab6dbab13906,
'ltx': 0xf2e85b3034e3caa4a852edfa1f3ec8b8,
'mtx': 0x9c548cc9545fedcccc807efe225cc447,
'ntx': 0xd039091f63184617605c7248899edf75,
'otx': 0x0ae8e64b998efd82996b67cd9f5ed382,
'ptx': 0xa32d1a4de51656e2fa1aff1e9a963361,
'qtx': 0xe199f0514c7676dedeea65c29cc95c90,
'rtx': 0xdbcb0d455a9db7b349f1ea3d9dc3f3f0,
'stx': 0xe01fd45bf10305e41d37459223778301,
'ttx': 0x1f8bc27ba992bc8f97d59f884d77452c,
'utx': 0x657ea5bd8d10ed21309050c359127b72,
'vtx': 0x99a3960278c068d6751a5cf6fa38c449,
'wtx': 0x40057dd251da285b4f6fa244c47a34ad,
'xtx': 0x47e450247428540aae5bb8a13759b20d,
'ytx': 0x112cf57b465e59f73cb6596f08675ea4,
'ztx': 0x5ee4576b685da05784dde41c7d4bccd6,
'aux': 0x53e5733361417423cbec0979f23b3def,
'bux': 0xa4298645c2a34a390914ae509869a377,
'cux': 0x569014996e740ed22f992b30232de2d7,
'dux': 0x81de98f70f97e7b3f0f87cfb96cd2522,
'eux': 0xa00db5fd17f7d0e295f8559ae775359f,
'fux': 0x6ab35678fd2164be79f1a261fdc6971b,
'gux': 0xdd5d08394837f966788e06df086c3ed6,
'hux': 0x9f73dd6f3a8b9ba22a653f5d0257bd6f,
'iux': 0xb14a7216c5b6de272e7b9ddc280a3ef9,
'jux': 0x0b16ba11b66b775629f979d64ee28760,
'kux': 0x87b1074243e58b6f262af5251c467dec,
'lux': 0xd877c55797fd430ce8150363cd86058f,
'mux': 0xf1e492ffa7562aa744240b8b3eb403c5,
'nux': 0x849a0a4fc24ce40d5b32b0082bff39e8,
'oux': 0x097d7730e8a0790a160b7bd4f062210f,
'pux': 0x03efaf5a568c5f0036ab0f08af9e7f42,
'qux': 0xd85b1213473c2fd7c2045020a6b9c62b,
'rux': 0xcf068ccdc13b8751bf7f38255ffa5734,
'sux': 0x455c99907225a26d1a6e3fa2ce99c9c0,
'tux': 0x5ab6e4b98bc7fb716fc07ed98fedf802,
'uux': 0x998a5f2f13bcd6b704e38c42db345d0a,
'vux': 0xe7c76aa32bf30ebfe3dfc4c96cc54e3c,
'wux': 0x5c47087089f54279c0baf49950bacfc8,
'xux': 0x62b0f1b5eccef2b5f2e55b7f13ccde6d,
'yux': 0x96b1cf0aab920815692cc3d465b6d2ef,
'zux': 0x0ceb1d918d841e921e8ba57707eb71a6,
'avx': 0x73758c37e4499f20ac5f995a144abba6,
'bvx': 0xcbe4efa0ca5519dfe144f35a8010b258,
'cvx': 0xf1a0be5294101737ccc1cb45b68fca8d,
'dvx': 0x11b5a4cc15089ee64161a64ea7ac0e66,
'evx': 0xa0480106192c124801c4d2eb94b489d1,
'fvx': 0xbf8b2f69d52d639abd893ff751aa48d3,
'gvx': 0xdffc8f0f3cabebc225ea470e3aae79c8,
'hvx': 0xf4c45137114667c9472b2cbd50fc9f50,
'ivx': 0x7f7d16cab3490b936aac9f8868902347,
'jvx': 0xba9b1667ce691639ba7f5530c599d29a,
'kvx': 0xf801e585b3ad23fa6e81f0a0ca55f298,
'lvx': 0x5b9abac90d36ca1570fab4e62a51c6f8,
'mvx': 0x80361bb04c8d5067a05806a81b71b91f,
'nvx': 0x9e632b05714bafe371c406f34ab059a6,
'ovx': 0x0390a8c1cb35df49c09cf7d700542ef1,
'pvx': 0x2d7a450856f024d87a8a40c7d1f2b9f3,
'qvx': 0x6b18c2c4b957afb245d7e2027698642c,
'rvx': 0xbe46beea9b30fd30e9f3822f423f5171,
'svx': 0x2fed65e470137eb2bf9aa3f81f55ee7a,
'tvx': 0xf1bd590c233d2ff43fad2b4e8bcb049f,
'uvx': 0x9b1da814f88fe439df0d84a6ef1dcec0,
'vvx': 0xa9735bb3f2cd850ee38d9bb934bac01f,
'wvx': 0x2fcd329e1f6d815d560071882ddb0b91,
'xvx': 0x1408eada0ced4b857adc3a1ee6401eb2,
'yvx': 0x44cb14b70c2675eaea3f19e3ec63007a,
'zvx': 0x1cfe1726f7a36f3519416c189cf55117,
'awx': 0x593f9a6f3262f172594e6436e5233922,
'bwx': 0xb07be8fbc891bb829547216ad9a88686,
'cwx': 0x3ef98185bdcf8f6de05dfb765deeb22c,
'dwx': 0x52c4b7482fd5cdd8c5f0cf86d8f2a20e,
'ewx': 0xf47cd21e90ef551bdfc17ee43487bd39,
'fwx': 0x72404ef00509991fe40c0eacd254addd,
'gwx': 0x2243e69b2b21b505adb1ca8426d8d94c,
'hwx': 0x917c631fcea4d1d98aa084b7552faf8b,
'iwx': 0xb5f41650589ffd0c96eaa76043edaf21,
'jwx': 0x3dee80a40dc1da26f35048942d1055ec,
'kwx': 0x9da310090291ae812ba913d6185255a6,
'lwx': 0x194af06ca2bb9554796a93422f54680a,
'mwx': 0xe75f5e766933fbe99872b95c02a19efa,
'nwx': 0xb1c367dcb07d9f0adf1a0c4406e513a0,
'owx': 0xb39ffdc99f6ed872ff6f457135dd921e,
'pwx': 0xbd4c10f6b0e7fe5a4b4b57b45d74a13e,
'qwx': 0xba00a47aea7070b04751eb7861aa1057,
'rwx': 0xc50f11e205b95d1471b31beb63d5301d,
'swx': 0x9b362a75d189b37e17c2999b003b2284,
'twx': 0x678dc95973fee384610610ba25152ee9,
'uwx': 0x53999e8726faa548b40866218629f6f9,
'vwx': 0x1caed5ffe4571c45a799ea2f89fa2fa5,
'wwx': 0x483a70a0179b1c6be6a9e83e22615358,
'xwx': 0x2fbdb7e3ea40303eb1b6da0335af3706,
'ywx': 0x6e7db57db1de3a90810fec8c6924be77,
'zwx': 0xe022d21a71f1325b45fdd83c66defda5,
'axx': 0xb1115f66b3714049d8753d41ef45daad,
'bxx': 0xa19b16e0b3c1e20346aaf3a61122ad74,
'cxx': 0xa91935a014854d444d2d79e6dca6c0dd,
'dxx': 0x640195b3b9c6c5ff2388acdc97b1114f,
'exx': 0x0655048ee7a0d2bf9cd4be5d9af4956f,
'fxx': 0x2131a961d37899516cb061079bd109a9,
'gxx': 0xbeeffefc7059e44843b3c35bc776feaf,
'hxx': 0xb1470b15769ab769666d754e9e465b51,
'ixx': 0x8acae71626faf1d76b2cb75b2aaae749,
'jxx': 0x45da66e3b35dac8cbc655a2aeba09225,
'kxx': 0xe44f0273448a2e624507ccdd379776d9,
'lxx': 0x73fe8f327b36932eb9d58b02ca722062,
'mxx': 0x784124f036e0ae31bb486a92bb2e9be2,
'nxx': 0x0467ce3358916561ef298d84a031feb7,
'oxx': 0x06bb8d46273815eaa7017ae91a6415c7,
'pxx': 0x95b99f318bbd5cdf45e10a17d357f32d,
'qxx': 0xbfbb5cdb4781a220d8fab375fb87f4f9,
'rxx': 0x3e19a674b9494bbe4798f29b506ff25b,
'sxx': 0x732ab54cba5f61c5d5b8722b59a3e4a1,
'txx': 0x7d17a0b95785ba7d95727ed2b8801d33,
'uxx': 0x1c81db898684a3c0b5b11b2104e6832d,
'vxx': 0x50b5392236b65929759d272f4f273f4f,
'wxx': 0xef85ad2e51dffc50c7c959df9da4efde,
'xxx': 0xf561aaf6ef0bf14d4208bb46a4ccb3ad,
'yxx': 0x9cce93846bf2dc36b80183ff0378677c,
'zxx': 0x45a5d2cb8e13de68db5da9f956138471,
'ayx': 0x89afacb06caf3135bc6d1ccb95d4dca7,
'byx': 0xfefbffcc18c15d88d4f23e36419ef58c,
'cyx': 0xab058c6d4bb7e0713ad1de921a56fd82,
'dyx': 0x6c0bd5458b8c055b6b737e5875443aa8,
'eyx': 0x377f52da49b11d3b305978bac1eb75ec,
'fyx': 0xe04dca5a89807ad13ce76f9314f3999d,
'gyx': 0x89fa00141924ca6f27a00171b3bf24f9,
'hyx': 0x6125e943f08d7272a07811cda02e5221,
'iyx': 0x5441705688a7685e78e1981ff2c84091,
'jyx': 0x623c8ffbd0a7711a0c5804e5d872858c,
'kyx': 0xe9060e1816f638c09b686157164d013b,
'lyx': 0x621baa35ab05dc1136101265d08509e5,
'myx': 0x55a9756627e2746c3eb82be416b0f4cf,
'nyx': 0xbdc499dc82257027fb3f0513dd312af2,
'oyx': 0x8c6ebeaf702fcacbfe62ba5d3f83a84b,
'pyx': 0xcf1369a5a0dac5a13981c396bbb10fbe,
'qyx': 0x995c6b9a581512e7ce484e20f6fddf82,
'ryx': 0x5a7c8cb7c35eed6d0113eb3b7178effd,
'syx': 0xaf7b5ead3ea6e1abcbfea6f8c0a64f69,
'tyx': 0xfcf2ec84bf6bbe746fc86d08611815d1,
'uyx': 0x59702e7dc004e39e9749142a70ad9243,
'vyx': 0xe0da1dc9942edb834ed180349c822ffd,
'wyx': 0x77b31159f034aebb6ee2cf074b0ac1c2,
'xyx': 0xf5d8782e1d2d51da0220da48875f4186,
'yyx': 0x9c47fc50338e71550dfd552a78223b3b,
'zyx': 0xfac97e579639be3f10db671a4462ed91,
'azx': 0x7f0a139238946e6ac43d8396926a524a,
'bzx': 0xce045d2197de66213f27f67d0602dba5,
'czx': 0x5cf965b033624583dc6f1a49fb6c77aa,
'dzx': 0xd095e757f4ff2ef7c3d85dd95f5258a4,
'ezx': 0x7a60a91a82653f8a81e90efde8e74396,
'fzx': 0x4ac71f8f9c3ae0d5f17b68e836c4f663,
'gzx': 0x293c853e72680148b80cee359b7e165b,
'hzx': 0x1a9787bdfa7bbb50141615087217f609,
'izx': 0xdff1166b7864a0790101a82150701446,
'jzx': 0x78b16769ef9af303df271009ee3c3fa0,
'kzx': 0xd3ff91995862f1bdc9733b4e017a460d,
'lzx': 0x5c8df559fbbe5156c06996be9c34397f,
'mzx': 0x964300cae83bc918da0ff3fb8bf81baf,
'nzx': 0xa7017ee91ad1dfe924d9f49e392ab221,
'ozx': 0xfef6b6dbe04fc3213e4f5555a05a406d,
'pzx': 0xc263a277c1b757eb3741a4df90eee443,
'qzx': 0xcc0f06547752496cbffd1651e7c21a88,
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'khz': 0x4782d8de857586199d704c8773a8c827,
'lhz': 0xff7c63c8de65b72404e46be69a107bf5,
'mhz': 0xddfbb7f02c38f62bf39d7a4f8f80c5f3,
'nhz': 0x15c38aa2581199246d9bd8889de2637f,
'ohz': 0x30f488b75e404ae13a9b5e3e7475a551,
'phz': 0x832dbedea7ae61116f47a72bf64835bc,
'qhz': 0xc7540feadbb31e42ec2cf5d358ea8847,
'rhz': 0x377de925179f909bbd19633842379829,
'shz': 0xbab9a317948c81793e98469117af3c3c,
'thz': 0xa2a1238086bfce5ab2c4315a0ad3941b,
'uhz': 0x76980ff30582d9ed7ffba6b654fb08c5,
'vhz': 0x60524490011a5a415351e5be6b7c281e,
'whz': 0x2c828f88626eb808544afaf1d8c954ad,
'xhz': 0x2e03f3607452e1e8f3506a9292cfdde0,
'yhz': 0x497a9ce2c9e1430898e373e0c3e127f9,
'zhz': 0x269d2be4617f754373fad754023e9777,
'aiz': 0x6a08b8d07bad8002e1f9d7efd28e9b86,
'biz': 0x300a1a224385acfb318149b6498f746d,
'ciz': 0x94a3dc640d51e0581000ffe1d0cef3a5,
'diz': 0x13ec2f2ed8c159dbfba5df2436d4cf42,
'eiz': 0xea8248e94e90452e6592c4f64d195bd5,
'fiz': 0x8f1944f4249471d7a35e82dd9aac92e1,
'giz': 0xf992d1c940053cd9544290d7a69342ff,
'hiz': 0xfe0901e738516d4cbf1114583cdc84a6,
'iiz': 0x0c5328c74806024d9b47dc33115b4fa8,
'jiz': 0x7ed57ac3f93d7f517095d53ca1944631,
'kiz': 0x7e732a29a006145e885d53b8ca584ae1,
'liz': 0x56d50a506cbbfb4a918a26898a51c1aa,
'miz': 0x1c4bcc46f09998e6842791d8374a066e,
'niz': 0x1df395683245ab0a40fa3838281f1b40,
'oiz': 0x32e0509327c2259458178a662fc2f66b,
'piz': 0xcf2fdbc5b6d5b14be58715d78463bec2,
'qiz': 0xaa002f711b7a6504ab3b7af2f7b0c4ab,
'riz': 0xa828a24b3c8a9f817820fea0d221dd1c,
'siz': 0x446cc80718ba4274fb67e2a03f2f6a1f,
'tiz': 0xb455ede106ac55608c6b121f80f24532,
'uiz': 0xd8ee48fe314ea11983e297a974a72d12,
'viz': 0x2ffd9e5be3549120efbe4e83e5c36237,
'wiz': 0xe3b77378c20be9f63f86c33efa6f8d47,
'xiz': 0x5af27b41435daf59eaeaa87f41cd019d,
'yiz': 0xc7020e1c8939626c1b93975b3837bbe5,
'ziz': 0xbd0eff9742bda0ebdec43dc354beda17,
'ajz': 0x387c9088d822110ec13737303a6e76d4,
'bjz': 0x14f43b4cfe28f9fa3a2871c5e67dd918,
'cjz': 0xed0cadbde8be0c98e473684c96228ba7,
'djz': 0x533de8a88fbdc9f10d1fb3a6a4ad9191,
'ejz': 0xc32cbf9d43e555b5120bc0725312ca69,
'fjz': 0xc33ede9b5912da8ba5669e9b7d8467b5,
'gjz': 0xbca905f834eaffe64fd5c09cc75eff5a,
'hjz': 0x9886067e6afc86c315a37d73057113c2,
'ijz': 0x799d0b634a94c3d25f30556363acd077,
'jjz': 0xfbe812da61cfe4f25e3e49aa8a427184,
'kjz': 0x223a227dd7514542311c37ce9752d408,
'ljz': 0x8a545972cb55bee645d78fd884901415,
'mjz': 0x0129d00854f5b5d3369a5de6bcb37dda,
'njz': 0x8a691a2bd6bd70b605fd5da35a2836a9,
'ojz': 0x3d90660e99510c574e90ad7289936fbd,
'pjz': 0x40c41917994b9c6e6bd430be0c2cfb43,
'qjz': 0xb464c4cd724cdbb2c5d5453e8710c3ea,
'rjz': 0xd66c5e0b18ffc0173b844dcc678be431,
'sjz': 0x5f47859188a602594556580532e814a3,
'tjz': 0x4f71feec6b46492e9a66073466d6e1d0,
'ujz': 0xe68725ff818b08ed59afbcbd0574a07c,
'vjz': 0xd343c15c53b32a6959ce3bc2f3bf2946,
'wjz': 0xd44aa1194e230ef05f49167b3c802449,
'xjz': 0x283e55a830a1bb6414c284beee2f7a73,
'yjz': 0xfb94a06b85623a0e71c56ebd0be9ae20,
'zjz': 0x24d64d4e061b0a84afeb2b00ce97fdbf,
'akz': 0x039a4f30aabde4d235bf7f3fb7c1c29d,
'bkz': 0x14399851ac77479e5d764176d4b2dc1b,
'ckz': 0xf78d5473534d40353840b7b529bf8ab2,
'dkz': 0x9e3631f2b5e1828a8dd3c58597b6178c,
'ekz': 0xe8a981db87b4f66af4aa751584804ad8,
'fkz': 0xa9f8917d11258ab1bb49d373a86c2b9e,
'gkz': 0x3504e779096fbf7739cedc572a573068,
'hkz': 0x949bf073d6944b4503e2e4a972cf56e2,
'ikz': 0xb912360bb726967b300e6272514a083b,
'jkz': 0x9f098b8da164c13193029ba496c58d9e,
'kkz': 0x108c739d8d18d317de151d72ff71eaff,
'lkz': 0x6e0e17f98f086761d450b8daa6a479fb,
'mkz': 0x64e0146735149da979c7d86092d26a56,
'nkz': 0xdf7ead801da8ca14a200f2cb684dac7b,
'okz': 0xeeb5c6bbe182030d7626332be586b96e,
'pkz': 0xec54a37c763476f73a03b3c466dfb50e,
'qkz': 0x05b18d66c6ea0fb7c40a2030cd67a25c,
'rkz': 0x4f6ba8d78fb63ead9766f35b635caef3,
'skz': 0x950e8a347468f286d23975abc86d3342,
'tkz': 0xd46e5560ab8f8645d2eff96e8d4c629d,
'ukz': 0x652d0684bed37946d732aab058f712cd,
'vkz': 0xa372b8632255b407639b17a383cb1dcd,
'wkz': 0xba305ee3459ae3827d6e219a6de03e73,
'xkz': 0x378fed74d2dac433550118008c65cf65,
'ykz': 0x90ef7e317d0ab25016660478636c48ed,
'zkz': 0xdb009cd62df36edc598cfd424e354fa2,
'alz': 0x10b20f8fdae06de05e332cb2348d7191,
'blz': 0xaefdfc4e3e719a7b67ebc1e011e3311c,
'clz': 0xc605b43a22a580a296bd855e988245d9,
'dlz': 0x914fd1457a9eeb19863d0f5a506add98,
'elz': 0x16306add44d7d975bb4d30dcd78eec3c,
'flz': 0x12016d864b6b2889ac5a96753e06c8d5,
'glz': 0xb13df5dc4d8724714fea345f2ce3f097,
'hlz': 0x0ec58966be7b5bda3bdba1ec69824e1f,
'ilz': 0xb515e0f75a92287788b6d9803fbef472,
'jlz': 0x89de32e4fbe2163115644d6b62054ec0,
'klz': 0x8c55ac6d27623e0ce18c8dc424adfb69,
'llz': 0x7b0b1898606043cfc143ef187ff4e4f5,
'mlz': 0xe94a9425ee88f2134621013734e2a578,
'nlz': 0x9c9e82efb71845a11ba69750d9d7b885,
'olz': 0x734f365c1d78f27671fe74b63319335b,
'plz': 0x9b952dbb76b48bfa463b1a4620425792,
'qlz': 0xf3ee5979ab381b6a2bf6258db7995b24,
'rlz': 0xbbdaa538a89c6d1f9ba40a10ceac8a92,
'slz': 0x5187ef462c7aba27c9c224399383befb,
'tlz': 0xe47b55a1d82597e6704145d0e0876321,
'ulz': 0x42701cc524d0be755a75920917f42477,
'vlz': 0x17ae6e5374b5a30a592fdeaa5ba4dd35,
'wlz': 0x65d790ae0e9c696da48f8b5ef40a7029,
'xlz': 0xe4f0f0326c456a94156a69452b85548c,
'ylz': 0x4a5573ebda6ffb81fe75638acb51292e,
'zlz': 0x73ca0bd0b089c177d3134dd772ab70f4,
'amz': 0xed5c08b7c09b160ade3a65ce3f560b06,
'bmz': 0x8a6afa9061bbc074ca44d7882450dc4d,
'cmz': 0x4afcc456ccfd59d852882f7c4082fc07,
'dmz': 0x6ffdb974418ac331f39c01fc396b5818,
'emz': 0x42e94333234092b13da2cc774b01d0be,
'fmz': 0x1ec7d7393d4b8f74cc9d27eb4c4107ca,
'gmz': 0x607ce75fcb6cc318f2604dbf3d6e06fb,
'hmz': 0xc25be4e582491fe40d6cf44df293bf11,
'imz': 0x484703112ea487df4160841ff4155f1f,
'jmz': 0xac5c2983f2860c5b340151053598bf87,
'kmz': 0x1116ccc127309521faa856e7ebb84451,
'lmz': 0xd80c02fdccb67b646c72c7036613257b,
'mmz': 0xc4236deea8f339a025c7dcd0673b9a10,
'nmz': 0xb2a8c600ccc374141f46fa6e44647a92,
'omz': 0x47f0e9f9c9aabff536081ef00ddf6b0f,
'pmz': 0x789c2ed54d67167dc5bb053b6574d7b1,
'qmz': 0x1f6209011727ba13f04980e1cda1a6fa,
'rmz': 0xf4f6a1dd59a67e4ed57cc6bb620c6906,
'smz': 0xe0af197398438826bd65fa69f10bf8b7,
'tmz': 0x6dd2abc202d5c30b152e755426f9cbd1,
'umz': 0xa0d3fbf112eb3db56e901d02d3d3e7f7,
'vmz': 0xadfe1ac080f892789c4152a282c2b801,
'wmz': 0xe03bfa2e2b6572bae164df43e78094ef,
'xmz': 0x44dc86be8fe87803a4aa207d8fb1d8d9,
'ymz': 0xc8e9c5bfcbc6e1214c593a081cb06df9,
'zmz': 0x699f02d3d0016a3ac737a0ddc5957915,
'anz': 0x88e1e1ca208e047be09ac68302716180,
'bnz': 0x1a5910537952f46b065e3d15eb4aa354,
'cnz': 0x69b582cf95bf9bcc4a5158e59b8c4409,
'dnz': 0xb874ec0c54202b41e67cc8e593c5701d,
'enz': 0x0b1ad72772f54fd760bc270028761bdb,
'fnz': 0x603b751cbaf8469f0e9df89f7d945b20,
'gnz': 0x355001848362c3f9601a834510a2e673,
'hnz': 0x0df2fb5603ee55e5335646e66156753e,
'inz': 0x4aeedad75170f46f10f76c422b503c6b,
'jnz': 0x9fe20c8a40e0703d94e461364a39fc8d,
'knz': 0xf19e067c3992d9dfde51b2196a11e256,
'lnz': 0xed4326be74815b1c2f391faf46b3dff7,
'mnz': 0x1b6b3768de403a8cb04728375e2d9ab2,
'nnz': 0x26c40f3894abd4e99874580f8bf25540,
'onz': 0x32a78180be3de8e131105684b205e0c8,
'pnz': 0x2b727bea763dacd3172e9778dc489c28,
'qnz': 0x6629d19fae366c9b629148bd93ff41a8,
'rnz': 0xe7672b3a6816197aeae4aa20e34f85ff,
'snz': 0xb57185b9c1e77a26ed6629e3a59a8b4b,
'tnz': 0x47b411692f96d7328a2c73abe70c0cb3,
'unz': 0x7fbc742e1a302efc1045507d7f4f1e6d,
'vnz': 0xc61a27a1c4e8e0d88fe3a964f0511f36,
'wnz': 0xab70273980c23033882f31e9d4d967da,
'xnz': 0xfdb8d0016315f84f03a15a6e408177ab,
'ynz': 0xee90217ef2052d3804f8869ed028d662,
'znz': 0x570e0599c92c5104946757f87344ec8b,
'aoz': 0x4f243899c1ad382046f782eacbc65689,
'boz': 0xea7d3eb14d8ff4de44f68b1656cceeac,
'coz': 0xc89dd2bbdbe28c9a7a85b8d9d34e67d4,
'doz': 0x3fa8d55f3e7aafc45759e8537e4a5a90,
'eoz': 0xb36b0403ba1ec5edf3f48170fc327ea5,
'foz': 0x34bc47bcacfdb9870f3d816c2089f722,
'goz': 0x24c8511859f7162a89274a8f81cd90f4,
'hoz': 0x11070013f046b9895721c446dcab26e6,
'ioz': 0xc355412939f139c60c6cf997491101f6,
'joz': 0x778cfb1d3232a9fc71546eed750ba58a,
'koz': 0x68c7e89dd0cbf3fa7bac7ac6a433c5ee,
'loz': 0x7f94461782b714fe904f92554ee97085,
'moz': 0x89364cb625249b3d478bace02699e05d,
'noz': 0xb675818cfcefea83a15d92ef70a73177,
'ooz': 0xe10b3737c26625a98242bf64e7a21a20,
'poz': 0x66fa5d8423591a35a2106e01b679cfb6,
'qoz': 0x6166814ee3556061ed6baa84470d9fd1,
'roz': 0xbece27a540b3ed869571e6b4c73d1906,
'soz': 0xe4caba2e128479a419cc2bd992de3a35,
'toz': 0x75a2c1a1abd1a7dd1fd5845f020ebb76,
'uoz': 0x1f62ced2267f1322b33e27e91671289d,
'voz': 0x430c414abb7dfde3a92b85833b0c9973,
'woz': 0x6fdbcefe890a1f6e1b927e3687177de3,
'xoz': 0x1b8a8d1cf898f0385c6e1f34e1347ef7,
'yoz': 0x34ac9cd22e43d2b390637e622955199b,
'zoz': 0x231e3f103e359cd184d2aac708ede167,
'apz': 0xde108e19e410b0443c23eeac7dc64fb2,
'bpz': 0x9e30a08aa11ac8714af151fd5e23f852,
'cpz': 0x4125a204b27dd922b6d3c3cf31db5657,
'dpz': 0xe23a81953efd40fe002cfa7c3c19f5ea,
'epz': 0x5762394762b9461160121be08cfe87e2,
'fpz': 0x703a47206591c5a7350ae7ed83293426,
'gpz': 0x0e279a1f4d8fbb5ba16498c86ac9aa0a,
'hpz': 0x463b87f1bf40ebb9681470eac0583058,
'ipz': 0x1a87dd168df3359a6c3014fef950f5b8,
'jpz': 0xb83cc3fa63110a30ed5e8dc8dc8f8381,
'kpz': 0xbdbefb359e83e67af9c5b05e1e344e4a,
'lpz': 0xcfd47456351699c5475f556479b49462,
'mpz': 0x672aac0f8006356039d498b542587948,
'npz': 0x62a9d7654d12a805b75477b61c159734,
'opz': 0x53c2b3524e98b04d105304b7aa5dc97e,
'ppz': 0xf87b337d6e4b0cb1681bc517e4c96cf9,
'qpz': 0x2d2f5b7ce6342c71b239ce02192c5eec,
'rpz': 0x533ff2cb2055365850dad736e049d3c7,
'spz': 0x43b701a8a5613f4783c0a267868df000,
'tpz': 0xdc78909c893fa45ab76fd72a50e0cec1,
'upz': 0x00c4add07d8f48d604811356b26f9276,
'vpz': 0x4090b105ed551c18f3e262d2c6fd42e0,
'wpz': 0x4c75fb619f2abb87d5d52cfba03c28e9,
'xpz': 0x22713b48cc9003fd610b074912bddc01,
'ypz': 0x6dd2d9a12f34ca47b52a4b2f54cbbc61,
'zpz': 0x824cbcfea0527e12b5964d7b6bc78e42,
'aqz': 0x12d45c3f59d9e01a1c3d429e88baf353,
'bqz': 0x525ee2691e1f30e9cb85dc3acbb8b9aa,
'cqz': 0x15c2ebdebedb817babfcb5e9eae66f3b,
'dqz': 0x75a11a5862e0087df59e2e1be5ab01cf,
'eqz': 0x605bf78188d0a6de464ce93ff5748a9b,
'fqz': 0xb398bbd84e246f8f3a30224984972b29,
'gqz': 0xb37bbd4094466196e173b6e941920096,
'hqz': 0x9f1a197bc588858039db195f7cd3d39d,
'iqz': 0xf9044c8573d3ed7b678d16f7257d23a4,
'jqz': 0xd1b14f722029b78a78ad4054ec44ab1a,
'kqz': 0xe53def6f7c5db643ddb189d95f33ebf4,
'lqz': 0xb050151a24f52691466c9a01a85c0526,
'mqz': 0x13c51fd76b5bf77f911ffbba3d5e10ce,
'nqz': 0x2a94de605adb1b2a3ac5dc265a0047c7,
'oqz': 0x8cca5eaa4c6eabf05faef32ddbcf4721,
'pqz': 0x2ce520981bbeacbd27d3548fcd6d918c,
'qqz': 0xc2bec9b800501ce0f43cb544a01083d5,
'rqz': 0x0924120dfdd0c90f1ceb0fd149a9bc0a,
'sqz': 0x436ee6d169337426828e25697f5018e7,
'tqz': 0xf35411eba5d62b442238e82b97c93f8c,
'uqz': 0x8e191075831c7694ed1fc426ae0274be,
'vqz': 0x2ae63bc4f82f0565b478cb8fd224033d,
'wqz': 0x2ae48a98648ba65e948ddecbaf165217,
'xqz': 0x96e1dfa3b359542cd9555137d8dea07a,
'yqz': 0xcaa2ae151e97c2c57365e7c00c213e9e,
'zqz': 0xc7e08e12a26e622fb2738baf1c4510e3,
'arz': 0xf1e84df374b610f1368c7a5133b9fdc7,
'brz': 0x900ba5ef2ca667fcd92567819aed3513,
'crz': 0x99aa4ed99db60bad7065dba06e678f8e,
'drz': 0x671915e3c04c9aff65cf083418d751ac,
'erz': 0xd6c66b97e132a16476f6a627c75602df,
'frz': 0xabd7363c58659e33b77870e20456cda5,
'grz': 0x970d7fe6ef9dba5d7460797f3903ccfe,
'hrz': 0xee84a8cfb056098fcc6c98ca697237e0,
'irz': 0x6682af6afb079ec919c6e5debb0f56f0,
'jrz': 0x941f4088d5bfaacd31551e2f3b562860,
'krz': 0xbabd5140bdf725562287069f365ff139,
'lrz': 0xf6c19cb1d873b33b48645d52278ecdf2,
'mrz': 0x8205c0ed66bf4060f8cdaa7a744a3c4b,
'nrz': 0x1854cee7a533d7bbad20471ec72d1cf5,
'orz': 0x21d6e79b3b2a0f9ad0373441e4f4a03e,
'prz': 0x76e02bf61ffd24fca7b05bbb1b35f1b3,
'qrz': 0x209410291e0addb5991818cbfc516e32,
'rrz': 0xd2c7b39409696ce5cd5c94e0884947e5,
'srz': 0xdf0df5670d4e4455531dc0095f7b5439,
'trz': 0xbfb030d858c745b0a72340de2ae7f1ad,
'urz': 0x878940bb3d1462616a45f5508f0630a2,
'vrz': 0xe8d112a5eabae0347d12b99a66286e11,
'wrz': 0x127102bb71b2283d78b2987f83df9187,
'xrz': 0x59c3bc6cc6566132696a588edbb1f359,
'yrz': 0x8f39db8c9ff18041970a148ca356efde,
'zrz': 0x41c4f4c22f5665e9347b5844392793bf,
'asz': 0xc0c1534f82f592afcd75a9b404e871ca,
'bsz': 0x0b334882956d5ee52a55b6cd2a714c1d,
'csz': 0xc15c3ff6a825f0b9773d8595d4bf9ffb,
'dsz': 0xcbf27e91efc647c0c94a0dc6432a22f9,
'esz': 0x6b83b0879ce85f29317985e1cc5c1d6d,
'fsz': 0x459f128445be8d6254334b3b894fc0d2,
'gsz': 0x3638aa49dc633892e7634f6ba6b5b317,
'hsz': 0x071a5dcfd09db850f1ff95131acc5f2c,
'isz': 0x49960fd911110a40c4cfec43e2e7603b,
'jsz': 0x9605cf916f3548ebcd45202e766725ce,
'ksz': 0xf24d02244192b7aa4a9988bf2cd09e72,
'lsz': 0xbd99bfdba45c50df680c85c7cd616aab,
'msz': 0xfb06d6f4135e4ef214f461c3b811de65,
'nsz': 0x83ad537246c68235c2ff05142fd9c91c,
'osz': 0xb247ddaf90f1435263171dc6b9976114,
'psz': 0x85d0697230936ecae2ca76b272f95e57,
'qsz': 0x5cb47c8f0af43c450043d1c873cbc170,
'rsz': 0x611bdbc1a25040d562a09384da155884,
'ssz': 0x8a50f1a98073407d925b82daf17cfb5d,
'tsz': 0x22ecf1a05ac07468891486d133da4379,
'usz': 0xbae58c8334de9da6e0d3d3e15187c727,
'vsz': 0x207fa7c2ee90b3b84f3d004c3cb3e70a,
'wsz': 0x95602600800c1aedae854321e7be66f0,
'xsz': 0xc84c5b65effa7201e43aabd604036ebe,
'ysz': 0x1993bd635c26887e4da900235cf39253,
'zsz': 0x13c773275f403aa44ef4c5251203bd94,
'atz': 0x50f1976039925df101e77acd00c679a3,
'btz': 0x28d00b62bdd2bd6e1563c9e11ada1543,
'ctz': 0xa4dc9f959e34fabab1728fd1a730f10d,
'dtz': 0x74b3490b5be51a1e110baaf044e35c93,
'etz': 0x2fa53c5561a78e9b9464c34201989f61,
'ftz': 0x8f6e444f4ee7095d2e79c529448eec95,
'gtz': 0xe6e99cf00281f12a0cd9b6d048f397ab,
'htz': 0xfc1ca4c7a5912cecec75331db77dc6a1,
'itz': 0xc0259905547c0713dad69edeafe8bbe5,
'jtz': 0x872bde93e112f16390aa50d66378b2f1,
'ktz': 0xc5f158daeb96a1c71aae3e088aa3b77a,
'ltz': 0x7d112fb3f6f0ef8459558056dd2fc36e,
'mtz': 0xa0286259168282d393c114d826559d96,
'ntz': 0x9ba89b4e17549270f97c74bb99d10a19,
'otz': 0x0fc58384e21e3f5717f366dd24fa16d8,
'ptz': 0xcf205a7dd510bc810a21cf1b88c62fa5,
'qtz': 0x4f3c526b58219f04f3a45febfaddf033,
'rtz': 0xcf5c5662f49f76f9bbd776d77935b422,
'stz': 0xbc2b883b4c051daf62598ea6024d38fc,
'ttz': 0xb4624c6107fce7d88d5fc3ba8ddc6e3f,
'utz': 0xa52955d8af64da1b86fafe54eaf3f0bd,
'vtz': 0x32f9192f7031d04e053b92ed0253947e,
'wtz': 0x3411106a83f07254cfde4e3106ea5d8f,
'xtz': 0x4bb540a2be0454967ac2843c1052d35d,
'ytz': 0x174de676895fbb5239d3a12b95a3a0fb,
'ztz': 0x436370082b257ecffd91d04804f86a6f,
'auz': 0xd03682ffcba3d5fc572791c9dc31eae3,
'buz': 0x2fbf0da2be14337c2919dc3c842166e7,
'cuz': 0x99d0ba8fd18c1d9a0dfdf8fa81825823,
'duz': 0x72b0c525c8a39b8fa8ecd2ac6ac9ea08,
'euz': 0x1b8a83a78dba5aba2ad13e7f0e3c40fc,
'fuz': 0x65b412d6e9d951540002f47c8b55e9d1,
'guz': 0x73316f3cd41c27508dbb6f4c5e7e7a8a,
'huz': 0x99c66c078ab6fb864fe2ffa560f6b7ad,
'iuz': 0x479f180550a4951325185891c123a3a4,
'juz': 0xd966546e81ea0adffedacaaa37c0481a,
'kuz': 0x9313b0984e3cf8e0aa706f56500e8f2a,
'luz': 0x4a45763fb8832658b8cfdf27bba1bd77,
'muz': 0x9c05417ff0f70e6b0969416cee26869e,
'nuz': 0x707023216516d0d926ac0ee090fce510,
'ouz': 0x0d39cd8d699bd48caf2c9e5082ea4904,
'puz': 0x35a94e6d4f4cdbcd7cf88f257b1d43f0,
'quz': 0x568f3dd88592a68ef99459a5491011cd,
'ruz': 0xaa97acb0c12855bbf5b30425a0fc6a3b,
'suz': 0xcae5d80ce5266d1b3ecccd796492e45a,
'tuz': 0x9a770bdd67f9510c1e4dc1c0bc116d80,
'uuz': 0xc41bbc6fa1c37b9e37cda8e140018ab1,
'vuz': 0xde7ba9174f9b0dc00e8c73a1b29e8a42,
'wuz': 0xcac5733ece00e74128c89886c71c6fa0,
'xuz': 0x9e0a15dc928e1a94e15628dffe678780,
'yuz': 0xb7eeaf465326a6205f22408c7c42620e,
'zuz': 0x9eab576f6a65202968559d1e0dcb87a5,
'avz': 0xdcbac74094dbb4de5af7922971c18800,
'bvz': 0xc38ea4ed4a7d4f514958a59353c3f85c,
'cvz': 0x99e1bf969458c7c20e6d139f11918154,
'dvz': 0xce7074d5836fa4c266aa07970b672a43,
'evz': 0xed9ebf8f89dc2f52f38a7a0306e21e93,
'fvz': 0x6e032d66f86fa994f0dd65d1e15fdc6a,
'gvz': 0x5a17fa8848fe834aac24e55d2dd25535,
'hvz': 0x7ecb9dc0aac08868a6a72b92f50ed50b,
'ivz': 0x09d76f48f8b0bf382a3d2fe5d245a3ea,
'jvz': 0x97a887e4fb138944664b21e982c690e7,
'kvz': 0x73967c1be8eaa02e92c475f6913cace3,
'lvz': 0x4f871613c75c5078ec65f2c4dca3bf3d,
'mvz': 0xa66591c98975b9cd878215d63069a80e,
'nvz': 0x4e9c79ad85db1e9f65d6e1916d000744,
'ovz': 0xb7c234a69734fda6b720ef162fd3ca29,
'pvz': 0xdfd5a7dd9d452231651a04782b79f557,
'qvz': 0x8d26e54326722eaca1a2cb0fd36cfb5e,
'rvz': 0xb51c3fe54861f598d970579e2dbdcb57,
'svz': 0x71286c93193ae97dd0a1f36e0da818f9,
'tvz': 0x07893fc7043bcd160974cfab408c1577,
'uvz': 0xad1dac8b635783339ad9029bf04ca3a0,
'vvz': 0x9b6e71acf91c71616eb275b1654c1f2d,
'wvz': 0xc09fa5e953f80cb3b1f56f27903078f2,
'xvz': 0xca686f80bf72389ab73fbc1d826b36a5,
'yvz': 0x1e10e1df6d0887c8c026cdf3f2c18905,
'zvz': 0x29e750d2b950f3b8e7e03a83e2c94c75,
'awz': 0x93a5a5354896a5eb7203ed0d704db2c5,
'bwz': 0xa21110dcdb82c52f988d3d9fcb5856ce,
'cwz': 0x5e07ba7fdedbd9a73ad82f8dd5f4398b,
'dwz': 0x3657f30781d07cd157bf2c0ea9679620,
'ewz': 0xc8001eb22207d287fd884fa5829e9dd8,
'fwz': 0x35d542786265a378392ed3c3ef39dc0b,
'gwz': 0x5bbecc0fe04e26940971a5efafac3b76,
'hwz': 0x8a1491fb01d6af8e9afcf7cb6c81f3dc,
'iwz': 0xa1975c24be2b6175b77c1219dece7591,
'jwz': 0x68c259d41b4484ae3391842f3067c5d0,
'kwz': 0x7e41ca0b45a072316edc7f534173559e,
'lwz': 0x2dd33aadc2e418848117df928876e353,
'mwz': 0x005f9c095cd48af43aa06c732f04c2e7,
'nwz': 0x6345746ba51b33784aa95d8e368e413e,
'owz': 0x3a7d6e1be0024d62d1d506a09787b651,
'pwz': 0x5bbd84002c50ad624fb8f9dbae1f48d2,
'qwz': 0x5f558f78854c47fa9f2b8bcaf75f6ed5,
'rwz': 0xc307e48024f3117e74362bbde661fd10,
'swz': 0xd1b2cbe43fd30f51f2488498287164f6,
'twz': 0x0a41a735fc23f3d167f6ac661eedf3f0,
'uwz': 0x9e06be76ed192a7f12190772563d2394,
'vwz': 0x4b2f587984273e63584cc71f4132b68e,
'wwz': 0xe235ac07af7a969a52bec0985f6a9f85,
'xwz': 0xe1a3accb868ce9be44e30e0c2ede9f76,
'ywz': 0x66876c06ee6396c5d357a24081cba693,
'zwz': 0xef05140c678b1e1046d9a8d0b24a5888,
'axz': 0xf0fbf4f5b5a12a047db2b6ea3bdc607c,
'bxz': 0x95ac3fba37f92ccde74634516b07f54c,
'cxz': 0x26178767bb0eb3bcb547bbc1b9560d03,
'dxz': 0x37f330ca440df8a4f33a31aa3862e721,
'exz': 0xc959e91e58534db253188b6b62d314b6,
'fxz': 0xd18495736a138091a97cba56e00ee547,
'gxz': 0x65f043e5c4c0e5c2c7f6e84fcf7f2fbe,
'hxz': 0x9f653e2b24ed7563f519c394636c33c6,
'ixz': 0xfb4bd0cd233481be4a7c5533a10c1940,
'jxz': 0x211741bafdae1c96966a54d7615f74a3,
'kxz': 0xbc7a683ebd1fe11dd05c8b4e06229ea6,
'lxz': 0x5d52a67facac487d8aa43f29de250510,
'mxz': 0xb963eebb752207ed139b5a0ae03a5cb9,
'nxz': 0x12b4534027611c9d247c56b106ce4ab3,
'oxz': 0x9ca6eecbe0e28fb2c6f8ee627a632c4a,
'pxz': 0xd4f80a6c16f13a45201877036a09b472,
'qxz': 0x498e89aa2f85b83ba9340555a13b96ec,
'rxz': 0xd4fa691b07b63de0e73e8b9148056064,
'sxz': 0x8cf9c943547923e7e7712b0a0d2ba7a5,
'txz': 0x2f6655471eadad727e38aba618de1809,
'uxz': 0x162ac222acb80300849b3d4f3b033bd2,
'vxz': 0xc7822d8c7725838b685b6cbc64f9eb2d,
'wxz': 0x4d3c1c3bd16ddd5af720b22937106129,
'xxz': 0x7693cf9650737bcfc93019df022e8a0c,
'yxz': 0x3fb54adfe44eea03344ec6b69ea31ef5,
'zxz': 0x0666d3bb926ccae05deb6307995a9a84,
'ayz': 0xf8e910120d41cf4f8cd8e5e94b6a51b9,
'byz': 0x6c51ca0a3cfbcc1a8e240946076b93e0,
'cyz': 0xd39569efd0e01dcd54ca24c4584e5b1e,
'dyz': 0x1fdddf3cf8b414c07cd99eb7b6c85af6,
'eyz': 0x8844a7a065551e1b86bd12ac1c7c52c9,
'fyz': 0xec28b4a2b55651dfe3e71c4dc2a9f8c7,
'gyz': 0x3f43b0b249522da511625cfe5b7edefc,
'hyz': 0x41d97610f9fab3dbe1ad81633f02b6cd,
'iyz': 0x5730883c4a64a4a474520ee45b2065cf,
'jyz': 0x68e35a3baa887ec2184be91789a3a6bf,
'kyz': 0x73d7e5547516a5ace6b9636af20419b0,
'lyz': 0x41299de22c46b9f179051414a408afc0,
'myz': 0x168325a69e6d7ed296b89358d7f9ba38,
'nyz': 0xebfddba32742e635014b2a87af207b0b,
'oyz': 0x83dd0bca3a7b3289ec26b4290acece42,
'pyz': 0x974631cc048e70fc5dbd784d76f324d2,
'qyz': 0x00e0679a620fd02134896f7ccd71384a,
'ryz': 0x3265150a54f845ab24da195a1530fb75,
'syz': 0xd8e2f78451cf9a89aec662db0657547f,
'tyz': 0xe1e4c0a32e30649a54068fdded95b8d6,
'uyz': 0x195961ac00c41e67f3f121fd4f059218,
'vyz': 0x7948ffe80965ce9b326d1bbd5e6433fa,
'wyz': 0x2a75448b4a4ddab6226a207e7a3a8447,
'xyz': 0xd16fb36f0911f878998c136191af705e,
'yyz': 0x37612607323c2c91f9c38619a76b4176,
'zyz': 0x4978d9eeb4efed2fd9d605dac313e7fc,
'azz': 0xbe021bf904406ee858fa8ee05356f9db,
'bzz': 0x32fc0c94c387bb0b63503dc85d08ce3a,
'czz': 0x254002be7fe7ce856d2e192dacff823f,
'dzz': 0x7491e82795267307ad3c1810ca303f16,
'ezz': 0x474764e47d1a6f0d90220e3729daed39,
'fzz': 0xcccce204c5ee9899b742676f62584426,
'gzz': 0x099567c7f05247629421418638059021,
'hzz': 0x2c06d93d60011551ece9c574dacc9c63,
'izz': 0x5a539513ccc6ec17ac23af227f60dc5f,
'jzz': 0x9d3e8bc84c8115b2ad2066084b1d3f02,
'kzz': 0xf1762738a6e5aa01c53abd4abd9169e1,
'lzz': 0x309e6d3b36f6ea6c9f04bcf5f1ff9616,
'mzz': 0x60e2566cfad3e4438f6c4a02cf676d82,
'nzz': 0xb021b1823c67bae630d1cbe29a46314c,
'ozz': 0x3aaf018941127dc788c49a5ef2554a44,
'pzz': 0x761e6fdc322207e6d412b783a8e2b821,
'qzz': 0xb09d22bbf5795d9d374493cd7adb4871,
'rzz': 0x55fa308a6cea33bd1128965b90c0302b,
'szz': 0xf94b1022296fd60bd1a8600bcd26dcd4,
'tzz': 0x15fdcedad0e275ad1982b21445dc1321,
'uzz': 0x20075ad3ea957ede1d2fbcd317e7592b,
'vzz': 0x9f50ac27ae752e1131a3012077e853c0,
'wzz': 0xaeea8760b9fe63eb9c65874e1d75719c,
'xzz': 0x8436a47abeec9845cdbd06a116cf936f,
'yzz': 0x44321627aa773dc17880c435a21ccec5,
'zzz': 0xf3abb86bd34cf4d52698f14c0da1dc60,
}
| likaiguo/simhashpy | simhash/simcache.py | Python | apache-2.0 | 785,241 | [
"ADF",
"ASE",
"BWA",
"CDK",
"EPW",
"Elk",
"MOE",
"VMD",
"VTK",
"xTB"
] | 4e21ea89f55a4909c25b7ce7773ec56bb800b14818304c53ffb9054e97dec50c |
# -*- coding: utf-8 -*-
# Copyright (C) 2009-2014 CEA/DEN, EDF R&D
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library 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
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
# See http://www.salome-platform.org/ or email : webmaster.salome@opencascade.com
#
####### Test for Extrude Quad ###############
import hexablock
doc = hexablock.addDocument("Extrude Quad Test")
dimx = 11
dimy = 11
dimz = 2
orig1 = doc.addVertex ( 0,0,0)
vx = doc.addVector ( 1,0,0)
vy = doc.addVector ( 0,1,0)
vz = doc.addVector ( 0,0,1)
dir1 = doc.addVector ( 1,1,1)
dir2 = doc.addVector ( 1,1,-1)
dx =1
dy=1
dz=1
grid1 = doc.makeCartesianUni (orig1, vx,vy,vz, dx,dy,dz, dimx,dimy,dimz)
mx = dimx/2
my = dimy/2
liste1 = []
liste2 = []
liste1.append(grid1.getQuadIJ (mx, my, dimz))
liste2.append(grid1.getQuadIJ (mx, my, 0))
for nx in range(dimx):
if nx!=mx:
liste1.append(grid1.getQuadIJ (nx, my, dimz))
liste2.append(grid1.getQuadIJ (nx, my, 0))
for ny in range(dimy):
if ny!=my:
liste1.append(grid1.getQuadIJ (mx, ny, dimz))
liste2.append(grid1.getQuadIJ (mx, ny, 0))
tlen = []
dh0 = 1
dh = 0.02
lh = 0
for nro in range(5):
dh = 1.5*dh + 1
lh += dh
tlen.append(lh)
nbiter = 5
doc.saveVtk ("prisme1.vtk")
prisme2 = doc.extrudeQuadsUni (liste2, dir2, dh0, nbiter)
doc.saveVtk ("prisme2.vtk")
prisme1 = doc.extrudeQuads (liste1, dir1, tlen)
doc.saveVtk ("prisme3.vtk")
| FedoraScientific/salome-hexablock | doc/test_doc/extrudeQuad/extrude_quad.py | Python | lgpl-2.1 | 2,051 | [
"VTK"
] | af989352d4ba19a56b9dd124cd525e66e1ef2d21e9a9cae7be18db160d096326 |
"""
Test courseware search
"""
import os
import json
from nose.plugins.attrib import attr
from ..helpers import UniqueCourseTest
from ...pages.common.logout import LogoutPage
from ...pages.studio.utils import add_html_component, click_css, type_in_codemirror
from ...pages.studio.auto_auth import AutoAuthPage
from ...pages.studio.overview import CourseOutlinePage
from ...pages.studio.container import ContainerPage
from ...pages.lms.courseware_search import CoursewareSearchPage
from ...fixtures.course import CourseFixture, XBlockFixtureDesc
@attr('shard_5')
class CoursewareSearchTest(UniqueCourseTest):
"""
Test courseware search.
"""
USERNAME = 'STUDENT_TESTER'
EMAIL = 'student101@example.com'
STAFF_USERNAME = "STAFF_TESTER"
STAFF_EMAIL = "staff101@example.com"
HTML_CONTENT = """
Someday I'll wish upon a star
And wake up where the clouds are far
Behind me.
Where troubles melt like lemon drops
Away above the chimney tops
That's where you'll find me.
"""
SEARCH_STRING = "chimney"
EDITED_CHAPTER_NAME = "Section 2 - edited"
EDITED_SEARCH_STRING = "edited"
TEST_INDEX_FILENAME = "test_root/index_file.dat"
def setUp(self):
"""
Create search page and course content to search
"""
# create test file in which index for this test will live
with open(self.TEST_INDEX_FILENAME, "w+") as index_file:
json.dump({}, index_file)
super(CoursewareSearchTest, self).setUp()
self.courseware_search_page = CoursewareSearchPage(self.browser, self.course_id)
self.course_outline = CourseOutlinePage(
self.browser,
self.course_info['org'],
self.course_info['number'],
self.course_info['run']
)
course_fix = CourseFixture(
self.course_info['org'],
self.course_info['number'],
self.course_info['run'],
self.course_info['display_name']
)
course_fix.add_children(
XBlockFixtureDesc('chapter', 'Section 1').add_children(
XBlockFixtureDesc('sequential', 'Subsection 1')
)
).add_children(
XBlockFixtureDesc('chapter', 'Section 2').add_children(
XBlockFixtureDesc('sequential', 'Subsection 2')
)
).install()
def tearDown(self):
os.remove(self.TEST_INDEX_FILENAME)
def _auto_auth(self, username, email, staff):
"""
Logout and login with given credentials.
"""
LogoutPage(self.browser).visit()
AutoAuthPage(self.browser, username=username, email=email,
course_id=self.course_id, staff=staff).visit()
def _studio_publish_content(self, section_index):
"""
Publish content on studio course page under specified section
"""
self._auto_auth(self.STAFF_USERNAME, self.STAFF_EMAIL, True)
self.course_outline.visit()
subsection = self.course_outline.section_at(section_index).subsection_at(0)
subsection.expand_subsection()
unit = subsection.unit_at(0)
unit.publish()
def _studio_edit_chapter_name(self, section_index):
"""
Edit chapter name on studio course page under specified section
"""
self._auto_auth(self.STAFF_USERNAME, self.STAFF_EMAIL, True)
self.course_outline.visit()
section = self.course_outline.section_at(section_index)
section.change_name(self.EDITED_CHAPTER_NAME)
def _studio_add_content(self, section_index):
"""
Add content on studio course page under specified section
"""
self._auto_auth(self.STAFF_USERNAME, self.STAFF_EMAIL, True)
# create a unit in course outline
self.course_outline.visit()
subsection = self.course_outline.section_at(section_index).subsection_at(0)
subsection.expand_subsection()
subsection.add_unit()
# got to unit and create an HTML component and save (not publish)
unit_page = ContainerPage(self.browser, None)
unit_page.wait_for_page()
add_html_component(unit_page, 0)
unit_page.wait_for_element_presence('.edit-button', 'Edit button is visible')
click_css(unit_page, '.edit-button', 0, require_notification=False)
unit_page.wait_for_element_visibility('.modal-editor', 'Modal editor is visible')
type_in_codemirror(unit_page, 0, self.HTML_CONTENT)
click_css(unit_page, '.action-save', 0)
def _studio_reindex(self):
"""
Reindex course content on studio course page
"""
self._auto_auth(self.STAFF_USERNAME, self.STAFF_EMAIL, True)
self.course_outline.visit()
self.course_outline.start_reindex()
self.course_outline.wait_for_ajax()
def _search_for_content(self, search_term):
"""
Login and search for specific content
Arguments:
search_term - term to be searched for
Returns:
(bool) True if search term is found in resulting content; False if not found
"""
self._auto_auth(self.USERNAME, self.EMAIL, False)
self.courseware_search_page.visit()
self.courseware_search_page.search_for_term(search_term)
return search_term in self.courseware_search_page.search_results.html[0]
def test_page_existence(self):
"""
Make sure that the page is accessible.
"""
self._auto_auth(self.USERNAME, self.EMAIL, False)
self.courseware_search_page.visit()
def test_search(self):
"""
Make sure that you can search for something.
"""
# Create content in studio without publishing.
self._studio_add_content(0)
# Do a search, there should be no results shown.
self.assertFalse(self._search_for_content(self.SEARCH_STRING))
# Publish in studio to trigger indexing.
self._studio_publish_content(0)
# Do the search again, this time we expect results.
self.assertTrue(self._search_for_content(self.SEARCH_STRING))
def test_reindex(self):
"""
Make sure new content gets reindexed on button press.
"""
# Create content in studio without publishing.
self._studio_add_content(1)
# Do a search, there should be no results shown.
self.assertFalse(self._search_for_content(self.EDITED_SEARCH_STRING))
# Publish in studio to trigger indexing, and edit chapter name afterwards.
self._studio_publish_content(1)
# Do a ReIndex from studio to ensure that our stuff is updated before the next stage of the test
self._studio_reindex()
# Search after publish, there should still be no results shown.
self.assertFalse(self._search_for_content(self.EDITED_SEARCH_STRING))
self._studio_edit_chapter_name(1)
# Do a ReIndex from studio to ensure that our stuff is updated before the next stage of the test
self._studio_reindex()
# Do the search again, this time we expect results.
self.assertTrue(self._search_for_content(self.EDITED_SEARCH_STRING))
| dkarakats/edx-platform | common/test/acceptance/tests/lms/test_lms_courseware_search.py | Python | agpl-3.0 | 7,300 | [
"VisIt"
] | 7d184558e55567577b4c94957f62e56b9453bad9a5d3dfb8fbccff31d941df84 |
# ImageNet classes
# Taken from:
# https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt
IMAGENET_CLASSES = \
{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
3: 'tiger shark, Galeocerdo cuvieri',
4: 'hammerhead, hammerhead shark',
5: 'electric ray, crampfish, numbfish, torpedo',
6: 'stingray',
7: 'cock',
8: 'hen',
9: 'ostrich, Struthio camelus',
10: 'brambling, Fringilla montifringilla',
11: 'goldfinch, Carduelis carduelis',
12: 'house finch, linnet, Carpodacus mexicanus',
13: 'junco, snowbird',
14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',
15: 'robin, American robin, Turdus migratorius',
16: 'bulbul',
17: 'jay',
18: 'magpie',
19: 'chickadee',
20: 'water ouzel, dipper',
21: 'kite',
22: 'bald eagle, American eagle, Haliaeetus leucocephalus',
23: 'vulture',
24: 'great grey owl, great gray owl, Strix nebulosa',
25: 'European fire salamander, Salamandra salamandra',
26: 'common newt, Triturus vulgaris',
27: 'eft',
28: 'spotted salamander, Ambystoma maculatum',
29: 'axolotl, mud puppy, Ambystoma mexicanum',
30: 'bullfrog, Rana catesbeiana',
31: 'tree frog, tree-frog',
32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',
33: 'loggerhead, loggerhead turtle, Caretta caretta',
34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',
35: 'mud turtle',
36: 'terrapin',
37: 'box turtle, box tortoise',
38: 'banded gecko',
39: 'common iguana, iguana, Iguana iguana',
40: 'American chameleon, anole, Anolis carolinensis',
41: 'whiptail, whiptail lizard',
42: 'agama',
43: 'frilled lizard, Chlamydosaurus kingi',
44: 'alligator lizard',
45: 'Gila monster, Heloderma suspectum',
46: 'green lizard, Lacerta viridis',
47: 'African chameleon, Chamaeleo chamaeleon',
48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',
49: 'African crocodile, Nile crocodile, Crocodylus niloticus',
50: 'American alligator, Alligator mississipiensis',
51: 'triceratops',
52: 'thunder snake, worm snake, Carphophis amoenus',
53: 'ringneck snake, ring-necked snake, ring snake',
54: 'hognose snake, puff adder, sand viper',
55: 'green snake, grass snake',
56: 'king snake, kingsnake',
57: 'garter snake, grass snake',
58: 'water snake',
59: 'vine snake',
60: 'night snake, Hypsiglena torquata',
61: 'boa constrictor, Constrictor constrictor',
62: 'rock python, rock snake, Python sebae',
63: 'Indian cobra, Naja naja',
64: 'green mamba',
65: 'sea snake',
66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',
67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',
68: 'sidewinder, horned rattlesnake, Crotalus cerastes',
69: 'trilobite',
70: 'harvestman, daddy longlegs, Phalangium opilio',
71: 'scorpion',
72: 'black and gold garden spider, Argiope aurantia',
73: 'barn spider, Araneus cavaticus',
74: 'garden spider, Aranea diademata',
75: 'black widow, Latrodectus mactans',
76: 'tarantula',
77: 'wolf spider, hunting spider',
78: 'tick',
79: 'centipede',
80: 'black grouse',
81: 'ptarmigan',
82: 'ruffed grouse, partridge, Bonasa umbellus',
83: 'prairie chicken, prairie grouse, prairie fowl',
84: 'peacock',
85: 'quail',
86: 'partridge',
87: 'African grey, African gray, Psittacus erithacus',
88: 'macaw',
89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
90: 'lorikeet',
91: 'coucal',
92: 'bee eater',
93: 'hornbill',
94: 'hummingbird',
95: 'jacamar',
96: 'toucan',
97: 'drake',
98: 'red-breasted merganser, Mergus serrator',
99: 'goose',
100: 'black swan, Cygnus atratus',
101: 'tusker',
102: 'echidna, spiny anteater, anteater',
103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',
104: 'wallaby, brush kangaroo',
105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',
106: 'wombat',
107: 'jellyfish',
108: 'sea anemone, anemone',
109: 'brain coral',
110: 'flatworm, platyhelminth',
111: 'nematode, nematode worm, roundworm',
112: 'conch',
113: 'snail',
114: 'slug',
115: 'sea slug, nudibranch',
116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',
117: 'chambered nautilus, pearly nautilus, nautilus',
118: 'Dungeness crab, Cancer magister',
119: 'rock crab, Cancer irroratus',
120: 'fiddler crab',
121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',
122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',
123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',
124: 'crayfish, crawfish, crawdad, crawdaddy',
125: 'hermit crab',
126: 'isopod',
127: 'white stork, Ciconia ciconia',
128: 'black stork, Ciconia nigra',
129: 'spoonbill',
130: 'flamingo',
131: 'little blue heron, Egretta caerulea',
132: 'American egret, great white heron, Egretta albus',
133: 'bittern',
134: 'crane',
135: 'limpkin, Aramus pictus',
136: 'European gallinule, Porphyrio porphyrio',
137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',
138: 'bustard',
139: 'ruddy turnstone, Arenaria interpres',
140: 'red-backed sandpiper, dunlin, Erolia alpina',
141: 'redshank, Tringa totanus',
142: 'dowitcher',
143: 'oystercatcher, oyster catcher',
144: 'pelican',
145: 'king penguin, Aptenodytes patagonica',
146: 'albatross, mollymawk',
147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',
148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',
149: 'dugong, Dugong dugon',
150: 'sea lion',
151: 'Chihuahua',
152: 'Japanese spaniel',
153: 'Maltese dog, Maltese terrier, Maltese',
154: 'Pekinese, Pekingese, Peke',
155: 'Shih-Tzu',
156: 'Blenheim spaniel',
157: 'papillon',
158: 'toy terrier',
159: 'Rhodesian ridgeback',
160: 'Afghan hound, Afghan',
161: 'basset, basset hound',
162: 'beagle',
163: 'bloodhound, sleuthhound',
164: 'bluetick',
165: 'black-and-tan coonhound',
166: 'Walker hound, Walker foxhound',
167: 'English foxhound',
168: 'redbone',
169: 'borzoi, Russian wolfhound',
170: 'Irish wolfhound',
171: 'Italian greyhound',
172: 'whippet',
173: 'Ibizan hound, Ibizan Podenco',
174: 'Norwegian elkhound, elkhound',
175: 'otterhound, otter hound',
176: 'Saluki, gazelle hound',
177: 'Scottish deerhound, deerhound',
178: 'Weimaraner',
179: 'Staffordshire bullterrier, Staffordshire bull terrier',
180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',
181: 'Bedlington terrier',
182: 'Border terrier',
183: 'Kerry blue terrier',
184: 'Irish terrier',
185: 'Norfolk terrier',
186: 'Norwich terrier',
187: 'Yorkshire terrier',
188: 'wire-haired fox terrier',
189: 'Lakeland terrier',
190: 'Sealyham terrier, Sealyham',
191: 'Airedale, Airedale terrier',
192: 'cairn, cairn terrier',
193: 'Australian terrier',
194: 'Dandie Dinmont, Dandie Dinmont terrier',
195: 'Boston bull, Boston terrier',
196: 'miniature schnauzer',
197: 'giant schnauzer',
198: 'standard schnauzer',
199: 'Scotch terrier, Scottish terrier, Scottie',
200: 'Tibetan terrier, chrysanthemum dog',
201: 'silky terrier, Sydney silky',
202: 'soft-coated wheaten terrier',
203: 'West Highland white terrier',
204: 'Lhasa, Lhasa apso',
205: 'flat-coated retriever',
206: 'curly-coated retriever',
207: 'golden retriever',
208: 'Labrador retriever',
209: 'Chesapeake Bay retriever',
210: 'German short-haired pointer',
211: 'vizsla, Hungarian pointer',
212: 'English setter',
213: 'Irish setter, red setter',
214: 'Gordon setter',
215: 'Brittany spaniel',
216: 'clumber, clumber spaniel',
217: 'English springer, English springer spaniel',
218: 'Welsh springer spaniel',
219: 'cocker spaniel, English cocker spaniel, cocker',
220: 'Sussex spaniel',
221: 'Irish water spaniel',
222: 'kuvasz',
223: 'schipperke',
224: 'groenendael',
225: 'malinois',
226: 'briard',
227: 'kelpie',
228: 'komondor',
229: 'Old English sheepdog, bobtail',
230: 'Shetland sheepdog, Shetland sheep dog, Shetland',
231: 'collie',
232: 'Border collie',
233: 'Bouvier des Flandres, Bouviers des Flandres',
234: 'Rottweiler',
235: 'German shepherd, German shepherd dog, German police dog, alsatian',
236: 'Doberman, Doberman pinscher',
237: 'miniature pinscher',
238: 'Greater Swiss Mountain dog',
239: 'Bernese mountain dog',
240: 'Appenzeller',
241: 'EntleBucher',
242: 'boxer',
243: 'bull mastiff',
244: 'Tibetan mastiff',
245: 'French bulldog',
246: 'Great Dane',
247: 'Saint Bernard, St Bernard',
248: 'Eskimo dog, husky',
249: 'malamute, malemute, Alaskan malamute',
250: 'Siberian husky',
251: 'dalmatian, coach dog, carriage dog',
252: 'affenpinscher, monkey pinscher, monkey dog',
253: 'basenji',
254: 'pug, pug-dog',
255: 'Leonberg',
256: 'Newfoundland, Newfoundland dog',
257: 'Great Pyrenees',
258: 'Samoyed, Samoyede',
259: 'Pomeranian',
260: 'chow, chow chow',
261: 'keeshond',
262: 'Brabancon griffon',
263: 'Pembroke, Pembroke Welsh corgi',
264: 'Cardigan, Cardigan Welsh corgi',
265: 'toy poodle',
266: 'miniature poodle',
267: 'standard poodle',
268: 'Mexican hairless',
269: 'timber wolf, grey wolf, gray wolf, Canis lupus',
270: 'white wolf, Arctic wolf, Canis lupus tundrarum',
271: 'red wolf, maned wolf, Canis rufus, Canis niger',
272: 'coyote, prairie wolf, brush wolf, Canis latrans',
273: 'dingo, warrigal, warragal, Canis dingo',
274: 'dhole, Cuon alpinus',
275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',
276: 'hyena, hyaena',
277: 'red fox, Vulpes vulpes',
278: 'kit fox, Vulpes macrotis',
279: 'Arctic fox, white fox, Alopex lagopus',
280: 'grey fox, gray fox, Urocyon cinereoargenteus',
281: 'tabby, tabby cat',
282: 'tiger cat',
283: 'Persian cat',
284: 'Siamese cat, Siamese',
285: 'Egyptian cat',
286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
287: 'lynx, catamount',
288: 'leopard, Panthera pardus',
289: 'snow leopard, ounce, Panthera uncia',
290: 'jaguar, panther, Panthera onca, Felis onca',
291: 'lion, king of beasts, Panthera leo',
292: 'tiger, Panthera tigris',
293: 'cheetah, chetah, Acinonyx jubatus',
294: 'brown bear, bruin, Ursus arctos',
295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',
296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',
297: 'sloth bear, Melursus ursinus, Ursus ursinus',
298: 'mongoose',
299: 'meerkat, mierkat',
300: 'tiger beetle',
301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',
302: 'ground beetle, carabid beetle',
303: 'long-horned beetle, longicorn, longicorn beetle',
304: 'leaf beetle, chrysomelid',
305: 'dung beetle',
306: 'rhinoceros beetle',
307: 'weevil',
308: 'fly',
309: 'bee',
310: 'ant, emmet, pismire',
311: 'grasshopper, hopper',
312: 'cricket',
313: 'walking stick, walkingstick, stick insect',
314: 'cockroach, roach',
315: 'mantis, mantid',
316: 'cicada, cicala',
317: 'leafhopper',
318: 'lacewing, lacewing fly',
319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
320: 'damselfly',
321: 'admiral',
322: 'ringlet, ringlet butterfly',
323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
324: 'cabbage butterfly',
325: 'sulphur butterfly, sulfur butterfly',
326: 'lycaenid, lycaenid butterfly',
327: 'starfish, sea star',
328: 'sea urchin',
329: 'sea cucumber, holothurian',
330: 'wood rabbit, cottontail, cottontail rabbit',
331: 'hare',
332: 'Angora, Angora rabbit',
333: 'hamster',
334: 'porcupine, hedgehog',
335: 'fox squirrel, eastern fox squirrel, Sciurus niger',
336: 'marmot',
337: 'beaver',
338: 'guinea pig, Cavia cobaya',
339: 'sorrel',
340: 'zebra',
341: 'hog, pig, grunter, squealer, Sus scrofa',
342: 'wild boar, boar, Sus scrofa',
343: 'warthog',
344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',
345: 'ox',
346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',
347: 'bison',
348: 'ram, tup',
349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',
350: 'ibex, Capra ibex',
351: 'hartebeest',
352: 'impala, Aepyceros melampus',
353: 'gazelle',
354: 'Arabian camel, dromedary, Camelus dromedarius',
355: 'llama',
356: 'weasel',
357: 'mink',
358: 'polecat, fitch, foulmart, foumart, Mustela putorius',
359: 'black-footed ferret, ferret, Mustela nigripes',
360: 'otter',
361: 'skunk, polecat, wood pussy',
362: 'badger',
363: 'armadillo',
364: 'three-toed sloth, ai, Bradypus tridactylus',
365: 'orangutan, orang, orangutang, Pongo pygmaeus',
366: 'gorilla, Gorilla gorilla',
367: 'chimpanzee, chimp, Pan troglodytes',
368: 'gibbon, Hylobates lar',
369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',
370: 'guenon, guenon monkey',
371: 'patas, hussar monkey, Erythrocebus patas',
372: 'baboon',
373: 'macaque',
374: 'langur',
375: 'colobus, colobus monkey',
376: 'proboscis monkey, Nasalis larvatus',
377: 'marmoset',
378: 'capuchin, ringtail, Cebus capucinus',
379: 'howler monkey, howler',
380: 'titi, titi monkey',
381: 'spider monkey, Ateles geoffroyi',
382: 'squirrel monkey, Saimiri sciureus',
383: 'Madagascar cat, ring-tailed lemur, Lemur catta',
384: 'indri, indris, Indri indri, Indri brevicaudatus',
385: 'Indian elephant, Elephas maximus',
386: 'African elephant, Loxodonta africana',
387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',
388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',
389: 'barracouta, snoek',
390: 'eel',
391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',
392: 'rock beauty, Holocanthus tricolor',
393: 'anemone fish',
394: 'sturgeon',
395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',
396: 'lionfish',
397: 'puffer, pufferfish, blowfish, globefish',
398: 'abacus',
399: 'abaya',
400: "academic gown, academic robe, judge's robe",
401: 'accordion, piano accordion, squeeze box',
402: 'acoustic guitar',
403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',
404: 'airliner',
405: 'airship, dirigible',
406: 'altar',
407: 'ambulance',
408: 'amphibian, amphibious vehicle',
409: 'analog clock',
410: 'apiary, bee house',
411: 'apron',
412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',
413: 'assault rifle, assault gun',
414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',
415: 'bakery, bakeshop, bakehouse',
416: 'balance beam, beam',
417: 'balloon',
418: 'ballpoint, ballpoint pen, ballpen, Biro',
419: 'Band Aid',
420: 'banjo',
421: 'bannister, banister, balustrade, balusters, handrail',
422: 'barbell',
423: 'barber chair',
424: 'barbershop',
425: 'barn',
426: 'barometer',
427: 'barrel, cask',
428: 'barrow, garden cart, lawn cart, wheelbarrow',
429: 'baseball',
430: 'basketball',
431: 'bassinet',
432: 'bassoon',
433: 'bathing cap, swimming cap',
434: 'bath towel',
435: 'bathtub, bathing tub, bath, tub',
436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',
437: 'beacon, lighthouse, beacon light, pharos',
438: 'beaker',
439: 'bearskin, busby, shako',
440: 'beer bottle',
441: 'beer glass',
442: 'bell cote, bell cot',
443: 'bib',
444: 'bicycle-built-for-two, tandem bicycle, tandem',
445: 'bikini, two-piece',
446: 'binder, ring-binder',
447: 'binoculars, field glasses, opera glasses',
448: 'birdhouse',
449: 'boathouse',
450: 'bobsled, bobsleigh, bob',
451: 'bolo tie, bolo, bola tie, bola',
452: 'bonnet, poke bonnet',
453: 'bookcase',
454: 'bookshop, bookstore, bookstall',
455: 'bottlecap',
456: 'bow',
457: 'bow tie, bow-tie, bowtie',
458: 'brass, memorial tablet, plaque',
459: 'brassiere, bra, bandeau',
460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',
461: 'breastplate, aegis, egis',
462: 'broom',
463: 'bucket, pail',
464: 'buckle',
465: 'bulletproof vest',
466: 'bullet train, bullet',
467: 'butcher shop, meat market',
468: 'cab, hack, taxi, taxicab',
469: 'caldron, cauldron',
470: 'candle, taper, wax light',
471: 'cannon',
472: 'canoe',
473: 'can opener, tin opener',
474: 'cardigan',
475: 'car mirror',
476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',
477: "carpenter's kit, tool kit",
478: 'carton',
479: 'car wheel',
480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',
481: 'cassette',
482: 'cassette player',
483: 'castle',
484: 'catamaran',
485: 'CD player',
486: 'cello, violoncello',
487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',
488: 'chain',
489: 'chainlink fence',
490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',
491: 'chain saw, chainsaw',
492: 'chest',
493: 'chiffonier, commode',
494: 'chime, bell, gong',
495: 'china cabinet, china closet',
496: 'Christmas stocking',
497: 'church, church building',
498: 'cinema, movie theater, movie theatre, movie house, picture palace',
499: 'cleaver, meat cleaver, chopper',
500: 'cliff dwelling',
501: 'cloak',
502: 'clog, geta, patten, sabot',
503: 'cocktail shaker',
504: 'coffee mug',
505: 'coffeepot',
506: 'coil, spiral, volute, whorl, helix',
507: 'combination lock',
508: 'computer keyboard, keypad',
509: 'confectionery, confectionary, candy store',
510: 'container ship, containership, container vessel',
511: 'convertible',
512: 'corkscrew, bottle screw',
513: 'cornet, horn, trumpet, trump',
514: 'cowboy boot',
515: 'cowboy hat, ten-gallon hat',
516: 'cradle',
517: 'crane',
518: 'crash helmet',
519: 'crate',
520: 'crib, cot',
521: 'Crock Pot',
522: 'croquet ball',
523: 'crutch',
524: 'cuirass',
525: 'dam, dike, dyke',
526: 'desk',
527: 'desktop computer',
528: 'dial telephone, dial phone',
529: 'diaper, nappy, napkin',
530: 'digital clock',
531: 'digital watch',
532: 'dining table, board',
533: 'dishrag, dishcloth',
534: 'dishwasher, dish washer, dishwashing machine',
535: 'disk brake, disc brake',
536: 'dock, dockage, docking facility',
537: 'dogsled, dog sled, dog sleigh',
538: 'dome',
539: 'doormat, welcome mat',
540: 'drilling platform, offshore rig',
541: 'drum, membranophone, tympan',
542: 'drumstick',
543: 'dumbbell',
544: 'Dutch oven',
545: 'electric fan, blower',
546: 'electric guitar',
547: 'electric locomotive',
548: 'entertainment center',
549: 'envelope',
550: 'espresso maker',
551: 'face powder',
552: 'feather boa, boa',
553: 'file, file cabinet, filing cabinet',
554: 'fireboat',
555: 'fire engine, fire truck',
556: 'fire screen, fireguard',
557: 'flagpole, flagstaff',
558: 'flute, transverse flute',
559: 'folding chair',
560: 'football helmet',
561: 'forklift',
562: 'fountain',
563: 'fountain pen',
564: 'four-poster',
565: 'freight car',
566: 'French horn, horn',
567: 'frying pan, frypan, skillet',
568: 'fur coat',
569: 'garbage truck, dustcart',
570: 'gasmask, respirator, gas helmet',
571: 'gas pump, gasoline pump, petrol pump, island dispenser',
572: 'goblet',
573: 'go-kart',
574: 'golf ball',
575: 'golfcart, golf cart',
576: 'gondola',
577: 'gong, tam-tam',
578: 'gown',
579: 'grand piano, grand',
580: 'greenhouse, nursery, glasshouse',
581: 'grille, radiator grille',
582: 'grocery store, grocery, food market, market',
583: 'guillotine',
584: 'hair slide',
585: 'hair spray',
586: 'half track',
587: 'hammer',
588: 'hamper',
589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',
590: 'hand-held computer, hand-held microcomputer',
591: 'handkerchief, hankie, hanky, hankey',
592: 'hard disc, hard disk, fixed disk',
593: 'harmonica, mouth organ, harp, mouth harp',
594: 'harp',
595: 'harvester, reaper',
596: 'hatchet',
597: 'holster',
598: 'home theater, home theatre',
599: 'honeycomb',
600: 'hook, claw',
601: 'hoopskirt, crinoline',
602: 'horizontal bar, high bar',
603: 'horse cart, horse-cart',
604: 'hourglass',
605: 'iPod',
606: 'iron, smoothing iron',
607: "jack-o'-lantern",
608: 'jean, blue jean, denim',
609: 'jeep, landrover',
610: 'jersey, T-shirt, tee shirt',
611: 'jigsaw puzzle',
612: 'jinrikisha, ricksha, rickshaw',
613: 'joystick',
614: 'kimono',
615: 'knee pad',
616: 'knot',
617: 'lab coat, laboratory coat',
618: 'ladle',
619: 'lampshade, lamp shade',
620: 'laptop, laptop computer',
621: 'lawn mower, mower',
622: 'lens cap, lens cover',
623: 'letter opener, paper knife, paperknife',
624: 'library',
625: 'lifeboat',
626: 'lighter, light, igniter, ignitor',
627: 'limousine, limo',
628: 'liner, ocean liner',
629: 'lipstick, lip rouge',
630: 'Loafer',
631: 'lotion',
632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',
633: "loupe, jeweler's loupe",
634: 'lumbermill, sawmill',
635: 'magnetic compass',
636: 'mailbag, postbag',
637: 'mailbox, letter box',
638: 'maillot',
639: 'maillot, tank suit',
640: 'manhole cover',
641: 'maraca',
642: 'marimba, xylophone',
643: 'mask',
644: 'matchstick',
645: 'maypole',
646: 'maze, labyrinth',
647: 'measuring cup',
648: 'medicine chest, medicine cabinet',
649: 'megalith, megalithic structure',
650: 'microphone, mike',
651: 'microwave, microwave oven',
652: 'military uniform',
653: 'milk can',
654: 'minibus',
655: 'miniskirt, mini',
656: 'minivan',
657: 'missile',
658: 'mitten',
659: 'mixing bowl',
660: 'mobile home, manufactured home',
661: 'Model T',
662: 'modem',
663: 'monastery',
664: 'monitor',
665: 'moped',
666: 'mortar',
667: 'mortarboard',
668: 'mosque',
669: 'mosquito net',
670: 'motor scooter, scooter',
671: 'mountain bike, all-terrain bike, off-roader',
672: 'mountain tent',
673: 'mouse, computer mouse',
674: 'mousetrap',
675: 'moving van',
676: 'muzzle',
677: 'nail',
678: 'neck brace',
679: 'necklace',
680: 'nipple',
681: 'notebook, notebook computer',
682: 'obelisk',
683: 'oboe, hautboy, hautbois',
684: 'ocarina, sweet potato',
685: 'odometer, hodometer, mileometer, milometer',
686: 'oil filter',
687: 'organ, pipe organ',
688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',
689: 'overskirt',
690: 'oxcart',
691: 'oxygen mask',
692: 'packet',
693: 'paddle, boat paddle',
694: 'paddlewheel, paddle wheel',
695: 'padlock',
696: 'paintbrush',
697: "pajama, pyjama, pj's, jammies",
698: 'palace',
699: 'panpipe, pandean pipe, syrinx',
700: 'paper towel',
701: 'parachute, chute',
702: 'parallel bars, bars',
703: 'park bench',
704: 'parking meter',
705: 'passenger car, coach, carriage',
706: 'patio, terrace',
707: 'pay-phone, pay-station',
708: 'pedestal, plinth, footstall',
709: 'pencil box, pencil case',
710: 'pencil sharpener',
711: 'perfume, essence',
712: 'Petri dish',
713: 'photocopier',
714: 'pick, plectrum, plectron',
715: 'pickelhaube',
716: 'picket fence, paling',
717: 'pickup, pickup truck',
718: 'pier',
719: 'piggy bank, penny bank',
720: 'pill bottle',
721: 'pillow',
722: 'ping-pong ball',
723: 'pinwheel',
724: 'pirate, pirate ship',
725: 'pitcher, ewer',
726: "plane, carpenter's plane, woodworking plane",
727: 'planetarium',
728: 'plastic bag',
729: 'plate rack',
730: 'plow, plough',
731: "plunger, plumber's helper",
732: 'Polaroid camera, Polaroid Land camera',
733: 'pole',
734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',
735: 'poncho',
736: 'pool table, billiard table, snooker table',
737: 'pop bottle, soda bottle',
738: 'pot, flowerpot',
739: "potter's wheel",
740: 'power drill',
741: 'prayer rug, prayer mat',
742: 'printer',
743: 'prison, prison house',
744: 'projectile, missile',
745: 'projector',
746: 'puck, hockey puck',
747: 'punching bag, punch bag, punching ball, punchball',
748: 'purse',
749: 'quill, quill pen',
750: 'quilt, comforter, comfort, puff',
751: 'racer, race car, racing car',
752: 'racket, racquet',
753: 'radiator',
754: 'radio, wireless',
755: 'radio telescope, radio reflector',
756: 'rain barrel',
757: 'recreational vehicle, RV, R.V.',
758: 'reel',
759: 'reflex camera',
760: 'refrigerator, icebox',
761: 'remote control, remote',
762: 'restaurant, eating house, eating place, eatery',
763: 'revolver, six-gun, six-shooter',
764: 'rifle',
765: 'rocking chair, rocker',
766: 'rotisserie',
767: 'rubber eraser, rubber, pencil eraser',
768: 'rugby ball',
769: 'rule, ruler',
770: 'running shoe',
771: 'safe',
772: 'safety pin',
773: 'saltshaker, salt shaker',
774: 'sandal',
775: 'sarong',
776: 'sax, saxophone',
777: 'scabbard',
778: 'scale, weighing machine',
779: 'school bus',
780: 'schooner',
781: 'scoreboard',
782: 'screen, CRT screen',
783: 'screw',
784: 'screwdriver',
785: 'seat belt, seatbelt',
786: 'sewing machine',
787: 'shield, buckler',
788: 'shoe shop, shoe-shop, shoe store',
789: 'shoji',
790: 'shopping basket',
791: 'shopping cart',
792: 'shovel',
793: 'shower cap',
794: 'shower curtain',
795: 'ski',
796: 'ski mask',
797: 'sleeping bag',
798: 'slide rule, slipstick',
799: 'sliding door',
800: 'slot, one-armed bandit',
801: 'snorkel',
802: 'snowmobile',
803: 'snowplow, snowplough',
804: 'soap dispenser',
805: 'soccer ball',
806: 'sock',
807: 'solar dish, solar collector, solar furnace',
808: 'sombrero',
809: 'soup bowl',
810: 'space bar',
811: 'space heater',
812: 'space shuttle',
813: 'spatula',
814: 'speedboat',
815: "spider web, spider's web",
816: 'spindle',
817: 'sports car, sport car',
818: 'spotlight, spot',
819: 'stage',
820: 'steam locomotive',
821: 'steel arch bridge',
822: 'steel drum',
823: 'stethoscope',
824: 'stole',
825: 'stone wall',
826: 'stopwatch, stop watch',
827: 'stove',
828: 'strainer',
829: 'streetcar, tram, tramcar, trolley, trolley car',
830: 'stretcher',
831: 'studio couch, day bed',
832: 'stupa, tope',
833: 'submarine, pigboat, sub, U-boat',
834: 'suit, suit of clothes',
835: 'sundial',
836: 'sunglass',
837: 'sunglasses, dark glasses, shades',
838: 'sunscreen, sunblock, sun blocker',
839: 'suspension bridge',
840: 'swab, swob, mop',
841: 'sweatshirt',
842: 'swimming trunks, bathing trunks',
843: 'swing',
844: 'switch, electric switch, electrical switch',
845: 'syringe',
846: 'table lamp',
847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',
848: 'tape player',
849: 'teapot',
850: 'teddy, teddy bear',
851: 'television, television system',
852: 'tennis ball',
853: 'thatch, thatched roof',
854: 'theater curtain, theatre curtain',
855: 'thimble',
856: 'thresher, thrasher, threshing machine',
857: 'throne',
858: 'tile roof',
859: 'toaster',
860: 'tobacco shop, tobacconist shop, tobacconist',
861: 'toilet seat',
862: 'torch',
863: 'totem pole',
864: 'tow truck, tow car, wrecker',
865: 'toyshop',
866: 'tractor',
867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',
868: 'tray',
869: 'trench coat',
870: 'tricycle, trike, velocipede',
871: 'trimaran',
872: 'tripod',
873: 'triumphal arch',
874: 'trolleybus, trolley coach, trackless trolley',
875: 'trombone',
876: 'tub, vat',
877: 'turnstile',
878: 'typewriter keyboard',
879: 'umbrella',
880: 'unicycle, monocycle',
881: 'upright, upright piano',
882: 'vacuum, vacuum cleaner',
883: 'vase',
884: 'vault',
885: 'velvet',
886: 'vending machine',
887: 'vestment',
888: 'viaduct',
889: 'violin, fiddle',
890: 'volleyball',
891: 'waffle iron',
892: 'wall clock',
893: 'wallet, billfold, notecase, pocketbook',
894: 'wardrobe, closet, press',
895: 'warplane, military plane',
896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',
897: 'washer, automatic washer, washing machine',
898: 'water bottle',
899: 'water jug',
900: 'water tower',
901: 'whiskey jug',
902: 'whistle',
903: 'wig',
904: 'window screen',
905: 'window shade',
906: 'Windsor tie',
907: 'wine bottle',
908: 'wing',
909: 'wok',
910: 'wooden spoon',
911: 'wool, woolen, woollen',
912: 'worm fence, snake fence, snake-rail fence, Virginia fence',
913: 'wreck',
914: 'yawl',
915: 'yurt',
916: 'web site, website, internet site, site',
917: 'comic book',
918: 'crossword puzzle, crossword',
919: 'street sign',
920: 'traffic light, traffic signal, stoplight',
921: 'book jacket, dust cover, dust jacket, dust wrapper',
922: 'menu',
923: 'plate',
924: 'guacamole',
925: 'consomme',
926: 'hot pot, hotpot',
927: 'trifle',
928: 'ice cream, icecream',
929: 'ice lolly, lolly, lollipop, popsicle',
930: 'French loaf',
931: 'bagel, beigel',
932: 'pretzel',
933: 'cheeseburger',
934: 'hotdog, hot dog, red hot',
935: 'mashed potato',
936: 'head cabbage',
937: 'broccoli',
938: 'cauliflower',
939: 'zucchini, courgette',
940: 'spaghetti squash',
941: 'acorn squash',
942: 'butternut squash',
943: 'cucumber, cuke',
944: 'artichoke, globe artichoke',
945: 'bell pepper',
946: 'cardoon',
947: 'mushroom',
948: 'Granny Smith',
949: 'strawberry',
950: 'orange',
951: 'lemon',
952: 'fig',
953: 'pineapple, ananas',
954: 'banana',
955: 'jackfruit, jak, jack',
956: 'custard apple',
957: 'pomegranate',
958: 'hay',
959: 'carbonara',
960: 'chocolate sauce, chocolate syrup',
961: 'dough',
962: 'meat loaf, meatloaf',
963: 'pizza, pizza pie',
964: 'potpie',
965: 'burrito',
966: 'red wine',
967: 'espresso',
968: 'cup',
969: 'eggnog',
970: 'alp',
971: 'bubble',
972: 'cliff, drop, drop-off',
973: 'coral reef',
974: 'geyser',
975: 'lakeside, lakeshore',
976: 'promontory, headland, head, foreland',
977: 'sandbar, sand bar',
978: 'seashore, coast, seacoast, sea-coast',
979: 'valley, vale',
980: 'volcano',
981: 'ballplayer, baseball player',
982: 'groom, bridegroom',
983: 'scuba diver',
984: 'rapeseed',
985: 'daisy',
986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
987: 'corn',
988: 'acorn',
989: 'hip, rose hip, rosehip',
990: 'buckeye, horse chestnut, conker',
991: 'coral fungus',
992: 'agaric',
993: 'gyromitra',
994: 'stinkhorn, carrion fungus',
995: 'earthstar',
996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',
997: 'bolete',
998: 'ear, spike, capitulum',
999: 'toilet tissue, toilet paper, bathroom tissue'} | Britefury/deep-learning-tutorial-pydata2016 | imagenet_classes.py | Python | mit | 30,763 | [
"Bowtie",
"ESPResSo",
"Jaguar",
"ORCA"
] | 31ab36be5fefad39f78591ac37cc345943f3d7a51c00a77dd11dfefd9861bb3c |
# Copyright (C) 2016
# Max Planck Institute for Polymer Research
#
# This file is part of ESPResSo++.
#
# ESPResSo++ is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ESPResSo++ is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
r"""
*****************************************
prepareComplexMolecules - set up proteins
*****************************************
various helper functions for setting up systems containing complex molecules such as proteins
.. function:: espressopp.tools.findConstrainedBonds(atomPids, bondtypes, bondtypeparams, masses, massCutoff = 1.1)
Finds all heavyatom-hydrogen bonds in a given list of particle IDs, and outputs a list describing the bonds, in a format suitable for use with the RATTLE algorithm for constrained bonds
:param atomPids: list of pids of atoms between which to search for bonds
:type atomPids: list of int
:param bondtypes: dictionary mapping from tuple of pids to bondtypeid, e.g. as returned by tools.gromacs.read()
:type bondtypes: dict, key: (int,int), value: int
:param bondtypeparams: dictionary mapping from bondtypeid to class storing parameters of that bond type, e.g. as returned by tools.gromacs.read()
:type bondtypeparams: dict, key: int, value: espressopp bond type
:param masses: list of masses, e.g. as returned by tools.gromacs.read()
:type masses: list of float
:param massCutoff: for identifying light atoms (hydrogens), default 1.1 mass units, can also be increased e.g. for use with deuterated systems
:type massCutoff: float
:returns:
| hydrogenIDs - list of pids (integer) of light atoms (hydrogens)
| constrainedBondsDict - dict, keys: pid (integer) of heavy atom, values: list of pids of light atoms that are bonded to it
| constrainedBondsList - list of lists, one entry for each constrained bond with format: [pid of heavy atom, pid of light atom, bond distance, mass of heavy atom ,mass of light atom]
Can then be used with RATTLE, e.g.
>>> rattle = espresso.integrator.Rattle(system, maxit = 1000, tol = 1e-6, rptol = 1e-6)
>>> rattle.addConstrainedBonds(constrainedBondsList)
>>> integrator.addExtension(rattle)
.. function:: espressopp.tools.getInternalNonbondedInteractions(atExclusions,pidlist)
Gets the non-bonded pairs within a list of particle indices, excluding those which are in a supplied list of exclusions. Useful for example for getting the internal atomistic non-bonded interactions in a coarse-grained particle and adding them as a fixedpairlist
:param atExclusions: list of excluded pairs
:type atExclusions: list of 2-tuples of int
:param pidlist: list of pids among which to create pairs
:type pidlist: list of int
:return: list of pairs which are not in atExclusions
:rtype: list of 2-tuples of int
.. function:: espressopp.tools.readSimpleSystem(filename,nparticles,header)
Read in a column-formatted file containing information about the
particles in a simple system, for example a coarsegrained protein.
This function expects the input file to have between 2 and 5 columns.
The number of columns in the file is automatically detected.
The function reads each column into a list and returns the lists.
Column types are interpreted as follows:
2 columns: float, float
3 columns: float, float, int
4 columns: float, float, int, str
5 columns: float, float, int, str, str
For example in the case of a coarsegrained protein model, these could be:
mass, charge, corresponding atomistic index, beadname, beadtype
:param filename: name of file to open and read
:type filename: string
:param nparticles: number of particles in file
:type nparticles: int
:param header: number of lines to skip at start of file (default 0)
:type header: int
Returns: between 2 and 5 lists
"""
def findConstrainedBonds(atomPids, bondtypes, bondtypeparams, masses, massCutoff = 1.1):
hydrogenIDs = []
constrainedBondsDict = {}
constraintDistances = {} #keys: (pid heavy, pid light), values: bond length
# first find hydrogens
for pid in atomPids:
if masses[pid-1] < massCutoff:
hydrogenIDs.append(pid)
# then find hydrogen-containing bonds
for bid, bondlist in bondtypes.iteritems():
for pair in bondlist:
pidHyd = pidHea = 0
if pair[0] in hydrogenIDs:
pidHyd = pair[0]
pidHea = pair[1]
elif pair[1] in hydrogenIDs:
pidHyd = pair[1]
pidHea = pair[0]
if (pidHyd > 0):
if pidHea in constrainedBondsDict.keys():
constrainedBondsDict[pidHea].append(pidHyd)
else:
constrainedBondsDict[pidHea] = [pidHyd]
constraintDistances[(pidHea,pidHyd)] = bondtypeparams[bid].parameters['b0']
constrainedBondsList = []
for pidHea in constrainedBondsDict.keys():
for pidHyd in constrainedBondsDict[pidHea]:
constrainedBondsList.append([pidHea,pidHyd,constraintDistances[(pidHea,pidHyd)],masses[pidHea-1],masses[pidHyd-1]])
return hydrogenIDs, constrainedBondsDict, constrainedBondsList
def getInternalNonbondedInteractions(atExclusions,pidlist):
nonBondPairs = []
for pid1 in pidlist:
for pid2 in pidlist:
if pid1==pid2: continue
if (pid1,pid2) in atExclusions: continue
if (pid2,pid1) in atExclusions: continue
if (pid1,pid2) in nonBondPairs: continue #to avoid this we'd have to assume pids in pidlist were ordered and continuous
if (pid2,pid1) in nonBondPairs: continue #to avoid this we'd have to assume pids in pidlist were ordered and continuous
nonBondPairs.append((pid1,pid2))
return nonBondPairs
def readSimpleSystem(filename,nparticles,header=0):
f = open(filename,'r')
mass=[]
charge=[]
index=[]
name=[]
ptype=[]
for i in range(header):
f.readline()
for i in range(nparticles):
line = f.readline()
line = line.split()
mass.append(float(line[0]))
charge.append(float(line[1]))
if len(line)>2:
index.append(int(line[2]))
if len(line)>3:
name.append(line[3])
if len(line)==5:
ptype.append(line[4])
f.close()
if len(line)==2:
return mass,charge
elif len(line)==3:
return mass,charge,index
elif len(line)==4:
return mass,charge,index,name
elif len(line)==5:
return mass,charge,index,name,ptype
| fedepad/espressopp | src/tools/prepareComplexMolecules.py | Python | gpl-3.0 | 6,805 | [
"ESPResSo",
"Gromacs"
] | fd4fec37ca880c6025d917ba42f43c5fe100d2340a5ad1dd11fe72226ba7b285 |
"""
Tests for geography support in PostGIS
"""
from __future__ import unicode_literals
import os
from unittest import skipUnless
from django.contrib.gis.gdal import HAS_GDAL
from django.contrib.gis.geos import HAS_GEOS
from django.contrib.gis.measure import D
from django.contrib.gis.tests.utils import postgis
from django.test import TestCase, skipUnlessDBFeature
from django.utils._os import upath
if HAS_GEOS:
from .models import City, County, Zipcode
@skipUnlessDBFeature("gis_enabled")
class GeographyTest(TestCase):
fixtures = ['initial']
def test01_fixture_load(self):
"Ensure geography features loaded properly."
self.assertEqual(8, City.objects.count())
@skipUnlessDBFeature("supports_distances_lookups", "supports_distance_geodetic")
def test02_distance_lookup(self):
"Testing GeoQuerySet distance lookup support on non-point geography fields."
z = Zipcode.objects.get(code='77002')
cities1 = list(City.objects
.filter(point__distance_lte=(z.poly, D(mi=500)))
.order_by('name')
.values_list('name', flat=True))
cities2 = list(City.objects
.filter(point__dwithin=(z.poly, D(mi=500)))
.order_by('name')
.values_list('name', flat=True))
for cities in [cities1, cities2]:
self.assertEqual(['Dallas', 'Houston', 'Oklahoma City'], cities)
@skipUnlessDBFeature("has_distance_method", "supports_distance_geodetic")
def test03_distance_method(self):
"Testing GeoQuerySet.distance() support on non-point geography fields."
# `GeoQuerySet.distance` is not allowed geometry fields.
htown = City.objects.get(name='Houston')
Zipcode.objects.distance(htown.point)
@skipUnless(postgis, "This is a PostGIS-specific test")
def test04_invalid_operators_functions(self):
"Ensuring exceptions are raised for operators & functions invalid on geography fields."
# Only a subset of the geometry functions & operator are available
# to PostGIS geography types. For more information, visit:
# http://postgis.refractions.net/documentation/manual-1.5/ch08.html#PostGIS_GeographyFunctions
z = Zipcode.objects.get(code='77002')
# ST_Within not available.
self.assertRaises(ValueError, City.objects.filter(point__within=z.poly).count)
# `@` operator not available.
self.assertRaises(ValueError, City.objects.filter(point__contained=z.poly).count)
# Regression test for #14060, `~=` was never really implemented for PostGIS.
htown = City.objects.get(name='Houston')
self.assertRaises(ValueError, City.objects.get, point__exact=htown.point)
@skipUnless(HAS_GDAL, "GDAL is required.")
def test05_geography_layermapping(self):
"Testing LayerMapping support on models with geography fields."
# There is a similar test in `layermap` that uses the same data set,
# but the County model here is a bit different.
from django.contrib.gis.utils import LayerMapping
# Getting the shapefile and mapping dictionary.
shp_path = os.path.realpath(os.path.join(os.path.dirname(upath(__file__)), '..', 'data'))
co_shp = os.path.join(shp_path, 'counties', 'counties.shp')
co_mapping = {'name': 'Name',
'state': 'State',
'mpoly': 'MULTIPOLYGON',
}
# Reference county names, number of polygons, and state names.
names = ['Bexar', 'Galveston', 'Harris', 'Honolulu', 'Pueblo']
num_polys = [1, 2, 1, 19, 1] # Number of polygons for each.
st_names = ['Texas', 'Texas', 'Texas', 'Hawaii', 'Colorado']
lm = LayerMapping(County, co_shp, co_mapping, source_srs=4269, unique='name')
lm.save(silent=True, strict=True)
for c, name, num_poly, state in zip(County.objects.order_by('name'), names, num_polys, st_names):
self.assertEqual(4326, c.mpoly.srid)
self.assertEqual(num_poly, len(c.mpoly))
self.assertEqual(name, c.name)
self.assertEqual(state, c.state)
@skipUnlessDBFeature("has_area_method", "supports_distance_geodetic")
def test06_geography_area(self):
"Testing that Area calculations work on geography columns."
# SELECT ST_Area(poly) FROM geogapp_zipcode WHERE code='77002';
ref_area = 5439084.70637573
tol = 5
z = Zipcode.objects.area().get(code='77002')
self.assertAlmostEqual(z.area.sq_m, ref_area, tol)
| diegoguimaraes/django | django/contrib/gis/tests/geogapp/tests.py | Python | bsd-3-clause | 4,650 | [
"VisIt"
] | ebf604b453b4f718ce53a93907817a86cf397d70c4ef7ed25aab248b1d2afbed |
import shutil
import os
import mdtraj as md
from openmoltools.utils import import_, enter_temp_directory, run_antechamber, create_ffxml_file
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG, format="LOG: %(message)s")
# Note: We recommend having every function return *copies* of input, to avoid headaches associated with in-place changes
def get_charges(molecule, max_confs=800, strictStereo=True, keep_confs=None):
"""Generate charges for an OpenEye OEMol molecule.
Parameters
----------
molecule : OEMol
Molecule for which to generate conformers.
Omega will be used to generate max_confs conformations.
max_confs : int, optional, default=800
Max number of conformers to generate
strictStereo : bool, optional, default=True
If False, permits smiles strings with unspecified stereochemistry.
See https://docs.eyesopen.com/omega/usage.html
keep_confs : int, optional, default=None
If None, apply the charges to the provided conformation and return
this conformation. Otherwise, return some or all of the generated
conformations. If -1, all generated conformations are returned.
Otherwise, keep_confs = N will return an OEMol with up to N
generated conformations. Multiple conformations are still used to
*determine* the charges.
Returns
-------
charged_copy : OEMol
A molecule with OpenEye's recommended AM1BCC charge selection scheme.
Notes
-----
Roughly follows
http://docs.eyesopen.com/toolkits/cookbook/python/modeling/am1-bcc.html
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for OEChem!"))
oequacpac = import_("openeye.oequacpac")
if not oequacpac.OEQuacPacIsLicensed(): raise(ImportError("Need License for oequacpac!"))
molecule = normalize_molecule(molecule)
charged_copy = generate_conformers(molecule, max_confs=max_confs, strictStereo=strictStereo) # Generate up to max_confs conformers
status = oequacpac.OEAssignPartialCharges(charged_copy, oequacpac.OECharges_AM1BCCSym) # AM1BCCSym recommended by Chris Bayly to KAB+JDC, Oct. 20 2014.
if not status:
raise(RuntimeError("OEAssignPartialCharges returned error code %d" % status))
#Determine conformations to return
if keep_confs == None:
#If returning original conformation
original = molecule.GetCoords()
#Delete conformers over 1
for k, conf in enumerate( charged_copy.GetConfs() ):
if k > 0:
charged_copy.DeleteConf(conf)
#Copy coordinates to single conformer
charged_copy.SetCoords( original )
elif keep_confs > 0:
#Otherwise if a number is provided, return this many confs if available
for k, conf in enumerate( charged_copy.GetConfs() ):
if k > keep_confs - 1:
charged_copy.DeleteConf(conf)
elif keep_confs == -1:
#If we want all conformations, continue
pass
else:
#Not a valid option to keep_confs
raise(ValueError('Not a valid option to keep_confs in get_charges.'))
return charged_copy
def normalize_molecule(molecule):
"""Normalize a copy of the molecule by checking aromaticity, adding explicit hydrogens, and renaming by IUPAC name.
Parameters
----------
molecule : OEMol
the molecule to be normalized.
Returns
-------
molcopy : OEMol
A (copied) version of the normalized molecule
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for OEChem!"))
oeiupac = import_("openeye.oeiupac")
if not oeiupac.OEIUPACIsLicensed(): raise(ImportError("Need License for OEOmega!"))
molcopy = oechem.OEMol(molecule)
# Assign aromaticity.
oechem.OEAssignAromaticFlags(molcopy, oechem.OEAroModelOpenEye)
# Add hydrogens.
oechem.OEAddExplicitHydrogens(molcopy)
# Set title to IUPAC name.
name = oeiupac.OECreateIUPACName(molcopy)
molcopy.SetTitle(name)
# Check for any missing atom names, if found reassign all of them.
if any([atom.GetName() == '' for atom in molcopy.GetAtoms()]):
oechem.OETriposAtomNames(molcopy)
return molcopy
def iupac_to_oemol(iupac_name):
"""Create a OEMolBuilder from a iupac name.
Parameters
----------
iupac_name : str
IUPAC name of desired molecule.
Returns
-------
molecule : OEMol
A normalized molecule with desired iupac name
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for OEChem!"))
oeiupac = import_("openeye.oeiupac")
if not oeiupac.OEIUPACIsLicensed(): raise(ImportError("Need License for OEOmega!"))
# Create an OEMol molecule from IUPAC name.
molecule = oechem.OEMol() # create a molecule
# Populate the MoleCule from the IUPAC name
if not oeiupac.OEParseIUPACName(molecule, iupac_name):
raise ValueError("The supplied IUPAC name '%s' could not be parsed." % iupac_name)
molecule = normalize_molecule(molecule)
return molecule
def smiles_to_oemol(smiles):
"""Create a OEMolBuilder from a smiles string.
Parameters
----------
smiles : str
SMILES representation of desired molecule.
Returns
-------
molecule : OEMol
A normalized molecule with desired smiles string.
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for OEChem!"))
molecule = oechem.OEMol()
if not oechem.OEParseSmiles(molecule, smiles):
raise ValueError("The supplied SMILES '%s' could not be parsed." % smiles)
molecule = normalize_molecule(molecule)
return molecule
def generate_conformers(molecule, max_confs=800, strictStereo=True, ewindow=15.0, rms_threshold=1.0, strictTypes = True):
"""Generate conformations for the supplied molecule
Parameters
----------
molecule : OEMol
Molecule for which to generate conformers
max_confs : int, optional, default=800
Max number of conformers to generate. If None, use default OE Value.
strictStereo : bool, optional, default=True
If False, permits smiles strings with unspecified stereochemistry.
strictTypes : bool, optional, default=True
If True, requires that Omega have exact MMFF types for atoms in molecule; otherwise, allows the closest atom type of the same element to be used.
Returns
-------
molcopy : OEMol
A multi-conformer molecule with up to max_confs conformers.
Notes
-----
Roughly follows
http://docs.eyesopen.com/toolkits/cookbook/python/modeling/am1-bcc.html
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for OEChem!"))
oeomega = import_("openeye.oeomega")
if not oeomega.OEOmegaIsLicensed(): raise(ImportError("Need License for OEOmega!"))
molcopy = oechem.OEMol(molecule)
omega = oeomega.OEOmega()
# These parameters were chosen to match http://docs.eyesopen.com/toolkits/cookbook/python/modeling/am1-bcc.html
omega.SetMaxConfs(max_confs)
omega.SetIncludeInput(True)
omega.SetCanonOrder(False)
omega.SetSampleHydrogens(True) # Word to the wise: skipping this step can lead to significantly different charges!
omega.SetEnergyWindow(ewindow)
omega.SetRMSThreshold(rms_threshold) # Word to the wise: skipping this step can lead to significantly different charges!
omega.SetStrictStereo(strictStereo)
omega.SetStrictAtomTypes(strictTypes)
omega.SetIncludeInput(False) # don't include input
if max_confs is not None:
omega.SetMaxConfs(max_confs)
status = omega(molcopy) # generate conformation
if not status:
raise(RuntimeError("omega returned error code %d" % status))
return molcopy
def get_names_to_charges(molecule):
"""Return a dictionary of atom names and partial charges, as well as a string representation.
Parameters
----------
molecule : OEMol
Molecule for which to grab charges
Returns
-------
data : dictionary
A dictinoary whose (key, val) pairs are the atom names and partial
charges, respectively.
molrepr : str
A string representation of data
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for oechem!"))
molcopy = oechem.OEMol(molecule)
molrepr = ""
data = {}
for atom in molcopy.GetAtoms():
name = atom.GetName()
charge = atom.GetPartialCharge()
data[name] = charge
molrepr += "%s %f \n" % (name, charge)
return data, molrepr
def molecule_to_mol2(molecule, tripos_mol2_filename=None, conformer=0, residue_name="MOL"):
"""Convert OE molecule to tripos mol2 file.
Parameters
----------
molecule : openeye.oechem.OEGraphMol
The molecule to be converted.
tripos_mol2_filename : str, optional, default=None
Output filename. If None, will create a filename similar to
name.tripos.mol2, where name is the name of the OE molecule.
conformer : int, optional, default=0
Save this frame
residue_name : str, optional, default="MOL"
OpenEye writes mol2 files with <0> as the residue / ligand name.
This chokes many mol2 parsers, so we replace it with a string of
your choosing.
Returns
-------
tripos_mol2_filename : str
Filename of output tripos mol2 file
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for oechem!"))
# Get molecule name.
molecule_name = molecule.GetTitle()
logger.debug(molecule_name)
# Write molecule as Tripos mol2.
if tripos_mol2_filename is None:
tripos_mol2_filename = molecule_name + '.tripos.mol2'
ofs = oechem.oemolostream(tripos_mol2_filename)
ofs.SetFormat(oechem.OEFormat_MOL2H)
for k, mol in enumerate(molecule.GetConfs()):
if k == conformer:
oechem.OEWriteMolecule(ofs, mol)
ofs.close()
# Replace <0> substructure names with valid text.
infile = open(tripos_mol2_filename, 'r')
lines = infile.readlines()
infile.close()
newlines = [line.replace('<0>', residue_name) for line in lines]
outfile = open(tripos_mol2_filename, 'w')
outfile.writelines(newlines)
outfile.close()
return molecule_name, tripos_mol2_filename
def oemols_to_ffxml(molecules, base_molecule_name="lig"):
"""Generate an OpenMM ffxml object and MDTraj trajectories from multiple OEMols
Parameters
----------
molecules : list(OEMole)
Molecules WITH CHARGES. Each can have multiple conformations.
WILL GIVE UNDEFINED RESULTS IF NOT CHARGED.
base_molecule_name : str, optional, default='lig'
Base name of molecule to use inside parameter files.
Returns
-------
trajectories : list(mdtraj.Trajectory)
List of MDTraj Trajectories for molecule. May contain multiple frames
ffxml : StringIO
StringIO representation of ffxml file.
Notes
-----
We allow multiple different molecules at once so that they can all be
included in a single ffxml file, which is currently the only recommended
way to simulate multiple GAFF molecules in a single simulation. For most
applications, you will have just a single molecule:
e.g. molecules = [my_oemol]
The resulting ffxml StringIO object can be directly input to OpenMM e.g.
`forcefield = app.ForceField(ffxml)`
This will generate a lot of temporary files, so you may want to use
utils.enter_temp_directory() to avoid clutter.
"""
all_trajectories = []
gaff_mol2_filenames = []
frcmod_filenames = []
print(os.getcwd())
for i, molecule in enumerate(molecules):
trajectories = []
for j in range(molecule.NumConfs()):
molecule_name = "%s-%d-%d" % (base_molecule_name, i, j)
mol2_filename = "./%s.mol2" % molecule_name
_unused = molecule_to_mol2(molecule, mol2_filename, conformer=j)
gaff_mol2_filename, frcmod_filename = run_antechamber(molecule_name, mol2_filename, charge_method=None) # It's redundant to run antechamber on each frame, fix me later.
traj = md.load(gaff_mol2_filename)
trajectories.append(traj)
if j == 0: # Only need 1 frame of forcefield files
gaff_mol2_filenames.append(gaff_mol2_filename)
frcmod_filenames.append(frcmod_filename)
# Create a trajectory with all frames of the current molecule
traj = trajectories[0].join(trajectories[1:])
all_trajectories.append(traj)
ffxml = create_ffxml_file(gaff_mol2_filenames, frcmod_filenames, override_mol2_residue_name=base_molecule_name)
return all_trajectories, ffxml
def smiles_to_antechamber(smiles_string, gaff_mol2_filename, frcmod_filename, residue_name="MOL", strictStereo=False):
"""Build a molecule from a smiles string and run antechamber,
generating GAFF mol2 and frcmod files from a smiles string. Charges
will be generated using the OpenEye QuacPac AM1-BCC implementation.
Parameters
----------
smiles_string : str
Smiles string of molecule to construct and charge
gaff_mol2_filename : str
Filename of mol2 file output of antechamber, with charges
created from openeye
frcmod_filename : str
Filename of frcmod file output of antechamber. Most likely
this file will be almost empty, at least for typical molecules.
residue_name : str, optional, default="MOL"
OpenEye writes mol2 files with <0> as the residue / ligand name.
This chokes many mol2 parsers, so we replace it with a string of
your choosing. This might be useful for downstream applications
if the residue names are required to be unique.
strictStereo : bool, optional, default=False
If False, permits smiles strings with unspecified stereochemistry.
See https://docs.eyesopen.com/omega/usage.html
"""
oechem = import_("openeye.oechem")
if not oechem.OEChemIsLicensed(): raise(ImportError("Need License for oechem!"))
# Get the absolute path so we can find these filenames from inside a temporary directory.
gaff_mol2_filename = os.path.abspath(gaff_mol2_filename)
frcmod_filename = os.path.abspath(frcmod_filename)
m = smiles_to_oemol(smiles_string)
m = get_charges(m, strictStereo=strictStereo, keep_confs=1)
with enter_temp_directory(): # Avoid dumping 50 antechamber files in local directory.
_unused = molecule_to_mol2(m, "./tmp.mol2", residue_name=residue_name)
net_charge = oechem.OENetCharge(m)
tmp_gaff_mol2_filename, tmp_frcmod_filename = run_antechamber("tmp", "./tmp.mol2", charge_method=None, net_charge=net_charge) # USE OE AM1BCC charges!
shutil.copy(tmp_gaff_mol2_filename, gaff_mol2_filename)
shutil.copy(tmp_frcmod_filename, frcmod_filename)
| Clyde-fare/openmoltools | openmoltools/openeye.py | Python | gpl-2.0 | 15,515 | [
"MDTraj",
"OpenMM"
] | 834f0dfaedd002bc571ad28de77c458678a14ede92302473259ac54829bf5289 |
# (c) 2013-2014, Michael DeHaan <michael.dehaan@gmail.com>
# (c) 2015 Toshio Kuratomi <tkuratomi@ansible.com>
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# Make coding more python3-ish
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import ast
import base64
import datetime
import imp
import json
import os
import shlex
import zipfile
import random
import re
from io import BytesIO
from ansible.release import __version__, __author__
from ansible import constants as C
from ansible.errors import AnsibleError
from ansible.module_utils._text import to_bytes, to_text
from ansible.plugins.loader import module_utils_loader, ps_module_utils_loader
from ansible.plugins.shell.powershell import async_watchdog, async_wrapper, become_wrapper, leaf_exec, exec_wrapper
# Must import strategy and use write_locks from there
# If we import write_locks directly then we end up binding a
# variable to the object and then it never gets updated.
from ansible.executor import action_write_locks
try:
from __main__ import display
except ImportError:
from ansible.utils.display import Display
display = Display()
REPLACER = b"#<<INCLUDE_ANSIBLE_MODULE_COMMON>>"
REPLACER_VERSION = b"\"<<ANSIBLE_VERSION>>\""
REPLACER_COMPLEX = b"\"<<INCLUDE_ANSIBLE_MODULE_COMPLEX_ARGS>>\""
REPLACER_WINDOWS = b"# POWERSHELL_COMMON"
REPLACER_JSONARGS = b"<<INCLUDE_ANSIBLE_MODULE_JSON_ARGS>>"
REPLACER_SELINUX = b"<<SELINUX_SPECIAL_FILESYSTEMS>>"
# We could end up writing out parameters with unicode characters so we need to
# specify an encoding for the python source file
ENCODING_STRING = u'# -*- coding: utf-8 -*-'
# module_common is relative to module_utils, so fix the path
_MODULE_UTILS_PATH = os.path.join(os.path.dirname(__file__), '..', 'module_utils')
# ******************************************************************************
ANSIBALLZ_TEMPLATE = u'''%(shebang)s
%(coding)s
ANSIBALLZ_WRAPPER = True # For test-module script to tell this is a ANSIBALLZ_WRAPPER
# This code is part of Ansible, but is an independent component.
# The code in this particular templatable string, and this templatable string
# only, is BSD licensed. Modules which end up using this snippet, which is
# dynamically combined together by Ansible still belong to the author of the
# module, and they may assign their own license to the complete work.
#
# Copyright (c), James Cammarata, 2016
# Copyright (c), Toshio Kuratomi, 2016
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
# IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE
# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import os.path
import sys
import __main__
# For some distros and python versions we pick up this script in the temporary
# directory. This leads to problems when the ansible module masks a python
# library that another import needs. We have not figured out what about the
# specific distros and python versions causes this to behave differently.
#
# Tested distros:
# Fedora23 with python3.4 Works
# Ubuntu15.10 with python2.7 Works
# Ubuntu15.10 with python3.4 Fails without this
# Ubuntu16.04.1 with python3.5 Fails without this
# To test on another platform:
# * use the copy module (since this shadows the stdlib copy module)
# * Turn off pipelining
# * Make sure that the destination file does not exist
# * ansible ubuntu16-test -m copy -a 'src=/etc/motd dest=/var/tmp/m'
# This will traceback in shutil. Looking at the complete traceback will show
# that shutil is importing copy which finds the ansible module instead of the
# stdlib module
scriptdir = None
try:
scriptdir = os.path.dirname(os.path.realpath(__main__.__file__))
except (AttributeError, OSError):
# Some platforms don't set __file__ when reading from stdin
# OSX raises OSError if using abspath() in a directory we don't have
# permission to read (realpath calls abspath)
pass
if scriptdir is not None:
sys.path = [p for p in sys.path if p != scriptdir]
import base64
import shutil
import zipfile
import tempfile
import subprocess
if sys.version_info < (3,):
bytes = str
PY3 = False
else:
unicode = str
PY3 = True
try:
# Python-2.6+
from io import BytesIO as IOStream
except ImportError:
# Python < 2.6
from StringIO import StringIO as IOStream
ZIPDATA = """%(zipdata)s"""
def invoke_module(module, modlib_path, json_params):
pythonpath = os.environ.get('PYTHONPATH')
if pythonpath:
os.environ['PYTHONPATH'] = ':'.join((modlib_path, pythonpath))
else:
os.environ['PYTHONPATH'] = modlib_path
p = subprocess.Popen([%(interpreter)s, module], env=os.environ, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE)
(stdout, stderr) = p.communicate(json_params)
if not isinstance(stderr, (bytes, unicode)):
stderr = stderr.read()
if not isinstance(stdout, (bytes, unicode)):
stdout = stdout.read()
if PY3:
sys.stderr.buffer.write(stderr)
sys.stdout.buffer.write(stdout)
else:
sys.stderr.write(stderr)
sys.stdout.write(stdout)
return p.returncode
def debug(command, zipped_mod, json_params):
# The code here normally doesn't run. It's only used for debugging on the
# remote machine.
#
# The subcommands in this function make it easier to debug ansiballz
# modules. Here's the basic steps:
#
# Run ansible with the environment variable: ANSIBLE_KEEP_REMOTE_FILES=1 and -vvv
# to save the module file remotely::
# $ ANSIBLE_KEEP_REMOTE_FILES=1 ansible host1 -m ping -a 'data=october' -vvv
#
# Part of the verbose output will tell you where on the remote machine the
# module was written to::
# [...]
# <host1> SSH: EXEC ssh -C -q -o ControlMaster=auto -o ControlPersist=60s -o KbdInteractiveAuthentication=no -o
# PreferredAuthentications=gssapi-with-mic,gssapi-keyex,hostbased,publickey -o PasswordAuthentication=no -o ConnectTimeout=10 -o
# ControlPath=/home/badger/.ansible/cp/ansible-ssh-%%h-%%p-%%r -tt rhel7 '/bin/sh -c '"'"'LANG=en_US.UTF-8 LC_ALL=en_US.UTF-8
# LC_MESSAGES=en_US.UTF-8 /usr/bin/python /home/badger/.ansible/tmp/ansible-tmp-1461173013.93-9076457629738/ping'"'"''
# [...]
#
# Login to the remote machine and run the module file via from the previous
# step with the explode subcommand to extract the module payload into
# source files::
# $ ssh host1
# $ /usr/bin/python /home/badger/.ansible/tmp/ansible-tmp-1461173013.93-9076457629738/ping explode
# Module expanded into:
# /home/badger/.ansible/tmp/ansible-tmp-1461173408.08-279692652635227/ansible
#
# You can now edit the source files to instrument the code or experiment with
# different parameter values. When you're ready to run the code you've modified
# (instead of the code from the actual zipped module), use the execute subcommand like this::
# $ /usr/bin/python /home/badger/.ansible/tmp/ansible-tmp-1461173013.93-9076457629738/ping execute
# Okay to use __file__ here because we're running from a kept file
basedir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'debug_dir')
args_path = os.path.join(basedir, 'args')
script_path = os.path.join(basedir, 'ansible_module_%(ansible_module)s.py')
if command == 'explode':
# transform the ZIPDATA into an exploded directory of code and then
# print the path to the code. This is an easy way for people to look
# at the code on the remote machine for debugging it in that
# environment
z = zipfile.ZipFile(zipped_mod)
for filename in z.namelist():
if filename.startswith('/'):
raise Exception('Something wrong with this module zip file: should not contain absolute paths')
dest_filename = os.path.join(basedir, filename)
if dest_filename.endswith(os.path.sep) and not os.path.exists(dest_filename):
os.makedirs(dest_filename)
else:
directory = os.path.dirname(dest_filename)
if not os.path.exists(directory):
os.makedirs(directory)
f = open(dest_filename, 'wb')
f.write(z.read(filename))
f.close()
# write the args file
f = open(args_path, 'wb')
f.write(json_params)
f.close()
print('Module expanded into:')
print('%%s' %% basedir)
exitcode = 0
elif command == 'execute':
# Execute the exploded code instead of executing the module from the
# embedded ZIPDATA. This allows people to easily run their modified
# code on the remote machine to see how changes will affect it.
# This differs slightly from default Ansible execution of Python modules
# as it passes the arguments to the module via a file instead of stdin.
# Set pythonpath to the debug dir
pythonpath = os.environ.get('PYTHONPATH')
if pythonpath:
os.environ['PYTHONPATH'] = ':'.join((basedir, pythonpath))
else:
os.environ['PYTHONPATH'] = basedir
p = subprocess.Popen([%(interpreter)s, script_path, args_path],
env=os.environ, shell=False, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, stdin=subprocess.PIPE)
(stdout, stderr) = p.communicate()
if not isinstance(stderr, (bytes, unicode)):
stderr = stderr.read()
if not isinstance(stdout, (bytes, unicode)):
stdout = stdout.read()
if PY3:
sys.stderr.buffer.write(stderr)
sys.stdout.buffer.write(stdout)
else:
sys.stderr.write(stderr)
sys.stdout.write(stdout)
return p.returncode
elif command == 'excommunicate':
# This attempts to run the module in-process (by importing a main
# function and then calling it). It is not the way ansible generally
# invokes the module so it won't work in every case. It is here to
# aid certain debuggers which work better when the code doesn't change
# from one process to another but there may be problems that occur
# when using this that are only artifacts of how we're invoking here,
# not actual bugs (as they don't affect the real way that we invoke
# ansible modules)
# stub the args and python path
sys.argv = ['%(ansible_module)s', args_path]
sys.path.insert(0, basedir)
from ansible_module_%(ansible_module)s import main
main()
print('WARNING: Module returned to wrapper instead of exiting')
sys.exit(1)
else:
print('WARNING: Unknown debug command. Doing nothing.')
exitcode = 0
return exitcode
if __name__ == '__main__':
#
# See comments in the debug() method for information on debugging
#
ANSIBALLZ_PARAMS = %(params)s
if PY3:
ANSIBALLZ_PARAMS = ANSIBALLZ_PARAMS.encode('utf-8')
try:
# There's a race condition with the controller removing the
# remote_tmpdir and this module executing under async. So we cannot
# store this in remote_tmpdir (use system tempdir instead)
temp_path = tempfile.mkdtemp(prefix='ansible_')
zipped_mod = os.path.join(temp_path, 'ansible_modlib.zip')
modlib = open(zipped_mod, 'wb')
modlib.write(base64.b64decode(ZIPDATA))
modlib.close()
if len(sys.argv) == 2:
exitcode = debug(sys.argv[1], zipped_mod, ANSIBALLZ_PARAMS)
else:
z = zipfile.ZipFile(zipped_mod, mode='r')
module = os.path.join(temp_path, 'ansible_module_%(ansible_module)s.py')
f = open(module, 'wb')
f.write(z.read('ansible_module_%(ansible_module)s.py'))
f.close()
# When installed via setuptools (including python setup.py install),
# ansible may be installed with an easy-install.pth file. That file
# may load the system-wide install of ansible rather than the one in
# the module. sitecustomize is the only way to override that setting.
z = zipfile.ZipFile(zipped_mod, mode='a')
# py3: zipped_mod will be text, py2: it's bytes. Need bytes at the end
sitecustomize = u'import sys\\nsys.path.insert(0,"%%s")\\n' %% zipped_mod
sitecustomize = sitecustomize.encode('utf-8')
# Use a ZipInfo to work around zipfile limitation on hosts with
# clocks set to a pre-1980 year (for instance, Raspberry Pi)
zinfo = zipfile.ZipInfo()
zinfo.filename = 'sitecustomize.py'
zinfo.date_time = ( %(year)i, %(month)i, %(day)i, %(hour)i, %(minute)i, %(second)i)
z.writestr(zinfo, sitecustomize)
z.close()
exitcode = invoke_module(module, zipped_mod, ANSIBALLZ_PARAMS)
finally:
try:
shutil.rmtree(temp_path)
except (NameError, OSError):
# tempdir creation probably failed
pass
sys.exit(exitcode)
'''
def _strip_comments(source):
# Strip comments and blank lines from the wrapper
buf = []
for line in source.splitlines():
l = line.strip()
if not l or l.startswith(u'#'):
continue
buf.append(line)
return u'\n'.join(buf)
if C.DEFAULT_KEEP_REMOTE_FILES:
# Keep comments when KEEP_REMOTE_FILES is set. That way users will see
# the comments with some nice usage instructions
ACTIVE_ANSIBALLZ_TEMPLATE = ANSIBALLZ_TEMPLATE
else:
# ANSIBALLZ_TEMPLATE stripped of comments for smaller over the wire size
ACTIVE_ANSIBALLZ_TEMPLATE = _strip_comments(ANSIBALLZ_TEMPLATE)
class ModuleDepFinder(ast.NodeVisitor):
# Caveats:
# This code currently does not handle:
# * relative imports from py2.6+ from . import urls
IMPORT_PREFIX_SIZE = len('ansible.module_utils.')
def __init__(self, *args, **kwargs):
"""
Walk the ast tree for the python module.
Save submodule[.submoduleN][.identifier] into self.submodules
self.submodules will end up with tuples like:
- ('basic',)
- ('urls', 'fetch_url')
- ('database', 'postgres')
- ('database', 'postgres', 'quote')
It's up to calling code to determine whether the final element of the
dotted strings are module names or something else (function, class, or
variable names)
"""
super(ModuleDepFinder, self).__init__(*args, **kwargs)
self.submodules = set()
def visit_Import(self, node):
# import ansible.module_utils.MODLIB[.MODLIBn] [as asname]
for alias in (a for a in node.names if a.name.startswith('ansible.module_utils.')):
py_mod = alias.name[self.IMPORT_PREFIX_SIZE:]
py_mod = tuple(py_mod.split('.'))
self.submodules.add(py_mod)
self.generic_visit(node)
def visit_ImportFrom(self, node):
# Specialcase: six is a special case because of its
# import logic
if node.names[0].name == '_six':
self.submodules.add(('_six',))
elif node.module.startswith('ansible.module_utils'):
where_from = node.module[self.IMPORT_PREFIX_SIZE:]
if where_from:
# from ansible.module_utils.MODULE1[.MODULEn] import IDENTIFIER [as asname]
# from ansible.module_utils.MODULE1[.MODULEn] import MODULEn+1 [as asname]
# from ansible.module_utils.MODULE1[.MODULEn] import MODULEn+1 [,IDENTIFIER] [as asname]
py_mod = tuple(where_from.split('.'))
for alias in node.names:
self.submodules.add(py_mod + (alias.name,))
else:
# from ansible.module_utils import MODLIB [,MODLIB2] [as asname]
for alias in node.names:
self.submodules.add((alias.name,))
self.generic_visit(node)
def _slurp(path):
if not os.path.exists(path):
raise AnsibleError("imported module support code does not exist at %s" % os.path.abspath(path))
fd = open(path, 'rb')
data = fd.read()
fd.close()
return data
def _get_shebang(interpreter, task_vars, args=tuple()):
"""
Note not stellar API:
Returns None instead of always returning a shebang line. Doing it this
way allows the caller to decide to use the shebang it read from the
file rather than trust that we reformatted what they already have
correctly.
"""
interpreter_config = u'ansible_%s_interpreter' % os.path.basename(interpreter).strip()
if interpreter_config not in task_vars:
return (None, interpreter)
interpreter = task_vars[interpreter_config].strip()
shebang = u'#!' + interpreter
if args:
shebang = shebang + u' ' + u' '.join(args)
return (shebang, interpreter)
def recursive_finder(name, data, py_module_names, py_module_cache, zf):
"""
Using ModuleDepFinder, make sure we have all of the module_utils files that
the module its module_utils files needs.
"""
# Parse the module and find the imports of ansible.module_utils
tree = ast.parse(data)
finder = ModuleDepFinder()
finder.visit(tree)
#
# Determine what imports that we've found are modules (vs class, function.
# variable names) for packages
#
normalized_modules = set()
# Loop through the imports that we've found to normalize them
# Exclude paths that match with paths we've already processed
# (Have to exclude them a second time once the paths are processed)
module_utils_paths = [p for p in module_utils_loader._get_paths(subdirs=False) if os.path.isdir(p)]
module_utils_paths.append(_MODULE_UTILS_PATH)
for py_module_name in finder.submodules.difference(py_module_names):
module_info = None
if py_module_name[0] == 'six':
# Special case the python six library because it messes up the
# import process in an incompatible way
module_info = imp.find_module('six', module_utils_paths)
py_module_name = ('six',)
idx = 0
elif py_module_name[0] == '_six':
# Special case the python six library because it messes up the
# import process in an incompatible way
module_info = imp.find_module('_six', [os.path.join(p, 'six') for p in module_utils_paths])
py_module_name = ('six', '_six')
idx = 0
else:
# Check whether either the last or the second to last identifier is
# a module name
for idx in (1, 2):
if len(py_module_name) < idx:
break
try:
module_info = imp.find_module(py_module_name[-idx],
[os.path.join(p, *py_module_name[:-idx]) for p in module_utils_paths])
break
except ImportError:
continue
# Could not find the module. Construct a helpful error message.
if module_info is None:
msg = ['Could not find imported module support code for %s. Looked for' % (name,)]
if idx == 2:
msg.append('either %s.py or %s.py' % (py_module_name[-1], py_module_name[-2]))
else:
msg.append(py_module_name[-1])
raise AnsibleError(' '.join(msg))
# Found a byte compiled file rather than source. We cannot send byte
# compiled over the wire as the python version might be different.
# imp.find_module seems to prefer to return source packages so we just
# error out if imp.find_module returns byte compiled files (This is
# fragile as it depends on undocumented imp.find_module behaviour)
if module_info[2][2] not in (imp.PY_SOURCE, imp.PKG_DIRECTORY):
msg = ['Could not find python source for imported module support code for %s. Looked for' % name]
if idx == 2:
msg.append('either %s.py or %s.py' % (py_module_name[-1], py_module_name[-2]))
else:
msg.append(py_module_name[-1])
raise AnsibleError(' '.join(msg))
if idx == 2:
# We've determined that the last portion was an identifier and
# thus, not part of the module name
py_module_name = py_module_name[:-1]
# If not already processed then we've got work to do
if py_module_name not in py_module_names:
# If not in the cache, then read the file into the cache
# We already have a file handle for the module open so it makes
# sense to read it now
if py_module_name not in py_module_cache:
if module_info[2][2] == imp.PKG_DIRECTORY:
# Read the __init__.py instead of the module file as this is
# a python package
normalized_name = py_module_name + ('__init__',)
normalized_path = os.path.join(os.path.join(module_info[1], '__init__.py'))
normalized_data = _slurp(normalized_path)
else:
normalized_name = py_module_name
normalized_path = module_info[1]
normalized_data = module_info[0].read()
module_info[0].close()
py_module_cache[normalized_name] = (normalized_data, normalized_path)
normalized_modules.add(normalized_name)
# Make sure that all the packages that this module is a part of
# are also added
for i in range(1, len(py_module_name)):
py_pkg_name = py_module_name[:-i] + ('__init__',)
if py_pkg_name not in py_module_names:
pkg_dir_info = imp.find_module(py_pkg_name[-1],
[os.path.join(p, *py_pkg_name[:-1]) for p in module_utils_paths])
normalized_modules.add(py_pkg_name)
py_module_cache[py_pkg_name] = (_slurp(pkg_dir_info[1]), pkg_dir_info[1])
#
# iterate through all of the ansible.module_utils* imports that we haven't
# already checked for new imports
#
# set of modules that we haven't added to the zipfile
unprocessed_py_module_names = normalized_modules.difference(py_module_names)
for py_module_name in unprocessed_py_module_names:
py_module_path = os.path.join(*py_module_name)
py_module_file_name = '%s.py' % py_module_path
zf.writestr(os.path.join("ansible/module_utils",
py_module_file_name), py_module_cache[py_module_name][0])
display.vvvvv("Using module_utils file %s" % py_module_cache[py_module_name][1])
# Add the names of the files we're scheduling to examine in the loop to
# py_module_names so that we don't re-examine them in the next pass
# through recursive_finder()
py_module_names.update(unprocessed_py_module_names)
for py_module_file in unprocessed_py_module_names:
recursive_finder(py_module_file, py_module_cache[py_module_file][0], py_module_names, py_module_cache, zf)
# Save memory; the file won't have to be read again for this ansible module.
del py_module_cache[py_module_file]
def _is_binary(b_module_data):
textchars = bytearray(set([7, 8, 9, 10, 12, 13, 27]) | set(range(0x20, 0x100)) - set([0x7f]))
start = b_module_data[:1024]
return bool(start.translate(None, textchars))
def _find_module_utils(module_name, b_module_data, module_path, module_args, task_vars, module_compression, async_timeout, become,
become_method, become_user, become_password, environment):
"""
Given the source of the module, convert it to a Jinja2 template to insert
module code and return whether it's a new or old style module.
"""
module_substyle = module_style = 'old'
# module_style is something important to calling code (ActionBase). It
# determines how arguments are formatted (json vs k=v) and whether
# a separate arguments file needs to be sent over the wire.
# module_substyle is extra information that's useful internally. It tells
# us what we have to look to substitute in the module files and whether
# we're using module replacer or ansiballz to format the module itself.
if _is_binary(b_module_data):
module_substyle = module_style = 'binary'
elif REPLACER in b_module_data:
# Do REPLACER before from ansible.module_utils because we need make sure
# we substitute "from ansible.module_utils basic" for REPLACER
module_style = 'new'
module_substyle = 'python'
b_module_data = b_module_data.replace(REPLACER, b'from ansible.module_utils.basic import *')
elif b'from ansible.module_utils.' in b_module_data:
module_style = 'new'
module_substyle = 'python'
elif REPLACER_WINDOWS in b_module_data or re.search(b'#Requires \-Module', b_module_data, re.IGNORECASE):
module_style = 'new'
module_substyle = 'powershell'
elif REPLACER_JSONARGS in b_module_data:
module_style = 'new'
module_substyle = 'jsonargs'
elif b'WANT_JSON' in b_module_data:
module_substyle = module_style = 'non_native_want_json'
shebang = None
# Neither old-style, non_native_want_json nor binary modules should be modified
# except for the shebang line (Done by modify_module)
if module_style in ('old', 'non_native_want_json', 'binary'):
return b_module_data, module_style, shebang
output = BytesIO()
py_module_names = set()
if module_substyle == 'python':
params = dict(ANSIBLE_MODULE_ARGS=module_args,)
python_repred_params = repr(json.dumps(params))
try:
compression_method = getattr(zipfile, module_compression)
except AttributeError:
display.warning(u'Bad module compression string specified: %s. Using ZIP_STORED (no compression)' % module_compression)
compression_method = zipfile.ZIP_STORED
lookup_path = os.path.join(C.DEFAULT_LOCAL_TMP, 'ansiballz_cache')
cached_module_filename = os.path.join(lookup_path, "%s-%s" % (module_name, module_compression))
zipdata = None
# Optimization -- don't lock if the module has already been cached
if os.path.exists(cached_module_filename):
display.debug('ANSIBALLZ: using cached module: %s' % cached_module_filename)
zipdata = open(cached_module_filename, 'rb').read()
else:
if module_name in action_write_locks.action_write_locks:
display.debug('ANSIBALLZ: Using lock for %s' % module_name)
lock = action_write_locks.action_write_locks[module_name]
else:
# If the action plugin directly invokes the module (instead of
# going through a strategy) then we don't have a cross-process
# Lock specifically for this module. Use the "unexpected
# module" lock instead
display.debug('ANSIBALLZ: Using generic lock for %s' % module_name)
lock = action_write_locks.action_write_locks[None]
display.debug('ANSIBALLZ: Acquiring lock')
with lock:
display.debug('ANSIBALLZ: Lock acquired: %s' % id(lock))
# Check that no other process has created this while we were
# waiting for the lock
if not os.path.exists(cached_module_filename):
display.debug('ANSIBALLZ: Creating module')
# Create the module zip data
zipoutput = BytesIO()
zf = zipfile.ZipFile(zipoutput, mode='w', compression=compression_method)
# Note: If we need to import from release.py first,
# remember to catch all exceptions: https://github.com/ansible/ansible/issues/16523
zf.writestr('ansible/__init__.py',
b'from pkgutil import extend_path\n__path__=extend_path(__path__,__name__)\n__version__="' +
to_bytes(__version__) + b'"\n__author__="' +
to_bytes(__author__) + b'"\n')
zf.writestr('ansible/module_utils/__init__.py', b'from pkgutil import extend_path\n__path__=extend_path(__path__,__name__)\n')
zf.writestr('ansible_module_%s.py' % module_name, b_module_data)
py_module_cache = {('__init__',): (b'', '[builtin]')}
recursive_finder(module_name, b_module_data, py_module_names, py_module_cache, zf)
zf.close()
zipdata = base64.b64encode(zipoutput.getvalue())
# Write the assembled module to a temp file (write to temp
# so that no one looking for the file reads a partially
# written file)
if not os.path.exists(lookup_path):
# Note -- if we have a global function to setup, that would
# be a better place to run this
os.makedirs(lookup_path)
display.debug('ANSIBALLZ: Writing module')
with open(cached_module_filename + '-part', 'wb') as f:
f.write(zipdata)
# Rename the file into its final position in the cache so
# future users of this module can read it off the
# filesystem instead of constructing from scratch.
display.debug('ANSIBALLZ: Renaming module')
os.rename(cached_module_filename + '-part', cached_module_filename)
display.debug('ANSIBALLZ: Done creating module')
if zipdata is None:
display.debug('ANSIBALLZ: Reading module after lock')
# Another process wrote the file while we were waiting for
# the write lock. Go ahead and read the data from disk
# instead of re-creating it.
try:
zipdata = open(cached_module_filename, 'rb').read()
except IOError:
raise AnsibleError('A different worker process failed to create module file. '
'Look at traceback for that process for debugging information.')
zipdata = to_text(zipdata, errors='surrogate_or_strict')
shebang, interpreter = _get_shebang(u'/usr/bin/python', task_vars)
if shebang is None:
shebang = u'#!/usr/bin/python'
# Enclose the parts of the interpreter in quotes because we're
# substituting it into the template as a Python string
interpreter_parts = interpreter.split(u' ')
interpreter = u"'{0}'".format(u"', '".join(interpreter_parts))
now = datetime.datetime.utcnow()
output.write(to_bytes(ACTIVE_ANSIBALLZ_TEMPLATE % dict(
zipdata=zipdata,
ansible_module=module_name,
params=python_repred_params,
shebang=shebang,
interpreter=interpreter,
coding=ENCODING_STRING,
year=now.year,
month=now.month,
day=now.day,
hour=now.hour,
minute=now.minute,
second=now.second,
)))
b_module_data = output.getvalue()
elif module_substyle == 'powershell':
# Powershell/winrm don't actually make use of shebang so we can
# safely set this here. If we let the fallback code handle this
# it can fail in the presence of the UTF8 BOM commonly added by
# Windows text editors
shebang = u'#!powershell'
exec_manifest = dict(
module_entry=to_text(base64.b64encode(b_module_data)),
powershell_modules=dict(),
module_args=module_args,
actions=['exec'],
environment=environment
)
exec_manifest['exec'] = to_text(base64.b64encode(to_bytes(leaf_exec)))
if async_timeout > 0:
exec_manifest["actions"].insert(0, 'async_watchdog')
exec_manifest["async_watchdog"] = to_text(base64.b64encode(to_bytes(async_watchdog)))
exec_manifest["actions"].insert(0, 'async_wrapper')
exec_manifest["async_wrapper"] = to_text(base64.b64encode(to_bytes(async_wrapper)))
exec_manifest["async_jid"] = str(random.randint(0, 999999999999))
exec_manifest["async_timeout_sec"] = async_timeout
if become and become_method == 'runas':
exec_manifest["actions"].insert(0, 'become')
exec_manifest["become_user"] = become_user
exec_manifest["become_password"] = become_password
exec_manifest["become"] = to_text(base64.b64encode(to_bytes(become_wrapper)))
lines = b_module_data.split(b'\n')
module_names = set()
requires_module_list = re.compile(to_bytes(r'(?i)^#\s*requires\s+\-module(?:s?)\s*(Ansible\.ModuleUtils\..+)'))
for line in lines:
# legacy, equivalent to #Requires -Modules powershell
if REPLACER_WINDOWS in line:
module_names.add(b'Ansible.ModuleUtils.Legacy')
line_match = requires_module_list.match(line)
if line_match:
module_names.add(line_match.group(1))
for m in set(module_names):
m = to_text(m)
mu_path = ps_module_utils_loader.find_plugin(m, ".psm1")
if not mu_path:
raise AnsibleError('Could not find imported module support code for \'%s\'.' % m)
exec_manifest["powershell_modules"][m] = to_text(
base64.b64encode(
to_bytes(
_slurp(mu_path)
)
)
)
# FUTURE: smuggle this back as a dict instead of serializing here; the connection plugin may need to modify it
module_json = json.dumps(exec_manifest)
b_module_data = exec_wrapper.replace(b"$json_raw = ''", b"$json_raw = @'\r\n%s\r\n'@" % to_bytes(module_json))
elif module_substyle == 'jsonargs':
module_args_json = to_bytes(json.dumps(module_args))
# these strings could be included in a third-party module but
# officially they were included in the 'basic' snippet for new-style
# python modules (which has been replaced with something else in
# ansiballz) If we remove them from jsonargs-style module replacer
# then we can remove them everywhere.
python_repred_args = to_bytes(repr(module_args_json))
b_module_data = b_module_data.replace(REPLACER_VERSION, to_bytes(repr(__version__)))
b_module_data = b_module_data.replace(REPLACER_COMPLEX, python_repred_args)
b_module_data = b_module_data.replace(REPLACER_SELINUX, to_bytes(','.join(C.DEFAULT_SELINUX_SPECIAL_FS)))
# The main event -- substitute the JSON args string into the module
b_module_data = b_module_data.replace(REPLACER_JSONARGS, module_args_json)
facility = b'syslog.' + to_bytes(task_vars.get('ansible_syslog_facility', C.DEFAULT_SYSLOG_FACILITY), errors='surrogate_or_strict')
b_module_data = b_module_data.replace(b'syslog.LOG_USER', facility)
return (b_module_data, module_style, shebang)
def modify_module(module_name, module_path, module_args, task_vars=None, module_compression='ZIP_STORED', async_timeout=0, become=False,
become_method=None, become_user=None, become_password=None, environment=None):
"""
Used to insert chunks of code into modules before transfer rather than
doing regular python imports. This allows for more efficient transfer in
a non-bootstrapping scenario by not moving extra files over the wire and
also takes care of embedding arguments in the transferred modules.
This version is done in such a way that local imports can still be
used in the module code, so IDEs don't have to be aware of what is going on.
Example:
from ansible.module_utils.basic import *
... will result in the insertion of basic.py into the module
from the module_utils/ directory in the source tree.
For powershell, this code effectively no-ops, as the exec wrapper requires access to a number of
properties not available here.
"""
task_vars = {} if task_vars is None else task_vars
environment = {} if environment is None else environment
with open(module_path, 'rb') as f:
# read in the module source
b_module_data = f.read()
(b_module_data, module_style, shebang) = _find_module_utils(module_name, b_module_data, module_path, module_args, task_vars, module_compression,
async_timeout=async_timeout, become=become, become_method=become_method,
become_user=become_user, become_password=become_password,
environment=environment)
if module_style == 'binary':
return (b_module_data, module_style, to_text(shebang, nonstring='passthru'))
elif shebang is None:
lines = b_module_data.split(b"\n", 1)
if lines[0].startswith(b"#!"):
shebang = lines[0].strip()
args = shlex.split(str(shebang[2:]))
interpreter = args[0]
interpreter = to_bytes(interpreter)
new_shebang = to_bytes(_get_shebang(interpreter, task_vars, args[1:])[0], errors='surrogate_or_strict', nonstring='passthru')
if new_shebang:
lines[0] = shebang = new_shebang
if os.path.basename(interpreter).startswith(b'python'):
lines.insert(1, to_bytes(ENCODING_STRING))
else:
# No shebang, assume a binary module?
pass
b_module_data = b"\n".join(lines)
else:
shebang = to_bytes(shebang, errors='surrogate_or_strict')
return (b_module_data, module_style, to_text(shebang, nonstring='passthru'))
| rmfitzpatrick/ansible | lib/ansible/executor/module_common.py | Python | gpl-3.0 | 40,314 | [
"VisIt"
] | 281a8c97eafdfa563da80b4dd3c3ebc7090efc799ec895c3c903b58bee8f7d94 |
#! /usr/bin/env python
'''
hmmerformat.py
takes input hmmscan output files (if using --domtblout option in hmmscan) and converts them to readable HTML
contact: jonathan.tietz@gmail.com
'''
# import modules
import csv, re, os, math, argparse, operator
from Bio import Entrez, Seq, SeqIO # BioPython
# input options
parser = argparse.ArgumentParser(description='hmmerformat - Produces an HTML readout of hmmscan results')
parser.add_argument('-i', help='Input hmmscan --domtblout output file', required=True) # input filename -- either GI list or CSV containing GI list
parser.add_argument('-o', help='Output file base (no suffix)', default='hmmerformat_out')
parser.add_argument('-f', help='FASTA file containing protein sequences')
parser.add_argument('-e', help='E-value significance threshold (default 1e-5)', default='1e-5', type=float)
args = parser.parse_args()
# initialize
input_file = args.i
output_html_file = args.o + '.html'
eval_threshold = args.e
# functions
def parse_hmmscan(hmmscan_filename):
'''
parses hmmscan outputs as a list of tuples for each identifier
'''
# open file
try:
f = open(hmmscan_filename, 'rU')
except IOError:
print "ERROR .. Could not read file in function parse_hmmscan .. file = ", hmmscan_filename
sys.exit()
result_list = []
with f:
reader = f.readlines()
for row in reader:
comments = re.match(r'^\#.*$',row)
if not comments:
values = re.match(r'(\S+?)\s+?(\S+?)\s+?\S+?\s+?(\S+?)\s+?\S+?\s+?\S+?\s+?(\S+?)\s+?\S+?\s+?\S+?\s+?\S+?\s+?\S+?\s+?(\S+?)\s+?(\S+?)\s+?(\S+?)\s+?\S+?\s+?\S+?\s+?\S+?\s+?\S+?\s+?\S+?\s+?(\S+?)\s+?(\S+?)\s+?\S+?\s+?(.+)\n',row)
if not values: # If didn't match expected regex, warn user
print("Encountered bad pHMMER row ...",row)
else:
'''
regex capture groups are:
(1) Target name (2) Target accession (3) Query name (4) E-value (5) c-Value (6) i-Value
(7) Score (8) Coord start (9) Coord end (10) Description
'''
props = (values.group(3), values.group(1), values.group(2), values.group(4), values.group(5), values.group(6), values.group(7), values.group(8), values.group(9), values.group(10))
result_list.append(props)
f.close()
return result_list
def parse_fasta(fasta_filename):
'''
gets a list of identifiers and sequences from a standard formatted FASTA file
'''
result_list = {}
order_counter = 0
for seq_record in SeqIO.parse(args.f, "fasta"):
result_list[seq_record.id] = [seq_record.seq, len(seq_record), order_counter]
order_counter += 1
return result_list
def sortkey(item):
return float(item[3])
def write_html(gene_list, hmmscan, html_file):
with open(html_file, 'w') as f:
# print header info
f.write('<!DOCTYPE html>\n'+'<html lang="en">\n'+'<head>\n'
+'<meta charset="utf-8">\n'
+'<meta http-equiv="X-UA-Compatible" content="IE=edge">\n'
+'<meta name="robots" content="noindex, nofollow" />\n'
+'<meta name="googlebot" content="noindex" />\n'
+'<meta name="viewport" content="width=device-width, initial-scale=1">\n'
+'<meta name="description" content="">\n'
+'<meta name="author" content="">\n'
+'<link rel="icon" href="../../favicon.ico">\n'
+'<title>hmmerformat</title>\n'
+'<!-- Latest compiled and minified CSS -->\n'
+'<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" crossorigin="anonymous">\n'
+'<!-- Optional theme -->\n'
+'<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap-theme.min.css" integrity="sha384-fLW2N01lMqjakBkx3l/M9EahuwpSfeNvV63J5ezn3uZzapT0u7EYsXMjQV+0En5r" crossorigin="anonymous">\n'
+'</head>\n'
+'<body>\n'
+' <div class="container">\n'
+' <div class="row">\n'
+' <div class="col-md-12">\n'
+' <h1>Content</h1>\n')
# obtain sorted gene list
unsorted_gene_list = []
for gene in gene_list:
unsorted_gene_list.append((gene, gene_list[gene][2]))
unsorted_gene_list = set(unsorted_gene_list)
sorted_gene_list = sorted(unsorted_gene_list, key=operator.itemgetter(1))
# print genes
for gene in sorted_gene_list:
# get hmms
hmmscan_specific = [x for x in hmmscan if x[0] == gene[0]]
hmmscan_specific = sorted(hmmscan_specific, key=sortkey)
how_many_hmms = len(hmmscan_specific)
hmmscan_threshold = [x for x in hmmscan_specific if float(x[3]) < eval_threshold]
how_many_significant_hmms = len(hmmscan_threshold)
# print sequence information
f.write( "<h2>%s</h2>" % gene[0])
f.write(' <p><b>%d</b> pHMM matches, <b>%d</b> of which meet %g threshold</p>\n' % (how_many_hmms, how_many_significant_hmms, eval_threshold))
# print progress bar of significant hits
if how_many_significant_hmms > 0: f.write('<div class="panel panel-default"><div class="panel-heading">Domain locations for significant pHMM hits</div><div class="panel-body">')
for row in hmmscan_threshold:
# calculate start and end coordinates
if gene[0] == 0:
start_coord = 0
else:
start_coord = 100 * int(row[7]) / int(gene_list[gene[0]][1])
if gene[0] == 0:
end_coord = 100
else:
end_coord = 100 * int(row[8]) / int(gene_list[gene[0]][1])
accession = row[2]
f.write('\n <div class="progress">\n'
+'<div class="progress-bar" style="background-image:none;background-color:#f5f5f5;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1);width: %f%%"> </div>\n' % start_coord
+'<div class="progress-bar" style="width: %f%%">%s</div></div>' % (float(end_coord - start_coord), accession))
if how_many_significant_hmms > 0: f.write('</div></div>')
f.write( '<div class="panel panel-default">')
f.write( '<div class="panel-heading">Sequence (%d aa)</div>' % gene_list[gene[0]][1])
f.write( '<div class="panel-body" style="word-wrap:break-word;"><p><samp>%s</samp></p><p><a href="http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins&PROGRAM=blastp&BLAST_PROGRAMS=blastp&QUERY=%s&LINK_LOC=protein&PAGE_TYPE=BlastSearch">BLASTP against nr database</a></p></div>' % (gene_list[gene[0]][0], gene_list[gene[0]][0]))
f.write( '</div>\n')
# print hmmscan information
f.write(' <div class="panel panel-default">\n'
+' <div class="panel-heading">pHMM Matches</div>\n'
+' <table class="table table-striped">\n'
+' <tr>\n'
+' <th>Target Name</th>\n'
+' <th>Accession</th>\n'
+' <th>E-value</th>\n'
+' <th>Individual E-value<br />(i-Value)</th>\n'
+' <th>Condensed E-value<br />(c-Value)</th>\n'
+' <th>Score</th>\n'
+' <th>Domain <br />Start</th>\n'
+' <th>Domain <br />End</th>\n'
+' <th>Description</th>\n'
+' </tr>')
# for each gene, print pHMM information
for row in hmmscan_specific:
if float(row[3]) > eval_threshold:
muted = ' class="text-muted"'
else:
muted = ''
f.write(' <tr%s>\n' % muted
+' <td>%s</td>\n' % row[1]
+' <td>%s</td>\n' % row[2]
+' <td>%s</td>\n' % row[3]
+' <td>%s</td>\n' % row[5]
+' <td>%s</td>\n' % row[4]
+' <td>%s</td>\n' % row[6]
+' <td>%s</td>\n' % row[7]
+' <td>%s</td>\n' % row[8]
+' <td>%s</td>\n' % row[9]
+' </tr>')
if how_many_hmms == 0:
f.write(' <tr><td colspan="9">no pHMMs matched</td></tr>')
f.write(' </table>\n'
+' </div>')
# print footer
f.write(' </div>\n'
+' </div>\n'
+' </div>\n'
+' </body>\n'
+' <!-- /container -->\n'
+' <!-- Bootstrap core JavaScript\n'
+' ================================================== -->\n'
+' <!-- Placed at the end of the document so the pages load faster -->\n'
+' <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>\n'
+' <script>window.jQuery || document.write(\'<script src="../../assets/js/vendor/jquery.min.js"><\/script>\')</script>\n'
+' <script src="../../dist/js/bootstrap.min.js"></script>\n'
+' <!-- IE10 viewport hack for Surface/desktop Windows 8 bug -->\n'
+' <script src="../../assets/js/ie10-viewport-bug-workaround.js"></script>\n'
+' </html>\n'
+'<!-- Latest compiled and minified JavaScript -->\n'
+'<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" crossorigin="anonymous"></script>')
return
# main
def main():
# convert FASTA to identifier list (if applicable)
gene_list = parse_fasta(args.f)
# fetch hmmscan results for each gene
hmmscan_results = parse_hmmscan(input_file)
# print HTML for each
write_html(gene_list, hmmscan_results, output_html_file)
if __name__ == '__main__':
main() | jtietz1/bioinf | hmmerformatdom.py | Python | gpl-3.0 | 10,154 | [
"BLAST",
"Biopython"
] | 527c6c8132d269ad44313eba8360ccfa0f2c3522fd27714a77e39124859769c0 |
"""Displays ImageData efficiently.
"""
# Author: Prabhu Ramachandran <prabhu_r@users.sf.net>
# Copyright (c) 2007, Enthought, Inc.
# License: BSD Style.
# Enthought library imports.
from traits.api import Instance, Bool, on_trait_change, \
Property
from traitsui.api import View, Group, Item
from tvtk.api import tvtk
# Local imports
from mayavi.core.module import Module
from mayavi.core.pipeline_info import PipelineInfo
######################################################################
# `ImageActor` class
######################################################################
class ImageActor(Module):
# An image actor.
actor = Instance(tvtk.ImageActor, allow_none=False, record=True)
input_info = PipelineInfo(datasets=['image_data'],
attribute_types=['any'],
attributes=['any'])
# An ImageMapToColors TVTK filter to adapt datasets without color
# information
image_map_to_color = Instance(tvtk.ImageMapToColors, (),
allow_none=False, record=True)
map_scalars_to_color = Bool
_force_map_scalars_to_color = Property(depends_on='module_manager.source')
########################################
# The view of this module.
view = View(Group(Item(name='actor', style='custom',
resizable=True),
show_labels=False, label='Actor'),
Group(
Group(Item('map_scalars_to_color',
enabled_when='not _force_map_scalars_to_color')),
Item('image_map_to_color', style='custom',
enabled_when='map_scalars_to_color',
show_label=False),
label='Map Scalars',
),
width=500,
height=600,
resizable=True)
######################################################################
# `Module` interface
######################################################################
def setup_pipeline(self):
self.actor = tvtk.ImageActor()
@on_trait_change('map_scalars_to_color,image_map_to_color.[output_format,pass_alpha_to_output]')
def update_pipeline(self):
"""Override this method so that it *updates* the tvtk pipeline
when data upstream is known to have changed.
"""
mm = self.module_manager
if mm is None:
return
src = mm.source
if self._force_map_scalars_to_color:
self.set(map_scalars_to_color=True, trait_change_notify=False)
if self.map_scalars_to_color:
self.configure_connection(self.image_map_to_color, src)
self.image_map_to_color.lookup_table = mm.scalar_lut_manager.lut
self.image_map_to_color.update()
self.configure_input_data(self.actor,
self.image_map_to_color.output)
else:
self.configure_input_data(self.actor, src.outputs[0])
self.pipeline_changed = True
def update_data(self):
"""Override this method so that it flushes the vtk pipeline if
that is necessary.
"""
# Just set data_changed, the component should do the rest.
self.data_changed = True
######################################################################
# Non-public methods.
######################################################################
def _actor_changed(self, old, new):
if old is not None:
self.actors.remove(old)
old.on_trait_change(self.render, remove=True)
self.actors.append(new)
new.on_trait_change(self.render)
def _get__force_map_scalars_to_color(self):
mm = self.module_manager
if mm is None:
return False
src = mm.source
return not isinstance(src.outputs[0].point_data.scalars,
tvtk.UnsignedCharArray)
| dmsurti/mayavi | mayavi/modules/image_actor.py | Python | bsd-3-clause | 4,083 | [
"Mayavi",
"VTK"
] | d1fe7317490429ba8029fad1d02a824abccae374c007c833704f56b6dcab3765 |
"""
climatology2.py
Compute a multi-epoch (multi-day) climatology from daily SST Level-3 grids.
Simple code to be run on Spark cluster, or using multi-core parallelism on single machine.
"""
import sys, os, urlparse, urllib, re, time
import numpy as N
import matplotlib
matplotlib.use('Agg')
import matplotlib.pylab as M
from variables import getVariables, close
from cache import retrieveFile, CachePath
from split import fixedSplit, splitByNDays
from netCDF4 import Dataset, default_fillvals
from pathos.multiprocessing import ProcessingPool as Pool
from plotlib import imageMap, makeMovie
from spatialFilter import spatialFilter
from gaussInterp import gaussInterp # calls into Fortran version gaussInterp_f.so
#from gaussInterp_slow import gaussInterp_slow as gaussInterp # pure python, slow debuggable version
#import pyximport; pyximport.install(pyimport=False)
#from gaussInterp import gaussInterp, gaussInterp_ # attempted cython version, had issues
VERBOSE = 1
# Possible execution modes
# Multicore & cluster modes use pathos pool.map(); Spark mode uses PySpark cluster.
ExecutionModes = ['sequential', 'multicore', 'cluster', 'spark']
# SST L3m 4.6km Metadata
# SST calues are scaled integers in degrees Celsius, lon/lat is 8640 x 4320
# Variable = 'sst', Mask = 'qual_sst', Coordinates = ['lon', 'lat']
# Generate algorithmic name for N-day Climatology product
SSTClimatologyTemplate = 'SST.L3.Global.Clim.%(period)s.%(date)s.%(version)s.nc' #??
# Simple mask and average functions to get us started, then add gaussian interpolation.
# MODIS L3 SST product, qual_sst is [-1, 2] - =0 is best data, can add =1 for better coverage
#def qcMask(var, mask): return N.ma.array(var, mask=N.ma.make_mask(mask == 0))
def qcMask(var, mask): return N.ma.masked_where(mask != 0, var)
#def qcMask(var, mask): return N.ma.masked_where(mask < 0, var)
def splitModisSst(seq, n):
for chunk in splitByNDays(seq, n, re.compile(r'(...).L3m'), keyed=False):
yield chunk
def mean(a): return N.ma.mean(a, axis=0)
AveragingFunctions = {'pixelMean': mean, 'gaussInterp': gaussInterp, 'spatialFilter': spatialFilter}
PixelMeanConfig = {'name': 'pixelMean'}
GaussInterpConfig = {'name': 'gaussInterp',
'latGrid': None, 'lonGrid': None, # None means use input lat/lon grid
'wlat': 3, 'wlon': 3,
'slat': 0.15, 'slon': 0.15, 'stime': 1,
'vfactor': -0.6931, 'missingValue': default_fillvals['f4']}
GaussInterpConfig1a = {'name': 'gaussInterp',
'latGrid': None, 'lonGrid': None, # None means use input lat/lon grid
'wlat': 0.30, 'wlon': 0.30,
'slat': 0.15, 'slon': 0.15, 'stime': 1,
'vfactor': -0.6931, 'missingValue': default_fillvals['f4']}
GaussInterpConfig1b = {'name': 'gaussInterp',
'latGrid': None, 'lonGrid': None, # None means use input lat/lon grid
'wlat': 0.08, 'wlon': 0.08,
'slat': 0.15, 'slon': 0.15, 'stime': 1,
'vfactor': -0.6931, 'missingValue': default_fillvals['f4']}
GaussInterpConfig2 = {'name': 'gaussInterp',
'latGrid': (89.5, -89.5, -0.25), 'lonGrid': (-180., 179., 0.25),
'wlat': 2., 'wlon': 2.,
'slat': 0.15, 'slon': 0.15, 'stime': 1,
'vfactor': -0.6931, 'missingValue': default_fillvals['f4']}
FilterGaussian = [[1, 2, 1], [2, 4, 2], [1, 2, 1]] # divide by 16
FilterLowPass = [[1, 1, 1], [1, 1, 1], [1, 1, 1]] # divide by 9
SpatialFilterConfig1 = {'name': 'spatialFilter', 'normalization': 16.,
'spatialFilter': N.array(FilterGaussian, dtype=N.int32),
'missingValue': default_fillvals['f4']}
SpatialFilterConfig2 = {'name': 'spatialFilter', 'normalization': 9.,
'spatialFilter': N.array(FilterLowPass, dtype=N.int32),
'missingValue': default_fillvals['f4']}
def climByAveragingPeriods(urls, # list of (daily) granule URLs for a long time period (e.g. a year)
nEpochs, # compute a climatology for every N epochs (days) by 'averaging'
nWindow, # number of epochs in window needed for averaging
nNeighbors, # number of neighbors on EACH side in lat/lon directions to use in averaging
variable, # name of primary variable in file
mask, # name of mask variable
coordinates, # names of coordinate arrays to read and pass on (e.g. 'lat' and 'lon')
splitFn=splitModisSst, # split function to use to partition the input URL list
maskFn=qcMask, # mask function to compute mask from mask variable
averager='pixelMean', # averaging function to use, one of ['pixelMean', 'gaussInterp', 'spatialFilter']
averagingConfig={}, # dict of parameters to control the averaging function (e.g. gaussInterp)
optimization='fortran', # optimization mode (fortran or cython)
mode='sequential', # Map across time periods of N-days for concurrent work, executed by:
# 'sequential' map, 'multicore' using pool.map(), 'cluster' using pathos pool.map(),
# or 'spark' using PySpark
numNodes=1, # number of cluster nodes to use
nWorkers=4, # number of parallel workers per node
averagingFunctions=AveragingFunctions, # dict of possible averaging functions
legalModes=ExecutionModes, # list of possible execution modes
cachePath=CachePath # directory to cache retrieved files in
):
'''Compute a climatology every N days by applying a mask and averaging function.
Writes the averaged variable grid, attributes of the primary variable, and the coordinate arrays in a dictionary.
***Assumption: This routine assumes that the N grids will fit in memory.***
'''
if averagingConfig['name'] == 'gaussInterp':
averagingConfig['wlat'] = nNeighbors
averagingConfig['wlon'] = nNeighbors
try:
averageFn = averagingFunctions[averager]
except:
print >>sys.stderr, 'climatology: Error, Averaging function must be one of: %s' % str(averagingFunctions)
sys.exit(1)
urlSplits = [s for s in splitFn(urls, nEpochs)]
def climsContoured(urls, plot=None, fillValue=default_fillvals['f4'], format='NETCDF4', cachePath=cachePath):
n = len(urls)
if VERBOSE: print >>sys.stderr, urls
var = climByAveraging(urls, variable, mask, coordinates, maskFn, averageFn, averagingConfig, optimization, cachePath)
fn = os.path.split(urls[0])[1]
inFile = os.path.join(cachePath, fn)
method = averagingConfig['name']
fn = os.path.splitext(fn)[0]
day = fn[5:8]
nDays = int(var['time'][0])
if 'wlat' in averagingConfig:
wlat = averagingConfig['wlat']
else:
wlat = 1
if int(wlat) == wlat:
outFile = 'A%s.L3m_%dday_clim_sst_4km_%s_%dnbrs.nc' % (day, nDays, method, int(wlat)) # mark each file with first day in period
else:
outFile = 'A%s.L3m_%dday_clim_sst_4km_%s_%4.2fnbrs.nc' % (day, nDays, method, wlat) # mark each file with first day in period
outFile = writeOutNetcdfVars(var, variable, mask, coordinates, inFile, outFile, fillValue, format)
if plot == 'contour':
figFile = contourMap(var, variable, coordinates, n, outFile)
elif plot == 'histogram':
# figFile = histogram(var, variable, n, outFile)
figFile = None
else:
figFile = None
return (outFile, figFile)
if mode == 'sequential':
results = map(climsContoured, urlSplits)
elif mode == 'multicore':
pool = Pool(nWorkers)
results = pool.map(climsContoured, urlSplits)
elif mode == 'cluster':
pass
elif mode == 'spark':
pass
return results
def climByAveraging(urls, # list of granule URLs for a time period
variable, # name of primary variable in file
mask, # name of mask variable
coordinates, # names of coordinate arrays to read and pass on (e.g. 'lat' and 'lon')
maskFn=qcMask, # mask function to compute mask from mask variable
averageFn=mean, # averaging function to use
averagingConfig={}, # parameters to control averaging function (e.g. gaussInterp)
optimization='fortran', # optimization mode (fortran or cython)
cachePath=CachePath
):
'''Compute a climatology over N arrays by applying a mask and averaging function.
Returns the averaged variable grid, attributes of the primary variable, and the coordinate arrays in a dictionary.
***Assumption: This routine assumes that the N grids will fit in memory.***
'''
n = len(urls)
varList = [variable, mask]
var = {}
vtime = N.zeros((n,), N.int32)
for i, url in enumerate(urls):
try:
path = retrieveFile(url, cachePath)
fn = os.path.split(path)[1]
vtime[i] = int(fn[5:8]) # KLUDGE: extract DOY from filename
except:
print >>sys.stderr, 'climByAveraging: Error, continuing without file %s' % url
accum[i] = emptyVar
continue
if path is None: continue
print >>sys.stderr, 'Read variables and mask ...'
try:
var, fh = getVariables(path, varList, arrayOnly=True, order='F', set_auto_mask=False) # return dict of variable objects by name
except:
print >>sys.stderr, 'climByAveraging: Error, cannot read file %s' % path
accum[i] = emptyVar
continue
if i == 0:
dtype = var[variable].dtype
if 'int' in dtype.name: dtype = N.float32
shape = (n,) + var[variable].shape
accum = N.ma.empty(shape, dtype, order='F')
emptyVar = N.array(N.ma.masked_all(var[variable].shape, dtype), order='F') # totally masked variable array for missing or bad file reads
print >>sys.stderr, 'Read coordinates ...'
var, fh = getVariables(path, coordinates, var, arrayOnly=True, order='F') # read coordinate arrays and add to dict
var[variable] = maskFn(var[variable], var[mask]) # apply quality mask variable to get numpy MA, turned off masking done by netCDF4 library
# var[variable] = var[variable][:]
# Echo variable range for sanity check
vals = var[variable].compressed()
print >>sys.stderr, 'Variable Range: min, max:', vals.min(), vals.max()
# Plot input grid
# figFile = histogram(vals, variable, n, os.path.split(path)[1])
# figFile = contourMap(var, variable, coordinates[1:], n, os.path.split(path)[1])
accum[i] = var[variable] # accumulate N arrays for 'averaging"""
# if i != 0 and i+1 != n: close(fh) # REMEMBER: closing fh loses netCDF4 in-memory data structures
close(fh)
coordinates = ['time'] + coordinates # add constructed time (days) as first coordinate
var['time'] = vtime
if averagingConfig['name'] == 'pixelMean':
print >>sys.stderr, 'Doing Pixel Average over %d grids ...' % n
start = time.time()
avg = averageFn(accum) # call pixel averaging function
end = time.time()
print >>sys.stderr, 'pixelMean execution time:', (end - start)
outlat = var[coordinates[1]].astype(N.float32)[:]
outlon = var[coordinates[2]].astype(N.float32)[:]
elif averagingConfig['name'] == 'gaussInterp':
print >>sys.stderr, 'Doing Gaussian Interpolation over %d grids ...' % n
var[variable] = accum
c = averagingConfig
latGrid = c['latGrid']; lonGrid = c['lonGrid']
if latGrid is not None and lonGrid is not None:
outlat = N.arange(latGrid[0], latGrid[1]+latGrid[2], latGrid[2], dtype=N.float32, order='F')
outlon = N.arange(lonGrid[0], lonGrid[1]+lonGrid[2], lonGrid[2], dtype=N.float32, order='F')
else:
outlat = N.array(var[coordinates[1]], dtype=N.float32, order='F')
outlon = N.array(var[coordinates[2]], dtype=N.float32, order='F')
varNames = [variable] + coordinates
start = time.time()
avg, weight, status = \
gaussInterp(var, varNames, outlat, outlon, c['wlat'], c['wlon'],
c['slat'], c['slon'], c['stime'], c['vfactor'], c['missingValue'],
VERBOSE, optimization)
end = time.time()
var['outweight'] = weight.astype(N.float32)
print >>sys.stderr, 'gaussInterp execution time:', (end - start)
elif averagingConfig['name'] == 'spatialFilter':
print >>sys.stderr, 'Applying Spatial 3x3 Filter and then averaging over %d grids ...' % n
var[variable] = accum
c = averagingConfig
varNames = [variable] + coordinates
start = time.time()
avg, count, status = \
spatialFilter(var, varNames, c['spatialFilter'], c['normalization'],
c['missingValue'], VERBOSE, optimization)
end = time.time()
print >>sys.stderr, 'spatialFilter execution time:', (end - start)
outlat = var[coordinates[1]].astype(N.float32)[:]
outlon = var[coordinates[2]].astype(N.float32)[:]
var['out'+variable] = avg.astype(N.float32) # return primary variable & mask arrays in dict
var['out'+mask] = N.ma.getmask(avg)
var['outlat'] = outlat
var['outlon'] = outlon
return var
def writeOutNetcdfVars(var, variable, mask, coordinates, inFile, outFile, fillValue=None, format='NETCDF4'):
'''Construct output bundle of arrays with NetCDF dimensions and attributes.
Output variables and attributes will have same names as the input file.
'''
din = Dataset(inFile, 'r')
dout = Dataset(outFile, 'w', format=format)
print >>sys.stderr, 'Writing %s ...' % outFile
# Transfer global attributes from input file
for a in din.ncattrs():
dout.setncattr(a, din.getncattr(a))
# Add dimensions and variables, copying data
coordDim = [dout.createDimension(coord, var['out'+coord].shape[0]) for coord in coordinates] # here output lon, lat, etc.
for coord in coordinates:
v = dout.createVariable(coord, var['out'+coord].dtype, (coord,))
v[:] = var['out'+coord][:]
primaryVar = dout.createVariable(variable, var['out'+variable].dtype, coordinates, fill_value=fillValue)
primaryVar[:] = var['out'+variable][:] # transfer array
maskVar = dout.createVariable(mask, 'i1', coordinates)
maskVar[:] = var['out'+mask].astype('i1')[:]
# Transfer variable attributes from input file
for k,v in dout.variables.iteritems():
for a in din.variables[k].ncattrs():
if a == 'scale_factor' or a == 'add_offset' or a == '_FillValue': continue
v.setncattr(a, din.variables[k].getncattr(a))
if k == variable:
try:
# if fillValue == None: fillValue = din.variables[k].getncattr('_FillValue') # total kludge
if fillValue == None: fillValue = default_fillvals['f4']
# print >>sys.stderr, default_fillvals
# v.setncattr('_FillValue', fillValue) # set proper _FillValue for climatology array
v.setncattr('missing_value', fillValue)
print >>sys.stderr, 'Setting missing_value for primary variable %s to %f' % (variable, fillValue)
except:
print >>sys.stderr, 'writeOutNetcdfVars: Warning, for variable %s no fill value specified or derivable from inputs.' % variable
din.close()
dout.close()
return outFile
def contourMap(var, variable, coordinates, n, outFile):
figFile = os.path.splitext(outFile)[0] + '_hist.png'
# TODO: Downscale variable array (SST) before contouring, matplotlib is TOO slow on large arrays
vals = var[variable][:]
# Fixed color scale, write file, turn off auto borders, set title, reverse lat direction so monotonically increasing??
imageMap(var[coordinates[1]][:], var[coordinates[0]][:], var[variable][:],
vmin=-2., vmax=45., outFile=figFile, autoBorders=False,
title='%s %d-day Mean from %s' % (variable.upper(), n, outFile))
print >>sys.stderr, 'Writing contour plot to %s' % figFile
return figFile
def histogram(vals, variable, n, outFile):
figFile = os.path.splitext(outFile)[0] + '_hist.png'
M.clf()
# M.hist(vals, 47, (-2., 45.))
M.hist(vals, 94)
M.xlim(-5, 45)
M.xlabel('SST in degrees Celsius')
M.ylim(0, 300000)
M.ylabel('Count')
M.title('Histogram of %s %d-day Mean from %s' % (variable.upper(), n, outFile))
M.show()
print >>sys.stderr, 'Writing histogram plot to %s' % figFile
M.savefig(figFile)
return figFile
def dailyFile2date(path, offset=1):
'''Convert YYYYDOY string in filename to date.'''
fn = os.path.split(path)[1]
year = int(fn[offset:offset+4])
doy = int(fn[offset+5:offset+8])
return fn[5:15].replace('.', '/')
def formatRegion(r):
"""Format lat/lon region specifier as string suitable for file name."""
if isinstance(r, str):
return r
else:
strs = [str(i).replace('-', 'm') for i in r]
return 'region-%s-%sby%s-%s' % tuple(strs)
def formatGrid(r):
"""Format lat/lon grid resolution specifier as string suitable for file name."""
if isinstance(r, str):
return r
else:
return str(r[0]) + 'by' + str(r[1])
def main(args):
nEpochs = int(args[0])
nWindow = int(args[1])
nNeighbors = int(args[2])
averager = args[3]
optimization = args[4]
mode = args[5]
nWorkers = int(args[6])
urlFile = args[7]
urls = [s.strip() for s in open(urlFile, 'r')]
if averager == 'gaussInterp':
results = climByAveragingPeriods(urls, nEpochs, nWindow, nNeighbors, 'sst', 'qual_sst', ['lat', 'lon'],
averager=averager, optimization=optimization, averagingConfig=GaussInterpConfig,
mode=mode, nWorkers=nWorkers)
elif averager == 'spatialFilter':
results = climByAveragingPeriods(urls, nEpochs, nWindow, nNeighbors, 'sst', 'qual_sst', ['lat', 'lon'],
averager=averager, optimization=optimization, averagingConfig=SpatialFilterConfig1,
mode=mode, nWorkers=nWorkers)
elif averager == 'pixelMean':
results = climByAveragingPeriods(urls, nEpochs, nWindow, nNeighbors, 'sst', 'qual_sst', ['lat', 'lon'],
averager=averager, optimization=optimization, averagingConfig=PixelMeanConfig,
mode=mode, nWorkers=nWorkers)
else:
print >>sys.stderr, 'climatology2: Error, averager must be one of', AveragingFunctions.keys()
sys.exit(1)
if results[0][1] is not None:
makeMovie([r[1] for r in results], 'clim.mpg')
return results
if __name__ == '__main__':
print main(sys.argv[1:])
# Old Tests:
# python climatology2.py 5 5 0 pixelMean fortran sequential 1 urls_sst_10days.txt
# python climatology2.py 5 5 3 gaussInterp fortran sequential 1 urls_sst_10days.txt
# python climatology2.py 5 5 0 pixelMean fortran sequential 1 urls_sst_40days.txt
# python climatology2.py 5 5 0 pixelMean fortran multicore 8 urls_sst_40days.txt
# python climatology2.py 5 5 3 gaussInterp fortran multicore 8 urls_sst_40days.txt
# Old Production:
# python climatology2.py 5 5 0 pixelMean fortran multicore 16 urls_sst_2015.txt >& log &
# python climatology2.py 10 10 0 pixelMean fortran multicore 16 urls_sst_2015.txt >& log &
# python climatology2.py 5 5 3 gaussInterp fortran multicore 16 urls_sst_2015.txt >& log &
# Tests:
# python climatology2.py 5 5 0 pixelMean fortran sequential 1 urls_sst_daynight_5days_sorted.txt
# python climatology2.py 5 5 0 pixelMean fortran multicore 4 urls_sst_daynight_5days_sorted.txt
# python climatology2.py 5 5 3 gaussInterp fortran sequential 1 urls_sst_daynight_5days_sorted.txt
# python climatology2.py 5 5 3 gaussInterp fortran multicore 4 urls_sst_daynight_5days_sorted.txt
# python climatology2.py 5 7 1 spatialFilter fortran sequential 1 urls_sst_daynight_5days_sorted.txt
# Test number of neighbors needed:
# python climatology2.py 5 7 3 gaussInterp fortran multicore 4 urls_sst_daynight_20days_sorted.txt
# Production:
# python climatology2.py 5 7 0 pixelMean fortran multicore 4 urls_sst_daynight_2003_2015_sorted.txt
# python climatology2.py 5 7 3 gaussInterp fortran sequential 1 urls_sst_daynight_2003_2015_sorted.txt
# python climatology2.py 5 7 3 gaussInterp fortran multicore 4 urls_sst_daynight_2003_2015_sorted.txt
# python climatology2.py 5 7 1 spatialFilter fortran multicore 4 urls_sst_daynight_2003_2015_sorted.txt
| dataplumber/nexus | climatology/clim/climatology2.py | Python | apache-2.0 | 21,756 | [
"Gaussian",
"NetCDF"
] | d11b51e3d1489668f5e25c569fec6580ad2b248a7cf5c0b6d1c49d6f100a8def |
#!/usr/bin/env python3
# ver 0.1 - coding python by Hyuntae Jung on 2/11/2017
# ver 0.2 - support pdb and dcd files for openmm on 5/8/2017
# ver 0.3 - support gro trajectory files for Monte Carlo on 6/6/2017
import argparse
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='# bonds and # group2 and trajectory gro files within distance of reference group1 every time step')
## args
parser.add_argument('-i', '--input', default='traj.trr', nargs='?',
help='input trajectory file')
parser.add_argument('-s', '--structure', default='topol.tpr', nargs='?',
help='.tpr or .gro structure file')
parser.add_argument('-select', '--select', nargs='?',
help='a file with two selection command-lines for group1 (1nd line) & 2(2nd line) in MDAnalysis')
parser.add_argument('-dist', '--dist', nargs='?',
help='reference distance betweeb group1 and group2')
parser.add_argument('-o', '--output', default='distc', nargs='?',
help='output file for #bonds and # groups2 near group 1 within distance')
parser.add_argument('-v', '--version', action='version', version='%(prog)s 0.2')
# read args
args = parser.parse_args()
# default for args
args.input = args.input if args.input is not None else 'traj.trr'
args.structure = args.structure if args.structure is not None else 'topol.tpr'
args.odnum = args.output
args.odnumas = args.output + '.acfs'
args.odnumat = args.output + '.acft'
args.oalign = args.output + '.align'
args.dist = int(args.dist)
## Check arguments for log
print("===============================")
print("input filename = ", args.input)
print("str filename = ", args.structure)
if args.select is not None:
print("select filename = ", args.select)
print("number of bins = ", args.nbin)
if args.tol == 0.0:
print("Set no bloack average")
elif args.tol <= 1.0:
print("tolerance for block average = %f" %args.tol)
elif args.tol > 1.0:
print("set block length = %d" %(int(args.tol)))
print("axis [0:2] = ", args.axis)
print("output delta_number trajectory = ", args.odnum)
print("output spatial delta_number autocorrelation = ", args.odnumas)
print("output temporal delta_number autocorrelation = ", args.odnumat)
print("output aligned delta_number trajectory = ", args.oalign)
## check vaulable setting
if args.nbin < 10:
raise ValueError("Too small nbin is usually not valid. (recommend > 10)")
if args.axis < 0 or args.axis > 2:
raise ValueError("wrong input of axis for histogram")
if args.tol < 0.0:
raise ValueError("wrong input of tolerance, %f" %args.tol)
elif args.tol >= 1.0:
print("Warning: tolerance %f is assigned to block_size, %d" %(args.tol, int(args.tol)))
else:
print("="*30)
## timer
import time
start_clock = time.clock() # process time
start_wall = time.time() # wall time
## import modules
import hjung
from hjung import *
import numpy as np
## read a topology and a trajectory using module MDAnalysis with selection
print("="*30)
coordinates1, coordinates2, unit_cells = hjung.io.read_coord_trr_3d_select2(args.structure, args.input, args.select1, args.select2)
print("Done: reading trajectory and topology file")
## reduce 3d-coordinates to 1d-coordinates
coordinates_1d = coordinates[:,:,args.axis]
## number histograms for each frame
print("="*30)
number_t_1d, bin_t_1d = hjung.analyze.histo_t_1d_nbin(coordinates_1d, unit_cells_1d, args.nbin)
print("Done: making number trajectory with respect to bins")
## block average to get stable volume (or number density)
block_length = 1
box_axis_avg, box_axis_std = hjung.coord.box_1d(unit_cells_1d)
print("box length avg = %f" %box_axis_avg)
print("bin size avg = %f" %(box_axis_avg/float(args.nbin)))
print("box length std = %f" %box_axis_std)
if args.tol > 0.0 and args.tol <= 1.0:
print("="*30)
print("To optimize, we use unit cell length on the axis you select.")
block_length = hjung.analyze.opt_block_length_1d(unit_cells_1d,args.tol)
print("Done: optimize block length")
elif args.tol > 1:
block_length = int(args.tol)
if block_length > 1:
print("="*30)
number_t_1d = hjung.analyze.block_average_1d(number_t_1d,block_length)
## save number histogram trajectory
#print("===============================")
#import numpy as np
#np.savetxt('traj.numb', number_t_1d, fmt='%f')
#print("Finished saving files of number and bin trajectory")
## Calculate delta_number, N[j-th frame, i-th slab] - <N>
# Assume: total #particle in a frame is fixed. (No particle incertion or deletion during simulation)
print("="*30)
ref_avg = np.mean(number_t_1d[0])
print("Assume: No particle incertion or deletion during simulation.")
delta_number_t_1d = number_t_1d - ref_avg
np.savetxt(args.odnum, delta_number_t_1d,
header='avg.number = %f, block_length = %d, generated by Hyuntae python code' %(ref_avg,block_length), fmt='%f', comments='# ')
print("Finished saving a file of delta_number trajectory.")
## autocorrelation function with periodic box system, but statistical autocorrelation on time trajectory
# \Delta N(i,{ t }_{ 1 })\ast \Delta N(j,{ t }_{ 2 })
# i.e. \Delta N(r)=N(r)-\left< c \right> This is what we did previous step
# assume we use periodic condition for spatial coordinate.
print("="*30)
# set matrix spatial delta_delta_number
print("spatial delta_number fluctuation")
acf_1d_wrap = hjung.analyze.autocorr_1d_t(delta_number_t_1d, 'wrap')
slab_shift = int(len(acf_1d_wrap[0])/2.0)
np.savetxt(args.odnumas, acf_1d_wrap,
header='spatial autocorr(slab_lag,i_frame) for delta_number, Plot u ($1-%d):2:3 when block_length = %d'
%(slab_shift,block_length), fmt='%f', comments='# ')
# set matrix temporal delta_delta_number
#print("temporal delta_number fluctuation")
#transp_delta_number = delta_number_t_1d.transpose()
#acf_1d_temp = hjung.analyze.autocorr_1d_t(transp_delta_number, 'constant')
#time_shift = int(len(acf_1d_temp[0])/2.0)
#np.savetxt(args.odnumat, acf_1d_temp,
# header='Temporal autocorr(time_lag,i_slab) for delta_number. Plot u ($1-%d):2:3 when block_length = %d'
# %(time_shift,block_length), fmt='%f', comments='# ')
#print("Finished saving a file of spatial and temporal delta_number autocorrelation")
# make gnuplot script
print("="*30)
print("for gnuplotting, please use following code:")
print("load '/home/hjung52/Utility/gnuplot/palette.plt'")
print("set pm3d map")
print("set multiplot layout 2, 2 title 'PEO plotting'")
#for first plot
print("set tmargin 2")
print("unset key")
print("set title 'PEO delta_Number'")
print("splot 'peo.dnum' u ($1*bin_length):($2*traj_1frame_ns*%d):($3/30) matrix" % (block_length))
# for second plot
print("set title 'PEO spatial autocorrelation with PBC'")
# load '/home/hjung52/Utility/gnuplot/palette.plt'
# set pm3d map
# set multiplot layout 2, 2 title 'PEO plotting'
# set tmargin 2
# unset key
# set title 'PEO delta_Number'
# set cbr[-2.5:2.5]
# splot 'peo.dnum' u ($1*0.19888):($2*0.01*2):($3/30) matrix
# set title 'PEO spatial autocorrelation with PBC'
# set cbr[-1:1]
# splot 'peo.dnumas' u ($1*0.19888-12.9274/2+0.09888):($2*0.02):3 matrix
# splot 'peo.dnumat' u ($1*0.02-10):($2*0.19888):3 matrix
# unset multiplot
## align delta_number using spatial autocorrelation function
print("="*30)
align_delta_number_t_1d = hjung.analyze.align_acf(delta_number_t_1d, acf_1d_wrap, 'wrap')
np.savetxt(args.oalign, align_delta_number_t_1d,
header='aligned delta_number by hjung.analyze.align_acf function', fmt='%f', comments='# ')
print("Finished saving a file of aligned delta_number using acf")
print("="*30)
print(time.clock() - start_clock, "seconds process time")
print(time.time() - start_wall, "seconds wall time") | jht0664/Utility_python_gromacs | python/select-groups-dist.py | Python | mit | 7,784 | [
"MDAnalysis",
"OpenMM"
] | 1f8296477f22ce6c10d40a920f705c540266f910e265e6d384af2e4db62bf218 |
from DIRAC.AccountingSystem.private.Policies.JobPolicy import JobPolicy as myJobPolicy
gPoliciesList = {"Job": myJobPolicy(), "WMSHistory": myJobPolicy(), "Pilot": myJobPolicy(), "Null": False}
| DIRACGrid/DIRAC | src/DIRAC/AccountingSystem/private/Policies/__init__.py | Python | gpl-3.0 | 195 | [
"DIRAC"
] | 319ffb254dbe860471c7d61dbc8df95b5dfb61bfc432dfbdaa4063a5f93c739a |
"""
ast
~~~
The `ast` module helps Python applications to process trees of the Python
abstract syntax grammar. The abstract syntax itself might change with
each Python release; this module helps to find out programmatically what
the current grammar looks like and allows modifications of it.
An abstract syntax tree can be generated by passing `ast.PyCF_ONLY_AST` as
a flag to the `compile()` builtin function or by using the `parse()`
function from this module. The result will be a tree of objects whose
classes all inherit from `ast.AST`.
A modified abstract syntax tree can be compiled into a Python code object
using the built-in `compile()` function.
Additionally various helper functions are provided that make working with
the trees simpler. The main intention of the helper functions and this
module in general is to provide an easy to use interface for libraries
that work tightly with the python syntax (template engines for example).
:copyright: Copyright 2008 by Armin Ronacher.
:license: Python License.
"""
from _ast import *
def parse(source, filename='<unknown>', mode='exec'):
"""
Parse the source into an AST node.
Equivalent to compile(source, filename, mode, PyCF_ONLY_AST).
"""
return compile(source, filename, mode, PyCF_ONLY_AST)
def literal_eval(node_or_string):
"""
Safely evaluate an expression node or a string containing a Python
expression. The string or node provided may only consist of the following
Python literal structures: strings, bytes, numbers, tuples, lists, dicts,
sets, booleans, and None.
"""
if isinstance(node_or_string, str):
node_or_string = parse(node_or_string, mode='eval')
if isinstance(node_or_string, Expression):
node_or_string = node_or_string.body
def _convert_num(node):
if isinstance(node, Constant):
if isinstance(node.value, (int, float, complex)):
return node.value
elif isinstance(node, Num):
return node.n
raise ValueError('malformed node or string: ' + repr(node))
def _convert_signed_num(node):
if isinstance(node, UnaryOp) and isinstance(node.op, (UAdd, USub)):
operand = _convert_num(node.operand)
if isinstance(node.op, UAdd):
return + operand
else:
return - operand
return _convert_num(node)
def _convert(node):
if isinstance(node, Constant):
return node.value
elif isinstance(node, (Str, Bytes)):
return node.s
elif isinstance(node, Num):
return node.n
elif isinstance(node, Tuple):
return tuple(map(_convert, node.elts))
elif isinstance(node, List):
return list(map(_convert, node.elts))
elif isinstance(node, Set):
return set(map(_convert, node.elts))
elif isinstance(node, Dict):
return dict(zip(map(_convert, node.keys),
map(_convert, node.values)))
elif isinstance(node, NameConstant):
return node.value
elif isinstance(node, BinOp) and isinstance(node.op, (Add, Sub)):
left = _convert_signed_num(node.left)
right = _convert_num(node.right)
if isinstance(left, (int, float)) and isinstance(right, complex):
if isinstance(node.op, Add):
return left + right
else:
return left - right
return _convert_signed_num(node)
return _convert(node_or_string)
def dump(node, annotate_fields=True, include_attributes=False):
"""
Return a formatted dump of the tree in *node*. This is mainly useful for
debugging purposes. The returned string will show the names and the values
for fields. This makes the code impossible to evaluate, so if evaluation is
wanted *annotate_fields* must be set to False. Attributes such as line
numbers and column offsets are not dumped by default. If this is wanted,
*include_attributes* can be set to True.
"""
def _format(node):
if isinstance(node, AST):
fields = [(a, _format(b)) for a, b in iter_fields(node)]
rv = '%s(%s' % (node.__class__.__name__, ', '.join(
('%s=%s' % field for field in fields)
if annotate_fields else
(b for a, b in fields)
))
if include_attributes and node._attributes:
rv += fields and ', ' or ' '
rv += ', '.join('%s=%s' % (a, _format(getattr(node, a)))
for a in node._attributes)
return rv + ')'
elif isinstance(node, list):
return '[%s]' % ', '.join(_format(x) for x in node)
return repr(node)
if not isinstance(node, AST):
raise TypeError('expected AST, got %r' % node.__class__.__name__)
return _format(node)
def copy_location(new_node, old_node):
"""
Copy source location (`lineno` and `col_offset` attributes) from
*old_node* to *new_node* if possible, and return *new_node*.
"""
for attr in 'lineno', 'col_offset':
if attr in old_node._attributes and attr in new_node._attributes \
and hasattr(old_node, attr):
setattr(new_node, attr, getattr(old_node, attr))
return new_node
def fix_missing_locations(node):
"""
When you compile a node tree with compile(), the compiler expects lineno and
col_offset attributes for every node that supports them. This is rather
tedious to fill in for generated nodes, so this helper adds these attributes
recursively where not already set, by setting them to the values of the
parent node. It works recursively starting at *node*.
"""
def _fix(node, lineno, col_offset):
if 'lineno' in node._attributes:
if not hasattr(node, 'lineno'):
node.lineno = lineno
else:
lineno = node.lineno
if 'col_offset' in node._attributes:
if not hasattr(node, 'col_offset'):
node.col_offset = col_offset
else:
col_offset = node.col_offset
for child in iter_child_nodes(node):
_fix(child, lineno, col_offset)
_fix(node, 1, 0)
return node
def increment_lineno(node, n=1):
"""
Increment the line number of each node in the tree starting at *node* by *n*.
This is useful to "move code" to a different location in a file.
"""
for child in walk(node):
if 'lineno' in child._attributes:
child.lineno = getattr(child, 'lineno', 0) + n
return node
def iter_fields(node):
"""
Yield a tuple of ``(fieldname, value)`` for each field in ``node._fields``
that is present on *node*.
"""
for field in node._fields:
try:
yield field, getattr(node, field)
except AttributeError:
pass
def iter_child_nodes(node):
"""
Yield all direct child nodes of *node*, that is, all fields that are nodes
and all items of fields that are lists of nodes.
"""
for name, field in iter_fields(node):
if isinstance(field, AST):
yield field
elif isinstance(field, list):
for item in field:
if isinstance(item, AST):
yield item
def get_docstring(node, clean=True):
"""
Return the docstring for the given node or None if no docstring can
be found. If the node provided does not have docstrings a TypeError
will be raised.
If *clean* is `True`, all tabs are expanded to spaces and any whitespace
that can be uniformly removed from the second line onwards is removed.
"""
if not isinstance(node, (AsyncFunctionDef, FunctionDef, ClassDef, Module)):
raise TypeError("%r can't have docstrings" % node.__class__.__name__)
if not(node.body and isinstance(node.body[0], Expr)):
return None
node = node.body[0].value
if isinstance(node, Str):
text = node.s
elif isinstance(node, Constant) and isinstance(node.value, str):
text = node.value
else:
return None
if clean:
import inspect
text = inspect.cleandoc(text)
return text
def walk(node):
"""
Recursively yield all descendant nodes in the tree starting at *node*
(including *node* itself), in no specified order. This is useful if you
only want to modify nodes in place and don't care about the context.
"""
from collections import deque
todo = deque([node])
while todo:
node = todo.popleft()
todo.extend(iter_child_nodes(node))
yield node
class NodeVisitor(object):
"""
A node visitor base class that walks the abstract syntax tree and calls a
visitor function for every node found. This function may return a value
which is forwarded by the `visit` method.
This class is meant to be subclassed, with the subclass adding visitor
methods.
Per default the visitor functions for the nodes are ``'visit_'`` +
class name of the node. So a `TryFinally` node visit function would
be `visit_TryFinally`. This behavior can be changed by overriding
the `visit` method. If no visitor function exists for a node
(return value `None`) the `generic_visit` visitor is used instead.
Don't use the `NodeVisitor` if you want to apply changes to nodes during
traversing. For this a special visitor exists (`NodeTransformer`) that
allows modifications.
"""
def visit(self, node):
"""Visit a node."""
method = 'visit_' + node.__class__.__name__
visitor = getattr(self, method, self.generic_visit)
return visitor(node)
def generic_visit(self, node):
"""Called if no explicit visitor function exists for a node."""
for field, value in iter_fields(node):
if isinstance(value, list):
for item in value:
if isinstance(item, AST):
self.visit(item)
elif isinstance(value, AST):
self.visit(value)
class NodeTransformer(NodeVisitor):
"""
A :class:`NodeVisitor` subclass that walks the abstract syntax tree and
allows modification of nodes.
The `NodeTransformer` will walk the AST and use the return value of the
visitor methods to replace or remove the old node. If the return value of
the visitor method is ``None``, the node will be removed from its location,
otherwise it is replaced with the return value. The return value may be the
original node in which case no replacement takes place.
Here is an example transformer that rewrites all occurrences of name lookups
(``foo``) to ``data['foo']``::
class RewriteName(NodeTransformer):
def visit_Name(self, node):
return copy_location(Subscript(
value=Name(id='data', ctx=Load()),
slice=Index(value=Str(s=node.id)),
ctx=node.ctx
), node)
Keep in mind that if the node you're operating on has child nodes you must
either transform the child nodes yourself or call the :meth:`generic_visit`
method for the node first.
For nodes that were part of a collection of statements (that applies to all
statement nodes), the visitor may also return a list of nodes rather than
just a single node.
Usually you use the transformer like this::
node = YourTransformer().visit(node)
"""
def generic_visit(self, node):
for field, old_value in iter_fields(node):
if isinstance(old_value, list):
new_values = []
for value in old_value:
if isinstance(value, AST):
value = self.visit(value)
if value is None:
continue
elif not isinstance(value, AST):
new_values.extend(value)
continue
new_values.append(value)
old_value[:] = new_values
elif isinstance(old_value, AST):
new_node = self.visit(old_value)
if new_node is None:
delattr(node, field)
else:
setattr(node, field, new_node)
return node
| FFMG/myoddweb.piger | monitor/api/python/Python-3.7.2/Lib/ast.py | Python | gpl-2.0 | 12,575 | [
"VisIt"
] | 280782a871e047aa888ac4b5ca06894f8ea892f2842be76249b9f9ffb2ff8fac |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2009-2013 Red Hat, Inc.
# This file is part of python-fedora
#
# python-fedora is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# python-fedora 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
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with python-fedora; if not, see <http://www.gnu.org/licenses/>
#
'''Implement a class that sets up simple communication to a Fedora Service.
.. moduleauthor:: Luke Macken <lmacken@redhat.com>
.. moduleauthor:: Toshio Kuratomi <tkuratom@redhat.com>
.. moduleauthor:: Ralph Bean <rbean@redhat.com>
'''
import copy
from hashlib import sha1
import logging
# For handling an exception that's coming from requests:
import ssl
import time
import warnings
from munch import munchify
from kitchen.text.converters import to_bytes
import requests
from six.moves import http_client as httplib
from six.moves import http_cookies as Cookie
from six.moves.urllib.parse import quote, urljoin, urlparse
from fedora import __version__
from fedora.client import AppError, AuthError, ServerError
log = logging.getLogger(__name__)
class ProxyClient(object):
# pylint: disable-msg=R0903
'''
A client to a Fedora Service. This class is optimized to proxy multiple
users to a service. ProxyClient is designed to be threadsafe so that
code can instantiate one instance of the class and use it for multiple
requests for different users from different threads.
If you want something that can manage one user's connection to a Fedora
Service, then look into using BaseClient instead.
This class has several attributes. These may be changed after
instantiation however, please note that this class is intended to be
threadsafe. Changing these values when another thread may affect more
than just the thread that you are making the change in. (For instance,
changing the debug option could cause other threads to start logging debug
messages in the middle of a method.)
.. attribute:: base_url
Initial portion of the url to contact the server. It is highly
recommended not to change this value unless you know that no other
threads are accessing this :class:`ProxyClient` instance.
.. attribute:: useragent
Changes the useragent string that is reported to the web server.
.. attribute:: session_name
Name of the cookie that holds the authentication value.
.. attribute:: session_as_cookie
If :data:`True`, then the session information is saved locally as
a cookie. This is here for backwards compatibility. New code should
set this to :data:`False` when constructing the :class:`ProxyClient`.
.. attribute:: debug
If :data:`True`, then more verbose logging is performed to aid in
debugging issues.
.. attribute:: insecure
If :data:`True` then the connection to the server is not checked to be
sure that any SSL certificate information is valid. That means that
a remote host can lie about who it is. Useful for development but
should not be used in production code.
.. attribute:: retries
Setting this to a positive integer will retry failed requests to the
web server this many times. Setting to a negative integer will retry
forever.
.. attribute:: timeout
A float describing the timeout of the connection. The timeout only
affects the connection process itself, not the downloading of the
response body. Defaults to 120 seconds.
.. versionchanged:: 0.3.33
Added the timeout attribute
'''
log = log
def __init__(self, base_url, useragent=None, session_name='tg-visit',
session_as_cookie=True, debug=False, insecure=False,
retries=None,
timeout=None):
'''Create a client configured for a particular service.
:arg base_url: Base of every URL used to contact the server
:kwarg useragent: useragent string to use. If not given, default to
"Fedora ProxyClient/VERSION"
:kwarg session_name: name of the cookie to use with session handling
:kwarg session_as_cookie: If set to True, return the session as a
SimpleCookie. If False, return a session_id. This flag allows us
to maintain compatibility for the 0.3 branch. In 0.4, code will
have to deal with session_id's instead of cookies.
:kwarg debug: If True, log debug information
:kwarg insecure: If True, do not check server certificates against
their CA's. This means that man-in-the-middle attacks are
possible against the `BaseClient`. You might turn this option on
for testing against a local version of a server with a self-signed
certificate but it should be off in production.
:kwarg retries: if we get an unknown or possibly transient error from
the server, retry this many times. Setting this to a negative
number makes it try forever. Defaults to zero, no retries.
:kwarg timeout: A float describing the timeout of the connection. The
timeout only affects the connection process itself, not the
downloading of the response body. Defaults to 120 seconds.
.. versionchanged:: 0.3.33
Added the timeout kwarg
'''
# Setup our logger
self._log_handler = logging.StreamHandler()
self.debug = debug
format = logging.Formatter("%(message)s")
self._log_handler.setFormatter(format)
self.log.addHandler(self._log_handler)
# When we are instantiated, go ahead and silence the python-requests
# log. It is kind of noisy in our app server logs.
if not debug:
requests_log = logging.getLogger("requests")
requests_log.setLevel(logging.WARN)
self.log.debug('proxyclient.__init__:entered')
if base_url[-1] != '/':
base_url = base_url + '/'
self.base_url = base_url
self.domain = urlparse(self.base_url).netloc
self.useragent = useragent or 'Fedora ProxyClient/%(version)s' % {
'version': __version__}
self.session_name = session_name
self.session_as_cookie = session_as_cookie
if session_as_cookie:
warnings.warn(
'Returning cookies from send_request() is'
' deprecated and will be removed in 0.4. Please port your'
' code to use a session_id instead by calling the ProxyClient'
' constructor with session_as_cookie=False',
DeprecationWarning, stacklevel=2)
self.insecure = insecure
# Have to do retries and timeout default values this way as BaseClient
# sends None for these values if not overridden by the user.
if retries is None:
self.retries = 0
else:
self.retries = retries
if timeout is None:
self.timeout = 120.0
else:
self.timeout = timeout
self.log.debug('proxyclient.__init__:exited')
def __get_debug(self):
'''Return whether we have debug logging turned on.
:Returns: True if debugging is on, False otherwise.
'''
if self._log_handler.level <= logging.DEBUG:
return True
return False
def __set_debug(self, debug=False):
'''Change debug level.
:kwarg debug: A true value to turn debugging on, false value to turn it
off.
'''
if debug:
self.log.setLevel(logging.DEBUG)
self._log_handler.setLevel(logging.DEBUG)
else:
self.log.setLevel(logging.ERROR)
self._log_handler.setLevel(logging.INFO)
debug = property(__get_debug, __set_debug, doc='''
When True, we log extra debugging statements. When False, we only log
errors.
''')
def send_request(self, method, req_params=None, auth_params=None,
file_params=None, retries=None, timeout=None):
'''Make an HTTP request to a server method.
The given method is called with any parameters set in ``req_params``.
If auth is True, then the request is made with an authenticated session
cookie. Note that path parameters should be set by adding onto the
method, not via ``req_params``.
:arg method: Method to call on the server. It's a url fragment that
comes after the base_url set in __init__(). Note that any
parameters set as extra path information should be listed here,
not in ``req_params``.
:kwarg req_params: dict containing extra parameters to send to the
server
:kwarg auth_params: dict containing one or more means of authenticating
to the server. Valid entries in this dict are:
:cookie: **Deprecated** Use ``session_id`` instead. If both
``cookie`` and ``session_id`` are set, only ``session_id`` will
be used. A ``Cookie.SimpleCookie`` to send as a session cookie
to the server
:session_id: Session id to put in a cookie to construct an identity
for the server
:username: Username to send to the server
:password: Password to use with username to send to the server
:httpauth: If set to ``basic`` then use HTTP Basic Authentication
to send the username and password to the server. This may be
extended in the future to support other httpauth types than
``basic``.
Note that cookie can be sent alone but if one of username or
password is set the other must as well. Code can set all of these
if it wants and all of them will be sent to the server. Be careful
of sending cookies that do not match with the username in this
case as the server can decide what to do in this case.
:kwarg file_params: dict of files where the key is the name of the
file field used in the remote method and the value is the local
path of the file to be uploaded. If you want to pass multiple
files to a single file field, pass the paths as a list of paths.
:kwarg retries: if we get an unknown or possibly transient error from
the server, retry this many times. Setting this to a negative
number makes it try forever. Default to use the :attr:`retries`
value set on the instance or in :meth:`__init__`.
:kwarg timeout: A float describing the timeout of the connection. The
timeout only affects the connection process itself, not the
downloading of the response body. Defaults to the :attr:`timeout`
value set on the instance or in :meth:`__init__`.
:returns: If ProxyClient is created with session_as_cookie=True (the
default), a tuple of session cookie and data from the server.
If ProxyClient was created with session_as_cookie=False, a tuple
of session_id and data instead.
:rtype: tuple of session information and data from server
.. versionchanged:: 0.3.17
No longer send tg_format=json parameter. We rely solely on the
Accept: application/json header now.
.. versionchanged:: 0.3.21
* Return data as a Bunch instead of a DictContainer
* Add file_params to allow uploading files
.. versionchanged:: 0.3.33
Added the timeout kwarg
'''
self.log.debug('proxyclient.send_request: entered')
# parameter mangling
file_params = file_params or {}
# Check whether we need to authenticate for this request
session_id = None
username = None
password = None
if auth_params:
if 'session_id' in auth_params:
session_id = auth_params['session_id']
elif 'cookie' in auth_params:
warnings.warn(
'Giving a cookie to send_request() to'
' authenticate is deprecated and will be removed in 0.4.'
' Please port your code to use session_id instead.',
DeprecationWarning, stacklevel=2)
session_id = auth_params['cookie'].output(attrs=[],
header='').strip()
if 'username' in auth_params and 'password' in auth_params:
username = auth_params['username']
password = auth_params['password']
elif 'username' in auth_params or 'password' in auth_params:
raise AuthError('username and password must both be set in'
' auth_params')
if not (session_id or username):
raise AuthError(
'No known authentication methods'
' specified: set "cookie" in auth_params or set both'
' username and password in auth_params')
# urljoin is slightly different than os.path.join(). Make sure method
# will work with it.
method = method.lstrip('/')
# And join to make our url.
url = urljoin(self.base_url, quote(method))
data = None # decoded JSON via json.load()
# Set standard headers
headers = {
'User-agent': self.useragent,
'Accept': 'application/json',
}
# Files to upload
for field_name, local_file_name in file_params:
file_params[field_name] = open(local_file_name, 'rb')
cookies = requests.cookies.RequestsCookieJar()
# If we have a session_id, send it
if session_id:
# Anytime the session_id exists, send it so that visit tracking
# works. Will also authenticate us if there's a need. Note that
# there's no need to set other cookie attributes because this is a
# cookie generated client-side.
cookies.set(self.session_name, session_id)
complete_params = req_params or {}
if session_id:
# Add the csrf protection token
token = sha1(to_bytes(session_id))
complete_params.update({'_csrf_token': token.hexdigest()})
auth = None
if username and password:
if auth_params.get('httpauth', '').lower() == 'basic':
# HTTP Basic auth login
auth = (username, password)
else:
# TG login
# Adding this to the request data prevents it from being
# logged by apache.
complete_params.update({
'user_name': to_bytes(username),
'password': to_bytes(password),
'login': 'Login',
})
# If debug, give people our debug info
self.log.debug('Creating request %(url)s' %
{'url': to_bytes(url)})
self.log.debug('Headers: %(header)s' %
{'header': to_bytes(headers, nonstring='simplerepr')})
if self.debug and complete_params:
debug_data = copy.deepcopy(complete_params)
if 'password' in debug_data:
debug_data['password'] = 'xxxxxxx'
self.log.debug('Data: %r' % debug_data)
if retries is None:
retries = self.retries
if timeout is None:
timeout = self.timeout
num_tries = 0
while True:
try:
response = requests.post(
url,
data=complete_params,
cookies=cookies,
headers=headers,
auth=auth,
verify=not self.insecure,
timeout=timeout,
)
except (requests.Timeout, requests.exceptions.SSLError) as e:
if isinstance(e, requests.exceptions.SSLError):
# And now we know how not to code a library exception
# hierarchy... We're expecting that requests is raising
# the following stupidity:
# requests.exceptions.SSLError(
# urllib3.exceptions.SSLError(
# ssl.SSLError('The read operation timed out')))
# If we weren't interested in reraising the exception with
# full tracdeback we could use a try: except instead of
# this gross conditional. But we need to code defensively
# because we don't want to raise an unrelated exception
# here and if requests/urllib3 can do this sort of
# nonsense, they may change the nonsense in the future
#
# And a note on the __class__.__name__/__module__ parsing:
# On top of all the other things it does wrong, requests
# is bundling a copy of urllib3. Distros can unbundle it.
# But because of the bundling, python will think
# exceptions raised by the version downloaded by pypi
# (requests.packages.urllib3.exceptions.SSLError) are
# different than the exceptions raised by the unbundled
# distro version (urllib3.exceptions.SSLError). We could
# do a try: except trying to import both of these
# SSLErrors and then code to detect either one of them but
# the whole thing is just stupid. So we'll use a stupid
# hack of our own that (1) means we don't have to depend
# on urllib3 just for this exception and (2) is (slightly)
# less likely to break on the whims of the requests
# author.
if not (e.args
and e.args[0].__class__.__name__ == 'SSLError'
and e.args[0].__class__.__module__.endswith(
'urllib3.exceptions')
and e.args[0].args
and isinstance(e.args[0].args[0], ssl.SSLError)
and e.args[0].args[0].args
and 'timed out' in e.args[0].args[0].args[0]):
# We're only interested in timeouts here
raise
self.log.debug('Request timed out')
if retries < 0 or num_tries < retries:
num_tries += 1
self.log.debug(
'Attempt #%(try)s failed' % {'try': num_tries})
time.sleep(0.5)
continue
# Fail and raise an error
# Raising our own exception protects the user from the
# implementation detail of requests vs pycurl vs urllib
raise ServerError(
url, -1, 'Request timed out after %s seconds' % timeout)
# When the python-requests module gets a response, it attempts to
# guess the encoding using chardet (or a fork)
# That process can take an extraordinarily long time for long
# response.text strings.. upwards of 30 minutes for FAS queries to
# /accounts/user/list JSON api! Therefore, we cut that codepath
# off at the pass by assuming that the response is 'utf-8'. We can
# make that assumption because we're only interfacing with servers
# that we run (and we know that they all return responses
# encoded 'utf-8').
response.encoding = 'utf-8'
# Check for auth failures
# Note: old TG apps returned 403 Forbidden on authentication
# failures.
# Updated apps return 401 Unauthorized
# We need to accept both until all apps are updated to return 401.
http_status = response.status_code
if http_status in (401, 403):
# Wrong username or password
self.log.debug('Authentication failed logging in')
raise AuthError(
'Unable to log into server. Invalid'
' authentication tokens. Send new username and password')
elif http_status >= 400:
if retries < 0 or num_tries < retries:
# Retry the request
num_tries += 1
self.log.debug(
'Attempt #%(try)s failed' % {'try': num_tries})
time.sleep(0.5)
continue
# Fail and raise an error
try:
msg = httplib.responses[http_status]
except (KeyError, AttributeError):
msg = 'Unknown HTTP Server Response'
raise ServerError(url, http_status, msg)
# Successfully returned data
break
# In case the server returned a new session cookie to us
new_session = response.cookies.get(self.session_name, '')
try:
data = response.json()
except ValueError as e:
# The response wasn't JSON data
raise ServerError(
url, http_status, 'Error returned from'
' json module while processing %(url)s: %(err)s' %
{'url': to_bytes(url), 'err': to_bytes(e)})
if 'exc' in data:
name = data.pop('exc')
message = data.pop('tg_flash')
raise AppError(name=name, message=message, extras=data)
# If we need to return a cookie for deprecated code, convert it here
if self.session_as_cookie:
cookie = Cookie.SimpleCookie()
cookie[self.session_name] = new_session
new_session = cookie
self.log.debug('proxyclient.send_request: exited')
data = munchify(data)
return new_session, data
__all__ = (ProxyClient,)
| fedora-infra/python-fedora | fedora/client/proxyclient.py | Python | lgpl-2.1 | 22,822 | [
"VisIt"
] | ac45b62a4a920eb0744c5203f8ae12e76cb9b933d0f8f2de0af21aa7daa8ea7a |
# $Id$
#
# Copyright (C) 2014 Seiji Matsuoka
#
# @@ All Rights Reserved @@
# This file is part of the RDKit.
# The contents are covered by the terms of the BSD license
# which is included in the file license.txt, found at the root
# of the RDKit source tree.
#
from rdkit.Chem.Draw.canvasbase import CanvasBase
from PySide import QtGui, QtCore
class Canvas(CanvasBase):
def __init__(self, size):
self.size = size
self.qsize = QtCore.QSize(*size)
self.pixmap = QtGui.QPixmap(self.qsize)
self.painter = QtGui.QPainter(self.pixmap)
self.painter.setRenderHint(QtGui.QPainter.Antialiasing, True)
self.painter.setRenderHint(QtGui.QPainter.SmoothPixmapTransform, True)
self.painter.fillRect(0, 0, size[0], size[1], QtCore.Qt.white)
def addCanvasLine(self, p1, p2, color=(0, 0, 0), color2=None, **kwargs):
if 'dash' in kwargs:
line_type = QtCore.Qt.DashLine
else:
line_type = QtCore.Qt.SolidLine
qp1 = QtCore.QPointF(*p1)
qp2 = QtCore.QPointF(*p2)
qpm = QtCore.QPointF((p1[0] + p2[0]) / 2, (p1[1] + p2[1]) / 2)
if color2 and color2 != color:
rgb = [int(c * 255) for c in color]
pen = QtGui.QPen(QtGui.QColor(*rgb), 1, line_type)
self.painter.setPen(pen)
self.painter.drawLine(qp1, qpm)
rgb2 = [int(c * 255) for c in color2]
pen.setColor(QtGui.QColor(*rgb2))
self.painter.setPen(pen)
self.painter.drawLine(qpm, qp2)
else:
rgb = [int(c * 255) for c in color]
pen = QtGui.QPen(QtGui.QColor(*rgb), 1, line_type)
self.painter.setPen(pen)
self.painter.drawLine(qp1, qp2)
def addCanvasText(self, text, pos, font, color=(0, 0, 0), **kwargs):
orientation = kwargs.get('orientation', 'E')
qfont = QtGui.QFont("Helvetica", font.size * 1.5)
qtext = QtGui.QTextDocument()
qtext.setDefaultFont(qfont)
colored = [int(c * 255) for c in color]
colored.append(text)
html_format = "<span style='color:rgb({},{},{})'>{}</span>"
formatted = html_format.format(*colored)
qtext.setHtml(formatted)
if orientation == 'N':
qpos = QtCore.QPointF(pos[0] - qtext.idealWidth() / 2,
pos[1] - font.size)
elif orientation == 'W':
qpos = QtCore.QPointF(pos[0] - qtext.idealWidth() + font.size,
pos[1] - font.size)
else:
qpos = QtCore.QPointF(pos[0] - font.size, pos[1] - font.size)
self.painter.save()
self.painter.translate(qpos)
qtext.drawContents(self.painter)
self.painter.restore()
return font.size * 1.8, font.size * 1.8, 0
def addCanvasPolygon(self, ps, color=(0, 0, 0), fill=True,
stroke=False, **kwargs):
polygon = QtGui.QPolygonF()
for ver in ps:
polygon.append(QtCore.QPointF(*ver))
pen = QtGui.QPen(QtGui.QColor(*color), 1, QtCore.Qt.SolidLine)
self.painter.setPen(pen)
self.painter.setBrush(QtGui.QColor(0, 0, 0))
self.painter.drawPolygon(polygon)
def addCanvasDashedWedge(self, p1, p2, p3, dash=(2, 2), color=(0, 0, 0),
color2=None, **kwargs):
rgb = [int(c * 255) for c in color]
pen = QtGui.QPen(QtGui.QColor(*rgb), 1, QtCore.Qt.SolidLine)
self.painter.setPen(pen)
dash = (4, 4)
pts1 = self._getLinePoints(p1, p2, dash)
pts2 = self._getLinePoints(p1, p3, dash)
if len(pts2) < len(pts1):
pts2, pts1 = pts1, pts2
for i in range(len(pts1)):
qp1 = QtCore.QPointF(pts1[i][0], pts1[i][1])
qp2 = QtCore.QPointF(pts2[i][0], pts2[i][1])
self.painter.drawLine(qp1, qp2)
def flush(self):
self.painter.end()
| soerendip42/rdkit | rdkit/Chem/Draw/qtCanvas.py | Python | bsd-3-clause | 3,963 | [
"RDKit"
] | 93579aa271037017c343c253280f140460b89290156431bdaec9cd4347a83b8c |
# coding: utf-8
"""
Works for Abinit:
"""
from __future__ import unicode_literals, division, print_function
import os
import shutil
import time
import abc
import collections
import numpy as np
import six
import copy
from six.moves import filter
from monty.collections import AttrDict
from monty.itertools import chunks
from monty.functools import lazy_property
from monty.fnmatch import WildCard
from pydispatch import dispatcher
from pymatgen.core.units import EnergyArray
from . import wrappers
from .tasks import (Task, AbinitTask, Dependency, Node, NodeResults, ScfTask, NscfTask, PhononTask, DdkTask,
BseTask, RelaxTask, DdeTask, ScrTask, SigmaTask)
from .strategies import HtcStrategy, NscfStrategy
from .utils import Directory
from .netcdf import ETSF_Reader
from .abitimer import AbinitTimerParser
import logging
logger = logging.getLogger(__name__)
__author__ = "Matteo Giantomassi"
__copyright__ = "Copyright 2013, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Matteo Giantomassi"
__all__ = [
"Work",
"BandStructureWork",
"RelaxWork",
"G0W0Work",
"QptdmWork",
"SigmaConvWork",
"BseMdfWork",
"PhononWork",
]
class WorkResults(NodeResults):
JSON_SCHEMA = NodeResults.JSON_SCHEMA.copy()
@classmethod
def from_node(cls, work):
"""Initialize an instance from a :class:`Work` instance."""
new = super(WorkResults, cls).from_node(work)
#new.update(
# #input=work.strategy
#)
# Will put all files found in outdir in GridFs
# Warning: assuming binary files.
d = {os.path.basename(f): f for f in work.outdir.list_filepaths()}
new.register_gridfs_files(**d)
return new
class WorkError(Exception):
"""Base class for the exceptions raised by Work objects."""
class BaseWork(six.with_metaclass(abc.ABCMeta, Node)):
Error = WorkError
Results = WorkResults
# interface modeled after subprocess.Popen
@abc.abstractproperty
def processes(self):
"""Return a list of objects that support the `subprocess.Popen` protocol."""
def poll(self):
"""
Check if all child processes have terminated. Set and return returncode attribute.
"""
return [task.poll() for task in self]
def wait(self):
"""
Wait for child processed to terminate. Set and return returncode attribute.
"""
return [task.wait() for task in self]
def communicate(self, input=None):
"""
Interact with processes: Send data to stdin. Read data from stdout and
stderr, until end-of-file is reached.
Wait for process to terminate. The optional input argument should be a
string to be sent to the child processed, or None, if no data should be
sent to the children.
communicate() returns a list of tuples (stdoutdata, stderrdata).
"""
return [task.communicate(input) for task in self]
@property
def returncodes(self):
"""
The children return codes, set by poll() and wait() (and indirectly by communicate()).
A None value indicates that the process hasn't terminated yet.
A negative value -N indicates that the child was terminated by signal N (Unix only).
"""
return [task.returncode for task in self]
@property
def ncores_reserved(self):
"""
Returns the number of cores reserved in this moment.
A core is reserved if it's still not running but
we have submitted the task to the queue manager.
"""
return sum(task.tot_cores for task in self if task.status == task.S_SUB)
@property
def ncores_allocated(self):
"""
Returns the number of CPUs allocated in this moment.
A core is allocated if it's running a task or if we have
submitted a task to the queue manager but the job is still pending.
"""
return sum(task.tot_cores for task in self if task.status in [task.S_SUB, task.S_RUN])
@property
def ncores_inuse(self):
"""
Returns the number of cores used in this moment.
A core is used if there's a job that is running on it.
"""
return sum(task.tot_cores for task in self if task.status == task.S_RUN)
def fetch_task_to_run(self):
"""
Returns the first task that is ready to run or
None if no task can be submitted at present"
Raises:
`StopIteration` if all tasks are done.
"""
# All the tasks are done so raise an exception
# that will be handled by the client code.
if all(task.is_completed for task in self):
raise StopIteration("All tasks completed.")
for task in self:
if task.can_run:
return task
# No task found, this usually happens when we have dependencies.
# Beware of possible deadlocks here!
logger.warning("Possible deadlock in fetch_task_to_run!")
return None
def fetch_alltasks_to_run(self):
"""
Returns a list with all the tasks that can be submitted.
Empty list if not task has been found.
"""
return [task for task in self if task.can_run]
@abc.abstractmethod
def setup(self, *args, **kwargs):
"""Method called before submitting the calculations."""
def _setup(self, *args, **kwargs):
self.setup(*args, **kwargs)
def connect_signals(self):
"""
Connect the signals within the work.
The :class:`Work` is responsible for catching the important signals raised from
its task and raise new signals when some particular condition occurs.
"""
for task in self:
dispatcher.connect(self.on_ok, signal=task.S_OK, sender=task)
@property
def all_ok(self):
return all(task.status == task.S_OK for task in self)
def on_ok(self, sender):
"""
This callback is called when one task reaches status `S_OK`.
It executes on_all_ok when all task in self have reached `S_OK`.
"""
logger.debug("in on_ok with sender %s" % sender)
if self.all_ok:
if self.finalized:
return AttrDict(returncode=0, message="Work has been already finalized")
else:
# Set finalized here, because on_all_ok might change it (e.g. Relax + EOS in a single work)
self._finalized = True
try:
results = AttrDict(**self.on_all_ok())
except:
self.finalized = False
raise
# Signal to possible observers that the `Work` reached S_OK
logger.info("Work %s is finalized and broadcasts signal S_OK" % str(self))
logger.info("Work %s status = %s" % (str(self), self.status))
if self._finalized:
dispatcher.send(signal=self.S_OK, sender=self)
return results
return AttrDict(returncode=1, message="Not all tasks are OK!")
def on_all_ok(self):
"""
This method is called once the `Work` is completed i.e. when all the tasks
have reached status S_OK. Subclasses should provide their own implementation
Returns:
Dictionary that must contain at least the following entries:
returncode:
0 on success.
message:
a string that should provide a human-readable description of what has been performed.
"""
return dict(returncode=0, message="Calling on_all_ok of the base class!")
def get_results(self, **kwargs):
"""
Method called once the calculations are completed.
The base version returns a dictionary task_name: TaskResults for each task in self.
"""
results = self.Results.from_node(self)
return results
class Work(BaseWork):
"""
A Work is a list of (possibly connected) tasks.
"""
def __init__(self, workdir=None, manager=None):
"""
Args:
workdir: Path to the working directory.
manager: :class:`TaskManager` object.
"""
super(Work, self).__init__()
self._tasks = []
if workdir is not None:
self.set_workdir(workdir)
if manager is not None:
self.set_manager(manager)
def set_manager(self, manager):
"""Set the :class:`TaskManager` to use to launch the :class:`Task`."""
self.manager = manager.deepcopy()
for task in self:
task.set_manager(manager)
@property
def flow(self):
"""The flow containing this :class:`Work`."""
return self._flow
def set_flow(self, flow):
"""Set the flow associated to this :class:`Work`."""
if not hasattr(self, "_flow"):
self._flow = flow
else:
if self._flow != flow:
raise ValueError("self._flow != flow")
@lazy_property
def pos(self):
"""The position of self in the :class:`Flow`"""
for i, work in enumerate(self.flow):
if self == work:
return i
raise ValueError("Cannot find the position of %s in flow %s" % (self, self.flow))
@property
def pos_str(self):
"""String representation of self.pos"""
return "w" + str(self.pos)
def set_workdir(self, workdir, chroot=False):
"""Set the working directory. Cannot be set more than once unless chroot is True"""
if not chroot and hasattr(self, "workdir") and self.workdir != workdir:
raise ValueError("self.workdir != workdir: %s, %s" % (self.workdir, workdir))
self.workdir = os.path.abspath(workdir)
# Directories with (input|output|temporary) data.
# The work will use these directories to connect
# itself to other works and/or to produce new data
# that will be used by its children.
self.indir = Directory(os.path.join(self.workdir, "indata"))
self.outdir = Directory(os.path.join(self.workdir, "outdata"))
self.tmpdir = Directory(os.path.join(self.workdir, "tmpdata"))
def chroot(self, new_workdir):
self.set_workdir(new_workdir, chroot=True)
for i, task in enumerate(self):
new_tdir = os.path.join(self.workdir, "t" + str(i))
task.set_workdir(new_tdir, chroot=True)
def __len__(self):
return len(self._tasks)
def __iter__(self):
return self._tasks.__iter__()
def __getitem__(self, slice):
return self._tasks[slice]
def chunks(self, chunk_size):
"""Yield successive chunks of tasks of lenght chunk_size."""
for tasks in chunks(self, chunk_size):
yield tasks
def opath_from_ext(self, ext):
"""
Returns the path of the output file with extension ext.
Use it when the file does not exist yet.
"""
return self.indir.path_in("in_" + ext)
def opath_from_ext(self, ext):
"""
Returns the path of the output file with extension ext.
Use it when the file does not exist yet.
"""
return self.outdir.path_in("out_" + ext)
@property
def processes(self):
return [task.process for task in self]
@property
def all_done(self):
"""True if all the :class:`Task` objects in the :class:`Work` are done."""
return all(task.status >= task.S_DONE for task in self)
@property
def isnc(self):
"""True if norm-conserving calculation."""
return all(task.isnc for task in self)
@property
def ispaw(self):
"""True if PAW calculation."""
return all(task.ispaw for task in self)
@property
def status_counter(self):
"""
Returns a `Counter` object that counts the number of task with
given status (use the string representation of the status as key).
"""
counter = collections.Counter()
for task in self:
counter[str(task.status)] += 1
return counter
def allocate(self, manager=None):
"""
This function is called once we have completed the initialization
of the :class:`Work`. It sets the manager of each task (if not already done)
and defines the working directories of the tasks.
Args:
manager: :class:`TaskManager` object or None
"""
for i, task in enumerate(self):
if not hasattr(task, "manager"):
# Set the manager
# Use the one provided in input else the one of the work.
task.set_manager(manager) if manager is not None else task.set_manager(self.manager)
task_workdir = os.path.join(self.workdir, "t" + str(i))
if not hasattr(task, "workdir"):
task.set_workdir(task_workdir)
else:
if task.workdir != task_workdir:
raise ValueError("task.workdir != task_workdir: %s, %s" % (task.workdir, task_workdir))
def register(self, obj, deps=None, required_files=None, manager=None, task_class=None):
"""
Registers a new :class:`Task` and add it to the internal list, taking into account possible dependencies.
Args:
obj: :class:`Strategy` object or :class:`AbinitInput` instance.
if Strategy object, we create a new `AbinitTask` from the input strategy and add it to the list.
deps: Dictionary specifying the dependency of this node.
None means that this obj has no dependency.
required_files: List of strings with the path of the files used by the task.
Note that the files must exist when the task is registered.
Use the standard approach based on Works, Tasks and deps
if the files will be produced in the future.
manager:
The :class:`TaskManager` responsible for the submission of the task. If manager is None, we use
the `TaskManager` specified during the creation of the :class:`Work`.
task_class: Task subclass to instantiate. Default: :class:`AbinitTask`
Returns:
:class:`Task` object
"""
task_workdir = None
if hasattr(self, "workdir"):
task_workdir = os.path.join(self.workdir, "t" + str(len(self)))
if isinstance(obj, Task):
task = obj
else:
# Set the class
if task_class is None:
task_class = AbinitTask
if isinstance(obj, HtcStrategy):
# Create the new task (note the factory so that we create subclasses easily).
task = task_class(obj, task_workdir, manager)
else:
task = task_class.from_input(obj, task_workdir, manager)
self._tasks.append(task)
# Handle possible dependencies.
if deps is not None:
deps = [Dependency(node, exts) for node, exts in deps.items()]
task.add_deps(deps)
# Handle possible dependencies.
if required_files is not None:
task.add_required_files(required_files)
return task
# Helper functions
def register_scf_task(self, *args, **kwargs):
"""Register a Scf task."""
kwargs["task_class"] = ScfTask
return self.register(*args, **kwargs)
def register_nscf_task(self, *args, **kwargs):
"""Register a nscf task."""
kwargs["task_class"] = NscfTask
return self.register(*args, **kwargs)
def register_relax_task(self, *args, **kwargs):
"""Register a task for structural optimization."""
kwargs["task_class"] = RelaxTask
return self.register(*args, **kwargs)
def register_phonon_task(self, *args, **kwargs):
"""Register a phonon task."""
kwargs["task_class"] = PhononTask
return self.register(*args, **kwargs)
def register_ddk_task(self, *args, **kwargs):
"""Register a ddk task."""
kwargs["task_class"] = DdkTask
return self.register(*args, **kwargs)
def register_scr_task(self, *args, **kwargs):
"""Register a screening task."""
kwargs["task_class"] = ScrTask
return self.register(*args, **kwargs)
def register_sigma_task(self, *args, **kwargs):
"""Register a sigma task."""
kwargs["task_class"] = SigmaTask
return self.register(*args, **kwargs)
def register_dde_task(self, *args, **kwargs):
"""Register a Dde task."""
kwargs["task_class"] = DdeTask
return self.register(*args, **kwargs)
def register_bse_task(self, *args, **kwargs):
"""Register a nscf task."""
kwargs["task_class"] = BseTask
return self.register(*args, **kwargs)
def path_in_workdir(self, filename):
"""Create the absolute path of filename in the working directory."""
return os.path.join(self.workdir, filename)
def setup(self, *args, **kwargs):
"""
Method called before running the calculations.
The default implementation is empty.
"""
def build(self, *args, **kwargs):
"""Creates the top level directory."""
# Create the directories of the work.
self.indir.makedirs()
self.outdir.makedirs()
self.tmpdir.makedirs()
# Build dirs and files of each task.
for task in self:
task.build(*args, **kwargs)
# Connect signals within the work.
self.connect_signals()
@property
def status(self):
"""
Returns the status of the work i.e. the minimum of the status of the tasks.
"""
return self.get_all_status(only_min=True)
#def set_status(self, status):
def get_all_status(self, only_min=False):
"""
Returns a list with the status of the tasks in self.
Args:
only_min: If True, the minimum of the status is returned.
"""
if len(self) == 0:
# The work will be created in the future.
if only_min:
return self.S_INIT
else:
return [self.S_INIT]
self.check_status()
status_list = [task.status for task in self]
if only_min:
return min(status_list)
else:
return status_list
def check_status(self):
"""Check the status of the tasks."""
# Recompute the status of the tasks
for task in self:
task.check_status()
# Take into account possible dependencies. Use a list instead of generators
for task in self:
if task.status < task.S_SUB and all([status == task.S_OK for status in task.deps_status]):
task.set_status(task.S_READY)
def rmtree(self, exclude_wildcard=""):
"""
Remove all files and directories in the working directory
Args:
exclude_wildcard: Optional string with regular expressions separated by `|`.
Files matching one of the regular expressions will be preserved.
example: exclude_wildard="*.nc|*.txt" preserves all the files
whose extension is in ["nc", "txt"].
"""
if not exclude_wildcard:
shutil.rmtree(self.workdir)
else:
w = WildCard(exclude_wildcard)
for dirpath, dirnames, filenames in os.walk(self.workdir):
for fname in filenames:
path = os.path.join(dirpath, fname)
if not w.match(fname):
os.remove(path)
def rm_indatadir(self):
"""Remove all the indata directories."""
for task in self:
task.rm_indatadir()
def rm_outdatadir(self):
"""Remove all the indata directories."""
for task in self:
task.rm_outatadir()
def rm_tmpdatadir(self):
"""Remove all the tmpdata directories."""
for task in self:
task.rm_tmpdatadir()
def move(self, dest, isabspath=False):
"""
Recursively move self.workdir to another location. This is similar to the Unix "mv" command.
The destination path must not already exist. If the destination already exists
but is not a directory, it may be overwritten depending on os.rename() semantics.
Be default, dest is located in the parent directory of self.workdir, use isabspath=True
to specify an absolute path.
"""
if not isabspath:
dest = os.path.join(os.path.dirname(self.workdir), dest)
shutil.move(self.workdir, dest)
def submit_tasks(self, wait=False):
"""
Submits the task in self and wait.
TODO: change name.
"""
for task in self:
print(task)
task.start()
if wait:
for task in self: task.wait()
def start(self, *args, **kwargs):
"""
Start the work. Calls build and _setup first, then submit the tasks.
Non-blocking call unless wait is set to True
"""
wait = kwargs.pop("wait", False)
# Initial setup
self._setup(*args, **kwargs)
# Build dirs and files.
self.build(*args, **kwargs)
# Submit tasks (does not block)
self.submit_tasks(wait=wait)
def read_etotals(self, unit="Ha"):
"""
Reads the total energy from the GSR file produced by the task.
Return a numpy array with the total energies in Hartree
The array element is set to np.inf if an exception is raised while reading the GSR file.
"""
if not self.all_done:
raise self.Error("Some task is still in running/submitted state")
etotals = []
for task in self:
# Open the GSR file and read etotal (Hartree)
gsr_path = task.outdir.has_abiext("GSR")
etot = np.inf
if gsr_path:
with ETSF_Reader(gsr_path) as r:
etot = r.read_value("etotal")
etotals.append(etot)
return EnergyArray(etotals, "Ha").to(unit)
def parse_timers(self):
"""
Parse the TIMER section reported in the ABINIT output files.
Returns:
:class:`AbinitTimerParser` object
"""
filenames = list(filter(os.path.exists, [task.output_file.path for task in self]))
parser = AbinitTimerParser()
parser.parse(filenames)
return parser
class BandStructureWork(Work):
"""Work for band structure calculations."""
def __init__(self, scf_input, nscf_input, dos_inputs=None, workdir=None, manager=None):
"""
Args:
scf_input: Input for the SCF run or :class:`SCFStrategy` object.
nscf_input: Input for the NSCF run or :class:`NSCFStrategy` object defining the band structure calculation.
dos_inputs: Input(s) for the DOS. DOS is computed only if dos_inputs is not None.
workdir: Working directory.
manager: :class:`TaskManager` object.
"""
super(BandStructureWork, self).__init__(workdir=workdir, manager=manager)
# Register the GS-SCF run.
self.scf_task = self.register_scf_task(scf_input)
# Register the NSCF run and its dependency.
self.nscf_task = self.register_nscf_task(nscf_input, deps={self.scf_task: "DEN"})
# Add DOS computation(s) if requested.
self.dos_tasks = []
if dos_inputs is not None:
if not isinstance(dos_inputs, (list, tuple)):
dos_inputs = [dos_inputs]
for dos_input in dos_inputs:
dos_task = self.register_nscf_task(dos_input, deps={self.scf_task: "DEN"})
self.dos_tasks.append(dos_task)
def plot_ebands(self, **kwargs):
"""
Plot the band structure. kwargs are passed to the plot method of :class:`ElectronBands`.
Returns:
`matplotlib` figure
"""
with self.nscf_task.open_gsr() as gsr:
return gsr.ebands.plot(**kwargs)
def plot_ebands_with_edos(self, dos_pos=0, method="gaussian", step=0.01, width=0.1, **kwargs):
"""
Plot the band structure and the DOS.
Args:
dos_pos: Index of the task from which the DOS should be obtained (note: 0 refers to the first DOS task).
method: String defining the method for the computation of the DOS.
step: Energy step (eV) of the linear mesh.
width: Standard deviation (eV) of the gaussian.
kwargs: Keyword arguments passed to `plot_with_edos` method to customize the plot.
Returns:
`matplotlib` figure.
"""
with self.nscf_task.open_gsr() as gsr:
gs_ebands = gsr.ebands
with self.dos_tasks[dos_pos].open_gsr() as gsr:
dos_ebands = gsr.ebands
edos = dos_ebands.get_edos(method=method, step=step, width=width)
return gs_ebands.plot_with_edos(edos, **kwargs)
def plot_edoses(self, dos_pos=None, method="gaussian", step=0.01, width=0.1, **kwargs):
"""
Plot the band structure and the DOS.
Args:
dos_pos: Index of the task from which the DOS should be obtained.
None is all DOSes should be displayed. Accepts integer or list of integers.
method: String defining the method for the computation of the DOS.
step: Energy step (eV) of the linear mesh.
width: Standard deviation (eV) of the gaussian.
kwargs: Keyword arguments passed to `plot` method to customize the plot.
Returns:
`matplotlib` figure.
"""
if dos_pos is not None and not isistance(dos_pos, (list, tuple)): dos_pos = [dos_pos]
from abipy.electrons.ebands import ElectronDosPlotter
plotter = ElectronDosPlotter()
for i, task in enumerate(self.dos_tasks):
if dos_pos is not None and i not in dos_pos: continue
with task.open_gsr() as gsr:
edos = gsr.ebands.get_edos(method=method, step=step, width=width)
ngkpt = task.get_inpvar("ngkpt")
plotter.add_edos("ngkpt %s" % str(ngkpt), edos)
return plotter.plot(**kwargs)
class RelaxWork(Work):
"""
Work for structural relaxations. The first task relaxes the atomic position
while keeping the unit cell parameters fixed. The second task uses the final
structure to perform a structural relaxation in which both the atomic positions
and the lattice parameters are optimized.
"""
def __init__(self, ion_input, ioncell_input, workdir=None, manager=None):
"""
Args:
ion_input: Input for the relaxation of the ions (cell is fixed)
ioncell_input: Input for the relaxation of the ions and the unit cell.
workdir: Working directory.
manager: :class:`TaskManager` object.
"""
super(RelaxWork, self).__init__(workdir=workdir, manager=manager)
self.ion_task = self.register_relax_task(ion_input)
# Note:
# 1) It would be nice to restart from the WFK file but ABINIT crashes due to the
# different unit cell parameters.
#
# 2) Restarting form DEN is not trivial because Abinit produces all these _TIM?_DEN files.
# and the syntax used to specify dependencies is not powerful enough.
#
# For the time being, we don't use any output from ion_tasl except for the
# the final structure that will be transferred in on_ok.
deps = {self.ion_task: "DEN"}
deps = {self.ion_task: "WFK"}
deps = None
self.ioncell_task = self.register_relax_task(ioncell_input, deps=deps)
# Lock ioncell_task as ion_task should communicate to ioncell_task that
# the calculation is OK and pass the final structure.
self.ioncell_task.set_status(self.S_LOCKED)
self.transfer_done = False
def on_ok(self, sender):
"""
This callback is called when one task reaches status S_OK.
If sender == self.ion_task, we update the initial structure
used by self.ioncell_task and we unlock it so that the job can be submitted.
"""
logger.debug("in on_ok with sender %s" % sender)
if sender == self.ion_task and not self.transfer_done:
# Get the relaxed structure from ion_task
ion_structure = self.ion_task.read_final_structure()
# Transfer it to the ioncell task (we do it only once).
self.ioncell_task._change_structure(ion_structure)
self.transfer_done = True
# Unlock ioncell_task so that we can submit it.
self.ioncell_task.set_status(self.S_READY)
return super(RelaxWork, self).on_ok(sender)
class G0W0Work(Work):
"""
Work for G0W0 calculations.
"""
def __init__(self, scf_input, nscf_input, scr_input, sigma_inputs,
workdir=None, manager=None, spread_scr=False, nksmall=None):
"""
Args:
scf_input: Input for the SCF run or :class:`SCFStrategy` object.
nscf_input: Input for the NSCF run or :class:`NSCFStrategy` object.
scr_input: Input for the screening run or :class:`ScrStrategy` object
sigma_inputs: List of Strategies for the self-energy run.
workdir: Working directory of the calculation.
manager: :class:`TaskManager` object.
spread_scr: Attach a screening task to every sigma task
if false only one screening task with the max ecuteps and nbands for all sigma tasks
nksmall: if not none add a dos and bands calculation to the Work
"""
super(G0W0Work, self).__init__(workdir=workdir, manager=manager)
# Register the GS-SCF run.
# register all scf_inputs but link the nscf only the last scf in the list
#MG: FIXME Why this?
if isinstance(scf_input, (list, tuple)):
for single_scf_input in scf_input:
self.scf_task = self.register_scf_task(single_scf_input)
else:
self.scf_task = self.register_scf_task(scf_input)
# Construct the input for the NSCF run.
self.nscf_task = nscf_task = self.register_nscf_task(nscf_input, deps={self.scf_task: "DEN"})
# Register the SCREENING run.
if not spread_scr:
self.scr_task = scr_task = self.register_scr_task(scr_input, deps={nscf_task: "WFK"})
else:
self.scr_tasks = []
if nksmall:
# if nksmall add bandstructure and dos calculations as well
from abiobjects import KSampling
scf_in = scf_input[-1] if isinstance(scf_input, (list, tuple)) else scf_input
logger.info('added band structure calculation')
bands_input = NscfStrategy(scf_strategy=scf_in,
ksampling=KSampling.path_from_structure(ndivsm=nksmall, structure=scf_in.structure),
nscf_nband=scf_in.electrons.nband*2, ecut=scf_in.ecut)
self.bands_task = self.register_nscf_task(bands_input, deps={self.scf_task: "DEN"})
dos_input = NscfStrategy(scf_strategy=scf_in,
ksampling=KSampling.automatic_density(kppa=nksmall**3, structure=scf_in.structure,
shifts=(0.0, 0.0, 0.0)),
nscf_nband=scf_in.electrons.nband*2, ecut=scf_in.ecut)
self.dos_task = self.register_nscf_task(dos_input, deps={self.scf_task: "DEN"})
# Register the SIGMA runs.
if not isinstance(sigma_inputs, (list, tuple)):
sigma_inputs = [sigma_inputs]
self.sigma_tasks = []
for sigma_input in sigma_inputs:
if spread_scr:
new_scr_input = copy.deepcopy(scr_input)
new_scr_input.screening.ecuteps = sigma_input.sigma.ecuteps
new_scr_input.screening.nband = sigma_input.sigma.nband
new_scr_input.electrons.nband = sigma_input.sigma.nband
scr_task = self.register_scr_task(new_scr_input, deps={nscf_task: "WFK"})
task = self.register_sigma_task(sigma_input, deps={nscf_task: "WFK", scr_task: "SCR"})
self.sigma_tasks.append(task)
class SigmaConvWork(Work):
"""
Work for self-energy convergence studies.
"""
def __init__(self, wfk_node, scr_node, sigma_inputs, workdir=None, manager=None):
"""
Args:
wfk_node: The node who has produced the WFK file or filepath pointing to the WFK file.
scr_node: The node who has produced the SCR file or filepath pointing to the SCR file.
sigma_inputs: List of Strategies for the self-energy run.
workdir: Working directory of the calculation.
manager: :class:`TaskManager` object.
"""
# Cast to node instances.
wfk_node, scr_node = Node.as_node(wfk_node), Node.as_node(scr_node)
super(SigmaConvWork, self).__init__(workdir=workdir, manager=manager)
# Register the SIGMA runs.
if not isinstance(sigma_inputs, (list, tuple)):
sigma_inputs = [sigma_inputs]
for sigma_input in sigma_inputs:
self.register_sigma_task(sigma_input, deps={wfk_node: "WFK", scr_node: "SCR"})
class BseMdfWork(Work):
"""
Work for simple BSE calculations in which the self-energy corrections
are approximated by the scissors operator and the screening is modeled
with the model dielectric function.
"""
def __init__(self, scf_input, nscf_input, bse_inputs, workdir=None, manager=None):
"""
Args:
scf_input: Input for the SCF run or :class:`ScfStrategy` object.
nscf_input: Input for the NSCF run or :class:`NscfStrategy` object.
bse_inputs: List of Inputs for the BSE run or :class:`BSEStrategy` object.
workdir: Working directory of the calculation.
manager: :class:`TaskManager`.
"""
super(BseMdfWork, self).__init__(workdir=workdir, manager=manager)
# Register the GS-SCF run.
self.scf_task = self.register_scf_task(scf_input)
# Construct the input for the NSCF run.
self.nscf_task = self.register_nscf_task(nscf_input, deps={self.scf_task: "DEN"})
# Construct the input(s) for the BSE run.
if not isinstance(bse_inputs, (list, tuple)):
bse_inputs = [bse_inputs]
for bse_input in bse_inputs:
self.register_bse_task(bse_input, deps={self.nscf_task: "WFK"})
class QptdmWork(Work):
"""
This work parallelizes the calculation of the q-points of the screening.
It also provides the callback `on_all_ok` that calls mrgscr to merge
all the partial screening files produced.
"""
def create_tasks(self, wfk_file, scr_input):
"""
Create the SCR tasks and register them in self.
Args:
wfk_file: Path to the ABINIT WFK file to use for the computation of the screening.
scr_input: Input for the screening calculation.
"""
assert len(self) == 0
wfk_file = self.wfk_file = os.path.abspath(wfk_file)
# Build a temporary work in the tmpdir that will use a shell manager
# to run ABINIT in order to get the list of q-points for the screening.
shell_manager = self.manager.to_shell_manager(mpi_procs=1)
w = Work(workdir=self.tmpdir.path_join("_qptdm_run"), manager=shell_manager)
fake_input = scr_input.deepcopy()
fake_task = w.register(fake_input)
w.allocate()
w.build()
# Create the symbolic link and add the magic value
# nqpdm = -1 to the input to get the list of q-points.
fake_task.inlink_file(wfk_file)
fake_task.strategy.add_extra_abivars({"nqptdm": -1})
fake_task.start_and_wait()
# Parse the section with the q-points
from pymatgen.io.abinitio.netcdf import NetcdfReader
with NetcdfReader(fake_task.outdir.has_abiext("qptdms.nc")) as reader:
qpoints = reader.read_value("reduced_coordinates_of_kpoints")
#print("qpoints)
#w.rmtree()
# Now we can register the task for the different q-points
for qpoint in qpoints:
qptdm_input = scr_input.deepcopy()
qptdm_input.set_vars(nqptdm=1, qptdm=qpoint)
new_task = self.register_scr_task(qptdm_input, manager=self.manager)
#new_task.set_cleanup_exts()
self.allocate()
def merge_scrfiles(self, remove_scrfiles=True):
"""
This method is called when all the q-points have been computed.
It runs `mrgscr` in sequential on the local machine to produce
the final SCR file in the outdir of the `Work`.
If remove_scrfiles is True, the partial SCR files are removed after the merge.
"""
scr_files = list(filter(None, [task.outdir.has_abiext("SCR") for task in self]))
logger.debug("will call mrgscr to merge %s:\n" % str(scr_files))
assert len(scr_files) == len(self)
# TODO: Propapagate the manager to the wrappers
mrgscr = wrappers.Mrgscr(verbose=1)
mrgscr.set_mpi_runner("mpirun")
final_scr = mrgscr.merge_qpoints(scr_files, out_prefix="out", cwd=self.outdir.path)
if remove_scrfiles:
for scr_file in scr_files:
try:
os.remove(scr_file)
except IOError:
pass
return final_scr
def on_all_ok(self):
"""
This method is called when all the q-points have been computed.
It runs `mrgscr` in sequential on the local machine to produce
the final SCR file in the outdir of the `Work`.
"""
final_scr = self.merge_scrfiles()
return self.Results(node=self, returncode=0, message="mrgscr done", final_scr=final_scr)
def build_oneshot_phononwork(scf_input, ph_inputs, workdir=None, manager=None, work_class=None):
"""
Returns a work for the computation of phonon frequencies
ph_inputs is a list of input for Phonon calculation in which all the independent perturbations
are explicitly computed i.e.
* rfdir 1 1 1
* rfatpol 1 natom
.. warning::
This work is mainly used for simple calculations, e.g. converge studies.
Use :class:`PhononWork` for better efficiency.
"""
work_class = OneShotPhononWork if work_class is None else work_class
work = work_class(workdir=workdir, manager=manager)
scf_task = work.register_scf_task(scf_input)
ph_inputs = [ph_inputs] if not isinstance(ph_inputs, (list, tuple)) else ph_inputs
# cannot use PhononTaks here because the Task is not able to deal with multiple phonon calculations
for phinp in ph_inputs:
ph_task = work.register(phinp, deps={scf_task: "WFK"})
return work
class OneShotPhononWork(Work):
"""
Simple and very inefficient work for the computation of the phonon frequencies
It consists of a GS task and a DFPT calculations for all the independent perturbations.
The main advantage is that one has direct access to the phonon frequencies that
can be computed at the end of the second task without having to call anaddb.
Use ``build_oneshot_phononwork`` to construct this work from the input files.
"""
def read_phonons(self):
"""Read phonon frequencies from the output file."""
#
# Phonon wavevector (reduced coordinates) : 0.00000 0.00000 0.00000
# Phonon energies in Hartree :
# 1.089934E-04 4.990512E-04 1.239177E-03 1.572715E-03 1.576801E-03
# 1.579326E-03
# Phonon frequencies in cm-1 :
# - 2.392128E+01 1.095291E+02 2.719679E+02 3.451711E+02 3.460677E+02
# - 3.466221E+02
BEGIN = " Phonon wavevector (reduced coordinates) :"
END = " Phonon frequencies in cm-1 :"
ph_tasks, qpts, phfreqs = self[1:], [], []
for task in ph_tasks:
with open(task.output_file.path, "r") as fh:
qpt, inside = None, 0
for line in fh:
if line.startswith(BEGIN):
qpts.append([float(s) for s in line[len(BEGIN):].split()])
inside, omegas = 1, []
elif line.startswith(END):
break
elif inside:
inside += 1
if inside > 2:
omegas.extend((float(s) for s in line.split()))
else:
raise ValueError("Cannot find %s in file %s" % (END, task.output_file.path))
phfreqs.append(omegas)
# Use namedtuple to store q-point and frequencies in meV
phonon = collections.namedtuple("phonon", "qpt freqs")
return [phonon(qpt=qpt, freqs=freqs_meV) for qpt, freqs_meV in zip(qpts, EnergyArray(phfreqs, "Ha").to("meV") )]
def get_results(self, **kwargs):
results = super(OneShotPhononWork, self).get_results()
phonons = self.read_phonons()
results.update(phonons=phonons)
return results
class PhononWork(Work):
"""
This work usually consists of nirred Phonon tasks where nirred is
the number of irreducible perturbations for a given q-point.
It provides the callback method (on_all_ok) that calls mrgddb to merge
the partial DDB files and mrgggkk to merge the GKK files.
"""
def merge_ddb_files(self):
"""
This method is called when all the q-points have been computed.
It runs `mrgddb` in sequential on the local machine to produce
the final DDB file in the outdir of the `Work`.
Returns:
path to the output DDB file
"""
ddb_files = list(filter(None, [task.outdir.has_abiext("DDB") for task in self]))
logger.debug("will call mrgddb to merge %s:\n" % str(ddb_files))
# assert len(ddb_files) == len(self)
#if len(ddb_files) == 1:
# Avoid the merge. Just move the DDB file to the outdir of the work
# Final DDB file will be produced in the outdir of the work.
out_ddb = self.outdir.path_in("out_DDB")
desc = "DDB file merged by %s on %s" % (self.__class__.__name__, time.asctime())
# TODO: propagate the taskmanager
mrgddb = wrappers.Mrgddb(verbose=1)
mrgddb.set_mpi_runner("mpirun")
mrgddb.merge(ddb_files, out_ddb=out_ddb, description=desc, cwd=self.outdir.path)
return out_ddb
def on_all_ok(self):
"""
This method is called when all the q-points have been computed.
Ir runs `mrgddb` in sequential on the local machine to produce
the final DDB file in the outdir of the `Work`.
"""
# Merge DDB files.
out_ddb = self.merge_ddb_files()
results = self.Results(node=self, returncode=0, message="DDB merge done")
results.register_gridfs_files(DDB=(out_ddb, "t"))
# TODO
# Call anaddb to compute the phonon frequencies for this q-point and
# store the results in the outdir of the work.
#atask = AnaddbTask(anaddb_input, ddb_node,
# gkk_node=None, md_node=None, ddk_node=None, workdir=None, manager=None)
#atask.start()
return results
| Dioptas/pymatgen | pymatgen/io/abinitio/works.py | Python | mit | 44,400 | [
"ABINIT",
"Gaussian",
"NetCDF",
"pymatgen"
] | b5c58aa0b3b3e58cabb88f7d741bdd8f7b03f7d644ed112fe78bc3b04966cac7 |
#!/usr/bin/env python
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
A convenience script engine to read Gaussian output in a directory tree.
"""
from __future__ import division, print_function
__author__ = "Shyue Ping Ong"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "1.0"
__maintainer__ = "Shyue Ping Ong"
__email__ = "shyue@mit.edu"
__date__ = "Jul 9, 2012"
import argparse
import os
import logging
import re
from pymatgen.apps.borg.hive import GaussianToComputedEntryDrone
from pymatgen.apps.borg.queen import BorgQueen
import multiprocessing
from tabulate import tabulate
save_file = "gau_data.gz"
def get_energies(rootdir, reanalyze, verbose, pretty):
if verbose:
FORMAT = "%(relativeCreated)d msecs : %(message)s"
logging.basicConfig(level=logging.INFO, format=FORMAT)
drone = GaussianToComputedEntryDrone(inc_structure=True,
parameters=['filename'])
ncpus = multiprocessing.cpu_count()
logging.info('Detected {} cpus'.format(ncpus))
queen = BorgQueen(drone, number_of_drones=ncpus)
if os.path.exists(save_file) and not reanalyze:
msg = 'Using previously assimilated data from {}. ' + \
'Use -f to force re-analysis'.format(save_file)
queen.load_data(save_file)
else:
queen.parallel_assimilate(rootdir)
msg = 'Results saved to {} for faster reloading.'.format(save_file)
queen.save_data(save_file)
entries = queen.get_data()
entries = sorted(entries, key=lambda x: x.parameters['filename'])
all_data = [(e.parameters['filename'].replace("./", ""),
re.sub(r"\s+", "", e.composition.formula),
"{}".format(e.parameters['charge']),
"{}".format(e.parameters['spin_mult']),
"{:.5f}".format(e.energy), "{:.5f}".format(e.energy_per_atom),
) for e in entries]
headers = ("Directory", "Formula", "Charge", "Spin Mult.", "Energy",
"E/Atom")
print(tabulate(all_data, headers=headers))
print("")
print(msg)
def main():
desc = '''
Convenient Gaussian run analyzer which can recursively go into a directory
to search results.
Author: Shyue Ping Ong
Version: 1.0
Last updated: Jul 6 2012'''
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('directories', metavar='dir', default='.', type=str,
nargs='*', help='directory to process')
parser.add_argument('-v', '--verbose', dest="verbose",
action='store_const', const=True,
help='verbose mode. Provides detailed output ' +
'on progress.')
parser.add_argument('-f', '--force', dest="reanalyze",
action='store_const',
const=True,
help='force reanalysis. Typically, gaussian_analyzer' +
' will just reuse a gaussian_analyzer_data.gz if ' +
'present. This forces the analyzer to reanalyze.')
args = parser.parse_args()
for d in args.directories:
get_energies(d, args.reanalyze, args.verbose, args.pretty)
if __name__ == "__main__":
main() | nisse3000/pymatgen | pymatgen/cli/gaussian_analyzer.py | Python | mit | 3,347 | [
"Gaussian",
"pymatgen"
] | 6c6159a9ecd364a514588e4c3343676a9cf156fe8f532eb86db874cb252c3e3a |
#
# This file is a part of Siesta Help Scripts
#
# (c) Andrey Sobolev, 2013
#
import numpy as np
from shs.geom import r
from abstract import OneTypeData, InteractingTypesData
class VAFData(OneTypeData):
""" Get VAF of evolution (type-wise)
NB: As it uses only one cell vector set, when calculating VAF for NPT ensemble one should expect getting strange results.
In:
-> atype (int?) - atomic type we need to calculate VAF for
"""
_shortDoc = "Velocity autocorrelation"
def calculatePartial(self, n):
''' Calc velocity autocorrelation function (VAF) for the evolution
In:
-> n: a list of atoms for which to calculate VAF
Out:
-> t: a list of \Delta t differences (in steps)
-> vaf: a list of average VAFss for every \Delta t
'''
coords = self.traj[:,n,:]
# assuming dt = 1, dx - in distance units!
v = coords[1:] - coords[:-1]
traj_len = len(v)
# time (in step units!)
t = np.arange(traj_len)
vaf = np.zeros(traj_len)
num = np.zeros(traj_len, dtype = int)
for delta_t in t:
for t_beg in range(traj_len - delta_t):
# correlation between v(t_beg) and v(t_beg + delta_t)
v_cor = (v[t_beg] * v[t_beg + delta_t]).sum(axis = 1)
num[delta_t] += len(v_cor.T)
vaf[delta_t] += np.sum(v_cor)
vaf = vaf / num
return t, vaf
class MSDData(OneTypeData):
""" Get MSD of evolution (type-wise)
NB: As it uses only one cell vector set, when calculating MSD for NPT ensemble one should expect getting strange results.
In:
-> atype (int?) - atomic type we need to calculate MSD for"""
_shortDoc = "Selfdiffusion (MSD)"
def calculatePartial(self, n):
''' Calc mean square displacement for the evolution
In:
-> n: a list of atoms for which to calculate MSD
Out:
-> t: a list of \Delta t differences (in steps)
-> vaf: a list of average VAFss for every \Delta t
'''
coords = self.traj[:,n,:]
traj_len = len(coords)
t = np.arange(traj_len)
msd = np.zeros(traj_len)
num = np.zeros(traj_len, dtype = int)
for delta_t in t:
for t_beg in range(traj_len - delta_t):
dx = coords[t_beg + delta_t] - coords[t_beg]
dr = (dx**2.0).sum(axis = 1)
num[delta_t] += len(dr.T)
msd[delta_t] += np.sum(dr)
msd = msd / num
return t, msd
class RDFData(InteractingTypesData):
""" Data class for calculating partial RDFs
"""
_shortDoc = "Partial RDFs"
def __init__(self, *args, **kwds):
self.rmax = kwds.get("rmax", 0.)
self.dr = kwds.get("dr", 0.05)
super(RDFData, self).__init__(*args, **kwds)
def getData(self, calc):
self.traj, self.vc = calc.evol.trajectory()
# get rmax
if self.rmax == 0.:
self.rmax = np.max(self.vc)/2.
self.y = []
self.x_title = "Distance"
self.y_titles = []
self.calculate()
def calculate(self):
if self.partial:
_, n = self.calc.evol.getAtomsByType()
labels = sorted(n.keys())
for i, ityp in enumerate(labels):
for jtyp in labels[i:]:
n1 = n[ityp]
n2 = n[jtyp]
x, y = self.calculatePartial(n1, n2)
self.x = x
self.y.append(y)
self.y_titles.append(ityp+'-'+jtyp)
else:
raise NotImplementedError('Calculating RDF from non-partial types is not implemented yet')
# n = None
# title.append('Total RDF')
# r, rdf = self.evol.rdf(n)
# total_rdf.append(rdf)
def calculatePartial(self, n1, n2):
''' Get RDF of evolution
In:
-> partial (bool) - indicates whether we need partial RDF
-> n (tuple of index lists or None) - if partial, then indicates which atoms we need to find geometry of
'''
# sij = coords[:,:,None,...] - coords[:,None,...]
# number of bins
nbins = int((self.rmax - self.dr) / self.dr)
nsteps = len(self.vc)
dists = np.zeros(nbins)
x = np.linspace(0., self.rmax, nbins)
for crd_i, vc_i, n1_i, n2_i in zip(self.traj, self.vc, n1, n2):
# fractional coordinates
nat1 = len(n1_i)
nat2 = len(n2_i)
vol = np.linalg.det(vc_i)
rij = r(crd_i, vc_i, (n1_i, n2_i))
dist = np.sqrt((rij**2.0).sum(axis = 1))
# found distances, now get histogram
hist, x = np.histogram(dist, bins=nbins, range=(self.dr, self.rmax))
dists += hist / (nat1 / vol * nat2)
# find rdf
rdf = dists / nsteps
# norm rdf
x = (x + self.dr/2.)[:-1]
rdf /= (4.*np.pi*(x**2.)*self.dr)
return x, rdf
class VPRDFData(InteractingTypesData):
""" Data class for calculating partial RDFs based on VP
"""
_shortDoc = "Partial VP RDFs"
def __init__(self, *args, **kwds):
self.dr = kwds.get("dr", 0.05)
super(VPRDFData, self).__init__(*args, **kwds)
def getData(self, calc):
self.data = [g.vp_distance() for _, g in calc.evol]
self.y = []
self.x_title = "Distance"
self.y_titles = []
self.calculate()
def calculate(self):
data = []
if self.partial:
_, types = self.calc.evol.getAtomsByType()
labels = sorted(types.keys())
# single
for ti in labels:
self.y_titles.append(ti)
data.append(self.calculatePartial(types[ti]))
# pairwise
for i, ti in enumerate(labels):
for tj in labels[i:]:
self.y_titles.append(ti + "-" + tj)
data.append(self.calculatePartial(types[ti], types[tj]))
else:
self.y_titles = ["Total"]
raise NotImplementedError
# histogram is calculated here, as it is a function
r_min = min([np.min(data_i) for data_i in data])
r_max = max([np.max(data_i) for data_i in data])
nbins = int((r_max-r_min) / self.dr)
for yi in data:
hist, x = np.histogram(yi, bins=nbins, range=(r_min, r_max))
self.y.append(hist)
self.x = (x + self.dr/2.)[:-1]
def calculatePartial(self, ti, tj = None):
if tj is None:
return np.hstack([np.ma.compressed(self.data[i][t_i]) for (i,t_i)
in enumerate(ti)])
else:
return np.hstack([np.ma.compressed(self.data[i][t_i][:,t_j])
for i,(t_i,t_j) in enumerate(zip(ti,tj))])
| ansobolev/shs | shs/data/per_type.py | Python | mit | 6,972 | [
"SIESTA"
] | 94164bdcf5e3f20ae33dcc168df28091a631072f7ab00f45001e132f613dbb65 |
import os
import unittest
import tempfile
from datetime import datetime, timedelta
from dateutil.relativedelta import relativedelta
import pytest
import pytz
from pyaxiom.netcdf.grids import Collection
import logging
from pyaxiom import logger
logger.level = logging.INFO
logger.addHandler(logging.StreamHandler())
class NetcdfCollectionTestFromDirectory(unittest.TestCase):
def setUp(self):
input_folder = os.path.join(os.path.dirname(__file__), "resources/coamps/cencoos_4km/wnd_tru/10m/")
self.c = Collection.from_directory(input_folder)
def test_name(self):
self.assertEqual(self.c.aggregation.name, "U.S. Navy Fleet Numerical Meteorology and Oceanography Center Forecast/Uninitialized Analysis/Image Product")
def test_members(self):
self.assertEqual(len(self.c.aggregation.members), 14)
def test_time(self):
self.assertEqual(self.c.aggregation.starting, datetime(2014, 6, 20, 0, 0, tzinfo=pytz.utc))
self.assertEqual(self.c.aggregation.ending, datetime(2014, 7, 19, 23, 0, tzinfo=pytz.utc))
class NetcdfCollectionTestFromDirectoryNoNcmlToMembers(unittest.TestCase):
def setUp(self):
input_folder = os.path.join(os.path.dirname(__file__), "resources/coamps/cencoos_4km/wnd_tru/10m/")
self.c = Collection.from_directory(input_folder, apply_to_members=False)
def test_name(self):
self.assertEqual(self.c.aggregation.name, "U.S. Navy Fleet Numerical Meteorology and Oceanography Center Forecast/Uninitialized Analysis/Image Product")
def test_members(self):
self.assertEqual(len(self.c.aggregation.members), 14)
def test_time(self):
self.assertEqual(self.c.aggregation.starting, datetime(2014, 6, 20, 0, 0, tzinfo=pytz.utc))
self.assertEqual(self.c.aggregation.ending, datetime(2014, 7, 19, 23, 0, tzinfo=pytz.utc))
class NetcdfCollectionTestFromGlob(unittest.TestCase):
def setUp(self):
glob_string = os.path.join(os.path.dirname(__file__), "resources/coamps/cencoos_4km/wnd_tru/10m/*.nc")
self.c = Collection.from_glob(glob_string)
def test_name(self):
self.assertEqual(self.c.aggregation.name, "U.S. Navy Fleet Numerical Meteorology and Oceanography Center Forecast/Uninitialized Analysis/Image Product")
def test_members(self):
self.assertEqual(len(self.c.aggregation.members), 14)
def test_time(self):
self.assertEqual(self.c.aggregation.starting, datetime(2014, 6, 20, 0, 0, tzinfo=pytz.utc))
self.assertEqual(self.c.aggregation.ending, datetime(2014, 7, 19, 23, 0, tzinfo=pytz.utc))
class NetcdfCollectionTestFromNestedGlobAndNcml(unittest.TestCase):
def setUp(self):
glob_string = os.path.join(os.path.dirname(__file__), "resources/coamps/cencoos_4km/wnd_tru/**/*.nc")
ncml = """<?xml version="1.0" encoding="UTF-8"?>
<netcdf xmlns="http://www.unidata.ucar.edu/namespaces/netcdf/ncml-2.2">
<attribute name="title" value="changed" />
</netcdf>
"""
self.c = Collection.from_glob(glob_string, ncml=ncml)
def test_name(self):
self.assertEqual(self.c.aggregation.name, "changed")
def test_members(self):
self.assertEqual(len(self.c.aggregation.members), 14)
def test_time(self):
self.assertEqual(self.c.aggregation.starting, datetime(2014, 6, 20, 0, 0, tzinfo=pytz.utc))
self.assertEqual(self.c.aggregation.ending, datetime(2014, 7, 19, 23, 0, tzinfo=pytz.utc))
class NetcdfCollectionTestFromNcml(unittest.TestCase):
def setUp(self):
input_ncml = os.path.join(os.path.dirname(__file__), "resources/coamps_10km_wind.ncml")
self.c = Collection.from_ncml_file(input_ncml)
def test_name(self):
self.assertEqual(self.c.aggregation.name, "U.S. Navy Fleet Numerical Meteorology and Oceanography Center Forecast/Uninitialized Analysis/Image Product")
def test_members(self):
self.assertEqual(len(self.c.aggregation.members), 14)
def test_time(self):
self.assertEqual(self.c.aggregation.starting, datetime(2014, 6, 20, 0, 0, tzinfo=pytz.utc))
self.assertEqual(self.c.aggregation.ending, datetime(2014, 7, 19, 23, 0, tzinfo=pytz.utc))
def test_yearly_bins(self):
starting = self.c.aggregation.starting.replace(microsecond=0, second=0, minute=0, hour=0, day=1, month=1)
bins = self.c.bins(delta=relativedelta(years=+1), starting=starting)
self.assertEqual(len(bins), 1)
first_month = bins[0]
self.assertEqual(first_month.starting, datetime(2014, 1, 1, 0, 0, tzinfo=pytz.utc))
self.assertEqual(first_month.ending, datetime(2015, 1, 1, 0, 0, tzinfo=pytz.utc))
def test_bimonthy_bins(self):
starting = self.c.aggregation.starting.replace(microsecond=0, second=0, minute=0, hour=0, day=1)
bins = self.c.bins(delta=relativedelta(months=+2), starting=starting)
self.assertEqual(len(bins), 1)
first_month = bins[0]
self.assertEqual(first_month.starting, datetime(2014, 6, 1, 0, 0, tzinfo=pytz.utc))
self.assertEqual(first_month.ending, datetime(2014, 8, 1, 0, 0, tzinfo=pytz.utc))
def test_monthly_bins(self):
starting = self.c.aggregation.starting.replace(microsecond=0, second=0, minute=0, hour=0, day=1)
bins = self.c.bins(delta=relativedelta(months=+1), starting=starting)
self.assertEqual(len(bins), 2)
first_month = bins[0]
self.assertEqual(first_month.starting, datetime(2014, 6, 1, 0, 0, tzinfo=pytz.utc))
self.assertEqual(first_month.ending, datetime(2014, 7, 1, 0, 0, tzinfo=pytz.utc))
second_month = bins[1]
self.assertEqual(second_month.starting, datetime(2014, 7, 1, 0, 0, tzinfo=pytz.utc))
self.assertEqual(second_month.ending, datetime(2014, 8, 1, 0, 0, tzinfo=pytz.utc))
def test_daily_bins(self):
starting = self.c.aggregation.starting.replace(microsecond=0, second=0, minute=0, hour=0)
bins = self.c.bins(delta=relativedelta(days=+1), starting=starting)
self.assertEqual(len(bins), 2)
first_day = bins[0]
self.assertEqual(first_day.starting, datetime(2014, 6, 20, 0, 0, tzinfo=pytz.utc))
self.assertEqual(first_day.ending, datetime(2014, 6, 21, 0, 0, tzinfo=pytz.utc))
second_day = bins[1]
self.assertEqual(second_day.starting, datetime(2014, 7, 19, 0, 0, tzinfo=pytz.utc))
self.assertEqual(second_day.ending, datetime(2014, 7, 20, 0, 0, tzinfo=pytz.utc))
def test_hard_start(self):
starting = self.c.aggregation.starting.replace(microsecond=0, second=0, minute=0, hour=0, day=1)
hard_start = datetime(2014, 7, 1, 0, 0, tzinfo=pytz.utc)
bins = self.c.bins(delta=relativedelta(months=+1), starting=starting, hard_start=hard_start)
self.assertEqual(len(bins), 1)
second_month = bins[0]
self.assertEqual(len(second_month.members), 4)
self.assertEqual(second_month.starting, datetime(2014, 7, 1, 0, 0, tzinfo=pytz.utc))
self.assertEqual(second_month.ending, datetime(2014, 8, 1, 0, 0, tzinfo=pytz.utc))
def test_hard_end(self):
starting = self.c.aggregation.starting.replace(microsecond=0, second=0, minute=0, hour=0, day=1)
hard_end = datetime(2014, 7, 1, 0, 0, tzinfo=pytz.utc)
bins = self.c.bins(delta=relativedelta(months=+1), starting=starting, hard_end=hard_end)
self.assertEqual(len(bins), 1)
first_month = bins[0]
self.assertEqual(len(first_month.members), 10)
self.assertEqual(first_month.starting, datetime(2014, 6, 1, 0, 0, tzinfo=pytz.utc))
self.assertEqual(first_month.ending, datetime(2014, 7, 1, 0, 0, tzinfo=pytz.utc))
@pytest.mark.skipif(os.environ.get("TRAVIS_PYTHON_VERSION") is not None,
reason="No workie in Travis")
def test_combine(self):
output_file = tempfile.NamedTemporaryFile().name
members = [ m.path for m in self.c.aggregation.members ]
Collection.combine(members=members, output_file=output_file)
self.assertTrue(os.path.isfile(output_file))
os.remove(output_file)
@pytest.mark.skipif(os.environ.get("TRAVIS_PYTHON_VERSION") is not None,
reason="No workie in Travis")
def test_combine_passing_members(self):
output_file = tempfile.NamedTemporaryFile().name
Collection.combine(members=self.c.aggregation.members, output_file=output_file)
self.assertTrue(os.path.isfile(output_file))
os.remove(output_file)
@pytest.mark.skipif(os.environ.get("TRAVIS_PYTHON_VERSION") is not None,
reason="No workie in Travis")
def test_combine_with_dimension(self):
output_file = tempfile.NamedTemporaryFile().name
members = [ m.path for m in self.c.aggregation.members ]
Collection.combine(members=members, output_file=output_file, dimension='time')
self.assertTrue(os.path.isfile(output_file))
os.remove(output_file)
@pytest.mark.skipif(os.environ.get("TRAVIS_PYTHON_VERSION") is not None,
reason="No workie in Travis")
def test_combine_with_dimension_and_stride(self):
output_file = tempfile.NamedTemporaryFile().name
members = [ m.path for m in self.c.aggregation.members ]
Collection.combine(members=members, output_file=output_file, dimension='time', start_index=1, stop_index=6, stride=2)
self.assertTrue(os.path.isfile(output_file))
os.remove(output_file)
| ocefpaf/pyaxiom | pyaxiom/tests/test_netcdf_collection.py | Python | mit | 9,573 | [
"NetCDF"
] | 683ae6f092d24a9517d8fb26c220dbb269decd464d08db244e1167e93b97d1f9 |
'''
synbiochem (c) University of Manchester 2015
synbiochem is licensed under the MIT License.
To view a copy of this license, visit <http://opensource.org/licenses/MIT/>.
@author: neilswainston
'''
# pylint: disable=too-many-public-methods
import unittest
import synbiochem.utils.chem_utils as chm_util
class Test(unittest.TestCase):
'''Test class for chemical_utils.'''
def test_get_molecular_mass(self):
'''Tests get_molecular_mass method.'''
self.assertAlmostEqual(chm_util.get_molecular_mass('H2O'),
18.010564684)
def test_get_elem_comp(self):
'''Tests get_elem_comp method.'''
self.assertEqual(chm_util.get_elem_comp('Fe4S'), {'Fe': 4, 'S': 1})
def test_parse_equation(self):
'''Tests parse_equation method.'''
eqn = '5.6 Fe4S + -3.2 water = 17.8 SiO2'
self.assertEqual(chm_util.parse_equation(eqn),
[['Fe4S', -5.6], ['water', 3.2], ['SiO2', 17.8]])
def test_parse_equation_error(self):
'''Tests parse_equation method (with error).'''
eqn = '5.6 Fe4S + -3.2 water = n+m SiO2'
self.assertRaises(ValueError, chm_util.parse_equation, eqn)
if __name__ == "__main__":
# import sys;sys.argv = ['', 'Test.testName']
unittest.main()
| synbiochem/synbiochem-py | synbiochem/utils/test/test_chem_utils.py | Python | mit | 1,308 | [
"VisIt"
] | ab128ed42c04977d6fa27b1964504fbf00cacf4d14b51e745eb909c4d6127741 |
"""
Alternation module
Copyright (c) 2009 John Markus Bjoerndalen <jmb@cs.uit.no>,
Brian Vinter <vinter@nbi.dk>, Rune M. Friborg <rune.m.friborg@gmail.com>.
See LICENSE.txt for licensing details (MIT License).
"""
# Imports
import inspect
import types
import collections
import pickle
from pycsp.parallel.guard import Guard
from pycsp.parallel.exceptions import *
from pycsp.parallel.const import *
# Decorators
def choice(func):
"""
@choice decorator for making a function into a Choice factory.
Each generated Choice object can be used as actions in one of
the four guards: InputGuard, OutputGuard, SkipGuard or TimeoutGuard.
The keyword variable channel_input is special and is provided in the
execution of the choice. Choice functions must accept the channel_input
parameter, when used in InputGuards.
Usage:
>>> @choice
... def add_service(serviceDB, channel_input):
... (id, request) = channel_input
... if serviceDB.has_key(id):
... serviceDB[id].append(request)
... else:
... serviceDB[id] = [request]
>>> @choice
... def quit(ch_end):
... poison(ch_end)
>>> _,_ = AltSelect(
InputGuard(request, action=add_service(services)),
TimeoutGuard(action=quit(request)))
The Choice factory returned by the @choice decorator:
func(*args, **kwargs)
"""
# __choice_fn func_name used to identify function in Alternation.execute
def __choice_fn(*args, **kwargs):
return Choice(func, *args, **kwargs)
return __choice_fn
# Classes
class Choice(object):
""" Choice(func, *args, **kwargs)
It is recommended to use the @choice decorator, to create Choice instances
"""
def __init__(self, fn, *args, **kwargs):
self.fn = fn
self.args = args
self.kwargs = kwargs
def invoke_on_input(self, channel_input):
self.kwargs['channel_input'] = channel_input
self.fn(*self.args, **self.kwargs)
del self.kwargs['channel_input']
def invoke_on_output(self):
self.fn(*self.args, **self.kwargs)
class Alternation(object):
""" Alternation([{cin0:None, (cout0,val):None}])
Alternation provides the basic interface to Alt. It is recommended
to use AltSelect / FairSelect as these are much more user-friendly.
Alternation supports the SkipGuard, TimeoutGuard, ChannelEndRead
or ChannelEndWrite objects.
Alternation guarantees priority if the flag ensurePriority = True
Note that alternation always performs the guard that was chosen,
i.e. channel input or output is executed within the alternation so
even the empty choice with an alternation execution or a choice where
the results are simply ignored, still performs the guarded input or
output.
Usage:
>>> L = []
>>> @choice
... def action(channel_input):
... L.append(channel_input)
>>> @process
... def P1(cout, n=5):
... for i in range(n):
... cout(i)
>>> @process
... def P2(cin1, cin2, n=10):
... alt = Alternation([{cin1:action(), cin2:action()}])
... for i in range(n):
... alt.execute()
>>> C1, C2 = Channel(), Channel()
>>> Parallel(P1(C1.writer()), P1(C2.writer()), P2(C1.reader(), C2.reader()))
>>> len(L)
10
>>> L.sort()
>>> L
[0, 0, 1, 1, 2, 2, 3, 3, 4, 4]
Performing a non-blocking write
>>> Alternation([
... { ( cout , datablock ): None } , # Try to write to a channel
... { SkipGuard (): " print('skipped !') } # Skip the alternation
... ]).execute()
Input with a timeout
>>> g, msg = Alternation([
... { cin : None } ,
... { TimeoutGuard (seconds=1): " print('Ignore this message !') }
... ]).select()
>>> if g == cin:
... print("Got: %s" % (msg))
"""
def __init__(self, guards, ensurePriority=False):
self.enableAcks = ensurePriority
# Preserve tuple entries and convert dictionary entries to tuple entries
self.guards = []
for g in guards:
if type(g) == tuple:
self.guards.append(g)
elif type(g) == dict:
for elem in list(g.keys()):
if type(elem) == tuple:
self.guards.append((elem[0], elem[1], g[elem]))
else:
self.guards.append((elem, g[elem]))
# The internal representation of guards is a prioritized list
# of tuples:
# input guard: (channel end, action)
# output guard: (channel end, msg, action)
# Default is to go one up in stackframe.
self.execute_frame = -1
def _set_execute_frame(self, steps):
if steps > 0:
self.execute_frame = -1*steps
else:
self.execute_frame = steps
def __result(self, reqs):
act=None
poison=False
retire=False
p, _ = getThreadAndName()
if p.state==SUCCESS:
for c in list(reqs.keys()):
if isinstance(c, Guard):
if c.id == p.result_ch:
act = c
c._close()
elif c.channel.name == p.result_ch:
act = c
elif p.state==POISON:
poison=True
elif p.state==RETIRE:
retire=True
return (act, poison, retire)
def _choose(self):
reqs={}
act = None
poison = False
retire = False
p, _ = getThreadAndName()
p.state = READY
p.sequence_number += 1
try:
idx = 0
for prio_item in self.guards:
if len(prio_item) == 3:
c, msg, action = prio_item
c._post_write(p, msg, ack=self.enableAcks)
op=WRITE
else:
c, action = prio_item
c._post_read(p, ack=self.enableAcks)
op=READ
reqs[c]=(idx, op)
if self.enableAcks:
p.wait_ack()
if p.state != READY:
# state has been changed by process lockthread, thus we can abort and read p.state.
break
idx += 1
except ChannelPoisonException:
act, poison, retire = self.__result(reqs)
if not act:
raise ChannelPoisonException
except ChannelRetireException:
act, poison, retire = self.__result(reqs)
if not act:
raise ChannelRetireException
# If noone have offered a channelrequest, we wait.
if p.state == READY:
p.wait()
if not act:
act, poison, retire = self.__result(reqs)
if not act:
if poison:
raise ChannelPoisonException()
if retire:
raise ChannelRetireException()
print('We should not get here in choice!!!')
idx, op = reqs[act]
# unpickle msg if necessary
msg = p.result_msg
if msg == None:
# Got successful write
pass
else:
# Got successful read
if type(msg) == list:
msg = msg[0]
else:
if msg == b"":
msg = None
else:
msg = pickle.loads(msg)[0]
return (idx, act, msg, op)
def execute(self):
"""
Selects the guard and executes the attached action. Action is a function or python code passed in a string.
>>> L1,L2 = [],[]
>>> @process
... def P1(cout, n):
... for i in range(n):
... cout(i)
>>> @process
... def P2(cin1, cin2, n):
... alt = Alternation([{
... cin1:"L1.append(channel_input)",
... cin2:"L2.append(channel_input)"
... }])
... for i in range(n):
... alt.execute()
>>> C1, C2 = Channel(), Channel()
>>> Parallel(P1(C1.writer(),n=10), P1(C2.writer(),n=5), P2(C1.reader(), C2.reader(), n=15))
>>> len(L1), len(L2)
(10, 5)
"""
idx, c, result_msg, op = self._choose()
if self.guards[idx]:
action = self.guards[idx][-1]
# Executing Choice object method
if isinstance(action, Choice):
if op==WRITE:
action.invoke_on_output()
else:
action.invoke_on_input(result_msg)
# Executing callback function object
elif isinstance(action, collections.Callable):
# Choice function not allowed as callback
if type(action) == types.FunctionType and action.__name__ == '__choice_fn':
raise InfoException('@choice function is not instantiated. Please use action() and not just action')
else:
# Execute callback function
if op==WRITE:
action()
else:
action(channel_input=result_msg)
# Compiling and executing string
elif type(action) == str:
# Fetch process frame and namespace
processframe= inspect.currentframe()
steps = self.execute_frame
while (steps < 0):
processframe = processframe.f_back
steps += 1
# Compile source provided in a string.
code = compile(action,processframe.f_code.co_filename + ' line ' + str(processframe.f_lineno) + ' in string' ,'exec')
f_globals = processframe.f_globals
f_locals = processframe.f_locals
if op==READ:
f_locals.update({'channel_input':result_msg})
# Execute action
exec(code, f_globals, f_locals)
elif type(action) == type(None):
pass
else:
raise Exception('Failed executing action: '+str(action))
return (c, result_msg)
def select(self):
"""
Selects the guard.
>>> L1,L2 = [],[]
>>> @process
... def P1(cout, n=5):
... for i in range(n):
... cout(i)
>>> @process
... def P2(cin1, cin2, n=10):
... alt = Alternation([{
... cin1:None,
... cin2:None
... }])
... for i in range(n):
... (g, msg) = alt.select()
... if g == cin1:
... L1.append(msg)
... if g == cin2:
... L2.append(msg)
>>> C1, C2 = Channel(), Channel()
>>> Parallel(P1(C1.writer()), P1(C2.writer()), P2(C1.reader(), C2.reader()))
>>> len(L1), len(L2)
(5, 5)
"""
idx, c, result_msg, op = self._choose()
return (c, result_msg)
| runefriborg/pycsp | pycsp/parallel/alternation.py | Python | mit | 11,461 | [
"Brian"
] | d299d49cd8b1595b28606faf22d5223de751dfcf01d4435012d3314cf2078187 |
#!/usr/bin/env python
"""
Convert data from Genbank format to GFF.
Usage:
python gbk_to_gff.py in.gbk > out.gff
Requirements:
BioPython:- http://biopython.org/
helper.py : https://github.com/vipints/GFFtools-GX/blob/master/helper.py
Copyright (C)
2009-2012 Friedrich Miescher Laboratory of the Max Planck Society, Tubingen, Germany.
2012-2014 Memorial Sloan Kettering Cancer Center New York City, USA.
"""
import os
import re
import sys
import collections
from Bio import SeqIO
import helper
def feature_table(chr_id, source, orient, genes, transcripts, cds, exons, unk):
"""
Write the feature information
"""
for gname, ginfo in genes.items():
line = [str(chr_id),
'gbk_to_gff',
ginfo[3],
str(ginfo[0]),
str(ginfo[1]),
'.',
ginfo[2],
'.',
'ID=%s;Name=%s' % (str(gname), str(gname))]
print '\t'.join(line)
## construct the transcript line is not defined in the original file
t_line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', ginfo[2], '.']
if not transcripts:
t_line.append('ID=Transcript:%s;Parent=%s' % (str(gname), str(gname)))
if exons: ## get the entire transcript region from the defined feature
t_line[3] = str(exons[gname][0][0])
t_line[4] = str(exons[gname][0][-1])
elif cds:
t_line[3] = str(cds[gname][0][0])
t_line[4] = str(cds[gname][0][-1])
print '\t'.join(t_line)
if exons:
exon_line_print(t_line, exons[gname], 'Transcript:'+str(gname), 'exon')
if cds:
exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'CDS')
if not exons:
exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'exon')
else: ## transcript is defined
for idx in transcripts[gname]:
t_line[2] = idx[3]
t_line[3] = str(idx[0])
t_line[4] = str(idx[1])
t_line.append('ID='+str(idx[2])+';Parent='+str(gname))
print '\t'.join(t_line)
## feature line print call
if exons:
exon_line_print(t_line, exons[gname], str(idx[2]), 'exon')
if cds:
exon_line_print(t_line, cds[gname], str(idx[2]), 'CDS')
if not exons:
exon_line_print(t_line, cds[gname], str(idx[2]), 'exon')
if len(genes) == 0: ## feature entry with fragment information
line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', orient, '.']
fStart = fStop = None
for eid, ex in cds.items():
fStart = ex[0][0]
fStop = ex[0][-1]
for eid, ex in exons.items():
fStart = ex[0][0]
fStop = ex[0][-1]
if fStart or fStart:
line[2] = 'gene'
line[3] = str(fStart)
line[4] = str(fStop)
line.append('ID=Unknown_Gene_' + str(unk) + ';Name=Unknown_Gene_' + str(unk))
print "\t".join(line)
if not cds:
line[2] = 'transcript'
else:
line[2] = 'mRNA'
line[8] = 'ID=Unknown_Transcript_' + str(unk) + ';Parent=Unknown_Gene_' + str(unk)
print "\t".join(line)
if exons:
exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')
if cds:
exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'CDS')
if not exons:
exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')
unk +=1
return unk
def exon_line_print(temp_line, trx_exons, parent, ftype):
"""
Print the EXON feature line
"""
for ex in trx_exons:
temp_line[2] = ftype
temp_line[3] = str(ex[0])
temp_line[4] = str(ex[1])
temp_line[8] = 'Parent=%s' % parent
print '\t'.join(temp_line)
def gbk_parse(fname):
"""
Extract genome annotation recods from genbank format
@args fname: gbk file name
@type fname: str
"""
fhand = helper.open_file(gbkfname)
unk = 1
for record in SeqIO.parse(fhand, "genbank"):
gene_tags = dict()
tx_tags = collections.defaultdict(list)
exon = collections.defaultdict(list)
cds = collections.defaultdict(list)
mol_type, chr_id = None, None
for rec in record.features:
if rec.type == 'source':
try:
mol_type = rec.qualifiers['mol_type'][0]
except:
mol_type = '.'
pass
try:
chr_id = rec.qualifiers['chromosome'][0]
except:
chr_id = record.name
continue
strand='-'
strand='+' if rec.strand>0 else strand
fid = None
try:
fid = rec.qualifiers['gene'][0]
except:
pass
transcript_id = None
try:
transcript_id = rec.qualifiers['transcript_id'][0]
except:
pass
if re.search(r'gene', rec.type):
gene_tags[fid] = (rec.location._start.position+1,
rec.location._end.position,
strand,
rec.type
)
elif rec.type == 'exon':
exon[fid].append((rec.location._start.position+1,
rec.location._end.position))
elif rec.type=='CDS':
cds[fid].append((rec.location._start.position+1,
rec.location._end.position))
else:
# get all transcripts
if transcript_id:
tx_tags[fid].append((rec.location._start.position+1,
rec.location._end.position,
transcript_id,
rec.type))
# record extracted, generate feature table
unk = feature_table(chr_id, mol_type, strand, gene_tags, tx_tags, cds, exon, unk)
fhand.close()
if __name__=='__main__':
try:
gbkfname = sys.argv[1]
except:
print __doc__
sys.exit(-1)
## extract gbk records
gbk_parse(gbkfname)
| vipints/oqtans | oqtans_tools/GFFtools/0.2/gbk_to_gff.py | Python | bsd-3-clause | 6,837 | [
"Biopython"
] | 06b5a6543aa7a346746f6d269acbc4718f30e21387591388e14eb8a4dc1e5564 |
"""
Order Steps
Steps file for Order.feature
"""
from os import getenv
import json
import requests
from behave import *
from compare import expect, ensure
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions
from app import server
WAIT_SECONDS = 30
BASE_URL = getenv('BASE_URL', 'http://localhost:5000/')
@given(u'the following orders')
def step_impl(context):
""" Delete all Orders and load new ones """
headers = {'Content-Type': 'application/json'}
context.resp = requests.delete(context.base_url + '/orders/reset', headers=headers)
expect(context.resp.status_code).to_equal(204)
create_url = context.base_url + '/orders'
for row in context.table:
data = {
"name": row['name'],
"time": row['time'],
"status": row['status'] in ['True', 'true', '1']
}
payload = json.dumps(data)
context.resp = requests.post(create_url, data=payload, headers=headers)
expect(context.resp.status_code).to_equal(201)
@when(u'I visit the "home page"')
def step_impl(context):
""" Make a call to the base URL """
context.driver.get(context.base_url)
@then(u'I should see "{message}" in the title')
def step_impl(context, message):
""" Check the document title for a message """
expect(context.driver.title).to_contain(message)
@then(u'I should not see "{message}"')
def step_impl(context, message):
error_msg = "I should not see '%s' in '%s'" % (message, context.resp.text)
ensure(message in context.resp.text, False, error_msg)
@when(u'I set the "{element_name}" to "{text_string}"')
def step_impl(context, element_name, text_string):
element_id = 'order_' + element_name.lower()
element = context.driver.find_element_by_id(element_id)
element.clear()
element.send_keys(text_string)
##################################################################
# This code works because of the following naming convention:
# The buttons have an id in the html hat is the button text
# in lowercase followed by '-btn' so the Clean button has an id of
# id='clear-btn'. That allows us to lowercase the name and add '-btn'
# to get the element id of any button
##################################################################
@when(u'I press the "{button}" button')
def step_impl(context, button):
button_id = button.lower() + '-btn'
context.driver.find_element_by_id(button_id).click()
@then(u'I should see "{name}" in the results')
def step_impl(context, name):
#element = context.driver.find_element_by_id('search_results')
#expect(element.text).to_contain(name)
found = WebDriverWait(context.driver, WAIT_SECONDS).until(
expected_conditions.text_to_be_present_in_element(
(By.ID, 'search_results'),
name
)
)
expect(found).to_be(True)
@then(u'I should not see "{name}" in the results')
def step_impl(context, name):
element = context.driver.find_element_by_id('search_results')
error_msg = "I should not see '%s' in '%s'" % (name, element.text)
ensure(name in element.text, False, error_msg)
@then(u'I should see the message "{message}"')
def step_impl(context, message):
#element = context.driver.find_element_by_id('flash_message')
#expect(element.text).to_contain(message)
found = WebDriverWait(context.driver, WAIT_SECONDS).until(
expected_conditions.text_to_be_present_in_element(
(By.ID, 'flash_message'),
message
)
)
expect(found).to_be(True)
##################################################################
# This code works because of the following naming convention:
# The id field for text input in the html is the element name
# prefixed by 'order_' so the Name field has an id='order_name'
# We can then lowercase the name and prefix with order_ to get the id
##################################################################
@then(u'I should see "{text_string}" in the "{element_name}" field')
def step_impl(context, text_string, element_name):
element_id = 'order_' + element_name.lower()
#element = context.driver.find_element_by_id(element_id)
found = WebDriverWait(context.driver, WAIT_SECONDS).until(
expected_conditions.text_to_be_present_in_element_value(
(By.ID, element_id),
text_string
)
)
#expect(element.get_attribute('value')).to_equal(text_string)
expect(found).to_be(True)
@when(u'I change "{element_name}" to "{text_string}"')
def step_impl(context, element_name, text_string):
element_id = 'order_' + element_name.lower()
#element = context.driver.find_element_by_id(element_id)
element = WebDriverWait(context.driver, WAIT_SECONDS).until(
expected_conditions.presence_of_element_located((By.ID, element_id))
)
element.clear()
element.send_keys(text_string)
# @when(u'I change "{key}" to "{value}"')
# def step_impl(context, key, value):
# context.data[key] = value
# @then(u'I should see "{message}" in "{field}"')
# def step_impl(context, message, field):
# """ Check a field for text """
# element = context.driver.find_element_by_id(field)
# assert message in element.text
| DevOps17-Bravo/orders | features/steps/orders_steps.py | Python | apache-2.0 | 5,302 | [
"VisIt"
] | 44453d0c84a3087d7e53a8c6ec0d99abad3b76cb763e804c2f8dff25574461cc |
"""
Forest of trees-based ensemble methods.
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` method of the base ``Forest``
class calls the ``fit`` method of each sub-estimator on random samples
(with replacement, a.k.a. bootstrap) of the training set.
The init of the sub-estimator is further delegated to the
``BaseEnsemble`` constructor.
- The ``ForestClassifier`` and ``ForestRegressor`` base classes further
implement the prediction logic by computing an average of the predicted
outcomes of the sub-estimators.
- The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived
classes provide the user with concrete implementations of
the forest ensemble method using classical, deterministic
``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as
sub-estimator implementations.
- The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived
classes provide the user with concrete implementations of the
forest ensemble method using the extremely randomized trees
``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as
sub-estimator implementations.
Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Joly Arnaud <arnaud.v.joly@gmail.com>
# Fares Hedayati <fares.hedayati@gmail.com>
#
# License: BSD 3 clause
import numbers
from warnings import catch_warnings, simplefilter, warn
import threading
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import issparse
from scipy.sparse import hstack as sparse_hstack
from joblib import Parallel, delayed
from ..base import ClassifierMixin, RegressorMixin, MultiOutputMixin
from ..metrics import r2_score
from ..preprocessing import OneHotEncoder
from ..tree import (DecisionTreeClassifier, DecisionTreeRegressor,
ExtraTreeClassifier, ExtraTreeRegressor)
from ..tree._tree import DTYPE, DOUBLE
from ..utils import check_random_state, check_array, compute_sample_weight
from ..exceptions import DataConversionWarning
from ._base import BaseEnsemble, _partition_estimators
from ..utils.fixes import _joblib_parallel_args
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted, _check_sample_weight
from ..utils.validation import _deprecate_positional_args
__all__ = ["RandomForestClassifier",
"RandomForestRegressor",
"ExtraTreesClassifier",
"ExtraTreesRegressor",
"RandomTreesEmbedding"]
MAX_INT = np.iinfo(np.int32).max
def _get_n_samples_bootstrap(n_samples, max_samples):
"""
Get the number of samples in a bootstrap sample.
Parameters
----------
n_samples : int
Number of samples in the dataset.
max_samples : int or float
The maximum number of samples to draw from the total available:
- if float, this indicates a fraction of the total and should be
the interval `(0, 1)`;
- if int, this indicates the exact number of samples;
- if None, this indicates the total number of samples.
Returns
-------
n_samples_bootstrap : int
The total number of samples to draw for the bootstrap sample.
"""
if max_samples is None:
return n_samples
if isinstance(max_samples, numbers.Integral):
if not (1 <= max_samples <= n_samples):
msg = "`max_samples` must be in range 1 to {} but got value {}"
raise ValueError(msg.format(n_samples, max_samples))
return max_samples
if isinstance(max_samples, numbers.Real):
if not (0 < max_samples < 1):
msg = "`max_samples` must be in range (0, 1) but got value {}"
raise ValueError(msg.format(max_samples))
return round(n_samples * max_samples)
msg = "`max_samples` should be int or float, but got type '{}'"
raise TypeError(msg.format(type(max_samples)))
def _generate_sample_indices(random_state, n_samples, n_samples_bootstrap):
"""
Private function used to _parallel_build_trees function."""
random_instance = check_random_state(random_state)
sample_indices = random_instance.randint(0, n_samples, n_samples_bootstrap)
return sample_indices
def _generate_unsampled_indices(random_state, n_samples, n_samples_bootstrap):
"""
Private function used to forest._set_oob_score function."""
sample_indices = _generate_sample_indices(random_state, n_samples,
n_samples_bootstrap)
sample_counts = np.bincount(sample_indices, minlength=n_samples)
unsampled_mask = sample_counts == 0
indices_range = np.arange(n_samples)
unsampled_indices = indices_range[unsampled_mask]
return unsampled_indices
def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees,
verbose=0, class_weight=None,
n_samples_bootstrap=None):
"""
Private function used to fit a single tree in parallel."""
if verbose > 1:
print("building tree %d of %d" % (tree_idx + 1, n_trees))
if forest.bootstrap:
n_samples = X.shape[0]
if sample_weight is None:
curr_sample_weight = np.ones((n_samples,), dtype=np.float64)
else:
curr_sample_weight = sample_weight.copy()
indices = _generate_sample_indices(tree.random_state, n_samples,
n_samples_bootstrap)
sample_counts = np.bincount(indices, minlength=n_samples)
curr_sample_weight *= sample_counts
if class_weight == 'subsample':
with catch_warnings():
simplefilter('ignore', DeprecationWarning)
curr_sample_weight *= compute_sample_weight('auto', y,
indices=indices)
elif class_weight == 'balanced_subsample':
curr_sample_weight *= compute_sample_weight('balanced', y,
indices=indices)
tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
else:
tree.fit(X, y, sample_weight=sample_weight, check_input=False)
return tree
class BaseForest(MultiOutputMixin, BaseEnsemble, metaclass=ABCMeta):
"""
Base class for forests of trees.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator,
n_estimators=100, *,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
max_samples=None):
super().__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params)
self.bootstrap = bootstrap
self.oob_score = oob_score
self.n_jobs = n_jobs
self.random_state = random_state
self.verbose = verbose
self.warm_start = warm_start
self.class_weight = class_weight
self.max_samples = max_samples
def apply(self, X):
"""
Apply trees in the forest to X, return leaf indices.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
X_leaves : ndarray of shape (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest,
return the index of the leaf x ends up in.
"""
X = self._validate_X_predict(X)
results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
**_joblib_parallel_args(prefer="threads"))(
delayed(tree.apply)(X, check_input=False)
for tree in self.estimators_)
return np.array(results).T
def decision_path(self, X):
"""
Return the decision path in the forest.
.. versionadded:: 0.18
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
indicator : sparse matrix of shape (n_samples, n_nodes)
Return a node indicator matrix where non zero elements indicates
that the samples goes through the nodes. The matrix is of CSR
format.
n_nodes_ptr : ndarray of shape (n_estimators + 1,)
The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
gives the indicator value for the i-th estimator.
"""
X = self._validate_X_predict(X)
indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
**_joblib_parallel_args(prefer='threads'))(
delayed(tree.decision_path)(X, check_input=False)
for tree in self.estimators_)
n_nodes = [0]
n_nodes.extend([i.shape[1] for i in indicators])
n_nodes_ptr = np.array(n_nodes).cumsum()
return sparse_hstack(indicators).tocsr(), n_nodes_ptr
def fit(self, X, y, sample_weight=None):
"""
Build a forest of trees from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted
to ``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csc_matrix``.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
Returns
-------
self : object
"""
# Validate or convert input data
if issparse(y):
raise ValueError(
"sparse multilabel-indicator for y is not supported."
)
X, y = self._validate_data(X, y, multi_output=True,
accept_sparse="csc", dtype=DTYPE)
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
# Remap output
self.n_features_ = X.shape[1]
y = np.atleast_1d(y)
if y.ndim == 2 and y.shape[1] == 1:
warn("A column-vector y was passed when a 1d array was"
" expected. Please change the shape of y to "
"(n_samples,), for example using ravel().",
DataConversionWarning, stacklevel=2)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
y, expanded_class_weight = self._validate_y_class_weight(y)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
# Get bootstrap sample size
n_samples_bootstrap = _get_n_samples_bootstrap(
n_samples=X.shape[0],
max_samples=self.max_samples
)
# Check parameters
self._validate_estimator()
if not self.bootstrap and self.oob_score:
raise ValueError("Out of bag estimation only available"
" if bootstrap=True")
random_state = check_random_state(self.random_state)
if not self.warm_start or not hasattr(self, "estimators_"):
# Free allocated memory, if any
self.estimators_ = []
n_more_estimators = self.n_estimators - len(self.estimators_)
if n_more_estimators < 0:
raise ValueError('n_estimators=%d must be larger or equal to '
'len(estimators_)=%d when warm_start==True'
% (self.n_estimators, len(self.estimators_)))
elif n_more_estimators == 0:
warn("Warm-start fitting without increasing n_estimators does not "
"fit new trees.")
else:
if self.warm_start and len(self.estimators_) > 0:
# We draw from the random state to get the random state we
# would have got if we hadn't used a warm_start.
random_state.randint(MAX_INT, size=len(self.estimators_))
trees = [self._make_estimator(append=False,
random_state=random_state)
for i in range(n_more_estimators)]
# Parallel loop: we prefer the threading backend as the Cython code
# for fitting the trees is internally releasing the Python GIL
# making threading more efficient than multiprocessing in
# that case. However, for joblib 0.12+ we respect any
# parallel_backend contexts set at a higher level,
# since correctness does not rely on using threads.
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
**_joblib_parallel_args(prefer='threads'))(
delayed(_parallel_build_trees)(
t, self, X, y, sample_weight, i, len(trees),
verbose=self.verbose, class_weight=self.class_weight,
n_samples_bootstrap=n_samples_bootstrap)
for i, t in enumerate(trees))
# Collect newly grown trees
self.estimators_.extend(trees)
if self.oob_score:
self._set_oob_score(X, y)
# Decapsulate classes_ attributes
if hasattr(self, "classes_") and self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self
@abstractmethod
def _set_oob_score(self, X, y):
"""
Calculate out of bag predictions and score."""
def _validate_y_class_weight(self, y):
# Default implementation
return y, None
def _validate_X_predict(self, X):
"""
Validate X whenever one tries to predict, apply, predict_proba."""
check_is_fitted(self)
return self.estimators_[0]._validate_X_predict(X, check_input=True)
@property
def feature_importances_(self):
"""
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
Returns
-------
feature_importances_ : ndarray of shape (n_features,)
The values of this array sum to 1, unless all trees are single node
trees consisting of only the root node, in which case it will be an
array of zeros.
"""
check_is_fitted(self)
all_importances = Parallel(n_jobs=self.n_jobs,
**_joblib_parallel_args(prefer='threads'))(
delayed(getattr)(tree, 'feature_importances_')
for tree in self.estimators_ if tree.tree_.node_count > 1)
if not all_importances:
return np.zeros(self.n_features_, dtype=np.float64)
all_importances = np.mean(all_importances,
axis=0, dtype=np.float64)
return all_importances / np.sum(all_importances)
def _accumulate_prediction(predict, X, out, lock):
"""
This is a utility function for joblib's Parallel.
It can't go locally in ForestClassifier or ForestRegressor, because joblib
complains that it cannot pickle it when placed there.
"""
prediction = predict(X, check_input=False)
with lock:
if len(out) == 1:
out[0] += prediction
else:
for i in range(len(out)):
out[i] += prediction[i]
class ForestClassifier(ClassifierMixin, BaseForest, metaclass=ABCMeta):
"""
Base class for forest of trees-based classifiers.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator,
n_estimators=100, *,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
max_samples=None):
super().__init__(
base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight,
max_samples=max_samples)
def _set_oob_score(self, X, y):
"""
Compute out-of-bag score."""
X = check_array(X, dtype=DTYPE, accept_sparse='csr')
n_classes_ = self.n_classes_
n_samples = y.shape[0]
oob_decision_function = []
oob_score = 0.0
predictions = [np.zeros((n_samples, n_classes_[k]))
for k in range(self.n_outputs_)]
n_samples_bootstrap = _get_n_samples_bootstrap(
n_samples, self.max_samples
)
for estimator in self.estimators_:
unsampled_indices = _generate_unsampled_indices(
estimator.random_state, n_samples, n_samples_bootstrap)
p_estimator = estimator.predict_proba(X[unsampled_indices, :],
check_input=False)
if self.n_outputs_ == 1:
p_estimator = [p_estimator]
for k in range(self.n_outputs_):
predictions[k][unsampled_indices, :] += p_estimator[k]
for k in range(self.n_outputs_):
if (predictions[k].sum(axis=1) == 0).any():
warn("Some inputs do not have OOB scores. "
"This probably means too few trees were used "
"to compute any reliable oob estimates.")
decision = (predictions[k] /
predictions[k].sum(axis=1)[:, np.newaxis])
oob_decision_function.append(decision)
oob_score += np.mean(y[:, k] ==
np.argmax(predictions[k], axis=1), axis=0)
if self.n_outputs_ == 1:
self.oob_decision_function_ = oob_decision_function[0]
else:
self.oob_decision_function_ = oob_decision_function
self.oob_score_ = oob_score / self.n_outputs_
def _validate_y_class_weight(self, y):
check_classification_targets(y)
y = np.copy(y)
expanded_class_weight = None
if self.class_weight is not None:
y_original = np.copy(y)
self.classes_ = []
self.n_classes_ = []
y_store_unique_indices = np.zeros(y.shape, dtype=int)
for k in range(self.n_outputs_):
classes_k, y_store_unique_indices[:, k] = \
np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
y = y_store_unique_indices
if self.class_weight is not None:
valid_presets = ('balanced', 'balanced_subsample')
if isinstance(self.class_weight, str):
if self.class_weight not in valid_presets:
raise ValueError('Valid presets for class_weight include '
'"balanced" and "balanced_subsample".'
'Given "%s".'
% self.class_weight)
if self.warm_start:
warn('class_weight presets "balanced" or '
'"balanced_subsample" are '
'not recommended for warm_start if the fitted data '
'differs from the full dataset. In order to use '
'"balanced" weights, use compute_class_weight '
'("balanced", classes, y). In place of y you can use '
'a large enough sample of the full training set '
'target to properly estimate the class frequency '
'distributions. Pass the resulting weights as the '
'class_weight parameter.')
if (self.class_weight != 'balanced_subsample' or
not self.bootstrap):
if self.class_weight == "balanced_subsample":
class_weight = "balanced"
else:
class_weight = self.class_weight
expanded_class_weight = compute_sample_weight(class_weight,
y_original)
return y, expanded_class_weight
def predict(self, X):
"""
Predict class for X.
The predicted class of an input sample is a vote by the trees in
the forest, weighted by their probability estimates. That is,
the predicted class is the one with highest mean probability
estimate across the trees.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)
else:
n_samples = proba[0].shape[0]
# all dtypes should be the same, so just take the first
class_type = self.classes_[0].dtype
predictions = np.empty((n_samples, self.n_outputs_),
dtype=class_type)
for k in range(self.n_outputs_):
predictions[:, k] = self.classes_[k].take(np.argmax(proba[k],
axis=1),
axis=0)
return predictions
def predict_proba(self, X):
"""
Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as
the mean predicted class probabilities of the trees in the forest.
The class probability of a single tree is the fraction of samples of
the same class in a leaf.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
p : ndarray of shape (n_samples, n_classes), or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
check_is_fitted(self)
# Check data
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# avoid storing the output of every estimator by summing them here
all_proba = [np.zeros((X.shape[0], j), dtype=np.float64)
for j in np.atleast_1d(self.n_classes_)]
lock = threading.Lock()
Parallel(n_jobs=n_jobs, verbose=self.verbose,
**_joblib_parallel_args(require="sharedmem"))(
delayed(_accumulate_prediction)(e.predict_proba, X, all_proba,
lock)
for e in self.estimators_)
for proba in all_proba:
proba /= len(self.estimators_)
if len(all_proba) == 1:
return all_proba[0]
else:
return all_proba
def predict_log_proba(self, X):
"""
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as
the log of the mean predicted class probabilities of the trees in the
forest.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
p : ndarray of shape (n_samples, n_classes), or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute :term:`classes_`.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return np.log(proba)
else:
for k in range(self.n_outputs_):
proba[k] = np.log(proba[k])
return proba
class ForestRegressor(RegressorMixin, BaseForest, metaclass=ABCMeta):
"""
Base class for forest of trees-based regressors.
Warning: This class should not be used directly. Use derived classes
instead.
"""
@abstractmethod
def __init__(self,
base_estimator,
n_estimators=100, *,
estimator_params=tuple(),
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
max_samples=None):
super().__init__(
base_estimator,
n_estimators=n_estimators,
estimator_params=estimator_params,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=max_samples)
def predict(self, X):
"""
Predict regression target for X.
The predicted regression target of an input sample is computed as the
mean predicted regression targets of the trees in the forest.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to
``dtype=np.float32``. If a sparse matrix is provided, it will be
converted into a sparse ``csr_matrix``.
Returns
-------
y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
The predicted values.
"""
check_is_fitted(self)
# Check data
X = self._validate_X_predict(X)
# Assign chunk of trees to jobs
n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
# avoid storing the output of every estimator by summing them here
if self.n_outputs_ > 1:
y_hat = np.zeros((X.shape[0], self.n_outputs_), dtype=np.float64)
else:
y_hat = np.zeros((X.shape[0]), dtype=np.float64)
# Parallel loop
lock = threading.Lock()
Parallel(n_jobs=n_jobs, verbose=self.verbose,
**_joblib_parallel_args(require="sharedmem"))(
delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock)
for e in self.estimators_)
y_hat /= len(self.estimators_)
return y_hat
def _set_oob_score(self, X, y):
"""
Compute out-of-bag scores."""
X = check_array(X, dtype=DTYPE, accept_sparse='csr')
n_samples = y.shape[0]
predictions = np.zeros((n_samples, self.n_outputs_))
n_predictions = np.zeros((n_samples, self.n_outputs_))
n_samples_bootstrap = _get_n_samples_bootstrap(
n_samples, self.max_samples
)
for estimator in self.estimators_:
unsampled_indices = _generate_unsampled_indices(
estimator.random_state, n_samples, n_samples_bootstrap)
p_estimator = estimator.predict(
X[unsampled_indices, :], check_input=False)
if self.n_outputs_ == 1:
p_estimator = p_estimator[:, np.newaxis]
predictions[unsampled_indices, :] += p_estimator
n_predictions[unsampled_indices, :] += 1
if (n_predictions == 0).any():
warn("Some inputs do not have OOB scores. "
"This probably means too few trees were used "
"to compute any reliable oob estimates.")
n_predictions[n_predictions == 0] = 1
predictions /= n_predictions
self.oob_prediction_ = predictions
if self.n_outputs_ == 1:
self.oob_prediction_ = \
self.oob_prediction_.reshape((n_samples, ))
self.oob_score_ = 0.0
for k in range(self.n_outputs_):
self.oob_score_ += r2_score(y[:, k],
predictions[:, k])
self.oob_score_ /= self.n_outputs_
def _compute_partial_dependence_recursion(self, grid, target_features):
"""Fast partial dependence computation.
Parameters
----------
grid : ndarray of shape (n_samples, n_target_features)
The grid points on which the partial dependence should be
evaluated.
target_features : ndarray of shape (n_target_features)
The set of target features for which the partial dependence
should be evaluated.
Returns
-------
averaged_predictions : ndarray of shape (n_samples,)
The value of the partial dependence function on each grid point.
"""
grid = np.asarray(grid, dtype=DTYPE, order='C')
averaged_predictions = np.zeros(shape=grid.shape[0],
dtype=np.float64, order='C')
for tree in self.estimators_:
# Note: we don't sum in parallel because the GIL isn't released in
# the fast method.
tree.tree_.compute_partial_dependence(
grid, target_features, averaged_predictions)
# Average over the forest
averaged_predictions /= len(self.estimators_)
return averaged_predictions
class RandomForestClassifier(ForestClassifier):
"""
A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
criterion : {"gini", "entropy"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
Note: this parameter is tree-specific.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
min_impurity_split : float, default=None
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead.
bootstrap : bool, default=True
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.
oob_score : bool, default=False
Whether to use out-of-bag samples to estimate
the generalization accuracy.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int or RandomState, default=None
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
default=None
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max_samples * X.shape[0]` samples. Thus,
`max_samples` should be in the interval `(0, 1)`.
.. versionadded:: 0.22
Attributes
----------
base_estimator_ : DecisionTreeClassifier
The child estimator template used to create the collection of fitted
sub-estimators.
estimators_ : list of DecisionTreeClassifier
The collection of fitted sub-estimators.
classes_ : ndarray of shape (n_classes,) or a list of such arrays
The classes labels (single output problem), or a list of arrays of
class labels (multi-output problem).
n_classes_ : int or list
The number of classes (single output problem), or a list containing the
number of classes for each output (multi-output problem).
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when ``oob_score`` is True.
oob_decision_function_ : ndarray of shape (n_samples, n_classes)
Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
`oob_decision_function_` might contain NaN. This attribute exists
only when ``oob_score`` is True.
See Also
--------
DecisionTreeClassifier, ExtraTreesClassifier
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, if the improvement
of the criterion is identical for several splits enumerated during the
search of the best split. To obtain a deterministic behaviour during
fitting, ``random_state`` has to be fixed.
References
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
Examples
--------
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
... n_informative=2, n_redundant=0,
... random_state=0, shuffle=False)
>>> clf = RandomForestClassifier(max_depth=2, random_state=0)
>>> clf.fit(X, y)
RandomForestClassifier(...)
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]
"""
@_deprecate_positional_args
def __init__(self,
n_estimators=100, *,
criterion="gini",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_decrease=0.,
min_impurity_split=None,
bootstrap=True,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
ccp_alpha=0.0,
max_samples=None):
super().__init__(
base_estimator=DecisionTreeClassifier(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes",
"min_impurity_decrease", "min_impurity_split",
"random_state", "ccp_alpha"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight,
max_samples=max_samples)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.ccp_alpha = ccp_alpha
class RandomForestRegressor(ForestRegressor):
"""
A random forest regressor.
A random forest is a meta estimator that fits a number of classifying
decision trees on various sub-samples of the dataset and uses averaging
to improve the predictive accuracy and control over-fitting.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
criterion : {"mse", "mae"}, default="mse"
The function to measure the quality of a split. Supported criteria
are "mse" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
min_impurity_split : float, default=None
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead.
bootstrap : bool, default=True
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.
oob_score : bool, default=False
whether to use out-of-bag samples to estimate
the R^2 on unseen data.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int or RandomState, default=None
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``).
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max_samples * X.shape[0]` samples. Thus,
`max_samples` should be in the interval `(0, 1)`.
.. versionadded:: 0.22
Attributes
----------
base_estimator_ : DecisionTreeRegressor
The child estimator template used to create the collection of fitted
sub-estimators.
estimators_ : list of DecisionTreeRegressor
The collection of fitted sub-estimators.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when ``oob_score`` is True.
oob_prediction_ : ndarray of shape (n_samples,)
Prediction computed with out-of-bag estimate on the training set.
This attribute exists only when ``oob_score`` is True.
See Also
--------
DecisionTreeRegressor, ExtraTreesRegressor
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
The features are always randomly permuted at each split. Therefore,
the best found split may vary, even with the same training data,
``max_features=n_features`` and ``bootstrap=False``, if the improvement
of the criterion is identical for several splits enumerated during the
search of the best split. To obtain a deterministic behaviour during
fitting, ``random_state`` has to be fixed.
The default value ``max_features="auto"`` uses ``n_features``
rather than ``n_features / 3``. The latter was originally suggested in
[1], whereas the former was more recently justified empirically in [2].
References
----------
.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
.. [2] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
trees", Machine Learning, 63(1), 3-42, 2006.
Examples
--------
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
... random_state=0, shuffle=False)
>>> regr = RandomForestRegressor(max_depth=2, random_state=0)
>>> regr.fit(X, y)
RandomForestRegressor(...)
>>> print(regr.predict([[0, 0, 0, 0]]))
[-8.32987858]
"""
@_deprecate_positional_args
def __init__(self,
n_estimators=100, *,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_decrease=0.,
min_impurity_split=None,
bootstrap=True,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
ccp_alpha=0.0,
max_samples=None):
super().__init__(
base_estimator=DecisionTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes",
"min_impurity_decrease", "min_impurity_split",
"random_state", "ccp_alpha"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=max_samples)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.ccp_alpha = ccp_alpha
class ExtraTreesClassifier(ForestClassifier):
"""
An extra-trees classifier.
This class implements a meta estimator that fits a number of
randomized decision trees (a.k.a. extra-trees) on various sub-samples
of the dataset and uses averaging to improve the predictive accuracy
and control over-fitting.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
criterion : {"gini", "entropy"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
min_impurity_split : float, default=None
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead.
bootstrap : bool, default=False
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.
oob_score : bool, default=False
Whether to use out-of-bag samples to estimate
the generalization accuracy.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int, RandomState, default=None
Controls 3 sources of randomness:
- the bootstrapping of the samples used when building trees
(if ``bootstrap=True``)
- the sampling of the features to consider when looking for the best
split at each node (if ``max_features < n_features``)
- the draw of the splits for each of the `max_features`
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
default=None
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}].
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max_samples * X.shape[0]` samples. Thus,
`max_samples` should be in the interval `(0, 1)`.
.. versionadded:: 0.22
Attributes
----------
base_estimator_ : ExtraTreesClassifier
The child estimator template used to create the collection of fitted
sub-estimators.
estimators_ : list of DecisionTreeClassifier
The collection of fitted sub-estimators.
classes_ : ndarray of shape (n_classes,) or a list of such arrays
The classes labels (single output problem), or a list of arrays of
class labels (multi-output problem).
n_classes_ : int or list
The number of classes (single output problem), or a list containing the
number of classes for each output (multi-output problem).
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when ``oob_score`` is True.
oob_decision_function_ : ndarray of shape (n_samples, n_classes)
Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
`oob_decision_function_` might contain NaN. This attribute exists
only when ``oob_score`` is True.
See Also
--------
sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble.
RandomForestClassifier : Ensemble Classifier based on trees with optimal
splits.
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
trees", Machine Learning, 63(1), 3-42, 2006.
Examples
--------
>>> from sklearn.ensemble import ExtraTreesClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0)
>>> clf.fit(X, y)
ExtraTreesClassifier(random_state=0)
>>> clf.predict([[0, 0, 0, 0]])
array([1])
"""
@_deprecate_positional_args
def __init__(self,
n_estimators=100, *,
criterion="gini",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_decrease=0.,
min_impurity_split=None,
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
class_weight=None,
ccp_alpha=0.0,
max_samples=None):
super().__init__(
base_estimator=ExtraTreeClassifier(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes",
"min_impurity_decrease", "min_impurity_split",
"random_state", "ccp_alpha"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight,
max_samples=max_samples)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.ccp_alpha = ccp_alpha
class ExtraTreesRegressor(ForestRegressor):
"""
An extra-trees regressor.
This class implements a meta estimator that fits a number of
randomized decision trees (a.k.a. extra-trees) on various sub-samples
of the dataset and uses averaging to improve the predictive accuracy
and control over-fitting.
Read more in the :ref:`User Guide <forest>`.
Parameters
----------
n_estimators : int, default=100
The number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
criterion : {"mse", "mae"}, default="mse"
The function to measure the quality of a split. Supported criteria
are "mse" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
max_depth : int, default=None
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a fraction and
`round(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
min_impurity_split : float, default=None
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead.
bootstrap : bool, default=False
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree.
oob_score : bool, default=False
Whether to use out-of-bag samples to estimate the R^2 on unseen data.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int or RandomState, default=None
Controls 3 sources of randomness:
- the bootstrapping of the samples used when building trees
(if ``bootstrap=True``)
- the sampling of the features to consider when looking for the best
split at each node (if ``max_features < n_features``)
- the draw of the splits for each of the `max_features`
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
ccp_alpha : non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
max_samples : int or float, default=None
If bootstrap is True, the number of samples to draw from X
to train each base estimator.
- If None (default), then draw `X.shape[0]` samples.
- If int, then draw `max_samples` samples.
- If float, then draw `max_samples * X.shape[0]` samples. Thus,
`max_samples` should be in the interval `(0, 1)`.
.. versionadded:: 0.22
Attributes
----------
base_estimator_ : ExtraTreeRegressor
The child estimator template used to create the collection of fitted
sub-estimators.
estimators_ : list of DecisionTreeRegressor
The collection of fitted sub-estimators.
feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance.
Warning: impurity-based feature importances can be misleading for
high cardinality features (many unique values). See
:func:`sklearn.inspection.permutation_importance` as an alternative.
n_features_ : int
The number of features.
n_outputs_ : int
The number of outputs.
oob_score_ : float
Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when ``oob_score`` is True.
oob_prediction_ : ndarray of shape (n_samples,)
Prediction computed with out-of-bag estimate on the training set.
This attribute exists only when ``oob_score`` is True.
See Also
--------
sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble.
RandomForestRegressor: Ensemble regressor using trees with optimal splits.
Notes
-----
The default values for the parameters controlling the size of the trees
(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
unpruned trees which can potentially be very large on some data sets. To
reduce memory consumption, the complexity and size of the trees should be
controlled by setting those parameter values.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
Examples
--------
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import ExtraTreesRegressor
>>> X, y = load_diabetes(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(
... X_train, y_train)
>>> reg.score(X_test, y_test)
0.2708...
"""
@_deprecate_positional_args
def __init__(self,
n_estimators=100, *,
criterion="mse",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
max_leaf_nodes=None,
min_impurity_decrease=0.,
min_impurity_split=None,
bootstrap=False,
oob_score=False,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False,
ccp_alpha=0.0,
max_samples=None):
super().__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes",
"min_impurity_decrease", "min_impurity_split",
"random_state", "ccp_alpha"),
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=max_samples)
self.criterion = criterion
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.ccp_alpha = ccp_alpha
class RandomTreesEmbedding(BaseForest):
"""
An ensemble of totally random trees.
An unsupervised transformation of a dataset to a high-dimensional
sparse representation. A datapoint is coded according to which leaf of
each tree it is sorted into. Using a one-hot encoding of the leaves,
this leads to a binary coding with as many ones as there are trees in
the forest.
The dimensionality of the resulting representation is
``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``,
the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.
Read more in the :ref:`User Guide <random_trees_embedding>`.
Parameters
----------
n_estimators : int, default=100
Number of trees in the forest.
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22.
max_depth : int, default=5
The maximum depth of each tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
min_samples_split : int or float, default=2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` is the minimum
number of samples for each split.
.. versionchanged:: 0.18
Added float values for fractions.
min_samples_leaf : int or float, default=1
The minimum number of samples required to be at a leaf node.
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression.
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` is the minimum
number of samples for each node.
.. versionchanged:: 0.18
Added float values for fractions.
min_weight_fraction_leaf : float, default=0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided.
max_leaf_nodes : int, default=None
Grow trees with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
min_impurity_decrease : float, default=0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed.
.. versionadded:: 0.19
min_impurity_split : float, default=None
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead.
sparse_output : bool, default=True
Whether or not to return a sparse CSR matrix, as default behavior,
or to return a dense array compatible with dense pipeline operators.
n_jobs : int, default=None
The number of jobs to run in parallel. :meth:`fit`, :meth:`transform`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details.
random_state : int or RandomState, default=None
Controls the generation of the random `y` used to fit the trees
and the draw of the splits for each feature at the trees' nodes.
See :term:`Glossary <random_state>` for details.
verbose : int, default=0
Controls the verbosity when fitting and predicting.
warm_start : bool, default=False
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`.
Attributes
----------
base_estimator_ : DecisionTreeClassifier instance
The child estimator template used to create the collection of fitted
sub-estimators.
estimators_ : list of DecisionTreeClassifier instances
The collection of fitted sub-estimators.
feature_importances_ : ndarray of shape (n_features,)
The feature importances (the higher, the more important the feature).
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
one_hot_encoder_ : OneHotEncoder instance
One-hot encoder used to create the sparse embedding.
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
.. [2] Moosmann, F. and Triggs, B. and Jurie, F. "Fast discriminative
visual codebooks using randomized clustering forests"
NIPS 2007
Examples
--------
>>> from sklearn.ensemble import RandomTreesEmbedding
>>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]]
>>> random_trees = RandomTreesEmbedding(
... n_estimators=5, random_state=0, max_depth=1).fit(X)
>>> X_sparse_embedding = random_trees.transform(X)
>>> X_sparse_embedding.toarray()
array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
[0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
[1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],
[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])
"""
criterion = 'mse'
max_features = 1
@_deprecate_positional_args
def __init__(self,
n_estimators=100, *,
max_depth=5,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_leaf_nodes=None,
min_impurity_decrease=0.,
min_impurity_split=None,
sparse_output=True,
n_jobs=None,
random_state=None,
verbose=0,
warm_start=False):
super().__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
estimator_params=("criterion", "max_depth", "min_samples_split",
"min_samples_leaf", "min_weight_fraction_leaf",
"max_features", "max_leaf_nodes",
"min_impurity_decrease", "min_impurity_split",
"random_state"),
bootstrap=False,
oob_score=False,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
max_samples=None)
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
self.min_impurity_split = min_impurity_split
self.sparse_output = sparse_output
def _set_oob_score(self, X, y):
raise NotImplementedError("OOB score not supported by tree embedding")
def fit(self, X, y=None, sample_weight=None):
"""
Fit estimator.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csc_matrix`` for maximum efficiency.
y : Ignored
Not used, present for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
Returns
-------
self : object
"""
self.fit_transform(X, y, sample_weight=sample_weight)
return self
def fit_transform(self, X, y=None, sample_weight=None):
"""
Fit estimator and transform dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data used to build forests. Use ``dtype=np.float32`` for
maximum efficiency.
y : Ignored
Not used, present for API consistency by convention.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
Returns
-------
X_transformed : sparse matrix of shape (n_samples, n_out)
Transformed dataset.
"""
X = check_array(X, accept_sparse=['csc'])
if issparse(X):
# Pre-sort indices to avoid that each individual tree of the
# ensemble sorts the indices.
X.sort_indices()
rnd = check_random_state(self.random_state)
y = rnd.uniform(size=X.shape[0])
super().fit(X, y, sample_weight=sample_weight)
self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output)
return self.one_hot_encoder_.fit_transform(self.apply(X))
def transform(self, X):
"""
Transform dataset.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input data to be transformed. Use ``dtype=np.float32`` for maximum
efficiency. Sparse matrices are also supported, use sparse
``csr_matrix`` for maximum efficiency.
Returns
-------
X_transformed : sparse matrix of shape (n_samples, n_out)
Transformed dataset.
"""
check_is_fitted(self)
return self.one_hot_encoder_.transform(self.apply(X))
| bnaul/scikit-learn | sklearn/ensemble/_forest.py | Python | bsd-3-clause | 95,540 | [
"Brian"
] | 858c76f48626546183deb057fe41ba472f17ff25db4e1d5f4fbf402d5be07307 |
from ase import *
from hotbit import Hotbit
from ase.lattice.cubic import FaceCenteredCubic
from hotbit.test.misc import default_param
import pylab as pl
from box import Atoms
for SCC in [True,False]:
timings=[]
norbs=[]
sccs=['non-SCC','SCC'][SCC]
for nx in [1,2,3,4,5]:
for ny in [1,2,3,4,5]:
print('nx',nx,ny)
atoms = FaceCenteredCubic(directions=[[1,-1,0], [1,1,-2], [1,1,1]],\
size=(nx,ny,1), symbol='Au', pbc=(0,0,0))
calc=Hotbit(verbose=True,SCC=SCC,gamma_cut=3,txt='size2.cal',**default_param)
atoms.set_calculator(calc)
try:
atoms.get_forces()
except:
continue
calc.timer.summary()
timings.append( calc.timer.get_timings() )
norbs.append( calc.el.get_nr_orbitals() )
calc.__del__()
order=argsort(norbs)
norbs=sort(norbs)
timings2=[]
for i in order:
timings2.append(timings[i])
times={}
maxtime=0.0
for key in timings2[0]:
times[key]=array([ts[key] for ts in timings2])
maxtime=max(maxtime,times[key].max())
s=0.1
for key in timings[0]:
s+=0.1
if times[key].max()<0.05*maxtime: continue
pl.plot(norbs,times[key],label=key,lw=s)
atoms=Atoms(atoms)
pl.title('Timings up to %s with %s' %(atoms.get_name(),sccs) )
pl.xlabel('Number of orbitals')
pl.ylabel('Time (s)')
pl.legend(loc='upper left')
pl.savefig('size_timing_%s.png' %sccs)
#pl.plot()
#pl.show()
pl.clf() | pekkosk/hotbit | hotbit/test/size2.py | Python | gpl-2.0 | 1,736 | [
"ASE"
] | ddcd71ca679fecfb16e119fbd3fe24b9d3096bc08c6cb8b8cf315d19b0d82713 |
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
# Credit to Dr. Shyue Ping Ong for the template of the calculator
"""
This module implements a TEM pattern calculator.
"""
import json
import os
from collections import namedtuple
from fractions import Fraction
from functools import lru_cache
from typing import Dict, List, Tuple, cast, Union
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import scipy.constants as sc
from pymatgen.analysis.diffraction.core import AbstractDiffractionPatternCalculator
from pymatgen.core.structure import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.util.string import latexify_spacegroup, unicodeify_spacegroup
with open(os.path.join(os.path.dirname(__file__), "atomic_scattering_params.json")) as f:
ATOMIC_SCATTERING_PARAMS = json.load(f)
__author__ = "Frank Wan, Jason Liang"
__copyright__ = "Copyright 2020, The Materials Project"
__version__ = "0.22"
__maintainer__ = "Jason Liang"
__email__ = "fwan@berkeley.edu, yhljason@berkeley.edu"
__date__ = "03/31/2020"
class TEMCalculator(AbstractDiffractionPatternCalculator):
"""
Computes the TEM pattern of a crystal structure for multiple Laue zones.
Code partially inspired from XRD calculation implementation. X-ray factor to electron factor
conversion based on the International Table of Crystallography.
#TODO: Could add "number of iterations", "magnification", "critical value of beam",
"twin direction" for certain materials, "sample thickness", and "excitation error s"
"""
def __init__(
self,
symprec: float = None,
voltage: float = 200,
beam_direction: Tuple[int, int, int] = (0, 0, 1),
camera_length: int = 160,
debye_waller_factors: Dict[str, float] = None,
cs: float = 1,
) -> None:
"""
Args:
symprec (float): Symmetry precision for structure refinement. If
set to 0, no refinement is done. Otherwise, refinement is
performed using spglib with provided precision.
voltage (float): The wavelength is a function of the TEM microscope's
voltage. By default, set to 200 kV. Units in kV.
beam_direction (tuple): The direction of the electron beam fired onto the sample.
By default, set to [0,0,1], which corresponds to the normal direction
of the sample plane.
camera_length (int): The distance from the sample to the projected diffraction pattern.
By default, set to 160 cm. Units in cm.
debye_waller_factors ({element symbol: float}): Allows the
specification of Debye-Waller factors. Note that these
factors are temperature dependent.
cs (float): the chromatic aberration coefficient. set by default to 1 mm.
"""
self.symprec = symprec
self.voltage = voltage
self.beam_direction = beam_direction
self.camera_length = camera_length
self.debye_waller_factors = debye_waller_factors or {}
self.cs = cs
@lru_cache(1)
def wavelength_rel(self) -> float:
"""
Calculates the wavelength of the electron beam with relativistic kinematic effects taken
into account.
Args:
none
Returns:
Relativistic Wavelength (in angstroms)
"""
wavelength_rel = (
sc.h
/ np.sqrt(
2 * sc.m_e * sc.e * 1000 * self.voltage * (1 + (sc.e * 1000 * self.voltage) / (2 * sc.m_e * sc.c ** 2))
)
* (10 ** 10)
)
return wavelength_rel
@staticmethod
def generate_points(coord_left: int = -10, coord_right: int = 10) -> np.ndarray:
"""
Generates a bunch of 3D points that span a cube.
Args:
coord_left (int): The minimum coordinate value.
coord_right (int): The maximum coordinate value.
Returns:
Numpy 2d array
"""
points = [0, 0, 0]
coord_values = np.arange(coord_left, coord_right + 1)
points[0], points[1], points[2] = np.meshgrid(coord_values, coord_values, coord_values)
points_matrix = (np.ravel(points[i]) for i in range(0, 3))
result = np.vstack(list(points_matrix)).transpose()
return result
def zone_axis_filter(
self, points: Union[List[Tuple[int, int, int]], np.ndarray], laue_zone: int = 0
) -> Union[List[Tuple[int, int, int]]]:
"""
Filters out all points that exist within the specified Laue zone according to the zone axis rule.
Args:
points (np.ndarray): The list of points to be filtered.
laue_zone (int): The desired Laue zone.
Returns:
list of 3-tuples
"""
if any(isinstance(n, tuple) for n in points):
return list(points)
if len(points) == 0:
return []
filtered = np.where(np.dot(np.array(self.beam_direction), np.transpose(points)) == laue_zone)
result = points[filtered]
result_tuples = cast(List[Tuple[int, int, int]], [tuple(x) for x in result.tolist()])
return result_tuples
def get_interplanar_spacings(
self, structure: Structure, points: Union[List[Tuple[int, int, int]], np.ndarray]
) -> Dict[Tuple[int, int, int], float]:
"""
Args:
structure (Structure): the input structure.
points (tuple): the desired hkl indices.
Returns:
Dict of hkl to its interplanar spacing, in angstroms (float).
"""
points_filtered = self.zone_axis_filter(points)
if (0, 0, 0) in points_filtered:
points_filtered.remove((0, 0, 0))
interplanar_spacings_val = np.array(list(map(lambda x: structure.lattice.d_hkl(x), points_filtered)))
interplanar_spacings = dict(zip(points_filtered, interplanar_spacings_val))
return interplanar_spacings
def bragg_angles(
self, interplanar_spacings: Dict[Tuple[int, int, int], float]
) -> Dict[Tuple[int, int, int], float]:
"""
Gets the Bragg angles for every hkl point passed in (where n = 1).
Args:
interplanar_spacings (dict): dictionary of hkl to interplanar spacing
Returns:
dict of hkl plane (3-tuple) to Bragg angle in radians (float)
"""
plane = list(interplanar_spacings.keys())
interplanar_spacings_val = np.array(list(interplanar_spacings.values()))
bragg_angles_val = np.arcsin(self.wavelength_rel() / (2 * interplanar_spacings_val))
bragg_angles = dict(zip(plane, bragg_angles_val))
return bragg_angles
def get_s2(self, bragg_angles: Dict[Tuple[int, int, int], float]) -> Dict[Tuple[int, int, int], float]:
"""
Calculates the s squared parameter (= square of sin theta over lambda) for each hkl plane.
Args:
bragg_angles (Dict): The bragg angles for each hkl plane.
Returns:
Dict of hkl plane to s2 parameter, calculates the s squared parameter
(= square of sin theta over lambda).
"""
plane = list(bragg_angles.keys())
bragg_angles_val = np.array(list(bragg_angles.values()))
s2_val = (np.sin(bragg_angles_val) / self.wavelength_rel()) ** 2
s2 = dict(zip(plane, s2_val))
return s2
def x_ray_factors(
self, structure: Structure, bragg_angles: Dict[Tuple[int, int, int], float]
) -> Dict[str, Dict[Tuple[int, int, int], float]]:
"""
Calculates x-ray factors, which are required to calculate atomic scattering factors. Method partially inspired
by the equivalent process in the xrd module.
Args:
structure (Structure): The input structure.
bragg_angles (Dict): Dictionary of hkl plane to Bragg angle.
Returns:
dict of atomic symbol to another dict of hkl plane to x-ray factor (in angstroms).
"""
x_ray_factors = {}
s2 = self.get_s2(bragg_angles)
atoms = structure.composition.elements
scattering_factors_for_atom = {}
for atom in atoms:
coeffs = np.array(ATOMIC_SCATTERING_PARAMS[atom.symbol])
for plane in bragg_angles:
scattering_factor_curr = atom.Z - 41.78214 * s2[plane] * np.sum(
coeffs[:, 0] * np.exp(-coeffs[:, 1] * s2[plane]), axis=None
)
scattering_factors_for_atom[plane] = scattering_factor_curr
x_ray_factors[atom.symbol] = scattering_factors_for_atom
scattering_factors_for_atom = {}
return x_ray_factors
def electron_scattering_factors(
self, structure: Structure, bragg_angles: Dict[Tuple[int, int, int], float]
) -> Dict[str, Dict[Tuple[int, int, int], float]]:
"""
Calculates atomic scattering factors for electrons using the Mott-Bethe formula (1st order Born approximation).
Args:
structure (Structure): The input structure.
bragg_angles (dict of 3-tuple to float): The Bragg angles for each hkl plane.
Returns:
dict from atomic symbol to another dict of hkl plane to factor (in angstroms)
"""
electron_scattering_factors = {}
x_ray_factors = self.x_ray_factors(structure, bragg_angles)
s2 = self.get_s2(bragg_angles)
atoms = structure.composition.elements
prefactor = 0.023934
scattering_factors_for_atom = {}
for atom in atoms:
for plane in bragg_angles:
scattering_factor_curr = prefactor * (atom.Z - x_ray_factors[atom.symbol][plane]) / s2[plane]
scattering_factors_for_atom[plane] = scattering_factor_curr
electron_scattering_factors[atom.symbol] = scattering_factors_for_atom
scattering_factors_for_atom = {}
return electron_scattering_factors
def cell_scattering_factors(
self, structure: Structure, bragg_angles: Dict[Tuple[int, int, int], float]
) -> Dict[Tuple[int, int, int], int]:
"""
Calculates the scattering factor for the whole cell.
Args:
structure (Structure): The input structure.
bragg_angles (dict of 3-tuple to float): The Bragg angles for each hkl plane.
Returns:
dict of hkl plane (3-tuple) to scattering factor (in angstroms).
"""
cell_scattering_factors = {}
electron_scattering_factors = self.electron_scattering_factors(structure, bragg_angles)
scattering_factor_curr = 0
for plane in bragg_angles:
for site in structure:
for sp, occu in site.species.items():
g_dot_r = np.dot(np.array(plane), np.transpose(site.frac_coords))
scattering_factor_curr += electron_scattering_factors[sp.symbol][plane] * np.exp(
2j * np.pi * g_dot_r
)
cell_scattering_factors[plane] = scattering_factor_curr
scattering_factor_curr = 0
return cell_scattering_factors
def cell_intensity(
self, structure: Structure, bragg_angles: Dict[Tuple[int, int, int], float]
) -> Dict[Tuple[int, int, int], float]:
"""
Calculates cell intensity for each hkl plane. For simplicity's sake, take I = |F|**2.
Args:
structure (Structure): The input structure.
bragg_angles (dict of 3-tuple to float): The Bragg angles for each hkl plane.
Returns:
dict of hkl plane to cell intensity
"""
csf = self.cell_scattering_factors(structure, bragg_angles)
plane = bragg_angles.keys()
csf_val = np.array(list(csf.values()))
cell_intensity_val = (csf_val * csf_val.conjugate()).real
cell_intensity = dict(zip(plane, cell_intensity_val))
return cell_intensity
def get_pattern(
self,
structure: Structure,
scaled: bool = None,
two_theta_range: Tuple[float, float] = None,
) -> pd.DataFrame:
"""
Returns all relevant TEM DP info in a pandas dataframe.
Args:
structure (Structure): The input structure.
scaled (boolean): Required value for inheritance, does nothing in TEM pattern
two_theta_range (Tuple): Required value for inheritance, does nothing in TEM pattern
Returns:
PandasDataFrame
"""
if self.symprec:
finder = SpacegroupAnalyzer(structure, symprec=self.symprec)
structure = finder.get_refined_structure()
points = self.generate_points(-10, 11)
tem_dots = self.tem_dots(structure, points)
field_names = [
"Position",
"(hkl)",
"Intensity (norm)",
"Film radius",
"Interplanar Spacing",
]
rows_list = []
for dot in tem_dots:
dict1 = {
"Pos": dot.position,
"(hkl)": dot.hkl,
"Intnsty (norm)": dot.intensity,
"Film rad": dot.film_radius,
"Interplanar Spacing": dot.d_spacing,
}
rows_list.append(dict1)
df = pd.DataFrame(rows_list, columns=field_names)
return df
def normalized_cell_intensity(
self, structure: Structure, bragg_angles: Dict[Tuple[int, int, int], float]
) -> Dict[Tuple[int, int, int], float]:
"""
Normalizes the cell_intensity dict to 1, for use in plotting.
Args:
structure (Structure): The input structure.
bragg_angles (dict of 3-tuple to float): The Bragg angles for each hkl plane.
Returns:
dict of hkl plane to normalized cell intensity
"""
normalized_cell_intensity = {}
cell_intensity = self.cell_intensity(structure, bragg_angles)
max_intensity = max(cell_intensity.values())
norm_factor = 1 / max_intensity
for plane in cell_intensity:
normalized_cell_intensity[plane] = cell_intensity[plane] * norm_factor
return normalized_cell_intensity
def is_parallel(
self,
structure: Structure,
plane: Tuple[int, int, int],
other_plane: Tuple[int, int, int],
) -> bool:
"""
Checks if two hkl planes are parallel in reciprocal space.
Args:
structure (Structure): The input structure.
plane (3-tuple): The first plane to be compared.
other_plane (3-tuple): The other plane to be compared.
Returns:
boolean
"""
phi = self.get_interplanar_angle(structure, plane, other_plane)
return phi in (180, 0) or np.isnan(phi)
def get_first_point(self, structure: Structure, points: list) -> Dict[Tuple[int, int, int], float]:
"""
Gets the first point to be plotted in the 2D DP, corresponding to maximum d/minimum R.
Args:
structure (Structure): The input structure.
points (list): All points to be checked.
Returns:
dict of a hkl plane to max interplanar distance.
"""
max_d = -100.0
max_d_plane = (0, 0, 1)
points = self.zone_axis_filter(points)
spacings = self.get_interplanar_spacings(structure, points)
for plane in sorted(spacings.keys()):
if spacings[plane] > max_d:
max_d_plane = plane
max_d = spacings[plane]
return {max_d_plane: max_d}
@staticmethod
def get_interplanar_angle(structure: Structure, p1: Tuple[int, int, int], p2: Tuple[int, int, int]) -> float:
"""
Returns the interplanar angle (in degrees) between the normal of two crystal planes.
Formulas from International Tables for Crystallography Volume C pp. 2-9.
Args:
structure (Structure): The input structure.
p1 (3-tuple): plane 1
p2 (3-tuple): plane 2
Returns:
float
"""
a, b, c = structure.lattice.a, structure.lattice.b, structure.lattice.c
alpha, beta, gamma = (
np.deg2rad(structure.lattice.alpha),
np.deg2rad(structure.lattice.beta),
np.deg2rad(structure.lattice.gamma),
)
v = structure.lattice.volume
a_star = b * c * np.sin(alpha) / v
b_star = a * c * np.sin(beta) / v
c_star = a * b * np.sin(gamma) / v
cos_alpha_star = (np.cos(beta) * np.cos(gamma) - np.cos(alpha)) / (np.sin(beta) * np.sin(gamma))
cos_beta_star = (np.cos(alpha) * np.cos(gamma) - np.cos(beta)) / (np.sin(alpha) * np.sin(gamma))
cos_gamma_star = (np.cos(alpha) * np.cos(beta) - np.cos(gamma)) / (np.sin(alpha) * np.sin(beta))
r1_norm = np.sqrt(
p1[0] ** 2 * a_star ** 2
+ p1[1] ** 2 * b_star ** 2
+ p1[2] ** 2 * c_star ** 2
+ 2 * p1[0] * p1[1] * a_star * b_star * cos_gamma_star
+ 2 * p1[0] * p1[2] * a_star * c_star * cos_beta_star
+ 2 * p1[1] * p1[2] * b_star * c_star * cos_gamma_star
)
r2_norm = np.sqrt(
p2[0] ** 2 * a_star ** 2
+ p2[1] ** 2 * b_star ** 2
+ p2[2] ** 2 * c_star ** 2
+ 2 * p2[0] * p2[1] * a_star * b_star * cos_gamma_star
+ 2 * p2[0] * p2[2] * a_star * c_star * cos_beta_star
+ 2 * p2[1] * p2[2] * b_star * c_star * cos_gamma_star
)
r1_dot_r2 = (
p1[0] * p2[0] * a_star ** 2
+ p1[1] * p2[1] * b_star ** 2
+ p1[2] * p2[2] * c_star ** 2
+ (p1[0] * p2[1] + p2[0] * p1[1]) * a_star * b_star * cos_gamma_star
+ (p1[0] * p2[2] + p2[0] * p1[1]) * a_star * c_star * cos_beta_star
+ (p1[1] * p2[2] + p2[1] * p1[2]) * b_star * c_star * cos_alpha_star
)
phi = np.arccos(r1_dot_r2 / (r1_norm * r2_norm))
return np.rad2deg(phi)
@staticmethod
def get_plot_coeffs(
p1: Tuple[int, int, int],
p2: Tuple[int, int, int],
p3: Tuple[int, int, int],
) -> np.ndarray:
"""
Calculates coefficients of the vector addition required to generate positions for each DP point
by the Moore-Penrose inverse method.
Args:
p1 (3-tuple): The first point. Fixed.
p2 (3-tuple): The second point. Fixed.
p3 (3-tuple): The point whose coefficients are to be calculted.
Returns:
Numpy array
"""
a = np.array([[p1[0], p2[0]], [p1[1], p2[1]], [p1[2], p2[2]]])
b = np.array([[p3[0], p3[1], p3[2]]]).T
a_pinv = np.linalg.pinv(a)
x = np.dot(a_pinv, b)
return np.ravel(x)
def get_positions(self, structure: Structure, points: list) -> Dict[Tuple[int, int, int], np.ndarray]:
"""
Calculates all the positions of each hkl point in the 2D diffraction pattern by vector addition.
Distance in centimeters.
Args:
structure (Structure): The input structure.
points (list): All points to be checked.
Returns:
dict of hkl plane to xy-coordinates.
"""
positions = {}
points = self.zone_axis_filter(points)
# first is the max_d, min_r
first_point_dict = self.get_first_point(structure, points)
for point, v in first_point_dict.items():
first_point = point
first_d = v
spacings = self.get_interplanar_spacings(structure, points)
# second is the first non-parallel-to-first-point vector when sorted.
# note 000 is "parallel" to every plane vector.
for plane in sorted(spacings.keys()):
second_point, second_d = plane, spacings[plane]
if not self.is_parallel(structure, first_point, second_point):
break
p1 = first_point
p2 = second_point
if (0, 0, 0) in points:
points.remove((0, 0, 0))
points.remove(first_point)
points.remove(second_point)
positions[(0, 0, 0)] = np.array([0, 0])
r1 = self.wavelength_rel() * self.camera_length / first_d
positions[first_point] = np.array([r1, 0])
r2 = self.wavelength_rel() * self.camera_length / second_d
phi = np.deg2rad(self.get_interplanar_angle(structure, first_point, second_point))
positions[second_point] = np.array([r2 * np.cos(phi), r2 * np.sin(phi)])
for plane in points:
coeffs = self.get_plot_coeffs(p1, p2, plane)
pos = np.array(
[
coeffs[0] * positions[first_point][0] + coeffs[1] * positions[second_point][0],
coeffs[0] * positions[first_point][1] + coeffs[1] * positions[second_point][1],
]
)
positions[plane] = pos
points.append((0, 0, 0))
points.append(first_point)
points.append(second_point)
return positions
def tem_dots(self, structure: Structure, points) -> List:
"""
Generates all TEM_dot as named tuples that will appear on the 2D diffraction pattern.
Args:
structure (Structure): The input structure.
points (list): All points to be checked.
Returns:
list of TEM_dots
"""
dots = []
interplanar_spacings = self.get_interplanar_spacings(structure, points)
bragg_angles = self.bragg_angles(interplanar_spacings)
cell_intensity = self.normalized_cell_intensity(structure, bragg_angles)
positions = self.get_positions(structure, points)
for hkl, intensity in cell_intensity.items():
dot = namedtuple("dot", ["position", "hkl", "intensity", "film_radius", "d_spacing"])
position = positions[hkl]
film_radius = 0.91 * (10 ** -3 * self.cs * self.wavelength_rel() ** 3) ** Fraction("1/4")
d_spacing = interplanar_spacings[hkl]
tem_dot = dot(position, hkl, intensity, film_radius, d_spacing)
dots.append(tem_dot)
return dots
def get_plot_2d(self, structure: Structure) -> go.Figure:
"""
Generates the 2D diffraction pattern of the input structure.
Args:
structure (Structure): The input structure.
Returns:
Figure
"""
if self.symprec:
finder = SpacegroupAnalyzer(structure, symprec=self.symprec)
structure = finder.get_refined_structure()
points = self.generate_points(-10, 11)
tem_dots = self.tem_dots(structure, points)
xs = []
ys = []
hkls = []
intensities = []
for dot in tem_dots:
xs.append(dot.position[0])
ys.append(dot.position[1])
hkls.append(str(dot.hkl))
intensities.append(dot.intensity)
hkls = list(map(unicodeify_spacegroup, list(map(latexify_spacegroup, hkls))))
data = [
go.Scatter(
x=xs,
y=ys,
text=hkls,
hoverinfo="text",
mode="markers",
marker=dict(
size=8,
cmax=1,
cmin=0,
color=intensities,
colorscale=[[0, "black"], [1.0, "white"]],
),
showlegend=False,
),
go.Scatter(
x=[0],
y=[0],
text="(0, 0, 0): Direct beam",
hoverinfo="text",
mode="markers",
marker=dict(size=14, cmax=1, cmin=0, color="white"),
showlegend=False,
),
]
layout = go.Layout(
title="2D Diffraction Pattern<br>Beam Direction: " + "".join(str(e) for e in self.beam_direction),
font=dict(size=14, color="#7f7f7f"),
hovermode="closest",
xaxis=dict(
range=[-4, 4],
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
),
yaxis=dict(
range=[-4, 4],
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
),
width=550,
height=550,
paper_bgcolor="rgba(100,110,110,0.5)",
plot_bgcolor="black",
)
fig = go.Figure(data=data, layout=layout)
return fig
def get_plot_2d_concise(self, structure: Structure) -> go.Figure:
"""
Generates the concise 2D diffraction pattern of the input structure of a smaller size and without layout.
Does not display.
Args:
structure (Structure): The input structure.
Returns:
Figure
"""
if self.symprec:
finder = SpacegroupAnalyzer(structure, symprec=self.symprec)
structure = finder.get_refined_structure()
points = self.generate_points(-10, 11)
tem_dots = self.tem_dots(structure, points)
xs = []
ys = []
hkls = []
intensities = []
for dot in tem_dots:
if dot.hkl != (0, 0, 0):
xs.append(dot.position[0])
ys.append(dot.position[1])
hkls.append(dot.hkl)
intensities.append(dot.intensity)
data = [
go.Scatter(
x=xs,
y=ys,
text=hkls,
mode="markers",
hoverinfo="skip",
marker=dict(
size=4,
cmax=1,
cmin=0,
color=intensities,
colorscale=[[0, "black"], [1.0, "white"]],
),
showlegend=False,
)
]
layout = go.Layout(
xaxis=dict(
range=[-4, 4],
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
),
yaxis=dict(
range=[-4, 4],
showgrid=False,
zeroline=False,
showline=False,
ticks="",
showticklabels=False,
),
plot_bgcolor="black",
margin={"l": 0, "r": 0, "t": 0, "b": 0},
width=121,
height=121,
)
fig = go.Figure(data=data, layout=layout)
fig.layout.update(showlegend=False)
return fig
| vorwerkc/pymatgen | pymatgen/analysis/diffraction/tem.py | Python | mit | 27,037 | [
"CRYSTAL",
"pymatgen"
] | dec4eb1dbd6dd8fdeea7ef20de0a2956c99244de93c5b7f15393fa8c59c91bd6 |
#
# @BEGIN LICENSE
#
# Psi4: an open-source quantum chemistry software package
#
# Copyright (c) 2007-2022 The Psi4 Developers.
#
# The copyrights for code used from other parties are included in
# the corresponding files.
#
# This file is part of Psi4.
#
# Psi4 is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, version 3.
#
# Psi4 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with Psi4; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
# @END LICENSE
#
"""Module with property-related helper functions."""
import psi4
from . import optproc
__all__ = ['free_atom_volumes']
def free_atom_volumes(wfn, **kwargs):
"""
Computes free-atom volumes using MBIS density partitioning.
The free-atom volumes are computed for all unique (inc. basis set)
atoms in a molecule and stored as wavefunction variables.
Free-atom densities are computed at the same level of theory as the molecule,
and we use unrestricted references as needed in computing the ground-state.
The free-atom volumes are used to compute volume ratios in routine MBIS computations
Parameters
----------
wfn : psi4.core.Wavefunction
The wave function associated with the molecule, method, and basis for
atomic computations
"""
# If we're already a free atom, break to avoid recursion
# We don't ever need volume ratios for free atoms since they
# are by definition 1.0
natom = wfn.molecule().natom()
if natom == 1:
return 0
# the level of theory
current_en = wfn.scalar_variable('CURRENT ENERGY')
total_energies = [k for k, v in wfn.scalar_variables().items() if abs(v - current_en) <= 1e-12]
theory = ""
for var in total_energies:
if 'TOTAL ENERGY' in var:
var = var.split()
if var[0] == 'SCF':
continue
elif var[0] == 'DFT':
theory = wfn.functional().name()
else:
theory = var[0]
# list of reference number of unpaired electrons.
# Note that this is not the same as the
# total spin of the ground state atom
reference_S = [
0, 1, 0, 1, 0, 1, 2, 3, 2, 1, 0, 1, 0, 1, 2, 3, 2, 1, 0, 1, 0, 1, 2, 3, 6, 5, 4, 3, 2, 1, 0, 1, 2, 3, 2, 1, 0,
1, 0, 1, 2, 5, 6, 5, 4, 3, 0, 1, 0, 1, 2, 3, 2, 1, 0, 1, 0, 1, 0, 3, 4, 5, 6, 7, 8, 5, 4, 3, 2, 1, 0, 1, 2, 3,
4, 5, 4, 3, 2, 1, 0, 1, 2, 3, 2, 1, 0
]
# the parent molecule and reference type
mol = wfn.molecule()
# Get unique atoms by input symbol,
# Be to handle different basis sets
unq_atoms = set()
for atom in range(mol.natom()):
symbol = mol.symbol(atom)
Z = int(mol.Z(atom))
basis = mol.basis_on_atom(atom)
unq_atoms.add((symbol, Z, basis))
psi4.core.print_out(f" Running {len(unq_atoms)} free-atom UHF computations")
optstash = optproc.OptionsState(["SCF", 'REFERENCE'])
for a_sym, a_z, basis in unq_atoms:
# make sure we do UHF/UKS if we're not a singlet
if reference_S[a_z] != 0:
psi4.core.set_local_option("SCF", "REFERENCE", "UHF")
else:
psi4.core.set_local_option("SCF", "REFERENCE", "RHF")
# Set the molecule, here just an atom
a_mol = psi4.core.Molecule.from_arrays(geom=[0, 0, 0],
elem=[a_sym],
molecular_charge=0,
molecular_multiplicity=int(1 + reference_S[a_z]))
a_mol.update_geometry()
psi4.molutil.activate(a_mol)
method = theory + "/" + basis
# Get the atomic wfn
at_e, at_wfn = psi4.energy(method, return_wfn=True)
# Now, re-run mbis for the atomic density, grabbing only the volume
psi4.oeprop(at_wfn, 'MBIS_CHARGES', title=a_sym + " " + method, free_atom=True)
vw = at_wfn.array_variable('MBIS RADIAL MOMENTS <R^3>') # P::e OEPROP
vw = vw.get(0, 0)
# set the atomic widths as wfn variables
wfn.set_variable("MBIS FREE ATOM " + a_sym.upper() + " VOLUME", vw)
# set_variable("MBIS FREE ATOM n VOLUME") # P::e OEPROP
psi4.core.clean()
psi4.core.clean_variables()
# reset mol and reference to original
optstash.restore()
mol.update_geometry()
psi4.molutil.activate(mol)
| psi4/psi4 | psi4/driver/p4util/prop_util.py | Python | lgpl-3.0 | 4,828 | [
"Psi4"
] | 35e96927a29f760ba67b76e91eb3538880f5c88aefb834f181aa054810a6938f |
''' Volume filter
This example script shows how to filter a volume using the PySPIDER interface. This
script can be used to lowpass filter a volume or, alternatively, bandpass filter
the volume using SPIDER commands.
Note that the code contains a "comment", a line starting with a `#`. This
comment explains the code on that line.
While this script is included with the installed package, it is intended
to be edited before being run. It is recommended you download it from
the link below.
:download:`Download script <../../arachnid/snippets/pyspider/filter_volume.py>`
.. seealso::
- List of :py:class:`SPIDER Commands <arachnid.core.spider.spider.Session>`
- :py:func:`open_session <arachnid.core.spider.spider.open_session>`
- :py:meth:`FQ <arachnid.core.spider.spider.Session.fq>`
- :py:meth:`CP <arachnid.core.spider.spider.Session.cp>`
Requirements
- :doc:`Installation <../install>` of the Arachnid Python package
- Installation of the SPIDER package (binary)
- Download: http://www.wadsworth.org/spider_doc/spider/docs/spi-register.html
- Install: http://www.wadsworth.org/spider_doc/spider/docs/installation.html
To run:
.. sourcecode:: sh
$ python filter_volume.py
.. literalinclude:: ../../arachnid/snippets/pyspider/filter_volume.py
:language: python
:lines: 42-
:linenos:
'''
from arachnid.core.spider import spider
if __name__ == '__main__': # This line is not necessary for script execution, but helps when building the documentation
input_volume = "input_volume.spi" # Filename for input file (should have extension)
output_volume = "filtered_output_volume" # Filename for output file (extension optional)
pixel_size = 1.2 # Pixel size of the volume
resolution = 20 # Resolution to lowpass filter the volume
# *** Uncomment the following to filter from 80 to 20 angstroms ***
#resolution = (80, 20)
# Create a SPIDER session using the extension of the input_volume
# - Alternatively, you can specify the extension with data_ext="dat" for the .dat extension
# - If no input file is given and no extension specified, the default is "spi"
# - Note that, if you specify an extension then this overrides the extension of the input file
spi = spider.open_session([input_volume], spider_path="", thread_count=0, enable_results=False, data_ext="")
# Test whether to perform a band pass filter
if not isinstance(resolution, tuple):
# Filter the volume using the Gaussian lowpass filter to the specified `resolution` with the given `pixel_size`
spi.fq(input_volume, spi.GAUS_LP, filter_radius=pixel_size/resolution, outputfile=output_volume) # Filter input_volume and write to output_volume
else:
# Filter the volume using the Butterworth highpass and Gaussian lowpass filter to the specified high and low `resolution` with the given `pixel_size`
involume = spi.cp(input_volume) # Read volume to an incore file
filtvolume = spi.fq(involume, spi.BUTER_LP, filter_radius=pixel_size/resolution[0]) # Highpass filter incore volume with Butterworth
filtvolume = spi.fq(filtvolume, spi.GAUS_HP, filter_radius=pixel_size/resolution[1]) # Lowpass filter incore volume with Gaussian
spi.cp(filtvolume, outputfile=output_volume) # Write incore filtered volume to a file
# The above example can be shortened to two lines as follows
if 1 == 0: # Do not run the following code - for illustrative purposes
filtvolume = spi.fq(involume, spi.BUTER_LP, filter_radius=pixel_size/resolution[0]) # Highpass filter incore volume with Butterworth
spi.fq(filtvolume, spi.GAUS_HP, filter_radius=pixel_size/resolution[1], outputfile=output_volume) # Lowpass filter incore volume with Gaussian
| ezralanglois/arachnid | arachnid/snippets/pyspider/filter_volume.py | Python | gpl-2.0 | 4,176 | [
"Gaussian"
] | eee892393d7056da56a21107557d4038f9908b7e53e274cac62fd63fcbe36d2b |
# Copyright 2018 Brian May
#
# This file is part of python-tldap.
#
# python-tldap is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# python-tldap is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with python-tldap If not, see <http://www.gnu.org/licenses/>.
""" Dictionary related classes. """
from typing import TypeVar, KeysView, ItemsView, Optional, Set, Dict
Entity = TypeVar('Entity', bound='CaseInsensitiveDict')
class CaseInsensitiveDict:
"""
Case insensitve dictionary for searches however preserves the case for
retrieval. Needs to be supplied with a set of allowed keys.
"""
def __init__(self, allowed_keys: Set[str], d: Optional[dict] = None) -> None:
self._lc: Dict[str, str] = {
value.lower(): value for value in allowed_keys
}
self._dict = dict()
if d is not None:
for k, v in d.items():
self[k] = v
def fix_key(self, key: str) -> str:
key = key.lower()
if key not in self._lc:
raise KeyError(key)
return self._lc[key.lower()]
def __setitem__(self, key: str, value: any):
key = self.fix_key(key)
self._dict.__setitem__(key, value)
def __delitem__(self, key: str):
key = self.fix_key(key)
del self._lc[key]
self._dict.__delitem__(key)
def __getitem__(self, key: str):
key = self.fix_key(key)
return self._dict.__getitem__(key)
def __contains__(self, key: str):
key = self.fix_key(key)
return self._dict.__contains__(key)
def get(self, key: str, default: any = None):
key = self.fix_key(key)
return self._dict.get(key, default)
def keys(self) -> KeysView[str]:
return self._dict.keys()
def items(self) -> ItemsView[str, any]:
return self._dict.items()
def to_dict(self) -> dict:
return self._dict
ImmutableDictEntity = TypeVar('ImmutableDictEntity', bound='ImmutableDict')
class ImmutableDict:
"""
Immutable dictionary that cannot be changed without creating a new instance.
"""
def __init__(self, allowed_keys: Optional[Set[str]] = None, d: Optional[dict] = None) -> None:
self._allowed_keys = allowed_keys
self._dict = CaseInsensitiveDict(allowed_keys)
if d is not None:
for key, value in d.items():
self._set(key, value)
def fix_key(self, key: str) -> str:
return self._dict.fix_key(key)
def __getitem__(self, key: str):
return self._dict.__getitem__(key)
def get(self, key: str, default: any = None):
key = self.fix_key(key)
try:
return self._dict.get(key, default)
except KeyError:
return default
def __contains__(self, key: str):
return self._dict.__contains__(key)
def keys(self) -> KeysView[str]:
return self._dict.keys()
def items(self) -> ItemsView[str, any]:
return self._dict.items()
def __copy__(self: ImmutableDictEntity) -> ImmutableDictEntity:
return self.__class__(self._allowed_keys, self._dict)
def _set(self, key: str, value: any) -> None:
self._dict[key] = value
def merge(self: ImmutableDictEntity, d: dict) -> ImmutableDictEntity:
clone = self.__copy__()
for key, value in d.items():
clone._set(key, value)
return clone
def set(self: ImmutableDictEntity, key: str, value: any) -> ImmutableDictEntity:
clone = self.__copy__()
clone._set(key, value)
return clone
def to_dict(self) -> dict:
return self._dict.to_dict()
| brianmay/python-tldap | tldap/dict.py | Python | gpl-3.0 | 4,084 | [
"Brian"
] | d2a322ed0338aa1094c4e291337bc52ec469341fa8a48cca161428f597f404fd |
"""
The geometric sector decomposition routines.
"""
from .common import Sector, refactorize
from ..polytope import convex_hull, triangulate, Polytope
from ..algebra import Polynomial, ExponentiatedPolynomial, Product
import itertools, numpy as np, sympy as sp
# *********************** primary decomposition ***********************
def Cheng_Wu(sector, index=-1):
'''
Replace one Feynman parameter by one.
This means integrating out the Dirac
delta according to the Cheng-Wu theorem.
:param sector:
:class:`.Sector`;
The container holding the polynomials (typically
:math:`U` and :math:`F`) to eliminate the Dirac
delta from.
:param index:
integer, optional;
The index of the Feynman parameter to eliminate.
Default: -1 (the last Feynman parameter)
'''
# do not remove the only parameter from the `polysymbols`
remove = sector.number_of_variables != 1
Jacobian = sector.Jacobian.replace(index, 1, remove)
other = [poly.replace(index, 1, remove) for poly in sector.other]
cast = [Product( *(product.factors[i].replace(index, 1, remove) for i in (0,1)) ) for product in sector.cast]
return Sector(cast, other, Jacobian)
# ********************** geometric decomposition **********************
def generate_fan(*polynomials):
'''
Calculate the fan of the polynomials in the input. The rays of a
cone are given by the exponent vectors after factoring out a monomial
together with the standard basis vectors. Each choice of factored out
monomials gives a different cone.
Only full (:math:`N`-) dimensional cones in :math:`R^N_{\geq 0}` need to be
considered.
:param polynomials:
abritrarily many instances of :class:`.Polynomial` where
all of these have an equal number of variables;
The polynomials to calculate the fan for.
'''
expolists = [poly.expolist for poly in polynomials]
factors = itertools.product(*expolists)
number_of_variables = polynomials[0].number_of_variables
identity_polynomial = Polynomial(np.identity(number_of_variables, dtype=int), np.ones(number_of_variables, dtype=int), copy=False)
fan = []
for factor in factors:
factor = np.array(factor)
# reshape to use numpy's broadcasting
cone = np.vstack( tuple(expolists[i] - factor[i].reshape(1,number_of_variables) for i in range(len(polynomials))) )
# use `Polynomial` class to remove duplicates
cone_poly = Polynomial(cone, np.ones(len(cone),dtype=int), copy=False)
cone_poly += identity_polynomial # implicit simplify
for i,hyperplane in enumerate(cone_poly.expolist):
if (hyperplane > 0).all() or (hyperplane == 0).all():
cone_poly.coeffs[i] = 0
cone = cone_poly.simplify().expolist
if (
len(cone) >= number_of_variables and
not (cone < 0).all(axis=1).any() and # if one hyperplane has only negative entries do not append to fan
not any( (hyperplane==cone).all(axis=-1).any() for hyperplane in -cone ) # do not append cones that have `hyperplane` and `-hyperplane`
):
fan.append(cone)
return fan
def transform_variables(polynomial, transformation, polysymbols='y'):
r'''
Transform the parameters :math:`x_i` of a
:class:`pySecDec.algebra.Polynomial`,
.. math::
x_i \rightarrow \prod_j x_j^{T_{ij}}
, where :math:`T_{ij}` is the transformation matrix.
:param polynomial:
:class:`pySecDec.algebra.Polynomial`;
The polynomial to transform the variables in.
:param transformation:
two dimensional array;
The transformation matrix :math:`T_{ij}`.
:param polysymbols:
string or iterable of strings;
The symbols for the new variables. This argument
is passed to the default constructor of
:class:`pySecDec.algebra.Polynomial`.
Refer to the documentation of
:class:`pySecDec.algebra.Polynomial`
for further details.
'''
new_expolist = polynomial.expolist.dot(transformation)
number_of_new_variables = transformation.shape[-1]
if isinstance(polysymbols, str):
polysymbols = [polysymbols + str(i) for i in range(number_of_new_variables)]
# keep the type (`polynomial` can have a subtype of `Polynomial`)
outpoly = polynomial.copy()
outpoly.expolist = new_expolist
outpoly.polysymbols = polysymbols
outpoly.number_of_variables = number_of_new_variables
return outpoly
def geometric_decomposition(sector, indices=None, normaliz='normaliz', workdir='normaliz_tmp'):
'''
Run the sector decomposition using the geomethod
as described in [BHJ+15]_.
.. note::
This function calls the command line executable of
`normaliz` [BIR]_. See :ref:`installation_normaliz`
for installation and a list of tested versions.
:param sector:
:class:`.Sector`;
The sector to be decomposed.
:param indices:
list of integers or None;
The indices of the parameters to be considered as
integration variables. By default (``indices=None``),
all parameters are considered as integration
variables.
:param normaliz:
string;
The shell command to run `normaliz`.
:param workdir:
string;
The directory for the communication with `normaliz`.
A directory with the specified name will be created
in the current working directory. If the specified
directory name already exists, an :class:`OSError`
is raised.
.. note::
The communication with `normaliz` is done via
files.
'''
original_sector = sector
sector = original_sector.copy()
if indices is None:
indices = range(original_sector.number_of_variables)
else:
# remove parameters that are not in `indices`
indices = list(indices)
sector.number_of_variables = len(indices)
sector.Jacobian.number_of_variables = len(indices)
sector.Jacobian.expolist = sector.Jacobian.expolist[:,indices]
sector.Jacobian.polysymbols = [sector.Jacobian.polysymbols[i] for i in indices]
for product in sector.cast:
for factor in product.factors:
factor.number_of_variables = len(indices)
factor.expolist = factor.expolist[:,indices]
factor.polysymbols = [factor.polysymbols[i] for i in indices]
for poly in sector.other:
poly.number_of_variables = len(indices)
poly.expolist = poly.expolist[:,indices]
poly.polysymbols = [poly.polysymbols[i] for i in indices]
dim = sector.number_of_variables
polytope_vertices = convex_hull( *(product.factors[1] for product in sector.cast) )
polytope = Polytope(vertices=polytope_vertices)
polytope.complete_representation(normaliz, workdir)
transformation = polytope.facets.T[:-1] # do not need offset term "a_F"
incidence_lists = polytope.vertex_incidence_lists()
# transform the variables for every polynomial of the sector
sector.Jacobian = transform_variables(sector.Jacobian, transformation, sector.Jacobian.polysymbols)
for i,product in enumerate(sector.cast):
transformed_monomial = transform_variables(product.factors[0], transformation, product.factors[0].polysymbols)
transformed_polynomial = transform_variables(product.factors[1], transformation, product.factors[1].polysymbols)
sector.cast[i] = Product(transformed_monomial, transformed_polynomial)
for i,polynomial in enumerate(sector.other):
sector.other[i] = transform_variables(polynomial, transformation, polynomial.polysymbols)
# this transformation produces an extra Jacobian factor
# can multiply part encoded in the `expolist` here but the coefficient is specific for each subsector
sector.Jacobian *= Polynomial([transformation.sum(axis=0) - 1], [1])
def make_sector(cone_indices, cone):
subsector = original_sector.copy()
Jacobian_coeff = abs(np.linalg.det(cone))
Jacobian_coeff_as_int = int(Jacobian_coeff + 0.5) # `Jacobian_coeff` is integral but numpy calculates it as float
assert abs(Jacobian_coeff_as_int - Jacobian_coeff) < 1.0e-5 * abs(Jacobian_coeff)
subsector.Jacobian *= Jacobian_coeff_as_int
# set variables to one that are not in `cone_indices`
number_of_variables = len(cone_indices) + original_sector.number_of_variables - len(indices)
assert number_of_variables == original_sector.number_of_variables
subsector.Jacobian.expolist[:,indices] = sector.Jacobian.expolist[:,cone_indices]
for resulting_product, output_product in zip(sector.cast, subsector.cast):
for j in range(2):
output_product.factors[j].expolist[:,indices] = resulting_product.factors[j].expolist[:,cone_indices]
refactorize(output_product)
for resulting_polynomial, output_polynomial in zip(sector.other, subsector.other):
output_polynomial.expolist[:,indices] = resulting_polynomial.expolist[:,cone_indices]
return subsector
for cone_indices in incidence_lists.values():
cone = transformation[:,cone_indices].T
# triangluate where neccessary
if len(cone_indices) != dim:
# assert len(cone) > dim # --> this check is done by `triangulate`
triangular_cones = triangulate(cone, normaliz, workdir)
assert len(triangular_cones.shape) == 3
for i, triangular_cone in enumerate(triangular_cones):
triangular_cone_indices = []
for vector in triangular_cone:
# find the indices of the vectors defining the triangular cone
triangular_cone_indices.append(int( np.where( (vector == transformation.T).all(axis=1) )[0] ))
yield make_sector(triangular_cone_indices, triangular_cone)
else:
yield make_sector(cone_indices, cone)
def geometric_decomposition_ku(sector, indices=None, normaliz='normaliz', workdir='normaliz_tmp'):
'''
Run the sector decomposition using the original geometric
decomposition strategy by Kaneko and Ueda as described
in [KU10]_.
.. note::
This function calls the command line executable of
`normaliz` [BIR]_. See :ref:`installation_normaliz`
for installation and a list of tested versions.
:param sector:
:class:`.Sector`;
The sector to be decomposed.
:param indices:
list of integers or None;
The indices of the parameters to be considered as
integration variables. By default (``indices=None``),
all parameters are considered as integration
variables.
:param normaliz:
string;
The shell command to run `normaliz`.
:param workdir:
string;
The directory for the communication with `normaliz`.
A directory with the specified name will be created
in the current working directory. If the specified
directory name already exists, an :class:`OSError`
is raised.
.. note::
The communication with `normaliz` is done via
files.
'''
original_sector = sector
sector = original_sector.copy()
if indices is None:
indices = range(original_sector.number_of_variables)
else:
# remove parameters that are not in `indices`
indices = list(indices)
sector.number_of_variables = len(indices)
sector.Jacobian.number_of_variables = len(indices)
sector.Jacobian.expolist = sector.Jacobian.expolist[:,indices]
sector.Jacobian.polysymbols = [sector.Jacobian.polysymbols[i] for i in indices]
for product in sector.cast:
for factor in product.factors:
factor.number_of_variables = len(indices)
factor.expolist = factor.expolist[:,indices]
factor.polysymbols = [factor.polysymbols[i] for i in indices]
for poly in sector.other:
poly.number_of_variables = len(indices)
poly.expolist = poly.expolist[:,indices]
poly.polysymbols = [poly.polysymbols[i] for i in indices]
def make_sector_ku(cone):
subsector = original_sector.copy()
transformation = np.identity(original_sector.number_of_variables, dtype = int)
index_array = np.array(indices)
transformation[index_array.reshape(-1,1),index_array] = cone
Jacobian_coeff = abs(np.linalg.det(cone))
Jacobian_coeff_as_int = int(Jacobian_coeff + 0.5) # `Jacobian_coeff` is integral but numpy calculates it as float
assert abs(Jacobian_coeff_as_int - Jacobian_coeff) < 1.0e-5 * abs(Jacobian_coeff)
subsector.Jacobian = Jacobian_coeff_as_int*transform_variables(subsector.Jacobian, transformation, subsector.Jacobian.polysymbols)
for i,product in enumerate(subsector.cast):
transformed_monomial = transform_variables(product.factors[0], transformation, product.factors[0].polysymbols)
transformed_polynomial = transform_variables(product.factors[1], transformation, product.factors[1].polysymbols)
subsector.cast[i] = Product(transformed_monomial, transformed_polynomial)
refactorize(subsector.cast[i])
for i,polynomial in enumerate(subsector.other):
subsector.other[i] = transform_variables(polynomial, transformation, polynomial.polysymbols)
# this transformation produces an extra Jacobian factor
# can multiply part encoded in the `expolist` here but the coefficient is specific for each subsector
subsector.Jacobian *= Polynomial([transformation.sum(axis=0) - 1], [1])
return subsector
fan = generate_fan( *(product.factors[1] for product in sector.cast) )
for cone in fan:
for dualcone in triangulate(cone, normaliz, workdir, switch_representation=True):
# exclude lower dimensional cones
if dualcone.shape[0] == cone.shape[1]:
yield make_sector_ku(dualcone.T)
| mppmu/secdec | pySecDec/decomposition/geometric.py | Python | gpl-3.0 | 14,294 | [
"DIRAC"
] | 1211476378b4c2df977fe92bbb4f65c6066d5aa580cba3f5218284bd4c961a7a |
# -*- coding: utf-8 -*-
"""Main IPython class."""
#-----------------------------------------------------------------------------
# Copyright (C) 2001 Janko Hauser <jhauser@zscout.de>
# Copyright (C) 2001-2007 Fernando Perez. <fperez@colorado.edu>
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-----------------------------------------------------------------------------
from __future__ import absolute_import, print_function
import __future__
import abc
import ast
import atexit
import functools
import os
import re
import runpy
import sys
import tempfile
import traceback
import types
import subprocess
from io import open as io_open
from IPython.config.configurable import SingletonConfigurable
from IPython.core import debugger, oinspect
from IPython.core import magic
from IPython.core import page
from IPython.core import prefilter
from IPython.core import shadowns
from IPython.core import ultratb
from IPython.core.alias import Alias, AliasManager
from IPython.core.autocall import ExitAutocall
from IPython.core.builtin_trap import BuiltinTrap
from IPython.core.events import EventManager, available_events
from IPython.core.compilerop import CachingCompiler, check_linecache_ipython
from IPython.core.display_trap import DisplayTrap
from IPython.core.displayhook import DisplayHook
from IPython.core.displaypub import DisplayPublisher
from IPython.core.error import InputRejected, UsageError
from IPython.core.extensions import ExtensionManager
from IPython.core.formatters import DisplayFormatter
from IPython.core.history import HistoryManager
from IPython.core.inputsplitter import IPythonInputSplitter, ESC_MAGIC, ESC_MAGIC2
from IPython.core.logger import Logger
from IPython.core.macro import Macro
from IPython.core.payload import PayloadManager
from IPython.core.prefilter import PrefilterManager
from IPython.core.profiledir import ProfileDir
from IPython.core.prompts import PromptManager
from IPython.core.usage import default_banner
from IPython.testing.skipdoctest import skip_doctest
from IPython.utils import PyColorize
from IPython.utils import io
from IPython.utils import py3compat
from IPython.utils import openpy
from IPython.utils.decorators import undoc
from IPython.utils.io import ask_yes_no
from IPython.utils.ipstruct import Struct
from IPython.utils.path import get_home_dir, get_ipython_dir, get_py_filename, unquote_filename, ensure_dir_exists
from IPython.utils.pickleshare import PickleShareDB
from IPython.utils.process import system, getoutput
from IPython.utils.py3compat import (builtin_mod, unicode_type, string_types,
with_metaclass, iteritems)
from IPython.utils.strdispatch import StrDispatch
from IPython.utils.syspathcontext import prepended_to_syspath
from IPython.utils.text import (format_screen, LSString, SList,
DollarFormatter)
from IPython.utils.traitlets import (Integer, Bool, CBool, CaselessStrEnum, Enum,
List, Unicode, Instance, Type)
from IPython.utils.warn import warn, error
import IPython.core.hooks
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
# compiled regexps for autoindent management
dedent_re = re.compile(r'^\s+raise|^\s+return|^\s+pass')
#-----------------------------------------------------------------------------
# Utilities
#-----------------------------------------------------------------------------
@undoc
def softspace(file, newvalue):
"""Copied from code.py, to remove the dependency"""
oldvalue = 0
try:
oldvalue = file.softspace
except AttributeError:
pass
try:
file.softspace = newvalue
except (AttributeError, TypeError):
# "attribute-less object" or "read-only attributes"
pass
return oldvalue
@undoc
def no_op(*a, **kw): pass
@undoc
class NoOpContext(object):
def __enter__(self): pass
def __exit__(self, type, value, traceback): pass
no_op_context = NoOpContext()
class SpaceInInput(Exception): pass
@undoc
class Bunch: pass
def get_default_colors():
if sys.platform=='darwin':
return "LightBG"
elif os.name=='nt':
return 'Linux'
else:
return 'Linux'
class SeparateUnicode(Unicode):
r"""A Unicode subclass to validate separate_in, separate_out, etc.
This is a Unicode based trait that converts '0'->'' and ``'\\n'->'\n'``.
"""
def validate(self, obj, value):
if value == '0': value = ''
value = value.replace('\\n','\n')
return super(SeparateUnicode, self).validate(obj, value)
class ReadlineNoRecord(object):
"""Context manager to execute some code, then reload readline history
so that interactive input to the code doesn't appear when pressing up."""
def __init__(self, shell):
self.shell = shell
self._nested_level = 0
def __enter__(self):
if self._nested_level == 0:
try:
self.orig_length = self.current_length()
self.readline_tail = self.get_readline_tail()
except (AttributeError, IndexError): # Can fail with pyreadline
self.orig_length, self.readline_tail = 999999, []
self._nested_level += 1
def __exit__(self, type, value, traceback):
self._nested_level -= 1
if self._nested_level == 0:
# Try clipping the end if it's got longer
try:
e = self.current_length() - self.orig_length
if e > 0:
for _ in range(e):
self.shell.readline.remove_history_item(self.orig_length)
# If it still doesn't match, just reload readline history.
if self.current_length() != self.orig_length \
or self.get_readline_tail() != self.readline_tail:
self.shell.refill_readline_hist()
except (AttributeError, IndexError):
pass
# Returning False will cause exceptions to propagate
return False
def current_length(self):
return self.shell.readline.get_current_history_length()
def get_readline_tail(self, n=10):
"""Get the last n items in readline history."""
end = self.shell.readline.get_current_history_length() + 1
start = max(end-n, 1)
ghi = self.shell.readline.get_history_item
return [ghi(x) for x in range(start, end)]
@undoc
class DummyMod(object):
"""A dummy module used for IPython's interactive module when
a namespace must be assigned to the module's __dict__."""
pass
class ExecutionResult(object):
"""The result of a call to :meth:`InteractiveShell.run_cell`
Stores information about what took place.
"""
execution_count = None
error_before_exec = None
error_in_exec = None
result = None
@property
def success(self):
return (self.error_before_exec is None) and (self.error_in_exec is None)
class InteractiveShell(SingletonConfigurable):
"""An enhanced, interactive shell for Python."""
_instance = None
ast_transformers = List([], config=True, help=
"""
A list of ast.NodeTransformer subclass instances, which will be applied
to user input before code is run.
"""
)
autocall = Enum((0,1,2), default_value=0, config=True, help=
"""
Make IPython automatically call any callable object even if you didn't
type explicit parentheses. For example, 'str 43' becomes 'str(43)'
automatically. The value can be '0' to disable the feature, '1' for
'smart' autocall, where it is not applied if there are no more
arguments on the line, and '2' for 'full' autocall, where all callable
objects are automatically called (even if no arguments are present).
"""
)
# TODO: remove all autoindent logic and put into frontends.
# We can't do this yet because even runlines uses the autoindent.
autoindent = CBool(True, config=True, help=
"""
Autoindent IPython code entered interactively.
"""
)
automagic = CBool(True, config=True, help=
"""
Enable magic commands to be called without the leading %.
"""
)
banner1 = Unicode(default_banner, config=True,
help="""The part of the banner to be printed before the profile"""
)
banner2 = Unicode('', config=True,
help="""The part of the banner to be printed after the profile"""
)
cache_size = Integer(1000, config=True, help=
"""
Set the size of the output cache. The default is 1000, you can
change it permanently in your config file. Setting it to 0 completely
disables the caching system, and the minimum value accepted is 20 (if
you provide a value less than 20, it is reset to 0 and a warning is
issued). This limit is defined because otherwise you'll spend more
time re-flushing a too small cache than working
"""
)
color_info = CBool(True, config=True, help=
"""
Use colors for displaying information about objects. Because this
information is passed through a pager (like 'less'), and some pagers
get confused with color codes, this capability can be turned off.
"""
)
colors = CaselessStrEnum(('NoColor','LightBG','Linux'),
default_value=get_default_colors(), config=True,
help="Set the color scheme (NoColor, Linux, or LightBG)."
)
colors_force = CBool(False, help=
"""
Force use of ANSI color codes, regardless of OS and readline
availability.
"""
# FIXME: This is essentially a hack to allow ZMQShell to show colors
# without readline on Win32. When the ZMQ formatting system is
# refactored, this should be removed.
)
debug = CBool(False, config=True)
deep_reload = CBool(False, config=True, help=
"""
Enable deep (recursive) reloading by default. IPython can use the
deep_reload module which reloads changes in modules recursively (it
replaces the reload() function, so you don't need to change anything to
use it). deep_reload() forces a full reload of modules whose code may
have changed, which the default reload() function does not. When
deep_reload is off, IPython will use the normal reload(), but
deep_reload will still be available as dreload().
"""
)
disable_failing_post_execute = CBool(False, config=True,
help="Don't call post-execute functions that have failed in the past."
)
display_formatter = Instance(DisplayFormatter)
displayhook_class = Type(DisplayHook)
display_pub_class = Type(DisplayPublisher)
data_pub_class = None
exit_now = CBool(False)
exiter = Instance(ExitAutocall)
def _exiter_default(self):
return ExitAutocall(self)
# Monotonically increasing execution counter
execution_count = Integer(1)
filename = Unicode("<ipython console>")
ipython_dir= Unicode('', config=True) # Set to get_ipython_dir() in __init__
# Input splitter, to transform input line by line and detect when a block
# is ready to be executed.
input_splitter = Instance('IPython.core.inputsplitter.IPythonInputSplitter',
(), {'line_input_checker': True})
# This InputSplitter instance is used to transform completed cells before
# running them. It allows cell magics to contain blank lines.
input_transformer_manager = Instance('IPython.core.inputsplitter.IPythonInputSplitter',
(), {'line_input_checker': False})
logstart = CBool(False, config=True, help=
"""
Start logging to the default log file in overwrite mode.
Use `logappend` to specify a log file to **append** logs to.
"""
)
logfile = Unicode('', config=True, help=
"""
The name of the logfile to use.
"""
)
logappend = Unicode('', config=True, help=
"""
Start logging to the given file in append mode.
Use `logfile` to specify a log file to **overwrite** logs to.
"""
)
object_info_string_level = Enum((0,1,2), default_value=0,
config=True)
pdb = CBool(False, config=True, help=
"""
Automatically call the pdb debugger after every exception.
"""
)
multiline_history = CBool(sys.platform != 'win32', config=True,
help="Save multi-line entries as one entry in readline history"
)
display_page = Bool(False, config=True,
help="""If True, anything that would be passed to the pager
will be displayed as regular output instead."""
)
# deprecated prompt traits:
prompt_in1 = Unicode('In [\\#]: ', config=True,
help="Deprecated, use PromptManager.in_template")
prompt_in2 = Unicode(' .\\D.: ', config=True,
help="Deprecated, use PromptManager.in2_template")
prompt_out = Unicode('Out[\\#]: ', config=True,
help="Deprecated, use PromptManager.out_template")
prompts_pad_left = CBool(True, config=True,
help="Deprecated, use PromptManager.justify")
def _prompt_trait_changed(self, name, old, new):
table = {
'prompt_in1' : 'in_template',
'prompt_in2' : 'in2_template',
'prompt_out' : 'out_template',
'prompts_pad_left' : 'justify',
}
warn("InteractiveShell.{name} is deprecated, use PromptManager.{newname}".format(
name=name, newname=table[name])
)
# protect against weird cases where self.config may not exist:
if self.config is not None:
# propagate to corresponding PromptManager trait
setattr(self.config.PromptManager, table[name], new)
_prompt_in1_changed = _prompt_trait_changed
_prompt_in2_changed = _prompt_trait_changed
_prompt_out_changed = _prompt_trait_changed
_prompt_pad_left_changed = _prompt_trait_changed
show_rewritten_input = CBool(True, config=True,
help="Show rewritten input, e.g. for autocall."
)
quiet = CBool(False, config=True)
history_length = Integer(10000, config=True)
# The readline stuff will eventually be moved to the terminal subclass
# but for now, we can't do that as readline is welded in everywhere.
readline_use = CBool(True, config=True)
readline_remove_delims = Unicode('-/~', config=True)
readline_delims = Unicode() # set by init_readline()
# don't use \M- bindings by default, because they
# conflict with 8-bit encodings. See gh-58,gh-88
readline_parse_and_bind = List([
'tab: complete',
'"\C-l": clear-screen',
'set show-all-if-ambiguous on',
'"\C-o": tab-insert',
'"\C-r": reverse-search-history',
'"\C-s": forward-search-history',
'"\C-p": history-search-backward',
'"\C-n": history-search-forward',
'"\e[A": history-search-backward',
'"\e[B": history-search-forward',
'"\C-k": kill-line',
'"\C-u": unix-line-discard',
], config=True)
_custom_readline_config = False
def _readline_parse_and_bind_changed(self, name, old, new):
# notice that readline config is customized
# indicates that it should have higher priority than inputrc
self._custom_readline_config = True
ast_node_interactivity = Enum(['all', 'last', 'last_expr', 'none'],
default_value='last_expr', config=True,
help="""
'all', 'last', 'last_expr' or 'none', specifying which nodes should be
run interactively (displaying output from expressions).""")
# TODO: this part of prompt management should be moved to the frontends.
# Use custom TraitTypes that convert '0'->'' and '\\n'->'\n'
separate_in = SeparateUnicode('\n', config=True)
separate_out = SeparateUnicode('', config=True)
separate_out2 = SeparateUnicode('', config=True)
wildcards_case_sensitive = CBool(True, config=True)
xmode = CaselessStrEnum(('Context','Plain', 'Verbose'),
default_value='Context', config=True)
# Subcomponents of InteractiveShell
alias_manager = Instance('IPython.core.alias.AliasManager')
prefilter_manager = Instance('IPython.core.prefilter.PrefilterManager')
builtin_trap = Instance('IPython.core.builtin_trap.BuiltinTrap')
display_trap = Instance('IPython.core.display_trap.DisplayTrap')
extension_manager = Instance('IPython.core.extensions.ExtensionManager')
payload_manager = Instance('IPython.core.payload.PayloadManager')
history_manager = Instance('IPython.core.history.HistoryAccessorBase')
magics_manager = Instance('IPython.core.magic.MagicsManager')
profile_dir = Instance('IPython.core.application.ProfileDir')
@property
def profile(self):
if self.profile_dir is not None:
name = os.path.basename(self.profile_dir.location)
return name.replace('profile_','')
# Private interface
_post_execute = Instance(dict)
# Tracks any GUI loop loaded for pylab
pylab_gui_select = None
def __init__(self, ipython_dir=None, profile_dir=None,
user_module=None, user_ns=None,
custom_exceptions=((), None), **kwargs):
# This is where traits with a config_key argument are updated
# from the values on config.
super(InteractiveShell, self).__init__(**kwargs)
self.configurables = [self]
# These are relatively independent and stateless
self.init_ipython_dir(ipython_dir)
self.init_profile_dir(profile_dir)
self.init_instance_attrs()
self.init_environment()
# Check if we're in a virtualenv, and set up sys.path.
self.init_virtualenv()
# Create namespaces (user_ns, user_global_ns, etc.)
self.init_create_namespaces(user_module, user_ns)
# This has to be done after init_create_namespaces because it uses
# something in self.user_ns, but before init_sys_modules, which
# is the first thing to modify sys.
# TODO: When we override sys.stdout and sys.stderr before this class
# is created, we are saving the overridden ones here. Not sure if this
# is what we want to do.
self.save_sys_module_state()
self.init_sys_modules()
# While we're trying to have each part of the code directly access what
# it needs without keeping redundant references to objects, we have too
# much legacy code that expects ip.db to exist.
self.db = PickleShareDB(os.path.join(self.profile_dir.location, 'db'))
self.init_history()
self.init_encoding()
self.init_prefilter()
self.init_syntax_highlighting()
self.init_hooks()
self.init_events()
self.init_pushd_popd_magic()
# self.init_traceback_handlers use to be here, but we moved it below
# because it and init_io have to come after init_readline.
self.init_user_ns()
self.init_logger()
self.init_builtins()
# The following was in post_config_initialization
self.init_inspector()
# init_readline() must come before init_io(), because init_io uses
# readline related things.
self.init_readline()
# We save this here in case user code replaces raw_input, but it needs
# to be after init_readline(), because PyPy's readline works by replacing
# raw_input.
if py3compat.PY3:
self.raw_input_original = input
else:
self.raw_input_original = raw_input
# init_completer must come after init_readline, because it needs to
# know whether readline is present or not system-wide to configure the
# completers, since the completion machinery can now operate
# independently of readline (e.g. over the network)
self.init_completer()
# TODO: init_io() needs to happen before init_traceback handlers
# because the traceback handlers hardcode the stdout/stderr streams.
# This logic in in debugger.Pdb and should eventually be changed.
self.init_io()
self.init_traceback_handlers(custom_exceptions)
self.init_prompts()
self.init_display_formatter()
self.init_display_pub()
self.init_data_pub()
self.init_displayhook()
self.init_magics()
self.init_alias()
self.init_logstart()
self.init_pdb()
self.init_extension_manager()
self.init_payload()
self.hooks.late_startup_hook()
self.events.trigger('shell_initialized', self)
atexit.register(self.atexit_operations)
def get_ipython(self):
"""Return the currently running IPython instance."""
return self
#-------------------------------------------------------------------------
# Trait changed handlers
#-------------------------------------------------------------------------
def _ipython_dir_changed(self, name, new):
ensure_dir_exists(new)
def set_autoindent(self,value=None):
"""Set the autoindent flag, checking for readline support.
If called with no arguments, it acts as a toggle."""
if value != 0 and not self.has_readline:
if os.name == 'posix':
warn("The auto-indent feature requires the readline library")
self.autoindent = 0
return
if value is None:
self.autoindent = not self.autoindent
else:
self.autoindent = value
#-------------------------------------------------------------------------
# init_* methods called by __init__
#-------------------------------------------------------------------------
def init_ipython_dir(self, ipython_dir):
if ipython_dir is not None:
self.ipython_dir = ipython_dir
return
self.ipython_dir = get_ipython_dir()
def init_profile_dir(self, profile_dir):
if profile_dir is not None:
self.profile_dir = profile_dir
return
self.profile_dir =\
ProfileDir.create_profile_dir_by_name(self.ipython_dir, 'default')
def init_instance_attrs(self):
self.more = False
# command compiler
self.compile = CachingCompiler()
# Make an empty namespace, which extension writers can rely on both
# existing and NEVER being used by ipython itself. This gives them a
# convenient location for storing additional information and state
# their extensions may require, without fear of collisions with other
# ipython names that may develop later.
self.meta = Struct()
# Temporary files used for various purposes. Deleted at exit.
self.tempfiles = []
self.tempdirs = []
# Keep track of readline usage (later set by init_readline)
self.has_readline = False
# keep track of where we started running (mainly for crash post-mortem)
# This is not being used anywhere currently.
self.starting_dir = py3compat.getcwd()
# Indentation management
self.indent_current_nsp = 0
# Dict to track post-execution functions that have been registered
self._post_execute = {}
def init_environment(self):
"""Any changes we need to make to the user's environment."""
pass
def init_encoding(self):
# Get system encoding at startup time. Certain terminals (like Emacs
# under Win32 have it set to None, and we need to have a known valid
# encoding to use in the raw_input() method
try:
self.stdin_encoding = sys.stdin.encoding or 'ascii'
except AttributeError:
self.stdin_encoding = 'ascii'
def init_syntax_highlighting(self):
# Python source parser/formatter for syntax highlighting
pyformat = PyColorize.Parser().format
self.pycolorize = lambda src: pyformat(src,'str',self.colors)
def init_pushd_popd_magic(self):
# for pushd/popd management
self.home_dir = get_home_dir()
self.dir_stack = []
def init_logger(self):
self.logger = Logger(self.home_dir, logfname='ipython_log.py',
logmode='rotate')
def init_logstart(self):
"""Initialize logging in case it was requested at the command line.
"""
if self.logappend:
self.magic('logstart %s append' % self.logappend)
elif self.logfile:
self.magic('logstart %s' % self.logfile)
elif self.logstart:
self.magic('logstart')
def init_builtins(self):
# A single, static flag that we set to True. Its presence indicates
# that an IPython shell has been created, and we make no attempts at
# removing on exit or representing the existence of more than one
# IPython at a time.
builtin_mod.__dict__['__IPYTHON__'] = True
# In 0.11 we introduced '__IPYTHON__active' as an integer we'd try to
# manage on enter/exit, but with all our shells it's virtually
# impossible to get all the cases right. We're leaving the name in for
# those who adapted their codes to check for this flag, but will
# eventually remove it after a few more releases.
builtin_mod.__dict__['__IPYTHON__active'] = \
'Deprecated, check for __IPYTHON__'
self.builtin_trap = BuiltinTrap(shell=self)
def init_inspector(self):
# Object inspector
self.inspector = oinspect.Inspector(oinspect.InspectColors,
PyColorize.ANSICodeColors,
'NoColor',
self.object_info_string_level)
def init_io(self):
# This will just use sys.stdout and sys.stderr. If you want to
# override sys.stdout and sys.stderr themselves, you need to do that
# *before* instantiating this class, because io holds onto
# references to the underlying streams.
if (sys.platform == 'win32' or sys.platform == 'cli') and self.has_readline:
io.stdout = io.stderr = io.IOStream(self.readline._outputfile)
else:
io.stdout = io.IOStream(sys.stdout)
io.stderr = io.IOStream(sys.stderr)
def init_prompts(self):
self.prompt_manager = PromptManager(shell=self, parent=self)
self.configurables.append(self.prompt_manager)
# Set system prompts, so that scripts can decide if they are running
# interactively.
sys.ps1 = 'In : '
sys.ps2 = '...: '
sys.ps3 = 'Out: '
def init_display_formatter(self):
self.display_formatter = DisplayFormatter(parent=self)
self.configurables.append(self.display_formatter)
def init_display_pub(self):
self.display_pub = self.display_pub_class(parent=self)
self.configurables.append(self.display_pub)
def init_data_pub(self):
if not self.data_pub_class:
self.data_pub = None
return
self.data_pub = self.data_pub_class(parent=self)
self.configurables.append(self.data_pub)
def init_displayhook(self):
# Initialize displayhook, set in/out prompts and printing system
self.displayhook = self.displayhook_class(
parent=self,
shell=self,
cache_size=self.cache_size,
)
self.configurables.append(self.displayhook)
# This is a context manager that installs/revmoes the displayhook at
# the appropriate time.
self.display_trap = DisplayTrap(hook=self.displayhook)
def init_virtualenv(self):
"""Add a virtualenv to sys.path so the user can import modules from it.
This isn't perfect: it doesn't use the Python interpreter with which the
virtualenv was built, and it ignores the --no-site-packages option. A
warning will appear suggesting the user installs IPython in the
virtualenv, but for many cases, it probably works well enough.
Adapted from code snippets online.
http://blog.ufsoft.org/2009/1/29/ipython-and-virtualenv
"""
if 'VIRTUAL_ENV' not in os.environ:
# Not in a virtualenv
return
# venv detection:
# stdlib venv may symlink sys.executable, so we can't use realpath.
# but others can symlink *to* the venv Python, so we can't just use sys.executable.
# So we just check every item in the symlink tree (generally <= 3)
p = os.path.normcase(sys.executable)
paths = [p]
while os.path.islink(p):
p = os.path.normcase(os.path.join(os.path.dirname(p), os.readlink(p)))
paths.append(p)
p_venv = os.path.normcase(os.environ['VIRTUAL_ENV'])
if any(p.startswith(p_venv) for p in paths):
# Running properly in the virtualenv, don't need to do anything
return
warn("Attempting to work in a virtualenv. If you encounter problems, please "
"install IPython inside the virtualenv.")
if sys.platform == "win32":
virtual_env = os.path.join(os.environ['VIRTUAL_ENV'], 'Lib', 'site-packages')
else:
virtual_env = os.path.join(os.environ['VIRTUAL_ENV'], 'lib',
'python%d.%d' % sys.version_info[:2], 'site-packages')
import site
sys.path.insert(0, virtual_env)
site.addsitedir(virtual_env)
#-------------------------------------------------------------------------
# Things related to injections into the sys module
#-------------------------------------------------------------------------
def save_sys_module_state(self):
"""Save the state of hooks in the sys module.
This has to be called after self.user_module is created.
"""
self._orig_sys_module_state = {}
self._orig_sys_module_state['stdin'] = sys.stdin
self._orig_sys_module_state['stdout'] = sys.stdout
self._orig_sys_module_state['stderr'] = sys.stderr
self._orig_sys_module_state['excepthook'] = sys.excepthook
self._orig_sys_modules_main_name = self.user_module.__name__
self._orig_sys_modules_main_mod = sys.modules.get(self.user_module.__name__)
def restore_sys_module_state(self):
"""Restore the state of the sys module."""
try:
for k, v in iteritems(self._orig_sys_module_state):
setattr(sys, k, v)
except AttributeError:
pass
# Reset what what done in self.init_sys_modules
if self._orig_sys_modules_main_mod is not None:
sys.modules[self._orig_sys_modules_main_name] = self._orig_sys_modules_main_mod
#-------------------------------------------------------------------------
# Things related to the banner
#-------------------------------------------------------------------------
@property
def banner(self):
banner = self.banner1
if self.profile and self.profile != 'default':
banner += '\nIPython profile: %s\n' % self.profile
if self.banner2:
banner += '\n' + self.banner2
return banner
def show_banner(self, banner=None):
if banner is None:
banner = self.banner
self.write(banner)
#-------------------------------------------------------------------------
# Things related to hooks
#-------------------------------------------------------------------------
def init_hooks(self):
# hooks holds pointers used for user-side customizations
self.hooks = Struct()
self.strdispatchers = {}
# Set all default hooks, defined in the IPython.hooks module.
hooks = IPython.core.hooks
for hook_name in hooks.__all__:
# default hooks have priority 100, i.e. low; user hooks should have
# 0-100 priority
self.set_hook(hook_name,getattr(hooks,hook_name), 100, _warn_deprecated=False)
if self.display_page:
self.set_hook('show_in_pager', page.as_hook(page.display_page), 90)
def set_hook(self,name,hook, priority=50, str_key=None, re_key=None,
_warn_deprecated=True):
"""set_hook(name,hook) -> sets an internal IPython hook.
IPython exposes some of its internal API as user-modifiable hooks. By
adding your function to one of these hooks, you can modify IPython's
behavior to call at runtime your own routines."""
# At some point in the future, this should validate the hook before it
# accepts it. Probably at least check that the hook takes the number
# of args it's supposed to.
f = types.MethodType(hook,self)
# check if the hook is for strdispatcher first
if str_key is not None:
sdp = self.strdispatchers.get(name, StrDispatch())
sdp.add_s(str_key, f, priority )
self.strdispatchers[name] = sdp
return
if re_key is not None:
sdp = self.strdispatchers.get(name, StrDispatch())
sdp.add_re(re.compile(re_key), f, priority )
self.strdispatchers[name] = sdp
return
dp = getattr(self.hooks, name, None)
if name not in IPython.core.hooks.__all__:
print("Warning! Hook '%s' is not one of %s" % \
(name, IPython.core.hooks.__all__ ))
if _warn_deprecated and (name in IPython.core.hooks.deprecated):
alternative = IPython.core.hooks.deprecated[name]
warn("Hook {} is deprecated. Use {} instead.".format(name, alternative))
if not dp:
dp = IPython.core.hooks.CommandChainDispatcher()
try:
dp.add(f,priority)
except AttributeError:
# it was not commandchain, plain old func - replace
dp = f
setattr(self.hooks,name, dp)
#-------------------------------------------------------------------------
# Things related to events
#-------------------------------------------------------------------------
def init_events(self):
self.events = EventManager(self, available_events)
self.events.register("pre_execute", self._clear_warning_registry)
def register_post_execute(self, func):
"""DEPRECATED: Use ip.events.register('post_run_cell', func)
Register a function for calling after code execution.
"""
warn("ip.register_post_execute is deprecated, use "
"ip.events.register('post_run_cell', func) instead.")
self.events.register('post_run_cell', func)
def _clear_warning_registry(self):
# clear the warning registry, so that different code blocks with
# overlapping line number ranges don't cause spurious suppression of
# warnings (see gh-6611 for details)
if "__warningregistry__" in self.user_global_ns:
del self.user_global_ns["__warningregistry__"]
#-------------------------------------------------------------------------
# Things related to the "main" module
#-------------------------------------------------------------------------
def new_main_mod(self, filename, modname):
"""Return a new 'main' module object for user code execution.
``filename`` should be the path of the script which will be run in the
module. Requests with the same filename will get the same module, with
its namespace cleared.
``modname`` should be the module name - normally either '__main__' or
the basename of the file without the extension.
When scripts are executed via %run, we must keep a reference to their
__main__ module around so that Python doesn't
clear it, rendering references to module globals useless.
This method keeps said reference in a private dict, keyed by the
absolute path of the script. This way, for multiple executions of the
same script we only keep one copy of the namespace (the last one),
thus preventing memory leaks from old references while allowing the
objects from the last execution to be accessible.
"""
filename = os.path.abspath(filename)
try:
main_mod = self._main_mod_cache[filename]
except KeyError:
main_mod = self._main_mod_cache[filename] = types.ModuleType(
py3compat.cast_bytes_py2(modname),
doc="Module created for script run in IPython")
else:
main_mod.__dict__.clear()
main_mod.__name__ = modname
main_mod.__file__ = filename
# It seems pydoc (and perhaps others) needs any module instance to
# implement a __nonzero__ method
main_mod.__nonzero__ = lambda : True
return main_mod
def clear_main_mod_cache(self):
"""Clear the cache of main modules.
Mainly for use by utilities like %reset.
Examples
--------
In [15]: import IPython
In [16]: m = _ip.new_main_mod(IPython.__file__, 'IPython')
In [17]: len(_ip._main_mod_cache) > 0
Out[17]: True
In [18]: _ip.clear_main_mod_cache()
In [19]: len(_ip._main_mod_cache) == 0
Out[19]: True
"""
self._main_mod_cache.clear()
#-------------------------------------------------------------------------
# Things related to debugging
#-------------------------------------------------------------------------
def init_pdb(self):
# Set calling of pdb on exceptions
# self.call_pdb is a property
self.call_pdb = self.pdb
def _get_call_pdb(self):
return self._call_pdb
def _set_call_pdb(self,val):
if val not in (0,1,False,True):
raise ValueError('new call_pdb value must be boolean')
# store value in instance
self._call_pdb = val
# notify the actual exception handlers
self.InteractiveTB.call_pdb = val
call_pdb = property(_get_call_pdb,_set_call_pdb,None,
'Control auto-activation of pdb at exceptions')
def debugger(self,force=False):
"""Call the pydb/pdb debugger.
Keywords:
- force(False): by default, this routine checks the instance call_pdb
flag and does not actually invoke the debugger if the flag is false.
The 'force' option forces the debugger to activate even if the flag
is false.
"""
if not (force or self.call_pdb):
return
if not hasattr(sys,'last_traceback'):
error('No traceback has been produced, nothing to debug.')
return
# use pydb if available
if debugger.has_pydb:
from pydb import pm
else:
# fallback to our internal debugger
pm = lambda : self.InteractiveTB.debugger(force=True)
with self.readline_no_record:
pm()
#-------------------------------------------------------------------------
# Things related to IPython's various namespaces
#-------------------------------------------------------------------------
default_user_namespaces = True
def init_create_namespaces(self, user_module=None, user_ns=None):
# Create the namespace where the user will operate. user_ns is
# normally the only one used, and it is passed to the exec calls as
# the locals argument. But we do carry a user_global_ns namespace
# given as the exec 'globals' argument, This is useful in embedding
# situations where the ipython shell opens in a context where the
# distinction between locals and globals is meaningful. For
# non-embedded contexts, it is just the same object as the user_ns dict.
# FIXME. For some strange reason, __builtins__ is showing up at user
# level as a dict instead of a module. This is a manual fix, but I
# should really track down where the problem is coming from. Alex
# Schmolck reported this problem first.
# A useful post by Alex Martelli on this topic:
# Re: inconsistent value from __builtins__
# Von: Alex Martelli <aleaxit@yahoo.com>
# Datum: Freitag 01 Oktober 2004 04:45:34 nachmittags/abends
# Gruppen: comp.lang.python
# Michael Hohn <hohn@hooknose.lbl.gov> wrote:
# > >>> print type(builtin_check.get_global_binding('__builtins__'))
# > <type 'dict'>
# > >>> print type(__builtins__)
# > <type 'module'>
# > Is this difference in return value intentional?
# Well, it's documented that '__builtins__' can be either a dictionary
# or a module, and it's been that way for a long time. Whether it's
# intentional (or sensible), I don't know. In any case, the idea is
# that if you need to access the built-in namespace directly, you
# should start with "import __builtin__" (note, no 's') which will
# definitely give you a module. Yeah, it's somewhat confusing:-(.
# These routines return a properly built module and dict as needed by
# the rest of the code, and can also be used by extension writers to
# generate properly initialized namespaces.
if (user_ns is not None) or (user_module is not None):
self.default_user_namespaces = False
self.user_module, self.user_ns = self.prepare_user_module(user_module, user_ns)
# A record of hidden variables we have added to the user namespace, so
# we can list later only variables defined in actual interactive use.
self.user_ns_hidden = {}
# Now that FakeModule produces a real module, we've run into a nasty
# problem: after script execution (via %run), the module where the user
# code ran is deleted. Now that this object is a true module (needed
# so docetst and other tools work correctly), the Python module
# teardown mechanism runs over it, and sets to None every variable
# present in that module. Top-level references to objects from the
# script survive, because the user_ns is updated with them. However,
# calling functions defined in the script that use other things from
# the script will fail, because the function's closure had references
# to the original objects, which are now all None. So we must protect
# these modules from deletion by keeping a cache.
#
# To avoid keeping stale modules around (we only need the one from the
# last run), we use a dict keyed with the full path to the script, so
# only the last version of the module is held in the cache. Note,
# however, that we must cache the module *namespace contents* (their
# __dict__). Because if we try to cache the actual modules, old ones
# (uncached) could be destroyed while still holding references (such as
# those held by GUI objects that tend to be long-lived)>
#
# The %reset command will flush this cache. See the cache_main_mod()
# and clear_main_mod_cache() methods for details on use.
# This is the cache used for 'main' namespaces
self._main_mod_cache = {}
# A table holding all the namespaces IPython deals with, so that
# introspection facilities can search easily.
self.ns_table = {'user_global':self.user_module.__dict__,
'user_local':self.user_ns,
'builtin':builtin_mod.__dict__
}
@property
def user_global_ns(self):
return self.user_module.__dict__
def prepare_user_module(self, user_module=None, user_ns=None):
"""Prepare the module and namespace in which user code will be run.
When IPython is started normally, both parameters are None: a new module
is created automatically, and its __dict__ used as the namespace.
If only user_module is provided, its __dict__ is used as the namespace.
If only user_ns is provided, a dummy module is created, and user_ns
becomes the global namespace. If both are provided (as they may be
when embedding), user_ns is the local namespace, and user_module
provides the global namespace.
Parameters
----------
user_module : module, optional
The current user module in which IPython is being run. If None,
a clean module will be created.
user_ns : dict, optional
A namespace in which to run interactive commands.
Returns
-------
A tuple of user_module and user_ns, each properly initialised.
"""
if user_module is None and user_ns is not None:
user_ns.setdefault("__name__", "__main__")
user_module = DummyMod()
user_module.__dict__ = user_ns
if user_module is None:
user_module = types.ModuleType("__main__",
doc="Automatically created module for IPython interactive environment")
# We must ensure that __builtin__ (without the final 's') is always
# available and pointing to the __builtin__ *module*. For more details:
# http://mail.python.org/pipermail/python-dev/2001-April/014068.html
user_module.__dict__.setdefault('__builtin__', builtin_mod)
user_module.__dict__.setdefault('__builtins__', builtin_mod)
if user_ns is None:
user_ns = user_module.__dict__
return user_module, user_ns
def init_sys_modules(self):
# We need to insert into sys.modules something that looks like a
# module but which accesses the IPython namespace, for shelve and
# pickle to work interactively. Normally they rely on getting
# everything out of __main__, but for embedding purposes each IPython
# instance has its own private namespace, so we can't go shoving
# everything into __main__.
# note, however, that we should only do this for non-embedded
# ipythons, which really mimic the __main__.__dict__ with their own
# namespace. Embedded instances, on the other hand, should not do
# this because they need to manage the user local/global namespaces
# only, but they live within a 'normal' __main__ (meaning, they
# shouldn't overtake the execution environment of the script they're
# embedded in).
# This is overridden in the InteractiveShellEmbed subclass to a no-op.
main_name = self.user_module.__name__
sys.modules[main_name] = self.user_module
def init_user_ns(self):
"""Initialize all user-visible namespaces to their minimum defaults.
Certain history lists are also initialized here, as they effectively
act as user namespaces.
Notes
-----
All data structures here are only filled in, they are NOT reset by this
method. If they were not empty before, data will simply be added to
therm.
"""
# This function works in two parts: first we put a few things in
# user_ns, and we sync that contents into user_ns_hidden so that these
# initial variables aren't shown by %who. After the sync, we add the
# rest of what we *do* want the user to see with %who even on a new
# session (probably nothing, so theye really only see their own stuff)
# The user dict must *always* have a __builtin__ reference to the
# Python standard __builtin__ namespace, which must be imported.
# This is so that certain operations in prompt evaluation can be
# reliably executed with builtins. Note that we can NOT use
# __builtins__ (note the 's'), because that can either be a dict or a
# module, and can even mutate at runtime, depending on the context
# (Python makes no guarantees on it). In contrast, __builtin__ is
# always a module object, though it must be explicitly imported.
# For more details:
# http://mail.python.org/pipermail/python-dev/2001-April/014068.html
ns = dict()
# make global variables for user access to the histories
ns['_ih'] = self.history_manager.input_hist_parsed
ns['_oh'] = self.history_manager.output_hist
ns['_dh'] = self.history_manager.dir_hist
ns['_sh'] = shadowns
# user aliases to input and output histories. These shouldn't show up
# in %who, as they can have very large reprs.
ns['In'] = self.history_manager.input_hist_parsed
ns['Out'] = self.history_manager.output_hist
# Store myself as the public api!!!
ns['get_ipython'] = self.get_ipython
ns['exit'] = self.exiter
ns['quit'] = self.exiter
# Sync what we've added so far to user_ns_hidden so these aren't seen
# by %who
self.user_ns_hidden.update(ns)
# Anything put into ns now would show up in %who. Think twice before
# putting anything here, as we really want %who to show the user their
# stuff, not our variables.
# Finally, update the real user's namespace
self.user_ns.update(ns)
@property
def all_ns_refs(self):
"""Get a list of references to all the namespace dictionaries in which
IPython might store a user-created object.
Note that this does not include the displayhook, which also caches
objects from the output."""
return [self.user_ns, self.user_global_ns, self.user_ns_hidden] + \
[m.__dict__ for m in self._main_mod_cache.values()]
def reset(self, new_session=True):
"""Clear all internal namespaces, and attempt to release references to
user objects.
If new_session is True, a new history session will be opened.
"""
# Clear histories
self.history_manager.reset(new_session)
# Reset counter used to index all histories
if new_session:
self.execution_count = 1
# Flush cached output items
if self.displayhook.do_full_cache:
self.displayhook.flush()
# The main execution namespaces must be cleared very carefully,
# skipping the deletion of the builtin-related keys, because doing so
# would cause errors in many object's __del__ methods.
if self.user_ns is not self.user_global_ns:
self.user_ns.clear()
ns = self.user_global_ns
drop_keys = set(ns.keys())
drop_keys.discard('__builtin__')
drop_keys.discard('__builtins__')
drop_keys.discard('__name__')
for k in drop_keys:
del ns[k]
self.user_ns_hidden.clear()
# Restore the user namespaces to minimal usability
self.init_user_ns()
# Restore the default and user aliases
self.alias_manager.clear_aliases()
self.alias_manager.init_aliases()
# Flush the private list of module references kept for script
# execution protection
self.clear_main_mod_cache()
def del_var(self, varname, by_name=False):
"""Delete a variable from the various namespaces, so that, as
far as possible, we're not keeping any hidden references to it.
Parameters
----------
varname : str
The name of the variable to delete.
by_name : bool
If True, delete variables with the given name in each
namespace. If False (default), find the variable in the user
namespace, and delete references to it.
"""
if varname in ('__builtin__', '__builtins__'):
raise ValueError("Refusing to delete %s" % varname)
ns_refs = self.all_ns_refs
if by_name: # Delete by name
for ns in ns_refs:
try:
del ns[varname]
except KeyError:
pass
else: # Delete by object
try:
obj = self.user_ns[varname]
except KeyError:
raise NameError("name '%s' is not defined" % varname)
# Also check in output history
ns_refs.append(self.history_manager.output_hist)
for ns in ns_refs:
to_delete = [n for n, o in iteritems(ns) if o is obj]
for name in to_delete:
del ns[name]
# displayhook keeps extra references, but not in a dictionary
for name in ('_', '__', '___'):
if getattr(self.displayhook, name) is obj:
setattr(self.displayhook, name, None)
def reset_selective(self, regex=None):
"""Clear selective variables from internal namespaces based on a
specified regular expression.
Parameters
----------
regex : string or compiled pattern, optional
A regular expression pattern that will be used in searching
variable names in the users namespaces.
"""
if regex is not None:
try:
m = re.compile(regex)
except TypeError:
raise TypeError('regex must be a string or compiled pattern')
# Search for keys in each namespace that match the given regex
# If a match is found, delete the key/value pair.
for ns in self.all_ns_refs:
for var in ns:
if m.search(var):
del ns[var]
def push(self, variables, interactive=True):
"""Inject a group of variables into the IPython user namespace.
Parameters
----------
variables : dict, str or list/tuple of str
The variables to inject into the user's namespace. If a dict, a
simple update is done. If a str, the string is assumed to have
variable names separated by spaces. A list/tuple of str can also
be used to give the variable names. If just the variable names are
give (list/tuple/str) then the variable values looked up in the
callers frame.
interactive : bool
If True (default), the variables will be listed with the ``who``
magic.
"""
vdict = None
# We need a dict of name/value pairs to do namespace updates.
if isinstance(variables, dict):
vdict = variables
elif isinstance(variables, string_types+(list, tuple)):
if isinstance(variables, string_types):
vlist = variables.split()
else:
vlist = variables
vdict = {}
cf = sys._getframe(1)
for name in vlist:
try:
vdict[name] = eval(name, cf.f_globals, cf.f_locals)
except:
print('Could not get variable %s from %s' %
(name,cf.f_code.co_name))
else:
raise ValueError('variables must be a dict/str/list/tuple')
# Propagate variables to user namespace
self.user_ns.update(vdict)
# And configure interactive visibility
user_ns_hidden = self.user_ns_hidden
if interactive:
for name in vdict:
user_ns_hidden.pop(name, None)
else:
user_ns_hidden.update(vdict)
def drop_by_id(self, variables):
"""Remove a dict of variables from the user namespace, if they are the
same as the values in the dictionary.
This is intended for use by extensions: variables that they've added can
be taken back out if they are unloaded, without removing any that the
user has overwritten.
Parameters
----------
variables : dict
A dictionary mapping object names (as strings) to the objects.
"""
for name, obj in iteritems(variables):
if name in self.user_ns and self.user_ns[name] is obj:
del self.user_ns[name]
self.user_ns_hidden.pop(name, None)
#-------------------------------------------------------------------------
# Things related to object introspection
#-------------------------------------------------------------------------
def _ofind(self, oname, namespaces=None):
"""Find an object in the available namespaces.
self._ofind(oname) -> dict with keys: found,obj,ospace,ismagic
Has special code to detect magic functions.
"""
oname = oname.strip()
#print '1- oname: <%r>' % oname # dbg
if not oname.startswith(ESC_MAGIC) and \
not oname.startswith(ESC_MAGIC2) and \
not py3compat.isidentifier(oname, dotted=True):
return dict(found=False)
alias_ns = None
if namespaces is None:
# Namespaces to search in:
# Put them in a list. The order is important so that we
# find things in the same order that Python finds them.
namespaces = [ ('Interactive', self.user_ns),
('Interactive (global)', self.user_global_ns),
('Python builtin', builtin_mod.__dict__),
]
# initialize results to 'null'
found = False; obj = None; ospace = None; ds = None;
ismagic = False; isalias = False; parent = None
# We need to special-case 'print', which as of python2.6 registers as a
# function but should only be treated as one if print_function was
# loaded with a future import. In this case, just bail.
if (oname == 'print' and not py3compat.PY3 and not \
(self.compile.compiler_flags & __future__.CO_FUTURE_PRINT_FUNCTION)):
return {'found':found, 'obj':obj, 'namespace':ospace,
'ismagic':ismagic, 'isalias':isalias, 'parent':parent}
# Look for the given name by splitting it in parts. If the head is
# found, then we look for all the remaining parts as members, and only
# declare success if we can find them all.
oname_parts = oname.split('.')
oname_head, oname_rest = oname_parts[0],oname_parts[1:]
for nsname,ns in namespaces:
try:
obj = ns[oname_head]
except KeyError:
continue
else:
#print 'oname_rest:', oname_rest # dbg
for idx, part in enumerate(oname_rest):
try:
parent = obj
# The last part is looked up in a special way to avoid
# descriptor invocation as it may raise or have side
# effects.
if idx == len(oname_rest) - 1:
obj = self._getattr_property(obj, part)
else:
obj = getattr(obj, part)
except:
# Blanket except b/c some badly implemented objects
# allow __getattr__ to raise exceptions other than
# AttributeError, which then crashes IPython.
break
else:
# If we finish the for loop (no break), we got all members
found = True
ospace = nsname
break # namespace loop
# Try to see if it's magic
if not found:
obj = None
if oname.startswith(ESC_MAGIC2):
oname = oname.lstrip(ESC_MAGIC2)
obj = self.find_cell_magic(oname)
elif oname.startswith(ESC_MAGIC):
oname = oname.lstrip(ESC_MAGIC)
obj = self.find_line_magic(oname)
else:
# search without prefix, so run? will find %run?
obj = self.find_line_magic(oname)
if obj is None:
obj = self.find_cell_magic(oname)
if obj is not None:
found = True
ospace = 'IPython internal'
ismagic = True
isalias = isinstance(obj, Alias)
# Last try: special-case some literals like '', [], {}, etc:
if not found and oname_head in ["''",'""','[]','{}','()']:
obj = eval(oname_head)
found = True
ospace = 'Interactive'
return {'found':found, 'obj':obj, 'namespace':ospace,
'ismagic':ismagic, 'isalias':isalias, 'parent':parent}
@staticmethod
def _getattr_property(obj, attrname):
"""Property-aware getattr to use in object finding.
If attrname represents a property, return it unevaluated (in case it has
side effects or raises an error.
"""
if not isinstance(obj, type):
try:
# `getattr(type(obj), attrname)` is not guaranteed to return
# `obj`, but does so for property:
#
# property.__get__(self, None, cls) -> self
#
# The universal alternative is to traverse the mro manually
# searching for attrname in class dicts.
attr = getattr(type(obj), attrname)
except AttributeError:
pass
else:
# This relies on the fact that data descriptors (with both
# __get__ & __set__ magic methods) take precedence over
# instance-level attributes:
#
# class A(object):
# @property
# def foobar(self): return 123
# a = A()
# a.__dict__['foobar'] = 345
# a.foobar # == 123
#
# So, a property may be returned right away.
if isinstance(attr, property):
return attr
# Nothing helped, fall back.
return getattr(obj, attrname)
def _object_find(self, oname, namespaces=None):
"""Find an object and return a struct with info about it."""
return Struct(self._ofind(oname, namespaces))
def _inspect(self, meth, oname, namespaces=None, **kw):
"""Generic interface to the inspector system.
This function is meant to be called by pdef, pdoc & friends."""
info = self._object_find(oname, namespaces)
if info.found:
pmethod = getattr(self.inspector, meth)
formatter = format_screen if info.ismagic else None
if meth == 'pdoc':
pmethod(info.obj, oname, formatter)
elif meth == 'pinfo':
pmethod(info.obj, oname, formatter, info, **kw)
else:
pmethod(info.obj, oname)
else:
print('Object `%s` not found.' % oname)
return 'not found' # so callers can take other action
def object_inspect(self, oname, detail_level=0):
"""Get object info about oname"""
with self.builtin_trap:
info = self._object_find(oname)
if info.found:
return self.inspector.info(info.obj, oname, info=info,
detail_level=detail_level
)
else:
return oinspect.object_info(name=oname, found=False)
def object_inspect_text(self, oname, detail_level=0):
"""Get object info as formatted text"""
with self.builtin_trap:
info = self._object_find(oname)
if info.found:
return self.inspector._format_info(info.obj, oname, info=info,
detail_level=detail_level
)
else:
raise KeyError(oname)
#-------------------------------------------------------------------------
# Things related to history management
#-------------------------------------------------------------------------
def init_history(self):
"""Sets up the command history, and starts regular autosaves."""
self.history_manager = HistoryManager(shell=self, parent=self)
self.configurables.append(self.history_manager)
#-------------------------------------------------------------------------
# Things related to exception handling and tracebacks (not debugging)
#-------------------------------------------------------------------------
def init_traceback_handlers(self, custom_exceptions):
# Syntax error handler.
self.SyntaxTB = ultratb.SyntaxTB(color_scheme='NoColor')
# The interactive one is initialized with an offset, meaning we always
# want to remove the topmost item in the traceback, which is our own
# internal code. Valid modes: ['Plain','Context','Verbose']
self.InteractiveTB = ultratb.AutoFormattedTB(mode = 'Plain',
color_scheme='NoColor',
tb_offset = 1,
check_cache=check_linecache_ipython)
# The instance will store a pointer to the system-wide exception hook,
# so that runtime code (such as magics) can access it. This is because
# during the read-eval loop, it may get temporarily overwritten.
self.sys_excepthook = sys.excepthook
# and add any custom exception handlers the user may have specified
self.set_custom_exc(*custom_exceptions)
# Set the exception mode
self.InteractiveTB.set_mode(mode=self.xmode)
def set_custom_exc(self, exc_tuple, handler):
"""set_custom_exc(exc_tuple,handler)
Set a custom exception handler, which will be called if any of the
exceptions in exc_tuple occur in the mainloop (specifically, in the
run_code() method).
Parameters
----------
exc_tuple : tuple of exception classes
A *tuple* of exception classes, for which to call the defined
handler. It is very important that you use a tuple, and NOT A
LIST here, because of the way Python's except statement works. If
you only want to trap a single exception, use a singleton tuple::
exc_tuple == (MyCustomException,)
handler : callable
handler must have the following signature::
def my_handler(self, etype, value, tb, tb_offset=None):
...
return structured_traceback
Your handler must return a structured traceback (a list of strings),
or None.
This will be made into an instance method (via types.MethodType)
of IPython itself, and it will be called if any of the exceptions
listed in the exc_tuple are caught. If the handler is None, an
internal basic one is used, which just prints basic info.
To protect IPython from crashes, if your handler ever raises an
exception or returns an invalid result, it will be immediately
disabled.
WARNING: by putting in your own exception handler into IPython's main
execution loop, you run a very good chance of nasty crashes. This
facility should only be used if you really know what you are doing."""
assert type(exc_tuple)==type(()) , \
"The custom exceptions must be given AS A TUPLE."
def dummy_handler(self,etype,value,tb,tb_offset=None):
print('*** Simple custom exception handler ***')
print('Exception type :',etype)
print('Exception value:',value)
print('Traceback :',tb)
#print 'Source code :','\n'.join(self.buffer)
def validate_stb(stb):
"""validate structured traceback return type
return type of CustomTB *should* be a list of strings, but allow
single strings or None, which are harmless.
This function will *always* return a list of strings,
and will raise a TypeError if stb is inappropriate.
"""
msg = "CustomTB must return list of strings, not %r" % stb
if stb is None:
return []
elif isinstance(stb, string_types):
return [stb]
elif not isinstance(stb, list):
raise TypeError(msg)
# it's a list
for line in stb:
# check every element
if not isinstance(line, string_types):
raise TypeError(msg)
return stb
if handler is None:
wrapped = dummy_handler
else:
def wrapped(self,etype,value,tb,tb_offset=None):
"""wrap CustomTB handler, to protect IPython from user code
This makes it harder (but not impossible) for custom exception
handlers to crash IPython.
"""
try:
stb = handler(self,etype,value,tb,tb_offset=tb_offset)
return validate_stb(stb)
except:
# clear custom handler immediately
self.set_custom_exc((), None)
print("Custom TB Handler failed, unregistering", file=io.stderr)
# show the exception in handler first
stb = self.InteractiveTB.structured_traceback(*sys.exc_info())
print(self.InteractiveTB.stb2text(stb), file=io.stdout)
print("The original exception:", file=io.stdout)
stb = self.InteractiveTB.structured_traceback(
(etype,value,tb), tb_offset=tb_offset
)
return stb
self.CustomTB = types.MethodType(wrapped,self)
self.custom_exceptions = exc_tuple
def excepthook(self, etype, value, tb):
"""One more defense for GUI apps that call sys.excepthook.
GUI frameworks like wxPython trap exceptions and call
sys.excepthook themselves. I guess this is a feature that
enables them to keep running after exceptions that would
otherwise kill their mainloop. This is a bother for IPython
which excepts to catch all of the program exceptions with a try:
except: statement.
Normally, IPython sets sys.excepthook to a CrashHandler instance, so if
any app directly invokes sys.excepthook, it will look to the user like
IPython crashed. In order to work around this, we can disable the
CrashHandler and replace it with this excepthook instead, which prints a
regular traceback using our InteractiveTB. In this fashion, apps which
call sys.excepthook will generate a regular-looking exception from
IPython, and the CrashHandler will only be triggered by real IPython
crashes.
This hook should be used sparingly, only in places which are not likely
to be true IPython errors.
"""
self.showtraceback((etype, value, tb), tb_offset=0)
def _get_exc_info(self, exc_tuple=None):
"""get exc_info from a given tuple, sys.exc_info() or sys.last_type etc.
Ensures sys.last_type,value,traceback hold the exc_info we found,
from whichever source.
raises ValueError if none of these contain any information
"""
if exc_tuple is None:
etype, value, tb = sys.exc_info()
else:
etype, value, tb = exc_tuple
if etype is None:
if hasattr(sys, 'last_type'):
etype, value, tb = sys.last_type, sys.last_value, \
sys.last_traceback
if etype is None:
raise ValueError("No exception to find")
# Now store the exception info in sys.last_type etc.
# WARNING: these variables are somewhat deprecated and not
# necessarily safe to use in a threaded environment, but tools
# like pdb depend on their existence, so let's set them. If we
# find problems in the field, we'll need to revisit their use.
sys.last_type = etype
sys.last_value = value
sys.last_traceback = tb
return etype, value, tb
def show_usage_error(self, exc):
"""Show a short message for UsageErrors
These are special exceptions that shouldn't show a traceback.
"""
self.write_err("UsageError: %s" % exc)
def get_exception_only(self, exc_tuple=None):
"""
Return as a string (ending with a newline) the exception that
just occurred, without any traceback.
"""
etype, value, tb = self._get_exc_info(exc_tuple)
msg = traceback.format_exception_only(etype, value)
return ''.join(msg)
def showtraceback(self, exc_tuple=None, filename=None, tb_offset=None,
exception_only=False):
"""Display the exception that just occurred.
If nothing is known about the exception, this is the method which
should be used throughout the code for presenting user tracebacks,
rather than directly invoking the InteractiveTB object.
A specific showsyntaxerror() also exists, but this method can take
care of calling it if needed, so unless you are explicitly catching a
SyntaxError exception, don't try to analyze the stack manually and
simply call this method."""
try:
try:
etype, value, tb = self._get_exc_info(exc_tuple)
except ValueError:
self.write_err('No traceback available to show.\n')
return
if issubclass(etype, SyntaxError):
# Though this won't be called by syntax errors in the input
# line, there may be SyntaxError cases with imported code.
self.showsyntaxerror(filename)
elif etype is UsageError:
self.show_usage_error(value)
else:
if exception_only:
stb = ['An exception has occurred, use %tb to see '
'the full traceback.\n']
stb.extend(self.InteractiveTB.get_exception_only(etype,
value))
else:
try:
# Exception classes can customise their traceback - we
# use this in IPython.parallel for exceptions occurring
# in the engines. This should return a list of strings.
stb = value._render_traceback_()
except Exception:
stb = self.InteractiveTB.structured_traceback(etype,
value, tb, tb_offset=tb_offset)
self._showtraceback(etype, value, stb)
if self.call_pdb:
# drop into debugger
self.debugger(force=True)
return
# Actually show the traceback
self._showtraceback(etype, value, stb)
except KeyboardInterrupt:
self.write_err('\n' + self.get_exception_only())
def _showtraceback(self, etype, evalue, stb):
"""Actually show a traceback.
Subclasses may override this method to put the traceback on a different
place, like a side channel.
"""
print(self.InteractiveTB.stb2text(stb), file=io.stdout)
def showsyntaxerror(self, filename=None):
"""Display the syntax error that just occurred.
This doesn't display a stack trace because there isn't one.
If a filename is given, it is stuffed in the exception instead
of what was there before (because Python's parser always uses
"<string>" when reading from a string).
"""
etype, value, last_traceback = self._get_exc_info()
if filename and issubclass(etype, SyntaxError):
try:
value.filename = filename
except:
# Not the format we expect; leave it alone
pass
stb = self.SyntaxTB.structured_traceback(etype, value, [])
self._showtraceback(etype, value, stb)
# This is overridden in TerminalInteractiveShell to show a message about
# the %paste magic.
def showindentationerror(self):
"""Called by run_cell when there's an IndentationError in code entered
at the prompt.
This is overridden in TerminalInteractiveShell to show a message about
the %paste magic."""
self.showsyntaxerror()
#-------------------------------------------------------------------------
# Things related to readline
#-------------------------------------------------------------------------
def init_readline(self):
"""Command history completion/saving/reloading."""
if self.readline_use:
import IPython.utils.rlineimpl as readline
self.rl_next_input = None
self.rl_do_indent = False
if not self.readline_use or not readline.have_readline:
self.has_readline = False
self.readline = None
# Set a number of methods that depend on readline to be no-op
self.readline_no_record = no_op_context
self.set_readline_completer = no_op
self.set_custom_completer = no_op
if self.readline_use:
warn('Readline services not available or not loaded.')
else:
self.has_readline = True
self.readline = readline
sys.modules['readline'] = readline
# Platform-specific configuration
if os.name == 'nt':
# FIXME - check with Frederick to see if we can harmonize
# naming conventions with pyreadline to avoid this
# platform-dependent check
self.readline_startup_hook = readline.set_pre_input_hook
else:
self.readline_startup_hook = readline.set_startup_hook
# Readline config order:
# - IPython config (default value)
# - custom inputrc
# - IPython config (user customized)
# load IPython config before inputrc if default
# skip if libedit because parse_and_bind syntax is different
if not self._custom_readline_config and not readline.uses_libedit:
for rlcommand in self.readline_parse_and_bind:
readline.parse_and_bind(rlcommand)
# Load user's initrc file (readline config)
# Or if libedit is used, load editrc.
inputrc_name = os.environ.get('INPUTRC')
if inputrc_name is None:
inputrc_name = '.inputrc'
if readline.uses_libedit:
inputrc_name = '.editrc'
inputrc_name = os.path.join(self.home_dir, inputrc_name)
if os.path.isfile(inputrc_name):
try:
readline.read_init_file(inputrc_name)
except:
warn('Problems reading readline initialization file <%s>'
% inputrc_name)
# load IPython config after inputrc if user has customized
if self._custom_readline_config:
for rlcommand in self.readline_parse_and_bind:
readline.parse_and_bind(rlcommand)
# Remove some chars from the delimiters list. If we encounter
# unicode chars, discard them.
delims = readline.get_completer_delims()
if not py3compat.PY3:
delims = delims.encode("ascii", "ignore")
for d in self.readline_remove_delims:
delims = delims.replace(d, "")
delims = delims.replace(ESC_MAGIC, '')
readline.set_completer_delims(delims)
# Store these so we can restore them if something like rpy2 modifies
# them.
self.readline_delims = delims
# otherwise we end up with a monster history after a while:
readline.set_history_length(self.history_length)
self.refill_readline_hist()
self.readline_no_record = ReadlineNoRecord(self)
# Configure auto-indent for all platforms
self.set_autoindent(self.autoindent)
def refill_readline_hist(self):
# Load the last 1000 lines from history
self.readline.clear_history()
stdin_encoding = sys.stdin.encoding or "utf-8"
last_cell = u""
for _, _, cell in self.history_manager.get_tail(1000,
include_latest=True):
# Ignore blank lines and consecutive duplicates
cell = cell.rstrip()
if cell and (cell != last_cell):
try:
if self.multiline_history:
self.readline.add_history(py3compat.unicode_to_str(cell,
stdin_encoding))
else:
for line in cell.splitlines():
self.readline.add_history(py3compat.unicode_to_str(line,
stdin_encoding))
last_cell = cell
except TypeError:
# The history DB can get corrupted so it returns strings
# containing null bytes, which readline objects to.
continue
@skip_doctest
def set_next_input(self, s, replace=False):
""" Sets the 'default' input string for the next command line.
Requires readline.
Example::
In [1]: _ip.set_next_input("Hello Word")
In [2]: Hello Word_ # cursor is here
"""
self.rl_next_input = py3compat.cast_bytes_py2(s)
# Maybe move this to the terminal subclass?
def pre_readline(self):
"""readline hook to be used at the start of each line.
Currently it handles auto-indent only."""
if self.rl_do_indent:
self.readline.insert_text(self._indent_current_str())
if self.rl_next_input is not None:
self.readline.insert_text(self.rl_next_input)
self.rl_next_input = None
def _indent_current_str(self):
"""return the current level of indentation as a string"""
return self.input_splitter.indent_spaces * ' '
#-------------------------------------------------------------------------
# Things related to text completion
#-------------------------------------------------------------------------
def init_completer(self):
"""Initialize the completion machinery.
This creates completion machinery that can be used by client code,
either interactively in-process (typically triggered by the readline
library), programatically (such as in test suites) or out-of-prcess
(typically over the network by remote frontends).
"""
from IPython.core.completer import IPCompleter
from IPython.core.completerlib import (module_completer,
magic_run_completer, cd_completer, reset_completer)
self.Completer = IPCompleter(shell=self,
namespace=self.user_ns,
global_namespace=self.user_global_ns,
use_readline=self.has_readline,
parent=self,
)
self.configurables.append(self.Completer)
# Add custom completers to the basic ones built into IPCompleter
sdisp = self.strdispatchers.get('complete_command', StrDispatch())
self.strdispatchers['complete_command'] = sdisp
self.Completer.custom_completers = sdisp
self.set_hook('complete_command', module_completer, str_key = 'import')
self.set_hook('complete_command', module_completer, str_key = 'from')
self.set_hook('complete_command', magic_run_completer, str_key = '%run')
self.set_hook('complete_command', cd_completer, str_key = '%cd')
self.set_hook('complete_command', reset_completer, str_key = '%reset')
# Only configure readline if we truly are using readline. IPython can
# do tab-completion over the network, in GUIs, etc, where readline
# itself may be absent
if self.has_readline:
self.set_readline_completer()
def complete(self, text, line=None, cursor_pos=None):
"""Return the completed text and a list of completions.
Parameters
----------
text : string
A string of text to be completed on. It can be given as empty and
instead a line/position pair are given. In this case, the
completer itself will split the line like readline does.
line : string, optional
The complete line that text is part of.
cursor_pos : int, optional
The position of the cursor on the input line.
Returns
-------
text : string
The actual text that was completed.
matches : list
A sorted list with all possible completions.
The optional arguments allow the completion to take more context into
account, and are part of the low-level completion API.
This is a wrapper around the completion mechanism, similar to what
readline does at the command line when the TAB key is hit. By
exposing it as a method, it can be used by other non-readline
environments (such as GUIs) for text completion.
Simple usage example:
In [1]: x = 'hello'
In [2]: _ip.complete('x.l')
Out[2]: ('x.l', ['x.ljust', 'x.lower', 'x.lstrip'])
"""
# Inject names into __builtin__ so we can complete on the added names.
with self.builtin_trap:
return self.Completer.complete(text, line, cursor_pos)
def set_custom_completer(self, completer, pos=0):
"""Adds a new custom completer function.
The position argument (defaults to 0) is the index in the completers
list where you want the completer to be inserted."""
newcomp = types.MethodType(completer,self.Completer)
self.Completer.matchers.insert(pos,newcomp)
def set_readline_completer(self):
"""Reset readline's completer to be our own."""
self.readline.set_completer(self.Completer.rlcomplete)
def set_completer_frame(self, frame=None):
"""Set the frame of the completer."""
if frame:
self.Completer.namespace = frame.f_locals
self.Completer.global_namespace = frame.f_globals
else:
self.Completer.namespace = self.user_ns
self.Completer.global_namespace = self.user_global_ns
#-------------------------------------------------------------------------
# Things related to magics
#-------------------------------------------------------------------------
def init_magics(self):
from IPython.core import magics as m
self.magics_manager = magic.MagicsManager(shell=self,
parent=self,
user_magics=m.UserMagics(self))
self.configurables.append(self.magics_manager)
# Expose as public API from the magics manager
self.register_magics = self.magics_manager.register
self.define_magic = self.magics_manager.define_magic
self.register_magics(m.AutoMagics, m.BasicMagics, m.CodeMagics,
m.ConfigMagics, m.DeprecatedMagics, m.DisplayMagics, m.ExecutionMagics,
m.ExtensionMagics, m.HistoryMagics, m.LoggingMagics,
m.NamespaceMagics, m.OSMagics, m.PylabMagics, m.ScriptMagics,
)
# Register Magic Aliases
mman = self.magics_manager
# FIXME: magic aliases should be defined by the Magics classes
# or in MagicsManager, not here
mman.register_alias('ed', 'edit')
mman.register_alias('hist', 'history')
mman.register_alias('rep', 'recall')
mman.register_alias('SVG', 'svg', 'cell')
mman.register_alias('HTML', 'html', 'cell')
mman.register_alias('file', 'writefile', 'cell')
# FIXME: Move the color initialization to the DisplayHook, which
# should be split into a prompt manager and displayhook. We probably
# even need a centralize colors management object.
self.magic('colors %s' % self.colors)
# Defined here so that it's included in the documentation
@functools.wraps(magic.MagicsManager.register_function)
def register_magic_function(self, func, magic_kind='line', magic_name=None):
self.magics_manager.register_function(func,
magic_kind=magic_kind, magic_name=magic_name)
def run_line_magic(self, magic_name, line):
"""Execute the given line magic.
Parameters
----------
magic_name : str
Name of the desired magic function, without '%' prefix.
line : str
The rest of the input line as a single string.
"""
fn = self.find_line_magic(magic_name)
if fn is None:
cm = self.find_cell_magic(magic_name)
etpl = "Line magic function `%%%s` not found%s."
extra = '' if cm is None else (' (But cell magic `%%%%%s` exists, '
'did you mean that instead?)' % magic_name )
error(etpl % (magic_name, extra))
else:
# Note: this is the distance in the stack to the user's frame.
# This will need to be updated if the internal calling logic gets
# refactored, or else we'll be expanding the wrong variables.
stack_depth = 2
magic_arg_s = self.var_expand(line, stack_depth)
# Put magic args in a list so we can call with f(*a) syntax
args = [magic_arg_s]
kwargs = {}
# Grab local namespace if we need it:
if getattr(fn, "needs_local_scope", False):
kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
with self.builtin_trap:
result = fn(*args,**kwargs)
return result
def run_cell_magic(self, magic_name, line, cell):
"""Execute the given cell magic.
Parameters
----------
magic_name : str
Name of the desired magic function, without '%' prefix.
line : str
The rest of the first input line as a single string.
cell : str
The body of the cell as a (possibly multiline) string.
"""
fn = self.find_cell_magic(magic_name)
if fn is None:
lm = self.find_line_magic(magic_name)
etpl = "Cell magic `%%{0}` not found{1}."
extra = '' if lm is None else (' (But line magic `%{0}` exists, '
'did you mean that instead?)'.format(magic_name))
error(etpl.format(magic_name, extra))
elif cell == '':
message = '%%{0} is a cell magic, but the cell body is empty.'.format(magic_name)
if self.find_line_magic(magic_name) is not None:
message += ' Did you mean the line magic %{0} (single %)?'.format(magic_name)
raise UsageError(message)
else:
# Note: this is the distance in the stack to the user's frame.
# This will need to be updated if the internal calling logic gets
# refactored, or else we'll be expanding the wrong variables.
stack_depth = 2
magic_arg_s = self.var_expand(line, stack_depth)
with self.builtin_trap:
result = fn(magic_arg_s, cell)
return result
def find_line_magic(self, magic_name):
"""Find and return a line magic by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics['line'].get(magic_name)
def find_cell_magic(self, magic_name):
"""Find and return a cell magic by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics['cell'].get(magic_name)
def find_magic(self, magic_name, magic_kind='line'):
"""Find and return a magic of the given type by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics[magic_kind].get(magic_name)
def magic(self, arg_s):
"""DEPRECATED. Use run_line_magic() instead.
Call a magic function by name.
Input: a string containing the name of the magic function to call and
any additional arguments to be passed to the magic.
magic('name -opt foo bar') is equivalent to typing at the ipython
prompt:
In[1]: %name -opt foo bar
To call a magic without arguments, simply use magic('name').
This provides a proper Python function to call IPython's magics in any
valid Python code you can type at the interpreter, including loops and
compound statements.
"""
# TODO: should we issue a loud deprecation warning here?
magic_name, _, magic_arg_s = arg_s.partition(' ')
magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
return self.run_line_magic(magic_name, magic_arg_s)
#-------------------------------------------------------------------------
# Things related to macros
#-------------------------------------------------------------------------
def define_macro(self, name, themacro):
"""Define a new macro
Parameters
----------
name : str
The name of the macro.
themacro : str or Macro
The action to do upon invoking the macro. If a string, a new
Macro object is created by passing the string to it.
"""
from IPython.core import macro
if isinstance(themacro, string_types):
themacro = macro.Macro(themacro)
if not isinstance(themacro, macro.Macro):
raise ValueError('A macro must be a string or a Macro instance.')
self.user_ns[name] = themacro
#-------------------------------------------------------------------------
# Things related to the running of system commands
#-------------------------------------------------------------------------
def system_piped(self, cmd):
"""Call the given cmd in a subprocess, piping stdout/err
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported. Should not be a command that expects input
other than simple text.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
# We do not support backgrounding processes because we either use
# pexpect or pipes to read from. Users can always just call
# os.system() or use ip.system=ip.system_raw
# if they really want a background process.
raise OSError("Background processes not supported.")
# we explicitly do NOT return the subprocess status code, because
# a non-None value would trigger :func:`sys.displayhook` calls.
# Instead, we store the exit_code in user_ns.
self.user_ns['_exit_code'] = system(self.var_expand(cmd, depth=1))
def system_raw(self, cmd):
"""Call the given cmd in a subprocess using os.system on Windows or
subprocess.call using the system shell on other platforms.
Parameters
----------
cmd : str
Command to execute.
"""
cmd = self.var_expand(cmd, depth=1)
# protect os.system from UNC paths on Windows, which it can't handle:
if sys.platform == 'win32':
from IPython.utils._process_win32 import AvoidUNCPath
with AvoidUNCPath() as path:
if path is not None:
cmd = '"pushd %s &&"%s' % (path, cmd)
cmd = py3compat.unicode_to_str(cmd)
try:
ec = os.system(cmd)
except KeyboardInterrupt:
self.write_err('\n' + self.get_exception_only())
ec = -2
else:
cmd = py3compat.unicode_to_str(cmd)
# For posix the result of the subprocess.call() below is an exit
# code, which by convention is zero for success, positive for
# program failure. Exit codes above 128 are reserved for signals,
# and the formula for converting a signal to an exit code is usually
# signal_number+128. To more easily differentiate between exit
# codes and signals, ipython uses negative numbers. For instance
# since control-c is signal 2 but exit code 130, ipython's
# _exit_code variable will read -2. Note that some shells like
# csh and fish don't follow sh/bash conventions for exit codes.
executable = os.environ.get('SHELL', None)
try:
# Use env shell instead of default /bin/sh
ec = subprocess.call(cmd, shell=True, executable=executable)
except KeyboardInterrupt:
# intercept control-C; a long traceback is not useful here
self.write_err('\n' + self.get_exception_only())
ec = 130
if ec > 128:
ec = -(ec - 128)
# We explicitly do NOT return the subprocess status code, because
# a non-None value would trigger :func:`sys.displayhook` calls.
# Instead, we store the exit_code in user_ns. Note the semantics
# of _exit_code: for control-c, _exit_code == -signal.SIGNIT,
# but raising SystemExit(_exit_code) will give status 254!
self.user_ns['_exit_code'] = ec
# use piped system by default, because it is better behaved
system = system_piped
def getoutput(self, cmd, split=True, depth=0):
"""Get output (possibly including stderr) from a subprocess.
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported.
split : bool, optional
If True, split the output into an IPython SList. Otherwise, an
IPython LSString is returned. These are objects similar to normal
lists and strings, with a few convenience attributes for easier
manipulation of line-based output. You can use '?' on them for
details.
depth : int, optional
How many frames above the caller are the local variables which should
be expanded in the command string? The default (0) assumes that the
expansion variables are in the stack frame calling this function.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
raise OSError("Background processes not supported.")
out = getoutput(self.var_expand(cmd, depth=depth+1))
if split:
out = SList(out.splitlines())
else:
out = LSString(out)
return out
#-------------------------------------------------------------------------
# Things related to aliases
#-------------------------------------------------------------------------
def init_alias(self):
self.alias_manager = AliasManager(shell=self, parent=self)
self.configurables.append(self.alias_manager)
#-------------------------------------------------------------------------
# Things related to extensions
#-------------------------------------------------------------------------
def init_extension_manager(self):
self.extension_manager = ExtensionManager(shell=self, parent=self)
self.configurables.append(self.extension_manager)
#-------------------------------------------------------------------------
# Things related to payloads
#-------------------------------------------------------------------------
def init_payload(self):
self.payload_manager = PayloadManager(parent=self)
self.configurables.append(self.payload_manager)
#-------------------------------------------------------------------------
# Things related to the prefilter
#-------------------------------------------------------------------------
def init_prefilter(self):
self.prefilter_manager = PrefilterManager(shell=self, parent=self)
self.configurables.append(self.prefilter_manager)
# Ultimately this will be refactored in the new interpreter code, but
# for now, we should expose the main prefilter method (there's legacy
# code out there that may rely on this).
self.prefilter = self.prefilter_manager.prefilter_lines
def auto_rewrite_input(self, cmd):
"""Print to the screen the rewritten form of the user's command.
This shows visual feedback by rewriting input lines that cause
automatic calling to kick in, like::
/f x
into::
------> f(x)
after the user's input prompt. This helps the user understand that the
input line was transformed automatically by IPython.
"""
if not self.show_rewritten_input:
return
rw = self.prompt_manager.render('rewrite') + cmd
try:
# plain ascii works better w/ pyreadline, on some machines, so
# we use it and only print uncolored rewrite if we have unicode
rw = str(rw)
print(rw, file=io.stdout)
except UnicodeEncodeError:
print("------> " + cmd)
#-------------------------------------------------------------------------
# Things related to extracting values/expressions from kernel and user_ns
#-------------------------------------------------------------------------
def _user_obj_error(self):
"""return simple exception dict
for use in user_expressions
"""
etype, evalue, tb = self._get_exc_info()
stb = self.InteractiveTB.get_exception_only(etype, evalue)
exc_info = {
u'status' : 'error',
u'traceback' : stb,
u'ename' : unicode_type(etype.__name__),
u'evalue' : py3compat.safe_unicode(evalue),
}
return exc_info
def _format_user_obj(self, obj):
"""format a user object to display dict
for use in user_expressions
"""
data, md = self.display_formatter.format(obj)
value = {
'status' : 'ok',
'data' : data,
'metadata' : md,
}
return value
def user_expressions(self, expressions):
"""Evaluate a dict of expressions in the user's namespace.
Parameters
----------
expressions : dict
A dict with string keys and string values. The expression values
should be valid Python expressions, each of which will be evaluated
in the user namespace.
Returns
-------
A dict, keyed like the input expressions dict, with the rich mime-typed
display_data of each value.
"""
out = {}
user_ns = self.user_ns
global_ns = self.user_global_ns
for key, expr in iteritems(expressions):
try:
value = self._format_user_obj(eval(expr, global_ns, user_ns))
except:
value = self._user_obj_error()
out[key] = value
return out
#-------------------------------------------------------------------------
# Things related to the running of code
#-------------------------------------------------------------------------
def ex(self, cmd):
"""Execute a normal python statement in user namespace."""
with self.builtin_trap:
exec(cmd, self.user_global_ns, self.user_ns)
def ev(self, expr):
"""Evaluate python expression expr in user namespace.
Returns the result of evaluation
"""
with self.builtin_trap:
return eval(expr, self.user_global_ns, self.user_ns)
def safe_execfile(self, fname, *where, **kw):
"""A safe version of the builtin execfile().
This version will never throw an exception, but instead print
helpful error messages to the screen. This only works on pure
Python files with the .py extension.
Parameters
----------
fname : string
The name of the file to be executed.
where : tuple
One or two namespaces, passed to execfile() as (globals,locals).
If only one is given, it is passed as both.
exit_ignore : bool (False)
If True, then silence SystemExit for non-zero status (it is always
silenced for zero status, as it is so common).
raise_exceptions : bool (False)
If True raise exceptions everywhere. Meant for testing.
shell_futures : bool (False)
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
"""
kw.setdefault('exit_ignore', False)
kw.setdefault('raise_exceptions', False)
kw.setdefault('shell_futures', False)
fname = os.path.abspath(os.path.expanduser(fname))
# Make sure we can open the file
try:
with open(fname) as thefile:
pass
except:
warn('Could not open file <%s> for safe execution.' % fname)
return
# Find things also in current directory. This is needed to mimic the
# behavior of running a script from the system command line, where
# Python inserts the script's directory into sys.path
dname = os.path.dirname(fname)
with prepended_to_syspath(dname):
try:
glob, loc = (where + (None, ))[:2]
py3compat.execfile(
fname, glob, loc,
self.compile if kw['shell_futures'] else None)
except SystemExit as status:
# If the call was made with 0 or None exit status (sys.exit(0)
# or sys.exit() ), don't bother showing a traceback, as both of
# these are considered normal by the OS:
# > python -c'import sys;sys.exit(0)'; echo $?
# 0
# > python -c'import sys;sys.exit()'; echo $?
# 0
# For other exit status, we show the exception unless
# explicitly silenced, but only in short form.
if kw['raise_exceptions']:
raise
if status.code and not kw['exit_ignore']:
self.showtraceback(exception_only=True)
except:
if kw['raise_exceptions']:
raise
# tb offset is 2 because we wrap execfile
self.showtraceback(tb_offset=2)
def safe_execfile_ipy(self, fname, shell_futures=False):
"""Like safe_execfile, but for .ipy or .ipynb files with IPython syntax.
Parameters
----------
fname : str
The name of the file to execute. The filename must have a
.ipy or .ipynb extension.
shell_futures : bool (False)
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
"""
fname = os.path.abspath(os.path.expanduser(fname))
# Make sure we can open the file
try:
with open(fname) as thefile:
pass
except:
warn('Could not open file <%s> for safe execution.' % fname)
return
# Find things also in current directory. This is needed to mimic the
# behavior of running a script from the system command line, where
# Python inserts the script's directory into sys.path
dname = os.path.dirname(fname)
def get_cells():
"""generator for sequence of code blocks to run"""
if fname.endswith('.ipynb'):
from IPython.nbformat import read
with io_open(fname) as f:
nb = read(f, as_version=4)
if not nb.cells:
return
for cell in nb.cells:
if cell.cell_type == 'code':
yield cell.source
else:
with open(fname) as f:
yield f.read()
with prepended_to_syspath(dname):
try:
for cell in get_cells():
# self.run_cell currently captures all exceptions
# raised in user code. It would be nice if there were
# versions of run_cell that did raise, so
# we could catch the errors.
result = self.run_cell(cell, silent=True, shell_futures=shell_futures)
if not result.success:
break
except:
self.showtraceback()
warn('Unknown failure executing file: <%s>' % fname)
def safe_run_module(self, mod_name, where):
"""A safe version of runpy.run_module().
This version will never throw an exception, but instead print
helpful error messages to the screen.
`SystemExit` exceptions with status code 0 or None are ignored.
Parameters
----------
mod_name : string
The name of the module to be executed.
where : dict
The globals namespace.
"""
try:
try:
where.update(
runpy.run_module(str(mod_name), run_name="__main__",
alter_sys=True)
)
except SystemExit as status:
if status.code:
raise
except:
self.showtraceback()
warn('Unknown failure executing module: <%s>' % mod_name)
def _run_cached_cell_magic(self, magic_name, line):
"""Special method to call a cell magic with the data stored in self.
"""
cell = self._current_cell_magic_body
self._current_cell_magic_body = None
return self.run_cell_magic(magic_name, line, cell)
def run_cell(self, raw_cell, store_history=False, silent=False, shell_futures=True):
"""Run a complete IPython cell.
Parameters
----------
raw_cell : str
The code (including IPython code such as %magic functions) to run.
store_history : bool
If True, the raw and translated cell will be stored in IPython's
history. For user code calling back into IPython's machinery, this
should be set to False.
silent : bool
If True, avoid side-effects, such as implicit displayhooks and
and logging. silent=True forces store_history=False.
shell_futures : bool
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
Returns
-------
result : :class:`ExecutionResult`
"""
result = ExecutionResult()
if (not raw_cell) or raw_cell.isspace():
return result
if silent:
store_history = False
if store_history:
result.execution_count = self.execution_count
def error_before_exec(value):
result.error_before_exec = value
return result
self.events.trigger('pre_execute')
if not silent:
self.events.trigger('pre_run_cell')
# If any of our input transformation (input_transformer_manager or
# prefilter_manager) raises an exception, we store it in this variable
# so that we can display the error after logging the input and storing
# it in the history.
preprocessing_exc_tuple = None
try:
# Static input transformations
cell = self.input_transformer_manager.transform_cell(raw_cell)
except SyntaxError:
preprocessing_exc_tuple = sys.exc_info()
cell = raw_cell # cell has to exist so it can be stored/logged
else:
if len(cell.splitlines()) == 1:
# Dynamic transformations - only applied for single line commands
with self.builtin_trap:
try:
# use prefilter_lines to handle trailing newlines
# restore trailing newline for ast.parse
cell = self.prefilter_manager.prefilter_lines(cell) + '\n'
except Exception:
# don't allow prefilter errors to crash IPython
preprocessing_exc_tuple = sys.exc_info()
# Store raw and processed history
if store_history:
self.history_manager.store_inputs(self.execution_count,
cell, raw_cell)
if not silent:
self.logger.log(cell, raw_cell)
# Display the exception if input processing failed.
if preprocessing_exc_tuple is not None:
self.showtraceback(preprocessing_exc_tuple)
if store_history:
self.execution_count += 1
return error_before_exec(preprocessing_exc_tuple[2])
# Our own compiler remembers the __future__ environment. If we want to
# run code with a separate __future__ environment, use the default
# compiler
compiler = self.compile if shell_futures else CachingCompiler()
with self.builtin_trap:
cell_name = self.compile.cache(cell, self.execution_count)
with self.display_trap:
# Compile to bytecode
try:
code_ast = compiler.ast_parse(cell, filename=cell_name)
except IndentationError as e:
self.showindentationerror()
if store_history:
self.execution_count += 1
return error_before_exec(e)
except (OverflowError, SyntaxError, ValueError, TypeError,
MemoryError) as e:
self.showsyntaxerror()
if store_history:
self.execution_count += 1
return error_before_exec(e)
# Apply AST transformations
try:
code_ast = self.transform_ast(code_ast)
except InputRejected as e:
self.showtraceback()
if store_history:
self.execution_count += 1
return error_before_exec(e)
# Give the displayhook a reference to our ExecutionResult so it
# can fill in the output value.
self.displayhook.exec_result = result
# Execute the user code
interactivity = "none" if silent else self.ast_node_interactivity
self.run_ast_nodes(code_ast.body, cell_name,
interactivity=interactivity, compiler=compiler, result=result)
# Reset this so later displayed values do not modify the
# ExecutionResult
self.displayhook.exec_result = None
self.events.trigger('post_execute')
if not silent:
self.events.trigger('post_run_cell')
if store_history:
# Write output to the database. Does nothing unless
# history output logging is enabled.
self.history_manager.store_output(self.execution_count)
# Each cell is a *single* input, regardless of how many lines it has
self.execution_count += 1
return result
def transform_ast(self, node):
"""Apply the AST transformations from self.ast_transformers
Parameters
----------
node : ast.Node
The root node to be transformed. Typically called with the ast.Module
produced by parsing user input.
Returns
-------
An ast.Node corresponding to the node it was called with. Note that it
may also modify the passed object, so don't rely on references to the
original AST.
"""
for transformer in self.ast_transformers:
try:
node = transformer.visit(node)
except InputRejected:
# User-supplied AST transformers can reject an input by raising
# an InputRejected. Short-circuit in this case so that we
# don't unregister the transform.
raise
except Exception:
warn("AST transformer %r threw an error. It will be unregistered." % transformer)
self.ast_transformers.remove(transformer)
if self.ast_transformers:
ast.fix_missing_locations(node)
return node
def run_ast_nodes(self, nodelist, cell_name, interactivity='last_expr',
compiler=compile, result=None):
"""Run a sequence of AST nodes. The execution mode depends on the
interactivity parameter.
Parameters
----------
nodelist : list
A sequence of AST nodes to run.
cell_name : str
Will be passed to the compiler as the filename of the cell. Typically
the value returned by ip.compile.cache(cell).
interactivity : str
'all', 'last', 'last_expr' or 'none', specifying which nodes should be
run interactively (displaying output from expressions). 'last_expr'
will run the last node interactively only if it is an expression (i.e.
expressions in loops or other blocks are not displayed. Other values
for this parameter will raise a ValueError.
compiler : callable
A function with the same interface as the built-in compile(), to turn
the AST nodes into code objects. Default is the built-in compile().
result : ExecutionResult, optional
An object to store exceptions that occur during execution.
Returns
-------
True if an exception occurred while running code, False if it finished
running.
"""
if not nodelist:
return
if interactivity == 'last_expr':
if isinstance(nodelist[-1], ast.Expr):
interactivity = "last"
else:
interactivity = "none"
if interactivity == 'none':
to_run_exec, to_run_interactive = nodelist, []
elif interactivity == 'last':
to_run_exec, to_run_interactive = nodelist[:-1], nodelist[-1:]
elif interactivity == 'all':
to_run_exec, to_run_interactive = [], nodelist
else:
raise ValueError("Interactivity was %r" % interactivity)
exec_count = self.execution_count
try:
for i, node in enumerate(to_run_exec):
mod = ast.Module([node])
code = compiler(mod, cell_name, "exec")
if self.run_code(code, result):
return True
for i, node in enumerate(to_run_interactive):
mod = ast.Interactive([node])
code = compiler(mod, cell_name, "single")
if self.run_code(code, result):
return True
# Flush softspace
if softspace(sys.stdout, 0):
print()
except:
# It's possible to have exceptions raised here, typically by
# compilation of odd code (such as a naked 'return' outside a
# function) that did parse but isn't valid. Typically the exception
# is a SyntaxError, but it's safest just to catch anything and show
# the user a traceback.
# We do only one try/except outside the loop to minimize the impact
# on runtime, and also because if any node in the node list is
# broken, we should stop execution completely.
if result:
result.error_before_exec = sys.exc_info()[1]
self.showtraceback()
return True
return False
def run_code(self, code_obj, result=None):
"""Execute a code object.
When an exception occurs, self.showtraceback() is called to display a
traceback.
Parameters
----------
code_obj : code object
A compiled code object, to be executed
result : ExecutionResult, optional
An object to store exceptions that occur during execution.
Returns
-------
False : successful execution.
True : an error occurred.
"""
# Set our own excepthook in case the user code tries to call it
# directly, so that the IPython crash handler doesn't get triggered
old_excepthook, sys.excepthook = sys.excepthook, self.excepthook
# we save the original sys.excepthook in the instance, in case config
# code (such as magics) needs access to it.
self.sys_excepthook = old_excepthook
outflag = 1 # happens in more places, so it's easier as default
try:
try:
self.hooks.pre_run_code_hook()
#rprint('Running code', repr(code_obj)) # dbg
exec(code_obj, self.user_global_ns, self.user_ns)
finally:
# Reset our crash handler in place
sys.excepthook = old_excepthook
except SystemExit as e:
if result is not None:
result.error_in_exec = e
self.showtraceback(exception_only=True)
warn("To exit: use 'exit', 'quit', or Ctrl-D.", level=1)
except self.custom_exceptions:
etype, value, tb = sys.exc_info()
if result is not None:
result.error_in_exec = value
self.CustomTB(etype, value, tb)
except:
if result is not None:
result.error_in_exec = sys.exc_info()[1]
self.showtraceback()
else:
outflag = 0
return outflag
# For backwards compatibility
runcode = run_code
#-------------------------------------------------------------------------
# Things related to GUI support and pylab
#-------------------------------------------------------------------------
def enable_gui(self, gui=None):
raise NotImplementedError('Implement enable_gui in a subclass')
def enable_matplotlib(self, gui=None):
"""Enable interactive matplotlib and inline figure support.
This takes the following steps:
1. select the appropriate eventloop and matplotlib backend
2. set up matplotlib for interactive use with that backend
3. configure formatters for inline figure display
4. enable the selected gui eventloop
Parameters
----------
gui : optional, string
If given, dictates the choice of matplotlib GUI backend to use
(should be one of IPython's supported backends, 'qt', 'osx', 'tk',
'gtk', 'wx' or 'inline'), otherwise we use the default chosen by
matplotlib (as dictated by the matplotlib build-time options plus the
user's matplotlibrc configuration file). Note that not all backends
make sense in all contexts, for example a terminal ipython can't
display figures inline.
"""
from IPython.core import pylabtools as pt
gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
if gui != 'inline':
# If we have our first gui selection, store it
if self.pylab_gui_select is None:
self.pylab_gui_select = gui
# Otherwise if they are different
elif gui != self.pylab_gui_select:
print ('Warning: Cannot change to a different GUI toolkit: %s.'
' Using %s instead.' % (gui, self.pylab_gui_select))
gui, backend = pt.find_gui_and_backend(self.pylab_gui_select)
pt.activate_matplotlib(backend)
pt.configure_inline_support(self, backend)
# Now we must activate the gui pylab wants to use, and fix %run to take
# plot updates into account
self.enable_gui(gui)
self.magics_manager.registry['ExecutionMagics'].default_runner = \
pt.mpl_runner(self.safe_execfile)
return gui, backend
def enable_pylab(self, gui=None, import_all=True, welcome_message=False):
"""Activate pylab support at runtime.
This turns on support for matplotlib, preloads into the interactive
namespace all of numpy and pylab, and configures IPython to correctly
interact with the GUI event loop. The GUI backend to be used can be
optionally selected with the optional ``gui`` argument.
This method only adds preloading the namespace to InteractiveShell.enable_matplotlib.
Parameters
----------
gui : optional, string
If given, dictates the choice of matplotlib GUI backend to use
(should be one of IPython's supported backends, 'qt', 'osx', 'tk',
'gtk', 'wx' or 'inline'), otherwise we use the default chosen by
matplotlib (as dictated by the matplotlib build-time options plus the
user's matplotlibrc configuration file). Note that not all backends
make sense in all contexts, for example a terminal ipython can't
display figures inline.
import_all : optional, bool, default: True
Whether to do `from numpy import *` and `from pylab import *`
in addition to module imports.
welcome_message : deprecated
This argument is ignored, no welcome message will be displayed.
"""
from IPython.core.pylabtools import import_pylab
gui, backend = self.enable_matplotlib(gui)
# We want to prevent the loading of pylab to pollute the user's
# namespace as shown by the %who* magics, so we execute the activation
# code in an empty namespace, and we update *both* user_ns and
# user_ns_hidden with this information.
ns = {}
import_pylab(ns, import_all)
# warn about clobbered names
ignored = set(["__builtins__"])
both = set(ns).intersection(self.user_ns).difference(ignored)
clobbered = [ name for name in both if self.user_ns[name] is not ns[name] ]
self.user_ns.update(ns)
self.user_ns_hidden.update(ns)
return gui, backend, clobbered
#-------------------------------------------------------------------------
# Utilities
#-------------------------------------------------------------------------
def var_expand(self, cmd, depth=0, formatter=DollarFormatter()):
"""Expand python variables in a string.
The depth argument indicates how many frames above the caller should
be walked to look for the local namespace where to expand variables.
The global namespace for expansion is always the user's interactive
namespace.
"""
ns = self.user_ns.copy()
try:
frame = sys._getframe(depth+1)
except ValueError:
# This is thrown if there aren't that many frames on the stack,
# e.g. if a script called run_line_magic() directly.
pass
else:
ns.update(frame.f_locals)
try:
# We have to use .vformat() here, because 'self' is a valid and common
# name, and expanding **ns for .format() would make it collide with
# the 'self' argument of the method.
cmd = formatter.vformat(cmd, args=[], kwargs=ns)
except Exception:
# if formatter couldn't format, just let it go untransformed
pass
return cmd
def mktempfile(self, data=None, prefix='ipython_edit_'):
"""Make a new tempfile and return its filename.
This makes a call to tempfile.mkstemp (created in a tempfile.mkdtemp),
but it registers the created filename internally so ipython cleans it up
at exit time.
Optional inputs:
- data(None): if data is given, it gets written out to the temp file
immediately, and the file is closed again."""
dirname = tempfile.mkdtemp(prefix=prefix)
self.tempdirs.append(dirname)
handle, filename = tempfile.mkstemp('.py', prefix, dir=dirname)
os.close(handle) # On Windows, there can only be one open handle on a file
self.tempfiles.append(filename)
if data:
tmp_file = open(filename,'w')
tmp_file.write(data)
tmp_file.close()
return filename
# TODO: This should be removed when Term is refactored.
def write(self,data):
"""Write a string to the default output"""
io.stdout.write(data)
# TODO: This should be removed when Term is refactored.
def write_err(self,data):
"""Write a string to the default error output"""
io.stderr.write(data)
def ask_yes_no(self, prompt, default=None):
if self.quiet:
return True
return ask_yes_no(prompt,default)
def show_usage(self):
"""Show a usage message"""
page.page(IPython.core.usage.interactive_usage)
def extract_input_lines(self, range_str, raw=False):
"""Return as a string a set of input history slices.
Parameters
----------
range_str : string
The set of slices is given as a string, like "~5/6-~4/2 4:8 9",
since this function is for use by magic functions which get their
arguments as strings. The number before the / is the session
number: ~n goes n back from the current session.
raw : bool, optional
By default, the processed input is used. If this is true, the raw
input history is used instead.
Notes
-----
Slices can be described with two notations:
* ``N:M`` -> standard python form, means including items N...(M-1).
* ``N-M`` -> include items N..M (closed endpoint).
"""
lines = self.history_manager.get_range_by_str(range_str, raw=raw)
return "\n".join(x for _, _, x in lines)
def find_user_code(self, target, raw=True, py_only=False, skip_encoding_cookie=True, search_ns=False):
"""Get a code string from history, file, url, or a string or macro.
This is mainly used by magic functions.
Parameters
----------
target : str
A string specifying code to retrieve. This will be tried respectively
as: ranges of input history (see %history for syntax), url,
correspnding .py file, filename, or an expression evaluating to a
string or Macro in the user namespace.
raw : bool
If true (default), retrieve raw history. Has no effect on the other
retrieval mechanisms.
py_only : bool (default False)
Only try to fetch python code, do not try alternative methods to decode file
if unicode fails.
Returns
-------
A string of code.
ValueError is raised if nothing is found, and TypeError if it evaluates
to an object of another type. In each case, .args[0] is a printable
message.
"""
code = self.extract_input_lines(target, raw=raw) # Grab history
if code:
return code
utarget = unquote_filename(target)
try:
if utarget.startswith(('http://', 'https://')):
return openpy.read_py_url(utarget, skip_encoding_cookie=skip_encoding_cookie)
except UnicodeDecodeError:
if not py_only :
# Deferred import
try:
from urllib.request import urlopen # Py3
except ImportError:
from urllib import urlopen
response = urlopen(target)
return response.read().decode('latin1')
raise ValueError(("'%s' seem to be unreadable.") % utarget)
potential_target = [target]
try :
potential_target.insert(0,get_py_filename(target))
except IOError:
pass
for tgt in potential_target :
if os.path.isfile(tgt): # Read file
try :
return openpy.read_py_file(tgt, skip_encoding_cookie=skip_encoding_cookie)
except UnicodeDecodeError :
if not py_only :
with io_open(tgt,'r', encoding='latin1') as f :
return f.read()
raise ValueError(("'%s' seem to be unreadable.") % target)
elif os.path.isdir(os.path.expanduser(tgt)):
raise ValueError("'%s' is a directory, not a regular file." % target)
if search_ns:
# Inspect namespace to load object source
object_info = self.object_inspect(target, detail_level=1)
if object_info['found'] and object_info['source']:
return object_info['source']
try: # User namespace
codeobj = eval(target, self.user_ns)
except Exception:
raise ValueError(("'%s' was not found in history, as a file, url, "
"nor in the user namespace.") % target)
if isinstance(codeobj, string_types):
return codeobj
elif isinstance(codeobj, Macro):
return codeobj.value
raise TypeError("%s is neither a string nor a macro." % target,
codeobj)
#-------------------------------------------------------------------------
# Things related to IPython exiting
#-------------------------------------------------------------------------
def atexit_operations(self):
"""This will be executed at the time of exit.
Cleanup operations and saving of persistent data that is done
unconditionally by IPython should be performed here.
For things that may depend on startup flags or platform specifics (such
as having readline or not), register a separate atexit function in the
code that has the appropriate information, rather than trying to
clutter
"""
# Close the history session (this stores the end time and line count)
# this must be *before* the tempfile cleanup, in case of temporary
# history db
self.history_manager.end_session()
# Cleanup all tempfiles and folders left around
for tfile in self.tempfiles:
try:
os.unlink(tfile)
except OSError:
pass
for tdir in self.tempdirs:
try:
os.rmdir(tdir)
except OSError:
pass
# Clear all user namespaces to release all references cleanly.
self.reset(new_session=False)
# Run user hooks
self.hooks.shutdown_hook()
def cleanup(self):
self.restore_sys_module_state()
class InteractiveShellABC(with_metaclass(abc.ABCMeta, object)):
"""An abstract base class for InteractiveShell."""
InteractiveShellABC.register(InteractiveShell)
| wolfram74/numerical_methods_iserles_notes | venv/lib/python2.7/site-packages/IPython/core/interactiveshell.py | Python | mit | 138,880 | [
"VisIt"
] | 7f0fba82cc8fc6179acb96cce454fff68c60e5f2fc8a0057c3f965fa58f4acc3 |
#!/usr/bin/env python
import boto
import boto.sqs
import boto.sqs.jsonmessage
import os
import iris
import json
import time
import sys
sys.path.append(".")
from config import manifest
class Job(object):
""" message should be URI to OpenDAP """
def __init__(self, message):
body = json.loads(message.get_body())["Message"]
self.open_dap = body
self.data_file = body.split("/")[-1]
info = manifest.runnames[self.data_file.split("_")[0]]
self.model = info["model"]
self.variable = "_".join(self.data_file.split("_")[1:-2])
self.timestamp = self.data_file.split("_")[-2]
self.profile_name = info["profile"]
self.message = message
self.nframes = info["nframes"]
def __str__(self):
self.data_file
def getTimes(self, tcoordname="time"):
d = iris.load_cube(os.path.join(os.getenv("DATA_DIR"), self.data_file))
with iris.FUTURE.context(cell_datetime_objects=True):
time_values = [t.point.isoformat() for t in d.coord(tcoordname).cells()]
forecast_periods = d.coord("forecast_period").points
return (time_values, forecast_periods)
def getImgSvcJobMsgs(self):
msgs = []
times, forecast_periods = self.getTimes()
for time, forecast_period in zip(times, forecast_periods):
msg = {"data_file": self.data_file,
"open_dap": self.open_dap,
"variable": self.variable,
"model": self.model,
"profile_name": self.profile_name,
"time_step": time,
"frame": forecast_period,
"nframes": self.nframes}
msgs.append(msg)
return msgs
class NoJobsError(Exception):
def __init__(self, value=""):
self.value = value
def __str__(self):
return repr(self.value)
def getQueue(queue_name):
conn = boto.sqs.connect_to_region(os.getenv("AWS_REGION"),
aws_access_key_id=os.getenv("AWS_KEY"),
aws_secret_access_key=os.getenv("AWS_SECRET_KEY"))
queue = conn.get_queue(queue_name)
return queue
def getTHREDDSJob(queue, visibility_timeout=60):
print "Getting job"
messages = queue.get_messages(num_messages=1, visibility_timeout=visibility_timeout)
try:
message = messages[0]
except IndexError:
print "No jobs, sleeping for 5s"
time.sleep(5)
raise NoJobsError()
job = Job(message)
return job
def postImgSvcJobs(msgs, queue):
nframes = len(msgs)
for i, msg in enumerate(msgs):
print "Adding " + str(msg) + " to the img svc job queue"
m = boto.sqs.jsonmessage.JSONMessage()
m.set_body(msg)
queue.write(m)
def postVidSvcJob(msg, queue):
nframes = len(msg)
print "Adding " + str(msg) + " to the vid svc job queue"
m = boto.sqs.jsonmessage.JSONMessage()
m.set_body(msg)
queue.write(m)
if __name__ == "__main__":
image_service_scheduler_queue = getQueue("image_service_scheduler_queue")
image_service_queue = getQueue("image_service_queue")
job = getTHREDDSJob(image_service_scheduler_queue)
msgs = job.getImgSvcJobMsgs()
postImgSvcJobs(msgs, image_service_queue)
image_service_scheduler_queue.delete_message(job.message)
for msg in msgs:
if msg["frame"] == 1: # 1 not 0 as possibly not getting the assimilated observations any more
print "Sleeping while the image service updates latest in the data service before posting the video jobs."
# The message should probable include the forecast reference time from the netCDF file and the video service should
# query the data service for that rather than latest. Then we wouldn't have to sleep. Ho hum!
time.sleep(60*5)
video_service_queue = getQueue("video_service_queue")
postVidSvcJob(msg, video_service_queue)
print "Done, sleeping for 5s"
time.sleep(5)
sys.exit()
| met-office-lab/image-service-scheduler | scheduler.py | Python | lgpl-3.0 | 4,111 | [
"NetCDF"
] | 6bd0475c9cc3b1d472074bc510528866fef2a311a45c4492232e11620444b664 |
from __future__ import (absolute_import, print_function)
from mantid.kernel import VMD
import math
import unittest
class VMDTest(unittest.TestCase):
def test_default_construction_gives_object_with_single_dimension(self):
one = VMD()
self.assertEquals(1, one.getNumDims())
def test_constructors_with_dimension_pts(self):
pts = [1]
for i in range(2,7):
pts.append(i)
vector = VMD(*pts) #unpack list
self.assertEquals(i, vector.getNumDims())
def test_scalar_prod_returns_expected_value(self):
a = VMD(1.0,2.0,1.0)
b = VMD(1.0,-2.0,-1.0)
sp = a.scalar_prod(b)
self.assertAlmostEquals(sp,-4.0)
def test_crossprod(self):
a = VMD(1.0,0.0,0.0)
b = VMD(0.0,1.0,0.0)
c = a.cross_prod(b)
self.assertAlmostEquals(c[0],0.0)
self.assertAlmostEquals(c[1],0.0)
self.assertAlmostEquals(c[2],1.0)
def test_norm(self):
p = VMD(1.0,-5.0,8.0);
self.assertAlmostEquals(p.norm(), math.sqrt(90.0),places=6)
def test_norm2(self):
p = VMD(1.0,-5.0,8.0);
self.assertAlmostEquals(p.norm2(), 90.0, places=6)
def test_normalize(self):
a = VMD(3,4, math.sqrt(39.0))
pre_norm = a.norm()
self.assertEquals(pre_norm, a.normalize())
b = VMD(3./8,4./8, math.sqrt(39.0)/8.) # normalized version
self.assertAlmostEquals(b[0], a[0], places=6)
self.assertAlmostEquals(b[1], a[1], places=6)
self.assertAlmostEquals(b[2], a[2], places=6)
def test_angle(self):
a = VMD(1,0,0);
b = VMD(0,1,0);
self.assertAlmostEqual(a.angle(b), math.pi/2, places=4);
def test_value_read_access_succeeds_for_valid_indices(self):
vector = VMD(1.0,2.0)
self.assertAlmostEqual(1.0, vector[0])
self.assertAlmostEqual(2.0, vector[1])
def test_value_write_access_succeeds_for_valid_indices(self):
vector = VMD(1.0,2.0)
vector[0] = 1.5
vector[1] = 1.6
self.assertAlmostEqual(1.5, vector[0])
self.assertAlmostEqual(1.6, vector[1])
def test_standard_mathematical_operators(self):
v1 = VMD(1.0,2.0)
v2 = VMD(5.0,-1.0)
v3 = v1 + v2
self.assertAlmostEqual(6.0, v3[0])
self.assertAlmostEqual(1.0, v3[1])
v3 = v1 - v2
self.assertAlmostEqual(-4.0, v3[0])
self.assertAlmostEqual(3.0, v3[1])
v3 = v1 * v2
self.assertAlmostEqual(5.0, v3[0])
self.assertAlmostEqual(-2.0, v3[1])
v3 = v1 / v2
self.assertAlmostEqual(1.0/5.0, v3[0])
self.assertAlmostEqual(-2.0, v3[1])
def test_inplace_mathematical_operators(self):
v1 = VMD(1.0,2.0)
v2 = VMD(5.0,-1.0)
v1 += v2
self.assertAlmostEqual(6.0, v1[0])
self.assertAlmostEqual(1.0, v1[1])
v1 = VMD(1.0,2.0)
v1 -= v2
self.assertAlmostEqual(-4.0, v1[0])
self.assertAlmostEqual(3.0, v1[1])
v1 = VMD(1.0,2.0)
v1 *= v2
self.assertAlmostEqual(5.0, v1[0])
self.assertAlmostEqual(-2.0, v1[1])
v1 = VMD(1.0,2.0)
v1 /= v2
self.assertAlmostEqual(1.0/5.0, v1[0])
self.assertAlmostEqual(-2.0, v1[1])
def test_equality_operators(self):
v1 = VMD(1.0,2.0)
self.assertTrue(v1 == v1)
v2 = VMD(1.0,2.0) # different object, same value
self.assertTrue(v1 == v2)
self.assertFalse(v1 != v2)
v3 = VMD(1.0,-5.0)
self.assertTrue(v1 != v3)
#==================== Failure cases =======================================
def test_value_read_access_raises_error_for_invalid_indices(self):
vector = VMD(1.0,2.0)
self.assertRaises(IndexError, vector.__getitem__, 2)
def test_value_write_access_raises_error_for_invalid_indices(self):
vector = VMD(1.0,2.0)
self.assertRaises(IndexError, vector.__setitem__, 2, 5.0)
def test_scalar_prod_raises_error_with_dimension_mismatch(self):
v1 = VMD(1,2,3)
v2 = VMD(1,2,3,4)
self.assertRaises(RuntimeError, v1.scalar_prod, v2)
def test_cross_prod_raises_error_with_dimension_mismatch(self):
v1 = VMD(1,2,3)
v2 = VMD(1,2,3,4)
self.assertRaises(RuntimeError, v1.cross_prod, v2)
def test_angle_raises_error_with_dimension_mismatch(self):
v1 = VMD(1,2,3)
v2 = VMD(1,2,3,4)
self.assertRaises(RuntimeError, v1.angle, v2)
if __name__ == '__main__':
unittest.main()
| dymkowsk/mantid | Framework/PythonInterface/test/python/mantid/kernel/VMDTest.py | Python | gpl-3.0 | 4,558 | [
"VMD"
] | 64c0423ccdf391dfccfa9e4c8a1f8882711895462e36912bc778c406ba858eca |
# Load dependencies
import ovito.io.particles
# Load the native code module
import OpenBabelPlugin
# Inject selected classes into parent module.
ovito.io.particles.OpenBabelImporter = OpenBabelPlugin.OpenBabelImporter
ovito.io.particles.CIFImporter = OpenBabelPlugin.CIFImporter
| srinath-chakravarthy/ovito | src/plugins/openbabel/python/ovito/io/particles/openbabel/__init__.py | Python | gpl-3.0 | 281 | [
"OVITO"
] | 9b09621712fcb400506bcb29fcf32c0ec7ebe5591a2bf4882018ac7dbb997843 |
# -*- coding: utf-8 -*-
#
# This file is part of Invenio-Query-Parser.
# Copyright (C) 2014, 2015, 2016 CERN.
#
# Invenio-Query-Parser is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or (at your option) any later version.
#
# Invenio-Query-Parser is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Invenio; if not, write to the Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
#
# In applying this licence, CERN does not waive the privileges and immunities
# granted to it by virtue of its status as an Intergovernmental Organization
# or submit itself to any jurisdiction.
"""Implement Pypeg to AST converter."""
from .. import ast, parser
from ..visitor import make_visitor
class PypegConverter(object):
visitor = make_visitor()
# pylint: disable=W0613,E0102
@visitor(parser.Whitespace)
def visit(self, node):
return ast.Value(node.value)
@visitor(parser.Not)
def visit(self, node, child):
return ast.NotOp(child)
@visitor(parser.And)
def visit(self, node, left, right):
return ast.AndOp(left, right)
@visitor(parser.Or)
def visit(self, node, left, right):
return ast.Or(left, right)
@visitor(parser.KeywordRule)
def visit(self, node):
return ast.Keyword(node.value)
@visitor(parser.SingleQuotedString)
def visit(self, node):
return ast.SingleQuotedValue(node.value)
@visitor(parser.DoubleQuotedString)
def visit(self, node):
return ast.DoubleQuotedValue(node.value)
@visitor(parser.SlashQuotedString)
def visit(self, node):
return ast.RegexValue(node.value)
@visitor(parser.SimpleValue)
def visit(self, node):
return ast.Value(node.value)
@visitor(parser.SimpleRangeValue)
def visit(self, node):
return ast.Value(node.value)
@visitor(parser.RangeValue)
def visit(self, node, child):
return child
@visitor(parser.RangeOp)
def visit(self, node, left, right):
return ast.RangeOp(left, right)
@visitor(parser.Number)
def visit(self, node):
return ast.Value(node.value)
@visitor(parser.Value)
def visit(self, node, child):
return child
@visitor(parser.ValueQuery)
def visit(self, node, child):
return ast.ValueQuery(child)
@visitor(parser.KeywordQuery)
def visit(self, node, keyword, value):
return ast.KeywordOp(keyword, value)
@visitor(parser.NotKeywordValue)
def visit(self, node):
return ast.ValueQuery(ast.Value(node.value))
@visitor(parser.NestedKeywordsRule)
def visit(self, node):
return ast.Value(node.value)
@visitor(parser.SimpleQuery)
def visit(self, node, child):
return child
@visitor(parser.ParenthesizedQuery)
def visit(self, node, child):
return child
@visitor(parser.NotQuery)
def visit(self, node, child):
return ast.NotOp(child)
@visitor(parser.AndQuery)
def visit(self, node, child):
return ast.AndOp(None, child)
@visitor(parser.ImplicitAndQuery)
def visit(self, node, child):
return ast.AndOp(None, child)
@visitor(parser.OrQuery)
def visit(self, node, child):
return ast.OrOp(None, child)
@visitor(parser.Query)
def visit(self, node, children):
# Build the boolean expression, left to right
# x and y or z and ... --> ((x and y) or z) and ...
tree = children[0]
for booleanNode in children[1:]:
booleanNode.left = tree
tree = booleanNode
return tree
@visitor(parser.EmptyQueryRule)
def visit(self, node):
return ast.EmptyQuery(node.value)
@visitor(parser.Main)
def visit(self, node, child):
return child
# pylint: enable=W0612,E0102
| tiborsimko/invenio-query-parser | invenio_query_parser/walkers/pypeg_to_ast.py | Python | gpl-2.0 | 4,234 | [
"VisIt"
] | 5f4d357283b0d20ed76920c17c6b9432b8cc19e9681d4d398077752da07d992a |
# Copyright (C) 2017 Matteo Franchin
#
# This file is part of Pyrtist.
# Pyrtist is free software: you can redistribute it and/or modify it
# under the terms of the GNU Lesser General Public License as published
# by the Free Software Foundation, either version 2.1 of the License, or
# (at your option) any later version.
#
# Pyrtist 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with Pyrtist. If not, see <http://www.gnu.org/licenses/>.
__all__ = ('CairoCmdExecutor',)
import math
import numbers
import cairo
from collections import namedtuple
from .core_types import *
from .cmd_stream import CmdStream
from .style import Cap, Join, FillRule, Font, ParagraphFormat
from .text_formatter import TextFormatter
def ext_arc_to(ctx, ctr, a, b, angle_begin, angle_end, direction):
prev_mx = ctx.get_matrix()
mx = cairo.Matrix(xx=a.x - ctr.x, yx=a.y - ctr.y, x0=ctr.x,
xy=b.x - ctr.x, yy=b.y - ctr.y, y0=ctr.y)
ctx.transform(mx)
arc = (ctx.arc_negative if direction < 0 else ctx.arc)
arc(0.0, 0.0, 1.0, # center x and y, radius.
angle_begin, angle_end) # angle begin and end.
ctx.set_matrix(prev_mx)
def ext_joinarc_to(ctx, a, b, c):
prev_mx = ctx.get_matrix()
mx_xx = b.x - c.x
mx_yx = b.y - c.y
mx = cairo.Matrix(xx=mx_xx, yx=mx_yx, x0=a.x - mx_xx,
xy=b.x - a.x, yy=b.y - a.y, y0=a.y - mx_yx)
ctx.transform(mx)
ctx.arc(0.0, 0.0, 1.0, # center x and y, radius.
0.0, 0.5*math.pi) # angle begin and end.
ctx.set_matrix(prev_mx)
class CairoTextFormatter(TextFormatter):
StackFrame = namedtuple('Stack', ['cur_pos', 'cur_mx'])
def __init__(self, context, paragraph_fmt):
super(CairoTextFormatter, self).__init__()
self.context = context
self.stack = []
self.fmt = paragraph_fmt or ParagraphFormat.default
def cmd_draw(self):
self.context.text_path(self.pop_text())
def cmd_save(self):
frame = self.StackFrame(cur_pos=self.context.get_current_point(),
cur_mx=self.context.get_matrix())
self.stack.append(frame)
def cmd_restore(self):
frame = self.stack.pop()
self.context.set_matrix(frame.cur_mx)
_, y = frame.cur_pos
x, _ = self.context.get_current_point()
self.context.move_to(x, y)
def change_fmt(self, translation, scale):
x, y = self.context.get_current_point()
mx = cairo.Matrix(xx=scale, xy=0.0,
yx=0.0, yy=scale,
x0=x, y0=y + translation)
self.context.transform(mx)
self.context.move_to(0.0, 0.0)
def cmd_superscript(self):
self.change_fmt(self.fmt.superscript_pos, self.fmt.superscript_scale)
def cmd_subscript(self):
self.change_fmt(self.fmt.subscript_pos, self.fmt.subscript_scale)
def cmd_newline(self):
self.context.translate(0.0, -self.fmt.line_spacing)
self.context.move_to(0.0, 0.0)
class CairoCmdExecutor(object):
formats = {'a1': cairo.FORMAT_A1,
'a8': cairo.FORMAT_A8,
'rgb24': cairo.FORMAT_RGB24,
'argb32': cairo.FORMAT_ARGB32}
caps = {Cap.butt: cairo.LINE_CAP_BUTT,
Cap.round: cairo.LINE_CAP_ROUND,
Cap.square: cairo.LINE_CAP_SQUARE}
joins = {Join.miter: cairo.LINE_JOIN_MITER,
Join.round: cairo.LINE_JOIN_ROUND,
Join.bevel: cairo.LINE_JOIN_BEVEL}
slants = {Font.Slant.normal: cairo.FONT_SLANT_NORMAL,
Font.Slant.italic: cairo.FONT_SLANT_ITALIC,
Font.Slant.oblique: cairo.FONT_SLANT_OBLIQUE,
None: cairo.FONT_SLANT_NORMAL}
weights = {Font.Weight.normal: cairo.FONT_WEIGHT_NORMAL,
Font.Weight.bold: cairo.FONT_WEIGHT_BOLD,
None: cairo.FONT_WEIGHT_NORMAL}
pattern_extends = {'none': cairo.EXTEND_NONE,
'repeat': cairo.EXTEND_REPEAT,
'reflect': cairo.EXTEND_REFLECT,
'pad': cairo.EXTEND_PAD}
pattern_filters = {'fast': cairo.FILTER_FAST,
'good': cairo.FILTER_GOOD,
'best': cairo.FILTER_BEST,
'nearest': cairo.FILTER_NEAREST,
'bilinear': cairo.FILTER_BILINEAR,
'gaussian': cairo.FILTER_GAUSSIAN}
@classmethod
def create_image_surface(cls, mode, width, height):
'''Create a new image surface.'''
cairo_fmt = cls.formats.get(mode, None)
if cairo_fmt is None:
raise ValueError('Invalid format {}'.format(mode))
return cairo.ImageSurface(cairo_fmt, width, height)
@staticmethod
def for_surface(cairo_surface, top_left=None, bot_right=None,
top_right=None, bot_left=None, bg_color=None):
'''Create a CairoCmdExecutor from a given cairo surface and the
coordinates of two opposite corners.
The user can either provide top_left, bot_right or top_right, bot_left.
These optional arguments should contain Point objects (or tuples)
specifying the coordinates of the corresponding corner. A reference
system will be set up to honor the user request. If no corner
coordinates are specified, then this function assumes bot_left=(0, 0)
and top_right=(width, height).
bg_color, if provided, is used to set the background with a uniform
color.
'''
if ((top_left is None) != (bot_right is None) or
(top_right is None) != (bot_left is None)):
raise TypeError('Only opposing corners should be set')
if bot_left is None:
if top_left is None:
bot_left = Point(0, 0)
top_right = Point(cairo_surface.get_width(),
cairo_surface.get_height())
else:
top_left, bot_right = Point(top_left), Point(bot_right)
else:
if top_left is not None:
raise TypeError('Only opposing corners should be set')
bot_left, top_right = Point(bot_left), Point(top_right)
if top_left is None:
top_left = Point(bot_left.x, top_right.y)
bot_right = Point(top_right.x, bot_left.y)
diag = bot_right - top_left
scale = Point(cairo_surface.get_width()/diag.x,
cairo_surface.get_height()/diag.y)
return CairoCmdExecutor(cairo_surface, top_left, scale,
bg_color=bg_color)
def __init__(self, surface, origin, resolution, bg_color=None):
context = cairo.Context(surface)
self.surface = surface
self.context = context
self.pattern = None
self.vector_transform = resolution
self.scalar_transform = 0.5*(abs(resolution.x) + abs(resolution.y))
self.origin = origin
self.size = (surface.get_width(), surface.get_height())
if bg_color is not None:
context.save()
context.rectangle(0, 0, *self.size)
context.set_source_rgba(*bg_color)
context.fill()
context.restore()
self.cmd_set_fill_rule(FillRule.even_odd)
self.cmd_set_font(None, None, None)
self.cmd_set_line_width(0.25*self.scalar_transform)
def execute(self, cmds):
for cmd in cmds:
method_name = 'cmd_{}'.format(cmd.get_name())
method = getattr(self, method_name, None)
if method is not None:
method(*self.transform_args(cmd.get_args()))
else:
print('Unknown method {}'.format(method_name))
return CmdStream()
def transform_args(self, args):
origin = self.origin
vtx = self.vector_transform
stx = self.scalar_transform
out = []
for arg in args:
if isinstance(arg, Point):
arg = arg - origin
arg.x *= vtx.x
arg.y *= vtx.y
elif isinstance(arg, Scalar):
arg = Scalar(arg*stx)
out.append(arg)
return out
def save(self, file_name):
self.surface.write_to_png(file_name)
def cmd_move_to(self, p):
self.context.move_to(p.x, p.y)
def cmd_line_to(self, p):
self.context.line_to(p.x, p.y)
def cmd_curve_to(self, p_out, p_in, p):
self.context.curve_to(p_out.x, p_out.y, p_in.x, p_in.y, p.x, p.y)
def cmd_ext_arc_to(self, ctr, a, b, angle_begin, angle_end, direction):
ext_arc_to(self.context, ctr, a, b, angle_begin, angle_end, direction)
def cmd_ext_joinarc_to(self, a, b, c):
ext_joinarc_to(self.context, a, b, c)
def cmd_close_path(self):
self.context.close_path()
def cmd_set_line_width(self, width):
self.context.set_line_width(float(width))
def cmd_set_line_join(self, join):
cairo_join = self.joins.get(join)
if cairo_join is None:
raise ValueError('Unknown join style {}'.format(join))
self.context.set_line_join(cairo_join)
def cmd_set_line_cap(self, cap):
cairo_cap = self.caps.get(cap)
if cairo_cap is None:
raise ValueError('Unknown cap style {}'.format(cap))
self.context.set_line_cap(cairo_cap)
def cmd_set_dash(self, offset, *dashes):
self.context.set_dash(dashes, offset)
def cmd_set_source_rgba(self, r, g, b, a):
self.context.set_source_rgba(r, g, b ,a)
def cmd_set_fill_rule(self, fill_rule):
if fill_rule == FillRule.winding:
self.context.set_fill_rule(cairo.FILL_RULE_WINDING)
else:
assert fill_rule is FillRule.even_odd, \
'Unknown fill rule'
self.context.set_fill_rule(cairo.FILL_RULE_EVEN_ODD)
def cmd_pattern_set_extend(self, extend):
if self.pattern is None:
return
cairo_extend = self.pattern_extends.get(extend)
if cairo_extend is None:
raise ValueError('Unknown pattern extend mode {}'.format(extend))
self.pattern.set_extend(cairo_extend)
def cmd_pattern_set_filter(self, flt):
if self.pattern is None:
return
cairo_filter = self.pattern_filters.get(flt)
if cairo_filter is None:
raise ValueError('Unknown pattern filter {}'.format(flt))
self.pattern.set_filter(cairo_filter)
def cmd_pattern_set_source(self):
if self.pattern is None:
return
self.context.set_source(self.pattern)
def cmd_pattern_create_image(self, origin, v10, v01, offset, surface):
lx = float(surface.get_width())
ly = float(surface.get_height())
mx = cairo.Matrix(xx=(v10.x - origin.x)/lx, yx=(v10.y - origin.y)/lx,
xy=(origin.x - v01.x)/ly, yy=(origin.y - v01.y)/ly,
x0=v01.x, y0=v01.y)
mx.invert()
mx = mx.multiply(cairo.Matrix(x0=offset[0]*lx, y0=-offset[1]*ly))
pattern = cairo.SurfacePattern(surface)
pattern.set_matrix(mx)
self.pattern = pattern
def cmd_pattern_create_linear(self, p_start, p_stop):
self.pattern = cairo.LinearGradient(p_start.x, p_start.y,
p_stop.x, p_stop.y)
def cmd_pattern_create_radial(self, ctr1, refx, refy, ctr2,
refr1, refr2):
xaxis = refx - ctr1
yaxis = refy - ctr1
mx = cairo.Matrix(xx=xaxis.x, yx=xaxis.y,
xy=yaxis.x, yy=yaxis.y,
x0=ctr1.x, y0=ctr1.y)
mx.invert()
c2x, c2y = mx.transform_point(ctr2.x, ctr2.y)
unity = xaxis.norm()
r1 = (refr1 - ctr1).norm()/unity
r2 = (refr2 - ctr2).norm()/unity
pattern = cairo.RadialGradient(0.0, 0.0, r1, c2x, c2y, r2)
pattern.set_matrix(mx)
self.pattern = pattern
def cmd_pattern_add_color_stop_rgba(self, offset, color):
if self.pattern is None:
return
self.pattern.add_color_stop_rgba(offset, *color)
def cmd_set_font(self, name, weight, slant):
name = name or 'sans'
cairo_weight = self.weights.get(weight)
cairo_slant = self.slants.get(slant)
self.context.select_font_face(name, cairo_slant, cairo_weight)
def cmd_text_path(self, origin, v10, v01, offset, par_fmt, text):
# Matrix for the reference system identified by origin, v10, v01.
mx = cairo.Matrix(xx=(v10.x - origin.x), xy=(v01.x - origin.x),
yx=(v10.y - origin.y), yy=(v01.y - origin.y),
x0=origin.x, y0=origin.y)
# Compute the text path in a separate context, to avoid polluting
# the current context.
new_context = cairo.Context(self.context.get_target())
prev_mx = None
try:
# Save the matrix (so it can be resored later) and change
# coordinates.
prev_mx = self.context.get_matrix()
self.context.transform(mx)
# Copy settings from self.context.
new_context.set_matrix(self.context.get_matrix())
new_context.move_to(0.0, 0.0)
new_context.set_font_face(self.context.get_font_face())
font_mx = cairo.Matrix(xx=1.0, yx=0.0,
xy=0.0, yy=-1.0,
x0=0.0, y0=0.0)
new_context.set_font_matrix(font_mx)
# Draw the path.
fmt = CairoTextFormatter(new_context, par_fmt)
fmt.run(text)
# Obtain the path extent in these coordinates.
x1, y1, x2, y2 = new_context.fill_extents()
text_path = new_context.copy_path()
# Position the text path correctly in the main context.
self.context.translate(-x1 - (x2 - x1)*offset[0],
-y1 - (y2 - y1)*offset[1])
self.context.append_path(text_path)
finally:
if prev_mx is not None:
self.context.set_matrix(prev_mx)
def cmd_stroke(self):
self.context.stroke()
def cmd_fill(self):
self.context.fill()
def cmd_fill_preserve(self):
self.context.fill_preserve()
def cmd_save(self):
self.context.save()
def cmd_restore(self):
self.context.restore()
def cmd_set_bbox(self, *args):
pass
| mfnch/pyrtist | pyrtist/lib2d/cairo_cmd_exec.py | Python | lgpl-2.1 | 14,883 | [
"Gaussian"
] | a5ebf2790ac787b31df4ee926c4354082764e8e86c0d596e2f941ed973cc04e4 |
"""
Copyright (c) 2015 Red Hat, Inc
All rights reserved.
This software may be modified and distributed under the terms
of the BSD license. See the LICENSE file for details.
"""
import json
import os
import pytest
from osbs.constants import DEFAULT_NAMESPACE
from osbs.cli.capture import setup_json_capture
from tests.fake_api import openshift, osbs
from tests.constants import TEST_BUILD
from osbs.conf import Configuration
API_VER = Configuration.get_openshift_api_version()
API_PREFIX = "/oapi/{v}/".format(v=API_VER)
PREFIX = API_PREFIX.replace('/', '_')
@pytest.fixture
def osbs_with_capture(osbs, tmpdir):
setup_json_capture(osbs, osbs.os_conf, str(tmpdir))
return osbs
def test_json_capture_no_watch(osbs_with_capture, tmpdir):
for visit in ["000", "001"]:
osbs_with_capture.list_builds()
filename = "get-namespaces_{n}_builds_-{v}.json"
path = os.path.join(str(tmpdir), filename.format(n=DEFAULT_NAMESPACE,
v=visit))
assert os.access(path, os.R_OK)
with open(path) as fp:
obj = json.load(fp)
assert obj
def test_json_capture_watch(osbs_with_capture, tmpdir):
osbs_with_capture.wait_for_build_to_finish(TEST_BUILD)
filename = "get-watch_namespaces_{n}_builds_{b}_-000-000.json"
path = os.path.join(str(tmpdir), filename.format(n=DEFAULT_NAMESPACE,
b=TEST_BUILD))
assert os.access(path, os.R_OK)
with open(path) as fp:
obj = json.load(fp)
assert obj
| jpopelka/osbs-client | tests/cli/test_capture.py | Python | bsd-3-clause | 1,581 | [
"VisIt"
] | 3a869687409c76d2991950535ab288daed185e1f902feac135eb6e89e18133df |
import ast as pyast
import re
import lxml.etree as ET
from calmjs.parse import asttypes as ast
from lxml.builder import E
invalid_unicode_re = re.compile(u"""[\u0001-\u0008\u000b\u000e-\u001f\u007f]""", re.U)
surrogate_unicode_re = re.compile(u"[\\ud800-\\udbff][\\udc00-\\udfff]", re.U)
def unescape_string(input_string):
input_string = invalid_unicode_re.sub(u"\ufffd", input_string)
return input_string.replace(r"\/", "/")
class XmlVisitor(object):
def visit(self, node):
method = "visit_%s" % node.__class__.__name__
return getattr(self, method, self.generic_visit)(node)
def generic_visit(self, node):
return "GEN: %r" % node
def visit_ES5Program(self, node):
program = E.program()
for child in node:
program.extend(self.visit(child))
return program
def visit_Block(self, node):
block = E.block()
for child in node:
block.extend(self.visit(child))
return [block]
def visit_VarStatement(self, node):
return [el for child in node for el in self.visit(child)]
def visit_VarDecl(self, node):
identifier = self.visit(node.identifier)[0]
varel = E.var(name=identifier.get("name"))
if node.initializer is not None:
varel.extend(self.visit(node.initializer))
return [varel]
def visit_VarDeclNoIn(self, node):
return self.visit_VarDecl(node)
def visit_Identifier(self, node):
if isinstance(node.value, (int, float)):
return [E.identifier(node.value)]
elif isinstance(node.value, str):
if node.value == "undefined":
return [E.undefined()]
idel = E.identifier()
idel.attrib["name"] = node.value
return [idel]
def visit_PropIdentifier(self, node):
return self.visit_Identifier(node)
def visit_Assign(self, node):
if node.op == ":":
propname = self.visit(node.left)[0]
if isinstance(node.left, ast.String):
propel = E.property(name=propname.text)
elif isinstance(node.left, ast.Identifier):
propel = E.property(name=propname.get("name"))
elif isinstance(node.left, ast.Number):
propel = E.property(name=propname.get("value"))
else:
print(type(node.left), type(propname))
raise RuntimeError
propel.extend(self.visit(node.right))
return [propel]
else:
assign = E.assign(operator=node.op)
left = E.left()
left.extend(self.visit(node.left))
right = E.right()
right.extend(self.visit(node.right))
assign.append(left)
assign.append(right)
return [assign]
def visit_GetPropAssign(self, node):
propel = E.property()
propel.extend(self.visit(node.prop_name))
getel = E.get()
getel.append(propel)
body = E.body()
for el in node.elements:
body.extend(self.visit(el))
getel.append(body)
return [getel]
def visit_SetPropAssign(self, node):
propel = E.property()
propel.extend(self.visit(node.prop_name))
setel = ET.Element("set")
params = E.params()
params.extend(self.visit(node.parameter))
body = E.body()
for el in node.elements:
body.extend(self.visit(el))
setel.append(body)
return [setel]
def visit_Number(self, node):
return [E.number(value=node.value)]
def visit_Comma(self, node):
comma = E.comma(E.left(self.visit(node.left)[0]), E.right(self.visit(node.right)[0]))
return [comma]
def visit_EmptyStatement(self, node):
return [E.empty(node.value)]
def visit_If(self, node):
ifel = ET.Element("if")
if node.predicate is not None:
predicate = E.predicate()
predicate.extend(self.visit(node.predicate))
ifel.append(predicate)
then = E.then()
then.extend(self.visit(node.consequent))
ifel.append(then)
if node.alternative is not None:
elseel = ET.Element("else")
elseel.extend(self.visit(node.alternative))
ifel.append(elseel)
return [ifel]
def visit_Boolean(self, node):
return [E.boolean(node.value)]
def visit_For(self, node):
forel = ET.Element("for")
if node.init is not None:
initel = E.init()
initel.extend(self.visit(node.init))
forel.append(initel)
if node.init is None:
forel.extend(E.init())
if node.cond is not None:
condition = E.condition()
condition.extend(self.visit(node.cond))
forel.append(condition)
if node.count is not None:
post = E.post()
post.extend(self.visit(node.count))
forel.append(post)
statement = E.statement()
statement.extend(self.visit(node.statement))
forel.append(statement)
return [forel]
def visit_ForIn(self, node):
variable = E.variable()
variable.extend(self.visit(node.item))
objel = ET.Element("object")
objel.extend(self.visit(node.iterable))
forel = ET.Element("forin")
forel.append(variable)
forel.append(objel)
statement = E.statement()
statement.extend(self.visit(node.statement))
forel.append(statement)
return [forel]
def visit_BinOp(self, node):
binop = E.binaryoperation(operation=node.op)
leftpart = E.left()
leftpart.extend(self.visit(node.left))
binop.append(leftpart)
rightpart = E.right(*self.visit(node.right))
binop.append(rightpart)
return [binop]
def visit_PostfixExpr(self, node):
postfix = E.postfix(operation=node.op)
postfix.extend(self.visit(node.value))
return [postfix]
def visit_UnaryExpr(self, node):
if node.op in ("delete", "void", "typeof"):
unary = E.unaryoperation(operation=node.op)
unary.extend(self.visit(node.value))
else:
# convert things like "+3.14" and "-22"
if node.op in ("-", "+") and isinstance(node.value, ast.Number):
node.value.value = "%s%s" % (node.op, node.value.value)
return self.visit(node.value)
else:
unary = E.unaryoperation(operation=node.op)
unary.extend(self.visit(node.value))
return [unary]
def visit_ExprStatement(self, node):
return self.visit(node.expr)
def visit_DoWhile(self, node):
dowhile = E.dowhile()
statement = E.statement()
statement.extend(self.visit(node.statement))
dowhile.append(statement)
whileel = ET.Element("while")
whileel.extend(self.visit(node.predicate))
dowhile.append(whileel)
return dowhile
def visit_While(self, node):
whileel = ET.Element("while")
predicate = E.predicate()
predicate.extend(self.visit(node.predicate))
whileel.append(predicate)
statement = E.statement()
statement.extend(self.visit(node.statement))
whileel.append(statement)
return [whileel]
def visit_Null(self, node):
return [E.null()]
def visit_Operator(self, node):
try:
return node.value
except Exception:
return node
def visit_str(self, node):
return node
def visit_String(self, node):
str_value = pyast.literal_eval("u" + node.value)
if surrogate_unicode_re.search(str_value):
in_utf16 = str_value.encode("utf16", "surrogatepass")
str_value = in_utf16.decode("utf16")
return [E.string(unescape_string(str_value))]
def visit_Continue(self, node):
continueel = ET.Element("continue")
if node.identifier is not None:
continueel.extend(self.visit_Identifier(node.identifier))
return [continueel]
def visit_Break(self, node):
breakel = ET.Element("break")
if node.identifier is not None:
breakel.extend(self.visit_Identifier(node.identifier))
return [breakel]
def visit_Return(self, node):
ret = ET.Element("return")
if node.expr is not None:
ret.extend(self.visit(node.expr))
return [ret]
def visit_With(self, node):
withel = ET.Element("with")
withel.extend(self.visit(node.expr))
statement = E.statement()
statement.extend(self.visit(node.statement))
withel.append(statement)
return [withel]
def visit_Label(self, node):
identifier = self.visit(node.identifier)[0]
label = E.label(name=identifier.get("name"))
statement = E.statement()
statement.extend(self.visit(node.statement))
label.append(statement)
return [label]
def visit_Switch(self, node):
expression = E.expression()
expression.extend(self.visit(node.expr))
switch = E.switch()
switch.append(expression)
for child in node.case_block.children():
switch.extend(self.visit(child))
return [switch]
def visit_Case(self, node):
expression = E.expression()
expression.extend(self.visit(node.expr))
case = E.case()
case.append(expression)
for element in node.elements:
case.extend(self.visit(element))
return [case]
def visit_Default(self, node):
default = E.default()
for element in node.elements:
default.extend(self.visit(element))
return [default]
def visit_Throw(self, node):
throwel = E.throw()
throwel.extend(self.visit(node.expr))
return [throwel]
def visit_Debugger(self, node):
return [E.debugger(node.value)]
def visit_Try(self, node):
tryel = ET.Element("try")
statements = E.statements()
statements.extend(self.visit(node.statements))
tryel.append(statements)
if node.catch is not None:
tryel.extend(self.visit(node.catch))
if node.fin is not None:
tryel.extend(self.visit(node.fin))
return [tryel]
def visit_Catch(self, node):
expression = E.expression()
expression.extend(self.visit(node.identifier))
body = E.body()
body.extend(self.visit(node.elements))
return [E.catch(expression, body)]
def visit_Finally(self, node):
finallyel = ET.Element("finally")
finallyel.extend(self.visit(node.elements))
return [finallyel]
def visit_FuncDecl(self, node):
funcdecl = E.funcdecl()
if node.identifier is not None:
identifier = self.visit(node.identifier)[0]
funcdecl.attrib["name"] = identifier.get("name")
parameters = E.parameters()
for param in node.parameters:
parameters.extend(self.visit(param))
funcdecl.append(parameters)
funcbody = E.body()
for element in node.elements:
funcbody.extend(self.visit(element))
funcdecl.append(funcbody)
return [funcdecl]
def visit_FuncExpr(self, node):
funcexpr = E.funcexpr()
if node.identifier is not None:
funcexpr.extend(self.visit(node.identifier))
else:
funcexpr.append(E.identifier())
parameters = E.parameters()
for param in node.parameters:
parameters.extend(self.visit(param))
funcexpr.append(parameters)
body = E.body()
for element in node.elements:
body.extend(self.visit(element))
funcexpr.append(body)
return [funcexpr]
def visit_Conditional(self, node):
conditional = E.conditional()
condition = E.condition()
condition.extend(self.visit(node.predicate))
value1 = E.value1()
value1.extend(self.visit(node.consequent))
value2 = E.value2()
value2.extend(self.visit(node.alternative))
conditional.append(condition)
conditional.append(value1)
conditional.append(value2)
return [conditional]
def visit_Regex(self, node):
return [E.regex(node.value)]
def visit_NewExpr(self, node):
newel = E.new()
newel.extend(self.visit(node.identifier))
arguments = E.arguments()
for arg in node.args or ():
arguments.extend(self.visit(arg))
newel.append(arguments)
return [newel]
def visit_DotAccessor(self, node):
dot = E.dotaccessor()
objel = E.object()
objel.extend(self.visit(node.node))
propel = E.property()
propel.extend(self.visit(node.identifier))
dot.append(objel)
dot.append(propel)
return [dot]
def visit_BracketAccessor(self, node):
objel = E.object()
objel.extend(self.visit(node.node))
propel = E.property()
propel.extend(self.visit(node.expr))
bracket = E.bracketaccessor(objel, propel)
return [bracket]
def visit_FunctionCall(self, node):
function = E.function()
function.extend(self.visit(node.identifier))
funccall = E.functioncall(function)
arguments = E.arguments()
for arg in node.args:
arguments.extend(self.visit(arg))
funccall.append(arguments)
return [funccall]
def visit_GroupingOp(self, node):
op = E.groupingoperator()
op.extend(self.visit(node.expr))
return [op]
def visit_Object(self, node):
obj = ET.Element("object")
for prop in node.properties:
obj.extend(self.visit(prop))
return [obj]
def visit_Array(self, node):
array = E.array()
for index, item in enumerate(node.items):
if isinstance(item, ast.Elision):
for _ in range(item.value):
array.append(E.undefined())
else:
array.extend(self.visit(item))
return [array]
def visit_This(self, node):
return [E.identifier("this")]
| redapple/js2xml | js2xml/xmlvisitor.py | Python | mit | 14,395 | [
"VisIt"
] | dd151b6d32ba33325c349b363f26bb3a8030389f233955903ffea8debc041689 |
from __future__ import print_function
from builtins import str
import os
import unittest
import shutil
import yaml
from sherlock.utKit import utKit
from fundamentals import tools
from os.path import expanduser
home = expanduser("~")
packageDirectory = utKit("").get_project_root()
settingsFile = packageDirectory + "/test_settings.yaml"
su = tools(
arguments={"settingsFile": settingsFile},
docString=__doc__,
logLevel="DEBUG",
options_first=False,
projectName=None,
defaultSettingsFile=False
)
arguments, settings, log, dbConn = su.setup()
# SETUP PATHS TO COMMON DIRECTORIES FOR TEST DATA
moduleDirectory = os.path.dirname(__file__)
pathToInputDir = moduleDirectory + "/input/"
pathToOutputDir = moduleDirectory + "/output/"
try:
shutil.rmtree(pathToOutputDir)
except:
pass
# COPY INPUT TO OUTPUT DIR
shutil.copytree(pathToInputDir, pathToOutputDir)
# Recursively create missing directories
if not os.path.exists(pathToOutputDir):
os.makedirs(pathToOutputDir)
settings["database settings"]["static catalogues"] = settings[
"database settings"]["static catalogues2"]
# SETUP ALL DATABASE CONNECTIONS
from sherlock import database
db = database(
log=log,
settings=settings
)
dbConns, dbVersions = db.connect()
transientsDbConn = dbConns["transients"]
cataloguesDbConn = dbConns["catalogues"]
from sherlock.commonutils import get_crossmatch_catalogues_column_map
colMaps = get_crossmatch_catalogues_column_map(
log=log,
dbConn=cataloguesDbConn
)
transients = [
{'ps1_designation': u'PS1-14aef',
'name': u'4L3Piiq',
'detection_list_id': 2,
'local_comments': u'',
'ra': 0.02548233704918263,
'followup_id': 2065412,
'dec': -4.284933417540423,
'id': 1,
'object_classification': 0
},
{'ps1_designation': u'PS1-13dcr',
'name': u'3I3Phzx',
'detection_list_id': 2,
'local_comments': u'',
'ra': 4.754236999477372,
'followup_id': 1140386,
'dec': 28.276703631398625,
'id': 2,
'object_classification': 0
},
{'ps1_designation': u'PS1-13dhc',
'name': u'3I3Pixd',
'detection_list_id': 2,
'local_comments': u'',
'ra': 1.3324973428505413,
'followup_id': 1202386,
'dec': 32.98869220595689,
'id': 3,
'object_classification': 0
},
{'ps1_designation': u'faint-sdss-star',
'name': u'faint-star',
'detection_list_id': 2,
'local_comments': u'',
'ra': 134.74209,
'followup_id': 1202386,
'dec': 59.18086,
'id': 4,
'object_classification': 0
},
{'ps1_designation': u'faint-sdss-galaxy',
'name': u'faint-star',
'detection_list_id': 2,
'local_comments': u'',
'ra': 134.77235,
'followup_id': 1202386,
'dec': 59.20961,
'id': 5,
'object_classification': 0
},
{'ps1_designation': u'faint-sdss-medium-galaxy',
'name': u'faint-star',
'detection_list_id': 2,
'local_comments': u'',
'ra': 134.76298,
'followup_id': 1202386,
'dec': 59.18248,
'id': 6,
'object_classification': 0
},
{'ps1_designation': u'faint-sdss-bright-star',
'name': u'faint-star',
'detection_list_id': 2,
'local_comments': u'',
'ra': 134.81694,
'followup_id': 1202386,
'dec': 59.19204,
'id': 7,
'object_classification': 0
},
{'ps1_designation': u'faint-sdss-medium-star',
'name': u'faint-star',
'detection_list_id': 2,
'local_comments': u'',
'ra': 134.84064,
'followup_id': 1202386,
'dec': 59.21254,
'id': 8,
'object_classification': 0
}
]
# transients = [
# {'ps1_designation': u'ATLAS17aeu',
# 'name': u'ATLAS17aeu',
# 'detection_list_id': 2,
# 'local_comments': u'',
# 'ra': 138.30789,
# 'followup_id': 1202386L,
# 'dec': +61.09267,
# 'id': 1000519791325919200L,
# 'object_classification': 0L
# }
# ]
sa = settings["search algorithm"]
class test_transient_catalogue_crossmatch(unittest.TestCase):
def test_transient_catalogue_crossmatch_function(self):
from sherlock import transient_catalogue_crossmatch
this = transient_catalogue_crossmatch(
log=log,
dbConn=cataloguesDbConn,
settings=settings,
colMaps=colMaps,
transients=transients
)
classifications = this.match()
def test_transient_catalogue_crossmatch_search_catalogue_function(self):
# brightnessFilters = ["bright", "faint", "general"]
# classificationType = ["synonym", "annotation", "association"]
from sherlock import transient_catalogue_crossmatch
this = transient_catalogue_crossmatch(
log=log,
dbConn=cataloguesDbConn,
settings=settings,
colMaps=colMaps,
transients=transients
)
search_name = "ned_d spec galaxy"
searchPara = sa[search_name]
print(searchPara)
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="general",
classificationType="synonym"
)
print(matchedObjects)
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="general",
classificationType="association"
)
print(matchedObjects)
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="general",
classificationType="association"
)
print(matchedObjects)
search_name = "ned phot galaxy"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="general",
classificationType="association"
)
print(matchedObjects)
search_name = "ned phot galaxy-like"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="general",
classificationType="annotation"
)
print(matchedObjects)
def test_transient_catalogue_phyiscal_crossmatch_search_catalogue_function(self):
from sherlock import transient_catalogue_crossmatch
this = transient_catalogue_crossmatch(
log=log,
dbConn=cataloguesDbConn,
settings=settings,
colMaps=colMaps,
transients=transients
)
search_name = "ned spec galaxy"
searchPara = sa[search_name]
matchedObjects = this.physical_separation_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " distance",
brightnessFilter="general",
classificationType="association"
)
print(matchedObjects)
search_name = "sdss spec galaxy"
searchPara = sa[search_name]
matchedObjects = this.physical_separation_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " distance",
brightnessFilter="general",
classificationType="association"
)
print(matchedObjects)
def test_transient_catalogue_faint_mag_search_catalogue_function(self):
from sherlock import transient_catalogue_crossmatch
this = transient_catalogue_crossmatch(
log=log,
dbConn=cataloguesDbConn,
settings=settings,
colMaps=colMaps,
transients=transients
)
search_name = "sdss star"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="faint",
classificationType="annotation"
)
print(matchedObjects)
print()
search_name = "2mass star"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="faint",
classificationType="annotation"
)
print(matchedObjects)
print()
search_name = "GSC star 1"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="faint",
classificationType="annotation"
)
print(matchedObjects)
print()
def test_transient_catalogue_bright_mag_search_catalogue_function(self):
from sherlock import transient_catalogue_crossmatch
this = transient_catalogue_crossmatch(
log=log,
dbConn=cataloguesDbConn,
settings=settings,
colMaps=colMaps,
transients=transients,
)
search_name = "sdss star"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="bright",
classificationType="association"
)
print(matchedObjects)
print()
search_name = "2mass star"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="general",
classificationType="association"
)
print(matchedObjects)
print()
search_name = "GSC star 1"
searchPara = sa[search_name]
matchedObjects = this.angular_crossmatch_against_catalogue(
objectList=transients,
searchPara=searchPara,
search_name=search_name + " angular",
brightnessFilter="bright",
classificationType="association"
)
print(matchedObjects)
print()
def test_transient_catalogue_crossmatch_function_exception(self):
from sherlock import transient_catalogue_crossmatch
try:
this = transient_catalogue_crossmatch(
log=log,
settings=settings,
fakeKey="break the code"
)
this.match()
assert False
except Exception as e:
assert True
print(str(e))
# x-class-to-test-named-worker-function
| thespacedoctor/sherlock | sherlock/tests/test_transient_catalogue_crossmatch.py | Python | mit | 11,473 | [
"Galaxy"
] | a4c07f37175b46015454a6a74cf0a242905207fe6856133a83c6b43c32dbab2f |
"""
This sample demonstrates a simple skill built with the Amazon Alexa Skills Kit.
The Intent Schema, Custom Slots, and Sample Utterances for this skill, as well
as testing instructions are located at http://amzn.to/1LzFrj6
For additional samples, visit the Alexa Skills Kit Getting Started guide at
http://amzn.to/1LGWsLG
"""
from __future__ import print_function
import urllib.request
# --------------- Helpers that build all of the responses ----------------------
def build_speechlet_response(title, output, reprompt_text, should_end_session, debug):
return {
'outputSpeech': {
'type': 'PlainText',
'text': output
},
'card': {
'type': 'Simple',
'title': "SessionSpeechlet - " + title,
'content': "SessionSpeechlet - " + output
},
'reprompt': {
'outputSpeech': {
'type': 'PlainText',
'text': reprompt_text
}
},
'shouldEndSession': should_end_session,
'debug': debug
}
def build_response(session_attributes, speechlet_response):
return {
'version': '1.0',
'sessionAttributes': session_attributes,
'response': speechlet_response
}
# --------------- Functions that control the skill's behavior ------------------
def get_welcome_response():
""" If we wanted to initialize the session to have some attributes we could
add those here
"""
session_attributes = {}
card_title = "Welcome"
speech_output = "Would you like to start or stop transcribing."
# If the user either does not reply to the welcome message or says something
# that is not understood, they will be prompted again with this text.
reprompt_text = "Please tell me to start or stop transcribing."
should_end_session = False
debug = " "
return build_response(session_attributes, build_speechlet_response(
card_title, speech_output, reprompt_text, should_end_session, debug))
def handle_session_end_request():
card_title = "Session Ended"
speech_output = "Thank you for trying the Alexa Skills for transcribing. " \
"Bye! "
# Setting this to true ends the session and exits the skill.
should_end_session = True
debug = " "
return build_response({}, build_speechlet_response(
card_title, speech_output, None, should_end_session, debug))
def create_transcribe_attribute(startStop):
return {"startStop": startStop}
def set_transcribe_in_session(intent, session):
""" Sets the startStop in the session and prepares the speech to reply to the
user.
"""
card_title = intent['name']
session_attributes = {}
should_end_session = False
if 'startStop' in intent['slots']:
startStop = intent['slots']['startStop']['value']
session_attributes = create_transcribe_attribute(startStop)
if startStop == "start transcribing":
speech_output = "I will " + \
"start transcribing" + \
". You can ask me to stop transcribing anytime. "
reprompt_text = "You can ask me to start or stop transcribing by saying, " \
"start transcribing or start transcribing."
debug = "starting reading"
captioning = urllib.request.urlopen("https://300af425.ngrok.io/start").read()
if startStop == "stop transcribing":
speech_output = "I will " + \
"stop transcribing." + \
". You can ask me to start transcribing again anytime."
reprompt_text = "You can ask me to start or stop transcribing by saying, " \
"start transcribing or start transcribing."
debug = "stopping reading"
captioning = urllib.request.urlopen("https://300af425.ngrok.io/stop").read()
else:
speech_output = "I'm not sure what you are trying to say. " \
"Please try again."
reprompt_text = "I'm not sure what you are trying to say. " \
". You can ask me to start or stop transcribing by saying, " \
"start transcribing or stop transcribing."
#print(captioning)
print(speech_output)
should_end_session = True
return build_response(session_attributes, build_speechlet_response(
card_title, speech_output, reprompt_text, should_end_session, debug))
def get_transcribe_from_session(intent, session):
session_attributes = {}
reprompt_text = None
if session.get('attributes', {}) and "startStop" in session.get('attributes', {}):
startStop = session['attributes']['startStop']
speech_output = "You can " + startStop + \
". Goodbye."
should_end_session = True
else:
speech_output = "I'm not sure what you mean. " \
"Please try again."
should_end_session = False
debug = " "
# Setting reprompt_text to None signifies that we do not want to reprompt
# the user. If the user does not respond or says something that is not
# understood, the session will end.
return build_response(session_attributes, build_speechlet_response(
intent['name'], speech_output, reprompt_text, should_end_session, debug))
# --------------- Events ------------------ (Alexa is called)
def on_session_started(session_started_request, session):
""" Called when the session starts """
print("on_session_started requestId=" + session_started_request['requestId']
+ ", sessionId=" + session['sessionId'])
def on_launch(launch_request, session):
""" Called when the user launches the skill without specifying what they
want
"""
print("on_launch requestId=" + launch_request['requestId'] +
", sessionId=" + session['sessionId'])
# Dispatch to your skill's launch
return get_welcome_response()
def on_intent(intent_request, session):
""" Called when the user specifies an intent for this skill """
print("on_intent requestId=" + intent_request['requestId'] +
", sessionId=" + session['sessionId'])
intent = intent_request['intent']
intent_name = intent_request['intent']['name']
# Dispatch to your skill's intent handlers
if intent_name == "startStopIsIntent":
return set_transcribe_in_session(intent, session)
elif intent_name == "AMAZON.HelpIntent":
return get_welcome_response()
elif intent_name == "AMAZON.CancelIntent" or intent_name == "AMAZON.StopIntent":
return handle_session_end_request()
else:
raise ValueError("Invalid intent")
def on_session_ended(session_ended_request, session):
""" Called when the user ends the session.
Is not called when the skill returns should_end_session=true
"""
print("on_session_ended requestId=" + session_ended_request['requestId'] +
", sessionId=" + session['sessionId'])
# add cleanup logic here
# --------------- Main handler ------------------
def lambda_handler(event, context):
""" Route the incoming request based on type (LaunchRequest, IntentRequest,
etc.) The JSON body of the request is provided in the event parameter.
"""
print("event.session.application.applicationId=" +
event['session']['application']['applicationId'])
"""
Uncomment this if statement and populate with your skill's application ID to
prevent someone else from configuring a skill that sends requests to this
function.
"""
# if (event['session']['application']['applicationId'] !=
# "amzn1.echo-sdk-ams.app.[unique-value-here]"):
# raise ValueError("Invalid Application ID")
if event['session']['new']:
on_session_started({'requestId': event['request']['requestId']},
event['session'])
if event['request']['type'] == "LaunchRequest":
return on_launch(event['request'], event['session'])
elif event['request']['type'] == "IntentRequest":
return on_intent(event['request'], event['session'])
elif event['request']['type'] == "SessionEndedRequest":
return on_session_ended(event['request'], event['session']) | ItsNotABugItsAFeature/transcribe | alexa/startStop.py | Python | mit | 8,405 | [
"VisIt"
] | 7d1f3778e11562da6d43dbe78a8e412e71b8bcce25fdf6cdf6031b37ef386b54 |
"""Test class for Remote Execution Management UI
:Requirement: Remoteexecution
:CaseAutomation: Automated
:CaseLevel: Acceptance
:CaseComponent: RemoteExecution
:Assignee: pondrejk
:TestType: Functional
:CaseImportance: High
:Upstream: No
"""
from datetime import datetime
from datetime import timedelta
from time import sleep
import pytest
from broker import VMBroker
from fauxfactory import gen_string
from nailgun import entities
from wait_for import wait_for
from robottelo.cli.factory import make_job_invocation
from robottelo.cli.factory import make_job_template
from robottelo.cli.globalparam import GlobalParameter
from robottelo.cli.host import Host
from robottelo.cli.job_invocation import JobInvocation
from robottelo.cli.recurring_logic import RecurringLogic
from robottelo.cli.repository import Repository
from robottelo.cli.repository_set import RepositorySet
from robottelo.cli.task import Task
from robottelo.config import settings
from robottelo.constants import PRDS
from robottelo.constants import REPOS
from robottelo.constants import REPOSET
from robottelo.hosts import ContentHost
from robottelo.utils.issue_handlers import is_open
@pytest.fixture()
def fixture_vmsetup(request, module_org, default_sat):
"""Create VM and register content host"""
if '_count' in request.param.keys():
with VMBroker(
nick=request.param['nick'],
host_classes={'host': ContentHost},
_count=request.param['_count'],
) as clients:
for client in clients:
client.configure_rex(satellite=default_sat, org=module_org)
yield clients
else:
with VMBroker(nick=request.param['nick'], host_classes={'host': ContentHost}) as client:
client.configure_rex(satellite=default_sat, org=module_org)
yield client
@pytest.fixture()
def fixture_sca_vmsetup(request, module_gt_manifest_org, default_sat):
"""Create VM and register content host to Simple Content Access organization"""
if '_count' in request.param.keys():
with VMBroker(
nick=request.param['nick'],
host_classes={'host': ContentHost},
_count=request.param['_count'],
) as clients:
for client in clients:
client.configure_rex(satellite=default_sat, org=module_gt_manifest_org)
yield clients
else:
with VMBroker(nick=request.param['nick'], host_classes={'host': ContentHost}) as client:
client.configure_rex(satellite=default_sat, org=module_gt_manifest_org)
yield client
class TestRemoteExecution:
"""Implements job execution tests in CLI."""
@pytest.mark.tier3
@pytest.mark.pit_client
@pytest.mark.pit_server
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
def test_positive_run_default_job_template_by_ip(self, fixture_vmsetup):
"""Run default template on host connected by ip and list task
:id: 811c7747-bec6-4a2d-8e5c-b5045d3fbc0d
:expectedresults: Verify the job was successfully ran against the host
and task can be listed by name and ID
:BZ: 1647582
:customerscenario: true
:parametrized: yes
"""
client = fixture_vmsetup
command = "echo {}".format(gen_string('alpha'))
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': f'command={command}',
'search-query': f"name ~ {client.hostname}",
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
task = Task.list_tasks({"search": command})[0]
search = Task.list_tasks({"search": 'id={}'.format(task["id"])})
assert search[0]["action"] == task["action"]
@pytest.mark.skip_if_open('BZ:1804685')
@pytest.mark.tier3
@pytest.mark.pit_client
@pytest.mark.pit_server
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
def test_positive_run_job_effective_user_by_ip(self, fixture_vmsetup):
"""Run default job template as effective user on a host by ip
:id: 0cd75cab-f699-47e6-94d3-4477d2a94bb7
:BZ: 1451675
:expectedresults: Verify the job was successfully run under the
effective user identity on host
:parametrized: yes
"""
client = fixture_vmsetup
# create a user on client via remote job
username = gen_string('alpha')
filename = gen_string('alpha')
make_user_job = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': f"command='useradd -m {username}'",
'search-query': f"name ~ {client.hostname}",
}
)
result = JobInvocation.info({'id': make_user_job['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output({'id': make_user_job['id'], 'host': client.hostname})
)
)
raise AssertionError(result)
# create a file as new user
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': f"command='touch /home/{username}/{filename}'",
'search-query': f"name ~ {client.hostname}",
'effective-user': f'{username}',
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
# check the file owner
result = client.execute(
f'''stat -c '%U' /home/{username}/{filename}''',
)
# assert the file is owned by the effective user
assert username == result.stdout.strip('\n')
nick_params = [{'nick': 'rhel7'}, {'nick': 'rhel7_fips'}, {'nick': 'rhel8'}]
if not is_open('BZ:1811166'):
nick_params.append({'nick': 'rhel8_fips'})
@pytest.mark.tier3
@pytest.mark.parametrize(
'fixture_vmsetup',
nick_params,
ids=[n['nick'] for n in nick_params],
indirect=True,
)
def test_positive_run_custom_job_template_by_ip(self, fixture_vmsetup, module_org, default_sat):
"""Run custom template on host connected by ip
:id: 9740eb1d-59f5-42b2-b3ab-659ca0202c74
:expectedresults: Verify the job was successfully ran against the host
:bz: 1872688, 1811166
:customerscenario: true
:CaseImportance: Critical
:parametrized: yes
"""
self.org = module_org
client = fixture_vmsetup
template_file = 'template_file.txt'
default_sat.execute(f'echo "echo Enforcing" > {template_file}')
template_name = gen_string('alpha', 7)
make_job_template(
{'organizations': self.org.name, 'name': template_name, 'file': template_file}
)
invocation_command = make_job_invocation(
{'job-template': template_name, 'search-query': f"name ~ {client.hostname}"}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
@pytest.mark.destructive
@pytest.mark.parametrize(
'fixture_vmsetup',
[{'nick': 'rhel7'}],
ids=['rhel7'],
indirect=True,
)
def test_positive_use_alternate_directory(self, fixture_vmsetup, module_org, default_sat):
"""Use alternate working directory on client to execute rex jobs
:id: a0181f18-d3dc-4bd9-a2a6-430c2a49809e
:expectedresults: Verify the job was successfully ran against the host
:customerscenario: true
:parametrized: yes
"""
client = fixture_vmsetup
testdir = gen_string('alpha')
result = client.run(f'mkdir /{testdir}')
assert result.status == 0
result = client.run(f'chcon --reference=/var /{testdir}')
assert result.status == 0
result = default_sat.execute(
f"sed -i r's/^:remote_working_dir:.*/:remote_working_dir: \\/{testdir}/' \
/etc/foreman-proxy/settings.d/remote_execution_ssh.yml",
)
assert result.status == 0
result = default_sat.execute('systemctl restart foreman-proxy')
assert result.status == 0
command = f'echo {gen_string("alpha")}'
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': f'command={command}',
'search-query': f"name ~ {client.hostname}",
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
output = ' '.join(
JobInvocation.get_output({'id': invocation_command['id'], 'host': client.hostname})
)
result = f'host output: {output}'
raise AssertionError(result)
task = Task.list_tasks({"search": command})[0]
search = Task.list_tasks({"search": f'id={task["id"]}'})
assert search[0]["action"] == task["action"]
@pytest.mark.tier3
@pytest.mark.upgrade
@pytest.mark.parametrize(
'fixture_vmsetup', [{'nick': 'rhel7', '_count': 2}], ids=['rhel7'], indirect=True
)
def test_positive_run_default_job_template_multiple_hosts_by_ip(
self, fixture_vmsetup, module_org
):
"""Run default job template against multiple hosts by ip
:id: 694a21d3-243b-4296-8bd0-4bad9663af15
:expectedresults: Verify the job was successfully ran against all hosts
:parametrized: yes
"""
clients = fixture_vmsetup
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': 'command="ls"',
'search-query': f'name ~ {clients[0].hostname} or name ~ {clients[1].hostname}',
}
)
# collect output messages from clients
output_msgs = []
for vm in clients:
output_msgs.append(
'host output from {}: {}'.format(
vm.hostname,
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': vm.hostname}
)
),
)
)
result = JobInvocation.info({'id': invocation_command['id']})
assert result['success'] == '2', output_msgs
@pytest.mark.tier3
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
@pytest.mark.skipif(
(not settings.robottelo.repos_hosting_url), reason='Missing repos_hosting_url'
)
def test_positive_install_multiple_packages_with_a_job_by_ip(self, fixture_vmsetup, module_org):
"""Run job to install several packages on host by ip
:id: 8b73033f-83c9-4024-83c3-5e442a79d320
:expectedresults: Verify the packages were successfully installed
on host
:parametrized: yes
"""
self.org = module_org
client = fixture_vmsetup
packages = ["cow", "dog", "lion"]
# Create a custom repo
repo = entities.Repository(
content_type='yum',
product=entities.Product(organization=self.org).create(),
url=settings.repos.yum_0.url,
).create()
repo.sync()
prod = repo.product.read()
subs = entities.Subscription(organization=self.org).search(
query={'search': f'name={prod.name}'}
)
assert len(subs) > 0, 'No subscriptions matching the product returned'
ak = entities.ActivationKey(
organization=self.org,
content_view=self.org.default_content_view,
environment=self.org.library,
).create()
ak.add_subscriptions(data={'subscriptions': [{'id': subs[0].id}]})
client.register_contenthost(org=self.org.label, activation_key=ak.name)
invocation_command = make_job_invocation(
{
'job-template': 'Install Package - Katello SSH Default',
'inputs': 'package={} {} {}'.format(*packages),
'search-query': f'name ~ {client.hostname}',
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
result = client.run(f'rpm -q {" ".join(packages)}')
assert result.status == 0
@pytest.mark.tier3
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
def test_positive_run_recurring_job_with_max_iterations_by_ip(self, fixture_vmsetup):
"""Run default job template multiple times with max iteration by ip
:id: 0a3d1627-95d9-42ab-9478-a908f2a7c509
:expectedresults: Verify the job was run not more than the specified
number of times.
:parametrized: yes
"""
client = fixture_vmsetup
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': 'command="ls"',
'search-query': f"name ~ {client.hostname}",
'cron-line': '* * * * *', # every minute
'max-iteration': 2, # just two runs
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['status'] == 'queued'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
sleep(150)
rec_logic = RecurringLogic.info({'id': result['recurring-logic-id']})
assert rec_logic['state'] == 'finished'
assert rec_logic['iteration'] == '2'
@pytest.mark.tier3
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
def test_positive_run_scheduled_job_template_by_ip(self, fixture_vmsetup, default_sat):
"""Schedule a job to be ran against a host
:id: 0407e3de-ef59-4706-ae0d-b81172b81e5c
:expectedresults: Verify the job was successfully ran after the
designated time
:parametrized: yes
"""
client = fixture_vmsetup
system_current_time = default_sat.execute('date --utc +"%b %d %Y %I:%M%p"').stdout
current_time_object = datetime.strptime(system_current_time.strip('\n'), '%b %d %Y %I:%M%p')
plan_time = (current_time_object + timedelta(seconds=30)).strftime("%Y-%m-%d %H:%M")
Host.set_parameter(
{
'host': client.hostname,
'name': 'remote_execution_connect_by_ip',
'value': 'True',
'parameter-type': 'boolean',
}
)
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - SSH Default',
'inputs': 'command="ls"',
'start-at': plan_time,
'search-query': f"name ~ {client.hostname}",
}
)
# Wait until the job runs
pending_state = '1'
while pending_state != '0':
invocation_info = JobInvocation.info({'id': invocation_command['id']})
pending_state = invocation_info['pending']
sleep(30)
invocation_info = JobInvocation.info({'id': invocation_command['id']})
try:
assert invocation_info['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
@pytest.mark.tier3
@pytest.mark.upgrade
def test_positive_run_receptor_installer(self, default_sat):
"""Run Receptor installer ("Configure Cloud Connector")
:CaseComponent: RHCloud-CloudConnector
:Assignee: lhellebr
:id: 811c7747-bec6-1a2d-8e5c-b5045d3fbc0d
:expectedresults: The job passes, installs Receptor that peers with c.r.c
:BZ: 1818076
"""
# Set Host parameter source_display_name to something random.
# To avoid 'name has already been taken' error when run multiple times
# on a machine with the same hostname.
host_id = Host.info({'name': default_sat.hostname})['id']
Host.set_parameter(
{'host-id': host_id, 'name': 'source_display_name', 'value': gen_string('alpha')}
)
template_name = 'Configure Cloud Connector'
invocation = make_job_invocation(
{
'async': True,
'job-template': template_name,
'inputs': f'satellite_user="{settings.server.admin_username}",\
satellite_password="{settings.server.admin_password}"',
'search-query': f'name ~ {default_sat.hostname}',
}
)
invocation_id = invocation['id']
wait_for(
lambda: entities.JobInvocation(id=invocation_id).read().status_label
in ["succeeded", "failed"],
timeout="1500s",
)
assert entities.JobInvocation(id=invocation_id).read().status == 0
result = ' '.join(
JobInvocation.get_output({'id': invocation_id, 'host': default_sat.hostname})
)
assert 'project-receptor.satellite_receptor_installer' in result
assert 'Exit status: 0' in result
# check that there is one receptor conf file and it's only readable
# by the receptor user and root
result = default_sat.execute('stat /etc/receptor/*/receptor.conf --format "%a:%U"')
assert result.stdout == '400:foreman-proxy'
result = default_sat.execute('ls -l /etc/receptor/*/receptor.conf | wc -l')
assert result.stdout == '1'
class TestAnsibleREX:
"""Test class for remote execution via Ansible"""
@pytest.mark.tier3
@pytest.mark.upgrade
@pytest.mark.pit_client
@pytest.mark.pit_server
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
def test_positive_run_effective_user_job(self, fixture_vmsetup):
"""Tests Ansible REX job having effective user runs successfully
:id: a5fa20d8-c2bd-4bbf-a6dc-bf307b59dd8c
:Steps:
0. Create a VM and register to SAT and prepare for REX (ssh key)
1. Run Ansible Command job for the host to create a user
2. Run Ansible Command job using effective user
3. Check the job result at the host is done under that user
:expectedresults: multiple asserts along the code
:CaseAutomation: Automated
:CaseLevel: System
:parametrized: yes
"""
client = fixture_vmsetup
# create a user on client via remote job
username = gen_string('alpha')
filename = gen_string('alpha')
make_user_job = make_job_invocation(
{
'job-template': 'Run Command - Ansible Default',
'inputs': f"command='useradd -m {username}'",
'search-query': f"name ~ {client.hostname}",
}
)
result = JobInvocation.info({'id': make_user_job['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output({'id': make_user_job['id'], 'host': client.hostname})
)
)
raise AssertionError(result)
# create a file as new user
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - Ansible Default',
'inputs': f"command='touch /home/{username}/{filename}'",
'search-query': f"name ~ {client.hostname}",
'effective-user': f'{username}',
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
# check the file owner
result = client.execute(
f'''stat -c '%U' /home/{username}/{filename}''',
)
# assert the file is owned by the effective user
assert username == result.stdout.strip('\n'), "file ownership mismatch"
@pytest.mark.tier3
@pytest.mark.upgrade
@pytest.mark.parametrize('fixture_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True)
def test_positive_run_reccuring_job(self, fixture_vmsetup):
"""Tests Ansible REX reccuring job runs successfully multiple times
:id: 49b0d31d-58f9-47f1-aa5d-561a1dcb0d66
:Steps:
0. Create a VM and register to SAT and prepare for REX (ssh key)
1. Run recurring Ansible Command job for the host
2. Check the multiple job results at the host
:expectedresults: multiple asserts along the code
:CaseAutomation: Automated
:CaseLevel: System
:parametrized: yes
"""
client = fixture_vmsetup
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - Ansible Default',
'inputs': 'command="ls"',
'search-query': f"name ~ {client.hostname}",
'cron-line': '* * * * *', # every minute
'max-iteration': 2, # just two runs
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['status'] == 'queued'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
sleep(150)
rec_logic = RecurringLogic.info({'id': result['recurring-logic-id']})
assert rec_logic['state'] == 'finished'
assert rec_logic['iteration'] == '2'
@pytest.mark.tier3
@pytest.mark.parametrize(
'fixture_vmsetup', [{'nick': 'rhel7', '_count': 2}], ids=['rhel7'], indirect=True
)
def test_positive_run_concurrent_jobs(self, fixture_vmsetup, module_org):
"""Tests Ansible REX concurent jobs without batch trigger
:id: ad0f108c-03f2-49c7-8732-b1056570567b
:Steps:
0. Create 2 hosts, disable foreman_tasks_proxy_batch_trigger
1. Run Ansible Command job with concurrency-setting
:expectedresults: multiple asserts along the code
:CaseAutomation: Automated
:customerscenario: true
:CaseLevel: System
:BZ: 1817320
:parametrized: yes
"""
param_name = 'foreman_tasks_proxy_batch_trigger'
GlobalParameter().set({'name': param_name, 'value': 'false'})
clients = fixture_vmsetup
output_msgs = []
invocation_command = make_job_invocation(
{
'job-template': 'Run Command - Ansible Default',
'inputs': 'command="ls"',
'search-query': f'name ~ {clients[0].hostname} or name ~ {clients[1].hostname}',
'concurrency-level': 2,
}
)
for vm in clients:
output_msgs.append(
'host output from {}: {}'.format(
vm.hostname,
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': vm.hostname}
)
),
)
)
result = JobInvocation.info({'id': invocation_command['id']})
assert result['success'] == '2', output_msgs
GlobalParameter().delete({'name': param_name})
assert len(GlobalParameter().list({'search': param_name})) == 0
nick_params = [{'nick': 'rhel7'}, {'nick': 'rhel7_fips'}, {'nick': 'rhel8'}]
if not is_open('BZ:1811166'):
nick_params.append({'nick': 'rhel8_fips'})
@pytest.mark.tier3
@pytest.mark.upgrade
@pytest.mark.pit_client
@pytest.mark.pit_server
@pytest.mark.parametrize(
'fixture_vmsetup',
nick_params,
ids=[n['nick'] for n in nick_params],
indirect=True,
)
@pytest.mark.skipif(
(not settings.robottelo.repos_hosting_url), reason='Missing repos_hosting_url'
)
def test_positive_run_packages_and_services_job(self, fixture_vmsetup, module_org):
"""Tests Ansible REX job can install packages and start services
:id: 47ed82fb-77ca-43d6-a52e-f62bae5d3a42
:Steps:
0. Create a VM and register to SAT and prepare for REX (ssh key)
1. Run Ansible Package job for the host to install a package
2. Check the package is present at the host
3. Run Ansible Service job for the host to start a service
4. Check the service is started on the host
:expectedresults: multiple asserts along the code
:CaseAutomation: Automated
:CaseLevel: System
:bz: 1872688, 1811166
:CaseImportance: Critical
:customerscenario: true
:parametrized: yes
"""
self.org = module_org
client = fixture_vmsetup
packages = ["cow"]
# Create a custom repo
repo = entities.Repository(
content_type='yum',
product=entities.Product(organization=self.org).create(),
url=settings.repos.yum_0.url,
).create()
repo.sync()
prod = repo.product.read()
subs = entities.Subscription(organization=self.org).search(
query={'search': f'name={prod.name}'}
)
assert len(subs), 'No subscriptions matching the product returned'
ak = entities.ActivationKey(
organization=self.org,
content_view=self.org.default_content_view,
environment=self.org.library,
).create()
ak.add_subscriptions(data={'subscriptions': [{'id': subs[0].id}]})
client.register_contenthost(org=self.org.label, activation_key=ak.name)
# install package
invocation_command = make_job_invocation(
{
'job-template': 'Package Action - Ansible Default',
'inputs': 'state=latest, name={}'.format(*packages),
'search-query': f'name ~ {client.hostname}',
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
result = client.run(f'rpm -q {" ".join(packages)}')
assert result.status == 0
# start a service
service = "postfix"
client.execute(
"sed -i 's/^inet_protocols.*/inet_protocols = ipv4/' /etc/postfix/main.cf",
)
invocation_command = make_job_invocation(
{
'job-template': 'Service Action - Ansible Default',
'inputs': f'state=started, name={service}',
'search-query': f"name ~ {client.hostname}",
}
)
result = JobInvocation.info({'id': invocation_command['id']})
try:
assert result['success'] == '1'
except AssertionError:
result = 'host output: {}'.format(
' '.join(
JobInvocation.get_output(
{'id': invocation_command['id'], 'host': client.hostname}
)
)
)
raise AssertionError(result)
result = client.execute(f"systemctl status {service}")
assert result.status == 0
@pytest.mark.tier3
@pytest.mark.parametrize(
'fixture_sca_vmsetup', [{'nick': 'rhel7'}], ids=['rhel7'], indirect=True
)
def test_positive_install_ansible_collection(self, fixture_sca_vmsetup, module_gt_manifest_org):
"""Test whether Ansible collection can be installed via REX
:Steps:
1. Upload a manifest.
2. Enable and sync Ansible repository.
3. Register content host to Satellite.
4. Enable Ansible repo on content host.
5. Install ansible package.
6. Run REX job to install Ansible collection on content host.
:id: ad25aee5-4ea3-4743-a301-1c6271856f79
:CaseComponent: Ansible
:Assignee: dsynk
"""
# Configure repository to prepare for installing ansible on host
RepositorySet.enable(
{
'basearch': 'x86_64',
'name': REPOSET['rhae2'],
'organization-id': module_gt_manifest_org.id,
'product': PRDS['rhae'],
'releasever': '7Server',
}
)
Repository.synchronize(
{
'name': REPOS['rhae2']['name'],
'organization-id': module_gt_manifest_org.id,
'product': PRDS['rhae'],
}
)
client = fixture_sca_vmsetup
client.execute(f'subscription-manager repos --enable {REPOS["rhae2"]["id"]}')
client.execute('yum -y install ansible')
collection_job = make_job_invocation(
{
'job-template': 'Ansible Collection - Install from Galaxy',
'inputs': 'ansible_collections_list="oasis_roles.system"',
'search-query': f'name ~ {client.hostname}',
}
)
result = JobInvocation.info({'id': collection_job['id']})
assert result['success'] == '1'
collection_path = str(client.execute('ls /etc/ansible/collections/ansible_collections'))
assert 'oasis' in collection_path
| rplevka/robottelo | tests/foreman/cli/test_remoteexecution.py | Python | gpl-3.0 | 32,749 | [
"Galaxy"
] | c441ee6de0dd5cbc0a8a6ccd4243383b51d15aae59df22131fcdeab798f77eff |
"""Find and deal with signatures in biological sequence data.
In addition to representing sequences according to motifs (see Motif.py
for more information), we can also use Signatures, which are conserved
regions that are not necessarily consecutive. This may be useful in
the case of very diverged sequences, where signatures may pick out
important conservation that can't be found by motifs (hopefully!).
"""
# biopython
from Bio.Alphabet import _verify_alphabet
from Bio.Seq import Seq
# local stuff
from Pattern import PatternRepository
class SignatureFinder:
"""Find Signatures in a group of sequence records.
In this simple implementation, signatures are just defined as a
two motifs separated by a gap. We need something a lot smarter than
this to find more complicated signatures.
"""
def __init__(self, alphabet_strict = 1):
"""Initialize a finder to get signatures.
Arguments:
o alphabet_strict - Specify whether signatures should be required
to have all letters in the signature be consistent with the
alphabet of the original sequence. This requires that all Seqs
used have a consistent alphabet. This helps protect against getting
useless signatures full of ambiguity signals.
"""
self._alphabet_strict = alphabet_strict
def find(self, seq_records, signature_size, max_gap):
"""Find all signatures in a group of sequences.
Arguments:
o seq_records - A list of SeqRecord objects we'll use the sequences
from to find signatures.
o signature_size - The size of each half of a signature (ie. if this
is set at 3, then the signature could be AGC-----GAC)
o max_gap - The maximum gap size between two parts of a signature.
"""
sig_info = self._get_signature_dict(seq_records, signature_size,
max_gap)
return PatternRepository(sig_info)
def _get_signature_dict(self, seq_records, sig_size, max_gap):
"""Return a dictionary with all signatures and their counts.
This internal function does all of the hard work for the
find_signatures function.
"""
if self._alphabet_strict:
alphabet = seq_records[0].seq.alphabet
else:
alphabet = None
# loop through all records to find signatures
all_sigs = {}
for seq_record in seq_records:
# if we are working with alphabets, make sure we are consistent
if alphabet is not None:
assert seq_record.seq.alphabet == alphabet, \
"Working with alphabet %s and got %s" % \
(alphabet, seq_record.seq.alphabet)
# now start finding signatures in the sequence
largest_sig_size = sig_size * 2 + max_gap
for start in range(len(seq_record.seq) - (largest_sig_size - 1)):
# find the first part of the signature
first_sig = seq_record.seq[start:start + sig_size].tostring()
# now find all of the second parts of the signature
for second in range(start + 1, (start + 1) + max_gap):
second_sig = seq_record.seq[second: second + sig_size].tostring()
# if we are being alphabet strict, make sure both parts
# of the sig fall within the specified alphabet
if alphabet is not None:
first_seq = Seq(first_sig, alphabet)
second_seq = Seq(second_sig, alphabet)
if _verify_alphabet(first_seq) \
and _verify_alphabet(second_seq):
all_sigs = self._add_sig(all_sigs,
(first_sig, second_sig))
# if we are not being strict, just add the motif
else:
all_sigs = self._add_sig(all_sigs,
(first_sig, second_sig))
return all_sigs
def _add_sig(self, sig_dict, sig_to_add):
"""Add a signature to the given dictionary.
"""
# incrememt the count of the signature if it is already present
if sig_to_add in sig_dict:
sig_dict[sig_to_add] += 1
# otherwise add it to the dictionary
else:
sig_dict[sig_to_add] = 1
return sig_dict
class SignatureCoder:
"""Convert a Sequence into its signature representatives.
This takes a sequence and a set of signatures, and converts the
sequence into a list of numbers representing the relative amounts
each signature is seen in the sequence. This allows a sequence to
serve as input into a neural network.
"""
def __init__(self, signatures, max_gap):
"""Initialize with the signatures to look for.
Arguments:
o signatures - A complete list of signatures, in order, that
are to be searched for in the sequences. The signatures should
be represented as a tuple of (first part of the signature,
second_part of the signature) -- ('GATC', 'GATC').
o max_gap - The maximum gap we can have between the two
elements of the signature.
"""
self._signatures = signatures
self._max_gap = max_gap
# check to be sure the signatures are all the same size
# only do this if we actually have signatures
if len(self._signatures) > 0:
first_sig_size = len(self._signatures[0][0])
second_sig_size = len(self._signatures[0][1])
assert first_sig_size == second_sig_size, \
"Ends of the signature do not match: %s" \
% self._signatures[0]
for sig in self._signatures:
assert len(sig[0]) == first_sig_size, \
"Got first part of signature %s, expected size %s" % \
(sig[0], first_sig_size)
assert len(sig[1]) == second_sig_size, \
"Got second part of signature %s, expected size %s" % \
(sig[1], second_sig_size)
def representation(self, sequence):
"""Convert a sequence into a representation of its signatures.
Arguments:
o sequence - A Seq object we are going to convert into a set of
signatures.
Returns:
A list of relative signature representations. Each item in the
list corresponds to the signature passed in to the initializer and
is the number of times that the signature was found, divided by the
total number of signatures found in the sequence.
"""
# check to be sure we have signatures to deal with,
# otherwise just return an empty list
if len(self._signatures) == 0:
return []
# initialize a dictionary to hold the signature counts
sequence_sigs = {}
for sig in self._signatures:
sequence_sigs[sig] = 0
# get a list of all of the first parts of the signatures
all_first_sigs = []
for sig_start, sig_end in self._signatures:
all_first_sigs.append(sig_start)
# count all of the signatures we are looking for in the sequence
sig_size = len(self._signatures[0][0])
smallest_sig_size = sig_size * 2
for start in range(len(sequence) - (smallest_sig_size - 1)):
# if the first part matches any of the signatures we are looking
# for, then expand out to look for the second part
first_sig = sequence[start:start + sig_size].tostring()
if first_sig in all_first_sigs:
for second in range(start + sig_size,
(start + sig_size + 1) + self._max_gap):
second_sig = sequence[second:second + sig_size].tostring()
# if we find the motif, increase the counts for it
if (first_sig, second_sig) in sequence_sigs:
sequence_sigs[(first_sig, second_sig)] += 1
# -- normalize the signature info to go between zero and one
min_count = min(sequence_sigs.values())
max_count = max(sequence_sigs.values())
# as long as we have some signatures present, normalize them
# otherwise we'll just return 0 for everything
if max_count > 0:
for sig in sequence_sigs:
sequence_sigs[sig] = (float(sequence_sigs[sig] - min_count)
/ float(max_count))
# return the relative signature info in the specified order
sig_amounts = []
for sig in self._signatures:
sig_amounts.append(sequence_sigs[sig])
return sig_amounts
| BlogomaticProject/Blogomatic | opt/blog-o-matic/usr/lib/python/Bio/NeuralNetwork/Gene/Signature.py | Python | gpl-2.0 | 8,956 | [
"Biopython"
] | 4217be3f0035eaf633df308332d926050ac6c357a302a24b1afdc7ee55f0c9f8 |
#
# @BEGIN LICENSE
#
# Psi4: an open-source quantum chemistry software package
#
# Copyright (c) 2007-2021 The Psi4 Developers.
#
# The copyrights for code used from other parties are included in
# the corresponding files.
#
# This file is part of Psi4.
#
# Psi4 is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, version 3.
#
# Psi4 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with Psi4; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
# @END LICENSE
#
import collections
from typing import Dict, List, Union
import numpy as np
from qcelemental.models import AtomicInput
import qcengine as qcng
from psi4 import core
from psi4.driver import p4util
from psi4.driver import driver_findif
from psi4.driver.p4util.exceptions import ValidationError
_engine_can_do = collections.OrderedDict([('libdisp', ['d1', 'd2', 'chg', 'das2009', 'das2010']),
('dftd3', ['d2', 'd3zero', 'd3bj', 'd3mzero', 'd3mbj']),
('nl', ['nl']),
('mp2d', ['dmp2']),
]) # yapf: disable
_capable_engines_for_disp = collections.defaultdict(list)
for eng, disps in _engine_can_do.items():
for disp in disps:
_capable_engines_for_disp[disp].append(eng)
class EmpiricalDispersion(object):
"""Lightweight unification of empirical dispersion calculation modes.
Attributes
----------
dashlevel : str
{'d1', 'd2', 'd3zero', 'd3bj', 'd3mzero', 'd3mbj', 'chg', 'das2009', 'das2010', 'nl', 'dmp2'}
Name of dispersion correction to be applied. Resolved
from `name_hint` and/or `level_hint` into a key of
`empirical_dispersion_resources.dashcoeff`.
dashparams : dict
Complete set of parameter values defining the flexible parts
of :py:attr:`dashlevel`. Number and parameter names vary by
:py:attr:`dashlevel`. Resolved into a complete set (keys of
dashcoeff[dashlevel]['default']) from `name_hint` and/or
`dashcoeff_supplement` and/or user `param_tweaks`.
fctldash : str
If :py:attr:`dashparams` for :py:attr:`dashlevel` corresponds to a defined,
named, untweaked "functional-dashlevel" set, then that
functional. Otherwise, empty string.
description : str
Tagline for dispersion :py:attr:`dashlevel`.
dashlevel_citation : str
Literature reference for dispersion :py:attr:`dashlevel` in general,
*not necessarily* for :py:attr:`dashparams`.
dashparams_citation : str
Literature reference for dispersion parameters, if :py:attr:`dashparams`
corresponds to a defined, named, untweaked "functional-dashlevel"
set with a citation. Otherwise, empty string.
dashcoeff_supplement : dict
See description in `qcengine.programs.empirical_dispersion_resources.from_arrays`. Used
here to "bless" the dispersion definitions attached to
the procedures/dft/<rung>_functionals-defined dictionaries
as legit, non-custom, and of equal validity to
`qcengine.programs.empirical_dispersion_resources.dashcoeff` itself for purposes of
validating :py:attr:`fctldash`.
engine : str
{'libdisp', 'dftd3', 'nl', 'mp2d'}
Compute engine for dispersion. One of Psi4's internal libdisp
library, Grimme's DFTD3 executable, or nl.
disp : Dispersion
Only present for :py:attr:`engine` `=libdisp`. Psi4 class instance prepared
to compute dispersion.
ordered_params : list
Fixed-order list of relevant parameters for :py:attr:`dashlevel`. Matches
:rst:psivar:`DISPERSION CORRECTION ENERGY` ordering. Used for printing.
Parameters
----------
name_hint
Name of functional (func only, func & disp, or disp only) for
which to compute dispersion (e.g., blyp, BLYP-D2, blyp-d3bj,
blyp-d3(bj), hf+d). Any or all parameters initialized from
``dashcoeff[dashlevel][functional-without-dashlevel]`` or
``dashcoeff_supplement[dashlevel][functional-with-dashlevel]``
can be overwritten via `param_tweaks`.
level_hint
Name of dispersion correction to be applied (e.g., d, D2,
d3(bj), das2010). Must be key in `dashcoeff` or "alias" or
"formal" to one.
param_tweaks
Values for the same keys as `dashcoeff[dashlevel]['default']`
(and same order if list) used to override any or all values
initialized by `name_hint`. Extra parameters will error.
engine
Override which code computes dispersion. See above for allowed
values. Really only relevant for -D2, which can be computed by
libdisp or dftd3.
"""
def __init__(self, *, name_hint: str = None, level_hint: str = None, param_tweaks: Union[Dict, List] = None, engine: str = None, save_pairwise_disp=False):
from .dft import dashcoeff_supplement
self.dashcoeff_supplement = dashcoeff_supplement
self.save_pairwise_disp = save_pairwise_disp
resolved = qcng.programs.empirical_dispersion_resources.from_arrays(
name_hint=name_hint,
level_hint=level_hint,
param_tweaks=param_tweaks,
dashcoeff_supplement=self.dashcoeff_supplement)
self.fctldash = resolved['fctldash']
self.dashlevel = resolved['dashlevel']
self.dashparams = resolved['dashparams']
self.description = qcng.programs.empirical_dispersion_resources.dashcoeff[self.dashlevel]['description']
self.ordered_params = qcng.programs.empirical_dispersion_resources.dashcoeff[self.dashlevel]['default'].keys()
self.dashlevel_citation = qcng.programs.empirical_dispersion_resources.dashcoeff[self.dashlevel]['citation']
self.dashparams_citation = resolved['dashparams_citation']
if engine is None:
self.engine = _capable_engines_for_disp[self.dashlevel][0]
else:
if self.dashlevel in _engine_can_do[engine]:
self.engine = engine
else:
raise ValidationError("""This little engine ({}) can't ({})""".format(engine, self.dashlevel))
if self.engine == 'libdisp':
self.disp = core.Dispersion.build(self.dashlevel, **resolved['dashparams'])
def print_out(self):
"""Format dispersion parameters of `self` for output file."""
text = []
text.append(" => {}: Empirical Dispersion <=".format(
(self.fctldash.upper() if self.fctldash.upper() else 'Custom')))
text.append('')
text.append(self.description)
text.append(self.dashlevel_citation.rstrip())
if self.dashparams_citation:
text.append(" Parametrisation from:{}".format(self.dashparams_citation.rstrip()))
text.append('')
for op in self.ordered_params:
text.append(" %6s = %14.6f" % (op, self.dashparams[op]))
text.append('\n')
core.print_out('\n'.join(text))
def compute_energy(self, molecule: core.Molecule, wfn: core.Wavefunction = None) -> float:
"""Compute dispersion energy based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
float
Dispersion energy [Eh].
Notes
-----
:psivar:`DISPERSION CORRECTION ENERGY`
Disp always set. Overridden in SCF finalization, but that only changes for "-3C" methods.
:psivar:`fctl DISPERSION CORRECTION ENERGY`
Set if :py:attr:`fctldash` nonempty.
"""
if self.engine in ['dftd3', 'mp2d']:
resi = AtomicInput(
**{
'driver': 'energy',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'pair_resolved': self.save_pairwise_disp,
'verbose': 1,
},
'molecule': molecule.to_schema(dtype=2),
'provenance': p4util.provenance_stamp(__name__),
})
jobrec = qcng.compute(
resi,
self.engine,
raise_error=True,
local_options={"scratch_directory": core.IOManager.shared_object().get_default_path()})
dashd_part = float(jobrec.extras['qcvars']['DISPERSION CORRECTION ENERGY'])
if wfn is not None:
for k, qca in jobrec.extras['qcvars'].items():
wfn.set_variable(k, float(qca) if isinstance(qca, str) else qca)
# Pass along the pairwise dispersion decomposition if we need it
if self.save_pairwise_disp is True:
wfn.set_variable("PAIRWISE DISPERSION CORRECTION ANALYSIS",
jobrec.extras['qcvars']["2-BODY PAIRWISE DISPERSION CORRECTION ANALYSIS"])
if self.fctldash in ['hf3c', 'pbeh3c']:
jobrec = qcng.compute(
resi,
"gcp",
raise_error=True,
local_options={"scratch_directory": core.IOManager.shared_object().get_default_path()})
gcp_part = jobrec.return_result
dashd_part += gcp_part
return dashd_part
else:
ene = self.disp.compute_energy(molecule)
core.set_variable('DISPERSION CORRECTION ENERGY', ene)
if self.fctldash:
core.set_variable(f"{self.fctldash} DISPERSION CORRECTION ENERGY", ene)
return ene
def compute_gradient(self,
molecule: core.Molecule,
wfn: core.Wavefunction = None) -> core.Matrix:
"""Compute dispersion gradient based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
Matrix
(nat, 3) dispersion gradient [Eh/a0].
"""
if self.engine in ['dftd3', 'mp2d']:
resi = AtomicInput(
**{
'driver': 'gradient',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'verbose': 1,
},
'molecule': molecule.to_schema(dtype=2),
'provenance': p4util.provenance_stamp(__name__),
})
jobrec = qcng.compute(
resi,
self.engine,
raise_error=True,
local_options={"scratch_directory": core.IOManager.shared_object().get_default_path()})
dashd_part = core.Matrix.from_array(jobrec.extras['qcvars']['DISPERSION CORRECTION GRADIENT'])
if wfn is not None:
for k, qca in jobrec.extras['qcvars'].items():
wfn.set_variable(k, float(qca) if isinstance(qca, str) else qca)
if self.fctldash in ['hf3c', 'pbeh3c']:
jobrec = qcng.compute(
resi,
"gcp",
raise_error=True,
local_options={"scratch_directory": core.IOManager.shared_object().get_default_path()})
gcp_part = core.Matrix.from_array(jobrec.return_result)
dashd_part.add(gcp_part)
return dashd_part
else:
return self.disp.compute_gradient(molecule)
def compute_hessian(self,
molecule: core.Molecule,
wfn: core.Wavefunction = None) -> core.Matrix:
"""Compute dispersion Hessian based on engine, dispersion level, and parameters in `self`.
Uses finite difference, as no dispersion engine has analytic second derivatives.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
Matrix
(3*nat, 3*nat) dispersion Hessian [Eh/a0/a0].
"""
optstash = p4util.OptionsState(['PRINT'], ['PARENT_SYMMETRY'])
core.set_global_option('PRINT', 0)
core.print_out("\n\n Analytical Dispersion Hessians are not supported by dftd3 or gcp.\n")
core.print_out(" Computing the Hessian through finite difference of gradients.\n\n")
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_global_option("PARENT_SYMMETRY", molecule.schoenflies_symbol())
findif_meta_dict = driver_findif.hessian_from_gradients_geometries(molclone, -1)
for displacement in findif_meta_dict["displacements"].values():
geom_array = np.reshape(displacement["geometry"], (-1, 3))
molclone.set_geometry(core.Matrix.from_array(geom_array))
molclone.update_geometry()
displacement["gradient"] = self.compute_gradient(molclone).np.ravel().tolist()
H = driver_findif.assemble_hessian_from_gradients(findif_meta_dict, -1)
if wfn is not None:
wfn.set_variable('DISPERSION CORRECTION HESSIAN', H)
optstash.restore()
return core.Matrix.from_array(H)
| psi-rking/psi4 | psi4/driver/procrouting/empirical_dispersion.py | Python | lgpl-3.0 | 14,773 | [
"Psi4"
] | 0dba6b336bfa873bf8d899e581f8237bbf89b48969cabdfbb2f9a63c14f2bf4b |
"""
Test atomic coordinates and neighbor lists.
"""
import os
import logging
import numpy as np
import unittest
from deepchem.utils import conformers
from deepchem.feat.atomic_coordinates import get_coords
from deepchem.feat.atomic_coordinates import AtomicCoordinates
from deepchem.feat.atomic_coordinates import NeighborListAtomicCoordinates
from deepchem.feat.atomic_coordinates import NeighborListComplexAtomicCoordinates
from deepchem.feat.atomic_coordinates import ComplexNeighborListFragmentAtomicCoordinates
logger = logging.getLogger(__name__)
class TestAtomicCoordinates(unittest.TestCase):
"""
Test AtomicCoordinates.
"""
def setUp(self):
"""
Set up tests.
"""
smiles = 'CC(=O)OC1=CC=CC=C1C(=O)O'
from rdkit import Chem
mol = Chem.MolFromSmiles(smiles)
engine = conformers.ConformerGenerator(max_conformers=1)
self.mol = engine.generate_conformers(mol)
assert self.mol.GetNumConformers() > 0
def test_atomic_coordinates(self):
"""
Simple test that atomic coordinates returns ndarray of right shape.
"""
N = self.mol.GetNumAtoms()
atomic_coords_featurizer = AtomicCoordinates()
# TODO(rbharath, joegomes): Why does AtomicCoordinates return a list? Is
# this expected behavior? Need to think about API.
coords = atomic_coords_featurizer._featurize(self.mol)[0]
assert isinstance(coords, np.ndarray)
assert coords.shape == (N, 3)
def test_neighbor_list_shape(self):
"""
Simple test that Neighbor Lists have right shape.
"""
nblist_featurizer = NeighborListAtomicCoordinates()
N = self.mol.GetNumAtoms()
coords = get_coords(self.mol)
nblist_featurizer = NeighborListAtomicCoordinates()
nblist = nblist_featurizer._featurize(self.mol)[1]
assert isinstance(nblist, dict)
assert len(nblist.keys()) == N
for (atom, neighbors) in nblist.items():
assert isinstance(atom, int)
assert isinstance(neighbors, list)
assert len(neighbors) <= N
# Do a manual distance computation and make
for i in range(N):
for j in range(N):
dist = np.linalg.norm(coords[i] - coords[j])
logger.info("Distance(%d, %d) = %f" % (i, j, dist))
if dist < nblist_featurizer.neighbor_cutoff and i != j:
assert j in nblist[i]
else:
assert j not in nblist[i]
def test_neighbor_list_extremes(self):
"""
Test Neighbor Lists with large/small boxes.
"""
N = self.mol.GetNumAtoms()
# Test with cutoff 0 angstroms. There should be no neighbors in this case.
nblist_featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=.1)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) == 0
# Test with cutoff 100 angstroms. Everything should be neighbors now.
nblist_featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=100)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) == N - 1
def test_neighbor_list_max_num_neighbors(self):
"""
Test that neighbor lists return only max_num_neighbors.
"""
N = self.mol.GetNumAtoms()
max_num_neighbors = 1
nblist_featurizer = NeighborListAtomicCoordinates(max_num_neighbors)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) <= max_num_neighbors
# Do a manual distance computation and ensure that selected neighbor is
# closest since we set max_num_neighbors = 1
coords = get_coords(self.mol)
for i in range(N):
closest_dist = np.inf
closest_nbr = None
for j in range(N):
if i == j:
continue
dist = np.linalg.norm(coords[i] - coords[j])
logger.info("Distance(%d, %d) = %f" % (i, j, dist))
if dist < closest_dist:
closest_dist = dist
closest_nbr = j
logger.info("Closest neighbor to %d is %d" % (i, closest_nbr))
logger.info("Distance: %f" % closest_dist)
if closest_dist < nblist_featurizer.neighbor_cutoff:
assert nblist[i] == [closest_nbr]
else:
assert nblist[i] == []
def test_neighbor_list_periodic(self):
"""Test building a neighbor list with periodic boundary conditions."""
cutoff = 4.0
box_size = np.array([10.0, 8.0, 9.0])
N = self.mol.GetNumAtoms()
coords = get_coords(self.mol)
featurizer = NeighborListAtomicCoordinates(
neighbor_cutoff=cutoff, periodic_box_size=box_size)
neighborlist = featurizer._featurize(self.mol)[1]
expected_neighbors = [set() for i in range(N)]
for i in range(N):
for j in range(i):
delta = coords[i] - coords[j]
delta -= np.round(delta / box_size) * box_size
if np.linalg.norm(delta) < cutoff:
expected_neighbors[i].add(j)
expected_neighbors[j].add(i)
for i in range(N):
assert (set(neighborlist[i]) == expected_neighbors[i])
def test_complex_featurization_simple(self):
"""Test Neighbor List computation on protein-ligand complex."""
dir_path = os.path.dirname(os.path.realpath(__file__))
ligand_file = os.path.join(dir_path, "data/3zso_ligand_hyd.pdb")
protein_file = os.path.join(dir_path, "data/3zso_protein.pdb")
max_num_neighbors = 4
complex_featurizer = NeighborListComplexAtomicCoordinates(max_num_neighbors)
system_coords, system_neighbor_list = complex_featurizer._featurize_complex(
ligand_file, protein_file)
N = system_coords.shape[0]
assert len(system_neighbor_list.keys()) == N
for atom in range(N):
assert len(system_neighbor_list[atom]) <= max_num_neighbors
def test_full_complex_featurization(self):
"""Unit test for ComplexNeighborListFragmentAtomicCoordinates."""
dir_path = os.path.dirname(os.path.realpath(__file__))
ligand_file = os.path.join(dir_path, "data/3zso_ligand_hyd.pdb")
protein_file = os.path.join(dir_path, "data/3zso_protein.pdb")
# Pulled from PDB files. For larger datasets with more PDBs, would use
# max num atoms instead of exact.
frag1_num_atoms = 44 # for ligand atoms
frag2_num_atoms = 2336 # for protein atoms
complex_num_atoms = 2380 # in total
max_num_neighbors = 4
# Cutoff in angstroms
neighbor_cutoff = 4
complex_featurizer = ComplexNeighborListFragmentAtomicCoordinates(
frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors,
neighbor_cutoff)
(frag1_coords, frag1_neighbor_list, frag1_z, frag2_coords,
frag2_neighbor_list, frag2_z, complex_coords,
complex_neighbor_list, complex_z) = complex_featurizer._featurize_complex(
ligand_file, protein_file)
self.assertEqual(frag1_coords.shape, (frag1_num_atoms, 3))
self.assertEqual(
sorted(list(frag1_neighbor_list.keys())), list(range(frag1_num_atoms)))
self.assertEqual(frag1_z.shape, (frag1_num_atoms,))
self.assertEqual(frag2_coords.shape, (frag2_num_atoms, 3))
self.assertEqual(
sorted(list(frag2_neighbor_list.keys())), list(range(frag2_num_atoms)))
self.assertEqual(frag2_z.shape, (frag2_num_atoms,))
self.assertEqual(complex_coords.shape, (complex_num_atoms, 3))
self.assertEqual(
sorted(list(complex_neighbor_list.keys())),
list(range(complex_num_atoms)))
self.assertEqual(complex_z.shape, (complex_num_atoms,))
| ktaneishi/deepchem | deepchem/feat/tests/test_atomic_coordinates.py | Python | mit | 7,419 | [
"RDKit"
] | ade1f98f8dfcb8d2e083b82cae8cae41624ae182bf40269618d63465f829450c |
import k3d
from k3d.headless import k3d_remote, get_headless_driver
import pathlib
import vtk
path = pathlib.Path(__file__).parent.resolve()
def generate():
plot = k3d.plot(screenshot_scale=1.0)
headless = k3d_remote(plot, get_headless_driver(), width=320, height=226)
model_matrix = (
1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 0.0,
0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0
)
reader = vtk.vtkXMLPolyDataReader()
reader.SetFileName(str(path) + '/assets/cow.vtp')
reader.Update()
cow3d = k3d.vtk_poly_data(reader.GetOutput(), color=0xff0000,
model_matrix=model_matrix)
plot += cow3d
headless.sync(hold_until_refreshed=True)
headless.camera_reset(1.0)
screenshot = headless.get_screenshot()
headless.close()
return screenshot
| K3D-tools/K3D-jupyter | docs/source/showcase/VTK_polydata.py | Python | mit | 841 | [
"VTK"
] | 3b2c2c07c77fac0acc3885535988fdcc574923bae3d81f21bffb422d68b2507e |
# class generated by DeVIDE::createDeVIDEModuleFromVTKObject
from module_kits.vtk_kit.mixins import SimpleVTKClassModuleBase
import vtk
class vtkThreshold(SimpleVTKClassModuleBase):
def __init__(self, module_manager):
SimpleVTKClassModuleBase.__init__(
self, module_manager,
vtk.vtkThreshold(), 'Processing.',
('vtkDataSet',), ('vtkUnstructuredGrid',),
replaceDoc=True,
inputFunctions=None, outputFunctions=None)
| nagyistoce/devide | modules/vtk_basic/vtkThreshold.py | Python | bsd-3-clause | 486 | [
"VTK"
] | 1cab1abab4027d2e9c7a469a0314b3a9cc6ded6b8d070b809c91b12953be9dec |
import datetime
import os
import sys
from unittest import mock
from datetime import timedelta
import pytest
from freezegun import freeze_time
from click.testing import CliRunner
from khal.cli import main_khal, main_ikhal
from .utils import _get_text, _get_ics_filepath
class CustomCliRunner(CliRunner):
def __init__(self, config_file, db=None, calendars=None,
xdg_data_home=None, xdg_config_home=None, tmpdir=None, **kwargs):
self.config_file = config_file
self.db = db
self.calendars = calendars
self.xdg_data_home = xdg_data_home
self.xdg_config_home = xdg_config_home
self.tmpdir = tmpdir
super(CustomCliRunner, self).__init__(**kwargs)
def invoke(self, cli, args=None, *a, **kw):
args = ['-c', str(self.config_file)] + (args or [])
return super(CustomCliRunner, self).invoke(cli, args, *a, **kw)
@pytest.fixture
def runner(tmpdir, monkeypatch):
db = tmpdir.join('khal.db')
calendar = tmpdir.mkdir('calendar')
calendar2 = tmpdir.mkdir('calendar2')
calendar3 = tmpdir.mkdir('calendar3')
xdg_data_home = tmpdir.join('vdirs')
xdg_config_home = tmpdir.join('.config')
config_file = xdg_config_home.join('khal').join('config')
# TODO create a vdir config on disk and let vdirsyncer actually read it
monkeypatch.setattr('vdirsyncer.cli.config.load_config', lambda: Config())
monkeypatch.setattr('xdg.BaseDirectory.xdg_data_home', str(xdg_data_home))
monkeypatch.setattr('xdg.BaseDirectory.xdg_config_home', str(xdg_config_home))
monkeypatch.setattr('xdg.BaseDirectory.xdg_config_dirs', [str(xdg_config_home)])
def inner(default_command='list', print_new=False, default_calendar=True, days=2,
**kwargs):
if default_calendar:
default_calendar = 'default_calendar = one'
else:
default_calendar = ''
if not os.path.exists(str(xdg_config_home.join('khal'))):
os.makedirs(str(xdg_config_home.join('khal')))
config_file.write(config_template.format(
default_command=default_command,
delta=str(days) + 'd',
calpath=str(calendar), calpath2=str(calendar2), calpath3=str(calendar3),
default_calendar=default_calendar,
print_new=print_new,
dbpath=str(db), **kwargs))
runner = CustomCliRunner(
config_file=config_file, db=db, calendars=dict(one=calendar),
xdg_data_home=xdg_data_home, xdg_config_home=xdg_config_home,
tmpdir=tmpdir,
)
return runner
return inner
config_template = '''
[calendars]
[[one]]
path = {calpath}
color = dark blue
[[two]]
path = {calpath2}
color = dark green
[[three]]
path = {calpath3}
[locale]
local_timezone = Europe/Berlin
default_timezone = Europe/Berlin
timeformat = %H:%M
dateformat = %d.%m.
longdateformat = %d.%m.%Y
datetimeformat = %d.%m. %H:%M
longdatetimeformat = %d.%m.%Y %H:%M
firstweekday = 0
[default]
default_command = {default_command}
{default_calendar}
timedelta = {delta}
print_new = {print_new}
[sqlite]
path = {dbpath}
'''
def test_direct_modification(runner):
runner = runner(default_command='list')
result = runner.invoke(main_khal, ['list'])
assert not result.exception
assert result.output == 'No events\n'
cal_dt = _get_text('event_dt_simple')
event = runner.calendars['one'].join('test.ics')
event.write(cal_dt)
format = '{start-end-time-style}: {title}'
args = ['list', '--format', format, '--day-format', '', '09.04.2014']
result = runner.invoke(main_khal, args)
assert not result.exception
assert result.output == '09:30-10:30: An Event\n'
os.remove(str(event))
result = runner.invoke(main_khal, ['list'])
assert not result.exception
assert result.output == 'No events\n'
def test_simple(runner):
runner = runner(default_command='list', days=2)
result = runner.invoke(main_khal)
assert not result.exception
assert result.output == 'No events\n'
now = datetime.datetime.now().strftime('%d.%m.%Y')
result = runner.invoke(
main_khal, 'new {} 18:00 myevent'.format(now).split())
assert result.output == ''
assert not result.exception
result = runner.invoke(main_khal)
print(result.output)
assert 'myevent' in result.output
assert '18:00' in result.output
# test show_all_days default value
assert 'Tomorrow:' not in result.output
assert not result.exception
def test_simple_color(runner):
runner = runner(default_command='list', days=2)
now = datetime.datetime.now().strftime('%d.%m.%Y')
result = runner.invoke(main_khal, 'new {} 18:00 myevent'.format(now).split())
assert result.output == ''
assert not result.exception
result = runner.invoke(main_khal, color=True)
assert not result.exception
assert '\x1b[34m' in result.output
def test_days(runner):
runner = runner(default_command='list', days=9)
when = (datetime.datetime.now() + timedelta(days=7)).strftime('%d.%m.%Y')
result = runner.invoke(main_khal, 'new {} 18:00 nextweek'.format(when).split())
assert result.output == ''
assert not result.exception
when = (datetime.datetime.now() + timedelta(days=30)).strftime('%d.%m.%Y')
result = runner.invoke(main_khal, 'new {} 18:00 nextmonth'.format(when).split())
assert result.output == ''
assert not result.exception
result = runner.invoke(main_khal)
assert 'nextweek' in result.output
assert 'nextmonth' not in result.output
assert '18:00' in result.output
assert not result.exception
def test_notstarted(runner):
with freeze_time('2015-6-1 15:00'):
runner = runner(default_command='calendar', days=2)
for command in [
'new 30.5.2015 5.6.2015 long event',
'new 2.6.2015 4.6.2015 two day event',
'new 1.6.2015 14:00 18:00 four hour event',
'new 1.6.2015 16:00 17:00 one hour event',
'new 2.6.2015 10:00 13:00 three hour event',
]:
result = runner.invoke(main_khal, command.split())
assert not result.exception
result = runner.invoke(main_khal, 'list now'.split())
assert result.output == \
"""Today, 01.06.2015
↔ long event
14:00-18:00 four hour event
16:00-17:00 one hour event
Tomorrow, 02.06.2015
↔ long event
↦ two day event
10:00-13:00 three hour event
Wednesday, 03.06.2015
↔ long event
↔ two day event
"""
assert not result.exception
result = runner.invoke(main_khal, 'list now --notstarted'.split())
assert result.output == \
"""Today, 01.06.2015
16:00-17:00 one hour event
Tomorrow, 02.06.2015
↦ two day event
10:00-13:00 three hour event
Wednesday, 03.06.2015
↔ two day event
"""
assert not result.exception
result = runner.invoke(main_khal, 'list now --once'.split())
assert result.output == \
"""Today, 01.06.2015
↔ long event
14:00-18:00 four hour event
16:00-17:00 one hour event
Tomorrow, 02.06.2015
↦ two day event
10:00-13:00 three hour event
"""
assert not result.exception
result = runner.invoke(main_khal, 'list now --once --notstarted'.split())
assert result.output == \
"""Today, 01.06.2015
16:00-17:00 one hour event
Tomorrow, 02.06.2015
↦ two day event
10:00-13:00 three hour event
"""
assert not result.exception
def test_calendar(runner):
with freeze_time('2015-6-1'):
runner = runner(default_command='calendar', days=0)
result = runner.invoke(main_khal)
assert not result.exception
assert result.exit_code == 0
output = '\n'.join([
" Mo Tu We Th Fr Sa Su No events",
"Jun 1 2 3 4 5 6 7 ",
" 8 9 10 11 12 13 14 ",
" 15 16 17 18 19 20 21 ",
" 22 23 24 25 26 27 28 ",
"Jul 29 30 1 2 3 4 5 ",
" 6 7 8 9 10 11 12 ",
" 13 14 15 16 17 18 19 ",
" 20 21 22 23 24 25 26 ",
"Aug 27 28 29 30 31 1 2 ",
" 3 4 5 6 7 8 9 ",
" 10 11 12 13 14 15 16 ",
" 17 18 19 20 21 22 23 ",
" 24 25 26 27 28 29 30 ",
"Sep 31 1 2 3 4 5 6 ",
"",
])
assert result.output == output
def test_long_calendar(runner):
with freeze_time('2015-6-1'):
runner = runner(default_command='calendar', days=100)
result = runner.invoke(main_khal)
assert not result.exception
assert result.exit_code == 0
output = '\n'.join([
" Mo Tu We Th Fr Sa Su No events",
"Jun 1 2 3 4 5 6 7 ",
" 8 9 10 11 12 13 14 ",
" 15 16 17 18 19 20 21 ",
" 22 23 24 25 26 27 28 ",
"Jul 29 30 1 2 3 4 5 ",
" 6 7 8 9 10 11 12 ",
" 13 14 15 16 17 18 19 ",
" 20 21 22 23 24 25 26 ",
"Aug 27 28 29 30 31 1 2 ",
" 3 4 5 6 7 8 9 ",
" 10 11 12 13 14 15 16 ",
" 17 18 19 20 21 22 23 ",
" 24 25 26 27 28 29 30 ",
"Sep 31 1 2 3 4 5 6 ",
" 7 8 9 10 11 12 13 ",
" 14 15 16 17 18 19 20 ",
" 21 22 23 24 25 26 27 ",
"Oct 28 29 30 1 2 3 4 ",
"",
])
assert result.output == output
def test_default_command_empty(runner):
runner = runner(default_command='', days=2)
result = runner.invoke(main_khal)
assert result.exception
assert result.exit_code == 1
assert result.output.startswith('Usage: ')
def test_default_command_nonempty(runner):
runner = runner(default_command='list', days=2)
result = runner.invoke(main_khal)
assert not result.exception
assert result.output == 'No events\n'
def test_invalid_calendar(runner):
runner = runner(default_command='', days=2)
result = runner.invoke(
main_khal, ['new'] + '-a one 18:00 myevent'.split())
assert not result.exception
result = runner.invoke(
main_khal, ['new'] + '-a inexistent 18:00 myevent'.split())
assert result.exception
assert result.exit_code == 2
assert 'Unknown calendar ' in result.output
def test_attach_calendar(runner):
runner = runner(default_command='calendar', days=2)
result = runner.invoke(main_khal, ['printcalendars'])
assert set(result.output.split('\n')[:3]) == set(['one', 'two', 'three'])
assert not result.exception
result = runner.invoke(main_khal, ['printcalendars', '-a', 'one'])
assert result.output == 'one\n'
assert not result.exception
result = runner.invoke(main_khal, ['printcalendars', '-d', 'one'])
assert set(result.output.split('\n')[:2]) == set(['two', 'three'])
assert not result.exception
@pytest.mark.parametrize('contents', [
'',
'BEGIN:VCALENDAR\nBEGIN:VTODO\nEND:VTODO\nEND:VCALENDAR\n'
])
def test_no_vevent(runner, tmpdir, contents):
runner = runner(default_command='list', days=2)
broken_item = runner.calendars['one'].join('broken_item.ics')
broken_item.write(contents.encode('utf-8'), mode='wb')
result = runner.invoke(main_khal)
assert not result.exception
assert 'No events' in result.output
def test_printformats(runner):
runner = runner(default_command='printformats', days=2)
result = runner.invoke(main_khal)
assert '\n'.join(['longdatetimeformat: 21.12.2013 10:09',
'datetimeformat: 21.12. 10:09',
'longdateformat: 21.12.2013',
'dateformat: 21.12.',
'timeformat: 10:09',
'']) == result.output
assert not result.exception
def test_repeating(runner):
runner = runner(default_command='list', days=2)
now = datetime.datetime.now().strftime('%d.%m.%Y')
end_date = datetime.datetime.now() + datetime.timedelta(days=10)
result = runner.invoke(
main_khal, 'new {} 18:00 myevent -r weekly -u {}'.format(
now, end_date.strftime('%d.%m.%Y')).split())
assert not result.exception
assert result.output == ''
def test_at(runner):
runner = runner(default_command='calendar', days=2)
now = datetime.datetime.now().strftime('%d.%m.%Y')
end_date = datetime.datetime.now() + datetime.timedelta(days=10)
result = runner.invoke(
main_khal,
'new {} {} 18:00 myevent'.format(now, end_date.strftime('%d.%m.%Y')).split())
args = ['--color', 'at', '--format', '{start-time}{title}', '--day-format', '', '18:30']
result = runner.invoke(main_khal, args)
assert not result.exception
assert result.output.startswith('myevent')
def test_at_day_format(runner):
runner = runner(default_command='calendar', days=2)
now = datetime.datetime.now().strftime('%d.%m.%Y')
end_date = datetime.datetime.now() + datetime.timedelta(days=10)
result = runner.invoke(
main_khal,
'new {} {} 18:00 myevent'.format(now, end_date.strftime('%d.%m.%Y')).split())
args = ['--color', 'at', '--format', '{start-time}{title}', '--day-format', '{name}', '18:30']
result = runner.invoke(main_khal, args)
assert not result.exception
assert result.output.startswith('Today\x1b[0m\nmyevent')
def test_list(runner):
runner = runner(default_command='calendar', days=2)
now = datetime.datetime.now().strftime('%d.%m.%Y')
end_date = datetime.datetime.now() + datetime.timedelta(days=10)
result = runner.invoke(
main_khal,
'new {} 18:00 myevent'.format(now, end_date.strftime('%d.%m.%Y')).split())
format = '{red}{start-end-time-style}{reset} {title} :: {description}'
args = ['--color', 'list', '--format', format, '--day-format', 'header', '18:30']
result = runner.invoke(main_khal, args)
expected = 'header\x1b[0m\n\x1b[31m18:00-19:00\x1b[0m myevent :: \x1b[0m\n'
assert not result.exception
assert result.output.startswith(expected)
def test_search(runner):
runner = runner(default_command='calendar', days=2)
now = datetime.datetime.now().strftime('%d.%m.%Y')
result = runner.invoke(main_khal, 'new {} 18:00 myevent'.format(now).split())
format = '{red}{start-end-time-style}{reset} {title} :: {description}'
result = runner.invoke(main_khal, ['--color', 'search', '--format', format, 'myevent'])
assert not result.exception
assert result.output.startswith('\x1b[34m\x1b[31m18:00')
def test_no_default_new(runner):
runner = runner(default_calendar=False)
result = runner.invoke(main_khal, 'new 18:00 beer'.split())
assert ("Error: Invalid value: No default calendar is configured, "
"please provide one explicitly.") in result.output
assert result.exit_code == 2
def test_import(runner, monkeypatch):
runner = runner()
result = runner.invoke(main_khal, 'import -a one -a two import file.ics'.split())
assert result.exception
assert result.exit_code == 2
assert 'Can\'t use "--include-calendar" / "-a" more than once' in result.output
class FakeImport():
args, kwargs = None, None
def clean(self):
self.args, self.kwargs = None, None
def import_ics(self, *args, **kwargs):
print('saving args')
print(args)
self.args = args
self.kwargs = kwargs
fake = FakeImport()
monkeypatch.setattr('khal.controllers.import_ics', fake.import_ics)
# as we are not actually parsing the file we want to import, we can use
# any readable file at all, therefore re-using the configuration file
result = runner.invoke(main_khal, 'import -a one {}'.format(runner.config_file).split())
assert not result.exception
assert {cal['name'] for cal in fake.args[0].calendars} == {'one'}
fake.clean()
result = runner.invoke(main_khal, 'import {}'.format(runner.config_file).split())
assert not result.exception
assert {cal['name'] for cal in fake.args[0].calendars} == {'one', 'two', 'three'}
def test_import_proper(runner):
runner = runner()
result = runner.invoke(main_khal, ['import', _get_ics_filepath('cal_d')], input='0\ny\n')
assert result.output.startswith('09.04.-09.04. An Event')
assert not result.exception
result = runner.invoke(main_khal, ['search', 'Event'])
assert result.output == '09.04.-09.04. An Event\n'
def test_import_invalid_choice_and_prefix(runner):
runner = runner()
result = runner.invoke(main_khal, ['import', _get_ics_filepath('cal_d')], input='9\nth\ny\n')
assert result.output.startswith('09.04.-09.04. An Event')
assert result.output.find('invalid choice') == 125
assert not result.exception
result = runner.invoke(main_khal, ['search', 'Event'])
assert result.output == '09.04.-09.04. An Event\n'
def test_import_from_stdin(runner):
ics_data = 'This is some really fake icalendar data'
with mock.patch('khal.controllers.import_ics') as mocked_import:
runner = runner()
result = runner.invoke(main_khal, ['import'], input=ics_data)
assert not result.exception
assert mocked_import.call_count == 1
assert mocked_import.call_args[1]['ics'] == ics_data
def test_interactive_command(runner, monkeypatch):
runner = runner(default_command='list', days=2)
token = "hooray"
def fake_ui(*a, **kw):
print(token)
sys.exit(0)
monkeypatch.setattr('khal.ui.start_pane', fake_ui)
result = runner.invoke(main_ikhal, ['-a', 'one'])
assert not result.exception
assert result.output.strip() == token
result = runner.invoke(main_khal, ['interactive', '-a', 'one'])
assert not result.exception
assert result.output.strip() == token
def test_color_option(runner):
runner = runner(default_command='list', days=2)
result = runner.invoke(main_khal, ['--no-color'])
assert result.output == 'No events\n'
result = runner.invoke(main_khal, ['--color'])
assert 'No events' in result.output
assert result.output != 'No events\n'
def choices(dateformat=0, timeformat=0,
parse_vdirsyncer_conf=True,
create_vdir=False,
write_config=True):
"""helper function to generate input for testing `configure`"""
confirm = {True: 'y', False: 'n'}
out = [
str(dateformat), str(timeformat),
confirm[parse_vdirsyncer_conf],
]
if not parse_vdirsyncer_conf:
out.append(confirm[create_vdir])
out.append(confirm[write_config])
return '\n'.join(out)
class Config():
"""helper class for mocking vdirsyncer's config objects"""
# TODO crate a vdir config on disk and let vdirsyncer actually read it
storages = {
'home_calendar_local': {
'type': 'filesystem',
'instance_name': 'home_calendar_local',
'path': '~/.local/share/calendars/home/',
'fileext': '.ics',
},
'events_local': {
'type': 'filesystem',
'instance_name': 'events_local',
'path': '~/.local/share/calendars/events/',
'fileext': '.ics',
},
'home_calendar_remote': {
'type': 'caldav',
'url': 'https://some.url/caldav',
'username': 'foo',
'password.fetch': ['command', 'get_secret'],
'instance_name': 'home_calendar_remote',
},
'home_contacts_remote': {
'type': 'carddav',
'url': 'https://another.url/caldav',
'username': 'bar',
'password.fetch': ['command', 'get_secret'],
'instance_name': 'home_contacts_remote',
},
'home_contacts_local': {
'type': 'filesystem',
'instance_name': 'home_contacts_local',
'path': '~/.local/share/contacts/',
'fileext': '.vcf',
},
'events_remote': {
'type': 'http',
'instance_name': 'events_remote',
'url': 'http://list.of/events/',
},
}
def test_configure_command(runner):
runner_factory = runner
runner = runner()
runner.config_file.remove()
result = runner.invoke(main_khal, ['configure'], input=choices())
assert 'Successfully wrote configuration to {}'.format(runner.config_file) in result.output
assert result.exit_code == 0
with open(str(runner.config_file)) as f:
actual_config = ''.join(f.readlines())
assert actual_config == '''[calendars]
[[events_local]]
path = ~/.local/share/calendars/events/*
type = discover
[[home_calendar_local]]
path = ~/.local/share/calendars/home/*
type = discover
[[home_contacts_local]]
path = ~/.local/share/contacts/*
type = discover
[locale]
timeformat = %H:%M
dateformat = %Y-%m-%d
longdateformat = %Y-%m-%d
datetimeformat = %Y-%m-%d %H:%M
longdatetimeformat = %Y-%m-%d %H:%M
'''
# if aborting, no config file should be written
runner = runner_factory()
assert os.path.exists(str(runner.config_file))
runner.config_file.remove()
assert not os.path.exists(str(runner.config_file))
result = runner.invoke(main_khal, ['configure'], input=choices(write_config=False))
assert 'aborted' in result.output
assert result.exit_code == 1
def test_print_ics_command(runner):
runner = runner(command='printics', days=2)
# Input is empty and loading from stdin
result = runner.invoke(main_khal, ['-'])
assert result.exception
# Non existing file
result = runner.invoke(main_khal, ['printics', 'nonexisting_file'])
assert result.exception
assert 'Error: Invalid value for "ics": Could not open file: ' \
in result.output
# Run on test files
result = runner.invoke(main_khal, ['printics', _get_ics_filepath('cal_d')])
assert not result.exception
result = runner.invoke(main_khal, ['printics', _get_ics_filepath('cal_dt_two_tz')])
assert not result.exception
# Test with some nice format strings
form = '{title}\t{description}\t{start}\t{start-long}\t{start-date}' \
'\t{start-date-long}\t{start-time}\t{end}\t{end-long}\t{end-date}' \
'\t{end-date-long}\t{end-time}\t{repeat-symbol}\t{description}' \
'\t{description-separator}\t{location}\t{calendar}' \
'\t{calendar-color}\t{start-style}\t{to-style}\t{end-style}' \
'\t{start-end-time-style}\t{end-necessary}\t{end-necessary-long}'
result = runner.invoke(main_khal, [
'printics', '-f', form, _get_ics_filepath('cal_dt_two_tz')])
assert not result.exception
assert 24 == len(result.output.split('\t'))
result = runner.invoke(main_khal, [
'printics', '-f', form, _get_ics_filepath('cal_dt_two_tz')])
assert not result.exception
assert 24 == len(result.output.split('\t'))
def test_printics_read_from_stdin(runner):
runner = runner(command='printics')
result = runner.invoke(main_khal, ['printics'], input=_get_text('cal_d'))
assert not result.exception
assert result.output == '1 events found in stdin input\n An Event\n'
def test_configure_command_config_exists(runner):
runner = runner()
result = runner.invoke(main_khal, ['configure'], input=choices())
assert 'Found an existing' in result.output
assert result.exit_code == 1
def test_configure_command_create_vdir(runner):
runner = runner()
runner.config_file.remove()
runner.xdg_config_home.remove()
result = runner.invoke(
main_khal, ['configure'],
input=choices(parse_vdirsyncer_conf=False, create_vdir=True),
)
assert 'Successfully wrote configuration to {}'.format(str(runner.config_file)) in result.output
assert result.exit_code == 0
with open(str(runner.config_file)) as f:
actual_config = ''.join(f.readlines())
assert actual_config == '''[calendars]
[[private]]
path = {}/khal/calendars/private
type = calendar
[locale]
timeformat = %H:%M
dateformat = %Y-%m-%d
longdateformat = %Y-%m-%d
datetimeformat = %Y-%m-%d %H:%M
longdatetimeformat = %Y-%m-%d %H:%M
'''.format(runner.xdg_data_home)
# running configure again, should yield another vdir path, as the old
# one still exists
runner.config_file.remove()
result = runner.invoke(
main_khal, ['configure'],
input=choices(parse_vdirsyncer_conf=False, create_vdir=True),
)
assert 'Successfully wrote configuration to {}'.format(str(runner.config_file)) in result.output
assert result.exit_code == 0
with open(str(runner.config_file)) as f:
actual_config = ''.join(f.readlines())
assert '{}/khal/calendars/private1' .format(runner.xdg_data_home) in actual_config
def test_configure_command_cannot_write_config_file(runner):
runner = runner()
runner.config_file.remove()
os.chmod(str(runner.xdg_config_home), 555)
result = runner.invoke(main_khal, ['configure'], input=choices())
assert result.exit_code == 1
def test_configure_command_cannot_create_vdir(runner):
runner = runner()
runner.config_file.remove()
os.mkdir(str(runner.xdg_data_home), mode=555)
result = runner.invoke(
main_khal, ['configure'],
input=choices(parse_vdirsyncer_conf=False, create_vdir=True),
)
assert 'Exiting' in result.output
assert result.exit_code == 1
def test_configure_no_vdir(runner):
runner = runner()
runner.config_file.remove()
result = runner.invoke(
main_khal, ['configure'],
input=choices(parse_vdirsyncer_conf=False, create_vdir=False),
)
assert 'khal will not be usable like this' in result.output
assert result.exit_code == 0
assert not result.exception
def test_edit(runner):
runner = runner()
result = runner.invoke(main_khal, ['list'])
assert not result.exception
assert result.output == 'No events\n'
for name in ['event_dt_simple', 'event_d_15']:
cal_dt = _get_text(name)
event = runner.calendars['one'].join('{}.ics'.format(name))
event.write(cal_dt)
format = '{start-end-time-style}: {title}'
result = runner.invoke(
main_khal, ['edit', '--show-past', 'Event'], input='s\nGreat Event\nn\nn\n')
assert not result.exception
args = ['list', '--format', format, '--day-format', '', '09.04.2014']
result = runner.invoke(main_khal, args)
assert '09:30-10:30: Great Event' in result.output
assert not result.exception
args = ['list', '--format', format, '--day-format', '', '09.04.2015']
result = runner.invoke(main_khal, args)
assert ': An Event' in result.output
assert not result.exception
def test_new(runner):
runner = runner(print_new='path')
result = runner.invoke(main_khal, 'new 13.03.2016 3d Visit'.split())
assert not result.exception
assert result.output.endswith('.ics\n')
assert result.output.startswith(str(runner.tmpdir))
@freeze_time('2015-6-1 8:00')
def test_new_interactive(runner):
runner = runner(print_new='path')
result = runner.invoke(
main_khal, 'new -i'.split(),
'Another event\n13:00 17:00\n\nNone\nn\n'
)
assert not result.exception
assert result.exit_code == 0
@freeze_time('2015-6-1 8:00')
def test_new_interactive_extensive(runner):
runner = runner(print_new='path', default_calendar=False)
result = runner.invoke(
main_khal, 'new -i 15:00 15:30'.split(),
'?\ninvalid\ntwo\n'
'Unicce Name\n'
'\n'
'Europe/London\n'
'bar\n'
'l\non a boat\n'
'p\nweekly\n'
'1.1.2018\n'
'a\n30m\n'
'c\nwork\n'
'n\n'
)
assert not result.exception
assert result.exit_code == 0
| hobarrera/khal | tests/cli_test.py | Python | mit | 28,017 | [
"VisIt"
] | 98a8fa851acd829589cca909d0ff831c352c2d94855b5baa6798e248dc1b54be |
from argparse import ArgumentParser
from bioblend import galaxy
def extract_users(user_list, all_users):
selected_users = []
for old_user in user_list:
for user in all_users:
if user[u'email'] == old_user:
selected_users.append(user)
continue
return selected_users
def get_user_apikeys(galaxy_instance, users):
selected_api_keys = []
for user in users:
api_key = galaxy_instance.users.get_user_apikey(user[u'id'])
if api_key == 'Not available.':
api_key = galaxy_instance.users.create_user_apikey(user[u'id'])
selected_api_keys.append(api_key)
return selected_api_keys
def purge_user_histories(api_key, GalaxyURL):
gui = galaxy.GalaxyInstance(url=GalaxyURL, key=api_key)
histories = gui.histories.get_histories()
for history in histories:
print("purging history %s" % history[u'id'])
gui.histories.delete_history(history[u'id'], purge=True)
def _parse_cli_options():
"""
Parse command line options, returning `parse_args` from `ArgumentParser`.
"""
parser = ArgumentParser(
description='bioblend-managed deletion of histories from a provided \
list of galaxy user emails',
usage=" python %(prog)s <options>")
parser.add_argument("-g", "--admin-galaxy-url",
dest="admin_galaxy_url",
required=True,
help="Galaxy url of an admin")
parser.add_argument("-a", "--api-key",
required=True,
dest="api_key",
help="Admin API key")
parser.add_argument("-e", "--emails",
required=True,
dest="emails",
nargs='+',
help="A list of user emails")
return parser.parse_args()
def __main__():
args = _parse_cli_options()
ADMIN_KEY = args.api_key
GalaxyURL = args.admin_galaxy_url
gi = galaxy.GalaxyInstance(url=GalaxyURL, key=ADMIN_KEY)
all_users = gi.users.get_users()
selected_users = extract_users(args.emails, all_users)
user_api_keys = get_user_apikeys(gi, selected_users)
for api_key in user_api_keys:
purge_user_histories(api_key, GalaxyURL)
if __name__ == "__main__":
__main__()
| ARTbio/tools-artbio | scripts/helper_scripts/purge_old_user_histories.py | Python | mit | 2,372 | [
"Galaxy"
] | 2ccefd818620d17be5ba5c038f4edf780202df1875f0bb204ec839c3f2442fc0 |
#!/usr/bin/python
"""
Copyright 2015 Paul Willworth <ioscode@gmail.com>
This file is part of Galaxy Harvester.
Galaxy Harvester 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.
Galaxy Harvester 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 Galaxy Harvester. If not, see <http://www.gnu.org/licenses/>.
"""
import os
import sys
import re
import Cookie
import dbSession
import dbShared
import cgi
import MySQLdb
from xml.dom import minidom
#
# Get current url
try:
url = os.environ['SCRIPT_NAME']
except KeyError:
url = ''
form = cgi.FieldStorage()
# Get Cookies
useCookies = 1
cookies = Cookie.SimpleCookie()
try:
cookies.load(os.environ['HTTP_COOKIE'])
except KeyError:
useCookies = 0
if useCookies:
try:
currentUser = cookies['userID'].value
except KeyError:
currentUser = ''
try:
loginResult = cookies['loginAttempt'].value
except KeyError:
loginResult = 'success'
try:
sid = cookies['gh_sid'].value
except KeyError:
sid = form.getfirst('gh_sid', '')
else:
currentUser = ''
loginResult = 'success'
sid = form.getfirst('gh_sid', '')
# Get form info
galaxy = form.getfirst("galaxy", "")
rowOrder = form.getfirst("rowOrder", "-1")
fltType = form.getfirst("fltType", "")
fltValue = form.getfirst("fltValue", "")
CRmin = form.getfirst("CRmin", "")
CDmin = form.getfirst("CDmin", "")
DRmin = form.getfirst("DRmin", "")
FLmin = form.getfirst("FLmin", "")
HRmin = form.getfirst("HRmin", "")
MAmin = form.getfirst("MAmin", "")
PEmin = form.getfirst("PEmin", "")
OQmin = form.getfirst("OQmin", "")
SRmin = form.getfirst("SRmin", "")
UTmin = form.getfirst("UTmin", "")
ERmin = form.getfirst("ERmin", "")
alertType = form.getfirst("alertType", "")
# escape input to prevent sql injection
sid = dbShared.dbInsertSafe(sid)
galaxy = dbShared.dbInsertSafe(galaxy)
rowOrder = dbShared.dbInsertSafe(rowOrder)
fltType = dbShared.dbInsertSafe(fltType)
fltValue = dbShared.dbInsertSafe(fltValue)
CRmin = dbShared.dbInsertSafe(CRmin)
CDmin = dbShared.dbInsertSafe(CDmin)
DRmin = dbShared.dbInsertSafe(DRmin)
FLmin = dbShared.dbInsertSafe(FLmin)
HRmin = dbShared.dbInsertSafe(HRmin)
MAmin = dbShared.dbInsertSafe(MAmin)
PEmin = dbShared.dbInsertSafe(PEmin)
OQmin = dbShared.dbInsertSafe(OQmin)
SRmin = dbShared.dbInsertSafe(SRmin)
UTmin = dbShared.dbInsertSafe(UTmin)
ERmin = dbShared.dbInsertSafe(ERmin)
alertType = dbShared.dbInsertSafe(alertType)
result = ""
# Get a session
logged_state = 0
sess = dbSession.getSession(sid, 2592000)
if (sess != ''):
logged_state = 1
currentUser = sess
def n2n(inVal):
if (inVal == '' or inVal == None or inVal == 'undefined' or inVal == 'None'):
return 'NULL'
else:
return str(inVal)
# Check for errors
errstr = ""
if (galaxy == ""):
errstr = errstr + "Error: no galaxy selected. \r\n"
if (fltType.isdigit() != True):
errstr = errstr + "Error: Type was not valid. \r\n"
if (alertType.isdigit() != True and alertType != "-1"):
errstr = errstr + "Error: Alert options was not valid. \r\n"
if (CRmin.isdigit() != True):
CRmin = 0
if (CDmin.isdigit() != True):
CDmin = 0
if (DRmin.isdigit() != True):
DRmin = 0
if (FLmin.isdigit() != True):
FLmin = 0
if (HRmin.isdigit() != True):
HRmin = 0
if (MAmin.isdigit() != True):
MAmin = 0
if (PEmin.isdigit() != True):
PEmin = 0
if (OQmin.isdigit() != True):
OQmin = 0
if (SRmin.isdigit() != True):
SRmin = 0
if (UTmin.isdigit() != True):
UTmin = 0
if (ERmin.isdigit() != True):
ERmin = 0
# Only process if no errors
if (errstr == ""):
result = ""
if (logged_state > 0):
conn = dbShared.ghConn()
# open list of users existing filters
cursor = conn.cursor()
cursor.execute("SELECT alertTypes FROM tFilters WHERE galaxy=" + str(galaxy) + " AND userID='" + currentUser + "' AND rowOrder=" + str(rowOrder) + " AND fltType=" + str(fltType) + " AND fltValue='" + fltValue + "';")
row = cursor.fetchone()
if str(alertType) == "-1":
# alert type -1 is code for delete it
if row != None:
cursor2 = conn.cursor()
tempSQL = "DELETE FROM tFilters WHERE userID='" + currentUser + "' AND galaxy=" + str(galaxy) + " AND rowOrder=" + str(rowOrder) + " AND fltType=" + str(fltType) + " AND fltValue='" + fltValue + "';"
cursor2.execute(tempSQL)
result = "alert deleted."
cursor2.close()
else:
result = "Error: could not find filter to remove."
else:
if row != None:
cursor2 = conn.cursor()
tempSQL = "UPDATE tFilters SET alertTypes=" + str(alertType) + ", CRmin=" + str(CRmin) + ", CDmin=" + str(CDmin) + ", DRmin=" + str(DRmin) + ", FLmin=" + str(FLmin) + ", HRmin=" + str(HRmin) + ", MAmin=" + str(MAmin) + ", PEmin=" + str(PEmin) + ", OQmin=" + str(OQmin) + ", SRmin=" + str(SRmin) + ", UTmin=" + str(UTmin) + ", ERmin=" + str(ERmin) + " WHERE userID='" + currentUser + "' AND galaxy=" + str(galaxy) + " AND rowOrder=" + str(rowOrder) + " AND fltType=" + str(fltType) + " AND fltValue='" + fltValue + "';"
cursor2.execute(tempSQL)
result = "alert updated."
cursor2.close()
else:
cursor.execute("SELECT Max(rowOrder) FROM tFilters WHERE galaxy=" + str(galaxy) + " AND userID='" + currentUser + "';")
row = cursor.fetchone()
if row[0] != None:
rowOrder = row[0] + 1
else:
rowOrder = 1
cursor2 = conn.cursor()
tempSQL = "INSERT INTO tFilters (userID, galaxy, rowOrder, fltType, fltValue, alertTypes, CRmin, CDmin, DRmin, FLmin, HRmin, MAmin, PEmin, OQmin, SRmin, UTmin, ERmin) VALUES ('" + currentUser + "', " + str(galaxy) + ", " + str(rowOrder) + ", " + str(fltType) + ", '" + fltValue + "', " + str(alertType) + ", " + str(CRmin) + ", " + str(CDmin) + ", " + str(DRmin) + ", " + str(FLmin) + ", " + str(HRmin) + ", " + str(MAmin) + ", " + str(PEmin) + ", " + str(OQmin) + ", " + str(SRmin) + ", " + str(UTmin) + ", " + str(ERmin) + ");"
cursor2.execute(tempSQL)
result = "alert added."
cursor2.close()
cursor.close()
conn.close()
else:
result = "Error: must be logged in to update alerts"
else:
result = errstr
print 'Content-type: text/xml\n'
doc = minidom.Document()
eRoot = doc.createElement("result")
doc.appendChild(eRoot)
eText = doc.createElement("resultText")
tText = doc.createTextNode(result)
eText.appendChild(tText)
eRoot.appendChild(eText)
print doc.toxml()
if (result.find("Error:") > -1):
sys.exit(500)
else:
sys.exit(200)
| druss316/G-Harvestor | html/postFilter.py | Python | gpl-3.0 | 6,748 | [
"Galaxy"
] | 9f3ee34a5346b309c3d75542baf6d2ac0db877d79f3d3e3f185db2040799090a |
"""Utilities for writing code that runs on Python 2 and 3"""
# Copyright (c) 2010-2014 Benjamin Peterson
#
# 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 operator
import sys
import types
__author__ = "Benjamin Peterson <benjamin@python.org>"
__version__ = "1.6.1"
# Useful for very coarse version differentiation.
PY2 = sys.version_info[0] == 2
PY3 = sys.version_info[0] == 3
if PY3:
string_types = str,
integer_types = int,
class_types = type,
text_type = str
binary_type = bytes
MAXSIZE = sys.maxsize
else:
string_types = basestring,
integer_types = (int, long)
class_types = (type, types.ClassType)
text_type = unicode
binary_type = str
if sys.platform.startswith("java"):
# Jython always uses 32 bits.
MAXSIZE = int((1 << 31) - 1)
else:
# It's possible to have sizeof(long) != sizeof(Py_ssize_t).
class X(object):
def __len__(self):
return 1 << 31
try:
len(X())
except OverflowError:
# 32-bit
MAXSIZE = int((1 << 31) - 1)
else:
# 64-bit
MAXSIZE = int((1 << 63) - 1)
del X
def _add_doc(func, doc):
"""Add documentation to a function."""
func.__doc__ = doc
def _import_module(name):
"""Import module, returning the module after the last dot."""
__import__(name)
return sys.modules[name]
class _LazyDescr(object):
def __init__(self, name):
self.name = name
def __get__(self, obj, tp):
try:
result = self._resolve()
except ImportError:
# See the nice big comment in MovedModule.__getattr__.
raise AttributeError("%s could not be imported " % self.name)
setattr(obj, self.name, result) # Invokes __set__.
# This is a bit ugly, but it avoids running this again.
delattr(obj.__class__, self.name)
return result
class MovedModule(_LazyDescr):
def __init__(self, name, old, new=None):
super(MovedModule, self).__init__(name)
if PY3:
if new is None:
new = name
self.mod = new
else:
self.mod = old
def _resolve(self):
return _import_module(self.mod)
def __getattr__(self, attr):
# It turns out many Python frameworks like to traverse sys.modules and
# try to load various attributes. This causes problems if this is a
# platform-specific module on the wrong platform, like _winreg on
# Unixes. Therefore, we silently pretend unimportable modules do not
# have any attributes. See issues #51, #53, #56, and #63 for the full
# tales of woe.
#
# First, if possible, avoid loading the module just to look at __file__,
# __name__, or __path__.
if (attr in ("__file__", "__name__", "__path__") and self.mod not in sys.modules):
raise AttributeError(attr)
try:
_module = self._resolve()
except ImportError:
raise AttributeError(attr)
value = getattr(_module, attr)
setattr(self, attr, value)
return value
class _LazyModule(types.ModuleType):
def __init__(self, name):
super(_LazyModule, self).__init__(name)
self.__doc__ = self.__class__.__doc__
def __dir__(self):
attrs = ["__doc__", "__name__"]
attrs += [attr.name for attr in self._moved_attributes]
return attrs
# Subclasses should override this
_moved_attributes = []
class MovedAttribute(_LazyDescr):
def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):
super(MovedAttribute, self).__init__(name)
if PY3:
if new_mod is None:
new_mod = name
self.mod = new_mod
if new_attr is None:
if old_attr is None:
new_attr = name
else:
new_attr = old_attr
self.attr = new_attr
else:
self.mod = old_mod
if old_attr is None:
old_attr = name
self.attr = old_attr
def _resolve(self):
module = _import_module(self.mod)
return getattr(module, self.attr)
class _MovedItems(_LazyModule):
"""Lazy loading of moved objects"""
_moved_attributes = [
MovedAttribute("cStringIO", "cStringIO", "io", "StringIO"),
MovedAttribute("filter", "itertools", "builtins", "ifilter", "filter"),
MovedAttribute("filterfalse", "itertools", "itertools", "ifilterfalse", "filterfalse"),
MovedAttribute("input", "__builtin__", "builtins", "raw_input", "input"),
MovedAttribute("map", "itertools", "builtins", "imap", "map"),
MovedAttribute("range", "__builtin__", "builtins", "xrange", "range"),
MovedAttribute("reload_module", "__builtin__", "imp", "reload"),
MovedAttribute("reduce", "__builtin__", "functools"),
MovedAttribute("StringIO", "StringIO", "io"),
MovedAttribute("UserDict", "UserDict", "collections"),
MovedAttribute("UserList", "UserList", "collections"),
MovedAttribute("UserString", "UserString", "collections"),
MovedAttribute("xrange", "__builtin__", "builtins", "xrange", "range"),
MovedAttribute("zip", "itertools", "builtins", "izip", "zip"),
MovedAttribute("zip_longest", "itertools", "itertools", "izip_longest", "zip_longest"),
MovedModule("builtins", "__builtin__"),
MovedModule("configparser", "ConfigParser"),
MovedModule("copyreg", "copy_reg"),
MovedModule("dbm_gnu", "gdbm", "dbm.gnu"),
MovedModule("http_cookiejar", "cookielib", "http.cookiejar"),
MovedModule("http_cookies", "Cookie", "http.cookies"),
MovedModule("html_entities", "htmlentitydefs", "html.entities"),
MovedModule("html_parser", "HTMLParser", "html.parser"),
MovedModule("http_client", "httplib", "http.client"),
MovedModule("email_mime_multipart", "email.MIMEMultipart", "email.mime.multipart"),
MovedModule("email_mime_text", "email.MIMEText", "email.mime.text"),
MovedModule("email_mime_base", "email.MIMEBase", "email.mime.base"),
MovedModule("BaseHTTPServer", "BaseHTTPServer", "http.server"),
MovedModule("CGIHTTPServer", "CGIHTTPServer", "http.server"),
MovedModule("SimpleHTTPServer", "SimpleHTTPServer", "http.server"),
MovedModule("cPickle", "cPickle", "pickle"),
MovedModule("queue", "Queue"),
MovedModule("reprlib", "repr"),
MovedModule("socketserver", "SocketServer"),
MovedModule("_thread", "thread", "_thread"),
MovedModule("tkinter", "Tkinter"),
MovedModule("tkinter_dialog", "Dialog", "tkinter.dialog"),
MovedModule("tkinter_filedialog", "FileDialog", "tkinter.filedialog"),
MovedModule("tkinter_scrolledtext", "ScrolledText", "tkinter.scrolledtext"),
MovedModule("tkinter_simpledialog", "SimpleDialog", "tkinter.simpledialog"),
MovedModule("tkinter_tix", "Tix", "tkinter.tix"),
MovedModule("tkinter_ttk", "ttk", "tkinter.ttk"),
MovedModule("tkinter_constants", "Tkconstants", "tkinter.constants"),
MovedModule("tkinter_dnd", "Tkdnd", "tkinter.dnd"),
MovedModule("tkinter_colorchooser", "tkColorChooser", "tkinter.colorchooser"),
MovedModule("tkinter_commondialog", "tkCommonDialog", "tkinter.commondialog"),
MovedModule("tkinter_tkfiledialog", "tkFileDialog", "tkinter.filedialog"),
MovedModule("tkinter_font", "tkFont", "tkinter.font"),
MovedModule("tkinter_messagebox", "tkMessageBox", "tkinter.messagebox"),
MovedModule("tkinter_tksimpledialog", "tkSimpleDialog", "tkinter.simpledialog"),
MovedModule("urllib_parse", __name__ + ".moves.urllib_parse", "urllib.parse"),
MovedModule("urllib_error", __name__ + ".moves.urllib_error", "urllib.error"),
MovedModule("urllib", __name__ + ".moves.urllib", __name__ + ".moves.urllib"),
MovedModule("urllib_robotparser", "robotparser", "urllib.robotparser"),
MovedModule("xmlrpc_client", "xmlrpclib", "xmlrpc.client"),
MovedModule("xmlrpc_server", "xmlrpclib", "xmlrpc.server"),
MovedModule("winreg", "_winreg"),
]
for attr in _moved_attributes:
setattr(_MovedItems, attr.name, attr)
if isinstance(attr, MovedModule):
sys.modules[__name__ + ".moves." + attr.name] = attr
del attr
_MovedItems._moved_attributes = _moved_attributes
moves = sys.modules[__name__ + ".moves"] = _MovedItems(__name__ + ".moves")
class Module_six_moves_urllib_parse(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_parse"""
_urllib_parse_moved_attributes = [
MovedAttribute("ParseResult", "urlparse", "urllib.parse"),
MovedAttribute("SplitResult", "urlparse", "urllib.parse"),
MovedAttribute("parse_qs", "urlparse", "urllib.parse"),
MovedAttribute("parse_qsl", "urlparse", "urllib.parse"),
MovedAttribute("urldefrag", "urlparse", "urllib.parse"),
MovedAttribute("urljoin", "urlparse", "urllib.parse"),
MovedAttribute("urlparse", "urlparse", "urllib.parse"),
MovedAttribute("urlsplit", "urlparse", "urllib.parse"),
MovedAttribute("urlunparse", "urlparse", "urllib.parse"),
MovedAttribute("urlunsplit", "urlparse", "urllib.parse"),
MovedAttribute("quote", "urllib", "urllib.parse"),
MovedAttribute("quote_plus", "urllib", "urllib.parse"),
MovedAttribute("unquote", "urllib", "urllib.parse"),
MovedAttribute("unquote_plus", "urllib", "urllib.parse"),
MovedAttribute("urlencode", "urllib", "urllib.parse"),
MovedAttribute("splitquery", "urllib", "urllib.parse"),
]
for attr in _urllib_parse_moved_attributes:
setattr(Module_six_moves_urllib_parse, attr.name, attr)
del attr
Module_six_moves_urllib_parse._moved_attributes = _urllib_parse_moved_attributes
sys.modules[__name__ + ".moves.urllib_parse"] = sys.modules[
__name__ + ".moves.urllib.parse"] = Module_six_moves_urllib_parse(__name__
+ ".moves.urllib_parse")
class Module_six_moves_urllib_error(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_error"""
_urllib_error_moved_attributes = [
MovedAttribute("URLError", "urllib2", "urllib.error"),
MovedAttribute("HTTPError", "urllib2", "urllib.error"),
MovedAttribute("ContentTooShortError", "urllib", "urllib.error"),
]
for attr in _urllib_error_moved_attributes:
setattr(Module_six_moves_urllib_error, attr.name, attr)
del attr
Module_six_moves_urllib_error._moved_attributes = _urllib_error_moved_attributes
sys.modules[__name__ + ".moves.urllib_error"] = sys.modules[
__name__ + ".moves.urllib.error"] = Module_six_moves_urllib_error(__name__
+ ".moves.urllib.error")
class Module_six_moves_urllib_request(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_request"""
_urllib_request_moved_attributes = [
MovedAttribute("urlopen", "urllib2", "urllib.request"),
MovedAttribute("install_opener", "urllib2", "urllib.request"),
MovedAttribute("build_opener", "urllib2", "urllib.request"),
MovedAttribute("pathname2url", "urllib", "urllib.request"),
MovedAttribute("url2pathname", "urllib", "urllib.request"),
MovedAttribute("getproxies", "urllib", "urllib.request"),
MovedAttribute("Request", "urllib2", "urllib.request"),
MovedAttribute("OpenerDirector", "urllib2", "urllib.request"),
MovedAttribute("HTTPDefaultErrorHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPRedirectHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPCookieProcessor", "urllib2", "urllib.request"),
MovedAttribute("ProxyHandler", "urllib2", "urllib.request"),
MovedAttribute("BaseHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPPasswordMgr", "urllib2", "urllib.request"),
MovedAttribute("HTTPPasswordMgrWithDefaultRealm", "urllib2", "urllib.request"),
MovedAttribute("AbstractBasicAuthHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPBasicAuthHandler", "urllib2", "urllib.request"),
MovedAttribute("ProxyBasicAuthHandler", "urllib2", "urllib.request"),
MovedAttribute("AbstractDigestAuthHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPDigestAuthHandler", "urllib2", "urllib.request"),
MovedAttribute("ProxyDigestAuthHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPSHandler", "urllib2", "urllib.request"),
MovedAttribute("FileHandler", "urllib2", "urllib.request"),
MovedAttribute("FTPHandler", "urllib2", "urllib.request"),
MovedAttribute("CacheFTPHandler", "urllib2", "urllib.request"),
MovedAttribute("UnknownHandler", "urllib2", "urllib.request"),
MovedAttribute("HTTPErrorProcessor", "urllib2", "urllib.request"),
MovedAttribute("urlretrieve", "urllib", "urllib.request"),
MovedAttribute("urlcleanup", "urllib", "urllib.request"),
MovedAttribute("URLopener", "urllib", "urllib.request"),
MovedAttribute("FancyURLopener", "urllib", "urllib.request"),
MovedAttribute("proxy_bypass", "urllib", "urllib.request"),
]
for attr in _urllib_request_moved_attributes:
setattr(Module_six_moves_urllib_request, attr.name, attr)
del attr
Module_six_moves_urllib_request._moved_attributes = _urllib_request_moved_attributes
sys.modules[__name__ + ".moves.urllib_request"] = sys.modules[
__name__ + ".moves.urllib.request"] = Module_six_moves_urllib_request(__name__
+ ".moves.urllib.request")
class Module_six_moves_urllib_response(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_response"""
_urllib_response_moved_attributes = [
MovedAttribute("addbase", "urllib", "urllib.response"),
MovedAttribute("addclosehook", "urllib", "urllib.response"),
MovedAttribute("addinfo", "urllib", "urllib.response"),
MovedAttribute("addinfourl", "urllib", "urllib.response"),
]
for attr in _urllib_response_moved_attributes:
setattr(Module_six_moves_urllib_response, attr.name, attr)
del attr
Module_six_moves_urllib_response._moved_attributes = _urllib_response_moved_attributes
sys.modules[__name__ + ".moves.urllib_response"] = sys.modules[
__name__ + ".moves.urllib.response"] = Module_six_moves_urllib_response(__name__
+ ".moves.urllib.response")
class Module_six_moves_urllib_robotparser(_LazyModule):
"""Lazy loading of moved objects in six.moves.urllib_robotparser"""
_urllib_robotparser_moved_attributes = [
MovedAttribute("RobotFileParser", "robotparser", "urllib.robotparser"),
]
for attr in _urllib_robotparser_moved_attributes:
setattr(Module_six_moves_urllib_robotparser, attr.name, attr)
del attr
Module_six_moves_urllib_robotparser._moved_attributes = _urllib_robotparser_moved_attributes
sys.modules[__name__ + ".moves.urllib_robotparser"] = sys.modules[
__name__ + ".moves.urllib.robotparser"] = Module_six_moves_urllib_robotparser(
__name__ + ".moves.urllib.robotparser")
class Module_six_moves_urllib(types.ModuleType):
"""Create a six.moves.urllib namespace that resembles the Python 3 namespace"""
parse = sys.modules[__name__ + ".moves.urllib_parse"]
error = sys.modules[__name__ + ".moves.urllib_error"]
request = sys.modules[__name__ + ".moves.urllib_request"]
response = sys.modules[__name__ + ".moves.urllib_response"]
robotparser = sys.modules[__name__ + ".moves.urllib_robotparser"]
def __dir__(self):
return ['parse', 'error', 'request', 'response', 'robotparser']
sys.modules[__name__ + ".moves.urllib"] = Module_six_moves_urllib(__name__ + ".moves.urllib")
def add_move(move):
"""Add an item to six.moves."""
setattr(_MovedItems, move.name, move)
def remove_move(name):
"""Remove item from six.moves."""
try:
delattr(_MovedItems, name)
except AttributeError:
try:
del moves.__dict__[name]
except KeyError:
raise AttributeError("no such move, %r" % (name, ))
if PY3:
_meth_func = "__func__"
_meth_self = "__self__"
_func_closure = "__closure__"
_func_code = "__code__"
_func_defaults = "__defaults__"
_func_globals = "__globals__"
else:
_meth_func = "im_func"
_meth_self = "im_self"
_func_closure = "func_closure"
_func_code = "func_code"
_func_defaults = "func_defaults"
_func_globals = "func_globals"
try:
advance_iterator = next
except NameError:
def advance_iterator(it):
return it.next()
next = advance_iterator
try:
callable = callable
except NameError:
def callable(obj):
return any("__call__" in klass.__dict__ for klass in type(obj).__mro__)
if PY3:
def get_unbound_function(unbound):
return unbound
create_bound_method = types.MethodType
Iterator = object
else:
def get_unbound_function(unbound):
return unbound.im_func
def create_bound_method(func, obj):
return types.MethodType(func, obj, obj.__class__)
class Iterator(object):
def next(self):
return type(self).__next__(self)
callable = callable
_add_doc(get_unbound_function, """Get the function out of a possibly unbound function""")
get_method_function = operator.attrgetter(_meth_func)
get_method_self = operator.attrgetter(_meth_self)
get_function_closure = operator.attrgetter(_func_closure)
get_function_code = operator.attrgetter(_func_code)
get_function_defaults = operator.attrgetter(_func_defaults)
get_function_globals = operator.attrgetter(_func_globals)
if PY3:
def iterkeys(d, **kw):
return iter(d.keys(**kw))
def itervalues(d, **kw):
return iter(d.values(**kw))
def iteritems(d, **kw):
return iter(d.items(**kw))
def iterlists(d, **kw):
return iter(d.lists(**kw))
else:
def iterkeys(d, **kw):
return iter(d.iterkeys(**kw))
def itervalues(d, **kw):
return iter(d.itervalues(**kw))
def iteritems(d, **kw):
return iter(d.iteritems(**kw))
def iterlists(d, **kw):
return iter(d.iterlists(**kw))
_add_doc(iterkeys, "Return an iterator over the keys of a dictionary.")
_add_doc(itervalues, "Return an iterator over the values of a dictionary.")
_add_doc(iteritems, "Return an iterator over the (key, value) pairs of a dictionary.")
_add_doc(iterlists, "Return an iterator over the (key, [values]) pairs of a dictionary.")
if PY3:
def b(s):
return s.encode("latin-1")
def u(s):
return s
unichr = chr
if sys.version_info[1] <= 1:
def int2byte(i):
return bytes((i, ))
else:
# This is about 2x faster than the implementation above on 3.2+
int2byte = operator.methodcaller("to_bytes", 1, "big")
byte2int = operator.itemgetter(0)
indexbytes = operator.getitem
iterbytes = iter
import io
StringIO = io.StringIO
BytesIO = io.BytesIO
else:
def b(s):
return s
# Workaround for standalone backslash
def u(s):
return unicode(s.replace(r'\\', r'\\\\'), "unicode_escape")
unichr = unichr
int2byte = chr
def byte2int(bs):
return ord(bs[0])
def indexbytes(buf, i):
return ord(buf[i])
def iterbytes(buf):
return (ord(byte) for byte in buf)
import StringIO
StringIO = BytesIO = StringIO.StringIO
_add_doc(b, """Byte literal""")
_add_doc(u, """Text literal""")
if PY3:
exec_ = getattr(moves.builtins, "exec")
def reraise(tp, value, tb=None):
if value.__traceback__ is not tb:
raise value.with_traceback(tb)
raise value
else:
def exec_(_code_, _globs_=None, _locs_=None):
"""Execute code in a namespace."""
if _globs_ is None:
frame = sys._getframe(1)
_globs_ = frame.f_globals
if _locs_ is None:
_locs_ = frame.f_locals
del frame
elif _locs_ is None:
_locs_ = _globs_
exec("""exec _code_ in _globs_, _locs_""")
exec_("""def reraise(tp, value, tb=None):
raise tp, value, tb
""")
print_ = getattr(moves.builtins, "print", None)
if print_ is None:
def print_(*args, **kwargs):
"""The new-style print function for Python 2.4 and 2.5."""
fp = kwargs.pop("file", sys.stdout)
if fp is None:
return
def write(data):
if not isinstance(data, basestring):
data = str(data)
# If the file has an encoding, encode unicode with it.
if (isinstance(fp, file) and isinstance(data, unicode) and fp.encoding is not None):
errors = getattr(fp, "errors", None)
if errors is None:
errors = "strict"
data = data.encode(fp.encoding, errors)
fp.write(data)
want_unicode = False
sep = kwargs.pop("sep", None)
if sep is not None:
if isinstance(sep, unicode):
want_unicode = True
elif not isinstance(sep, str):
raise TypeError("sep must be None or a string")
end = kwargs.pop("end", None)
if end is not None:
if isinstance(end, unicode):
want_unicode = True
elif not isinstance(end, str):
raise TypeError("end must be None or a string")
if kwargs:
raise TypeError("invalid keyword arguments to print()")
if not want_unicode:
for arg in args:
if isinstance(arg, unicode):
want_unicode = True
break
if want_unicode:
newline = unicode("\n")
space = unicode(" ")
else:
newline = "\n"
space = " "
if sep is None:
sep = space
if end is None:
end = newline
for i, arg in enumerate(args):
if i:
write(sep)
write(arg)
write(end)
_add_doc(reraise, """Reraise an exception.""")
def with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
return meta("NewBase", bases, {})
def add_metaclass(metaclass):
"""Class decorator for creating a class with a metaclass."""
def wrapper(cls):
orig_vars = cls.__dict__.copy()
orig_vars.pop('__dict__', None)
orig_vars.pop('__weakref__', None)
slots = orig_vars.get('__slots__')
if slots is not None:
if isinstance(slots, str):
slots = [slots]
for slots_var in slots:
orig_vars.pop(slots_var)
return metaclass(cls.__name__, cls.__bases__, orig_vars)
return wrapper
# added as part of the RDKit port
if PY3:
def cmp(t1, t2):
return (t1 < t2) * -1 or (t1 > t2) * 1
else:
cmp = cmp
| bp-kelley/rdkit | rdkit/six.py | Python | bsd-3-clause | 23,799 | [
"RDKit"
] | 52258e9a01aa8214c31991fdc0641af7b2d524341476afc4dedf87e6427bce56 |
import numpy as np
from tqdm import tqdm
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
import statsmodels.nonparametric.smoothers_lowess as sm
import spectralspace.sample.access_spectrum as acs
from empca import empca,MAD,meanMed
from spectralspace.sample.mask_data import mask,maskFilter,noFilter
from spectralspace.sample.star_sample import aspcappix
import os
from galpy.util import multi as ml
font = {'family': 'serif',
'weight': 'normal',
'size' : 20
}
matplotlib.rc('font',**font)
# Specify which independent variables to use when fitting different sample types
independentVariables = {'apogee':{'clusters':['TEFF'],
'OCs':['TEFF'],
'GCs':['TEFF'],
'red_clump':['TEFF','LOGG','FE_H'],
'red_giant':['TEFF','LOGG','FE_H'],
'syn':['TEFF','LOGG'],
'elem':['TEFF','LOGG','C_H','N_H','O_H','FE_H']}}
def smoothMedian(diag,frac=None,numpix=None):
"""
Uses Locally Weighted Scatterplot Smoothing to smooth an array on
each detector separately. Interpolates at masked pixels and concatenates
the result.
Returns the smoothed median value of the input array, with the same
dimension.
"""
mask = diag.mask==False
smoothmedian = np.zeros(diag.shape)
for i in range(len(detectors)-1):
xarray = np.arange(detectors[i],detectors[i+1])
yarray = diag[detectors[i]:detectors[i+1]]
array_mask = mask[detectors[i]:detectors[i+1]]
if frac:
low_smooth = sm.lowess(yarray[array_mask],xarray[array_mask],
frac=frac,it=3,return_sorted=False)
if numpix:
frac = float(numpix)/len(xarray)
low_smooth = sm.lowess(yarray[array_mask],xarray[array_mask],
frac=frac,it=3,return_sorted=False)
smooth_interp = sp.interpolate.interp1d(xarray[array_mask],
low_smooth,bounds_error=False)
smoothmedian[detectors[i]:detectors[i+1]] = smooth_interp(xarray)
nanlocs = np.where(np.isnan(smoothmedian))
smoothmedian[nanlocs] = 1
return smoothmedian
def getsmallEMPCAarrays(model):
"""
Read out arrays
"""
if model.savename:
arc = np.load('{0}_data.npz'.format(model.savename))
model.eigval = arc['eigval']
model.eigvec = np.ma.masked_array(arc['eigvec'],
mask=arc['eigvecmask'])
model.coeff = arc['coeff']
class smallEMPCA(object):
"""
Class to contain crucial EMPCA-related objects.
"""
def __init__(self,model,correction=None,savename=None):
"""
Set all relevant data for the EMPCA.
model: EMPCA model object
correction: information about correction used on data
"""
self.savename = savename
self.R2Array = model.R2Array
self.R2noise = model.R2noise
self.Vnoise = model.Vnoise
self.Vdata = model.Vdata
self.eigvec = model.eigvec
self.coeff = model.coeff
self.correction = correction
self.eigval = model.eigvals
self.savename= savename
self.cleararrays()
def cleararrays(self):
"""
Clear memory allocation for bigger arrays by writing them to file if
self.savename is set
"""
if self.savename:
np.savez_compressed('{0}_data.npz'.format(self.savename),
eigval=self.eigval,eigvec=self.eigvec.data,
eigvecmask = self.eigvec.mask,
coeff=self.coeff)
del self.eigval
del self.eigvec
del self.coeff
class empca_residuals(mask):
"""
Contains functions to find polynomial fits.
"""
def __init__(self,dataSource,sampleType,maskMaker,ask=True,datadict=None,
datadir='.',func=None,badcombpixmask=4351,minSNR=50,degree=2,
nvecs=5,fibfit=False):
"""
Fit a masked subsample.
sampleType: designator of the sample type - must be a key in readfn
and independentVariables in data.py
maskMaker: function that decides on elements to be masked
ask: if True, function asks for user input to make
filter_function.py, if False, uses existing
filter_function.py
datadir path to directory in which to write results
badcombpixmask sum of 2**<bits on which to mask> default value masks
bits 0-7 and 12
minSNR minimum allowable signal to noise - pixels below this
threshold are masked
degree: degree of polynomial to fit
"""
mask.__init__(self,dataSource,sampleType,maskMaker,ask=ask,datadict=datadict,
minSNR=minSNR,datadir=datadir,func=func,
badcombpixmask=badcombpixmask)
self.degree = degree
self.polynomial = PolynomialFeatures(degree=degree)
self.fibfit=fibfit
if self.fibfit:
fwhminfo = np.load(self.datadir+'/apogee_dr12_fiberfwhm_atpixel.npy')
self.fwhms_sample = fwhminfo[(np.round(self.matchingData['MEANFIB']).astype(int),)]
self.name += '/fibfit'
self.getDirectory()
self.testM = self.makeMatrix(0)
self.nvecs = nvecs
def sample_wrapper(self,i):
"""
A wrapper to run subsamples in parallele.
i: index of subsample
If self.division is True, define the subsample as
where the randomly assigned sample numbers (self.inds) match i
If self.division is False, define the subsamples as
where the randomly assigned sample numbers (self.inds) do not match i
"""
print('Working on {0}'.format(i+1))
# Select ith subsample and update arrays
# If sample is to be divided, find where assigned indices match i
if self.division:
self.matchingData = self.filterData[self.inds==i]
self.teff = self.originalteff[self.inds==i]
self.logg = self.originallogg[self.inds==i]
self.fe_h = self.originalfe_h[self.inds==i]
self.spectra = self.originalspectra[self.inds==i]
self.spectra_errs = self.originalspectra_errs[self.inds==i]
self._bitmasks = self.originalbitmasks[self.inds==i]
self._maskHere = self.originalmaskHere[self.inds==i]
# If sample is to be jackknived, find where assigned indices do not match i
if not self.division:
self.matchingData = self.filterData[self.inds!=i]
self.teff = self.originalteff[self.inds!=i]
self.logg = self.originallogg[self.inds!=i]
self.fe_h = self.originalfe_h[self.inds!=i]
self.spectra = self.originalspectra[self.inds!=i]
self.spectra_errs = self.originalspectra_errs[self.inds!=i]
self._bitmasks = self.originalbitmasks[self.inds!=i]
self._maskHere = self.originalmaskHere[self.inds!=i]
# Update mask
self.applyMask()
# Update name
self.name = '{0}/seed{1}_subsample{2}of{3}/'.format(self.originalname,
self.seed,i+1,
self.subsamples)
# Create directory and solve for polynomial fit coefficients
self.getDirectory()
self.findResiduals()
# Create output arrays to hold EMPCA results for each variance function
self.R2As = np.zeros((len(self.varfuncs),self.nvecs+1))
self.R2ns = np.zeros((len(self.varfuncs)))
self.cvcs = np.zeros((len(self.varfuncs)))
self.labs = np.zeros((len(self.varfuncs)),dtype='S100')
# Store information about which sample you're on
self.samplenum = i+1
# Call EMPCA solver for all variance functions in parallel
stat = ml.parallel_map(self.EMPCA_wrapper,range(len(self.varfuncs)))
print('Did EMPCA')
# Unpack results of running in parallel and store
for s in range(len(stat)):
R2A,R2n,cvc,lab = stat[s]
self.R2As[s] = R2A
self.R2ns[s] = R2n
self.cvcs[s] = cvc
self.labs[s] = lab
print('Got stats')
# Clear arrays from memory
del self.matchingData
del self.teff
del self.logg
del self.fe_h
del self.spectra
del self.spectra_errs
del self._bitmasks
del self._maskHere
# Clean directory of all but EMPCA files (don't store residuals)
self.directoryClean()
print('Cleaned')
print('R2, R2noise, cvcs, labels'.format(self.R2As.T,self.R2ns,self.cvcs,self.labs))
return (self.R2As.T,self.R2ns,self.cvcs,self.labs)
def EMPCA_wrapper(self,v):
"""
Run EMPCA for vth variance function and compute statistics.
v: Index of function to calculate variance in self.varfuncs
"""
# run EMPCA
self.pixelEMPCA(varfunc=self.varfuncs[v],nvecs=self.nvecs,
savename='eig{0}_minSNR{1}_corrNone_{2}.pkl'.format(self.nvecs,self.minSNR,self.varfuncs[v].__name__))
# Find R^2 values
R2A = self.empcaModelWeight.R2Array
R2n = self.empcaModelWeight.R2noise
print('Got R^2')
# Find where R^2 intersects R^2_noise
cvc = np.interp(R2n,R2A,np.arange(len(R2A)),left=0,right=-1)
lab = 'subsamp {0}, {1} stars, func {2} - {3} vec'.format(self.samplenum,self.numberStars(),self.varfuncs[v].__name__,cvc)
print('Found intersection')
return (R2A,R2n,cvc,lab)
def samplesplit(self,division=False,seed=None,fullsamp=True,maxsamp=5,subsamples=5,varfuncs=[np.ma.var,meanMed],numcores=None,ctmnorm=None):
"""
Take self.subsamples random subsamples of the original data set and
run EMPCA.
division: if True, split sample - if False, jackknife sample
seed: seed to randomly distribute stars into subsamples
fullsamp: if True, also process undivided full sample
maxsamp: maximum number of samples to run simultaneously
subsamples: number of subsamples to divide up full samples
varfuncs: list of functions to compute variance in EMPCA
numcores: maximum number of simultaneous parallel processes
ctnnorm: if set, renormalize for continuum
Creates a plot comparing R^2 statistics for the subsamples.
"""
self.subsamples = subsamples
self.varfuncs = varfuncs
self.division=division
if numcores:
maxsamp = np.floor(float(numcores)/len(self.varfuncs))
# If no subsamples, just run regular EMCPA
if self.subsamples==1:
self.continuumNormalize(source=ctmnorm)
self.findResiduals()
R2Arrays = np.zeros((len(self.varfuncs),self.nvecs+1))
R2noises = np.zeros((len(self.varfuncs)))
crossvecs = np.zeros((len(self.varfuncs)))
labels = np.zeros(len(self.varfuncs),dtype='S100')
self.samplenum=1
# Call EMPCA solver for all variance functions in parallel
stat = ml.parallel_map(self.EMPCA_wrapper,range(len(self.varfuncs)))
# Unpack results of running in parallel and store
for s in range(len(stat)):
R2A,R2n,cvc,lab = stat[s]
R2Arrays[s] = R2A
R2noises[s] = R2n
crossvecs[s] = cvc
labels[s] = lab
# If subsamples, run EMPCA on many subsamples
elif self.subsamples!=1:
# Pick seed to initialize randomization for reproducibility
if not seed:
self.seed = np.random.randint(0,100)
elif seed:
self.seed=seed
np.random.seed(self.seed)
self.continuumNormalize(source=ctmnorm)
# Make copies of original data to use for slicing
self.filterData = np.copy(self.matchingData)
self.originalteff = np.ma.copy(self.teff)
self.originallogg = np.ma.copy(self.logg)
self.originalfe_h = np.ma.copy(self.fe_h)
self.originalspectra = np.ma.copy(self.spectra)
self.originalspectra_errs = np.ma.copy(self.spectra_errs)
self.originalbitmasks = np.copy(self._bitmasks)
self.originalmaskHere = np.copy(self._maskHere)
self.originalname = np.copy(self.name)
# Initialize array that assigns each star to a group from 0
# To self.subsamples-1
self.inds = np.zeros((self.numberStars()))-1
# Number of stars in each subsample
subnum = self.numberStars()/self.subsamples
# Randomly choose stars to belong to each subsample
for i in range(self.subsamples):
group = np.random.choice(np.where(self.inds==-1)[0],size=subnum,replace=False)
self.inds[group] = i
# Distribute leftover stars one at a time to each subsample
leftovers = [i for i in range(self.numberStars()) if self.inds[i]==-1]
if leftovers != []:
k = 0
for i in leftovers:
self.inds[i] = k
k+=1
# Create arrays to hold R^2 statistics and their labels
if fullsamp:
self.sampnum = self.subsamples+1
elif not fullsamp:
self.sampnum = self.subsamples
R2Arrays = np.zeros((len(self.varfuncs)*(self.sampnum),
self.nvecs+1))
R2noises = np.zeros((len(self.varfuncs)*(self.sampnum)))
crossvecs = np.zeros((len(self.varfuncs)*(self.sampnum)))
labels = np.zeros((len(self.varfuncs)*(self.sampnum)),dtype='S200')
# Run all samples in parallel but serialize if too large
if self.sampnum <=maxsamp:
stats = ml.parallel_map(self.sample_wrapper, range(self.sampnum))
elif self.sampnum >maxsamp:
sample = 0
stats = []
# Find the number of sets of size maxsamp that need to run
number_sets = self.sampnum/maxsamp + int(self.sampnum % maxsamp > 0)
# If maxsamp a factor of self.sampnum take the easy route
if self.sampnum/maxsamp == number_sets:
for i in range(number_sets):
ss = ml.parallel_map(self.sample_wrapper, range(sample,sample+maxsamp))
sample += maxsamp
stats.append(ss)
# If maxsamp not a factor of self.sampnum, run the regular sized runs then one smaller run
elif self.sampnum/maxsamp != number_sets:
for i in range(number_sets):
if i < number_sets-1:
ss = ml.parallel_map(self.sample_wrapper, range(sample,sample+maxsamp))
sample += maxsamp
stats.append(ss)
if i == number_sets-1:
ss = ml.parallel_map(self.sample_wrapper, range(sample,sample+self.sampnum % maxsamp))
sample += self.sampnum% maxsamp
stats.append(ss)
# Unpack run statistics from sublist
stats = [item for sublist in stats for item in sublist]
# Unpack information from parallel runs into appropriate arrays
print('stats ',stats)
k = 0
for s in range(len(stats)):
R2As,R2ns,cvcs,labs = stats[s]
R2Arrays[k:k+len(self.varfuncs)] = R2As.T
R2noises[k:k+len(self.varfuncs)] = R2ns
crossvecs[k:k+len(self.varfuncs)] = cvcs
labels[k:k+len(self.varfuncs)] = labs
k+=len(self.varfuncs)
# Restore original arrays
self.matchingData = self.filterData
self.teff = self.originalteff
self.logg = self.originallogg
self.fe_h = self.originalfe_h
self.spectra = self.originalspectra
self.spectra_errs = self.originalspectra_errs
self._bitmasks = self.originalbitmasks
self._maskHere = self.originalmaskHere
self.name = str(self.originalname)
# Update mask
self.applyMask()
# Calculate uncertainty on number of eigenvectors.
sort = self.func_sort(R2Arrays,R2noises,crossvecs,labels)
labs = sort[3]
cvecs = sort[2]
start = 0
# Calculate for each variance function separately
for v in range(len(self.varfuncs)):
# Number of crossover values to use for this variance function
num = len(cvecs)/len(self.varfuncs)
lab = labs[start:start+num]
cvec = cvecs[start:start+num]
start+=num
# Don't use the full sample value if its in hte list
safe = np.array([i for i in range(len(lab)) if 'subsamp{0}'.format(self.subsamples+1) not in lab[i]])
cvec = cvec[safe]
# Don't use crossover values flagged as greater than the total amount found
cvec = cvec[cvec!=-1]
if cvec.size:
avgvec = np.median(cvec)
if not self.division:
varvec = ((len(cvec)-1.)/float(len(cvec)))*np.sum((cvec-avgvec)**2)
elif self.division:
varvec = np.var(cvec)
elif not cvec.size:
avgvec = -1
varvec = -1
print('{0} +/- {1}'.format(avgvec,np.sqrt(varvec)))
# Save results
self.numeigvec = avgvec
self.numeigvec_std = np.sqrt(varvec)
numeigvec_file = np.array([self.numeigvec,self.numeigvec_std])
numeigvec_file.tofile('{0}/subsamples{1}_{2}_seed{3}_numeigvec.npy'.format(self.name,self.subsamples,self.varfuncs[v].__name__,self.seed))
# Move full sample analysis to parent directory
if fullsamp:
os.system('mv {0}/seed{1}_subsample{2}of{3}/* {4}'.format(self.name,self.seed,self.subsamples+1,self.subsamples,self.name))
os.system('rmdir {0}/seed{1}_subsample{2}of{3}/'.format(self.name,self.seed,self.subsamples+1,self.subsamples))
# Make plots sorting by function
self.R2compare(R2Arrays,R2noises,crossvecs,labels,funcsort=True)
self.R2compare(R2Arrays,R2noises,crossvecs,labels,funcsort=False)
def func_sort(self,R2Arrays,R2noises,crossvecs,labels):
"""
Sort arrays by function used rather than subsample.
R2Arrays: array of R2 values for each subsample
R2noises: array of R2noise for each subsample
crossvecs: number of eigenvectors for R2Array to intersect R2noise
labels: labels for each sample
Returns newly sorted arrays
"""
newR2 = np.zeros(R2Arrays.shape)
newR2n = np.zeros(R2noises.shape)
newvec = np.zeros(crossvecs.shape)
newlab = []
k = 0
for i in range(len(self.varfuncs)):
newR2[k:k+self.sampnum] = R2Arrays[i::len(self.varfuncs)]
newR2n[k:k+self.sampnum] = R2noises[i::len(self.varfuncs)]
newvec[k:k+self.sampnum] = crossvecs[i::len(self.varfuncs)\
]
newlab.append(labels[i::len(self.varfuncs)])
k += self.sampnum
return newR2,newR2n,newvec,[item for sublist in newlab for item in sublist]
def R2compare(self,R2Arrays,R2noises,crossvecs,labels,funcsort=True):
"""
Make plots comparing R2 values for different samples and different
functions. If there are less than 10 subsamples (including each
variance function as a different sample), this plots a 1D comparison
of R^2 values as a function of eigenvector number. Regardless of the
number of subsamples, this plots a 2D comparison of R^2.
R2Arrays: An array of R2 values with size number of samples by
number of eigenvectors
R2noises: An array of R2noise values with size number of samples
crossvecs: An array of the location of the intersection of R2 and
R2noise with size number of samples
labels: Labels for each subsample
funcsort: Keyword to sort input to group similar functions instead
of similar samples
Saves 1 or 2 figures.
"""
# If sorting by function instead of sample, slice and reorient arrays
# accordingly
if funcsort:
R2Arrays,R2noises,crossvecs,labels = self.func_sort(R2Arrays,R2noises,crossvecs,labels)
# Get colours for line plot
colors = plt.get_cmap('plasma')(np.linspace(0,0.85,len(labels)))
# If there aren't too many lines, make a 1D line plot of R2
if (self.subsamples+1)*len(self.varfuncs) < 10:
plt.figure(figsize=(10,8))
for i in range(len(labels)):
plt.plot(R2Arrays[i],lw=4,color=colors[i],label=labels[i])
plt.axhline(R2noises[i],lw=3,ls='--',color=colors[i])
plt.ylim(0,1)
plt.xlim(-1,len(R2Arrays[0]))
plt.ylabel('$R^2$',fontsize=20)
plt.xlabel('n')
legend = plt.legend(loc='best',fontsize = 13)
if hasattr(self,'seed'):
plt.savefig('{0}/seed{1}_R2comp.png'.format(self.name,self.seed))
elif not hasattr(self,'seed'):
plt.savefig('{0}/R2comp.png'.format(self.name))
# Make a 2D plot of R2
plt.figure(figsize=(10,8))
plt.subplot(111)
plt.imshow(R2Arrays,cmap = 'viridis',interpolation = 'nearest',
aspect = R2Arrays.shape[1]/float(R2Arrays.shape[0]),
vmin=0,vmax=1.0)
# Plot lines to split up samples or function types, and vertical lines
# to mark cross over points between R2 and R2noise
k = 0
for i in range(len(labels)):
# Find bounds on the scale of the plotting area (0-1)
ymin = i*(1./len(labels))
ymax = ymin + (1./len(labels))
# Plot marker of where R2 crosses R2noise
plt.axvline(crossvecs[i],ymin=ymin,ymax=ymax,color='w',lw=2)
# Plot horizontal lines to guide the eye
# If not sorted, mark bounds of samples
if not funcsort:
if i == k*len(self.varfuncs):
if k!=0:
plt.axhline(i-0.5,color='w',lw=2,ls='--')
k+=1
# If sorted, mark bounds of functions
elif funcsort:
if i == k*(self.subsamples+1):
if k!=0:
plt.axhline(i-0.5,color='w',lw=2,ls='--')
k+=1
# Find shorter labels for the 2D plots
shortlabels = [i[:-10] for i in labels]
# If there are too many labels, reduce them for readability
if (self.sampnum*len(self.varfuncs)) > 10:
few = int(np.ceil(np.log10(self.sampnum)))
else:
few = 1
# Plot ylables
plt.yticks(np.arange(len(labels))[::few],shortlabels[::-1][::few],
fontsize=12)
# Constrain x-axis since adding axhline/axvline can make the axes stretch
plt.xlim(-0.5,self.nvecs+0.5)
plt.xlabel('$n$')
plt.colorbar(label='$R^2$')
plt.tight_layout()
# Save the plot
if hasattr(self,'seed'):
if funcsort:
plt.savefig('{0}/seed{1}_fsort_2D_R2comp.png'.format(self.name,self.seed))
elif not funcsort:
plt.savefig('{0}/seed{1}_2D_R2comp.png'.format(self.name,self.seed))
elif not hasattr(self,'seed'):
if funcsort:
plt.savefig('{0}/fsort_2D_R2comp.png'.format(self.name))
elif not funcsort:
plt.savefig('{0}/2D_R2comp.png'.format(self.name))
def makeMatrix(self,pixel,matrix='default'):
"""
Find independent variable matrix
pixel: pixel to use, informs the mask on the matrix
matrix: choose which variables to fit
Returns the matrix of independent variables for fit
"""
if matrix=='default':
matrix=self._sampleType
# Find the number of unmasked stars at this pixel
numberStars = len(self.spectra[:,pixel][self.unmasked[:,pixel]])
# Create basic independent variable array
if not self.fibfit:
indeps = np.zeros((numberStars,
len(independentVariables[self._dataSource][matrix])))
elif self.fibfit:
indeps = np.zeros((numberStars,
len(independentVariables[self._dataSource][matrix])+1))
for i in range(len(independentVariables[self._dataSource][matrix])):
variable = independentVariables[self._dataSource][matrix][i]
indep = self.keywordMap[variable][self.unmasked[:,pixel]]
indeps[:,i] = indep-np.ma.median(indep)
if self.fibfit:
indeps[:,-1] = self.fwhms_sample[:,pixel][self.unmasked[:,pixel]]
# use polynomial to produce matrix with all necessary columns
return np.matrix(self.polynomial.fit_transform(indeps))
def fibFit(self):
fwhminfo = np.load(self.datadir+'/apogee_dr12_fiberfwhm_atpixel.npy')
fwhms_sample = fwhminfo[(np.round(self.matchingData['MEANFIB']).astype(int),)]
print(whms_sample.shape)
self.fibspectra = np.ma.masked_array(np.copy(self.spectra),mask=np.copy(self.spectra.mask))
bestFits = np.ma.masked_array(np.copy(self.spectra),mask=np.copy(self.spectra.mask))
for p in tqdm(range(aspcappix),'fibfit'):
fullindeps = fwhms_sample[:,p]
fullindeps -= np.ma.median(fullindeps)
indeps = fullindeps[self.unmasked[:,p]]
indeps = np.matrix(self.polynomial.fit_transform(indeps.reshape(-1,1)))
fullindeps = np.matrix(self.polynomial.fit_transform(fullindeps.reshape(-1,1)))
covInverse = np.diag(1./self.spectra_errs[:,p][self.unmasked[:,p]]**2)
starsAtPixel = np.matrix(self.spectra[:,p][self.unmasked[:,p]])
newIndeps = np.dot(indeps.T,np.dot(covInverse,indeps))
newStarsAtPixel = np.dot(indeps.T,np.dot(covInverse,starsAtPixel.T))
invNewIndeps = np.linalg.inv(newIndeps)
coeffs = np.dot(invNewIndeps,newStarsAtPixel)
#coeffs = np.linalg.lstsq(newIndeps,newStarsAtPixel)[0]
coeff_errs = np.array([np.sqrt(np.array(invNewIndeps)[i][i]) for i in range(newIndeps.shape[1])])
bestFit = fullindeps*coeffs
bestFits = bestFit.T[0]
self.fibspectra[:,p] = self.spectra[:,p]-bestFit.T[0]
np.save('{0}/bestfibfit.npy'.format(self.name),bestFits)
np.save('{0}/fibfitspec.npy'.format(self.name),self.spectra.data)
self.spectra = self.fibspectra
def findFit(self,pixel,eigcheck=False,givencoeffs=[],matrix='default',
fibfit=False):
"""
Fits polynomial to all spectra at a given pixel, weighted by spectra
uncertainties.
pixel: pixel at which to perform fit
eigcheck: check for degeneracy between pixels
givencoeffs: if fit coefficient are given, use them instead of finding new ones
matrix: choose which variables to fit
Return polynomial values and coefficients
"""
# find matrix for polynomial of independent values
indeps = self.makeMatrix(pixel,matrix=matrix)
self.numparams = indeps.shape[1]
# If no coefficients given, find them
if givencoeffs == []:
# calculate inverse covariance matrix
covInverse = np.diag(1./self.spectra_errs[:,pixel][self.unmasked[:,pixel]]**2)
# find matrix for spectra values
starsAtPixel = np.matrix(self.spectra[:,pixel][self.unmasked[:,pixel]])
# transform to matrices that have been weighted by the inverse
# covariance
newIndeps = np.dot(indeps.T,np.dot(covInverse,indeps))
# Degeneracy check
degen = False
if eigcheck:
eigvals,eigvecs = np.linalg.eig(newIndeps)
if any(np.fabs(eigvals)<1e-10) and indeps.shape[1] > self.degree+1:
print('degenerate pixel ',pixel,' coeffs ',np.where(np.fabs(eigvals) < 1e-10)[0])
degen = True
indeps = indeps.T[self.noncrossInds].T
newStarsAtPixel = np.dot(indeps.T,np.dot(covInverse,starsAtPixel.T))
try:
invNewIndeps = np.linalg.inv(newIndeps)
# calculate fit coefficients
coeffs = np.dot(invNewIndeps,newStarsAtPixel)
#coeffs = np.linalg.lstsq(newIndeps,newStarsAtPixel)[0]
coeff_errs = np.array([np.sqrt(np.array(invNewIndeps)[i][i]) for i in range(newIndeps.shape[1])])
bestFit = indeps*coeffs
# If degeneracy, make coefficients into the correct shape and mask appropriately
except np.linalg.LinAlgError:
newcoeffs = np.ma.masked_array(np.zeros(self.numparams),
mask = np.zeros(self.numparams))
newcoeff_errs = np.ma.masked_array(np.zeros(self.numparams),
mask = np.zeros(self.numparams))
try:
newcoeffs[self.noncrossInds] = coeffs
newcoeff_errs[self.noncrossInds] = coeff_errs
newcoeffs.mask[self.crossInds] = True
newcoeff_errs.mask[self.crossInds] = True
coeffs = newcoeffs
coeff_errs = newcoeff_errs
bestFit = indeps*coeffs
except UnboundLocalError:
coeffs = newcoeffs.T
coeff_errs = newcoeff_errs
bestFit = np.zeros(len(self.spectra[:,pixel][self.unmasked[:,pixel]]))
# If coefficients given, use those
elif givencoeffs != []:
coeffs,coeff_errs = givencoeffs
bestFit = indeps*coeffs
return bestFit,coeffs.T,coeff_errs
def multiFit(self,minStarNum='default',eigcheck=False,coeffs=None,matrix='default'):
"""
Loop over all pixels and find fit. Mask where there aren't enough
stars to fit.
minStarNum: (optional) number of stars required to perform fit
(default:'default' which sets minStarNum to the number
of fit parameters plus one)
eigcheck: check for degeneracy between pixels
coeffs: file containing alternate coefficients to use
matrix: choose which variables to fit
Saves fit coefficients, and resulting approximate spectra
"""
# create sample matrix to confirm the number of parameters
self.testM = self.makeMatrix(0,matrix=matrix)
self.numparams = self.testM.shape[1]
# set minimum number of stars needed for the fit
if minStarNum=='default':
self.minStarNum = self.testM.shape[1]+1
elif minStarNum!='default':
self.minStarNum = minStarNum
# create arrays to hold fit results
self.fitCoeffs = np.ma.masked_array(np.zeros((aspcappix,
self.numparams)))
self.fitCoeffErrs = np.ma.masked_array(np.zeros((aspcappix,
self.numparams)))
self.fitSpectra = np.ma.masked_array(np.zeros((self.spectra.shape)),
mask = self.spectra.mask)
if not coeffs:
# perform fit at all pixels with enough stars
for pixel in tqdm(range(aspcappix),desc='fit'):
if np.sum(self.unmasked[:,pixel].astype(int)) < self.minStarNum:
# if too many stars missing, update mask
self.fitSpectra[:,pixel].mask = np.ones(self.spectra.shape[1])
self.fitCoeffs[pixel].mask = np.ones(self.numparams)
self.fitCoeffErrs[pixel].mask = np.ones(self.numparams)
self.unmasked[:,pixel] = np.zeros(self.unmasked[:,pixel].shape)
self.masked[:,pixel] = np.ones(self.masked[:,pixel].shape)
else:
# if fit possible update arrays
fitSpectrum,coefficients,coefficient_uncertainty = self.findFit(pixel,eigcheck=eigcheck,matrix=matrix)
self.fitSpectra[:,pixel][self.unmasked[:,pixel]] = np.array(fitSpectrum).flatten()
self.fitCoeffs[pixel] = coefficients
self.fitCoeffErrs[pixel] = coefficient_uncertainty
elif coeffs:
fmask = np.load(self.name+'/fitcoeffmask.npy')
self.fitCoeffs = np.ma.masked_array(np.load(self.name+'/fitcoeffs.npy'),mask=fmask)
self.fitCoeffErrs = np.ma.masked_array(np.load(self.name+'/fitcoefferrs.npy'),mask=fmask)
for pixel in tqdm(range(aspcappix),desc='fit'):
if np.sum(self.unmasked[:,pixel].astype(int)) < self.minStarNum:
# if too many stars missing, update mask
self.fitSpectra[:,pixel].mask = np.ones(self.spectra.shape[1])
self.fitCoeffs[pixel].mask = np.ones(self.numparams)
self.fitCoeffErrs[pixel].mask = np.ones(self.numparams)
self.unmasked[:,pixel] = np.zeros(self.unmasked[:,pixel].shape)
self.masked[:,pixel] = np.ones(self.masked[:,pixel].shape)
else:
# if fit possible update arrays
fitSpectrum,coefficients,coefficient_uncertainty = self.findFit(pixel,eigcheck=eigcheck,givencoeffs = [self.fitCoeffs[pixel],self.fitCoeffErrs[pixel]],matrix=matrix)
self.fitSpectra[:,pixel][self.unmasked[:,pixel]] = fitSpectrum
# update mask on input data
self.applyMask()
def plot_example_fit(self,indep=1,pixel=0,figsize=(12,8),
xlabel='$T_{\mathrm{eff}}$ - median($T_{\mathrm{eff}}$) (K)'):
"""
Show a two-dimensional representation of the fit at a given pixel.
indep: Column of matrix from makeMatrix corresponding to the
independent variable to plot against.
pixel: Pixel at which to plot the fit.
figsize: Tuple that determines figure size
xlabel: Label for the x-axis of the plot.
Creates a plot showing an example of a fit in one parameter
"""
# Create figure
plt.figure(figsize=figsize)
ax=plt.subplot2grid((3,1),(0,0),rowspan=2)
# Find independent variable
indeps = self.makeMatrix(pixel)
# Recreate fit
fitresult = np.dot(indeps,self.fitCoeffs[pixel].T)
# Choose the independent variable to plot on the x-axis
indep = np.array(np.reshape(indeps[:,indep],len(fitresult)))[0]
# Sort stars by their independent variable values
sortd = indep.argsort()
# Create an array of independent variables to plot smooth fit
fitindep = np.arange(np.floor((min(indep)-100)/100.)*100,np.ceil((max(indep)+100)/100.)*100,100)
fit = np.dot(np.matrix([np.ones(len(fitindep)),fitindep,fitindep**2]).T,self.fitCoeffs[pixel].T)
# Get colors for points
c = plt.get_cmap('viridis')(np.linspace(0.6, 1, 1))[0]
# Find where data is unmasked
unmasked = np.where(self.spectra[:,pixel].mask==False)
# Plot a fit line and errorbar points of data
plt.plot(fitindep,fit,lw=3,color='k',
label='polynomial fit')
plt.plot(indep,self.spectra[:,pixel][unmasked],'o',color=c,
markersize=10,markeredgecolor='k',markeredgewidth=2)
plt.ylabel('normalized flux',fontsize=20)
xticks = np.arange(np.floor((min(indep)-100)/100.)*100+100,
np.ceil((max(indep)+100)/100.)*100,200)
plt.xticks(xticks,['']*len(xticks))
plt.ylim(0.6,1.1)
plt.xlim(np.floor((min(indep)-100)/100.)*100+100,
np.ceil((max(indep)+100)/100.)*100-100)
# Fix axis ticks
plt.yticks(np.arange(0.7,1.1,0.1),np.arange(0.7,1.1,0.1).astype(str),
fontsize=20)
yminorlocator = MultipleLocator(0.05)
ax.yaxis.set_minor_locator(yminorlocator)
xminorlocator = MultipleLocator(50)
ax.xaxis.set_minor_locator(xminorlocator)
plt.tick_params(which='both', width=2)
plt.tick_params(which='major',length=5)
plt.tick_params(which='minor',length=3)
plt.legend(loc='best',frameon=False,fontsize=18)
# Plot residuals of the fit
ax=plt.subplot2grid((3,1),(2,0))
plt.axhline(0,lw=2,color='k')
plt.errorbar(indep,self.residuals[:,pixel][unmasked],yerr=self.spectra_errs[:,pixel][unmasked],fmt='o',color=c,ecolor='k',markersize=8,elinewidth=3,capthick=2,capsize=4,markeredgecolor='k',markeredgewidth=1.2)
resname = 'residuals'# $\delta_{'+'{0}'.format(pixel) + '}(s)$'
plt.ylabel(resname,fontsize=20)
plt.xlabel(xlabel,fontsize=20)
# Fix axis ticks
plt.xticks(xticks,xticks.astype(int))
plt.ylim(-0.05,0.05)
plt.xlim(np.floor((min(indep)-100)/100.)*100+100,
np.ceil((max(indep)+100)/100.)*100-100)
yminorlocator = MultipleLocator(0.01)
ax.yaxis.set_minor_locator(yminorlocator)
xminorlocator = MultipleLocator(50)
ax.xaxis.set_minor_locator(xminorlocator)
plt.tick_params(which='both', width=2)
plt.tick_params(which='major',length=5)
plt.tick_params(which='minor',length=3)
# Compress plots
plt.subplots_adjust(hspace=0)
def fitStatistic(self):
"""
Adds to fit object chi squared and reduced chi squared properties.
"""
self.fitChiSquared = np.ma.sum((self.spectra-self.fitSpectra)**2/self.spectra_errs**2,axis=0)
# Calculate degrees of freedom
if isinstance(self.fitCoeffs.mask,np.ndarray):
dof = self.numberStars() - np.sum(self.fitCoeffs.mask==False,axis=1) - 1
else:
dof = self.numberStars() - self.numparams - 1
self.fitReducedChi = self.fitChiSquared/dof
def findResiduals(self,minStarNum='default',gen=True,coeffs=None,matrix='default',eigcheck=False):
"""
Calculate residuals from polynomial fits.
minStarNum: (optional) number of stars required to perform fit
(default:'default' which sets minStarNum to the number
of fit parameters plus one)
gen: if true, generate residuals from scratch rather than reading from file
coeffs: path to file containing fit coefficients
matrix: choose which independent variables to use
Save fit information
"""
if gen:
self.multiFit(minStarNum=minStarNum,coeffs=coeffs,matrix=matrix,eigcheck=eigcheck)
self.residuals = self.spectra - self.fitSpectra
np.save(self.name+'/fitcoeffs.npy',self.fitCoeffs.data)
np.save(self.name+'/fitcoeffmask.npy',self.fitCoeffs.mask)
np.save(self.name+'/fitcoefferrs.npy',self.fitCoeffErrs.data)
np.save(self.name+'/fitspectra.npy',self.fitSpectra.data)
np.save(self.name+'/residuals.npy',self.residuals.data)
np.save(self.name+'/mask.npy',self.masked)
if not gen:
self.testM = self.makeMatrix(0)
self.minStarNum = self.testM.shape[1]+1
fmask = np.load(self.name+'/fitcoeffmask.npy')
self.fitCoeffs = np.ma.masked_array(np.load(self.name+'/fitcoeffs.npy'),mask=fmask)
self.fitCoeffErrs = np.ma.masked_array(np.load(self.name+'/fitcoefferrs.npy'),mask=fmask)
self.fitSpectra = np.ma.masked_array(np.load(self.name+'/fitspectra.npy'),mask=self.masked)
self.residuals = np.ma.masked_array(np.load(self.name+'/residuals.npy'),mask=self.masked)
'''
def testFit(self,errs=None,randomize=False, params=defaultparams, singlepix=None,minStarNum='default'):
"""
Test fit accuracy by generating a data set from given input parameters.
errs: Sets errors to use in the fit. If a int or float, set
constant errors. Else, use data set errors.
randomize: Sets whether generated data gets Gaussian noise added,
drawn from flux uncertainty.
params: Either an array of parameters to use or an index to select
from existing fitCoeffs array.
singlepix: Sets whether to fit all pixels, or just a single one
(random if singlepix not set as integer)
"""
if minStarNum=='default':
self.minStarNum = self.testM.shape[1]+1
elif minStarNum!='default':
self.minStarNum = minStarNum
# Choose parameters if necessary
if isinstance(params,(int)):
params = self.fitCoeffs[params]
if isinstance(params,(dict)):
params = params[self._sampleType]
if isinstance(params,(list)):
params = np.array(params)
# Store parameters for future comparison
self.testParams = np.matrix(params).T
# Keep old spectra information
self.old_spectra = np.ma.masked_array(np.zeros(self.spectra.shape))
self.old_spectra[:] = self.spectra
self.old_spectra_errs = np.ma.masked_array(np.zeros(self.spectra_errs.shape))
self.old_spectra_errs[:] = self.spectra_errs
if not errs:
# assign errors as constant with consistent max
self.spectra_errs = np.ma.masked_array(np.ones(self.old_spectra_errs.shape))
self.spectra_errs.mask=self.old_spectra_errs.mask
# Generate data
for pixel in range(aspcappix):
if np.sum(self.unmasked[:,pixel].astype(int)) > self.minStarNum:
indeps = self.makeMatrix(pixel)
self.spectra[:,pixel][self.unmasked[:,pixel]] = np.reshape(np.array(indeps*self.testParams),self.spectra[:,pixel][self.unmasked[:,pixel]].shape)
# If requested, added noise to the data
if randomize:
self.spectra += self.spectra_errs*np.random.randn(self.spectra.shape[0],
self.spectra.shape[1])
self.testParams = np.reshape(np.array(self.testParams),(len(params),))
if not singlepix:
# Calculate residuals
self.multiFit()
# Find the difference between the calculated parameters and original input
self.diff = np.ma.masked_array([self.testParams-self.fitCoeffs[i] for i in range(len(self.fitCoeffs))],mask = self.fitCoeffs.mask)
elif singlepix:
fitSpectrum,coefficients,coefficient_uncertainty = self.findFit(singlepix)
self.fitCoeffs = coefficients
self.fitCoeffErrs = coefficient_uncertainty
self.diff = self.testParams-coefficients
# Normalize the difference by standard error size
self.errNormDiff = self.diff/np.ma.median(self.spectra_errs)
# Restore previous values
self.spectra[:] = self.old_spectra
self.spectra_errs[:] = self.old_spectra_errs
'''
def findCorrection(self,cov=None,median=True,numpix=10.,frac=None,
savename='pickles/correction_factor.pkl',tol=0.005):
"""
Calculates the diagonal of a square matrix and smooths it
either over a fraction of the data or a number of elements,
where number of elements takes precedence if both are set.
cov: Square matrix. If unspecified, calculate from residuals and
uncertainties
median: If true, returns smoothed median, not raw diagonal
numpix: Number of elements to smooth over
frac: Fraction of data to smooth over
tol: Acceptable distance from 0, if greater than this, smooth to
adjacent values when median is True
Returns the diagonal of a covariance matrix
"""
if isinstance(cov,(list,np.ndarray)):
self.cov=cov
if not isinstance(cov,(list,np.ndarray)):
arr = self.residuals.T/self.spectra_errs.T
cov = np.ma.cov(arr)
self.cov=cov
diagonal = np.ma.diag(cov)
if median:
median = smoothMedian(diagonal,frac=frac,numpix=float(numpix))
offtol = np.where(np.fabs(median)<tol)[0]
if offtol.shape > 0:
median[offtol] = median[offtol-1]
acs.pklwrite(savename,median)
return median
elif not median:
acs.pklwrite(savename,diagonal)
return diagonal
def pixelEMPCA(self,randomSeed=1,nvecs=5,deltR2=0,varfunc=np.ma.var,correction=None,savename=None,gen=True,weight=True):
"""
Calculates EMPCA on residuals in pixel space.
randomSeed: seed to initialize starting EMPCA vectors
nvecs: number of eigenvectors to use
deltR2: minimum difference between R2 values at which to truncate iterations
varfunc: function to use to compute variance
correction: correction to apply to measurement uncertainties
savename: file in which to save results
gen: if True, find EMPCA results from scratch
weight: if True, use measurement uncertainties to weight residuals
"""
# If allowed, try to read result from file
if savename and not gen:
try:
self.empcaModelWeight = acs.pklread(self.name+'/'+savename)
except IOError:
gen = True
if gen:
# Apply correction measurement uncertainties
self.correctUncertainty(correction=correction)
# Apply new mask based on corrected uncertainties
self.applyMask()
self.nvecs = nvecs
self.deltR2 = deltR2
# Find pixels with enough stars to do EMPCA
self.goodPixels=([i for i in range(aspcappix) if np.sum(self.residuals[:,i].mask) < self.residuals.shape[0]-self.minStarNum],)
self.empcaResiduals = self.residuals.T[self.goodPixels].T
# Calculate weights that just mask missing elements
unmasked = (self.empcaResiduals.mask==False)
errorWeights = unmasked.astype(float)
if weight:
errorWeights[unmasked] = 1./((self.spectra_errs.T[self.goodPixels].T[unmasked])**2)
self.empcaModelWeight = empca(self.empcaResiduals.data,weights=errorWeights,
nvec=self.nvecs,deltR2=self.deltR2,
randseed=randomSeed,varfunc=varfunc)
# Calculate eigenvalues
self.empcaModelWeight.eigvals = np.zeros(len(self.empcaModelWeight.eigvec))
for e in range(len(self.empcaModelWeight.eigvec)):
self.empcaModelWeight.eigvals[e] = self.empcaModelWeight.eigval(e+1)
# Find R2 and R2noise for this model, and resize eigenvectors appropriately
self.setR2(self.empcaModelWeight)
self.setDeltaR2(self.empcaModelWeight)
self.setR2noise(self.empcaModelWeight)
self.resizePixelEigvec(self.empcaModelWeight)
# Restore original measurement uncertainties
self.uncorrectUncertainty(correction=correction)
self.applyMask()
# Save only basic statistics
self.smallModel = smallEMPCA(self.empcaModelWeight,correction=correction,savename=self.name+'/'+savename)
if savename:
acs.pklwrite(self.name+'/'+savename,self.smallModel)
def setR2(self,model):
"""
Add R2 values for each eigenvector as array to model.
model: EMPCA model
"""
vecs = len(model.eigvec)
# Create R2 array
R2Array = np.zeros(vecs+1)
for vec in range(vecs+1):
R2Array[vec] = model.R2(vec)
# Add R2 array to model
model.R2Array = R2Array
def setDeltaR2(self,model):
"""
Add Delta R2 values for each eigenvectors as array to model
model: EMPCA model
"""
self.setR2(model)
model.DeltaR2 = (np.roll(model.R2Array,-1)-model.R2Array)[:-1]
def resizePixelEigvec(self,model):
"""
Resize eigenvectors to span full pixel space, masking where necessary.
model: EMPCA model
"""
# Create array for reshape eigenvectors
neweigvec = np.ma.masked_array(np.zeros((self.nvecs,aspcappix)))
# Add eigenvectors to array with appropriate mask
for vec in range(self.nvecs):
newvec = np.ma.masked_array(np.zeros((aspcappix)),
mask=np.ones(aspcappix))
newvec[self.goodPixels] = model.eigvec[vec][:len(self.goodPixels[0])]
newvec.mask[self.goodPixels] = 0
# Normalize the vector
neweigvec[vec] = newvec/np.sqrt(np.sum(newvec**2))
# Change model eigenvectors to reshaped ones
model.eigvec = neweigvec
def setR2noise(self,model):
"""
Calculate R2 noise, the threshold at which additional vectors are only
explaining noise.
model: EMPCA model
"""
model.Vdata = model._unmasked_data_var
# Calculate data noise
model.Vnoise = np.mean(1./(model.weights[model.weights!=0]))
# Calculate R2noise
model.R2noise = 1.-(model.Vnoise/model.Vdata)
| npricejones/spectralspace | spectralspace/analysis/empca_residuals.py | Python | bsd-3-clause | 51,257 | [
"Gaussian"
] | 81be6523160517999efbc9002e9654da670dc613e2b3a7465e0654911aa9252a |
import os
import pyemu
import pandas as pd
import numpy as np
if not os.path.exists("temp"):
os.mkdir("temp")
def add_base_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 10
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst,num_reals=num_reals)
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst,num_reals=num_reals)
pet = pe.copy()
pet.transform()
pe.add_base()
assert pe.shape[0] == num_reals
pet.add_base()
assert pet.shape[0] == num_reals
assert "base" in pe.index
assert "base" in pet.index
p = pe.loc["base",:]
d = (pst.parameter_data.parval1 - pe.loc["base",:]).apply(np.abs)
pst.add_transform_columns()
d = (pst.parameter_data.parval1_trans - pet.loc["base", :]).apply(np.abs)
assert d.max() == 0.0
try:
pe.add_base()
except:
pass
else:
raise Exception("should have failed")
oe.add_base()
assert oe.shape[0] == num_reals
d = (pst.observation_data.loc[oe.columns,"obsval"] - oe.loc["base",:]).apply(np.abs)
assert d.max() == 0
try:
oe.add_base()
except:
pass
else:
raise Exception("should have failed")
def nz_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 10
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst, fill=True, num_reals=num_reals)
assert oe.shape[1] == pst.nobs
oe_nz = oe.nonzero
assert oe_nz.shape[1] == pst.nnz_obs
assert list(oe_nz.columns.values) == pst.nnz_obs_names
def par_gauss_draw_consistency_test():
pst = pyemu.Pst(os.path.join("pst","pest.pst"))
pst.parameter_data.loc[pst.par_names[3::2],"partrans"] = "fixed"
pst.parameter_data.loc[pst.par_names[0],"pargp"] = "test"
num_reals = 5000
pe1 = pyemu.ParameterEnsemble.from_gaussian_draw(pst,num_reals=num_reals)
sigma_range = 4
cov = pyemu.Cov.from_parameter_data(pst,sigma_range=sigma_range).to_2d()
pe2 = pyemu.ParameterEnsemble.from_gaussian_draw(pst,cov=cov,num_reals=num_reals)
pe3 = pyemu.ParameterEnsemble.from_gaussian_draw(pst,cov=cov,num_reals=num_reals,by_groups=False)
pst.add_transform_columns()
theo_mean = pst.parameter_data.parval1_trans
adj_par = pst.parameter_data.loc[pst.adj_par_names,:].copy()
ub,lb = adj_par.parubnd_trans,adj_par.parlbnd_trans
theo_std = ((ub - lb) / sigma_range)
for pe in [pe1,pe2,pe3]:
assert pe.shape[0] == num_reals
assert pe.shape[1] == pst.npar
pe.transform()
pe_mean = pe.loc[:,theo_mean.index].mean()
d = (pe_mean - theo_mean).apply(np.abs)
assert d.max() < 0.05,d.max()
d = (pe.loc[:,pst.adj_par_names].std() - theo_std)
assert d.max() < 0.05,d.max()
# ensemble should be transformed so now lets test the I/O
pe_org = pe.copy()
pe.to_binary("test.jcb")
pe = pyemu.ParameterEnsemble.from_binary(pst=pst, filename="test.jcb")
pe.transform()
pe._df.index = pe.index.map(np.int64)
d = (pe - pe_org).apply(np.abs)
assert d.max().max() < 1.0e-10, d.max().sort_values(ascending=False)
pe.to_csv("test.csv")
pe = pyemu.ParameterEnsemble.from_csv(pst=pst,filename="test.csv")
pe.transform()
d = (pe - pe_org).apply(np.abs)
assert d.max().max() < 1.0e-10,d.max().sort_values(ascending=False)
def obs_gauss_draw_consistency_test():
pst = pyemu.Pst(os.path.join("pst","pest.pst"))
num_reals = 1000
oe1 = pyemu.ObservationEnsemble.from_gaussian_draw(pst,num_reals=num_reals)
cov = pyemu.Cov.from_observation_data(pst).to_2d()
oe2 = pyemu.ObservationEnsemble.from_gaussian_draw(pst,cov=cov,num_reals=num_reals)
oe3 = pyemu.ObservationEnsemble.from_gaussian_draw(pst,cov=cov,num_reals=num_reals,by_groups=False)
theo_mean = pst.observation_data.obsval.copy()
#theo_mean.loc[pst.nnz_obs_names] = 0.0
theo_std = 1.0 / pst.observation_data.loc[pst.nnz_obs_names,"weight"]
for oe in [oe1,oe2,oe3]:
assert oe.shape[0] == num_reals
assert oe.shape[1] == pst.nnz_obs
d = (oe.mean() - theo_mean).apply(np.abs)
assert d.max() < 0.01,d.sort_values()
d = (oe.loc[:,pst.nnz_obs_names].std() - theo_std)
assert d.max() < 0.01,d.sort_values()
# ensemble should be transformed so now lets test the I/O
oe_org = oe.copy()
oe.to_binary("test.jcb")
oe = pyemu.ObservationEnsemble.from_binary(pst=pst, filename="test.jcb")
oe._df.index = oe.index.map(np.int64)
d = (oe - oe_org).apply(np.abs)
assert d.max().max() < 1.0e-10, d.max().sort_values(ascending=False)
oe.to_csv("test.csv")
oe = pyemu.ObservationEnsemble.from_csv(pst=pst,filename="test.csv")
d = (oe - oe_org).apply(np.abs)
assert d.max().max() < 1.0e-10,d.max().sort_values(ascending=False)
def phi_vector_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 10
oe1 = pyemu.ObservationEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pv = oe1.phi_vector
for real in oe1.index:
pst.res.loc[oe1.columns,"modelled"] = oe1.loc[real,:].values
d = np.abs(pst.phi - pv.loc[real])
assert d < 1.0e-10
def deviations_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 10
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pe_devs = pe.get_deviations()
oe_devs = oe.get_deviations()
pe.add_base()
pe_base_devs = pe.get_deviations(center_on="base")
s = pe_base_devs.loc["base",:].apply(np.abs).sum()
assert s == 0.0
pe.transform()
pe_base_devs = pe.get_deviations(center_on="base")
s = pe_base_devs.loc["base", :].apply(np.abs).sum()
assert s == 0.0
oe.add_base()
oe_base_devs = oe.get_deviations(center_on="base")
s = oe_base_devs.loc["base", :].apply(np.abs).sum()
assert s == 0.0
def as_pyemu_matrix_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 10
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pe.add_base()
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
oe.add_base()
pe_mat = pe.as_pyemu_matrix()
assert type(pe_mat) == pyemu.Matrix
assert pe_mat.shape == pe.shape
pe._df.index = pe._df.index.map(str)
d = (pe_mat.to_dataframe() - pe._df).apply(np.abs).values.sum()
assert d == 0.0
oe_mat = oe.as_pyemu_matrix(typ=pyemu.Cov)
assert type(oe_mat) == pyemu.Cov
assert oe_mat.shape == oe.shape
oe._df.index = oe._df.index.map(str)
d = (oe_mat.to_dataframe() - oe._df).apply(np.abs).values.sum()
assert d == 0.0
def dropna_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 10
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pe.iloc[::3,:] = np.NaN
ped = pe.dropna()
assert type(ped) == pyemu.ParameterEnsemble
assert ped.shape == pe._df.dropna().shape
def enforce_test():
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
# make sure sanity check is working
num_reals = 10
broke_pst = pst.get()
broke_pst.parameter_data.loc[:, "parval1"] = broke_pst.parameter_data.parubnd
pe = pyemu.ParameterEnsemble.from_gaussian_draw(broke_pst, num_reals=num_reals)
try:
pe.enforce(how="scale")
except:
pass
else:
raise Exception("should have failed")
broke_pst.parameter_data.loc[:, "parval1"] = broke_pst.parameter_data.parlbnd
pe = pyemu.ParameterEnsemble.from_gaussian_draw(broke_pst, num_reals=num_reals)
try:
pe.enforce(how="scale")
except:
pass
else:
raise Exception("should have failed")
# check that all pars at parval1 values don't change
num_reals = 1
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pe._df.loc[:, :] = pst.parameter_data.parval1.values
pe._df.loc[0, pst.par_names[0]] = pst.parameter_data.parlbnd.loc[pst.par_names[0]] * 0.5
pe.enforce(how="scale")
assert (pe.loc[0,pst.par_names[1:]] - pst.parameter_data.loc[pst.par_names[1:], "parval1"]).apply(np.abs).max() == 0
#check that all pars are in bounds
pe.reseed()
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
print(pe.head())
pe.enforce(how="scale")
for ridx in pe._df.index:
real = pe._df.loc[ridx,pst.adj_par_names]
ub_diff = pe.ubnd - real
assert ub_diff.min() >= 0.0,ub_diff
lb_diff = real - pe.lbnd
assert lb_diff.min() >= 0.0,lb_diff
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pe._df.loc[0, :] += pst.parameter_data.parubnd
pe.enforce(how="scale")
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
pe._df.loc[0,:] += pst.parameter_data.parubnd
pe.enforce()
assert (pe._df.loc[0,:] - pst.parameter_data.parubnd).apply(np.abs).sum() == 0.0
pe._df.loc[0, :] += pst.parameter_data.parubnd
pe._df.loc[1:,:] = pst.parameter_data.parval1.values
pe.enforce(how="drop")
assert pe.shape[0] == num_reals - 1
def pnulpar_test():
import os
import pyemu
ev = pyemu.ErrVar(jco=os.path.join("mc","freyberg_ord.jco"))
ev.get_null_proj(maxsing=1).to_ascii("ev_new_proj.mat")
pst = ev.pst
par_dir = os.path.join("mc","prior_par_draws")
par_files = [os.path.join(par_dir,f) for f in os.listdir(par_dir) if f.endswith('.par')]
pe = pyemu.ParameterEnsemble.from_parfiles(pst=pst,parfile_names=par_files)
real_num = [int(os.path.split(f)[-1].split('.')[0].split('_')[1]) for f in par_files]
pe._df.index = real_num
pe_proj = pe.project(ev.get_null_proj(maxsing=1), enforce_bounds='reset')
par_dir = os.path.join("mc", "proj_par_draws")
par_files = [os.path.join(par_dir, f) for f in os.listdir(par_dir) if f.endswith('.par')]
real_num = [int(os.path.split(f)[-1].split('.')[0].split('_')[1]) for f in par_files]
pe_pnul = pyemu.ParameterEnsemble.from_parfiles(pst=pst,parfile_names=par_files)
pe_pnul._df.index = real_num
pe_proj._df.sort_index(axis=1, inplace=True)
pe_proj._df.sort_index(axis=0, inplace=True)
pe_pnul._df.sort_index(axis=1, inplace=True)
pe_pnul._df.sort_index(axis=0, inplace=True)
diff = 100.0 * ((pe_proj._df - pe_pnul._df) / pe_proj._df)
assert max(diff.max()) < 1.0e-4,diff
def triangular_draw_test():
import os
import matplotlib.pyplot as plt
import pyemu
pst = pyemu.Pst(os.path.join("pst","pest.pst"))
pst.parameter_data.loc[:,"partrans"] = "none"
pe = pyemu.ParameterEnsemble.from_triangular_draw(pst,1000)
def uniform_draw_test():
import os
import numpy as np
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
pe = pyemu.ParameterEnsemble.from_uniform_draw(pst, 5000)
def fill_test():
import os
import numpy as np
pst = pyemu.Pst(os.path.join("pst", "pest.pst"))
num_reals = 100
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst)
assert oe.shape == (num_reals,pst.nnz_obs),oe.shape
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst,fill=True)
assert oe.shape == (num_reals,pst.nobs)
std = oe.std().loc[pst.zero_weight_obs_names].apply(np.abs)
assert std.max() < 1e-10
print(std)
obs = pst.observation_data
obs.loc[:,"weight"] = 0.0
oe = pyemu.ObservationEnsemble.from_gaussian_draw(pst, fill=True)
assert oe.shape == (num_reals, pst.nobs)
std = oe.std().apply(np.abs)
print(std)
assert std.max() < 1e-10
return
par = pst.parameter_data
# first test that all ensembles are filled with parval1
par.loc[:,"partrans"] = "fixed"
pe = pyemu.ParameterEnsemble.from_uniform_draw(pst, num_reals=num_reals)
assert pe.shape == (num_reals,pst.npar)
std = pe._df.std().apply(np.abs)
assert std.max() == 0.0
pe = pyemu.ParameterEnsemble.from_triangular_draw(pst, num_reals=num_reals)
assert pe.shape == (num_reals, pst.npar)
std = pe._df.std().apply(np.abs)
assert std.max() == 0.0
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals)
assert pe.shape == (num_reals,pst.npar)
std = pe._df.std().apply(np.abs)
assert std.max() == 0.0
cov = pyemu.Cov.from_parameter_data(pst)
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst,cov=cov, num_reals=num_reals)
assert pe.shape == (num_reals,pst.npar)
std = pe._df.std().apply(np.abs)
assert std.max() == 0.0
cov = cov.to_2d()
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals)
assert pe.shape == (num_reals, pst.npar)
std = pe._df.std().apply(np.abs)
assert std.max() == 0.0
# test that fill=False is working
pe = pyemu.ParameterEnsemble.from_uniform_draw(pst, num_reals=num_reals,fill=False)
assert pe.shape == (num_reals, pst.npar_adj),pe.shape
pe = pyemu.ParameterEnsemble.from_triangular_draw(pst, num_reals=num_reals,fill=False)
assert pe.shape == (num_reals, pst.npar_adj),pe.shape
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals,fill=False)
assert pe.shape == (num_reals, pst.npar_adj),pe.shape
cov = pyemu.Cov.from_parameter_data(pst)
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals,fill=False)
assert pe.shape == (num_reals, pst.npar_adj),pe.shape
cov = cov.to_2d()
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals,fill=False)
assert pe.shape == (num_reals, pst.npar_adj),pe.shape
# unfix one par
par.loc[pst.par_names[0],"partrans"] = "log"
pe = pyemu.ParameterEnsemble.from_uniform_draw(pst, num_reals=num_reals, fill=False)
print(pe.shape)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
pe = pyemu.ParameterEnsemble.from_triangular_draw(pst, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
cov = pyemu.Cov.from_parameter_data(pst)
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
cov = cov.to_2d()
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
par.loc[pst.par_names[1], "partrans"] = "tied"
par.loc[pst.par_names[1], "partied"] = pst.par_names[0]
pe = pyemu.ParameterEnsemble.from_uniform_draw(pst, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
pe = pyemu.ParameterEnsemble.from_triangular_draw(pst, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
cov = pyemu.Cov.from_parameter_data(pst)
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
cov = cov.to_2d()
pe = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, fill=False)
assert pe.shape == (num_reals, pst.npar_adj), pe.shape
def emp_cov_test():
import os
import numpy as np
import pyemu
pst = pyemu.Pst(os.path.join("en", "pest.pst"))
cov = pyemu.Cov.from_binary(os.path.join("en", "cov.jcb"))
print(pst.npar, cov.shape)
num_reals = 10000
pe_eig = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, factor="eigen")
emp_cov = pe_eig.covariance_matrix()
assert isinstance(emp_cov,pyemu.Cov)
assert emp_cov.row_names == pst.adj_par_names
cov_df = cov.to_dataframe()
emp_df = emp_cov.to_dataframe()
for p in pst.adj_par_names:
print(p,cov_df.loc[p,p],emp_df.loc[p,p])
diff = np.diag(cov.x) - np.diag(emp_cov.x)
print(diff.max())
assert diff.max() < 0.5,diff.max()
def factor_draw_test():
import os
import numpy as np
import pyemu
pst = pyemu.Pst(os.path.join("en","pest.pst"))
cov = pyemu.Cov.from_binary(os.path.join("en","cov.jcb"))
print(pst.npar,cov.shape)
num_reals = 5000
pe_eig = pyemu.ParameterEnsemble.from_gaussian_draw(pst,cov=cov,num_reals=num_reals,factor="eigen")
pe_svd = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, factor="svd")
pe_eig.transform()
pe_svd.transform()
mn_eig = pe_eig.mean()
mn_svd = pe_svd.mean()
sd_eig = pe_eig.std()
sd_svd = pe_svd.std()
pst.add_transform_columns()
par = pst.parameter_data
df = cov.to_dataframe()
for p in pst.adj_par_names:
print(p,par.loc[p,"parval1_trans"],mn_eig[p],mn_svd[p],np.sqrt(df.loc[p,p]),sd_eig[p],sd_svd[p])
d = (mn_eig - mn_svd).apply(np.abs)
assert d.max() < 0.5,d.sort_values()
d = (sd_eig - sd_svd).apply(np.abs)
assert d.max() < 0.5,d.sort_values()
num_reals = 10
pe_eig = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, factor="eigen")
emp_cov = pe_eig.covariance_matrix()
pe_eig = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=emp_cov, num_reals=num_reals, factor="eigen")
def emp_cov_draw_test():
import os
import numpy as np
import pyemu
pst = pyemu.Pst(os.path.join("en","pest.pst"))
cov = pyemu.Cov.from_binary(os.path.join("en","cov.jcb"))
num_reals = 10
pe_eig = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=cov, num_reals=num_reals, factor="eigen")
emp_cov = pe_eig.covariance_matrix()
num_reals = 1000
pe_eig = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=emp_cov, num_reals=num_reals, factor="eigen")
pe_svd = pyemu.ParameterEnsemble.from_gaussian_draw(pst, cov=emp_cov, num_reals=num_reals, factor="svd")
pe_eig.transform()
pe_svd.transform()
mn_eig = pe_eig.mean()
mn_svd = pe_svd.mean()
sd_eig = pe_eig.std()
sd_svd = pe_svd.std()
pst.add_transform_columns()
par = pst.parameter_data
df = cov.to_dataframe()
for p in pst.adj_par_names:
print(p,par.loc[p,"parval1_trans"],mn_eig[p],mn_svd[p],np.sqrt(df.loc[p,p]),sd_eig[p],sd_svd[p])
d = (mn_eig - mn_svd).apply(np.abs)
assert d.max() < 0.5,d.sort_values()
d = (sd_eig - sd_svd).apply(np.abs)
assert d.max() < 0.5,d.sort_values()
def mixed_par_draw_test():
import os
import matplotlib.pyplot as plt
import pandas as pd
import pyemu
pst = pyemu.Pst(os.path.join("pst","pest.pst"))
pname = pst.par_names[0]
pst.parameter_data.loc[pname,"partrans"] = "none"
npar = pst.npar
num_reals = 100
pe1 = pyemu.ParameterEnsemble.from_mixed_draws(pst, {}, num_reals=num_reals)
pe2 = pyemu.ParameterEnsemble.from_mixed_draws(pst, {},default="uniform",num_reals=num_reals)
pe3 = pyemu.ParameterEnsemble.from_mixed_draws(pst, {}, default="triangular", num_reals=num_reals)
# ax = plt.subplot(111)
# pe1.loc[:,pname].hist(ax=ax,alpha=0.5,bins=25)
# pe2.loc[:, pname].hist(ax=ax,alpha=0.5,bins=25)
# pe3.loc[:, pname].hist(ax=ax,alpha=0.5,bins=25)
# plt.show()
how = {}
for p in pst.adj_par_names[:10]:
how[p] = "gaussian"
for p in pst.adj_par_names[12:30]:
how[p] = "uniform"
for p in pst.adj_par_names[40:100]:
how[p] = "triangular"
#for pnames in how.keys():
# pst.parameter_data.loc[::3,"partrans"] = "fixed"
pe = pyemu.ParameterEnsemble.from_mixed_draws(pst,how)
pst.parameter_data.loc[pname, "partrans"] = "none"
how = {p:"uniform" for p in pst.par_names}
how["junk"] = "uniform"
try:
pe = pyemu.ParameterEnsemble.from_mixed_draws(pst,how)
except:
pass
else:
raise Exception("should have failed")
try:
pe = pyemu.ParameterEnsemble.from_mixed_draws(pst,{p:"junk" for p in pst.par_names})
except:
pass
else:
raise Exception("should have failed")
try:
pe = pyemu.ParameterEnsemble.from_mixed_draws(pst,{},default="junk")
except:
pass
else:
raise Exception("should have failed")
cov = pyemu.Cov.from_parameter_data(pst)
cov.drop(pst.par_names[:2],0)
how = {p:"gaussian" for p in pst.par_names}
try:
pe = pyemu.ParameterEnsemble.from_mixed_draws(pst, how,cov=cov)
except:
pass
else:
raise Exception("should have failed")
how = {p: "uniform" for p in pst.par_names}
pe = pyemu.ParameterEnsemble.from_mixed_draws(pst, how, cov=cov)
#cov.drop(pst.par_names[:2], 0)
assert pst.npar == npar
def binary_test():
from datetime import datetime
import numpy as np
import pandas as pd
import pyemu
npar = 100000
nobs = 500
par_names = ["p{0}".format(i) for i in range(npar)]
obs_names = ["o{0}".format(i) for i in range(nobs)]
arr = np.random.random((nobs,npar))
pst = pyemu.Pst.from_par_obs_names(par_names,obs_names)
df = pd.DataFrame(data=arr,columns=par_names,index=obs_names)
pe = pyemu.ParameterEnsemble(pst=pst,df=df)
s1 = datetime.now()
pe.to_dense("par.bin")
pe1 = pyemu.ParameterEnsemble.from_binary(pst=pst,filename="par.bin")
e1 = datetime.now()
d = (pe - pe1).apply(np.abs)
print(d.max().max())
assert d.max().max() < 1.0e-10
s2 = datetime.now()
pe.to_binary("par.bin")
pe1 = pyemu.ParameterEnsemble.from_binary(pst=pst,filename="par.bin")
e2 = datetime.now()
print((e1 - s1).total_seconds())
print((e2 - s2).total_seconds())
if __name__ == "__main__":
#par_gauss_draw_consistency_test()
#obs_gauss_draw_consistency_test()
phi_vector_test()
#add_base_test()
#nz_test()
#deviations_test()
# as_pyemu_matrix_test()
# dropna_test()
#enforce_test()
#pnulpar_test()
# triangular_draw_test()
# uniform_draw_test()
# fill_test()
#factor_draw_test()
#emp_cov_test()
#emp_cov_draw_test()
#mixed_par_draw_test()
#binary_test()
| jtwhite79/pyemu | autotest/en_tests.py | Python | bsd-3-clause | 22,500 | [
"Gaussian"
] | 5e5d99b6415154d8baa259569d656f201b24080982a16917d424d82b65a14793 |
"""
Codes for Canstrat ASCII files; only used by canstrat.py.
:copyright: 2016 Agile Geoscience
:license: Apache 2.0
"""
# Codes for rtc.
rtc_txt = """X ROC-C_X ROCKTP_IGNEB ROCK TYPE 1 Igneous Basic
N ROC-C_N ROCKTP_IGNEA ROCK TYPE 2 Igneous Acidic
Z ROC-C_Z ROCKTP_META ROCK TYPE 3 Metamorphic
V ROC-C_V ROCKTP_VOLC ROCK TYPE 4 Volcanic
R ROC-C_R ROCKTP_SID ROCK TYPE 8 Siderite
U ROC-C_U ROCKTP_TILL ROCK TYPE 9 Glacial Till
E ROC-C_E ROCKTP_CONGL ROCK TYPE 12 Conglomerate
F ROC-C_F ROCKTP_BRECC ROCK TYPE 13 Breccia
H ROC-C_H ROCKTP_CHERT ROCK TYPE 16 Chert
J ROC-C_J ROCKTP_SAND ROCK TYPE 17 Sandstone
I ROC-C_I ROCKTP_SILT ROCK TYPE 18 Siltstone
C ROC-C_C ROCKTP_CLAY ROCK TYPE 19 Clay
S ROC-C_S ROCKTP_SHALE ROCK TYPE 20 Shale
B ROC-C_B ROCKTP_BENT ROCK TYPE 22 Bentonite
Q ROC-C_Q ROCKTP_COAL ROCK TYPE 24 Coal
M ROC-C_M ROCKTP_MARL ROCK TYPE 26 Marlstone
L ROC-C_L ROCKTP_LST ROCK TYPE 27 Limestone
D ROC-C_D ROCKTP_DOL ROCK TYPE 30 Dolomite
A ROC-C_A ROCKTP_ANHY ROCK TYPE 35 Anhydrite
T ROC-C_T ROCKTP_SALT ROCK TYPE 37 Salt
G ROC-C_G ROCKTP_GYP ROCK TYPE 39 Gypsum
P ROC-C_P ROCKTP_PHOS ROCK TYPE 40 Phosphate"""
rtc = {w[0]: ' '.join(w[6:]) for w in [r.split() for r in rtc_txt.split('\n')]}
# Code for grain size.
grains_txt = """X GRA-C_X GRAINS_CRYPTOX GRAIN, CRYSTAL OR 0.001 Cryptocrystalline (carbonate, chert, coal)
L GRA-C_L GRAINS_LITHO GRAIN, CRYSTAL OR 0.001 Lithographic (carbonate, chert, coal)
M GRA-C_M GRAINS_SHALE GRAIN, CRYSTAL OR 0.001 Shale, Clay, Marlstone, Bentonite
V GRA-C_V GRAINS_VOLC GRAIN, CRYSTAL OR 0.001 Volcanics (equivalent to size M)
1 GRA-C_1 GRAINS_SILT2 GRAIN, CRYSTAL OR 0.0176 1/2 Silt Size
2 GRA-C_2 GRAINS_SILT GRAIN, CRYSTAL OR 0.0473 Silt Size
3 GRA-C_3 GRAINS_VFGR2 GRAIN, CRYSTAL OR 0.0781 1/2 Very fine grained
4 GRA-C_4 GRAINS_VFGR GRAIN, CRYSTAL OR 0.1094 Very fine grained
5 GRA-C_5 GRAINS_FGR2 GRAIN, CRYSTAL OR 0.156 1/2 Fine grained
6 GRA-C_6 GRAINS_FGR GRAIN, CRYSTAL OR 0.2185 Fine grained
7 GRA-C_7 GRAINS_MGR2 GRAIN, CRYSTAL OR 0.313 1/2 Medium grained
8 GRA-C_8 GRAINS_MGR GRAIN, CRYSTAL OR 0.438 Medium grained
9 GRA-C_9 GRAINS_CGR2 GRAIN, CRYSTAL OR 0.625 1/2 Coarse grained
0 GRA-C_0 GRAINS_CGR GRAIN, CRYSTAL OR 0.875 Coarse grained
C GRA-C_C GRAINS_VCGR GRAIN, CRYSTAL OR 1.500 Greater than 1.000 mm"""
grains = {w[0]: float(w[6]) for w in [r.split() for r in grains_txt.split('\n')]}
grains['L'] += 0.0001
grains['M'] += 0.0002
grains['V'] += 0.0003
grains['X'] += 0.0004
# Code for fwork.
fwork_txt = """** FRA-N_XX FRAMEW_-1 FRAMEWORK -1 uninterpretable
00 FRA-N_00 FRAMEW_0 FRAMEWORK 0 0%
01 FRA-N_01 FRAMEW_10 FRAMEWORK 10 10%
02 FRA-N_02 FRAMEW_20 FRAMEWORK 20 20%
03 FRA-N_03 FRAMEW_30 FRAMEWORK 30 30%
04 FRA-N_04 FRAMEW_40 FRAMEWORK 40 40%
05 FRA-N_05 FRAMEW_50 FRAMEWORK 50 50%
06 FRA-N_06 FRAMEW_60 FRAMEWORK 60 60%
07 FRA-N_07 FRAMEW_70 FRAMEWORK 70 70%
08 FRA-N_08 FRAMEW_80 FRAMEWORK 80 80%
09 FRA-N_09 FRAMEW_90 FRAMEWORK 90 90%
10 FRA-N_10 FRAMEW_100 FRAMEWORK 100 100%"""
fwork = {w[0]: int(w[4]) for w in [r.split() for r in fwork_txt.split('\n')]}
# Code for colour.
colour_txt = """W COL-C_W COLOUR_WHITE COLOUR 1 White
C COL-C_C COLOUR_CREAM COLOUR 2 Cream
F COL-C_F COLOUR_BUFF COLOUR 3 Buff-Tan
Y COL-C_Y COLOUR_YELLOW COLOUR 4 Yellow
S COL-C_S COLOUR_S&P COLOUR 5 Salt and Pepper
V COL-C_V COLOUR_VARIC COLOUR 6 Varicoloured
O COL-C_O COLOUR_ORANGE COLOUR 7 Orange
R COL-C_R COLOUR_RED COLOUR 8 Red
P COL-C_P COLOUR_PURPLE COLOUR 9 Purple
U COL-C_U COLOUR_BLUE COLOUR 10 Blue
N COL-C_N COLOUR_GREEN COLOUR 11 Green
B COL-C_B COLOUR_BROWN COLOUR 12 Brown
G COL-C_G COLOUR_GRAY COLOUR 13 Gray
K COL-C_K COLOUR_BLACK COLOUR 14 Black"""
colour = {w[0]: ' '.join(w[5:]) for w in [r.split() for r in colour_txt.split('\n')]}
colour[' '] = ''
# Code for colour modifier.
cmod_txt = """V COI-C_V COLOURINT_VLIGHT COLOUR INTENSITY 1 Very Light
L COI-C_L COLOURINT_LIGHT COLOUR INTENSITY 3 Light
M COI-C_M COLOURINT_MEDIUM COLOUR INTENSITY 5 Medium
D COI-C_D COLOURINT_DARK COLOUR INTENSITY 7 Dark
K COI-C_K COLOURINT_VDARK COLOUR INTENSITY 9 Very Dark"""
cmod = {w[0]: ' '.join(w[6:]) for w in [r.split() for r in cmod_txt.split('\n')]}
cmod[' '] = ''
# Code for porgrade.
porg_txt = """1 POR-N_1 PORGRADE_3 POROSITY GRADE 3 3%
2 POR-N_2 PORGRADE_6 POROSITY GRADE 6 6%
3 POR-N_3 PORGRADE_9 POROSITY GRADE 9 9%
4 POR-N_4 PORGRADE_12 POROSITY GRADE 12 12%
5 POR-N_5 PORGRADE_15 POROSITY GRADE 15 15%
6 POR-N_6 PORGRADE_20 POROSITY GRADE 20 20%
7 POR-N_7 PORGRADE_26 POROSITY GRADE 26 26%
8 POR-N_8 PORGRADE_33 POROSITY GRADE 33 33%
9 POR-N_9 PORGRADE_39 POROSITY GRADE 39 >33%"""
porgrade = {w[0]: float(w[5])/100 for w in [r.split() for r in porg_txt.split('\n')]}
# Codes for oil stains.
stain_txt = """Q OIL-C_Q OILSTN_QUEST OIL STAIN 1 Questionable stain
D OIL-C_D OILSTN_DEAD OIL STAIN 2 Dead Stain
M OIL-C_M OILSTN_MEDSPOT OIL STAIN 3 Medium-Spotted Stain
G OIL-C_G OILSTN_GOOD OIL STAIN 4 Good Stain"""
stain = {w[0]: w[2][7:].title() for w in [r.split() for r in stain_txt.split('\n')]}
stain[' '] = 'None'
oil = {' ': 0, 'Q': 1, 'D': 2, 'M': 3, 'G': 4}
| agile-geoscience/striplog | striplog/canstrat_codes.py | Python | apache-2.0 | 5,071 | [
"CRYSTAL"
] | 7f1bc1cb742d943db550032a2359de27bbd93fbeff133236d6ee77ec0a5185d5 |
#
# @BEGIN LICENSE
#
# Psi4: an open-source quantum chemistry software package
#
# Copyright (c) 2007-2021 The Psi4 Developers.
#
# The copyrights for code used from other parties are included in
# the corresponding files.
#
# This file is part of Psi4.
#
# Psi4 is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, version 3.
#
# Psi4 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with Psi4; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
# @END LICENSE
#
"""
Runs a JSON input psi file.
"""
import atexit
import copy
import datetime
import json
import os
import sys
import traceback
import uuid
import warnings
import pprint
pp = pprint.PrettyPrinter(width=120, compact=True, indent=1)
import numpy as np
import qcelemental as qcel
import qcengine as qcng
from collections import defaultdict
from psi4 import core
from psi4.extras import exit_printing
from psi4.header import print_header
from psi4.metadata import __version__
from psi4.driver import driver, p4util
__all__ = ["run_qcschema", "run_json"]
## Methods and properties blocks
methods_dict_ = {
'energy': driver.energy,
'gradient': driver.gradient,
'properties': driver.properties,
'hessian': driver.hessian,
'frequency': driver.frequency,
}
can_do_properties_ = {
"dipole", "quadrupole", "mulliken_charges", "lowdin_charges", "wiberg_lowdin_indices", "mayer_indices"
}
## QCSchema translation blocks
_qcschema_translation = {
# Generics
"generics": {
"return_energy": {"variables": "CURRENT ENERGY"},
# "nuclear_repulsion_energy": {"variables": "NUCLEAR REPULSION ENERGY"}, # use mol instead
},
# Properties
"properties": {
"mulliken_charges": {"variables": "MULLIKEN_CHARGES", "skip_null": True},
"lowdin_charges": {"variables": "LOWDIN_CHARGES", "skip_null": True},
"wiberg_lowdin_indices": {"variables": "WIBERG_LOWDIN_INDICES", "skip_null": True},
"mayer_indices": {"variables": "MAYER_INDICES", "skip_null": True},
},
# SCF variables
"scf": {
"scf_one_electron_energy": {"variables": "ONE-ELECTRON ENERGY"},
"scf_two_electron_energy": {"variables": "TWO-ELECTRON ENERGY"},
"scf_dipole_moment": {"variables": "SCF DIPOLE"},
"scf_iterations": {"variables": "SCF ITERATIONS", "cast": int},
"scf_total_energy": {"variables": "SCF TOTAL ENERGY"},
"scf_vv10_energy": {"variables": "DFT VV10 ENERGY", "skip_zero": True},
"scf_xc_energy": {"variables": "DFT XC ENERGY", "skip_zero": True},
"scf_dispersion_correction_energy": {"variables": "DISPERSION CORRECTION ENERGY", "skip_zero": True},
# SCF Properties (experimental)
# "scf_quadrupole_moment": {"variables": ["SCF QUADRUPOLE " + x for x in ["XX", "XY", "XZ", "YY", "YZ", "ZZ"]], "skip_null": True},
# "scf_mulliken_charges": {"variables": "MULLIKEN_CHARGES", "skip_null": True},
# "scf_lowdin_charges": {"variables": "LOWDIN_CHARGES", "skip_null": True},
# "scf_wiberg_lowdin_indices": {"variables": "WIBERG_LOWDIN_INDICES", "skip_null": True},
# "scf_mayer_indices": {"variables": "MAYER_INDICES", "skip_null": True},
},
# MP2 variables
"mp2": {
"mp2_same_spin_correlation_energy": {"variables": "MP2 SAME-SPIN CORRELATION ENERGY"},
"mp2_opposite_spin_correlation_energy": {"variables": "MP2 OPPOSITE-SPIN CORRELATION ENERGY"},
"mp2_singles_energy": {"variables": "MP2 SINGLES ENERGY"},
"mp2_doubles_energy": {"variables": "MP2 DOUBLES ENERGY"},
"mp2_correlation_energy": {"variables": "MP2 CORRELATION ENERGY"},
"mp2_total_energy": {"variables": "MP2 TOTAL ENERGY"},
"mp2_dipole_moment": {"variables": "NYI"}
},
"ccsd": {
"ccsd_same_spin_correlation_energy": {"variables": "CCSD SAME-SPIN CORRELATION ENERGY"},
"ccsd_opposite_spin_correlation_energy": {"variables": "CCSD OPPOSITE-SPIN CORRELATION ENERGY"},
"ccsd_singles_energy": {"variables": "CCSD SINGLES ENERGY"},
"ccsd_doubles_energy": {"variables": "CCSD DOUBLES ENERGY"},
"ccsd_correlation_energy": {"variables": "CCSD CORRELATION ENERGY"},
"ccsd_total_energy": {"variables": "CCSD TOTAL ENERGY"},
"ccsd_dipole_moment": {"variables": "NYI"},
"ccsd_iterations": {"variables": "CCSD ITERATIONS", "cast": int},
},
"ccsd(t)": {
"ccsd_prt_pr_correlation_energy": {"variables": "CCSD(T) CORRELATION ENERGY"},
"ccsd_prt_pr_total_energy": {"variables": "CCSD(T) TOTAL ENERGY"},
"ccsd_prt_pr_dipole_moment": {"variables": "NYI"},
}
# "ccsd_singles_energy": {"variables": "NYI", "default": 0.0},
# "": {"variables": },
} # yapf: disable
def _serial_translation(value, json=False):
"""
Translates from Psi4 to JSON data types
"""
if isinstance(value, (core.Dimension)):
return value.to_tuple()
if json:
if isinstance(value, (core.Matrix, core.Vector)):
return value.np.ravel().tolist()
elif isinstance(value, np.ndarray):
return value.ravel().tolist()
else:
if isinstance(value, (core.Matrix, core.Vector)):
return value.np
return value
def _convert_variables(data, context=None, json=False):
"""
Converts dictionaries of variables based on translation metadata
"""
# Build the correct translation units
if context is None:
needed_vars = {}
for v in _qcschema_translation.values():
needed_vars.update(v)
else:
needed_vars = _qcschema_translation[context]
ret = {}
for key, var in needed_vars.items():
conversion_factor = var.get("conversion_factor", None)
# Get the actual variables
if isinstance(var["variables"], str):
value = data.get(var["variables"], None)
if value is not None and conversion_factor:
if isinstance(value, (int, float, np.ndarray)):
value *= conversion_factor
elif isinstance(value, (core.Matrix, core.Vector)):
value.scale(conversion_factor)
elif isinstance(var["variables"], (list, tuple)):
value = [data.get(x, None) for x in var["variables"]]
if not any(value):
value = None
elif conversion_factor:
value = [x * conversion_factor for x in value]
else:
raise TypeError("variables type not understood.")
# Handle skips
if var.get("skip_zero", False) and (value == 0):
continue
if (var.get("skip_zero") or var.get("skip_null", False)) and (value is None):
continue
# Add defaults
if (value is None) and ("default" in var):
value = var["default"]
# Cast if called
if (value is not None) and ("cast" in var):
value = var["cast"](value)
ret[key] = _serial_translation(value, json=json)
return ret
def _convert_basis(basis):
"""Converts a Psi4 basis object to a QCElemental basis.
"""
centers = []
symbols = []
# Loop over centers
for c in range(basis.molecule().natom()):
center_shells = []
symbols.append(basis.molecule().symbol(c).title())
# Loop over shells *on* a center
for s in range(basis.nshell_on_center(c)):
shell = basis.shell(basis.shell_on_center(c, s))
if shell.is_pure():
htype = "spherical"
else:
htype = "cartesian"
# Build the shell
coefs = [[shell.coef(x) for x in range(shell.nprimitive)]]
exps = [shell.exp(x) for x in range(shell.nprimitive)]
qshell = qcel.models.basis.ElectronShell(angular_momentum=[shell.am],
harmonic_type=htype,
exponents=exps,
coefficients=coefs)
center_shells.append(qshell)
centers.append(qcel.models.basis.BasisCenter(electron_shells=center_shells))
# Take unique to prevent duplicate data, doesn't matter too much
hashes = [hash(json.dumps(centers[x].dict(), sort_keys=True)) for x in range(len(centers))]
uniques = {k: v for k, v in zip(hashes, centers)}
name_map = {}
counter = defaultdict(int)
# Generate reasonable names
for symbol, h in zip(symbols, hashes):
if h in name_map:
continue
counter[symbol] += 1
name_map[h] = f"{basis.name()}_{symbol}{counter[symbol]}"
center_data = {name_map[k]: v for k, v in uniques.items()}
atom_map = [name_map[x] for x in hashes]
ret = qcel.models.BasisSet(name=basis.name(), center_data=center_data, atom_map=atom_map)
return ret
def _convert_wavefunction(wfn, context=None):
basis = _convert_basis(wfn.basisset())
# We expect CCA ordering.
# Psi4 Cartesian is CCA (nothing to do)
# Psi4 Spherical is in "Gaussian" reorder
# Spherical Map: 0 -1 +1, ... -> -1, ..., 0, ..., +1
spherical_maps = {}
for L in range(wfn.basisset().max_am() + 1):
mapper = list(range(L * 2 - 1, 0, -2)) + [0] + list(range(2, L * 2 + 1, 2))
spherical_maps[L] = np.array(mapper)
# Build a flat index that we can transform the AO quantities
reorder = True
ao_map = []
cnt = 0
for atom in basis.atom_map:
center = basis.center_data[atom]
for shell in center.electron_shells:
if shell.harmonic_type == "cartesian":
ao_map.append(np.arange(cnt, cnt + shell.nfunctions()))
else:
smap = spherical_maps[shell.angular_momentum[0]]
ao_map.append(smap + cnt)
reorder = True
cnt += shell.nfunctions()
ao_map = np.hstack(ao_map)
# Build remap functions
def re2d(mat, both=True):
arr = np.array(mat)
if reorder:
if both:
arr = arr[ao_map[:, None], ao_map]
else:
arr = arr[ao_map[:, None]]
return arr
def re1d(mat):
arr = np.array(mat)
if reorder:
arr = arr[ao_map]
return arr
# get occupations in orbital-energy ordering
def sort_occs(noccpi, epsilon):
occs = []
for irrep, nocc in enumerate(noccpi):
for i, e in enumerate(epsilon[irrep]):
occs.append((e, int(i < nocc)))
occs.sort(key = lambda x : x[0])
return np.array([occ[1] for occ in occs])
# Map back out what we can
ret = {
"basis": basis,
"restricted": (wfn.same_a_b_orbs() and wfn.same_a_b_dens()),
# Return results
"orbitals_a": "scf_orbitals_a",
"orbitals_b": "scf_orbitals_b",
"density_a": "scf_density_a",
"density_b": "scf_density_b",
"fock_a": "scf_fock_a",
"fock_b": "scf_fock_b",
"eigenvalues_a": "scf_eigenvalues_a",
"eigenvalues_b": "scf_eigenvalues_b",
"occupations_a": "scf_occupations_a",
"occupations_b": "scf_occupations_b",
# "h_effective_a": re2d(wfn.H()),
# "h_effective_b": re2d(wfn.H()),
# SCF quantities
"scf_orbitals_a": re2d(wfn.Ca_subset("AO", "ALL"), both=False),
"scf_orbitals_b": re2d(wfn.Cb_subset("AO", "ALL"), both=False),
"scf_density_a": re2d(wfn.Da_subset("AO")),
"scf_density_b": re2d(wfn.Db_subset("AO")),
"scf_fock_a": re2d(wfn.Fa_subset("AO")),
"scf_fock_b": re2d(wfn.Fa_subset("AO")),
"scf_eigenvalues_a": re1d(wfn.epsilon_a_subset("AO", "ALL")),
"scf_eigenvalues_b": re1d(wfn.epsilon_b_subset("AO", "ALL")),
"scf_occupations_a": re1d(sort_occs((wfn.doccpi() + wfn.soccpi()).to_tuple(), wfn.epsilon_a().nph)),
"scf_occupations_b": re1d(sort_occs(wfn.doccpi().to_tuple(), wfn.epsilon_b().nph)),
}
return ret
## Execution functions
def _clean_psi_environ(do_clean):
if do_clean:
core.clean_variables()
core.clean_options()
core.clean()
def _read_output(outfile):
try:
with open(outfile, 'r') as f:
output = f.read()
return output
except OSError:
return "Could not read output file."
def _quiet_remove(filename):
"""
Destroy the created at file at exit, pass if error.
"""
try:
os.unlink(filename)
except OSError:
pass
def run_qcschema(input_data, clean=True):
outfile = os.path.join(core.IOManager.shared_object().get_default_path(), str(uuid.uuid4()) + ".qcschema_tmpout")
core.set_output_file(outfile, False)
print_header()
start_time = datetime.datetime.now()
try:
input_model = qcng.util.model_wrapper(input_data, qcel.models.AtomicInput)
# Echo the infile on the outfile
core.print_out("\n ==> Input QCSchema <==\n")
core.print_out("\n--------------------------------------------------------------------------\n")
core.print_out(pp.pformat(json.loads(input_model.json())))
core.print_out("\n--------------------------------------------------------------------------\n")
keep_wfn = input_model.protocols.wavefunction != 'none'
# qcschema should be copied
ret_data = run_json_qcschema(input_model.dict(), clean, False, keep_wfn=keep_wfn)
ret_data["provenance"] = {
"creator": "Psi4",
"version": __version__,
"routine": "psi4.schema_runner.run_qcschema"
}
exit_printing(start_time=start_time, success=True)
ret = qcel.models.AtomicResult(**ret_data, stdout=_read_output(outfile))
except Exception as exc:
if not isinstance(input_data, dict):
input_data = input_data.dict()
input_data = input_data.copy()
input_data["stdout"] = _read_output(outfile)
ret = qcel.models.FailedOperation(input_data=input_data,
success=False,
error={
'error_type': type(exc).__name__,
'error_message': ''.join(traceback.format_exception(*sys.exc_info())),
})
atexit.register(_quiet_remove, outfile)
return ret
def run_json(json_data, clean=True):
warnings.warn(
"Using `psi4.schema_wrapper.run_schema` instead of `psi4.json_wrapper.run_qcschema` is deprecated, and in 1.5 it will stop working\n",
category=FutureWarning)
# Set scratch
if "scratch_location" in json_data:
psi4_io = core.IOManager.shared_object()
psi4_io.set_default_path(json_data["scratch_location"])
# Direct output
outfile = os.path.join(core.IOManager.shared_object().get_default_path(), str(uuid.uuid4()) + ".json_out")
core.set_output_file(outfile, False)
# Set memory
if "memory" in json_data:
p4util.set_memory(json_data["memory"])
# Do we return the output?
return_output = json_data.pop("return_output", False)
if return_output:
json_data["raw_output"] = "Not yet run."
# Set a few flags
json_data["raw_output"] = None
json_data["success"] = False
json_data["provenance"] = {"creator": "Psi4", "version": __version__, "routine": "psi4.json_wrapper.run_json"}
# Attempt to run the computer
try:
# qcschema should be copied
json_data = run_json_qcschema(copy.deepcopy(json_data), clean, True)
except Exception as exc:
json_data["error"] = {
'error_type': type(exc).__name__,
'error_message': ''.join(traceback.format_exception(*sys.exc_info())),
}
json_data["success"] = False
json_data["raw_output"] = _read_output(outfile)
if return_output:
json_data["raw_output"] = _read_output(outfile)
atexit.register(_quiet_remove, outfile)
return json_data
def run_json_qcschema(json_data, clean, json_serialization, keep_wfn=False):
"""
An implementation of the QC JSON Schema (molssi-qc-schema.readthedocs.io/en/latest/index.html#) implementation in Psi4.
Parameters
----------
json_data : JSON
Please see molssi-qc-schema.readthedocs.io/en/latest/spec_components.html for further details.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/MolSSI/QC_JSON_Schema.
Examples
--------
"""
# Clean a few things
_clean_psi_environ(clean)
# This is currently a forced override
if json_data["schema_name"] in ["qc_schema_input", "qcschema_input"]:
json_data["schema_name"] = "qcschema_input"
else:
raise KeyError("Schema name of '{}' not understood".format(json_data["schema_name"]))
if json_data["schema_version"] != 1:
raise KeyError("Schema version of '{}' not understood".format(json_data["schema_version"]))
if json_data.get("nthreads", False) is not False:
core.set_num_threads(json_data["nthreads"], quiet=True)
# Build molecule
if "schema_name" in json_data["molecule"]:
molschemus = json_data["molecule"] # dtype >=2
else:
molschemus = json_data # dtype =1
mol = core.Molecule.from_schema(molschemus)
# Update molecule geometry as we orient and fix_com
json_data["molecule"]["geometry"] = mol.geometry().np.ravel().tolist()
# Set options
kwargs = json_data["keywords"].pop("function_kwargs", {})
p4util.set_options(json_data["keywords"])
# Setup the computation
method = json_data["model"]["method"]
core.set_global_option("BASIS", json_data["model"]["basis"])
kwargs.update({"return_wfn": True, "molecule": mol})
# Handle special properties case
if json_data["driver"] == "properties":
if "properties" in json_data["model"]:
kwargs["properties"] = [x.lower() for x in json_data["model"]["properties"]]
extra = set(kwargs["properties"]) - can_do_properties_
if len(extra):
raise KeyError("Did not understand property key %s." % kwargs["properties"])
else:
kwargs["properties"] = list(can_do_properties_)
# Actual driver run
val, wfn = methods_dict_[json_data["driver"]](method, **kwargs)
# Pull out a standard set of SCF properties
if "extras" not in json_data:
json_data["extras"] = {}
json_data["extras"]["qcvars"] = {}
if json_data["extras"].get("wfn_qcvars_only", False):
psi_props = wfn.variables()
else:
psi_props = core.variables()
for k, v in psi_props.items():
if k not in json_data["extras"]["qcvars"]:
json_data["extras"]["qcvars"][k] = _serial_translation(v, json=json_serialization)
# Still a bit of a mess at the moment add in local vars as well.
for k, v in wfn.variables().items():
if k not in json_data["extras"]["qcvars"]:
# interpreting wfn_qcvars_only as no deprecated qcvars either
if not (json_data["extras"].get("wfn_qcvars_only", False) and (
any([k.upper().endswith(" DIPOLE " + cart) for cart in ["X", "Y", "Z"]])
or any([k.upper().endswith(" QUADRUPOLE " + cart) for cart in ["XX", "YY", "ZZ", "XY", "XZ", "YZ"]])
or k.upper()
in [
"SOS-MP2 CORRELATION ENERGY",
"SOS-MP2 TOTAL ENERGY",
"SOS-PI-MP2 CORRELATION ENERGY",
"SOS-PI-MP2 TOTAL ENERGY",
"SCS-MP3 CORRELATION ENERGY",
"SCS-MP3 TOTAL ENERGY",
]
)):
json_data["extras"]["qcvars"][k] = _serial_translation(v, json=json_serialization)
# Handle the return result
if json_data["driver"] == "energy":
json_data["return_result"] = val
elif json_data["driver"] in ["gradient", "hessian"]:
json_data["return_result"] = _serial_translation(val, json=json_serialization)
elif json_data["driver"] == "properties":
ret = {}
mtd = json_data["model"]["method"].upper()
# Dipole/quadrupole still special case
if "dipole" in kwargs["properties"]:
ret["dipole"] = _serial_translation(psi_props[mtd + " DIPOLE"], json=json_serialization)
if "quadrupole" in kwargs["properties"]:
ret["quadrupole"] = _serial_translation(psi_props[mtd + " QUADRUPOLE"], json=json_serialization)
ret.update(_convert_variables(wfn.variables(), context="properties", json=json_serialization))
json_data["return_result"] = ret
else:
raise KeyError("Did not understand Driver key %s." % json_data["driver"])
props = {
"calcinfo_nbasis": wfn.nso(),
"calcinfo_nmo": wfn.nmo(),
"calcinfo_nalpha": wfn.nalpha(),
"calcinfo_nbeta": wfn.nbeta(),
"calcinfo_natom": mol.geometry().shape[0],
"nuclear_repulsion_energy": mol.nuclear_repulsion_energy(), # use this b/c psivar is monomer for SAPT
}
props.update(_convert_variables(psi_props, context="generics", json=json_serialization))
if not list(set(['CBS NUMBER', 'NBODY NUMBER', 'FINDIF NUMBER']) & set(json_data["extras"]["qcvars"].keys())):
props.update(_convert_variables(psi_props, context="scf", json=json_serialization))
# Write out post-SCF keywords
if "MP2 CORRELATION ENERGY" in psi_props:
props.update(_convert_variables(psi_props, context="mp2", json=json_serialization))
if "CCSD CORRELATION ENERGY" in psi_props:
props.update(_convert_variables(psi_props, context="ccsd", json=json_serialization))
if "CCSD(T) CORRELATION ENERGY" in psi_props:
props.update(_convert_variables(psi_props, context="ccsd(t)", json=json_serialization))
json_data["properties"] = props
json_data["success"] = True
if keep_wfn:
json_data["wavefunction"] = _convert_wavefunction(wfn)
# Reset state
_clean_psi_environ(clean)
json_data["schema_name"] = "qcschema_output"
return json_data
| ashutoshvt/psi4 | psi4/driver/schema_wrapper.py | Python | lgpl-3.0 | 22,829 | [
"Gaussian",
"Psi4"
] | aeaa2f659cf39b5b1b88a875862165d41b82928c8d4cdcab9a7ee481e38f5d49 |
"""
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Joly Arnaud <arnaud.v.joly@gmail.com>
# Fares Hedayati <fares.hedayati@gmail.com>
# Nelson Liu <nelson@nelsonliu.me>
#
# License: BSD 3 clause
from __future__ import division
import numbers
from abc import ABCMeta
from abc import abstractmethod
from math import ceil
import numpy as np
from scipy.sparse import issparse
from ..base import BaseEstimator
from ..base import ClassifierMixin
from ..base import RegressorMixin
from ..externals import six
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_array
from ..utils import check_random_state
from ..utils import compute_sample_weight
from ..utils.multiclass import check_classification_targets
from ..exceptions import NotFittedError
from ._criterion import Criterion
from ._splitter import Splitter
from ._tree import DepthFirstTreeBuilder
from ._tree import BestFirstTreeBuilder
from ._tree import Tree
from . import _tree, _splitter, _criterion
__all__ = ["DecisionTreeClassifier",
"DecisionTreeRegressor",
"ExtraTreeClassifier",
"ExtraTreeRegressor"]
# =============================================================================
# Types and constants
# =============================================================================
DTYPE = _tree.DTYPE
DOUBLE = _tree.DOUBLE
CRITERIA_CLF = {"gini": _criterion.Gini, "entropy": _criterion.Entropy}
CRITERIA_REG = {"mse": _criterion.MSE, "friedman_mse": _criterion.FriedmanMSE,
"mae": _criterion.MAE}
DENSE_SPLITTERS = {"best": _splitter.BestSplitter,
"random": _splitter.RandomSplitter}
SPARSE_SPLITTERS = {"best": _splitter.BestSparseSplitter,
"random": _splitter.RandomSparseSplitter}
# =============================================================================
# Base decision tree
# =============================================================================
class BaseDecisionTree(six.with_metaclass(ABCMeta, BaseEstimator,
_LearntSelectorMixin)):
"""Base class for decision trees.
Warning: This class should not be used directly.
Use derived classes instead.
"""
@abstractmethod
def __init__(self,
criterion,
splitter,
max_depth,
min_samples_split,
min_samples_leaf,
min_weight_fraction_leaf,
max_features,
max_leaf_nodes,
random_state,
min_impurity_split,
class_weight=None,
presort=False):
self.criterion = criterion
self.splitter = splitter
self.max_depth = max_depth
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_fraction_leaf = min_weight_fraction_leaf
self.max_features = max_features
self.random_state = random_state
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_split = min_impurity_split
self.class_weight = class_weight
self.presort = presort
self.n_features_ = None
self.n_outputs_ = None
self.classes_ = None
self.n_classes_ = None
self.tree_ = None
self.max_features_ = None
def fit(self, X, y, sample_weight=None, check_input=True,
X_idx_sorted=None):
"""Build a decision tree from the training set (X, y).
Parameters
----------
X : array-like or sparse matrix, shape = [n_samples, n_features]
The training input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csc_matrix``.
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The target values (class labels in classification, real numbers in
regression). In the regression case, use ``dtype=np.float64`` and
``order='C'`` for maximum efficiency.
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted. Splits
that would create child nodes with net zero or negative weight are
ignored while searching for a split in each node. In the case of
classification, splits are also ignored if they would result in any
single class carrying a negative weight in either child node.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
X_idx_sorted : array-like, shape = [n_samples, n_features], optional
The indexes of the sorted training input samples. If many tree
are grown on the same dataset, this allows the ordering to be
cached between trees. If None, the data will be sorted here.
Don't use this parameter unless you know what to do.
Returns
-------
self : object
Returns self.
"""
random_state = check_random_state(self.random_state)
if check_input:
X = check_array(X, dtype=DTYPE, accept_sparse="csc")
y = check_array(y, ensure_2d=False, dtype=None)
if issparse(X):
X.sort_indices()
if X.indices.dtype != np.intc or X.indptr.dtype != np.intc:
raise ValueError("No support for np.int64 index based "
"sparse matrices")
# Determine output settings
n_samples, self.n_features_ = X.shape
is_classification = isinstance(self, ClassifierMixin)
y = np.atleast_1d(y)
expanded_class_weight = None
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
if is_classification:
check_classification_targets(y)
y = np.copy(y)
self.classes_ = []
self.n_classes_ = []
if self.class_weight is not None:
y_original = np.copy(y)
y_encoded = np.zeros(y.shape, dtype=np.int)
for k in range(self.n_outputs_):
classes_k, y_encoded[:, k] = np.unique(y[:, k],
return_inverse=True)
self.classes_.append(classes_k)
self.n_classes_.append(classes_k.shape[0])
y = y_encoded
if self.class_weight is not None:
expanded_class_weight = compute_sample_weight(
self.class_weight, y_original)
else:
self.classes_ = [None] * self.n_outputs_
self.n_classes_ = [1] * self.n_outputs_
self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
# Check parameters
max_depth = ((2 ** 31) - 1 if self.max_depth is None
else self.max_depth)
max_leaf_nodes = (-1 if self.max_leaf_nodes is None
else self.max_leaf_nodes)
if isinstance(self.min_samples_leaf, (numbers.Integral, np.integer)):
min_samples_leaf = self.min_samples_leaf
else: # float
min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))
if isinstance(self.min_samples_split, (numbers.Integral, np.integer)):
min_samples_split = self.min_samples_split
else: # float
min_samples_split = int(ceil(self.min_samples_split * n_samples))
min_samples_split = max(2, min_samples_split)
min_samples_split = max(min_samples_split, 2 * min_samples_leaf)
if isinstance(self.max_features, six.string_types):
if self.max_features == "auto":
if is_classification:
max_features = max(1, int(np.sqrt(self.n_features_)))
else:
max_features = self.n_features_
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_)))
else:
raise ValueError(
'Invalid value for max_features. Allowed string '
'values are "auto", "sqrt" or "log2".')
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, (numbers.Integral, np.integer)):
max_features = self.max_features
else: # float
if self.max_features > 0.0:
max_features = max(1,
int(self.max_features * self.n_features_))
else:
max_features = 0
self.max_features_ = max_features
if len(y) != n_samples:
raise ValueError("Number of labels=%d does not match "
"number of samples=%d" % (len(y), n_samples))
if not (0. < self.min_samples_split <= 1. or
2 <= self.min_samples_split):
raise ValueError("min_samples_split must be in at least 2"
" or in (0, 1], got %s" % min_samples_split)
if not (0. < self.min_samples_leaf <= 0.5 or
1 <= self.min_samples_leaf):
raise ValueError("min_samples_leaf must be at least than 1 "
"or in (0, 0.5], got %s" % min_samples_leaf)
if not 0 <= self.min_weight_fraction_leaf <= 0.5:
raise ValueError("min_weight_fraction_leaf must in [0, 0.5]")
if max_depth <= 0:
raise ValueError("max_depth must be greater than zero. ")
if not (0 < max_features <= self.n_features_):
raise ValueError("max_features must be in (0, n_features]")
if not isinstance(max_leaf_nodes, (numbers.Integral, np.integer)):
raise ValueError("max_leaf_nodes must be integral number but was "
"%r" % max_leaf_nodes)
if -1 < max_leaf_nodes < 2:
raise ValueError(("max_leaf_nodes {0} must be either smaller than "
"0 or larger than 1").format(max_leaf_nodes))
if sample_weight is not None:
if (getattr(sample_weight, "dtype", None) != DOUBLE or
not sample_weight.flags.contiguous):
sample_weight = np.ascontiguousarray(
sample_weight, dtype=DOUBLE)
if len(sample_weight.shape) > 1:
raise ValueError("Sample weights array has more "
"than one dimension: %d" %
len(sample_weight.shape))
if len(sample_weight) != n_samples:
raise ValueError("Number of weights=%d does not match "
"number of samples=%d" %
(len(sample_weight), n_samples))
if expanded_class_weight is not None:
if sample_weight is not None:
sample_weight = sample_weight * expanded_class_weight
else:
sample_weight = expanded_class_weight
# Set min_weight_leaf from min_weight_fraction_leaf
if self.min_weight_fraction_leaf != 0. and sample_weight is not None:
min_weight_leaf = (self.min_weight_fraction_leaf *
np.sum(sample_weight))
else:
min_weight_leaf = 0.
if self.min_impurity_split < 0.:
raise ValueError("min_impurity_split must be greater than or equal "
"to 0")
presort = self.presort
# Allow presort to be 'auto', which means True if the dataset is dense,
# otherwise it will be False.
if self.presort == 'auto' and issparse(X):
presort = False
elif self.presort == 'auto':
presort = True
if presort is True and issparse(X):
raise ValueError("Presorting is not supported for sparse "
"matrices.")
# If multiple trees are built on the same dataset, we only want to
# presort once. Splitters now can accept presorted indices if desired,
# but do not handle any presorting themselves. Ensemble algorithms
# which desire presorting must do presorting themselves and pass that
# matrix into each tree.
if X_idx_sorted is None and presort:
X_idx_sorted = np.asfortranarray(np.argsort(X, axis=0),
dtype=np.int32)
if presort and X_idx_sorted.shape != X.shape:
raise ValueError("The shape of X (X.shape = {}) doesn't match "
"the shape of X_idx_sorted (X_idx_sorted"
".shape = {})".format(X.shape,
X_idx_sorted.shape))
# Build tree
criterion = self.criterion
if not isinstance(criterion, Criterion):
if is_classification:
criterion = CRITERIA_CLF[self.criterion](self.n_outputs_,
self.n_classes_)
else:
criterion = CRITERIA_REG[self.criterion](self.n_outputs_,
n_samples)
SPLITTERS = SPARSE_SPLITTERS if issparse(X) else DENSE_SPLITTERS
splitter = self.splitter
if not isinstance(self.splitter, Splitter):
splitter = SPLITTERS[self.splitter](criterion,
self.max_features_,
min_samples_leaf,
min_weight_leaf,
random_state,
self.presort)
self.tree_ = Tree(self.n_features_, self.n_classes_, self.n_outputs_)
# Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise
if max_leaf_nodes < 0:
builder = DepthFirstTreeBuilder(splitter, min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth, self.min_impurity_split)
else:
builder = BestFirstTreeBuilder(splitter, min_samples_split,
min_samples_leaf,
min_weight_leaf,
max_depth,
max_leaf_nodes, self.min_impurity_split)
builder.build(self.tree_, X, y, sample_weight, X_idx_sorted)
if self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self
def _validate_X_predict(self, X, check_input):
"""Validate X whenever one tries to predict, apply, predict_proba"""
if self.tree_ is None:
raise NotFittedError("Estimator not fitted, "
"call `fit` before exploiting the model.")
if check_input:
X = check_array(X, dtype=DTYPE, accept_sparse="csr")
if issparse(X) and (X.indices.dtype != np.intc or
X.indptr.dtype != np.intc):
raise ValueError("No support for np.int64 index based "
"sparse matrices")
n_features = X.shape[1]
if self.n_features_ != n_features:
raise ValueError("Number of features of the model must "
"match the input. Model n_features is %s and "
"input n_features is %s "
% (self.n_features_, n_features))
return X
def predict(self, X, check_input=True):
"""Predict class or regression value for X.
For a classification model, the predicted class for each sample in X is
returned. For a regression model, the predicted value based on X is
returned.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes, or the predict values.
"""
X = self._validate_X_predict(X, check_input)
proba = self.tree_.predict(X)
n_samples = X.shape[0]
# Classification
if isinstance(self, ClassifierMixin):
if self.n_outputs_ == 1:
return self.classes_.take(np.argmax(proba, axis=1), axis=0)
else:
predictions = np.zeros((n_samples, self.n_outputs_))
for k in range(self.n_outputs_):
predictions[:, k] = self.classes_[k].take(
np.argmax(proba[:, k], axis=1),
axis=0)
return predictions
# Regression
else:
if self.n_outputs_ == 1:
return proba[:, 0]
else:
return proba[:, :, 0]
def apply(self, X, check_input=True):
"""
Returns the index of the leaf that each sample is predicted as.
.. versionadded:: 0.17
Parameters
----------
X : array_like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
X_leaves : array_like, shape = [n_samples,]
For each datapoint x in X, return the index of the leaf x
ends up in. Leaves are numbered within
``[0; self.tree_.node_count)``, possibly with gaps in the
numbering.
"""
X = self._validate_X_predict(X, check_input)
return self.tree_.apply(X)
def decision_path(self, X, check_input=True):
"""Return the decision path in the tree
Parameters
----------
X : array_like or sparse matrix, shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Returns
-------
indicator : sparse csr array, shape = [n_samples, n_nodes]
Return a node indicator matrix where non zero elements
indicates that the samples goes through the nodes.
"""
X = self._validate_X_predict(X, check_input)
return self.tree_.decision_path(X)
@property
def feature_importances_(self):
"""Return the feature importances.
The importance of a feature is computed as the (normalized) total
reduction of the criterion brought by that feature.
It is also known as the Gini importance.
Returns
-------
feature_importances_ : array, shape = [n_features]
"""
if self.tree_ is None:
raise NotFittedError("Estimator not fitted, call `fit` before"
" `feature_importances_`.")
return self.tree_.compute_feature_importances()
# =============================================================================
# Public estimators
# =============================================================================
class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
"""A decision tree classifier.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : string, optional (default="gini")
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain.
splitter : string, optional (default="best")
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a percentage and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=sqrt(n_features)`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_depth : int or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if ``max_leaf_nodes`` is not None.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the input samples required to be at a
leaf node.
max_leaf_nodes : int or None, optional (default=None)
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
If not None then ``max_depth`` will be ignored.
class_weight : dict, list of dicts, "balanced" or None, optional (default=None)
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf.
.. versionadded:: 0.18
presort : bool, optional (default=False)
Whether to presort the data to speed up the finding of best splits in
fitting. For the default settings of a decision tree on large
datasets, setting this to true may slow down the training process.
When using either a smaller dataset or a restricted depth, this may
speed up the training.
Attributes
----------
classes_ : array of shape = [n_classes] or a list of such arrays
The classes labels (single output problem),
or a list of arrays of class labels (multi-output problem).
feature_importances_ : array of shape = [n_features]
The feature importances. The higher, the more important the
feature. The importance of a feature is computed as the (normalized)
total reduction of the criterion brought by that feature. It is also
known as the Gini importance [4]_.
max_features_ : int,
The inferred value of max_features.
n_classes_ : int or list
The number of classes (for single output problems),
or a list containing the number of classes for each
output (for multi-output problems).
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
tree_ : Tree object
The underlying Tree object.
See also
--------
DecisionTreeRegressor
References
----------
.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
and Regression Trees", Wadsworth, Belmont, CA, 1984.
.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
Learning", Springer, 2009.
.. [4] L. Breiman, and A. Cutler, "Random Forests",
http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier(random_state=0)
>>> iris = load_iris()
>>> cross_val_score(clf, iris.data, iris.target, cv=10)
... # doctest: +SKIP
...
array([ 1. , 0.93..., 0.86..., 0.93..., 0.93...,
0.93..., 0.93..., 1. , 0.93..., 1. ])
"""
def __init__(self,
criterion="gini",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features=None,
random_state=None,
max_leaf_nodes=None,
min_impurity_split=1e-7,
class_weight=None,
presort=False):
super(DecisionTreeClassifier, self).__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
class_weight=class_weight,
random_state=random_state,
min_impurity_split=min_impurity_split,
presort=presort)
def predict_proba(self, X, check_input=True):
"""Predict class probabilities of the input samples X.
The predicted class probability is the fraction of samples of the same
class in a leaf.
check_input : boolean, (default=True)
Allow to bypass several input checking.
Don't use this parameter unless you know what you do.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
X = self._validate_X_predict(X, check_input)
proba = self.tree_.predict(X)
if self.n_outputs_ == 1:
proba = proba[:, :self.n_classes_]
normalizer = proba.sum(axis=1)[:, np.newaxis]
normalizer[normalizer == 0.0] = 1.0
proba /= normalizer
return proba
else:
all_proba = []
for k in range(self.n_outputs_):
proba_k = proba[:, k, :self.n_classes_[k]]
normalizer = proba_k.sum(axis=1)[:, np.newaxis]
normalizer[normalizer == 0.0] = 1.0
proba_k /= normalizer
all_proba.append(proba_k)
return all_proba
def predict_log_proba(self, X):
"""Predict class log-probabilities of the input samples X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, it will be converted to
``dtype=np.float32`` and if a sparse matrix is provided
to a sparse ``csr_matrix``.
Returns
-------
p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1.
The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute `classes_`.
"""
proba = self.predict_proba(X)
if self.n_outputs_ == 1:
return np.log(proba)
else:
for k in range(self.n_outputs_):
proba[k] = np.log(proba[k])
return proba
class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
"""A decision tree regressor.
Read more in the :ref:`User Guide <tree>`.
Parameters
----------
criterion : string, optional (default="mse")
The function to measure the quality of a split. Supported criteria
are "mse" for the mean squared error, which is equal to variance
reduction as feature selection criterion, and "mae" for the mean
absolute error.
.. versionadded:: 0.18
Mean Absolute Error (MAE) criterion.
splitter : string, optional (default="best")
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split.
max_features : int, float, string or None, optional (default=None)
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split.
- If float, then `max_features` is a percentage and
`int(max_features * n_features)` features are considered at each
split.
- If "auto", then `max_features=n_features`.
- If "sqrt", then `max_features=sqrt(n_features)`.
- If "log2", then `max_features=log2(n_features)`.
- If None, then `max_features=n_features`.
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features.
max_depth : int or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples.
Ignored if ``max_leaf_nodes`` is not None.
min_samples_split : int, float, optional (default=2)
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number.
- If float, then `min_samples_split` is a percentage and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split.
min_samples_leaf : int, float, optional (default=1)
The minimum number of samples required to be at a leaf node:
- If int, then consider `min_samples_leaf` as the minimum number.
- If float, then `min_samples_leaf` is a percentage and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node.
min_weight_fraction_leaf : float, optional (default=0.)
The minimum weighted fraction of the input samples required to be at a
leaf node.
max_leaf_nodes : int or None, optional (default=None)
Grow a tree with ``max_leaf_nodes`` in best-first fashion.
Best nodes are defined as relative reduction in impurity.
If None then unlimited number of leaf nodes.
If not None then ``max_depth`` will be ignored.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
min_impurity_split : float, optional (default=1e-7)
Threshold for early stopping in tree growth. If the impurity
of a node is below the threshold, the node is a leaf.
.. versionadded:: 0.18
presort : bool, optional (default=False)
Whether to presort the data to speed up the finding of best splits in
fitting. For the default settings of a decision tree on large
datasets, setting this to true may slow down the training process.
When using either a smaller dataset or a restricted depth, this may
speed up the training.
Attributes
----------
feature_importances_ : array of shape = [n_features]
The feature importances.
The higher, the more important the feature.
The importance of a feature is computed as the
(normalized) total reduction of the criterion brought
by that feature. It is also known as the Gini importance [4]_.
max_features_ : int,
The inferred value of max_features.
n_features_ : int
The number of features when ``fit`` is performed.
n_outputs_ : int
The number of outputs when ``fit`` is performed.
tree_ : Tree object
The underlying Tree object.
See also
--------
DecisionTreeClassifier
References
----------
.. [1] https://en.wikipedia.org/wiki/Decision_tree_learning
.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
and Regression Trees", Wadsworth, Belmont, CA, 1984.
.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
Learning", Springer, 2009.
.. [4] L. Breiman, and A. Cutler, "Random Forests",
http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Examples
--------
>>> from sklearn.datasets import load_boston
>>> from sklearn.model_selection import cross_val_score
>>> from sklearn.tree import DecisionTreeRegressor
>>> boston = load_boston()
>>> regressor = DecisionTreeRegressor(random_state=0)
>>> cross_val_score(regressor, boston.data, boston.target, cv=10)
... # doctest: +SKIP
...
array([ 0.61..., 0.57..., -0.34..., 0.41..., 0.75...,
0.07..., 0.29..., 0.33..., -1.42..., -1.77...])
"""
def __init__(self,
criterion="mse",
splitter="best",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features=None,
random_state=None,
max_leaf_nodes=None,
min_impurity_split=1e-7,
presort=False):
super(DecisionTreeRegressor, self).__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
random_state=random_state,
min_impurity_split=min_impurity_split,
presort=presort)
class ExtraTreeClassifier(DecisionTreeClassifier):
"""An extremely randomized tree classifier.
Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_features` randomly
selected features and the best split among those is chosen. When
`max_features` is set 1, this amounts to building a totally random
decision tree.
Warning: Extra-trees should only be used within ensemble methods.
Read more in the :ref:`User Guide <tree>`.
See also
--------
ExtraTreeRegressor, ExtraTreesClassifier, ExtraTreesRegressor
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
"""
def __init__(self,
criterion="gini",
splitter="random",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
random_state=None,
max_leaf_nodes=None,
min_impurity_split=1e-7,
class_weight=None):
super(ExtraTreeClassifier, self).__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
class_weight=class_weight,
min_impurity_split=min_impurity_split,
random_state=random_state)
class ExtraTreeRegressor(DecisionTreeRegressor):
"""An extremely randomized tree regressor.
Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_features` randomly
selected features and the best split among those is chosen. When
`max_features` is set 1, this amounts to building a totally random
decision tree.
Warning: Extra-trees should only be used within ensemble methods.
Read more in the :ref:`User Guide <tree>`.
See also
--------
ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor
References
----------
.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
Machine Learning, 63(1), 3-42, 2006.
"""
def __init__(self,
criterion="mse",
splitter="random",
max_depth=None,
min_samples_split=2,
min_samples_leaf=1,
min_weight_fraction_leaf=0.,
max_features="auto",
random_state=None,
min_impurity_split=1e-7,
max_leaf_nodes=None):
super(ExtraTreeRegressor, self).__init__(
criterion=criterion,
splitter=splitter,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
min_impurity_split=min_impurity_split,
random_state=random_state)
| sonnyhu/scikit-learn | sklearn/tree/tree.py | Python | bsd-3-clause | 41,818 | [
"Brian"
] | ca327c95ec1bb92f67e779ad2d437772ffa378c55510e0850e174c77f34290ec |
# Principal Component Analysis Code :
from numpy import mean,cov,double,cumsum,dot,linalg,array,rank,size,flipud
from pylab import *
import numpy as np
import matplotlib.pyplot as pp
#from enthought.mayavi import mlab
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import rospy
#import hrl_lib.mayavi2_util as mu
import hrl_lib.viz as hv
import hrl_lib.util as ut
import hrl_lib.matplotlib_util as mpu
import pickle
from mvpa.clfs.knn import kNN
from mvpa.datasets import Dataset
from mvpa.clfs.transerror import TransferError
from mvpa.misc.data_generators import normalFeatureDataset
from mvpa.algorithms.cvtranserror import CrossValidatedTransferError
from mvpa.datasets.splitters import NFoldSplitter
import sys
sys.path.insert(0, '/home/tapo/svn/robot1_data/usr/tapo/data_code/Classification/Data/Single_Contact_kNN/Scaled')
from data_method_IV import Fmat_original
def pca(X):
#get dimensions
num_data,dim = X.shape
#center data
mean_X = X.mean(axis=1)
M = (X-mean_X) # subtract the mean (along columns)
Mcov = cov(M)
###### Sanity Check ######
i=0
n=0
while i < 123:
j=0
while j < 140:
if X[i,j] != X[i,j]:
print X[i,j]
print i,j
n=n+1
j = j+1
i=i+1
print n
##########################
print 'PCA - COV-Method used'
val,vec = linalg.eig(Mcov)
#return the projection matrix, the variance and the mean
return vec,val,mean_X, M, Mcov
def my_mvpa(Y,num2):
#Using PYMVPA
PCA_data = np.array(Y)
PCA_label_2 = ['Styrofoam-Fixed']*5 + ['Books-Fixed']*5 + ['Bucket-Fixed']*5 + ['Bowl-Fixed']*5 + ['Can-Fixed']*5 + ['Box-Fixed']*5 + ['Pipe-Fixed']*5 + ['Styrofoam-Movable']*5 + ['Container-Movable']*5 + ['Books-Movable']*5 + ['Cloth-Roll-Movable']*5 + ['Black-Rubber-Movable']*5 + ['Can-Movable']*5 + ['Box-Movable']*5 + ['Rug-Fixed']*5 + ['Bubble-Wrap-1-Fixed']*5 + ['Pillow-1-Fixed']*5 + ['Bubble-Wrap-2-Fixed']*5 + ['Sponge-Fixed']*5 + ['Foliage-Fixed']*5 + ['Pillow-2-Fixed']*5 + ['Rug-Movable']*5 + ['Bubble-Wrap-1-Movable']*5 + ['Pillow-1-Movable']*5 + ['Bubble-Wrap-2-Movable']*5 + ['Pillow-2-Movable']*5 + ['Cushion-Movable']*5 + ['Sponge-Movable']*5
clf = kNN(k=num2)
terr = TransferError(clf)
ds1 = Dataset(samples=PCA_data,labels=PCA_label_2)
cvterr = CrossValidatedTransferError(terr,NFoldSplitter(cvtype=1),enable_states=['confusion'])
error = cvterr(ds1)
return (1-error)*100
def result(eigvec_total,eigval_total,mean_data_total,B,C,num_PC):
# Reduced Eigen-Vector Matrix according to highest Eigenvalues..(Considering First 20 based on above figure)
W = eigvec_total[:,0:num_PC]
m_W, n_W = np.shape(W)
# Normalizes the data set with respect to its variance (Not an Integral part of PCA, but useful)
length = len(eigval_total)
s = np.matrix(np.zeros(length)).T
i = 0
while i < length:
s[i] = sqrt(C[i,i])
i = i+1
Z = np.divide(B,s)
m_Z, n_Z = np.shape(Z)
#Projected Data:
Y = (W.T)*B # 'B' for my Laptop: otherwise 'Z' instead of 'B'
m_Y, n_Y = np.shape(Y.T)
return Y.T
if __name__ == '__main__':
Fmat = Fmat_original
# Checking the Data-Matrix
m_tot, n_tot = np.shape(Fmat)
print 'Total_Matrix_Shape:',m_tot,n_tot
eigvec_total, eigval_total, mean_data_total, B, C = pca(Fmat)
#print eigvec_total
#print eigval_total
#print mean_data_total
m_eigval_total, n_eigval_total = np.shape(np.matrix(eigval_total))
m_eigvec_total, n_eigvec_total = np.shape(eigvec_total)
m_mean_data_total, n_mean_data_total = np.shape(np.matrix(mean_data_total))
print 'Eigenvalue Shape:',m_eigval_total, n_eigval_total
print 'Eigenvector Shape:',m_eigvec_total, n_eigvec_total
print 'Mean-Data Shape:',m_mean_data_total, n_mean_data_total
#Recall that the cumulative sum of the eigenvalues shows the level of variance accounted by each of the corresponding eigenvectors. On the x axis there is the number of eigenvalues used.
perc_total = cumsum(eigval_total)/sum(eigval_total)
num_PC=1
while num_PC <=20:
Proj = np.zeros((140,num_PC))
Proj = result(eigvec_total,eigval_total,mean_data_total,B,C,num_PC)
# PYMVPA:
num=0
cv_acc = np.zeros(21)
while num <=20:
cv_acc[num] = my_mvpa(Proj,num)
num = num+1
plot(np.arange(21),cv_acc,'-s')
grid('True')
hold('True')
num_PC = num_PC+1
legend(('1-PC', '2-PCs', '3-PCs', '4-PCs', '5-PCs', '6-PCs', '7-PCs', '8-PCs', '9-PCs', '10-PCs', '11-PC', '12-PCs', '13-PCs', '14-PCs', '15-PCs', '16-PCs', '17-PCs', '18-PCs', '19-PCs', '20-PCs'))
ylabel('Cross-Validation Accuracy')
xlabel('k in k-NN Classifier')
show()
| tapomayukh/projects_in_python | classification/Classification_with_kNN/Single_Contact_Classification/Scaled_Features/best_kNN_PCA/objects/test11_cross_validate_objects_1200ms_scaled_method_iv.py | Python | mit | 4,916 | [
"Mayavi"
] | 1ffd4a6743c897021833c8ba69d83ac76a132c9cd7e21606d3b1fa296d74a593 |
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from datetime import datetime, date
from lxml import etree
import time
from openerp import api
from openerp import SUPERUSER_ID
from openerp import tools
from openerp.osv import fields, osv
from openerp.tools.translate import _
from openerp.exceptions import UserError
class project_task_type(osv.osv):
_name = 'project.task.type'
_description = 'Task Stage'
_order = 'sequence, id'
_columns = {
'name': fields.char('Stage Name', required=True, translate=True),
'description': fields.text('Description', translate=True),
'sequence': fields.integer('Sequence'),
'project_ids': fields.many2many('project.project', 'project_task_type_rel', 'type_id', 'project_id', 'Projects'),
'legend_priority': fields.char(
'Priority Management Explanation', translate=True,
help='Explanation text to help users using the star and priority mechanism on stages or issues that are in this stage.'),
'legend_blocked': fields.char(
'Kanban Blocked Explanation', translate=True,
help='Override the default value displayed for the blocked state for kanban selection, when the task or issue is in that stage.'),
'legend_done': fields.char(
'Kanban Valid Explanation', translate=True,
help='Override the default value displayed for the done state for kanban selection, when the task or issue is in that stage.'),
'legend_normal': fields.char(
'Kanban Ongoing Explanation', translate=True,
help='Override the default value displayed for the normal state for kanban selection, when the task or issue is in that stage.'),
'fold': fields.boolean('Folded in Tasks Pipeline',
help='This stage is folded in the kanban view when '
'there are no records in that stage to display.'),
}
def _get_default_project_ids(self, cr, uid, ctx=None):
if ctx is None:
ctx = {}
default_project_id = ctx.get('default_project_id')
return [default_project_id] if default_project_id else None
_defaults = {
'sequence': 1,
'project_ids': _get_default_project_ids,
}
class project(osv.osv):
_name = "project.project"
_description = "Project"
_inherits = {'account.analytic.account': "analytic_account_id",
"mail.alias": "alias_id"}
_inherit = ['mail.thread', 'ir.needaction_mixin']
_period_number = 5
def _auto_init(self, cr, context=None):
""" Installation hook: aliases, project.project """
# create aliases for all projects and avoid constraint errors
alias_context = dict(context, alias_model_name='project.task')
return self.pool.get('mail.alias').migrate_to_alias(cr, self._name, self._table, super(project, self)._auto_init,
'project.task', self._columns['alias_id'], 'id', alias_prefix='project+', alias_defaults={'project_id':'id'}, context=alias_context)
def onchange_partner_id(self, cr, uid, ids, part=False, context=None):
partner_obj = self.pool.get('res.partner')
val = {}
if not part:
return {'value': val}
if 'pricelist_id' in self.fields_get(cr, uid, context=context):
pricelist = partner_obj.read(cr, uid, part, ['property_product_pricelist'], context=context)
pricelist_id = pricelist.get('property_product_pricelist', False) and pricelist.get('property_product_pricelist')[0] or False
val['pricelist_id'] = pricelist_id
return {'value': val}
def unlink(self, cr, uid, ids, context=None):
alias_ids = []
mail_alias = self.pool.get('mail.alias')
analytic_account_to_delete = set()
for proj in self.browse(cr, uid, ids, context=context):
if proj.tasks:
raise UserError(_('You cannot delete a project containing tasks. You can either delete all the project\'s tasks and then delete the project or simply deactivate the project.'))
elif proj.alias_id:
alias_ids.append(proj.alias_id.id)
if proj.analytic_account_id and not proj.analytic_account_id.line_ids:
analytic_account_to_delete.add(proj.analytic_account_id.id)
res = super(project, self).unlink(cr, uid, ids, context=context)
mail_alias.unlink(cr, uid, alias_ids, context=context)
self.pool['account.analytic.account'].unlink(cr, uid, list(analytic_account_to_delete), context=context)
return res
def _get_attached_docs(self, cr, uid, ids, field_name, arg, context):
res = {}
attachment = self.pool.get('ir.attachment')
task = self.pool.get('project.task')
for id in ids:
project_attachments = attachment.search(cr, uid, [('res_model', '=', 'project.project'), ('res_id', '=', id)], context=context, count=True)
task_ids = task.search(cr, uid, [('project_id', '=', id)], context=context)
task_attachments = attachment.search(cr, uid, [('res_model', '=', 'project.task'), ('res_id', 'in', task_ids)], context=context, count=True)
res[id] = (project_attachments or 0) + (task_attachments or 0)
return res
def _task_count(self, cr, uid, ids, field_name, arg, context=None):
if context is None:
context = {}
res={}
for project in self.browse(cr, uid, ids, context=context):
res[project.id] = len(project.task_ids)
return res
def _task_needaction_count(self, cr, uid, ids, field_name, arg, context=None):
Task = self.pool['project.task']
res = dict.fromkeys(ids, 0)
projects = Task.read_group(cr, uid, [('project_id', 'in', ids), ('message_needaction', '=', True)], ['project_id'], ['project_id'], context=context)
res.update({project['project_id'][0]: int(project['project_id_count']) for project in projects})
return res
def _get_alias_models(self, cr, uid, context=None):
""" Overriden in project_issue to offer more options """
return [('project.task', "Tasks")]
def _get_visibility_selection(self, cr, uid, context=None):
""" Overriden in portal_project to offer more options """
return [('portal', _('Customer Project: visible in portal if the customer is a follower')),
('employees', _('All Employees Project: all employees can access')),
('followers', _('Private Project: followers only'))]
def attachment_tree_view(self, cr, uid, ids, context):
task_ids = self.pool.get('project.task').search(cr, uid, [('project_id', 'in', ids)])
domain = [
'|',
'&', ('res_model', '=', 'project.project'), ('res_id', 'in', ids),
'&', ('res_model', '=', 'project.task'), ('res_id', 'in', task_ids)]
res_id = ids and ids[0] or False
return {
'name': _('Attachments'),
'domain': domain,
'res_model': 'ir.attachment',
'type': 'ir.actions.act_window',
'view_id': False,
'view_mode': 'kanban,tree,form',
'view_type': 'form',
'help': _('''<p class="oe_view_nocontent_create">
Documents are attached to the tasks and issues of your project.</p><p>
Send messages or log internal notes with attachments to link
documents to your project.
</p>'''),
'limit': 80,
'context': "{'default_res_model': '%s','default_res_id': %d}" % (self._name, res_id)
}
# Lambda indirection method to avoid passing a copy of the overridable method when declaring the field
_alias_models = lambda self, *args, **kwargs: self._get_alias_models(*args, **kwargs)
_visibility_selection = lambda self, *args, **kwargs: self._get_visibility_selection(*args, **kwargs)
_columns = {
'active': fields.boolean('Active', help="If the active field is set to False, it will allow you to hide the project without removing it."),
'sequence': fields.integer('Sequence', help="Gives the sequence order when displaying a list of Projects."),
'analytic_account_id': fields.many2one(
'account.analytic.account', 'Contract/Analytic',
help="Link this project to an analytic account if you need financial management on projects. "
"It enables you to connect projects with budgets, planning, cost and revenue analysis, timesheets on projects, etc.",
ondelete="cascade", required=True, auto_join=True),
'label_tasks': fields.char('Use Tasks as', help="Gives label to tasks on project's kanban view."),
'tasks': fields.one2many('project.task', 'project_id', "Task Activities"),
'resource_calendar_id': fields.many2one('resource.calendar', 'Working Time', help="Timetable working hours to adjust the gantt diagram report", states={'close':[('readonly',True)]} ),
'type_ids': fields.many2many('project.task.type', 'project_task_type_rel', 'project_id', 'type_id', 'Tasks Stages', states={'close':[('readonly',True)], 'cancelled':[('readonly',True)]}),
'task_count': fields.function(_task_count, type='integer', string="Tasks",),
'task_needaction_count': fields.function(_task_needaction_count, type='integer', string="Tasks",),
'task_ids': fields.one2many('project.task', 'project_id',
domain=['|', ('stage_id.fold', '=', False), ('stage_id', '=', False)]),
'color': fields.integer('Color Index'),
'user_id': fields.many2one('res.users', 'Project Manager'),
'alias_id': fields.many2one('mail.alias', 'Alias', ondelete="restrict", required=True,
help="Internal email associated with this project. Incoming emails are automatically synchronized "
"with Tasks (or optionally Issues if the Issue Tracker module is installed)."),
'alias_model': fields.selection(_alias_models, "Alias Model", select=True, required=True,
help="The kind of document created when an email is received on this project's email alias"),
'privacy_visibility': fields.selection(_visibility_selection, 'Privacy / Visibility', required=True,
help="Holds visibility of the tasks or issues that belong to the current project:\n"
"- Portal : employees see everything;\n"
" if portal is activated, portal users see the tasks or issues followed by\n"
" them or by someone of their company\n"
"- Employees Only: employees see all tasks or issues\n"
"- Followers Only: employees see only the followed tasks or issues; if portal\n"
" is activated, portal users see the followed tasks or issues."),
'state': fields.selection([('draft','New'),
('open','In Progress'),
('cancelled', 'Cancelled'),
('pending','Pending'),
('close','Closed')],
'Status', required=True, copy=False),
'doc_count': fields.function(
_get_attached_docs, string="Number of documents attached", type='integer'
),
'date_start': fields.date('Start Date'),
'date': fields.date('Expiration Date', select=True, track_visibility='onchange'),
}
_order = "sequence, name, id"
_defaults = {
'active': True,
'type': 'contract',
'label_tasks': 'Tasks',
'state': 'open',
'sequence': 10,
'user_id': lambda self,cr,uid,ctx: uid,
'alias_model': 'project.task',
'privacy_visibility': 'employees',
}
# TODO: Why not using a SQL contraints ?
def _check_dates(self, cr, uid, ids, context=None):
for leave in self.read(cr, uid, ids, ['date_start', 'date'], context=context):
if leave['date_start'] and leave['date']:
if leave['date_start'] > leave['date']:
return False
return True
_constraints = [
(_check_dates, 'Error! project start-date must be lower than project end-date.', ['date_start', 'date'])
]
def set_template(self, cr, uid, ids, context=None):
return self.setActive(cr, uid, ids, value=False, context=context)
def reset_project(self, cr, uid, ids, context=None):
return self.setActive(cr, uid, ids, value=True, context=context)
def map_tasks(self, cr, uid, old_project_id, new_project_id, context=None):
""" copy and map tasks from old to new project """
if context is None:
context = {}
map_task_id = {}
task_obj = self.pool.get('project.task')
proj = self.browse(cr, uid, old_project_id, context=context)
for task in proj.tasks:
# preserve task name and stage, normally altered during copy
defaults = {'stage_id': task.stage_id.id,
'name': task.name}
map_task_id[task.id] = task_obj.copy(cr, uid, task.id, defaults, context=context)
self.write(cr, uid, [new_project_id], {'tasks':[(6,0, map_task_id.values())]})
task_obj.duplicate_task(cr, uid, map_task_id, context=context)
return True
def copy(self, cr, uid, id, default=None, context=None):
if default is None:
default = {}
context = dict(context or {})
context['active_test'] = False
proj = self.browse(cr, uid, id, context=context)
if not default.get('name'):
default.update(name=_("%s (copy)") % (proj.name))
res = super(project, self).copy(cr, uid, id, default, context)
for follower in proj.message_follower_ids:
self.message_subscribe(cr, uid, res, partner_ids=[follower.partner_id.id], subtype_ids=[subtype.id for subtype in follower.subtype_ids])
self.map_tasks(cr, uid, id, res, context=context)
return res
def duplicate_template(self, cr, uid, ids, context=None):
context = dict(context or {})
data_obj = self.pool.get('ir.model.data')
result = []
for proj in self.browse(cr, uid, ids, context=context):
context.update({'analytic_project_copy': True})
new_date_start = time.strftime('%Y-%m-%d')
new_date_end = False
if proj.date_start and proj.date:
start_date = date(*time.strptime(proj.date_start,'%Y-%m-%d')[:3])
end_date = date(*time.strptime(proj.date,'%Y-%m-%d')[:3])
new_date_end = (datetime(*time.strptime(new_date_start,'%Y-%m-%d')[:3])+(end_date-start_date)).strftime('%Y-%m-%d')
context.update({'copy':True})
new_id = self.copy(cr, uid, proj.id, default = {
'name':_("%s (copy)") % (proj.name),
'state':'open',
'date_start':new_date_start,
'date':new_date_end}, context=context)
result.append(new_id)
if result and len(result):
res_id = result[0]
form_view_id = data_obj._get_id(cr, uid, 'project', 'edit_project')
form_view = data_obj.read(cr, uid, form_view_id, ['res_id'])
tree_view_id = data_obj._get_id(cr, uid, 'project', 'view_project')
tree_view = data_obj.read(cr, uid, tree_view_id, ['res_id'])
search_view_id = data_obj._get_id(cr, uid, 'project', 'view_project_project_filter')
search_view = data_obj.read(cr, uid, search_view_id, ['res_id'])
return {
'name': _('Projects'),
'view_type': 'form',
'view_mode': 'form,tree',
'res_model': 'project.project',
'view_id': False,
'res_id': res_id,
'views': [(form_view['res_id'],'form'),(tree_view['res_id'],'tree')],
'type': 'ir.actions.act_window',
'search_view_id': search_view['res_id'],
}
@api.multi
def setActive(self, value=True):
""" Set a project as active/inactive, and its tasks as well. """
self.write({'active': value})
def create(self, cr, uid, vals, context=None):
if context is None:
context = {}
# Prevent double project creation when 'use_tasks' is checked + alias management
create_context = dict(context, project_creation_in_progress=True,
alias_model_name=vals.get('alias_model', 'project.task'),
alias_parent_model_name=self._name,
mail_create_nosubscribe=True)
ir_values = self.pool.get('ir.values').get_default(cr, uid, 'project.config.settings', 'generate_project_alias')
if ir_values:
vals['alias_name'] = vals.get('alias_name') or vals.get('name')
project_id = super(project, self).create(cr, uid, vals, context=create_context)
project_rec = self.browse(cr, uid, project_id, context=context)
values = {'alias_parent_thread_id': project_id, 'alias_defaults': {'project_id': project_id}}
self.pool.get('mail.alias').write(cr, uid, [project_rec.alias_id.id], values, context=context)
return project_id
def write(self, cr, uid, ids, vals, context=None):
# if alias_model has been changed, update alias_model_id accordingly
if vals.get('alias_model'):
model_ids = self.pool.get('ir.model').search(cr, uid, [('model', '=', vals.get('alias_model', 'project.task'))])
vals.update(alias_model_id=model_ids[0])
res = super(project, self).write(cr, uid, ids, vals, context=context)
if 'active' in vals:
# archiving/unarchiving a project does it on its tasks, too
projects = self.browse(cr, uid, ids, context)
tasks = projects.with_context(active_test=False).mapped('tasks')
tasks.write({'active': vals['active']})
return res
class task(osv.osv):
_name = "project.task"
_description = "Task"
_date_name = "date_start"
_inherit = ['mail.thread', 'ir.needaction_mixin']
_mail_post_access = 'read'
def _get_default_partner(self, cr, uid, context=None):
if context is None:
context = {}
if 'default_project_id' in context:
project = self.pool.get('project.project').browse(cr, uid, context['default_project_id'], context=context)
if project and project.partner_id:
return project.partner_id.id
return False
def _get_default_stage_id(self, cr, uid, context=None):
""" Gives default stage_id """
if context is None:
context = {}
default_project_id = context.get('default_project_id')
if not default_project_id:
return False
return self.stage_find(cr, uid, [], default_project_id, [('fold', '=', False)], context=context)
def _read_group_stage_ids(self, cr, uid, ids, domain, read_group_order=None, access_rights_uid=None, context=None):
if context is None:
context = {}
stage_obj = self.pool.get('project.task.type')
order = stage_obj._order
access_rights_uid = access_rights_uid or uid
if read_group_order == 'stage_id desc':
order = '%s desc' % order
if 'default_project_id' in context:
search_domain = ['|', ('project_ids', '=', context['default_project_id']), ('id', 'in', ids)]
else:
search_domain = [('id', 'in', ids)]
stage_ids = stage_obj._search(cr, uid, search_domain, order=order, access_rights_uid=access_rights_uid, context=context)
result = stage_obj.name_get(cr, access_rights_uid, stage_ids, context=context)
# restore order of the search
result.sort(lambda x, y: cmp(stage_ids.index(x[0]), stage_ids.index(y[0])))
fold = {}
for stage in stage_obj.browse(cr, access_rights_uid, stage_ids, context=context):
fold[stage.id] = stage.fold or False
return result, fold
_group_by_full = {
'stage_id': _read_group_stage_ids,
}
def onchange_remaining(self, cr, uid, ids, remaining=0.0, planned=0.0):
if remaining and not planned:
return {'value': {'planned_hours': remaining}}
return {}
def onchange_planned(self, cr, uid, ids, planned=0.0, effective=0.0):
return {'value': {'remaining_hours': planned - effective}}
@api.cr_uid_ids_context
def onchange_project(self, cr, uid, id, project_id, context=None):
values = {}
if project_id:
project = self.pool.get('project.project').browse(cr, uid, project_id, context=context)
if project.exists():
values['partner_id'] = project.partner_id.id
values['stage_id'] = self.stage_find(cr, uid, [], project_id, [('fold', '=', False)], context=context)
else:
values['stage_id'] = False
return {'value': values}
def onchange_user_id(self, cr, uid, ids, user_id, context=None):
vals = {}
if user_id:
vals['date_start'] = fields.datetime.now()
return {'value': vals}
def duplicate_task(self, cr, uid, map_ids, context=None):
mapper = lambda t: map_ids.get(t.id, t.id)
for task in self.browse(cr, uid, map_ids.values(), context):
new_child_ids = set(map(mapper, task.child_ids))
new_parent_ids = set(map(mapper, task.parent_ids))
if new_child_ids or new_parent_ids:
task.write({'parent_ids': [(6,0,list(new_parent_ids))],
'child_ids': [(6,0,list(new_child_ids))]})
def copy_data(self, cr, uid, id, default=None, context=None):
if default is None:
default = {}
current = self.browse(cr, uid, id, context=context)
if not default.get('name'):
default['name'] = _("%s (copy)") % current.name
if 'remaining_hours' not in default:
default['remaining_hours'] = current.planned_hours
return super(task, self).copy_data(cr, uid, id, default, context)
_columns = {
'active': fields.boolean('Active'),
'name': fields.char('Task Title', track_visibility='onchange', size=128, required=True, select=True),
'description': fields.html('Description'),
'priority': fields.selection([('0','Normal'), ('1','High')], 'Priority', select=True),
'sequence': fields.integer('Sequence', select=True, help="Gives the sequence order when displaying a list of tasks."),
'stage_id': fields.many2one('project.task.type', 'Stage', track_visibility='onchange', select=True,
domain="[('project_ids', '=', project_id)]", copy=False),
'tag_ids': fields.many2many('project.tags', string='Tags', oldname='categ_ids'),
'kanban_state': fields.selection([('normal', 'In Progress'),('done', 'Ready for next stage'),('blocked', 'Blocked')], 'Kanban State',
track_visibility='onchange',
help="A task's kanban state indicates special situations affecting it:\n"
" * Normal is the default situation\n"
" * Blocked indicates something is preventing the progress of this task\n"
" * Ready for next stage indicates the task is ready to be pulled to the next stage",
required=True, copy=False),
'create_date': fields.datetime('Create Date', readonly=True, select=True),
'write_date': fields.datetime('Last Modification Date', readonly=True, select=True), #not displayed in the view but it might be useful with base_action_rule module (and it needs to be defined first for that)
'date_start': fields.datetime('Starting Date', select=True, copy=False),
'date_end': fields.datetime('Ending Date', select=True, copy=False),
'date_assign': fields.datetime('Assigning Date', select=True, copy=False, readonly=True),
'date_deadline': fields.date('Deadline', select=True, copy=False),
'date_last_stage_update': fields.datetime('Last Stage Update', select=True, copy=False, readonly=True),
'project_id': fields.many2one('project.project', 'Project', ondelete='set null', select=True, track_visibility='onchange', change_default=True),
'parent_ids': fields.many2many('project.task', 'project_task_parent_rel', 'task_id', 'parent_id', 'Parent Tasks'),
'child_ids': fields.many2many('project.task', 'project_task_parent_rel', 'parent_id', 'task_id', 'Delegated Tasks'),
'notes': fields.text('Notes'),
'planned_hours': fields.float('Initially Planned Hours', help='Estimated time to do the task, usually set by the project manager when the task is in draft state.'),
'remaining_hours': fields.float('Remaining Hours', digits=(16,2), help="Total remaining time, can be re-estimated periodically by the assignee of the task."),
'user_id': fields.many2one('res.users', 'Assigned to', select=True, track_visibility='onchange'),
'partner_id': fields.many2one('res.partner', 'Customer'),
'manager_id': fields.related('project_id', 'user_id', type='many2one', relation='res.users', string='Project Manager'),
'company_id': fields.many2one('res.company', 'Company'),
'id': fields.integer('ID', readonly=True),
'color': fields.integer('Color Index'),
'user_email': fields.related('user_id', 'email', type='char', string='User Email', readonly=True),
'attachment_ids': fields.one2many('ir.attachment', 'res_id', domain=lambda self: [('res_model', '=', self._name)], auto_join=True, string='Attachments'),
# In the domain of displayed_image_id, we couln't use attachment_ids because a one2many is represented as a list of commands so we used res_model & res_id
'displayed_image_id': fields.many2one('ir.attachment', domain="[('res_model', '=', 'project.task'), ('res_id', '=', id), ('mimetype', 'ilike', 'image')]", string='Displayed Image'),
'legend_blocked': fields.related("stage_id", "legend_blocked", type="char", string='Kanban Blocked Explanation'),
'legend_done': fields.related("stage_id", "legend_done", type="char", string='Kanban Valid Explanation'),
'legend_normal': fields.related("stage_id", "legend_normal", type="char", string='Kanban Ongoing Explanation'),
}
_defaults = {
'stage_id': _get_default_stage_id,
'project_id': lambda self, cr, uid, ctx=None: ctx.get('default_project_id') if ctx is not None else False,
'date_last_stage_update': fields.datetime.now,
'kanban_state': 'normal',
'priority': '0',
'sequence': 10,
'active': True,
'user_id': lambda obj, cr, uid, ctx=None: uid,
'company_id': lambda self, cr, uid, ctx=None: self.pool.get('res.company')._company_default_get(cr, uid, 'project.task', context=ctx),
'partner_id': lambda self, cr, uid, ctx=None: self._get_default_partner(cr, uid, context=ctx),
'date_start': fields.datetime.now,
}
_order = "priority desc, sequence, date_start, name, id"
def _check_recursion(self, cr, uid, ids, context=None):
for id in ids:
visited_branch = set()
visited_node = set()
res = self._check_cycle(cr, uid, id, visited_branch, visited_node, context=context)
if not res:
return False
return True
def _check_cycle(self, cr, uid, id, visited_branch, visited_node, context=None):
if id in visited_branch: #Cycle
return False
if id in visited_node: #Already tested don't work one more time for nothing
return True
visited_branch.add(id)
visited_node.add(id)
#visit child using DFS
task = self.browse(cr, uid, id, context=context)
for child in task.child_ids:
res = self._check_cycle(cr, uid, child.id, visited_branch, visited_node, context=context)
if not res:
return False
visited_branch.remove(id)
return True
def _check_dates(self, cr, uid, ids, context=None):
if context == None:
context = {}
obj_task = self.browse(cr, uid, ids[0], context=context)
start = obj_task.date_start or False
end = obj_task.date_end or False
if start and end :
if start > end:
return False
return True
_constraints = [
(_check_recursion, 'Error ! You cannot create recursive tasks.', ['parent_ids']),
(_check_dates, 'Error ! Task starting date must be lower than its ending date.', ['date_start','date_end'])
]
# Override view according to the company definition
def fields_view_get(self, cr, uid, view_id=None, view_type='form', context=None, toolbar=False, submenu=False):
users_obj = self.pool.get('res.users')
if context is None: context = {}
# read uom as admin to avoid access rights issues, e.g. for portal/share users,
# this should be safe (no context passed to avoid side-effects)
obj_tm = users_obj.browse(cr, SUPERUSER_ID, uid, context=context).company_id.project_time_mode_id
tm = obj_tm and obj_tm.name or 'Hours'
res = super(task, self).fields_view_get(cr, uid, view_id=view_id, view_type=view_type, context=context, toolbar=toolbar, submenu=submenu)
# read uom as admin to avoid access rights issues, e.g. for portal/share users,
# this should be safe (no context passed to avoid side-effects)
obj_tm = users_obj.browse(cr, SUPERUSER_ID, uid, context=context).company_id.project_time_mode_id
try:
# using get_object to get translation value
uom_hour = self.pool['ir.model.data'].get_object(cr, uid, 'product', 'product_uom_hour', context=context)
except ValueError:
uom_hour = False
if not obj_tm or not uom_hour or obj_tm.id == uom_hour.id:
return res
eview = etree.fromstring(res['arch'])
# if the project_time_mode_id is not in hours (so in days), display it as a float field
def _check_rec(eview):
if eview.attrib.get('widget','') == 'float_time':
eview.set('widget','float')
for child in eview:
_check_rec(child)
return True
_check_rec(eview)
res['arch'] = etree.tostring(eview)
# replace reference of 'Hours' to 'Day(s)'
for f in res['fields']:
# TODO this NOT work in different language than english
# the field 'Initially Planned Hours' should be replaced by 'Initially Planned Days'
# but string 'Initially Planned Days' is not available in translation
if 'Hours' in res['fields'][f]['string']:
res['fields'][f]['string'] = res['fields'][f]['string'].replace('Hours', obj_tm.name)
return res
def get_empty_list_help(self, cr, uid, help, context=None):
context = dict(context or {})
context['empty_list_help_id'] = context.get('default_project_id')
context['empty_list_help_model'] = 'project.project'
context['empty_list_help_document_name'] = _("tasks")
return super(task, self).get_empty_list_help(cr, uid, help, context=context)
# ----------------------------------------
# Case management
# ----------------------------------------
def stage_find(self, cr, uid, cases, section_id, domain=[], order='sequence', context=None):
""" Override of the base.stage method
Parameter of the stage search taken from the lead:
- section_id: if set, stages must belong to this section or
be a default stage; if not set, stages must be default
stages
"""
if isinstance(cases, (int, long)):
cases = self.browse(cr, uid, cases, context=context)
# collect all section_ids
section_ids = []
if section_id:
section_ids.append(section_id)
for task in cases:
if task.project_id:
section_ids.append(task.project_id.id)
search_domain = []
if section_ids:
search_domain = [('|')] * (len(section_ids) - 1)
for section_id in section_ids:
search_domain.append(('project_ids', '=', section_id))
search_domain += list(domain)
# perform search, return the first found
stage_ids = self.pool.get('project.task.type').search(cr, uid, search_domain, order=order, context=context)
if stage_ids:
return stage_ids[0]
return False
def _check_child_task(self, cr, uid, ids, context=None):
if context == None:
context = {}
tasks = self.browse(cr, uid, ids, context=context)
for task in tasks:
if task.child_ids:
for child in task.child_ids:
if child.stage_id and not child.stage_id.fold:
raise UserError(_("Child task still open.\nPlease cancel or complete child task first."))
return True
def _store_history(self, cr, uid, ids, context=None):
for task in self.browse(cr, uid, ids, context=context):
self.pool.get('project.task.history').create(cr, uid, {
'task_id': task.id,
'remaining_hours': task.remaining_hours,
'planned_hours': task.planned_hours,
'kanban_state': task.kanban_state,
'type_id': task.stage_id.id,
'user_id': task.user_id.id
}, context=context)
return True
# ------------------------------------------------
# CRUD overrides
# ------------------------------------------------
def create(self, cr, uid, vals, context=None):
context = dict(context or {})
# for default stage
if vals.get('project_id') and not context.get('default_project_id'):
context['default_project_id'] = vals.get('project_id')
# user_id change: update date_assign
if vals.get('user_id'):
vals['date_assign'] = fields.datetime.now()
# context: no_log, because subtype already handle this
create_context = dict(context, mail_create_nolog=True)
task_id = super(task, self).create(cr, uid, vals, context=create_context)
self._store_history(cr, uid, [task_id], context=context)
return task_id
def write(self, cr, uid, ids, vals, context=None):
if isinstance(ids, (int, long)):
ids = [ids]
# stage change: update date_last_stage_update
if 'stage_id' in vals:
vals['date_last_stage_update'] = fields.datetime.now()
# user_id change: update date_assign
if vals.get('user_id'):
vals['date_assign'] = fields.datetime.now()
# Overridden to reset the kanban_state to normal whenever
# the stage (stage_id) of the task changes.
if vals and not 'kanban_state' in vals and 'stage_id' in vals:
new_stage = vals.get('stage_id')
vals_reset_kstate = dict(vals, kanban_state='normal')
for t in self.browse(cr, uid, ids, context=context):
write_vals = vals_reset_kstate if t.stage_id.id != new_stage else vals
super(task, self).write(cr, uid, [t.id], write_vals, context=context)
result = True
else:
result = super(task, self).write(cr, uid, ids, vals, context=context)
if any(item in vals for item in ['stage_id', 'remaining_hours', 'user_id', 'kanban_state']):
self._store_history(cr, uid, ids, context=context)
return result
def unlink(self, cr, uid, ids, context=None):
if context is None:
context = {}
self._check_child_task(cr, uid, ids, context=context)
res = super(task, self).unlink(cr, uid, ids, context)
return res
def _get_total_hours(self):
return self.remaining_hours
def _generate_task(self, cr, uid, tasks, ident=4, context=None):
context = context or {}
result = ""
ident = ' '*ident
company = self.pool["res.users"].browse(cr, uid, uid, context=context).company_id
duration_uom = {
'day(s)': 'd', 'days': 'd', 'day': 'd', 'd': 'd',
'month(s)': 'm', 'months': 'm', 'month': 'month', 'm': 'm',
'week(s)': 'w', 'weeks': 'w', 'week': 'w', 'w': 'w',
'hour(s)': 'H', 'hours': 'H', 'hour': 'H', 'h': 'H',
}.get(company.project_time_mode_id.name.lower(), "hour(s)")
for task in tasks:
if task.stage_id and task.stage_id.fold:
continue
result += '''
%sdef Task_%s():
%s todo = \"%.2f%s\"
%s effort = \"%.2f%s\"''' % (ident, task.id, ident, task.remaining_hours, duration_uom, ident, task._get_total_hours(), duration_uom)
start = []
for t2 in task.parent_ids:
start.append("up.Task_%s.end" % (t2.id,))
if start:
result += '''
%s start = max(%s)
''' % (ident,','.join(start))
if task.user_id:
result += '''
%s resource = %s
''' % (ident, 'User_'+str(task.user_id.id))
result += "\n"
return result
# ---------------------------------------------------
# Mail gateway
# ---------------------------------------------------
def _track_subtype(self, cr, uid, ids, init_values, context=None):
record = self.browse(cr, uid, ids[0], context=context)
if 'kanban_state' in init_values and record.kanban_state == 'blocked':
return 'project.mt_task_blocked'
elif 'kanban_state' in init_values and record.kanban_state == 'done':
return 'project.mt_task_ready'
elif 'user_id' in init_values and record.user_id: # assigned -> new
return 'project.mt_task_new'
elif 'stage_id' in init_values and record.stage_id and record.stage_id.sequence <= 1: # start stage -> new
return 'project.mt_task_new'
elif 'stage_id' in init_values:
return 'project.mt_task_stage'
return super(task, self)._track_subtype(cr, uid, ids, init_values, context=context)
def _notification_group_recipients(self, cr, uid, ids, message, recipients, done_ids, group_data, context=None):
""" Override the mail.thread method to handle project users and officers
recipients. Indeed those will have specific action in their notification
emails: creating tasks, assigning it. """
group_project_user = self.pool['ir.model.data'].xmlid_to_res_id(cr, uid, 'project.group_project_user')
for recipient in recipients:
if recipient.id in done_ids:
continue
if recipient.user_ids and group_project_user in recipient.user_ids[0].groups_id.ids:
group_data['group_project_user'] |= recipient
done_ids.add(recipient.id)
return super(task, self)._notification_group_recipients(cr, uid, ids, message, recipients, done_ids, group_data, context=context)
def _notification_get_recipient_groups(self, cr, uid, ids, message, recipients, context=None):
res = super(task, self)._notification_get_recipient_groups(cr, uid, ids, message, recipients, context=context)
take_action = self._notification_link_helper(cr, uid, ids, 'assign', context=context)
new_action_id = self.pool['ir.model.data'].xmlid_to_res_id(cr, uid, 'project.action_view_task')
new_action = self._notification_link_helper(cr, uid, ids, 'new', context=context, action_id=new_action_id)
task_record = self.browse(cr, uid, ids[0], context=context)
actions = []
if not task_record.user_id:
actions.append({'url': take_action, 'title': _('I take it')})
else:
actions.append({'url': new_action, 'title': _('New Task')})
res['group_project_user'] = {
'actions': actions
}
return res
@api.cr_uid_context
def message_get_reply_to(self, cr, uid, ids, default=None, context=None):
""" Override to get the reply_to of the parent project. """
tasks = self.browse(cr, SUPERUSER_ID, ids, context=context)
project_ids = set([task.project_id.id for task in tasks if task.project_id])
aliases = self.pool['project.project'].message_get_reply_to(cr, uid, list(project_ids), default=default, context=context)
return dict((task.id, aliases.get(task.project_id and task.project_id.id or 0, False)) for task in tasks)
def email_split(self, cr, uid, ids, msg, context=None):
email_list = tools.email_split((msg.get('to') or '') + ',' + (msg.get('cc') or ''))
# check left-part is not already an alias
task_ids = self.browse(cr, uid, ids, context=context)
aliases = [task.project_id.alias_name for task in task_ids if task.project_id]
return filter(lambda x: x.split('@')[0] not in aliases, email_list)
def message_new(self, cr, uid, msg, custom_values=None, context=None):
""" Override to updates the document according to the email. """
if custom_values is None:
custom_values = {}
defaults = {
'name': msg.get('subject'),
'planned_hours': 0.0,
'partner_id': msg.get('author_id', False)
}
defaults.update(custom_values)
res = super(task, self).message_new(cr, uid, msg, custom_values=defaults, context=context)
email_list = self.email_split(cr, uid, [res], msg, context=context)
partner_ids = filter(None, self._find_partner_from_emails(cr, uid, [res], email_list, force_create=False, context=context))
self.message_subscribe(cr, uid, [res], partner_ids, context=context)
return res
def message_update(self, cr, uid, ids, msg, update_vals=None, context=None):
""" Override to update the task according to the email. """
if update_vals is None:
update_vals = {}
maps = {
'cost': 'planned_hours',
}
for line in msg['body'].split('\n'):
line = line.strip()
res = tools.command_re.match(line)
if res:
match = res.group(1).lower()
field = maps.get(match)
if field:
try:
update_vals[field] = float(res.group(2).lower())
except (ValueError, TypeError):
pass
email_list = self.email_split(cr, uid, ids, msg, context=context)
partner_ids = filter(None, self._find_partner_from_emails(cr, uid, ids, email_list, force_create=False, context=context))
self.message_subscribe(cr, uid, ids, partner_ids, context=context)
return super(task, self).message_update(cr, uid, ids, msg, update_vals=update_vals, context=context)
def message_get_suggested_recipients(self, cr, uid, ids, context=None):
recipients = super(task, self).message_get_suggested_recipients(cr, uid, ids, context=context)
for data in self.browse(cr, uid, ids, context=context):
if data.partner_id:
reason = _('Customer Email') if data.partner_id.email else _('Customer')
data._message_add_suggested_recipient(recipients, partner=data.partner_id, reason=reason)
return recipients
class account_analytic_account(osv.osv):
_inherit = 'account.analytic.account'
_description = 'Analytic Account'
def _compute_project_count(self, cr, uid, ids, fieldnames, args, context=None):
result = dict.fromkeys(ids, 0)
for account in self.browse(cr, uid, ids, context=context):
result[account.id] = len(account.project_ids)
return result
_columns = {
'use_tasks': fields.boolean('Tasks', help="Check this box to manage internal activities through this project"),
'company_uom_id': fields.related('company_id', 'project_time_mode_id', string="Company UOM", type='many2one', relation='product.uom'),
'project_ids': fields.one2many('project.project', 'analytic_account_id', 'Projects'),
'project_count': fields.function(_compute_project_count, 'Project Count', type='integer')
}
def on_change_template(self, cr, uid, ids, template_id, date_start=False, context=None):
res = super(account_analytic_account, self).on_change_template(cr, uid, ids, template_id, date_start=date_start, context=context)
if template_id and 'value' in res:
template = self.browse(cr, uid, template_id, context=context)
res['value']['use_tasks'] = template.use_tasks
return res
def _trigger_project_creation(self, cr, uid, vals, context=None):
'''
This function is used to decide if a project needs to be automatically created or not when an analytic account is created. It returns True if it needs to be so, False otherwise.
'''
if context is None: context = {}
return vals.get('use_tasks') and not 'project_creation_in_progress' in context
@api.cr_uid_id_context
def project_create(self, cr, uid, analytic_account_id, vals, context=None):
'''
This function is called at the time of analytic account creation and is used to create a project automatically linked to it if the conditions are meet.
'''
project_pool = self.pool.get('project.project')
project_id = project_pool.search(cr, uid, [('analytic_account_id','=', analytic_account_id)])
if not project_id and self._trigger_project_creation(cr, uid, vals, context=context):
project_values = {
'name': vals.get('name'),
'analytic_account_id': analytic_account_id,
'use_tasks': True,
}
return project_pool.create(cr, uid, project_values, context=context)
return False
def create(self, cr, uid, vals, context=None):
if context is None:
context = {}
if vals.get('child_ids', False) and context.get('analytic_project_copy', False):
vals['child_ids'] = []
analytic_account_id = super(account_analytic_account, self).create(cr, uid, vals, context=context)
self.project_create(cr, uid, analytic_account_id, vals, context=context)
return analytic_account_id
def write(self, cr, uid, ids, vals, context=None):
if isinstance(ids, (int, long)):
ids = [ids]
vals_for_project = vals.copy()
for account in self.browse(cr, uid, ids, context=context):
if not vals.get('name'):
vals_for_project['name'] = account.name
self.project_create(cr, uid, account.id, vals_for_project, context=context)
return super(account_analytic_account, self).write(cr, uid, ids, vals, context=context)
def unlink(self, cr, uid, ids, context=None):
proj_ids = self.pool['project.project'].search(cr, uid, [('analytic_account_id', 'in', ids)])
has_tasks = self.pool['project.task'].search(cr, uid, [('project_id', 'in', proj_ids)], count=True, context=context)
if has_tasks:
raise UserError(_('Please remove existing tasks in the project linked to the accounts you want to delete.'))
return super(account_analytic_account, self).unlink(cr, uid, ids, context=context)
def name_search(self, cr, uid, name, args=None, operator='ilike', context=None, limit=100):
if args is None:
args = []
if context is None:
context={}
if context.get('current_model') == 'project.project':
project_ids = self.search(cr, uid, args + [('name', operator, name)], limit=limit, context=context)
return self.name_get(cr, uid, project_ids, context=context)
return super(account_analytic_account, self).name_search(cr, uid, name, args=args, operator=operator, context=context, limit=limit)
def projects_action(self, cr, uid, ids, context=None):
accounts = self.browse(cr, uid, ids, context=context)
project_ids = sum([account.project_ids.ids for account in accounts], [])
result = {
"type": "ir.actions.act_window",
"res_model": "project.project",
"views": [[False, "tree"], [False, "form"]],
"domain": [["id", "in", project_ids]],
"context": {"create": False},
"name": "Projects",
}
if len(project_ids) == 1:
result['views'] = [(False, "form")]
result['res_id'] = project_ids[0]
else:
result = {'type': 'ir.actions.act_window_close'}
return result
class project_project(osv.osv):
_inherit = 'project.project'
_defaults = {
'use_tasks': True
}
class project_task_history(osv.osv):
"""
Tasks History, used for cumulative flow charts (Lean/Agile)
"""
_name = 'project.task.history'
_description = 'History of Tasks'
_rec_name = 'task_id'
_log_access = False
def _get_date(self, cr, uid, ids, name, arg, context=None):
result = {}
for history in self.browse(cr, uid, ids, context=context):
if history.type_id and history.type_id.fold:
result[history.id] = history.date
continue
cr.execute('''select
date
from
project_task_history
where
task_id=%s and
id>%s
order by id limit 1''', (history.task_id.id, history.id))
res = cr.fetchone()
result[history.id] = res and res[0] or False
return result
def _get_related_date(self, cr, uid, ids, context=None):
result = []
for history in self.browse(cr, uid, ids, context=context):
cr.execute('''select
id
from
project_task_history
where
task_id=%s and
id<%s
order by id desc limit 1''', (history.task_id.id, history.id))
res = cr.fetchone()
if res:
result.append(res[0])
return result
_columns = {
'task_id': fields.many2one('project.task', 'Task', ondelete='cascade', required=True, select=True),
'type_id': fields.many2one('project.task.type', 'Stage'),
'kanban_state': fields.selection([('normal', 'Normal'), ('blocked', 'Blocked'), ('done', 'Ready for next stage')], 'Kanban State', required=False),
'date': fields.date('Date', select=True),
'end_date': fields.function(_get_date, string='End Date', type="date", store={
'project.task.history': (_get_related_date, None, 20)
}),
'remaining_hours': fields.float('Remaining Time', digits=(16, 2)),
'planned_hours': fields.float('Planned Time', digits=(16, 2)),
'user_id': fields.many2one('res.users', 'Responsible'),
}
_defaults = {
'date': fields.date.context_today,
}
class project_task_history_cumulative(osv.osv):
_name = 'project.task.history.cumulative'
_table = 'project_task_history_cumulative'
_inherit = 'project.task.history'
_auto = False
_columns = {
'end_date': fields.date('End Date'),
'nbr_tasks': fields.integer('# of Tasks', readonly=True),
'project_id': fields.many2one('project.project', 'Project'),
}
def init(self, cr):
tools.drop_view_if_exists(cr, 'project_task_history_cumulative')
cr.execute(""" CREATE VIEW project_task_history_cumulative AS (
SELECT
history.date::varchar||'-'||history.history_id::varchar AS id,
history.date AS end_date,
*
FROM (
SELECT
h.id AS history_id,
h.date+generate_series(0, CAST((coalesce(h.end_date, DATE 'tomorrow')::date - h.date) AS integer)-1) AS date,
h.task_id, h.type_id, h.user_id, h.kanban_state,
count(h.task_id) as nbr_tasks,
greatest(h.remaining_hours, 1) AS remaining_hours, greatest(h.planned_hours, 1) AS planned_hours,
t.project_id
FROM
project_task_history AS h
JOIN project_task AS t ON (h.task_id = t.id)
GROUP BY
h.id,
h.task_id,
t.project_id
) AS history
)
""")
class project_tags(osv.Model):
""" Tags of project's tasks (or issues) """
_name = "project.tags"
_description = "Tags of project's tasks, issues..."
_columns = {
'name': fields.char('Name', required=True),
'color': fields.integer('Color Index'),
}
_sql_constraints = [
('name_uniq', 'unique (name)', "Tag name already exists !"),
]
| bplancher/odoo | addons/project/project.py | Python | agpl-3.0 | 53,906 | [
"VisIt"
] | 1ab893ee6f2ad42571809356a3268ef63f20569086414c46e27a3f7de9213d3d |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2016-06-05 10:20
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('visit', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='visit',
name='user',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL),
),
]
| anhelus/emw-museum-output | visit/migrations/0002_auto_20160605_1220.py | Python | gpl-3.0 | 563 | [
"VisIt"
] | a95be434ba8d3cfb201087df875595c80e5b94644d00536316f9da555da3886a |
import numpy as np
import os
import math
from skimage.io import imread
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import MaxNLocator
import scipy.stats as stats
import matplotlib.pyplot as plt
def create_isotropic_gaussian_twindataset(pos, amount_data, variance, sep_vec):
"""
Creates two isotropic gaussian datasets with a given variance.
Args:
pos: centroid of the two datasets
amount_data: amount of data per dataset
sep_vec: sepration vector between centroid and each centerpoint of the datasets
variance: varaince of the gaussian distribution
Returns:
"""
sigma = np.sqrt(variance)
mu1 = np.add(pos,sep_vec)
mu2 = np.subtract(pos, sep_vec)
d1 = np.random.normal(0.0, sigma, (amount_data, 2))
d2 = np.random.normal(0.0, sigma, (amount_data, 2))
return np.asmatrix(np.add(d1, mu1)), np.asmatrix(np.add(d2, mu2)), ['DarkGreen', 'Yellow']
def align(data):
"""
Return the data aligned with its mean values.
Args:
data (pandas.core.frame.DataFrame):
Dataset which should be aligned
Returns:
pandas.core.frame.DataFrame: New DataFrame with aligned data.
"""
mean = data.mean()
return data.sub(mean)
def get_person_images(path, ext, min):
"""
Returns all directories which have a min amount of files of type ext.
Args:
path (string): path entrypoint wehre to start
ext (string): extension of the files
min (int): minimal amount of files in directory
Returns (list):
A list with tuples containing the root path an the containing files
of the matching directories.
"""
import re
# for all leaves in directory tree
for root, dirs, files in os.walk(path):
if not dirs:
filtered_files = [x for x in files if re.search('{}$'.format(ext), x)]
if len(filtered_files) >= min:
yield (root, files)
def imstack2vectors(image):
"""
Args:
image:
Returns:
"""
s = image.shape
if len(s) == 3:
return np.asarray([image[:,:,index].flatten() for index in range(s[2])]).T
else:
return image.flatten()
def get_dataset(root, files, scale_factor=1, crop_factor=1):
"""
Args:
root (string): path to images
files (list): list of image files in directory root
scale_factor (int): scale image by this factor
cropfactor: 1/crop_factor
Returns (dict):
Returns _data_ in a numpy array and metadata (_name_ and _amount_ of data)
keys: 'amount', 'name', 'data'
"""
name = root.split('/')[-1]
amount_img = len(files)
frame = []
for f in files:
img = imread('{}/{}'.format(root,f), as_grey=True)
lx,ly = img.shape
cropped_img = img[lx / crop_factor: - lx / crop_factor, ly / crop_factor: - ly / crop_factor]
# make it work if someone
scale = int(scale_factor)
if scale > 1:
cropped_img = cropped_img[::scale,::scale]
cropped_img = imstack2vectors(cropped_img)
frame.append(cropped_img)
nparray = np.array(frame)
return name, nparray, amount_img
class Perceptron(object):
"""
Perceptron learning machine
"""
def __init__(self, data):
"""
Args:
data: List of 2 Datasets (2 Classes)
Each must datapoint must be in the same vector space R^d
"""
_data = np.vstack(data)
amount1 = data[0].shape[0]
amount2 = data[1].shape[0]
self.l, self.d = _data.shape
self.R = np.max(np.linalg.norm(_data, axis=1)) ** 2
self.data = np.zeros((self.l, self.d))
self.data[:,:] = _data
self.y = np.vstack([1]*amount1 + [-1]*amount2)
self.w = np.zeros(self.d)
self.b = 0
self.eta = 0.1 # learnrate
print("data set length: {}".format(self.l))
print("data point dimension: {}".format(self.d))
print("R: {}".format(self.R))
self.wsteps = []
self.mi = np.zeros(self.l)
def learn(self, max_iterations=0):
"""
Args:
max_iterations: limits how often the dataset will be iterrated
Returns:
None
"""
steps = 0
i = 0
while min(self.mi) <= 0.0:
i = (i + 1) % self.l
# update functional margin
self.mi[i] = self.y[i] * (np.dot(self.w, self.data[i]) + self.b)
if self.mi[i] <= 0.0: # update on error classification
# update weight vector
self.w = self.w + self.y[i] * self.eta * self.data[i]
self.b = self.b + self.y[i] * self.eta * self.R
self.wsteps.append((self.w.copy(), self.b))
if i == 0:
steps += 1
if max_iterations > 0 and steps > max_iterations:
print("Did not find linear seperation after {} steps".format(self.l * steps))
break
print("correction steps: {}".format(len(self.wsteps)))
print("Last step: b = {}, \n\t w = {}".format(self.b, self.w))
print("Functional margin min and max: {}, {}".format(min(self.mi), max(self.mi)))
def classify(self, data, labels, c=0, verbose=True):
"""
Args:
data: data matrix which should be classified
labels: vector with labels to check classification
Returns:
"""
false_negative, true_negative, false_positive, true_positive = 0,0,0,0
for xi, yi in zip(data, labels):
m = xi.dot(self.w) + self.b + c
if m <= 0.0: # negative
if int(yi) < 0:
true_negative += 1
else:
false_negative += 1
else: # positive
if int(yi) > 0:
true_positive += 1
else:
false_positive += 1
positives = len([i for i in labels if i > 0])
negatives = len([i for i in labels if i < 0])
if verbose:
print("False negative (Miss): {} --> {:.2f}%".format(false_negative, 100 * false_negative / positives))
print("False positive (Fehlalarmrate): {} --> {:.2f}%".format(false_positive, 100 * false_positive / negatives))
print("True negative (korrekte Rückweisung): {} --> {:.2f}%".format(true_negative, 100 * true_negative / negatives))
print("True positive (Detektionswahrscheinlichkeit): {} --> {:.2f}%".format(true_positive, 100 * true_positive / positives))
return true_positive/positives, false_positive/negatives
def plot_result2D(self, colors, amount_correction_steps=2, ):
"""
Does only work with 2 dimensional data
Args:
amount_correction_steps: limits plots of the seperating planes
Returns:
plot object
"""
if self.d != 2:
raise TypeError("Datapoints must be 2 dimensional")
x_min = np.min(self.data[:,0])
x_max = np.max(self.data[:,0])
y_min = np.min(self.data[:,1])
y_max = np.max(self.data[:,1])
min_value = min(x_min, y_min) - 1
max_value = max(x_max, y_max) + 1
amount = len(self.wsteps)
if amount_correction_steps >= 0 and amount_correction_steps < amount:
amount = amount_correction_steps + 1
rows = np.ceil(amount / 3) + 1
plt.figure(figsize=(15,5*rows))
tmp_ax = plt.subplot(rows, 3, 2)
tmp_ax.axis([min_value,max_value, min_value,max_value])
ax = [tmp_ax]
for j in range(0, amount):
tmp_ax = plt.subplot(rows, 3, 4 + j)
tmp_ax.axis([min_value,max_value, min_value,max_value])
ax.append(tmp_ax)
mask1 = self.y == 1
mask2 = self.y == -1
d1 = np.compress(mask1[:,0], self.data, axis=0)
d2 = np.compress(mask2[:,0], self.data, axis=0)
for axi in ax:
axi.scatter(d1[:,0], d1[:,1], c=colors[0])
axi.scatter(d2[:,0], d2[:,1], c=colors[1])
self.plot_separating_plane('Blue', ax[0], "End result")
lower = len(self.wsteps) - amount
i = lower
for axi, plane_param in zip(ax[1:], self.wsteps[lower:]):
self.plot_separating_plane('Blue', axi, 'Step {}'.format(i + 1), plane_param)
i += 1
return ax
def plot_separating_plane(self, color, ax, name, plane_param=None):
"""
Args:
color: color for the plane
ax: matplotlib ax object
weights: weight vector, if none the current weight vector of the object is used
"""
x_min = np.min(self.data[:,0])
x_max = np.max(self.data[:,0])
y_min = np.min(self.data[:,1])
y_max = np.max(self.data[:,1])
min_value = min(x_min, y_min) - 1
max_value = max(x_max, y_max) + 1
if plane_param is None:
w = self.w
b = self.b
else:
w = plane_param[0]
b = plane_param[1]
axis_x = np.arange(min_value,max_value)
axis_y = -(b + axis_x * w[0])/w[1]
ax.plot(axis_x, axis_y,'--', color=color)
ax.set_title(name)
def plot_discriminant_function(self):
x_min = np.min(self.data[:,0])
x_max = np.max(self.data[:,0])
y_min = np.min(self.data[:,1])
y_max = np.max(self.data[:,1])
min_value = min(x_min, y_min) - 1
max_value = max(x_max, y_max) + 1
window_size = np.asarray([min_value, max_value])
x = np.arange(window_size[0]-1, window_size[1]+1)
y = np.arange(window_size[0]-1, window_size[1]+1)
X, Y = np.meshgrid(x, y)
Z = np.zeros(X.shape)
tmp_array = np.zeros(2)
for i in range(0, len(Z)):
for j in range (0, len(Z)):
tmp_array[0] = X[i][j]
tmp_array[1] = Y[i][j]
scalar = np.dot(self.w,tmp_array)
res = scalar + self.b
res = (res * 100) / 100.0
Z[i][j] = res
fig, ax = plt.subplots(nrows=1)
levels = MaxNLocator(nbins=3).tick_values(Z.min(), Z.max())
cmap = plt.get_cmap('afmhot')
cf = ax.contourf(X,Y,Z, levels=levels, cmap=cmap)
fig.colorbar(cf, ax=ax)
ax.set_title('heatmap')
mask1 = self.y == 1
mask2 = self.y == -1
d1 = np.compress(mask1[:,0], self.data, axis=0)
d2 = np.compress(mask2[:,0], self.data, axis=0)
ax.scatter(d1[:,0], d1[:,1], color='DarkBlue')
ax.scatter(d2[:,0], d2[:,1], color='Yellow')
fig.tight_layout()
plt.show()
class GaussianNaiveBayes(object):
def __init__(self, data):
self.data_class1 , self.data_class2 = data
self.l_class1 = len(self.data_class1)
self.l_class2 = len(self.data_class2)
self.l_all = self.l_class1 + self.l_class2
def learn(self):
self.p_class1 = self.l_class1 / self.l_all
self.p_class2 = self.l_class2 / self.l_all
# A1 flattens the resulting (1,d) matrix
self.mean_class1 = np.mean(self.data_class1, axis=0).A1
self.mean_class2 = np.mean(self.data_class2, axis=0).A1
self.std_class1 = np.std(self.data_class1, axis=0).A1
self.std_class2 = np.std(self.data_class2, axis=0).A1
rows = np.ceil(len(self.mean_class1) / 3.0)
fig = plt.figure(figsize=(15,5*rows))
fig.suptitle('positives:', fontsize=20)
for index, (mu, std) in enumerate(zip(self.mean_class1, self.std_class1)):
ax = plt.subplot(rows, 3, index + 1)
ax.set_title("{}: mu={:.3f}, std={:.3f}".format(index, mu, std))
h = np.sort(self.data_class1[:,index],axis=0)
fit = stats.norm.pdf(h, mu, std)
plt.plot(h,fit,'-o')
plt.hist(h,normed=True)
fig = plt.figure(figsize=(15,5*rows))
fig.suptitle('negatives:', fontsize=20)
for index, (mu, std) in enumerate(zip(self.mean_class2, self.std_class2)):
ax = plt.subplot(rows, 3, index + 1)
ax.set_title("{}: mu={:.3f}, std={:.3f}".format(index, mu, std))
h = np.sort(self.data_class2[:,index],axis=0)
fit = stats.norm.pdf(h, mu, std)
plt.plot(h,fit,'-o')
plt.hist(h,normed=True)
plt.show()
def classify(self, data, label, c=None, verbose=True):
"""
Args:
data: datapoints which should be classified
label: label_vector to check if classification is correct
Returns:
"""
true_positive = 0
true_negative = 0
false_positive = 0
false_negative = 0
classifaction_vector = []
for datapoint, y in zip(data, label):
dp = datapoint.A1
likelihood_class1 = np.multiply.reduce([GaussianNaiveBayes.GNB(x,mu,sigma) for x, mu, sigma in zip(dp, self.mean_class1, self.std_class1)])
likelihood_class2 = np.multiply.reduce([GaussianNaiveBayes.GNB(x,mu,sigma) for x, mu, sigma in zip(dp, self.mean_class2, self.std_class2)])
if self.discriminant_function(self.p_class1, self.p_class2, likelihood_class1, likelihood_class2, c) >= 0:
# detected as positive
if int(y) > 0:
true_positive += 1
else:
false_positive += 1
classifaction_vector.append(1)
else:
#detected as negative
if int(y) < 0:
true_negative += 1
else:
false_negative += 1
classifaction_vector.append(-1)
positives = len([i for i in label if i > 0])
negatives = len([i for i in label if i < 0])
if verbose:
print("False negative (Miss): {} --> {:.2f}%".format(false_negative, 100 * false_negative / positives))
print("False positive (Fehlalarmrate): {} --> {:.2f}%".format(false_positive, 100 * false_positive / negatives))
print("True negative (korrekte Rückweisung): {} --> {:.2f}%".format(true_negative, 100 * true_negative / negatives))
print("True positive (Detektionsrate): {} --> {:.2f}%".format(true_positive, 100 * true_positive / positives))
return true_positive/positives, false_positive/negatives
@staticmethod
def discriminant_function(apriori1, apriori2, likelihood1, likelihood2, c=None):
if c:
res = (c*likelihood1/likelihood2) - 1
return res
else:
return ((apriori1*likelihood1)/(apriori2*likelihood2)) - 1
def plot_discriminant_function(self, colors):
min_val = -2
max_val = 2
window_size = np.arange(min_val-1, max_val+1)
X,Y = np.meshgrid(window_size,window_size)
Z = np.zeros(X.shape)
x = np.zeros(2)
for i in range(0, Z.shape[0]):
for j in range (0, Z.shape[1]):
x[0] = X[i][j]
x[1] = Y[i][j]
likelihood_class1 = np.multiply.reduce([GaussianNaiveBayes.GNB(x,mu,sigma) for x, mu, sigma in zip(x, self.mean_class1, self.std_class1)])
likelihood_class2 = np.multiply.reduce([GaussianNaiveBayes.GNB(x,mu,sigma) for x, mu, sigma in zip(x, self.mean_class2, self.std_class2)])
Z[i][j] = self.discriminant_function(self.p_class1, self.p_class2, likelihood_class1, likelihood_class2)
levels = MaxNLocator(nbins=15).tick_values(Z.min(), Z.max())
cmap = plt.get_cmap('gray')
fig, ax = plt.subplots(nrows=1)
cf = ax.contourf(X,Y,Z, levels=levels, cmap=cmap)
fig.colorbar(cf, ax=ax)
ax.set_title('heatmap')
ax.scatter(self.data_class1[:,0], self.data_class1[:,1], c=colors[0])
ax.scatter(self.data_class2[:,0], self.data_class2[:,1], c=colors[1])
fig.tight_layout()
plt.show()
@staticmethod
def GNB(x, mu, sigma):
variance = sigma**2
pi = math.pi
a = 1 / np.sqrt(2*pi*variance)
exp = np.exp((-0.5 / variance) * (x - mu)**2)
return a*exp
| mahieke/maschinelles_lernen | a3/util/__init__.py | Python | mit | 16,551 | [
"Gaussian"
] | d1bdd6f960284dd63d852724db99fbacab7f6b05f258e021cbb0dca207628659 |
#!/usr/bin/env python
import numpy as np
import matplotlib
# matplotlib.use("Agg")
import matplotlib.pyplot as plt
import math
import scipy.signal
"""
Usage:
som = Planar(3,20,10)
; set other parameters?
for i in range(0,1000):
som.train([red,green,blue])
self.clasify([0.1,0.4,0.5])
"""
class Planar:
def __init__(self, vector_size, map_size):
self._length = vector_size
self._width = map_size[0]
self._height = map_size[1]
self._map = np.random.random(map_size + (vector_size, ))
self._train = 0
self._coef = 0.05
self._delta = min(self._width, self._height)/20
# self._sim = scipy.signal.ricker(self._delta, (self._delta-1)/2)
# self._sim /= np.max(self._sim)
self._sim = scipy.signal.gaussian(1+2*self._delta, self._delta/2)
self._sim /= np.max(self._sim)
# self._sim = scipy.signal.flattop(self._delta)
def delta(self, delta = None):
if delta:
self._delta = delta
self._sim = scipy.signal.gaussian(1+2*self._delta, self._delta/2)
self._sim /= np.max(self._sim)
else:
return self._delta
def train(self, vector):
self._train += 1
# bou_score = float('inf') # best maching unit
bou_score = 0 # best maching unit
bou_index = (0,0)
for i in range(0, self._width):
for j in range(0, self._height):
score = 0
tile = self._map[i,j]
# score = math.sqrt( sum( (a - b)**2 for a,b in zip(vector,self._map[i,j])))
# score = sum( (a - b)**2 for a,b in zip(vector,self._map[i,j]))
mvector = np.sqrt( np.dot(vector,vector) )
mtile = np.sqrt( np.dot(tile,tile) )
dotp = np.dot(vector,tile)
score = dotp/(mvector*mtile)
if (score > 1):
import pdb; pdb.set_trace()
# score = abs( sum( (a - b) for a,b in zip(vector,self._map[i,j])))
if score > bou_score:
bou_score = score
bou_index = (i,j)
# min_x = max(bou_index[0] - self._delta, 0);
# max_x = min(bou_index[0] + self._delta, self._width - 1)
# min_y = max(bou_index[1] - self._delta, 0);
# max_y = min(bou_index[1] + self._delta, self._height - 1)
min_x = bou_index[0] - self._delta
max_x = bou_index[0] + self._delta
min_y = bou_index[1] - self._delta
max_y = bou_index[1] + self._delta
for i in range(min_x, max_x + 1):
if i < 0 or i > self._width - 1:
continue
for j in range(min_y, max_y + 1):
if j < 0 or j > self._height - 1:
continue
sim = self._sim[i - min_x] * self._sim[j - min_y]
# print self._sim
# print sim
# import pdb; pdb.set_trace()
for k in range(0, self._length):
self._map[i,j,k] -= sim * self._coef * (self._map[i,j,k] - vector[k])
self._map[i,j,k] = max(self._map[i,j,k],0)
self._map[i,j,k] = min(self._map[i,j,k],1)
def clasify(self, vector):
pass
"""
Usage:
som = Planar(3,20,10)
vis = Visualizer(som)
som.train([red,green,blue])
vis.show()
for i in range(0,1000):
som.train([red,green,blue])
vis.animate()
"""
class Visualizer:
def __init__(self, kohonen):
self._som = kohonen
# plt.ion()
self._im = plt.imshow(self._som._map)
self._anim = []
def show(self):
# print "Show ", self._som
self._im.set_data(self._som._map)
self._anim.append( np.copy(self._som._map) )
plt.draw()
plt.pause(0.1)
def animate(self):
# print "Animate ", self._som
# self.show()
self._anim.append( np.copy(self._som._map) )
# self._play()
def _play(self):
for i in self._anim:
self._im.set_data(i)
plt.draw()
plt.pause(0.1)
def save(self, filename):
import matplotlib
import matplotlib.animation
fig = plt.figure()
ims = []
y = 0
for i in self._anim:
im = plt.imshow(i)
# fname = "file%03d.png" % y
# y+=1
# plt.savefig(fname)
ims.append( [im] )
# Writer = matplotlib.animation.writers['ffmpeg']
# writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
print ims
im_ani = matplotlib.animation.ArtistAnimation(fig, ims)
# im_ani.save('im.mp4', writer=writer)
im_ani.save('im.mp4')
if __name__ == "__main__":
side = 100
som = Planar(3, (side,side))
som.delta(side/20)
som._coef = 0.1
vis = Visualizer(som)
vis.animate()
print ""
for i in range(0,100000):
if (i%100 == 0):
print "Train no ", i
vis.show()
vector = np.random.random(3)
som.train(vector)
vis.save('foobar')
# try:
# while True:
# vis.animate()
# except KeyboardInterrupt:
# pass
| karolciba/playground | kohonen/planar.py | Python | unlicense | 5,289 | [
"Gaussian"
] | 1d4afa59504d4f42f0c1380e235b953dd6a2dd7504b4a7aa80048f846bebba25 |
"""
.. versionadded:: 0.2
.. versionchanged:: 0.4
This function generates random steps according to given width of steps and
desired number of steps.
Example Usage
==================
Simplest example (10 steps with width 50 samples, normal distribution
with unit standard deviation and zero mean) follows.
.. code-block:: python
import signalz
x = signalz.random_steps(50, 10)
More complicated example with normal distribution (standard deviation and
mean value is changed) follows.
.. code-block:: python
import signalz
x = signalz.random_steps(50, steps_count=10, distribution="normal", std=30, mean=-10)
Another example, this time the size of the data is not requested by number of
steps, but by number of samples (`size=500`).
.. code-block:: python
import signalz
x = signalz.random_steps(50, size=500)
Function Documentation
======================================
"""
import numpy as np
from signalz.misc import check_type_or_raise
import signalz
def random_steps(step_width, steps_count=10, size=None, distribution="normal",
maximum=1, minimum=0, std=1, mean=0):
"""
This function generates random steps.
**Args:**
* `step_width` - desired width of every step (int)
**Kwargs:**
* `steps_count` - desired number steps (int), this variable is used,
if the `size` is not defined
* `size` - lenght of desired output in samples (int),
if this variable is defined,
it determines the size of data instead of `steps_count`
* `distribution` - distribution of random numbers (str), Options are
`normal` and `uniform`.
* `maximum` - maximal value for steps (float), this value is used
in case of uniform distribution.
* `minimum` - minimal value for steps (float), this value is used
in case of uniform distribution.
* `std` - standard deviation of random variable (float), this value
is used in case of gaussian (normal) distribution.
* `mean` - mean value of random variable (float), this value
is used in case of gaussian (normal) distribution.
**Returns:**
* vector of values representing desired steps (1d array)
"""
# check values
check_type_or_raise(steps_count, int, "steps count")
check_type_or_raise(step_width, int, "step width")
# get correct number of steps if size is defined
if not size is None:
check_type_or_raise(size, int, "size")
steps_count = int(np.ceil(size / float(step_width)))
# generate random values
if distribution in ["normal", "gaussian"]:
values = signalz.gaussian_white_noise(steps_count, offset=mean, std=std)
elif distribution == "uniform":
values = signalz.uniform_white_noise(steps_count, minimum=minimum,
maximum=maximum)
# generate steps
x = signalz.steps(step_width, values)
if size is None:
return x
else:
return x[:size]
| matousc89/signalz | signalz/generators/random_steps.py | Python | mit | 3,018 | [
"Gaussian"
] | 165f9386c96ee6feb80f2f22068b9c1e74657bbee94e357abcd302b887264cce |
import copy as cp
import logging
import lib.const as C
import lib.visit as v
from .. import util
from ..meta import class_lookup
from ..meta.template import Template
from ..meta.clazz import Clazz
from ..meta.method import Method
from ..meta.field import Field
from ..meta.statement import Statement, to_statements
from ..meta.expression import Expression
class Proxy(object):
@classmethod
def find_proxy(cls):
return lambda anno: anno.by_name(C.A.PROXY)
def __init__(self, smpls):
self._smpls = smpls
@v.on("node")
def visit(self, node):
"""
This is the generic method to initialize the dynamic dispatcher
"""
@v.when(Template)
def visit(self, node): pass
## @Proxy(P)
## class C { }
## =>
## class C {
## P _proxy;
## C(P p) { _proxy = p; }
## m$i$(...) { _proxy.m$i$(...); }
## }
@v.when(Clazz)
def visit(self, node):
if not util.exists(Proxy.find_proxy(), node.annos): return
_anno = util.find(Proxy.find_proxy(), node.annos)
if hasattr(_anno, "cid"): proxy = _anno.cid
elif len(node.sups) == 1: proxy = node.sups[0]
else: raise Exception("ambiguous proxy", _anno)
cname = node.name
logging.debug("reducing: @{}({}) class {}".format(C.A.PROXY, proxy, cname))
cls_p = class_lookup(proxy)
setattr(node, "proxy", cls_p)
# introduce a field to hold the proxy instance: P _proxy
fld = Field(clazz=node, typ=proxy, name=u"_proxy")
node.add_flds([fld])
node.init_fld(fld)
# if purely empty proxy
# add methods that delegate desired operations to the proxy
if not node.mtds:
for mtd_p in cls_p.mtds:
mname = mtd.name
mtd_cp = cp.deepcopy(mtd_p)
mtd_cp.clazz = node
if not mtd_p.is_init:
args = ", ".join(map(lambda (_, nm): nm, mtd_p.params))
body = u"_proxy.{mname}({args});".format(**locals())
if mtd_p.typ != C.J.v: body = u"return " + body
else: # cls' own <init>: cname(P p) { _proxy = p; }
mtd_cp.name = cname
mtd_cp.typ = cname
mtd_cp.params = [ (proxy, u"x") ]
body = u"_proxy = x;"
mtd_cp.body = to_statements(mtd_p, body)
logging.debug("{} ~> {}".format(mtd_cp.signature, mtd_p.signature))
node.add_mtds([mtd_cp])
@v.when(Field)
def visit(self, node): pass
@v.when(Method)
def visit(self, node):
if not hasattr(node.clazz, "proxy"): return
cls_p = getattr(node.clazz, "proxy")
mtd_p = cls_p.mtd_by_sig(node.name, node.param_typs)
if mtd_p: # method delegation
logging.debug("{} ~> {}".format(node.signature, mtd_p.signature))
mname = node.name
args = ", ".join(map(lambda (_, nm): nm, node.params))
body = u"_proxy.{mname}({args});".format(**locals())
if node.typ != C.J.v: body = u"return " + body
node.body = to_statements(node, body)
mname = node.name
if mname.startswith("get") and mname.endswith(cls_p.name):
body = u"return _proxy;"
node.body = to_statements(node, body)
@v.when(Statement)
def visit(self, node): return [node]
@v.when(Expression)
def visit(self, node): return node
| plum-umd/pasket | pasket/rewrite/proxy.py | Python | mit | 3,168 | [
"VisIt"
] | d2b6221c5ab1d34225b77198ae04508eae0042c138081675c7e536f14405453f |
import os.path
from .action_mapper import FileActionMapper
from .action_mapper import path_type
from .util import PathHelper
from galaxy.util import in_directory
class PathMapper(object):
""" Ties together a FileActionMapper and remote job configuration returned
by the Pulsar setup method to pre-determine the location of files for staging
on the remote Pulsar server.
This is not useful when rewrite_paths (as has traditionally been done with
the Pulsar) because when doing that the Pulsar determines the paths as files are
uploaded. When rewrite_paths is disabled however, the destination of files
needs to be determined prior to transfer so an object of this class can be
used.
"""
def __init__(
self,
client,
remote_job_config,
local_working_directory,
action_mapper=None,
):
self.local_working_directory = local_working_directory
if not action_mapper:
action_mapper = FileActionMapper(client)
self.action_mapper = action_mapper
self.input_directory = remote_job_config["inputs_directory"]
self.output_directory = remote_job_config["outputs_directory"]
self.working_directory = remote_job_config["working_directory"]
self.unstructured_files_directory = remote_job_config["unstructured_files_directory"]
self.config_directory = remote_job_config["configs_directory"]
separator = remote_job_config["system_properties"]["separator"]
self.path_helper = PathHelper(separator)
def remote_output_path_rewrite(self, local_path):
output_type = path_type.OUTPUT
if in_directory(local_path, self.local_working_directory):
output_type = path_type.OUTPUT_WORKDIR
remote_path = self.__remote_path_rewrite(local_path, output_type)
return remote_path
def remote_input_path_rewrite(self, local_path):
remote_path = self.__remote_path_rewrite(local_path, path_type.INPUT)
return remote_path
def remote_version_path_rewrite(self, local_path):
remote_path = self.__remote_path_rewrite(local_path, path_type.OUTPUT, name="COMMAND_VERSION")
return remote_path
def check_for_arbitrary_rewrite(self, local_path):
path = str(local_path) # Use false_path if needed.
action = self.action_mapper.action(path, path_type.UNSTRUCTURED)
if not action.staging_needed:
return action.path_rewrite(self.path_helper), []
unique_names = action.unstructured_map()
name = unique_names[path]
remote_path = self.path_helper.remote_join(self.unstructured_files_directory, name)
return remote_path, unique_names
def __remote_path_rewrite(self, dataset_path, dataset_path_type, name=None):
""" Return remote path of this file (if staging is required) else None.
"""
path = str(dataset_path) # Use false_path if needed.
action = self.action_mapper.action(path, dataset_path_type)
if action.staging_needed:
if name is None:
name = os.path.basename(path)
remote_directory = self.__remote_directory(dataset_path_type)
remote_path_rewrite = self.path_helper.remote_join(remote_directory, name)
else:
# Actions which don't require staging MUST define a path_rewrite
# method.
remote_path_rewrite = action.path_rewrite(self.path_helper)
return remote_path_rewrite
def __action(self, dataset_path, dataset_path_type):
path = str(dataset_path) # Use false_path if needed.
action = self.action_mapper.action(path, dataset_path_type)
return action
def __remote_directory(self, dataset_path_type):
if dataset_path_type in [path_type.OUTPUT]:
return self.output_directory
elif dataset_path_type in [path_type.WORKDIR, path_type.OUTPUT_WORKDIR]:
return self.working_directory
elif dataset_path_type in [path_type.INPUT]:
return self.input_directory
else:
message = "PathMapper cannot handle path type %s" % dataset_path_type
raise Exception(message)
__all__ = [PathMapper]
| mikel-egana-aranguren/SADI-Galaxy-Docker | galaxy-dist/lib/pulsar/client/path_mapper.py | Python | gpl-3.0 | 4,244 | [
"Galaxy"
] | a65637ff661fd7171af6d2dcb66ae2b00469e2988c23cd79202b0783cb496202 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# (c) 2015, Heyo
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
# this is a windows documentation stub. actual code lives in the .ps1
# file of the same name
DOCUMENTATION = '''
---
module: win_nssm
version_added: "2.0"
short_description: NSSM - the Non-Sucking Service Manager
description:
- nssm is a service helper which doesn't suck. See https://nssm.cc/ for more information.
requirements:
- "nssm >= 2.24.0 # (install via win_chocolatey) win_chocolatey: name=nssm"
options:
name:
description:
- Name of the service to operate on
required: true
state:
description:
- State of the service on the system
- Note that NSSM actions like "pause", "continue", "rotate" do not fit the declarative style of ansible, so these should be implemented via the ansible command module
required: false
choices:
- present
- started
- stopped
- restarted
- absent
default: started
application:
description:
- The application binary to run as a service
- "Specify this whenever the service may need to be installed (state: present, started, stopped, restarted)"
- "Note that the application name must look like the following, if the directory includes spaces:"
- 'nssm install service "c:\\Program Files\\app.exe\\" "C:\\Path with spaces\\"'
- "See commit 0b386fc1984ab74ee59b7bed14b7e8f57212c22b in the nssm.git project for more info (https://git.nssm.cc/?p=nssm.git;a=commit;h=0b386fc1984ab74ee59b7bed14b7e8f57212c22b)"
required: false
default: null
stdout_file:
description:
- Path to receive output
required: false
default: null
stderr_file:
description:
- Path to receive error output
required: false
default: null
app_parameters:
description:
- Parameters to be passed to the application when it starts
required: false
default: null
dependencies:
description:
- Service dependencies that has to be started to trigger startup, separated by comma.
required: false
default: null
user:
description:
- User to be used for service startup
required: false
default: null
password:
description:
- Password to be used for service startup
required: false
default: null
password:
description:
- Password to be used for service startup
required: false
default: null
start_mode:
description:
- If C(auto) is selected, the service will start at bootup. C(manual) means that the service will start only when another service needs it. C(disabled) means that the service will stay off, regardless if it is needed or not.
required: true
default: auto
choices:
- auto
- manual
- disabled
author:
- "Adam Keech (@smadam813)"
- "George Frank (@georgefrank)"
- "Hans-Joachim Kliemeck (@h0nIg)"
'''
EXAMPLES = '''
# Install and start the foo service
- win_nssm:
name: foo
application: C:\windows\\foo.exe
# Install and start the foo service with a key-value pair argument
# This will yield the following command: C:\windows\\foo.exe bar "true"
- win_nssm:
name: foo
application: C:\windows\\foo.exe
app_parameters:
bar: true
# Install and start the foo service with a key-value pair argument, where the argument needs to start with a dash
# This will yield the following command: C:\windows\\foo.exe -bar "true"
- win_nssm:
name: foo
application: C:\windows\\foo.exe
app_parameters:
"-bar": true
# Install and start the foo service with a single parameter
# This will yield the following command: C:\windows\\foo.exe bar
- win_nssm:
name: foo
application: C:\windows\\foo.exe
app_parameters:
_: bar
# Install and start the foo service with a mix of single params, and key value pairs
# This will yield the following command: C:\windows\\foo.exe bar -file output.bat
- win_nssm:
name: foo
application: C:\windows\\foo.exe
app_parameters:
_: bar
"-file": "output.bat"
# Install and start the foo service, redirecting stdout and stderr to the same file
- win_nssm:
name: foo
application: C:\windows\\foo.exe
stdout_file: C:\windows\\foo.log
stderr_file: C:\windows\\foo.log
# Install and start the foo service, but wait for dependencies tcpip and adf
- win_nssm:
name: foo
application: C:\windows\\foo.exe
dependencies: 'adf,tcpip'
# Install and start the foo service with dedicated user
- win_nssm:
name: foo
application: C:\windows\\foo.exe
user: foouser
password: secret
# Install the foo service but do not start it automatically
- win_nssm:
name: foo
application: C:\windows\\foo.exe
state: present
start_mode: manual
# Remove the foo service
- win_nssm:
name: foo
state: absent
'''
| georgefrank/ansible-modules-extras | windows/win_nssm.py | Python | gpl-3.0 | 5,495 | [
"ADF"
] | eb031416e654c68e4bc3c0509bf376d903e471426d9bf6ca04fec1b4398a1bc1 |
#!/usr/bin/env python
"""
Variational Bayes method to solve phylgoenetic HMM for histone modifications
Need to:
* Preprocess
** load each dataset
** call significant sites for each dataset (vs. one control dataset)
** save out resulting histogrammed data
* Learn
** Init parameters randomly
** E step: optimize each q_{ij} for fixed \Theta
** M step: optimize \Theta for fixed Q
for a complete hg19, we have:
T = 15,478,482
I = 9
K = 15
L = 9
\Theta is:
e = K * 2**L
\theta = K**2 * K
\alpha = K * K
\beta = K * K
\gamma = K
X = I * T * L * 1 byte for bool => 2050 MB RAM
for mf:
Q = I * T * K * 4 bytes for float64 =>
15181614 * 9 * 15 * (4 bytes) / 1e6 = 8198 MB RAM
\therefore should be okay for 12GB RAM
for poc:
\Theta = T * K * K * 4 bytes => 30 GB RAM
Q = I * T * K * 4 bytes => 24 GB RAM
Q_pairs = I * T * K * K * 4 bytes => :(
Chromosome 1:
T = 1246254
=> Q = .9 GB
=> Q_pairs = 8.9 GB
=> X = .1 GB
"""
import argparse
import sys
import operator
import glob
import urllib
import os
import hashlib
import multiprocessing
import time
import cPickle as pickle
import copy
import re
import tarfile
from cStringIO import StringIO
import time
import random
try:
import pysam
except ImportError:
print 'pysam not installed. Cannot convert data'
import scipy as sp
from scipy.stats import poisson
import scipy.io
import scipy.signal
try:
import matplotlib
matplotlib.use('Agg', warn=False)
#matplotlib.rc('text', usetex=True)
#matplotlib.rc('ps', usedistiller='xpdf')
from matplotlib import pyplot
allow_plots = True
except ImportError:
allow_plots = False
print 'matplotlib not installed. Cannot plot!'
sp.seterr(all='raise')
sp.seterr(under='print')
#sp.random.seed([5])
from treehmm.static import valid_species, valid_marks, mark_avail, phylogeny, inference_types, float_type
from treehmm import vb_mf
from treehmm import vb_prodc
#import loopy_bp
from treehmm import loopy_bp
from treehmm import clique_hmm
#import concatenate_hmm
from treehmm import vb_gmtkExact_continuous as vb_gmtkExact
from treehmm import vb_independent
from treehmm.plot import plot_params, plot_data, plot_Q, plot_energy, plot_energy_comparison
from treehmm.do_parallel import do_parallel_inference
def main(argv=sys.argv[1:]):
"""run a variational EM algorithm"""
# parse arguments, then call convert_data or do_inference
parser = make_parser()
args = parser.parse_args(argv)
if not hasattr(args, 'mark_avail'):
args.mark_avail = mark_avail
elif isinstance(args.mark_avail, basestring):
args.mark_avail = sp.load(args.mark_avail)
if args.func == do_inference:
# allow patterns on the command line
global phylogeny
args.phylogeny = eval(args.phylogeny)
phylogeny = args.phylogeny
all_obs = []
for obs_pattern in args.observe_matrix:
obs_files = glob.glob(obs_pattern)
if len(obs_files) == 0:
parser.error('No files matched the pattern %s' % obs_pattern)
all_obs.extend(obs_files)
args.observe_matrix = all_obs
if args.approx == 'gmtk':
args.subtask = False
obs_mat = args.observe_matrix
args.observe_matrix = args.observe_matrix[0]
init_args_for_inference(args)
args.observe_matrix = obs_mat
del args.func
vb_gmtkExact.mark_avail = args.mark_avail
vb_gmtkExact.run_gmtk_lineagehmm(args)
return
if len(args.observe_matrix) > 1:
print 'parallel inference on %s jobs' % len(args.observe_matrix)
args.func = do_parallel_inference
else:
args.subtask = False
args.observe_matrix = args.observe_matrix[0]
args.observe = os.path.split(args.observe_matrix)[1]
args.func = do_inference
if args.range_k is not None:
out_template = args.out_dir + "_rangeK"
for args.K in eval(args.range_k):
print 'trying K=', args.K
args.out_dir = out_template
args.func(args)
return
args.func(args) # do inference, downloading, or data conversion
def do_inference(args):
"""Perform the type of inference specified in args"""
# set up
if args.quiet_mode:
sys.stdout = open('log_%s.log' , 'a')
init_args_for_inference(args)
print 'done making args'
args.out_dir = args.out_dir.format(timestamp=time.strftime('%x_%X').replace('/','-'), **args.__dict__)
try:
print 'making', args.out_dir
os.makedirs(args.out_dir)
except OSError:
pass
if not args.subtask:
args.iteration = '0_initial'
plot_params(args)
if args.plot_iter >= 2:
plot_data(args)
for i in xrange(1, args.max_iterations+1):
if not args.subtask:
args.iteration = i
print 'iteration', i
# run a few times rather than checking free energy
for j in xrange(1, args.max_E_iter+1 if args.approx != 'clique' else 2):
args.update_q_func(args)
if args.approx !='loopy':
f = args.free_energy_func(args)
print 'free energy after %s E steps' % j, f
try:
print abs(args.last_free_energy - f) / args.last_free_energy
if abs(abs(args.last_free_energy - f) / args.last_free_energy) < args.epsilon_e:
args.last_free_energy = f
break
args.last_free_energy = f
except: # no previous free energy
args.last_free_energy = f
else:
print 'loopy %s E steps' %j
if loopy_bp.bp_check_convergence(args):
args.last_free_energy = f = abs(args.free_energy_func(args))
break
print '# saving Q distribution'
if args.continuous_observations:
for k in range(args.K):
print 'means[%s,:] = ' % k, args.means[k,:]
print 'stdev[%s,:] = ' % k, sp.sqrt(args.variances[k,:])
if args.save_Q >= 2:
for p in args.Q_to_save:
sp.save(os.path.join(args.out_dir,
args.out_params.format(param=p, **args.__dict__)),
args.__dict__[p])
if args.subtask:
# save the weights without renormalizing
print 'saving weights for parameters'
args.update_param_func(args, renormalize=False)
#plot_Q(args)
args.free_energy.append(args.last_free_energy)
for p in args.params_to_save:
sp.save(os.path.join(args.out_dir, args.out_params.format(param=p, **args.__dict__)),
args.__dict__[p])
break
else:
# optimize parameters with new Q
args.update_param_func(args)
f = args.free_energy_func(args)
try:
if args.approx != 'clique':
print abs(args.last_free_energy - f) / args.last_free_energy
if abs(abs(args.last_free_energy - f) / args.last_free_energy) < args.epsilon:
args.last_free_energy = f
break
args.last_free_energy = f
except: # no previous free energy
args.last_free_energy = f
#args.last_free_energy = args.free_energy_func(args)
args.free_energy.append(args.last_free_energy)
print 'free energy after M-step', args.free_energy[-1]
# save current parameter state
for p in args.params_to_save:
sp.save(os.path.join(args.out_dir,
args.out_params.format(param=p, **args.__dict__)),
args.__dict__[p])
if args.plot_iter != 0 and i % args.plot_iter == 0:
plot_params(args)
plot_energy(args)
if args.plot_iter >= 2:
plot_Q(args)
#import ipdb; ipdb.set_trace()
if args.compare_inf is not None:
args.log_obs_mat = sp.zeros((args.I,args.T,args.K), dtype=float_type)
vb_mf.make_log_obs_matrix(args)
if 'mf' in args.compare_inf:
tmpargs = copy.deepcopy(args)
tmpargs.Q = vb_mf.mf_random_q(args.I,args.T,args.K)
print 'comparing '
for j in xrange(1, args.max_E_iter+1):
vb_mf.mf_update_q(tmpargs)
if vb_mf.mf_check_convergence(tmpargs):
break
print 'mf convergence after %s iterations' % j
e = vb_mf.mf_free_energy(tmpargs)
args.cmp_energy['mf'].append(e)
if 'poc' in args.compare_inf:
tmpargs = copy.deepcopy(args)
if args.approx != 'poc':
tmpargs.Q, tmpargs.Q_pairs = vb_prodc.prodc_initialize_qs(args.theta, args.alpha, args.beta,
args.gamma, args.emit_probs, args.X, args.log_obs_mat)
for j in xrange(1, args.max_E_iter+1):
vb_prodc.prodc_update_q(tmpargs)
if vb_mf.mf_check_convergence(tmpargs):
break
print 'poc convergence after %s iterations' % j
e = vb_prodc.prodc_free_energy(tmpargs)
args.cmp_energy['poc'].append(e)
#sp.io.savemat(os.path.join(args.out_dir, 'Artfdata_poc_inferred_params_K{K}_{T}.mat'.format(K=args.K, T=args.max_bins)), dict(alpha = args.alpha, theta=args.theta, beta=args.beta, gamma=args.gamma, emit_probs=args.emit_probs))
if 'pot' in args.compare_inf:
#del args.cmp_energy['pot']
pass
if 'concat' in args.compare_inf:
#del args.cmp_energy['concat']
pass
if 'clique' in args.compare_inf:
tmpargs = copy.deepcopy(args)
if args.approx != 'clique':
clique_hmm.clique_init_args(tmpargs)
for j in xrange(1):
clique_hmm.clique_update_q(tmpargs)
e = clique_hmm.clique_likelihood(tmpargs)
args.cmp_energy['clique'].append(-e)
if 'loopy' in args.compare_inf:
tmpargs = copy.deepcopy(args)
if args.approx != 'loopy':
tmpargs.lmds, tmpargs.pis = loopy_bp.bp_initialize_msg(args.I, args.T, args.K, args.vert_children)
for j in xrange(1, args.max_E_iter+1):
loopy_bp.bp_update_msg_new(tmpargs)
if loopy_bp.bp_check_convergence(tmpargs):
break
print 'loopy convergence after %s iterations' % j
#e = loopy_bp.bp_bethe_free_energy(tmpargs)
e = loopy_bp.bp_mf_free_energy(tmpargs)
args.cmp_energy['loopy'].append(e)
if args.plot_iter != 0:
plot_energy_comparison(args)
# save the final parameters and free energy to disk
print 'done iteration'
if args.save_Q >= 1:
for p in args.Q_to_save:
sp.save(os.path.join(args.out_dir,
args.out_params.format(param=p, **args.__dict__)),
args.__dict__[p])
for p in args.params_to_save:
sp.save(os.path.join(args.out_dir, args.out_params.format(param=p, **args.__dict__)),
args.__dict__[p])
#pickle.dump(args, os.path.join(args.out_dir, args.out_params.format(param='args', **args.__dict__)))
print 'done savin'
if not args.subtask and args.plot_iter != 0:
plot_energy(args)
plot_params(args)
plot_Q(args)
#scipy.io.savemat('poc_inferred_params_K{K}_{T}.mat'.format(K=args.K, T=args.max_bins), dict(alpha = args.alpha, theta=args.theta, beta=args.beta, gamma=args.gamma, emit_probs=args.emit_probs))
def init_args_for_inference(args):
"""Initialize args with inference variables according to learning method"""
# read in the datafiles to X array
print '# loading observations'
X = sp.load(args.observe_matrix)
if args.max_bins is not None:
X = X[:, :args.max_bins, :]
if args.max_species is not None:
X = X[:args.max_species, :, :]
args.X = X
args.I, args.T, args.L = X.shape
if args.X.dtype != scipy.int8:
args.continuous_observations = True
print 'Inference for continuous observations'
args.X = X.astype(float_type)
#args.means = sp.rand(args.K, args.L)
#args.variances = sp.rand(args.K, args.L)
args.means, args.variances = initialize_mean_variance(args)
else:
args.continuous_observations = False
print 'Inference for discrete observations'
match = re.search(r'\.i(\d+)\.', args.observe_matrix)
args.real_species_i = int(match.groups()[0]) if match and args.I == 1 else None
args.free_energy = []
make_tree(args)
args.Q_to_save = ['Q']
#if args.approx == 'poc':
# args.Q_to_save += ['Q_pairs']
#elif args.approx == 'clique':
# args.Q_to_save += ['clq_Q', 'clq_Q_pairs']
args.params_to_save = ['free_energy', 'alpha', 'gamma', 'last_free_energy']
if True: #args.approx not in ['clique', 'concat']:
args.params_to_save += ['theta', 'beta']
if args.continuous_observations:
args.params_to_save += ['means', 'variances']
else:
args.params_to_save += ['emit_probs', 'emit_sum']
if args.compare_inf is not None:
if 'all' in args.compare_inf:
args.compare_inf = inference_types
args.cmp_energy = dict((inf, []) for inf in args.compare_inf if inf not in ['pot', 'concat'])
args.params_to_save += ['cmp_energy']
if args.warm_start: # need to load params
print '# loading previous params for warm start from %s' % args.warm_start
tmpargs = copy.deepcopy(args)
tmpargs.out_dir = args.warm_start
#tmpargs.observe = 'all.npy'
args.free_energy, args.theta, args.alpha, args.beta, args.gamma, args.emit_probs, args.emit_sum = load_params(tmpargs)
try:
args.free_energy = list(args.free_energy)
except TypeError: # no previous free energy
args.free_energy = []
print 'done'
elif args.subtask: # params in args already
print '# using previous params from parallel driver'
else:
print '# generating random parameters'
(args.theta, args.alpha, args.beta, args.gamma, args.emit_probs) = \
random_params(args.I, args.K, args.L, args.separate_theta)
if args.continuous_observations:
del args.emit_probs
if args.approx == 'mf': # mean-field approximation
if not args.subtask or args.iteration == 0:
args.Q = vb_mf.mf_random_q(args.I,args.T,args.K)
#else:
# q_path = os.path.join(args.out_dir, args.out_params.format(param='Q', **args.__dict__))
# print 'loading previous Q from %s' % q_path
# args.Q = sp.load(q_path)
args.log_obs_mat = sp.zeros((args.I,args.T,args.K), dtype=float_type)
vb_mf.make_log_obs_matrix(args)
args.update_q_func = vb_mf.mf_update_q
args.update_param_func = vb_mf.mf_update_params
args.free_energy_func = vb_mf.mf_free_energy
args.converged_func = vb_mf.mf_check_convergence
elif args.approx == 'poc': # product-of-chains approximation
if not args.separate_theta:
import vb_prodc
else:
import vb_prodc_sepTheta as vb_prodc
args.log_obs_mat = sp.zeros((args.I,args.T,args.K), dtype=float_type)
if args.continuous_observations:
vb_mf.make_log_obs_matrix_gaussian(args)
else:
vb_mf.make_log_obs_matrix(args)
if not args.subtask or args.iteration == 0:
print '# generating Qs'
args.Q, args.Q_pairs = vb_prodc.prodc_initialize_qs(args.theta, args.alpha, args.beta,
args.gamma, args.X, args.log_obs_mat)
#else:
# q_path = os.path.join(args.out_dir, args.out_params.format(param='Q', **args.__dict__))
# print 'loading previous Q from %s' % q_path
# args.Q = sp.load(q_path)
# args.Q_pairs = sp.load(os.path.join(args.out_dir, args.out_params.format(param='Q_pairs', **args.__dict__)))
args.update_q_func = vb_prodc.prodc_update_q
args.update_param_func = vb_prodc.prodc_update_params
args.free_energy_func = vb_prodc.prodc_free_energy
args.converged_func = vb_mf.mf_check_convergence
elif args.approx == 'indep': # completely independent chains
args.log_obs_mat = sp.zeros((args.I, args.T, args.K), dtype=float_type)
if args.continuous_observations:
vb_mf.make_log_obs_matrix_gaussian(args)
else:
vb_mf.make_log_obs_matrix(args)
if not args.subtask or args.iteration == 0:
print '# generating Qs'
args.Q = sp.zeros((args.I, args.T, args.K), dtype=float_type)
args.Q_pairs = sp.zeros((args.I, args.T, args.K, args.K), dtype=float_type)
vb_independent.independent_update_qs(args)
#else:
# q_path = os.path.join(args.out_dir, args.out_params.format(param='Q', **args.__dict__))
# print 'loading previous Q from %s' % q_path
# args.Q = sp.load(q_path)
# args.Q_pairs = sp.load(os.path.join(args.out_dir, args.out_params.format(param='Q_pairs', **args.__dict__)))
args.update_q_func = vb_independent.independent_update_qs
args.update_param_func = vb_independent.independent_update_params
args.free_energy_func = vb_independent.independent_free_energy
args.converged_func = vb_mf.mf_check_convergence
elif args.approx == 'pot': # product-of-trees approximation
raise NotImplementedError("Product of Trees is not implemented yet!")
elif args.approx == 'clique':
if args.separate_theta:
raise RuntimeError('separate_theta not implemented yet for clique')
print 'making cliqued Q'
args.Q = sp.zeros((args.I, args.T, args.K), dtype=float_type)
clique_hmm.clique_init_args(args)
args.update_q_func = clique_hmm.clique_update_q
args.update_param_func = clique_hmm.clique_update_params
args.free_energy_func = clique_hmm.clique_likelihood
args.converged_func = vb_mf.mf_check_convergence
elif args.approx == 'concat':
raise NotImplementedError("Concatenated HMM is not implemented yet!")
elif args.approx == 'loopy':
if args.separate_theta:
raise RuntimeError('separate_theta not implemented yet for clique')
if not args.subtask or args.iteration == 0:
args.Q = vb_mf.mf_random_q(args.I, args.T, args.K)
#else:
# q_path = os.path.join(args.out_dir, args.out_params.format(param='Q', **args.__dict__))
# print 'loading previous Q from %s' % q_path
# args.Q = sp.load(q_path)
args.lmds, args.pis = loopy_bp.bp_initialize_msg(args)
args.log_obs_mat = sp.zeros((args.I,args.T,args.K), dtype=float_type)
vb_mf.make_log_obs_matrix(args)
args.update_q_func = loopy_bp.bp_update_msg_new
args.update_param_func = loopy_bp.bp_update_params_new
#args.free_energy_func = loopy_bp.bp_bethe_free_energy
args.free_energy_func = loopy_bp.bp_mf_free_energy
args.converged_func = loopy_bp.bp_check_convergence
elif args.approx == 'gmtk':
pass
else:
raise RuntimeError('%s not recognized as valid inference method!' % args.approx)
def distance(x1, x2):
return scipy.sqrt((x1 - x2) * (x1 - x2)).sum()
def initialize_mean_variance(args):
"""Initialize the current mean and variance values semi-intelligently.
Inspired by the kmeans++ algorithm: iteratively choose new centers from the data
by weighted sampling, favoring points that are distant from those already chosen
"""
X = args.X.reshape(args.X.shape[0] * args.X.shape[1], args.X.shape[2])
# kmeans++ inspired choice
centers = [random.choice(X)]
min_dists = scipy.array([distance(centers[-1], x) for x in X])
for l in range(1, args.K):
weights = min_dists * min_dists
new_center = weighted_sample(zip(weights, X), 1).next()
centers.append(new_center)
min_dists = scipy.fmin(min_dists, scipy.array([distance(centers[-1], x) for x in X]))
means = scipy.array(centers)
# for the variance, get the variance of the data in this cluster
variances = []
for c in centers:
idxs = tuple(i for i, (x, m) in enumerate(zip(X, min_dists)) if distance(c, x) == m)
v = scipy.var(X[idxs, :], axis=0)
variances.append(v)
variances = scipy.array(variances) + args.pseudocount
#import pdb; pdb.set_trace()
#for k in range(args.K):
# print sp.sqrt(variances[k,:])
variances[variances < .1] = .1
return means, variances
def weighted_sample(items, n):
total = float(sum(w for w, v in items))
i = 0
w, v = items[0]
while n:
x = total * (1 - random.random() ** (1.0 / n))
total -= x
while x > w:
x -= w
i += 1
w, v = items[i]
w -= x
yield v
n -= 1
def make_parser():
"""Make a parser for variational inference"""
parser = argparse.ArgumentParser()
tasks_parser = parser.add_subparsers()
# parameters for converting datasets from BAM to observation matrix
convert_parser = tasks_parser.add_parser('convert', help='Convert BAM reads'
' into a matrix of observations')
convert_parser.add_argument('--download_first', action='store_true',
help='Download the raw sequence data from UCSC,'
' then convert it.')
convert_parser.add_argument('--base_url', default='http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/%s',
help='When downloading, string-format the template into this url.')
convert_parser.add_argument('--species', nargs='+', default=valid_species,
help='The set of species with observations. By '
'default, use all species: %(default)s')
convert_parser.add_argument('--marks', nargs='+', default=valid_marks,
help='The set of marks with observations. By '
'default, use all histone marks: %(default)s')
convert_parser.add_argument('--windowsize', type=int, default=200,
help='histogram bin size used in conversion')
convert_parser.add_argument('--chromosomes', nargs='+', default='all',
help='which chromosomes to convert. By default,'
' convert all autosomes')
convert_parser.add_argument('--min_reads', type=float, default=.5,
help='The minimum number of reads for a region to be included. default: %(default)s')
convert_parser.add_argument('--min_size', type=int, default=25,
help='The minimum length (in bins) to include a chunk. default: %(default)s')
convert_parser.add_argument('--max_pvalue', type=float, default=1e-4,
help='p-value threshold to consider the read count'
' significant, using a local poisson rate defined by'
' the control data')
convert_parser.add_argument('--outfile', default='observations.{chrom}.npy',
help='Where to save the binarized reads')
#convert_parser.add_argument('--bam_template', help='bam file template.',
# default='wgEncodeBroadHistone{species}{mark}StdAlnRep*.bam')
convert_parser.add_argument('--bam_template', help='bam file template. default: %(default)s',
default='wgEncode*{species}{mark}StdAlnRep{repnum}.bam')
convert_parser.set_defaults(func=convert_data)
# # to trim off telomeric regions
# trim_parser = tasks_parser.add_parser('trim', help='trim off regions without'
# 'any observations in them')
# trim_parser.add_argument('observe_matrix', nargs='+',
# help='Files to be trimmed (converted from bam'
# ' using "%(prog)s convert" command).')
# trim_parser.set_defaults(func=trim_data)
# to split a converted dataset into pieces
split_parser = tasks_parser.add_parser('split', help='split observations '
'into smaller pieces, retaining only regions with '
'a smoothed minimum read count.')
split_parser.add_argument('observe_matrix', nargs='+',
help='Files containing observed data (converted from bam'
' using "%(prog)s convert" command). If multiple files '
'are specified, each is treated as its own chain but '
'the parameters are shared across all chains')
split_parser.add_argument('start_positions', help='start_positions.pkl file generated during `convert` step.')
#split_parser.add_argument('--chunksize', type=int, default=100000,
# help='the number of bins per chunk. default: %(default)s')
split_parser.add_argument('--min_reads', type=float, default=.5,
help='The minimum number of reads for a region to be included. default: %(default)s')
split_parser.add_argument('--gauss_window_size', type=int, default=200,
help='The size of the gaussian smoothing window. default: %(default)s')
split_parser.add_argument('--min_size', type=int, default=25,
help='The minimum length (in bins) to include a chunk. default: %(default)s')
split_parser.set_defaults(func=split_data)
# parameters for learning and inference with converted observations
infer_parser = tasks_parser.add_parser('infer')
infer_parser.add_argument('K', type=int, help='The number of hidden states'
' to infer')
infer_parser.add_argument('observe_matrix', nargs='+',
help='Files containing observed data (converted from bam'
' using "%(prog)s convert" command). If multiple files '
'are specified, each is treated as its own chain but '
'the parameters are shared across all chains')
infer_parser.add_argument('--approx', choices=inference_types,
default='mf',
help='Which approximation to make in inference')
infer_parser.add_argument('--out_params', default='{approx}_{param}_{observe}',
help='Where to save final parameters')
infer_parser.add_argument('--epsilon', type=float, default=1e-4,
help='Convergence criteria: change in Free energy'
' during M step must be < epsilon')
infer_parser.add_argument('--epsilon_e', type=float, default=1e-3,
help='Convergence criteria: change in Free energy'
' during E step must be < epsilon')
infer_parser.add_argument('--max_iterations', type=int, default=50,
help='Maximum number of EM steps before stopping')
infer_parser.add_argument('--max_E_iter', type=int, default=10,
help='Maximum number of E steps per M step')
infer_parser.add_argument('--max_bins', default=None, type=int,
help='Restrict the total number of bins (T)')
infer_parser.add_argument('--max_species', default=None, type=int,
help='Restrict the total number of species (I)')
infer_parser.add_argument('--pseudocount', type=float_type, default=1e-6,
help='pseudocount to add to each parameter matrix')
infer_parser.add_argument('--plot_iter', type=int, default=1,
help='draw a plot per *plot_iter* iterations.'
'0 => plot only at the end. Default is %(default)s')
infer_parser.add_argument('--out_dir', type=str, default='{run_name}_out/{approx}/I{I}_K{K}_T{T}_{timestamp}',
help='Output parameters and plots in this directory'
' (default: %(default)s')
infer_parser.add_argument('--run_name', type=str, default='infer',
help='name of current run type (default: %(default)s')
infer_parser.add_argument('--num_processes', type=int, default=None,
help='Maximum number of processes to use '
'simultaneously (default: all)')
infer_parser.add_argument('--warm_start', type=str, default=None,
help="Resume iterations using parameters and Q's "
"from a previous run. Q's that are not found are "
"regenerated")
infer_parser.add_argument('--compare_inf', nargs='+', type=str, default=None, choices=inference_types + ['all'],
help="While learning using --approx method, "
"compare the inferred hidden states and energies "
"from these inference methods.")
infer_parser.add_argument('--range_k', type=str, default=None,
help="perform inference over a range of K values. Argument is passed as range(*arg*)")
infer_parser.add_argument('--save_Q', type=int, choices=[0,1,2,3], default=1,
help="Whether to save the inferred marginals for hidden variables. 0 => no saving, 1 => save at end, 2 => save at each iteration. 3 => for parallel jobs, reconstruct the chromsomal Q distribution at each iteration. Default: %(default)s")
infer_parser.add_argument('--quiet_mode', action='store_true', help="Turn off printing for this run")
infer_parser.add_argument('--run_local', action='store_true', help="Force parallel jobs to run on the local computer, even when SGE is available")
infer_parser.add_argument('--separate_theta', action='store_true', help='use a separate theta matrix for each node of the tree (only works for GMTK)')
infer_parser.add_argument('--mark_avail', help='npy matrix of available marks',
default=mark_avail)
infer_parser.add_argument('--phylogeny', help='the phylogeny connecting each species, as a python dictionary with children for keys and parents for values. Note: this does not have to be a singly-rooted or even a bifurcating phylogeny! You may specify multiple trees, chains, stars, etc, but should not have loops in the phylogeny.',
default=str(phylogeny))
infer_parser.add_argument('--chunksize', help='The number of chunks (for convert+split data) or chromosomes (for convert only) to submit to each runner. When running on SGE, you should set this number relatively high (in 100s?) since each job has a very slow startup time. When running locally, this is the number of chunks each subprocess will handle at a time.',
default=1)
infer_parser.set_defaults(func=do_inference)
bed_parser = tasks_parser.add_parser('q_to_bed')
bed_parser.add_argument('q_root_dir', help='Root directory for the Q outputs to convert. '
'Should look something like: infer_out/mf/<timestamp>/')
bed_parser.add_argument('start_positions', help='the pickled offsets generated by `tree-hmm convert` or `tree-hmm split`.',)
bed_parser.add_argument('--bed_template', help='template for bed output files. Default: %(default)s',
default='treehmm_states.{species}.state{state}.bed')
bed_parser.add_argument('--save_probs', action='store_true', help='Instead of saving the most likely state for each bin, record the probability of being in that state at each position. NOTE: this will greatly increase the BED file size!')
bed_parser.set_defaults(func=q_to_bed)
return parser
def q_to_bed(args):
attrs = pickle.load(open(args.start_positions))
windowsize = attrs['windowsize']
start_positions = attrs['start_positions']
valid_species = attrs['valid_species']
valid_marks = attrs['valid_marks']
outfiles = {}
for f in glob.glob(os.path.join(args.q_root_dir, '*_Q_*.npy')):
Q = scipy.load(f)
if not args.save_probs:
best_states = Q.argmax(axis=2)
obs = f.split('_Q_')[1]
I, T, K = Q.shape
chrom, bin_offset = start_positions[obs]
for i in range(I):
for t in range(T):
if args.save_probs:
for k in range(K):
bedline = '\t'.join([chrom, str((bin_offset + t) * windowsize),
str((bin_offset + t + 1) * windowsize),
'{species}.state{k}'.format(species=valid_species[i], k=k),
str(Q[i,t,k]), '+']) + '\n'
if (i,k) not in outfiles:
outfiles[(i,k)] = open(args.bed_template.format(species=valid_species[i], state=k), 'w')
outfiles[(i,k)].write(bedline)
else:
k = best_states[i,t]
bedline = '\t'.join([chrom, str((bin_offset + t) * windowsize),
str((bin_offset + t + 1) * windowsize),
'{species}.state{k}'.format(species=valid_species[i], k=k),
str(Q[i,t,k]), '+']) + '\n'
if (i,k) not in outfiles:
outfiles[(i,k)] = open(args.bed_template.format(species=valid_species[i], state=k), 'w')
outfiles[(i,k)].write(bedline)
def load_params(args):
#print args.out_params
#print args.__dict__.keys()
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='last_free_energy', **args.__dict__))
free_energy = sp.load(os.path.join(args.out_dir, args.out_params.format(param='free_energy', **args.__dict__)))
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='theta', **args.__dict__))
theta = sp.load(os.path.join(args.out_dir, args.out_params.format(param='theta', **args.__dict__)))
if len(theta.shape)==3 and args.separate_theta:
tmp = sp.zeros((args.I-1, args.K, args.K, args.K), dtype=float_type)
for i in range(args.I-1):
tmp[i,:,:,:] = theta
theta = tmp
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='alpha', **args.__dict__))
alpha = sp.load(os.path.join(args.out_dir, args.out_params.format(param='alpha', **args.__dict__)))
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='beta', **args.__dict__))
beta = sp.load(os.path.join(args.out_dir, args.out_params.format(param='beta', **args.__dict__)))
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='gamma', **args.__dict__))
gamma = sp.load(os.path.join(args.out_dir, args.out_params.format(param='gamma', **args.__dict__)))
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='emit_probs', **args.__dict__))
emit_probs = sp.load(os.path.join(args.out_dir, args.out_params.format(param='emit_probs', **args.__dict__)))
#print 'loading from', os.path.join(args.out_dir, args.out_params.format(param='emit_sum', **args.__dict__))
emit_sum = sp.load(os.path.join(args.out_dir, args.out_params.format(param='emit_sum', **args.__dict__)))
return free_energy, theta, alpha, beta, gamma, emit_probs, emit_sum
def make_tree(args):
"""build a tree from the vertical parents specified in args"""
I = args.I
# define the tree structure
#tree_by_parents = {0:sp.inf, 1:0, 2:0} # 3 species, 2 with one parent
#tree_by_parents = {0:sp.inf, 1:0} # 3 species, 2 with one parent
#tree_by_parents = dict((args.species.index(k), args.species.index(v)) for k, v in phylogeny.items())
tree_by_parents = dict((valid_species.index(k), valid_species.index(v))
for k, v in args.phylogeny.items() if
valid_species.index(k) in xrange(I) and
valid_species.index(v) in xrange(I))
tree_by_parents[0] = sp.inf #'Null'
print tree_by_parents.keys()
# [inf, parent(1), parent(2), ...]
global vert_parent
#I = max(tree_by_parents) + 1
vert_parent = sp.array([tree_by_parents[c] if c > 0 else I for c in
xrange(I)], dtype=sp.int8) # error if 0's parent is accessed
args.vert_parent = vert_parent
print 'vert_parent', vert_parent
# args.vert_parent = tree_by_parents
# {inf:0, 0:[1,2], 1:[children(1)], ...}
global vert_children
vert_children = dict((pa, []) for pa in
tree_by_parents.keys())# + tree_by_parents.values())
for pa in tree_by_parents.values():
for ch in tree_by_parents.keys():
if tree_by_parents[ch] == pa:
if pa not in vert_children:
vert_children[pa] = []
if ch not in vert_children[pa]:
vert_children[pa].append(ch)
del vert_children[sp.inf]
for pa in vert_children:
vert_children[pa] = sp.array(vert_children[pa], dtype=sp.int32)
args.vert_children = vert_children
# vert_children = sp.ones(I, dtype = 'object')
# for pa in range(I):
# vert_children[pa] = []
# for child, parent in tree_by_parents.items():
# if pa == parent:
# vert_children[pa].append(child)
# print vert_children
# args.vert_children = vert_children
def random_params(I, K, L, separate_theta):
"""Create and normalize random parameters for inference"""
#sp.random.seed([5])
if separate_theta:
theta = sp.rand(I-1, K, K, K).astype(float_type)
else:
theta = sp.rand(K, K, K).astype(float_type)
alpha = sp.rand(K, K).astype(float_type)
beta = sp.rand(K, K).astype(float_type)
gamma = sp.rand(K).astype(float_type)
emit_probs = sp.rand(K, L).astype(float_type)
vb_mf.normalize_trans(theta, alpha, beta, gamma)
return theta, alpha, beta, gamma, emit_probs
# def trim_data(args):
# """Trim regions without any observations from the start and end of the
# obervation matrices
# """
# for f in args.observe_matrix:
# print '# trimming ', f, 'start is ',
# X = sp.load(f).astype(sp.int8)
# S = X.cumsum(axis=0).cumsum(axis=2) # any species has any observation
# for start_t in xrange(X.shape[1]):
# if S[-1, start_t, -1] > 0:
# break
# for end_t in xrange(X.shape[1] - 1, -1, -1):
# if S[-1, end_t, -1] > 0:
# break
# tmpX = X[:, start_t:end_t, :]
# print start_t
# sp.save(os.path.splitext(f)[0] + '.trimmed', tmpX)
def split_data(args):
"""Split the given observation matrices into smaller chunks"""
sizes = []
total_size = 0
covered_size = 0
attrs = pickle.load(open(args.start_positions))
valid_species = attrs['valid_species']
valid_marks = attrs['valid_marks']
windowsize = attrs['windowsize']
old_starts = attrs['start_positions']
start_positions = {}
for f in args.observe_matrix:
print '# splitting ', f
chrom = old_starts[os.path.split(f)[1]][0]
X = sp.load(f).astype(sp.int8)
total_size += X.shape[1]
#start_ts = xrange(0, X.shape[1], args.chunksize)
#end_ts = xrange(args.chunksize, X.shape[1] + args.chunksize, args.chunksize)
density = X.sum(axis=0).sum(axis=1) # sumation over I, then L
#from ipdb import set_trace; set_trace()
gk = _gauss_kernel(args.gauss_window_size)
smoothed_density = scipy.signal.convolve(density, gk, mode='same')
regions_to_keep = smoothed_density >= args.min_reads
# find the regions where a transition is made from no reads to reads, and reads to no reads
start_ts = sp.where(sp.diff(regions_to_keep.astype(sp.int8)) > 0)[0]
end_ts = sp.where(sp.diff(regions_to_keep.astype(sp.int8)) < 0)[0]
cur_regions = [r for r in zip(start_ts, end_ts) if r[1] - r[0] >= args.min_size]
sizes.extend([end_t - start_t for start_t, end_t in cur_regions])
print 'saving %s regions' % len(sizes)
for chunknum, (start_t, end_t) in enumerate(cur_regions):
covered_size += end_t - start_t
tmpX = X[:, start_t:end_t, :]
name = os.path.splitext(f)[0] + '.chunk%s.npy' % chunknum
sp.save(name, tmpX)
fname = os.path.split(name)[1]
start_positions[fname] = (chrom, start_t)
print '# plotting size distribution'
pyplot.figure()
pyplot.figtext(.5,.01,'%s regions; %s bins total; %s bins covered; coverage = %.3f' % (len(sizes),total_size, covered_size, covered_size / float(total_size)), ha='center')
pyplot.hist(sizes, bins=100)
pyplot.title('chunk sizes for all chroms, min_reads %s, min_size %s, gauss_window_size %s' % (args.min_reads, args.min_size, args.gauss_window_size))
pyplot.savefig('chunk_sizes.minreads%s.minsize%s.windowsize%s.png' % (args.min_reads, args.min_size, args.gauss_window_size))
with open('start_positions_split.pkl', 'w') as outfile:
attrs = dict(windowsize=windowsize, start_positions=start_positions,
valid_species=valid_species, valid_marks=valid_marks)
pickle.dump(attrs, outfile, -1)
# --min_reads .5 --min_size 25 --window_size 200;
# def extract_local_features(args):
# """extract some local features from the given data, saving an X array with extra dimensions"""
# sizes = []
# total_size = 0
# covered_size = 0
# start_positions = {}
# for f in args.observe_matrix:
# print '# features on ', f
# X = sp.load(f).astype(sp.int8)
# total_size += X.shape[1]
# #start_ts = xrange(0, X.shape[1], args.chunksize)
# #end_ts = xrange(args.chunksize, X.shape[1] + args.chunksize, args.chunksize)
# density = X.sum(axis=0).sum(axis=1) # summation over I, then L
# #from ipdb import set_trace; set_trace()
# gk = _gauss_kernel(args.window_size)
# smoothed_density = scipy.signal.convolve(density, gk, mode='same')
# regions_to_keep = smoothed_density >= args.min_reads
# # find the regions where a transition is made from no reads to reads, and reads to no reads
# start_ts = sp.where(sp.diff(regions_to_keep.astype(sp.int8)) > 0)[0]
# end_ts = sp.where(sp.diff(regions_to_keep.astype(sp.int8)) < 0)[0]
# cur_regions = [r for r in zip(start_ts, end_ts) if r[1] - r[0] >= args.min_size]
# sizes.extend([end_t - start_t for start_t, end_t in cur_regions])
# print 'saving %s regions' % len(sizes)
# for chunknum, (start_t, end_t) in enumerate(cur_regions):
# covered_size += end_t - start_t
# tmpX = X[:, start_t:end_t, :]
# name = os.path.splitext(f)[0] + '.chunk%s.npy' % chunknum
# sp.save(name, tmpX)
# start_positions[name] = start_t
# print '# plotting size distribution'
# pyplot.figure()
# pyplot.figtext(.5,.01,'%s regions; %s bins total; %s bins covered; coverage = %.3f' % (len(sizes),total_size, covered_size, covered_size / float(total_size)), ha='center')
# pyplot.hist(sizes, bins=100)
# pyplot.title('chunk sizes for all chroms, min_reads %s, min_size %s, window_size %s' % (args.min_reads, args.min_size, args.window_size))
# pyplot.savefig('chunk_sizes.minreads%s.minsize%s.windowsize%s.png' % (args.min_reads, args.min_size, args.window_size))
# pickle.dump(start_positions, open('start_positions.pkl', 'w'))
# # --min_reads .5 --min_size 25 --window_size 200;
# def convert_data_continuous_features_and_split(args):
# """histogram both treatment and control data as specified by args
# This saves the complete X matrix
# This version doesn't binarize the data, smooths out the read signal (gaussian convolution)
# and adds derivative information
# """
# if args.download_first:
# download_data(args)
# I = len(args.species)
# L = len(args.marks)
# final_data = None
# total_size = 0
# covered_size = 0
# start_positions = {}
# # make sure all the data is present...
# for species in args.species:
# for mark in args.marks:
# d_files = [f for f in glob.glob(args.bam_template.format(
# species=species, mark=mark))]
# if len(d_files) == 0:
# print("No histone data for species %s mark %s Expected: %s" %
# (species, mark, args.bam_template.format(
# species=species, mark=mark)))
# for i, species in enumerate(args.species):
# for l, mark in enumerate(args.marks):
# d_obs = {}
# d_files = [f for f in glob.glob(args.bam_template.format(
# species=species, mark=mark))]
# if len(d_files) == 0:
# args.mark_avail[i, l] = 0
# else:
# args.mark_avail[i, l] = 1
# for mark_file in d_files:
# read_counts = histogram_reads(mark_file, args.windowsize, args.chromosomes)
# # d_obs.append(histogram_reads(mark_file, args.windowsize,
# # args.chromosomes))
# for
# d_obs = reduce(operator.add, d_obs) # add all replicants together
# #print 'before per million:', d_obs.sum()
# #d_obs /= (d_obs.sum() / 1e7) # convert to reads mapping per ten million
# # convert to a binary array with global poisson
# #genome_rate = d_obs / (d_obs.sum() / 1e6)
# if final_data is None:
# final_data = sp.zeros((I, len(d_obs), L), dtype=sp.float32)
# final_data[i, :, l] = d_obs
# total_size = final_data.shape[1]
# regions_to_keep = (final_data[:, :, tuple(range(L))].sum(axis=0).sum(axis=1) >= args.min_reads).astype(sp.int8)
# # find the regions where a transition is made from no reads to reads, and reads to no reads
# start_ts = sp.where(sp.diff(regions_to_keep) > 0)[0]
# end_ts = sp.where(sp.diff(regions_to_keep) < 0)[0]
# cur_regions = [r for r in zip(start_ts, end_ts) if r[1] - r[0] >= args.min_size]
# sizes = [end_t - start_t for start_t, end_t in cur_regions]
# print 'saving %s regions' % len(sizes)
# tarout = tarfile.open(args.outfile + '.tar.gz', 'w:gz')
# for chunknum, (start_t, end_t) in enumerate(cur_regions):
# covered_size += end_t - start_t
# tmpX = final_data[:, start_t:end_t, :]
# print 'adding chunk', chunknum, 'of', len(cur_regions)
# s = StringIO()
# sp.save(s, tmpX)
# name = args.outfile + '.chunk%s.npy' % chunknum
# info = tarfile.TarInfo(name)
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# start_positions[name] = start_t
# print '# plotting size distribution'
# pyplot.figure()
# pyplot.figtext(.5,.01,'%s regions; %s bins total; %s bins covered; coverage = %.3f' % (len(sizes),total_size, covered_size, covered_size / float(total_size)), ha='center')
# pyplot.hist(sizes, bins=100)
# pyplot.title('chunk sizes for all chroms, min_reads %s, min_size %s, windowsize %s' % (args.min_reads, args.min_size, args.windowsize))
# pyplot.savefig('chunk_sizes.minreads%s.minsize%s.windowsize%s.png' % (args.min_reads, args.min_size, args.windowsize))
# s = StringIO()
# pyplot.savefig(s)
# info = tarfile.TarInfo('chunk_sizes.minreads%s.minsize%s.windowsize%s.png' % (args.min_reads, args.min_size, args.windowsize))
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# s = StringIO()
# pickle.dump(start_positions, s)
# info = tarfile.TarInfo('start_positions.pkl')
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# pickle.dump(start_positions, open('start_positions.pkl', 'w'))
# # --min_reads .5 --min_size 25 --window_size 200;
# s = StringIO()
# sp.save(s, args.mark_avail)
# info = tarfile.TarInfo('available_marks.npy')
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# pickle.dump(start_positions, open('start_positions.pkl', 'w'))
# # --min_reads .5 --min_size 25 --window_size 200;
# tarout.close()
# print "output file:", args.outfile
# print 'available marks:', args.mark_avail
# #with open(args.outfile, 'wb') as outfile:
# # sp.save(outfile, final_data)
# with open(args.outfile + '.available_marks', 'wb') as outfile:
# sp.save(outfile, args.mark_avail)
# def convert_data_continuous_features_and_split_old(args):
# """histogram both treatment and control data as specified by args
# This saves the complete X matrix
# This version doesn't binarize the data, smooths out the read signal (gaussian convolution)
# and adds derivative information
# """
# if args.download_first:
# download_data(args)
# I = len(args.species)
# L = len(args.marks)
# final_data = None
# total_size = 0
# covered_size = 0
# start_positions = {}
# # make sure all the data is present...
# for species in args.species:
# for mark in args.marks:
# d_files = [f for f in glob.glob(args.bam_template.format(
# species=species, mark=mark))]
# if len(d_files) == 0:
# print("No histone data for species %s mark %s Expected: %s" %
# (species, mark, args.bam_template.format(
# species=species, mark=mark)))
# for i, species in enumerate(args.species):
# for l, mark in enumerate(args.marks):
# l = l * 3
# d_obs = []
# d_files = [f for f in glob.glob(args.bam_template.format(
# species=species, mark=mark))]
# if len(d_files) == 0:
# args.mark_avail[i, l] = 0
# args.mark_avail[i, l+1] = 0
# args.mark_avail[i, l+2] = 0
# else:
# args.mark_avail[i, l] = 1
# args.mark_avail[i, l+1] = 1
# args.mark_avail[i, l+2] = 1
# for mark_file in d_files:
# try:
# d_obs.append(histogram_reads(mark_file, args.windowsize,
# args.chromosomes))
# except ValueError as e:
# print e.message
# print d_obs[-1].sum()
# print d_obs[-1].shape
# d_obs = reduce(operator.add, d_obs) # add all replicants together
# #print 'before per million:', d_obs.sum()
# #d_obs /= (d_obs.sum() / 1e7) # convert to reads mapping per ten million
# # convert to a binary array with global poisson
# genome_rate = d_obs / (d_obs.sum() / 1e6)
# if final_data is None:
# final_data = sp.zeros((I, len(d_obs), L * 3), dtype=sp.float32)
# asinh_obs = sp.log(genome_rate + sp.sqrt(genome_rate * genome_rate + 1))
# gk = _gauss_kernel(3)
# smoothed_obs = scipy.signal.convolve(asinh_obs, gk, mode='same')
# smooth_deriv = sp.gradient(smoothed_obs)
# smooth_deriv2 = sp.gradient(smooth_deriv)
# final_data[i, :, l] = smoothed_obs
# final_data[i, :, l + 1] = smooth_deriv
# final_data[i, :, l + 2] = smooth_deriv2
# total_size = final_data.shape[1]
# regions_to_keep = (final_data[:, :, tuple(range(0, L * 3, 3))].sum(axis=0).sum(axis=1) >= args.min_reads).astype(sp.int8)
# # find the regions where a transition is made from no reads to reads, and reads to no reads
# start_ts = sp.where(sp.diff(regions_to_keep) > 0)[0]
# end_ts = sp.where(sp.diff(regions_to_keep) < 0)[0]
# cur_regions = [r for r in zip(start_ts, end_ts) if r[1] - r[0] >= args.min_size]
# sizes = [end_t - start_t for start_t, end_t in cur_regions]
# print 'saving %s regions' % len(sizes)
# tarout = tarfile.open(args.outfile + '.tar.gz', 'w:gz')
# for chunknum, (start_t, end_t) in enumerate(cur_regions):
# covered_size += end_t - start_t
# tmpX = final_data[:, start_t:end_t, :]
# print 'adding chunk', chunknum, 'of', len(cur_regions)
# s = StringIO()
# sp.save(s, tmpX)
# name = args.outfile + '.chunk%s.npy' % chunknum
# info = tarfile.TarInfo(name)
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# start_positions[name] = start_t
# print '# plotting size distribution'
# pyplot.figure()
# pyplot.figtext(.5,.01,'%s regions; %s bins total; %s bins covered; coverage = %.3f' % (len(sizes),total_size, covered_size, covered_size / float(total_size)), ha='center')
# pyplot.hist(sizes, bins=100)
# pyplot.title('chunk sizes for all chroms, min_reads %s, min_size %s, windowsize %s' % (args.min_reads, args.min_size, args.windowsize))
# pyplot.savefig('chunk_sizes.minreads%s.minsize%s.windowsize%s.png' % (args.min_reads, args.min_size, args.windowsize))
# s = StringIO()
# pyplot.savefig(s)
# info = tarfile.TarInfo('chunk_sizes.minreads%s.minsize%s.windowsize%s.png' % (args.min_reads, args.min_size, args.windowsize))
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# s = StringIO()
# pickle.dump(start_positions, s)
# info = tarfile.TarInfo('start_positions.pkl')
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# pickle.dump(start_positions, open('start_positions.pkl', 'w'))
# # --min_reads .5 --min_size 25 --window_size 200;
# s = StringIO()
# sp.save(s, args.mark_avail)
# info = tarfile.TarInfo('available_marks.npy')
# info.size = s.tell(); info.mtime = time.time()
# s.seek(0)
# tarout.addfile(info, s)
# pickle.dump(start_positions, open('start_positions.pkl', 'w'))
# # --min_reads .5 --min_size 25 --window_size 200;
# tarout.close()
# print "output file:", args.outfile
# print 'available marks:', args.mark_avail
# #with open(args.outfile, 'wb') as outfile:
# # sp.save(outfile, final_data)
# with open(args.outfile + '.available_marks', 'wb') as outfile:
# sp.save(outfile, args.mark_avail)
def _gauss_kernel(winsize):
x = sp.mgrid[-int(winsize):int(winsize)+1]
g = sp.exp(-(x**2/float(winsize)))
return g / g.sum()
def convert_data(args):
"""histogram both treatment and control data as specified by args
This saves the complete X matrix
"""
if args.download_first:
download_data(args)
I = len(args.species)
L = len(args.marks)
final_data = None
start_positions = {}
# make sure all the data is present...
for species in args.species:
for mark in args.marks:
d_files = [f for f in glob.glob(args.bam_template.format(
species=species, mark=mark, repnum='*'))]
if len(d_files) == 0:
raise RuntimeError("No histone data for species %s mark %s Expected: %s" %
(species, mark, args.bam_template.format(
species=species, mark=mark, repnum='*')))
for i, species in enumerate(args.species):
for l, mark in enumerate(args.marks):
d_obs = {}
d_files = [f for f in glob.glob(args.bam_template.format(
species=species, mark=mark, repnum='*'))]
if len(d_files) == 0:
pass
else:
for mark_file in d_files:
read_counts = histogram_reads(mark_file, args.windowsize, args.chromosomes)
# d_obs.append(histogram_reads(mark_file, args.windowsize,
# args.chromosomes))
for chrom in read_counts:
d_obs.setdefault(chrom, []).append(read_counts[chrom])
for chrom in d_obs:
d_obs[chrom] = reduce(operator.add, d_obs[chrom]) # add all replicants together
#print 'before per million:', d_obs.sum()
#d_obs /= (d_obs.sum() / 1e7) # convert to reads mapping per ten million
# convert to a binary array with global poisson
num_reads = sum(x.sum() for x in d_obs.values())
num_bins = float(sum(len(x) for x in d_obs.values()))
genome_rate = num_reads / num_bins
print 'after per million', num_reads, num_bins, genome_rate
if final_data is None:
final_data = {}
for chrom in d_obs:
d_obs[chrom] = call_significant_sites(d_obs[chrom], genome_rate, args.max_pvalue)
if chrom not in final_data:
final_data[chrom] = sp.zeros((I, len(d_obs[chrom]), L), dtype=sp.int8)
final_data[chrom][i, :, l] = d_obs[chrom]
for chrom in final_data:
start_positions[os.path.split(args.outfile.format(chrom=chrom))[1]] = (chrom, 0)
print "output file:", args.outfile.format(chrom=chrom)
with open(args.outfile.format(chrom=chrom), 'wb') as outfile:
sp.save(outfile, final_data[chrom])
with open('start_positions.pkl', 'w') as outfile:
pickle.dump(dict(windowsize=args.windowsize, valid_species=valid_species,
valid_marks=valid_marks, start_positions=start_positions),
outfile, -1)
def download_data(args):
"""Download any missing histone modification data from UCSC and check md5s.
"""
md5s = urllib.urlopen(args.base_url % 'md5sum.txt').read().strip().split('\n')
md5s = dict(reversed(l.strip().split()) for l in md5s)
for species in args.species:
for mark in args.marks:
for rep in range(10):
fname = args.bam_template.format(species=species,
mark=mark, repnum=rep)
if fname not in md5s:
continue
if os.path.exists(fname):
#m = hashlib.md5(open(fname, 'rb').read()).hexdigest()
#if m != md5s[fname]: # destroy if md5 doesn't match
# print 'removing incomplete file: %s' % fname
# print m, md5s[fname]
# os.unlink(fname)
#else:
print 'skipping already downloaded %s' % fname
continue
with open(fname, 'wb') as outfile:
try:
print 'downloading %s' % fname
page = urllib.urlopen(args.base_url % fname)
while True:
data = page.read(81920)
if not data:
break
outfile.write(data)
except RuntimeError as e:
print 'Skipping...', e.message
def histogram_reads(bam_file, windowsize, chromosomes='all', exclude_chroms=['chrM', 'chrY', 'chrX'],
skip_qc_fail=True):
"""Histogram the counts along bam_file, resulting in a vector.
This will concatenate all chromosomes, together, so to get the
counts for a particular chromosome, pass it as a list, a la
>>> histogram_reads(my_bam_file, chromosomes=['chr1'])
"""
print 'histogramming', bam_file
reads_bam = pysam.Samfile(bam_file, 'rb')
# get the chromosome name and lengths for our subset
if chromosomes == 'all':
chromosomes = filter(lambda c: c not in exclude_chroms,
reads_bam.references)
chrom_set = set(chromosomes)
chrom_lengths = {c : reads_bam.lengths[reads_bam.references.index(c)]
for c in chromosomes}
else:
chromosomes = filter(lambda c: c not in exclude_chroms,
chromosomes)
chrom_lengths = {c : reads_bam.lengths[reads_bam.references.index(c)]
for c in chromosomes if c not in
exclude_chroms}
chrom_set = set(chromosomes)
# # offset of each chromosome into concatenated chrom bins
# chrom_ends = list(((sp.array(chrom_lengths) // windowsize) + 1).cumsum())
# chrom_starts = dict(zip(chromosomes, [0] + chrom_ends[:-1]))
read_counts = {}
# create the histogram: 1 x sum(lengths) array
# read_counts = sp.zeros(chrom_ends[-1], dtype=float_type)
# count the reads in the input
for read in reads_bam:
if skip_qc_fail and (read.is_qcfail or read.is_unmapped or read.is_secondary or
read.is_duplicate or read.mapq == 0):
continue # filter out non-mapping reads
chrom = reads_bam.references[read.tid]
if chrom in chrom_set: # chrom requested?
# offset = chrom_starts[chrom]
offset = 0
if read.is_paired:
if read.is_proper_pair:
# add at the middle of the mates
bin = offset + ((read.pos + read.mpos +
read.rlen) / 2) // windowsize
# read_counts[min(chrom_ends[-1] - 1, bin)] += 1.
if chrom not in read_counts:
read_counts[chrom] = sp.zeros(chrom_lengths[chrom] // windowsize, dtype=float_type)
read_counts[chrom][min(chrom_lengths[chrom] // windowsize - 1, bin)] += 1.
else:
# add at the middle of the fragment
bin = offset + (read.pos + 100) // windowsize
if chrom not in read_counts:
read_counts[chrom] = sp.zeros(chrom_lengths[chrom] // windowsize, dtype=float_type)
read_counts[chrom][min(chrom_lengths[chrom] // windowsize - 1, bin)] += 1.
return read_counts
def call_significant_sites(fg_counts, bg_counts, max_pvalue):
"""binarize fg_counts (significant=1) using bg_counts as a local poisson
rate. the poisson survival must be < sig_level.
"""
print 'most reads in a bin:', fg_counts.max(), 'poisson expected rate:', bg_counts
print 'read count vs binary present:' , {i: poisson.sf(i, bg_counts) < max_pvalue for i in range(20)}
return poisson.sf(fg_counts, bg_counts) < max_pvalue
if __name__ == '__main__':
main()
| satwik77/tree-hmm | treehmm/__init__.py | Python | bsd-3-clause | 65,101 | [
"Gaussian",
"pysam"
] | 328529e71083a24cf26acda00a491d23601ca00ac6346674d72f75691b4a6d99 |
# Requires:
# - netcdf4-python (HDF5, NetCDF-4)
# - NumPy
try:
from netCDF4 import Dataset
except ImportError:
print "ERROR: netcdf4-python module not found"
raise
try:
import os
import sys
import glob
except ImportError:
print "ERROR: os, sys or glob modules not available"
raise
try:
import numpy as np
except ImportError:
print "ERROR: NumPy module not available"
raise
print " data = collect('variable', path='.')"
def collect(varname, xind=None, yind=None, zind=None, tind=None, path="."):
"""Collect a variable from a set of BOUT++ outputs."""
# Little helper function to read a variable
def read_var(file, name):
var = file.variables[name]
return var[:]
# Search for BOUT++ dump files in NetCDF format
file_list = glob.glob(os.path.join(path, "BOUT.dmp.*.nc"))
if file_list == []:
print "ERROR: No data files found"
return None
nfiles = len(file_list)
print "Number of files: " + str(nfiles)
# Read data from the first file
f = Dataset(file_list[0], "r")
print "File format : " + f.file_format
try:
v = f.variables[varname]
except KeyError:
print "ERROR: Variable '"+varname+"' not found"
return None
dims = v.dimensions
ndims = len(dims)
if ndims == 0:
# Just read from this file and return
data = v.getValue()
f.close()
return data[0]
elif ndims == 1:
data = v[:]
f.close()
return data
elif ndims > 4:
print "ERROR: Too many dimensions"
f.close()
raise CollectError
mxsub = read_var(f, "MXSUB")[0]
mysub = read_var(f, "MYSUB")[0]
mz = read_var(f, "MZ")[0]
myg = read_var(f, "MYG")[0]
t_array = read_var(f, "t_array")
nt = len(t_array)
print "Time-points : " + str(nt)
# Get the version of BOUT++ (should be > 0.6 for NetCDF anyway)
try:
v = f.variables["BOUT_VERSION"]
print "BOUT++ version : " + str(v.getValue()[0])
# 2D decomposition
nxpe = read_var(f, "NXPE")[0]
mxg = read_var(f, "MXG")[0]
nype = read_var(f, "NYPE")[0]
npe = nxpe * nype
if npe < nfiles:
print "WARNING: More files than expected (" + str(npe) + ")"
elif npe > nfiles:
print "WARNING: Some files missing. Expected " + str(npe)
nx = nxpe * mxsub + 2*mxg
except KeyError:
print "BOUT++ version : Pre-0.2"
# Assume number of files is correct
# No decomposition in X
nx = mxsub
mxg = 0
nxpe = 1
nype = nfiles
ny = mysub * nype
f.close()
# Check ranges
def check_range(r, low, up, name="range"):
r2 = r
if r != None:
if (len(r) < 1) or (len(r) > 2):
print "WARNING: "+name+" must be [min, max]"
r2 = None
else:
if len(r) == 1:
r = [r,r]
if r2[0] < low:
r2[0] = low
if r2[0] > up:
r2[0] = up
if r2[1] < 0:
r2[1] = 0
if r2[1] > up:
r2[1] = up
if r2[0] > r2[1]:
tmp = r2[0]
r2[0] = r2[1]
r2[1] = tmp
else:
r2 = [low, up]
return r2
xind = check_range(xind, 0, nx-1, "xind")
yind = check_range(yind, 0, ny-1, "yind")
zind = check_range(zind, 0, mz-2, "zind")
tind = check_range(tind, 0, nt-1, "tind")
xsize = xind[1] - xind[0] + 1
ysize = yind[1] - yind[0] + 1
zsize = zind[1] - zind[0] + 1
tsize = tind[1] - tind[0] + 1
# Map between dimension names and output size
sizes = {'x':xsize, 'y':ysize, 'z':zsize, 't':tsize}
# Create a list with size of each dimension
ddims = map(lambda d: sizes[d], dims)
# Create the data array
data = np.zeros(ddims)
for i in range(nfiles):
# Get X and Y processor indices
pe_yind = int(i / nxpe)
pe_xind = i % nxpe
# Get local ranges
ymin = yind[0] - pe_yind*mysub + myg
ymax = yind[1] - pe_yind*mysub + myg
xmin = xind[0] - pe_xind*mxsub
xmax = xind[1] - pe_xind*mxsub
inrange = True
if (ymin >= (mysub + myg)) or (ymax < myg):
inrange = False # Y out of range
if ymin < myg:
ymin = myg
if ymax >= mysub+myg:
ymax = myg + mysub - 1
# Check lower x boundary
if pe_xind == 0:
# Keeping inner boundary
if xmax < 0: inrange = False
if xmin < 0: xmin = 0
else:
if xmax < mxg: inrange = False
if xmin < mxg: xmin = mxg
# Upper x boundary
if pe_xind == (nxpe - 1):
# Keeping outer boundary
if xmin >= (mxsub + 2*mxg): inrange = False
if xmax > (mxsub + 2*mxg - 1): xmax = (mxsub + 2*mxg - 1)
else:
if xmin >= (mxsub + mxg): inrange = False
if xmax >= (mxsub + mxg): xmax = (mxsub+mxg-1)
# Number of local values
nx_loc = xmax - xmin + 1
ny_loc = ymax - ymin + 1
# Calculate global indices
xgmin = xmin + pe_xind * mxsub
xgmax = xmax + pe_xind * mxsub
ygmin = ymin + pe_yind * mysub - myg
ygmax = ymax + pe_yind * mysub - myg
if not inrange:
continue # Don't need this file
filename = os.path.join(path, "BOUT.dmp." + str(i) + ".nc")
sys.stdout.write("\rReading from " + filename + ": [" + \
str(xmin) + "-" + str(xmax) + "][" + \
str(ymin) + "-" + str(ymax) + "] -> [" + \
str(xgmin) + "-" + str(xgmax) + "][" + \
str(ygmin) + "-" + str(ygmax) + "]")
f = Dataset(filename, "r")
var = f.variables[varname]
if ndims == 4:
d = var[tind[0]:(tind[1]+1), xmin:(xmax+1), ymin:(ymax+1), zind[0]:(zind[1]+1)]
data[:, (xgmin-xind[0]):(xgmin-xind[0]+nx_loc), (ygmin-yind[0]):(ygmin-yind[0]+ny_loc), :] = d
elif ndims == 3:
# Could be xyz or txy
if dims[3] == 'z': # xyz
d = var[xmin:(xmax+1), ymin:(ymax+1), zind[0]:(zind[1]+1)]
data[(xgmin-xind[0]):(xgmin-xind[0]+nx_loc), (ygmin-yind[0]):(ygmin-yind[0]+ny_loc), :] = d
else: # txy
d = var[tind[0]:(tind[1]+1), xmin:(xmax+1), ymin:(ymax+1)]
data[:, (xgmin-xind[0]):(xgmin-xind[0]+nx_loc), (ygmin-yind[0]):(ygmin-yind[0]+ny_loc)] = d
elif ndims == 2:
# xy
d = var[xmin:(xmax+1), ymin:(ymax+1)]
data[(xgmin-xind[0]):(xgmin-xind[0]+nx_loc), (ygmin-yind[0]):(ygmin-yind[0]+ny_loc)] = d
# Finished looping over all files
sys.stdout.write("\n")
return data
| bendudson/BOUT-1.0 | tools/pylib/boutdata/collect.py | Python | gpl-3.0 | 7,138 | [
"NetCDF"
] | f368f962c25a96f212324dac2ea2d76b3c513165bcfb42a93ea4a7827574e4b5 |
from typing import Optional, Set
import click
import copy
from datetime import datetime
import json
import logging
import os
import subprocess
import sys
import time
import urllib
import urllib.parse
import yaml
from socket import socket
import ray
import psutil
from ray._private.gcs_utils import use_gcs_for_bootstrap
import ray._private.services as services
import ray.ray_constants as ray_constants
import ray._private.utils
from ray.util.annotations import PublicAPI
from ray.autoscaler._private.commands import (
attach_cluster,
exec_cluster,
create_or_update_cluster,
monitor_cluster,
rsync,
teardown_cluster,
get_head_node_ip,
kill_node,
get_worker_node_ips,
get_local_dump_archive,
get_cluster_dump_archive,
debug_status,
RUN_ENV_TYPES,
)
from ray.autoscaler._private.constants import RAY_PROCESSES
from ray.autoscaler._private.fake_multi_node.node_provider import FAKE_HEAD_NODE_ID
from ray.autoscaler._private.kuberay.run_autoscaler import run_autoscaler_with_retries
from ray.internal.internal_api import memory_summary
from ray.autoscaler._private.cli_logger import add_click_logging_options, cli_logger, cf
from ray.core.generated import gcs_service_pb2
from ray.core.generated import gcs_service_pb2_grpc
from ray.dashboard.modules.job.cli import job_cli_group
from distutils.dir_util import copy_tree
logger = logging.getLogger(__name__)
@click.group()
@click.option(
"--logging-level",
required=False,
default=ray_constants.LOGGER_LEVEL,
type=str,
help=ray_constants.LOGGER_LEVEL_HELP,
)
@click.option(
"--logging-format",
required=False,
default=ray_constants.LOGGER_FORMAT,
type=str,
help=ray_constants.LOGGER_FORMAT_HELP,
)
@click.version_option()
def cli(logging_level, logging_format):
level = logging.getLevelName(logging_level.upper())
ray._private.ray_logging.setup_logger(level, logging_format)
cli_logger.set_format(format_tmpl=logging_format)
@click.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--port",
"-p",
required=False,
type=int,
default=ray_constants.DEFAULT_DASHBOARD_PORT,
help="The local port to forward to the dashboard",
)
@click.option(
"--remote-port",
required=False,
type=int,
default=ray_constants.DEFAULT_DASHBOARD_PORT,
help="The remote port your dashboard runs on",
)
@click.option(
"--no-config-cache",
is_flag=True,
default=False,
help="Disable the local cluster config cache.",
)
@PublicAPI
def dashboard(cluster_config_file, cluster_name, port, remote_port, no_config_cache):
"""Port-forward a Ray cluster's dashboard to the local machine."""
# Sleeping in a loop is preferable to `sleep infinity` because the latter
# only works on linux.
# Find the first open port sequentially from `remote_port`.
try:
port_forward = [
(port, remote_port),
]
click.echo(
"Attempting to establish dashboard locally at"
" localhost:{} connected to"
" remote port {}".format(port, remote_port)
)
# We want to probe with a no-op that returns quickly to avoid
# exceptions caused by network errors.
exec_cluster(
cluster_config_file,
override_cluster_name=cluster_name,
port_forward=port_forward,
no_config_cache=no_config_cache,
)
click.echo("Successfully established connection.")
except Exception as e:
raise click.ClickException(
"Failed to forward dashboard from remote port {1} to local port "
"{0}. There are a couple possibilities: \n 1. The remote port is "
"incorrectly specified \n 2. The local port {0} is already in "
"use.\n The exception is: {2}".format(port, remote_port, e)
) from None
def continue_debug_session(live_jobs: Set[str]):
"""Continue active debugging session.
This function will connect 'ray debug' to the right debugger
when a user is stepping between Ray tasks.
"""
active_sessions = ray.experimental.internal_kv._internal_kv_list(
"RAY_PDB_", namespace=ray_constants.KV_NAMESPACE_PDB
)
for active_session in active_sessions:
if active_session.startswith(b"RAY_PDB_CONTINUE"):
# Check to see that the relevant job is still alive.
data = ray.experimental.internal_kv._internal_kv_get(
active_session, namespace=ray_constants.KV_NAMESPACE_PDB
)
if json.loads(data)["job_id"] not in live_jobs:
ray.experimental.internal_kv._internal_kv_del(
active_session, namespace=ray_constants.KV_NAMESPACE_PDB
)
continue
print("Continuing pdb session in different process...")
key = b"RAY_PDB_" + active_session[len("RAY_PDB_CONTINUE_") :]
while True:
data = ray.experimental.internal_kv._internal_kv_get(
key, namespace=ray_constants.KV_NAMESPACE_PDB
)
if data:
session = json.loads(data)
if "exit_debugger" in session or session["job_id"] not in live_jobs:
ray.experimental.internal_kv._internal_kv_del(
key, namespace=ray_constants.KV_NAMESPACE_PDB
)
return
host, port = session["pdb_address"].split(":")
ray.util.rpdb.connect_pdb_client(host, int(port))
ray.experimental.internal_kv._internal_kv_del(
key, namespace=ray_constants.KV_NAMESPACE_PDB
)
continue_debug_session(live_jobs)
return
time.sleep(1.0)
def format_table(table):
"""Format a table as a list of lines with aligned columns."""
result = []
col_width = [max(len(x) for x in col) for col in zip(*table)]
for line in table:
result.append(
" | ".join("{0:{1}}".format(x, col_width[i]) for i, x in enumerate(line))
)
return result
@cli.command()
@click.option(
"--address", required=False, type=str, help="Override the address to connect to."
)
def debug(address):
"""Show all active breakpoints and exceptions in the Ray debugger."""
address = services.canonicalize_bootstrap_address(address)
logger.info(f"Connecting to Ray instance at {address}.")
ray.init(address=address, log_to_driver=False)
while True:
# Used to filter out and clean up entries from dead jobs.
live_jobs = {job["JobID"] for job in ray.state.jobs() if not job["IsDead"]}
continue_debug_session(live_jobs)
active_sessions = ray.experimental.internal_kv._internal_kv_list(
"RAY_PDB_", namespace=ray_constants.KV_NAMESPACE_PDB
)
print("Active breakpoints:")
sessions_data = []
for active_session in active_sessions:
data = json.loads(
ray.experimental.internal_kv._internal_kv_get(
active_session, namespace=ray_constants.KV_NAMESPACE_PDB
)
)
# Check that the relevant job is alive, else clean up the entry.
if data["job_id"] in live_jobs:
sessions_data.append(data)
else:
ray.experimental.internal_kv._internal_kv_del(
active_session, namespace=ray_constants.KV_NAMESPACE_PDB
)
sessions_data = sorted(
sessions_data, key=lambda data: data["timestamp"], reverse=True
)
table = [["index", "timestamp", "Ray task", "filename:lineno"]]
for i, data in enumerate(sessions_data):
date = datetime.utcfromtimestamp(data["timestamp"]).strftime(
"%Y-%m-%d %H:%M:%S"
)
table.append(
[
str(i),
date,
data["proctitle"],
data["filename"] + ":" + str(data["lineno"]),
]
)
for i, line in enumerate(format_table(table)):
print(line)
if i >= 1 and not sessions_data[i - 1]["traceback"].startswith(
"NoneType: None"
):
print(sessions_data[i - 1]["traceback"])
inp = input("Enter breakpoint index or press enter to refresh: ")
if inp == "":
print()
continue
else:
index = int(inp)
session = json.loads(
ray.experimental.internal_kv._internal_kv_get(
active_sessions[index], namespace=ray_constants.KV_NAMESPACE_PDB
)
)
host, port = session["pdb_address"].split(":")
ray.util.rpdb.connect_pdb_client(host, int(port))
@cli.command()
@click.option(
"--node-ip-address", required=False, type=str, help="the IP address of this node"
)
@click.option("--address", required=False, type=str, help="the address to use for Ray")
@click.option(
"--port",
type=int,
required=False,
help=f"the port of the head ray process. If not provided, defaults to "
f"{ray_constants.DEFAULT_PORT}; if port is set to 0, we will"
f" allocate an available port.",
)
@click.option(
"--redis-password",
required=False,
hidden=True,
type=str,
default=ray_constants.REDIS_DEFAULT_PASSWORD,
help="If provided, secure Redis ports with this password",
)
@click.option(
"--redis-shard-ports",
required=False,
hidden=True,
type=str,
help="the port to use for the Redis shards other than the " "primary Redis shard",
)
@click.option(
"--object-manager-port",
required=False,
type=int,
help="the port to use for starting the object manager",
)
@click.option(
"--node-manager-port",
required=False,
type=int,
default=0,
help="the port to use for starting the node manager",
)
@click.option(
"--gcs-server-port",
required=False,
type=int,
help="Port number for the GCS server.",
)
@click.option(
"--min-worker-port",
required=False,
type=int,
default=10002,
help="the lowest port number that workers will bind on. If not set, "
"random ports will be chosen.",
)
@click.option(
"--max-worker-port",
required=False,
type=int,
default=19999,
help="the highest port number that workers will bind on. If set, "
"'--min-worker-port' must also be set.",
)
@click.option(
"--worker-port-list",
required=False,
help="a comma-separated list of open ports for workers to bind on. "
"Overrides '--min-worker-port' and '--max-worker-port'.",
)
@click.option(
"--ray-client-server-port",
required=False,
type=int,
default=10001,
help="the port number the ray client server will bind on. If not set, "
"the ray client server will not be started.",
)
@click.option(
"--memory",
required=False,
hidden=True,
type=int,
help="The amount of memory (in bytes) to make available to workers. "
"By default, this is set to the available memory on the node.",
)
@click.option(
"--object-store-memory",
required=False,
type=int,
help="The amount of memory (in bytes) to start the object store with. "
"By default, this is capped at 20GB but can be set higher.",
)
@click.option(
"--redis-max-memory",
required=False,
hidden=True,
type=int,
help="The max amount of memory (in bytes) to allow redis to use. Once the "
"limit is exceeded, redis will start LRU eviction of entries. This only "
"applies to the sharded redis tables (task, object, and profile tables). "
"By default this is capped at 10GB but can be set higher.",
)
@click.option(
"--num-cpus", required=False, type=int, help="the number of CPUs on this node"
)
@click.option(
"--num-gpus", required=False, type=int, help="the number of GPUs on this node"
)
@click.option(
"--resources",
required=False,
default="{}",
type=str,
help="a JSON serialized dictionary mapping resource name to " "resource quantity",
)
@click.option(
"--head",
is_flag=True,
default=False,
help="provide this argument for the head node",
)
@click.option(
"--include-dashboard",
default=None,
type=bool,
help="provide this argument to start the Ray dashboard GUI",
)
@click.option(
"--dashboard-host",
required=False,
default="localhost",
help="the host to bind the dashboard server to, either localhost "
"(127.0.0.1) or 0.0.0.0 (available from all interfaces). By default, this"
"is localhost.",
)
@click.option(
"--dashboard-port",
required=False,
type=int,
default=ray_constants.DEFAULT_DASHBOARD_PORT,
help="the port to bind the dashboard server to--defaults to {}".format(
ray_constants.DEFAULT_DASHBOARD_PORT
),
)
@click.option(
"--dashboard-agent-listen-port",
type=int,
hidden=True,
default=0,
help="the port for dashboard agents to listen for http on.",
)
@click.option(
"--dashboard-agent-grpc-port",
type=int,
hidden=True,
default=None,
help="the port for dashboard agents to listen for grpc on.",
)
@click.option(
"--block",
is_flag=True,
default=False,
help="provide this argument to block forever in this command",
)
@click.option(
"--plasma-directory",
required=False,
type=str,
help="object store directory for memory mapped files",
)
@click.option(
"--autoscaling-config",
required=False,
type=str,
help="the file that contains the autoscaling config",
)
@click.option(
"--no-redirect-output",
is_flag=True,
default=False,
help="do not redirect non-worker stdout and stderr to files",
)
@click.option(
"--plasma-store-socket-name",
default=None,
help="manually specify the socket name of the plasma store",
)
@click.option(
"--raylet-socket-name",
default=None,
help="manually specify the socket path of the raylet process",
)
@click.option(
"--temp-dir",
hidden=True,
default=None,
help="manually specify the root temporary dir of the Ray process",
)
@click.option(
"--system-config",
default=None,
hidden=True,
type=json.loads,
help="Override system configuration defaults.",
)
@click.option(
"--enable-object-reconstruction",
is_flag=True,
default=False,
hidden=True,
help="Specify whether object reconstruction will be used for this cluster.",
)
@click.option(
"--metrics-export-port",
type=int,
hidden=True,
default=None,
help="the port to use to expose Ray metrics through a " "Prometheus endpoint.",
)
@click.option(
"--no-monitor",
is_flag=True,
hidden=True,
default=False,
help="If True, the ray autoscaler monitor for this cluster will not be " "started.",
)
@click.option(
"--tracing-startup-hook",
type=str,
hidden=True,
default=None,
help="The function that sets up tracing with a tracing provider, remote "
"span processor, and additional instruments. See docs.ray.io/tracing.html "
"for more info.",
)
@click.option(
"--ray-debugger-external",
is_flag=True,
default=False,
help="Make the Ray debugger available externally to the node. This is only"
"safe to activate if the node is behind a firewall.",
)
@add_click_logging_options
@PublicAPI
def start(
node_ip_address,
address,
port,
redis_password,
redis_shard_ports,
object_manager_port,
node_manager_port,
gcs_server_port,
min_worker_port,
max_worker_port,
worker_port_list,
ray_client_server_port,
memory,
object_store_memory,
redis_max_memory,
num_cpus,
num_gpus,
resources,
head,
include_dashboard,
dashboard_host,
dashboard_port,
dashboard_agent_listen_port,
dashboard_agent_grpc_port,
block,
plasma_directory,
autoscaling_config,
no_redirect_output,
plasma_store_socket_name,
raylet_socket_name,
temp_dir,
system_config,
enable_object_reconstruction,
metrics_export_port,
no_monitor,
tracing_startup_hook,
ray_debugger_external,
):
"""Start Ray processes manually on the local machine."""
if use_gcs_for_bootstrap() and gcs_server_port is not None:
cli_logger.error(
"`{}` is deprecated and ignored. Use {} to specify "
"GCS server port on head node.",
cf.bold("--gcs-server-port"),
cf.bold("--port"),
)
# Whether the original arguments include node_ip_address.
include_node_ip_address = False
if node_ip_address is not None:
include_node_ip_address = True
node_ip_address = services.resolve_ip_for_localhost(node_ip_address)
try:
resources = json.loads(resources)
except Exception:
cli_logger.error("`{}` is not a valid JSON string.", cf.bold("--resources"))
cli_logger.abort(
"Valid values look like this: `{}`",
cf.bold('--resources=\'{"CustomResource3": 1, ' '"CustomResource2": 2}\''),
)
raise Exception(
"Unable to parse the --resources argument using "
"json.loads. Try using a format like\n\n"
' --resources=\'{"CustomResource1": 3, '
'"CustomReseource2": 2}\''
)
redirect_output = None if not no_redirect_output else True
ray_params = ray._private.parameter.RayParams(
node_ip_address=node_ip_address,
min_worker_port=min_worker_port,
max_worker_port=max_worker_port,
worker_port_list=worker_port_list,
ray_client_server_port=ray_client_server_port,
object_manager_port=object_manager_port,
node_manager_port=node_manager_port,
memory=memory,
object_store_memory=object_store_memory,
redis_password=redis_password,
redirect_output=redirect_output,
num_cpus=num_cpus,
num_gpus=num_gpus,
resources=resources,
autoscaling_config=autoscaling_config,
plasma_directory=plasma_directory,
huge_pages=False,
plasma_store_socket_name=plasma_store_socket_name,
raylet_socket_name=raylet_socket_name,
temp_dir=temp_dir,
include_dashboard=include_dashboard,
dashboard_host=dashboard_host,
dashboard_port=dashboard_port,
dashboard_agent_listen_port=dashboard_agent_listen_port,
metrics_agent_port=dashboard_agent_grpc_port,
_system_config=system_config,
enable_object_reconstruction=enable_object_reconstruction,
metrics_export_port=metrics_export_port,
no_monitor=no_monitor,
tracing_startup_hook=tracing_startup_hook,
ray_debugger_external=ray_debugger_external,
)
if head:
# Start head node.
if port is None:
port = ray_constants.DEFAULT_PORT
# Set bootstrap port.
assert ray_params.redis_port is None
assert ray_params.gcs_server_port is None
if use_gcs_for_bootstrap():
ray_params.gcs_server_port = port
else:
if port == 0:
with socket() as s:
s.bind(("", 0))
port = s.getsockname()[1]
ray_params.redis_port = port
ray_params.gcs_server_port = gcs_server_port
if os.environ.get("RAY_FAKE_CLUSTER"):
ray_params.env_vars = {
"RAY_OVERRIDE_NODE_ID_FOR_TESTING": FAKE_HEAD_NODE_ID
}
num_redis_shards = None
# Start Ray on the head node.
if redis_shard_ports is not None and address is None:
redis_shard_ports = redis_shard_ports.split(",")
# Infer the number of Redis shards from the ports if the number is
# not provided.
num_redis_shards = len(redis_shard_ports)
# This logic is deprecated and will be removed later.
if address is not None:
cli_logger.warning(
"Specifying {} for external Redis address is deprecated. "
"Please specify environment variable {}={} instead.",
cf.bold("--address"),
cf.bold("RAY_REDIS_ADDRESS"),
address,
)
cli_logger.print(
"Will use `{}` as external Redis server address(es). "
"If the primary one is not reachable, we starts new one(s) "
"with `{}` in local.",
cf.bold(address),
cf.bold("--port"),
)
external_addresses = address.split(",")
# We reuse primary redis as sharding when there's only one
# instance provided.
if len(external_addresses) == 1:
external_addresses.append(external_addresses[0])
reachable = False
try:
[primary_redis_ip, port] = external_addresses[0].split(":")
ray._private.services.wait_for_redis_to_start(
primary_redis_ip, port, password=redis_password
)
reachable = True
# We catch a generic Exception here in case someone later changes
# the type of the exception.
except Exception:
cli_logger.print(
"The primary external redis server `{}` is not reachable. "
"Will starts new one(s) with `{}` in local.",
cf.bold(external_addresses[0]),
cf.bold("--port"),
)
if reachable:
ray_params.update_if_absent(external_addresses=external_addresses)
num_redis_shards = len(external_addresses) - 1
if redis_password == ray_constants.REDIS_DEFAULT_PASSWORD:
cli_logger.warning(
"`{}` should not be specified as empty string if "
"external redis server(s) `{}` points to requires "
"password.",
cf.bold("--redis-password"),
cf.bold("--address"),
)
# Get the node IP address if one is not provided.
ray_params.update_if_absent(node_ip_address=services.get_node_ip_address())
cli_logger.labeled_value("Local node IP", ray_params.node_ip_address)
# Initialize Redis settings.
ray_params.update_if_absent(
redis_shard_ports=redis_shard_ports,
redis_max_memory=redis_max_memory,
num_redis_shards=num_redis_shards,
redis_max_clients=None,
)
# Fail early when starting a new cluster when one is already running
if address is None:
default_address = f"{ray_params.node_ip_address}:{port}"
bootstrap_addresses = services.find_bootstrap_address()
if default_address in bootstrap_addresses:
raise ConnectionError(
f"Ray is trying to start at {default_address}, "
f"but is already running at {bootstrap_addresses}. "
"Please specify a different port using the `--port`"
" flag of `ray start` command."
)
node = ray.node.Node(
ray_params, head=True, shutdown_at_exit=block, spawn_reaper=block
)
bootstrap_addresses = node.address
if temp_dir is None:
# Default temp directory.
temp_dir = ray._private.utils.get_user_temp_dir()
# Using the user-supplied temp dir unblocks on-prem
# users who can't write to the default temp.
current_cluster_path = os.path.join(temp_dir, "ray_current_cluster")
# TODO: Consider using the custom temp_dir for this file across the
# code base. (https://github.com/ray-project/ray/issues/16458)
with open(current_cluster_path, "w") as f:
print(bootstrap_addresses, file=f)
# this is a noop if new-style is not set, so the old logger calls
# are still in place
cli_logger.newline()
startup_msg = "Ray runtime started."
cli_logger.success("-" * len(startup_msg))
cli_logger.success(startup_msg)
cli_logger.success("-" * len(startup_msg))
cli_logger.newline()
with cli_logger.group("Next steps"):
cli_logger.print("To connect to this Ray runtime from another node, run")
# NOTE(kfstorm): Java driver rely on this line to get the address
# of the cluster. Please be careful when updating this line.
cli_logger.print(
cf.bold(" ray start --address='{}'{}"),
bootstrap_addresses,
f" --redis-password='{redis_password}'" if redis_password else "",
)
cli_logger.newline()
cli_logger.print("Alternatively, use the following Python code:")
with cli_logger.indented():
cli_logger.print("{} ray", cf.magenta("import"))
# Note: In the case of joining an existing cluster using
# `address="auto"`, the _node_ip_address parameter is
# unnecessary.
cli_logger.print(
"ray{}init(address{}{}{}{})",
cf.magenta("."),
cf.magenta("="),
cf.yellow("'auto'"),
", _redis_password{}{}".format(
cf.magenta("="), cf.yellow("'" + redis_password + "'")
)
if redis_password
else "",
", _node_ip_address{}{}".format(
cf.magenta("="), cf.yellow("'" + node_ip_address + "'")
)
if include_node_ip_address
else "",
)
cli_logger.newline()
cli_logger.print(
"To connect to this Ray runtime from outside of "
"the cluster, for example to"
)
cli_logger.print(
"connect to a remote cluster from your laptop "
"directly, use the following"
)
cli_logger.print("Python code:")
with cli_logger.indented():
cli_logger.print("{} ray", cf.magenta("import"))
cli_logger.print(
"ray{}init(address{}{})",
cf.magenta("."),
cf.magenta("="),
cf.yellow(
"'ray://<head_node_ip_address>:" f"{ray_client_server_port}'"
),
)
cli_logger.newline()
cli_logger.print(
cf.underlined(
"If connection fails, check your "
"firewall settings and "
"network configuration."
)
)
cli_logger.newline()
cli_logger.print("To terminate the Ray runtime, run")
cli_logger.print(cf.bold(" ray stop"))
else:
# Start worker node.
# Ensure `--address` flag is specified.
if address is None:
cli_logger.abort(
"`{}` is a required flag unless starting a head " "node with `{}`.",
cf.bold("--address"),
cf.bold("--head"),
)
raise Exception(
"`--address` is a required flag unless starting a "
"head node with `--head`."
)
# Raise error if any head-only flag are specified.
head_only_flags = {
"--port": port,
"--redis-shard-ports": redis_shard_ports,
"--include-dashboard": include_dashboard,
}
for flag, val in head_only_flags.items():
if val is None:
continue
cli_logger.abort(
"`{}` should only be specified when starting head " "node with `{}`.",
cf.bold(flag),
cf.bold("--head"),
)
raise ValueError(
f"{flag} should only be specified when starting head node "
"with `--head`."
)
# Start Ray on a non-head node.
bootstrap_address = services.canonicalize_bootstrap_address(address)
if bootstrap_address is None:
cli_logger.abort(
"Cannot canonicalize address `{}={}`.",
cf.bold("--address"),
cf.bold(address),
)
raise Exception("Cannot canonicalize address " f"`--address={address}`.")
if use_gcs_for_bootstrap():
ray_params.gcs_address = bootstrap_address
else:
ray_params.redis_address = bootstrap_address
address_ip, address_port = services.extract_ip_port(bootstrap_address)
# Wait for the Redis server to be started. And throw an exception
# if we can't connect to it.
services.wait_for_redis_to_start(
address_ip, address_port, password=redis_password
)
# Get the node IP address if one is not provided.
ray_params.update_if_absent(
node_ip_address=services.get_node_ip_address(bootstrap_address)
)
cli_logger.labeled_value("Local node IP", ray_params.node_ip_address)
node = ray.node.Node(
ray_params, head=False, shutdown_at_exit=block, spawn_reaper=block
)
# Ray and Python versions should probably be checked before
# initializing Node.
node.check_version_info()
cli_logger.newline()
startup_msg = "Ray runtime started."
cli_logger.success("-" * len(startup_msg))
cli_logger.success(startup_msg)
cli_logger.success("-" * len(startup_msg))
cli_logger.newline()
cli_logger.print("To terminate the Ray runtime, run")
cli_logger.print(cf.bold(" ray stop"))
cli_logger.flush()
if block:
cli_logger.newline()
with cli_logger.group(cf.bold("--block")):
cli_logger.print(
"This command will now block until terminated by a signal."
)
cli_logger.print(
"Running subprocesses are monitored and a message will be "
"printed if any of them terminate unexpectedly."
)
cli_logger.flush()
while True:
time.sleep(1)
deceased = node.dead_processes()
if len(deceased) > 0:
cli_logger.newline()
cli_logger.error("Some Ray subprcesses exited unexpectedly:")
with cli_logger.indented():
for process_type, process in deceased:
cli_logger.error(
"{}",
cf.bold(str(process_type)),
_tags={"exit code": str(process.returncode)},
)
# shutdown_at_exit will handle cleanup.
cli_logger.newline()
cli_logger.error("Remaining processes will be killed.")
sys.exit(1)
@cli.command()
@click.option(
"-f",
"--force",
is_flag=True,
help="If set, ray will send SIGKILL instead of SIGTERM.",
)
@click.option(
"-g",
"--grace-period",
default=10,
help=(
"The time ray waits for processes to be properly terminated. "
"If processes are not terminated within the grace period, "
"they are forcefully terminated after the grace period."
),
)
@add_click_logging_options
@PublicAPI
def stop(force, grace_period):
"""Stop Ray processes manually on the local machine."""
# Note that raylet needs to exit before object store, otherwise
# it cannot exit gracefully.
is_linux = sys.platform.startswith("linux")
processes_to_kill = RAY_PROCESSES
process_infos = []
for proc in psutil.process_iter(["name", "cmdline"]):
try:
process_infos.append((proc, proc.name(), proc.cmdline()))
except psutil.Error:
pass
stopped = []
for keyword, filter_by_cmd in processes_to_kill:
if filter_by_cmd and is_linux and len(keyword) > 15:
# getting here is an internal bug, so we do not use cli_logger
msg = (
"The filter string should not be more than {} "
"characters. Actual length: {}. Filter: {}"
).format(15, len(keyword), keyword)
raise ValueError(msg)
found = []
for candidate in process_infos:
proc, proc_cmd, proc_args = candidate
corpus = proc_cmd if filter_by_cmd else subprocess.list2cmdline(proc_args)
if keyword in corpus:
# This is a way to avoid killing redis server that's not started by Ray.
# We are using a simple hacky solution here since
# Redis server will anyway removed soon from the ray repository.
# This feature is only supported on MacOS/Linux temporarily until
# Redis is removed from Ray.
if (
keyword == "redis-server"
and sys.platform != "win32"
and "core/src/ray/thirdparty/redis/src/redis-server" not in corpus
):
continue
found.append(candidate)
for proc, proc_cmd, proc_args in found:
proc_string = str(subprocess.list2cmdline(proc_args))
try:
if force:
proc.kill()
else:
# TODO(mehrdadn): On Windows, this is forceful termination.
# We don't want CTRL_BREAK_EVENT, because that would
# terminate the entire process group. What to do?
proc.terminate()
if force:
cli_logger.verbose(
"Killed `{}` {} ",
cf.bold(proc_string),
cf.dimmed("(via SIGKILL)"),
)
else:
cli_logger.verbose(
"Send termination request to `{}` {}",
cf.bold(proc_string),
cf.dimmed("(via SIGTERM)"),
)
stopped.append(proc)
except psutil.NoSuchProcess:
cli_logger.verbose(
"Attempted to stop `{}`, but process was already dead.",
cf.bold(proc_string),
)
except (psutil.Error, OSError) as ex:
cli_logger.error(
"Could not terminate `{}` due to {}", cf.bold(proc_string), str(ex)
)
try:
os.remove(
os.path.join(ray._private.utils.get_user_temp_dir(), "ray_current_cluster")
)
except OSError:
# This just means the file doesn't exist.
pass
# Wait for the processes to actually stop.
# Dedup processes.
stopped, alive = psutil.wait_procs(stopped, timeout=0)
procs_to_kill = stopped + alive
total_found = len(procs_to_kill)
# Wait for grace period to terminate processes.
gone_procs = set()
def on_terminate(proc):
gone_procs.add(proc)
cli_logger.print(f"{len(gone_procs)}/{total_found} stopped.", end="\r")
stopped, alive = psutil.wait_procs(
procs_to_kill, timeout=grace_period, callback=on_terminate
)
total_stopped = len(stopped)
# Print the termination result.
if total_found == 0:
cli_logger.print("Did not find any active Ray processes.")
else:
if total_stopped == total_found:
cli_logger.success("Stopped all {} Ray processes.", total_stopped)
else:
cli_logger.warning(
f"Stopped only {total_stopped} out of {total_found} Ray processes "
f"within the grace period {grace_period} seconds. "
f"Set `{cf.bold('-v')}` to see more details. "
f"Remaining processes {alive} will be forcefully terminated.",
)
cli_logger.warning(
f"You can also use `{cf.bold('--force')}` to forcefully terminate "
"processes or set higher `--grace-period` to wait longer time for "
"proper termination."
)
# For processes that are not killed within the grace period,
# we send force termination signals.
for proc in alive:
proc.kill()
# Wait a little bit to make sure processes are killed forcefully.
psutil.wait_procs(alive, timeout=2)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--min-workers",
required=False,
type=int,
help="Override the configured min worker node count for the cluster.",
)
@click.option(
"--max-workers",
required=False,
type=int,
help="Override the configured max worker node count for the cluster.",
)
@click.option(
"--no-restart",
is_flag=True,
default=False,
help=(
"Whether to skip restarting Ray services during the update. "
"This avoids interrupting running jobs."
),
)
@click.option(
"--restart-only",
is_flag=True,
default=False,
help=(
"Whether to skip running setup commands and only restart Ray. "
"This cannot be used with 'no-restart'."
),
)
@click.option(
"--yes", "-y", is_flag=True, default=False, help="Don't ask for confirmation."
)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--no-config-cache",
is_flag=True,
default=False,
help="Disable the local cluster config cache.",
)
@click.option(
"--redirect-command-output",
is_flag=True,
default=False,
help="Whether to redirect command output to a file.",
)
@click.option(
"--use-login-shells/--use-normal-shells",
is_flag=True,
default=True,
help=(
"Ray uses login shells (bash --login -i) to run cluster commands "
"by default. If your workflow is compatible with normal shells, "
"this can be disabled for a better user experience."
),
)
@add_click_logging_options
@PublicAPI
def up(
cluster_config_file,
min_workers,
max_workers,
no_restart,
restart_only,
yes,
cluster_name,
no_config_cache,
redirect_command_output,
use_login_shells,
):
"""Create or update a Ray cluster."""
if restart_only or no_restart:
cli_logger.doassert(
restart_only != no_restart,
"`{}` is incompatible with `{}`.",
cf.bold("--restart-only"),
cf.bold("--no-restart"),
)
assert restart_only != no_restart, (
"Cannot set both 'restart_only' " "and 'no_restart' at the same time!"
)
if urllib.parse.urlparse(cluster_config_file).scheme in ("http", "https"):
try:
response = urllib.request.urlopen(cluster_config_file, timeout=5)
content = response.read()
file_name = cluster_config_file.split("/")[-1]
with open(file_name, "wb") as f:
f.write(content)
cluster_config_file = file_name
except urllib.error.HTTPError as e:
cli_logger.warning("{}", str(e))
cli_logger.warning("Could not download remote cluster configuration file.")
create_or_update_cluster(
config_file=cluster_config_file,
override_min_workers=min_workers,
override_max_workers=max_workers,
no_restart=no_restart,
restart_only=restart_only,
yes=yes,
override_cluster_name=cluster_name,
no_config_cache=no_config_cache,
redirect_command_output=redirect_command_output,
use_login_shells=use_login_shells,
)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--yes", "-y", is_flag=True, default=False, help="Don't ask for confirmation."
)
@click.option(
"--workers-only", is_flag=True, default=False, help="Only destroy the workers."
)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--keep-min-workers",
is_flag=True,
default=False,
help="Retain the minimal amount of workers specified in the config.",
)
@add_click_logging_options
@PublicAPI
def down(cluster_config_file, yes, workers_only, cluster_name, keep_min_workers):
"""Tear down a Ray cluster."""
teardown_cluster(
cluster_config_file, yes, workers_only, cluster_name, keep_min_workers
)
@cli.command(hidden=True)
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--yes", "-y", is_flag=True, default=False, help="Don't ask for confirmation."
)
@click.option(
"--hard",
is_flag=True,
default=False,
help="Terminates the node via node provider (defaults to a 'soft kill'"
" which terminates Ray but does not actually delete the instances).",
)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
def kill_random_node(cluster_config_file, yes, hard, cluster_name):
"""Kills a random Ray node. For testing purposes only."""
click.echo(
"Killed node with IP " + kill_node(cluster_config_file, yes, hard, cluster_name)
)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--lines", required=False, default=100, type=int, help="Number of lines to tail."
)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@add_click_logging_options
def monitor(cluster_config_file, lines, cluster_name):
"""Tails the autoscaler logs of a Ray cluster."""
monitor_cluster(cluster_config_file, lines, cluster_name)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--start", is_flag=True, default=False, help="Start the cluster if needed."
)
@click.option(
"--screen", is_flag=True, default=False, help="Run the command in screen."
)
@click.option("--tmux", is_flag=True, default=False, help="Run the command in tmux.")
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--no-config-cache",
is_flag=True,
default=False,
help="Disable the local cluster config cache.",
)
@click.option("--new", "-N", is_flag=True, help="Force creation of a new screen.")
@click.option(
"--port-forward",
"-p",
required=False,
multiple=True,
type=int,
help="Port to forward. Use this multiple times to forward multiple ports.",
)
@add_click_logging_options
@PublicAPI
def attach(
cluster_config_file,
start,
screen,
tmux,
cluster_name,
no_config_cache,
new,
port_forward,
):
"""Create or attach to a SSH session to a Ray cluster."""
port_forward = [(port, port) for port in list(port_forward)]
attach_cluster(
cluster_config_file,
start,
screen,
tmux,
cluster_name,
no_config_cache=no_config_cache,
new=new,
port_forward=port_forward,
)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.argument("source", required=False, type=str)
@click.argument("target", required=False, type=str)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@add_click_logging_options
def rsync_down(cluster_config_file, source, target, cluster_name):
"""Download specific files from a Ray cluster."""
rsync(cluster_config_file, source, target, cluster_name, down=True)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.argument("source", required=False, type=str)
@click.argument("target", required=False, type=str)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--all-nodes",
"-A",
is_flag=True,
required=False,
help="Upload to all nodes (workers and head).",
)
@add_click_logging_options
def rsync_up(cluster_config_file, source, target, cluster_name, all_nodes):
"""Upload specific files to a Ray cluster."""
if all_nodes:
cli_logger.warning(
"WARNING: the `all_nodes` option is deprecated and will be "
"removed in the future. "
"Rsync to worker nodes is not reliable since workers may be "
"added during autoscaling. Please use the `file_mounts` "
"feature instead for consistent file sync in autoscaling clusters"
)
rsync(
cluster_config_file,
source,
target,
cluster_name,
down=False,
all_nodes=all_nodes,
)
@cli.command(context_settings={"ignore_unknown_options": True})
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--stop",
is_flag=True,
default=False,
help="Stop the cluster after the command finishes running.",
)
@click.option(
"--start", is_flag=True, default=False, help="Start the cluster if needed."
)
@click.option(
"--screen", is_flag=True, default=False, help="Run the command in a screen."
)
@click.option("--tmux", is_flag=True, default=False, help="Run the command in tmux.")
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--no-config-cache",
is_flag=True,
default=False,
help="Disable the local cluster config cache.",
)
@click.option(
"--port-forward",
"-p",
required=False,
multiple=True,
type=int,
help="Port to forward. Use this multiple times to forward multiple ports.",
)
@click.argument("script", required=True, type=str)
@click.option(
"--args",
required=False,
type=str,
help="(deprecated) Use '-- --arg1 --arg2' for script args.",
)
@click.argument("script_args", nargs=-1)
@add_click_logging_options
def submit(
cluster_config_file,
screen,
tmux,
stop,
start,
cluster_name,
no_config_cache,
port_forward,
script,
args,
script_args,
):
"""Uploads and runs a script on the specified cluster.
The script is automatically synced to the following location:
os.path.join("~", os.path.basename(script))
Example:
>>> ray submit [CLUSTER.YAML] experiment.py -- --smoke-test
"""
cli_logger.doassert(
not (screen and tmux),
"`{}` and `{}` are incompatible.",
cf.bold("--screen"),
cf.bold("--tmux"),
)
cli_logger.doassert(
not (script_args and args),
"`{0}` and `{1}` are incompatible. Use only `{1}`.\n" "Example: `{2}`",
cf.bold("--args"),
cf.bold("-- <args ...>"),
cf.bold("ray submit script.py -- --arg=123 --flag"),
)
assert not (screen and tmux), "Can specify only one of `screen` or `tmux`."
assert not (script_args and args), "Use -- --arg1 --arg2 for script args."
if args:
cli_logger.warning(
"`{}` is deprecated and will be removed in the future.", cf.bold("--args")
)
cli_logger.warning(
"Use `{}` instead. Example: `{}`.",
cf.bold("-- <args ...>"),
cf.bold("ray submit script.py -- --arg=123 --flag"),
)
cli_logger.newline()
if start:
create_or_update_cluster(
config_file=cluster_config_file,
override_min_workers=None,
override_max_workers=None,
no_restart=False,
restart_only=False,
yes=True,
override_cluster_name=cluster_name,
no_config_cache=no_config_cache,
redirect_command_output=False,
use_login_shells=True,
)
target = os.path.basename(script)
target = os.path.join("~", target)
rsync(
cluster_config_file,
script,
target,
cluster_name,
no_config_cache=no_config_cache,
down=False,
)
command_parts = ["python", target]
if script_args:
command_parts += list(script_args)
elif args is not None:
command_parts += [args]
port_forward = [(port, port) for port in list(port_forward)]
cmd = " ".join(command_parts)
exec_cluster(
cluster_config_file,
cmd=cmd,
run_env="docker",
screen=screen,
tmux=tmux,
stop=stop,
start=False,
override_cluster_name=cluster_name,
no_config_cache=no_config_cache,
port_forward=port_forward,
)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.argument("cmd", required=True, type=str)
@click.option(
"--run-env",
required=False,
type=click.Choice(RUN_ENV_TYPES),
default="auto",
help="Choose whether to execute this command in a container or directly on"
" the cluster head. Only applies when docker is configured in the YAML.",
)
@click.option(
"--stop",
is_flag=True,
default=False,
help="Stop the cluster after the command finishes running.",
)
@click.option(
"--start", is_flag=True, default=False, help="Start the cluster if needed."
)
@click.option(
"--screen", is_flag=True, default=False, help="Run the command in a screen."
)
@click.option("--tmux", is_flag=True, default=False, help="Run the command in tmux.")
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
@click.option(
"--no-config-cache",
is_flag=True,
default=False,
help="Disable the local cluster config cache.",
)
@click.option(
"--port-forward",
"-p",
required=False,
multiple=True,
type=int,
help="Port to forward. Use this multiple times to forward multiple ports.",
)
@add_click_logging_options
def exec(
cluster_config_file,
cmd,
run_env,
screen,
tmux,
stop,
start,
cluster_name,
no_config_cache,
port_forward,
):
"""Execute a command via SSH on a Ray cluster."""
port_forward = [(port, port) for port in list(port_forward)]
exec_cluster(
cluster_config_file,
cmd=cmd,
run_env=run_env,
screen=screen,
tmux=tmux,
stop=stop,
start=start,
override_cluster_name=cluster_name,
no_config_cache=no_config_cache,
port_forward=port_forward,
_allow_uninitialized_state=True,
)
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
def get_head_ip(cluster_config_file, cluster_name):
"""Return the head node IP of a Ray cluster."""
click.echo(get_head_node_ip(cluster_config_file, cluster_name))
@cli.command()
@click.argument("cluster_config_file", required=True, type=str)
@click.option(
"--cluster-name",
"-n",
required=False,
type=str,
help="Override the configured cluster name.",
)
def get_worker_ips(cluster_config_file, cluster_name):
"""Return the list of worker IPs of a Ray cluster."""
worker_ips = get_worker_node_ips(cluster_config_file, cluster_name)
click.echo("\n".join(worker_ips))
@cli.command()
def stack():
"""Take a stack dump of all Python workers on the local machine."""
COMMAND = """
pyspy=`which py-spy`
if [ ! -e "$pyspy" ]; then
echo "ERROR: Please 'pip install py-spy'" \
"or 'pip install ray[default]' first."
exit 1
fi
# Set IFS to iterate over lines instead of over words.
export IFS="
"
# Call sudo to prompt for password before anything has been printed.
sudo true
workers=$(
ps aux | grep -E ' ray::|default_worker.py' | grep -v raylet | grep -v grep
)
for worker in $workers; do
echo "Stack dump for $worker";
pid=`echo $worker | awk '{print $2}'`;
sudo $pyspy dump --pid $pid --native;
echo;
done
"""
subprocess.call(COMMAND, shell=True)
@cli.command()
def microbenchmark():
"""Run a local Ray microbenchmark on the current machine."""
from ray._private.ray_perf import main
main()
@cli.command()
@click.option(
"--address",
required=False,
type=str,
help="Override the redis address to connect to.",
)
def timeline(address):
"""Take a Chrome tracing timeline for a Ray cluster."""
address = services.canonicalize_bootstrap_address(address)
logger.info(f"Connecting to Ray instance at {address}.")
ray.init(address=address)
time = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
filename = os.path.join(
ray._private.utils.get_user_temp_dir(), f"ray-timeline-{time}.json"
)
ray.timeline(filename=filename)
size = os.path.getsize(filename)
logger.info(f"Trace file written to {filename} ({size} bytes).")
logger.info("You can open this with chrome://tracing in the Chrome browser.")
@cli.command()
@click.option(
"--address", required=False, type=str, help="Override the address to connect to."
)
@click.option(
"--redis_password",
required=False,
type=str,
default=ray_constants.REDIS_DEFAULT_PASSWORD,
help="Connect to ray with redis_password.",
)
@click.option(
"--group-by",
type=click.Choice(["NODE_ADDRESS", "STACK_TRACE"]),
default="NODE_ADDRESS",
help="Group object references by a GroupByType \
(e.g. NODE_ADDRESS or STACK_TRACE).",
)
@click.option(
"--sort-by",
type=click.Choice(["PID", "OBJECT_SIZE", "REFERENCE_TYPE"]),
default="OBJECT_SIZE",
help="Sort object references in ascending order by a SortingType \
(e.g. PID, OBJECT_SIZE, or REFERENCE_TYPE).",
)
@click.option(
"--units",
type=click.Choice(["B", "KB", "MB", "GB"]),
default="B",
help="Specify unit metrics for displaying object sizes \
(e.g. B, KB, MB, GB).",
)
@click.option(
"--no-format",
is_flag=True,
type=bool,
default=True,
help="Display unformatted results. Defaults to true when \
terminal width is less than 137 characters.",
)
@click.option(
"--stats-only", is_flag=True, default=False, help="Display plasma store stats only."
)
@click.option(
"--num-entries",
"--n",
type=int,
default=None,
help="Specify number of sorted entries per group.",
)
def memory(
address,
redis_password,
group_by,
sort_by,
units,
no_format,
stats_only,
num_entries,
):
"""Print object references held in a Ray cluster."""
address = services.canonicalize_bootstrap_address(address)
time = datetime.now()
header = "=" * 8 + f" Object references status: {time} " + "=" * 8
mem_stats = memory_summary(
address,
redis_password,
group_by,
sort_by,
units,
no_format,
stats_only,
num_entries,
)
print(f"{header}\n{mem_stats}")
@cli.command()
@click.option(
"--address", required=False, type=str, help="Override the address to connect to."
)
@click.option(
"--redis_password",
required=False,
type=str,
default=ray_constants.REDIS_DEFAULT_PASSWORD,
help="Connect to ray with redis_password.",
)
@PublicAPI
def status(address, redis_password):
"""Print cluster status, including autoscaling info."""
address = services.canonicalize_bootstrap_address(address)
if use_gcs_for_bootstrap():
gcs_client = ray._private.gcs_utils.GcsClient(address=address)
else:
redis_client = ray._private.services.create_redis_client(
address, redis_password
)
gcs_client = ray._private.gcs_utils.GcsClient.create_from_redis(redis_client)
ray.experimental.internal_kv._initialize_internal_kv(gcs_client)
status = ray.experimental.internal_kv._internal_kv_get(
ray_constants.DEBUG_AUTOSCALING_STATUS
)
error = ray.experimental.internal_kv._internal_kv_get(
ray_constants.DEBUG_AUTOSCALING_ERROR
)
print(debug_status(status, error))
@cli.command(hidden=True)
@click.option(
"--stream",
"-S",
required=False,
type=bool,
is_flag=True,
default=False,
help="If True, will stream the binary archive contents to stdout",
)
@click.option(
"--output", "-o", required=False, type=str, default=None, help="Output file."
)
@click.option(
"--logs/--no-logs",
is_flag=True,
default=True,
help="Collect logs from ray session dir",
)
@click.option(
"--debug-state/--no-debug-state",
is_flag=True,
default=True,
help="Collect debug_state.txt from ray session dir",
)
@click.option(
"--pip/--no-pip", is_flag=True, default=True, help="Collect installed pip packages"
)
@click.option(
"--processes/--no-processes",
is_flag=True,
default=True,
help="Collect info on running processes",
)
@click.option(
"--processes-verbose/--no-processes-verbose",
is_flag=True,
default=True,
help="Increase process information verbosity",
)
@click.option(
"--tempfile",
"-T",
required=False,
type=str,
default=None,
help="Temporary file to use",
)
def local_dump(
stream: bool = False,
output: Optional[str] = None,
logs: bool = True,
debug_state: bool = True,
pip: bool = True,
processes: bool = True,
processes_verbose: bool = False,
tempfile: Optional[str] = None,
):
"""Collect local data and package into an archive.
Usage:
ray local-dump [--stream/--output file]
This script is called on remote nodes to fetch their data.
"""
# This may stream data to stdout, so no printing here
get_local_dump_archive(
stream=stream,
output=output,
logs=logs,
debug_state=debug_state,
pip=pip,
processes=processes,
processes_verbose=processes_verbose,
tempfile=tempfile,
)
@cli.command()
@click.argument("cluster_config_file", required=False, type=str)
@click.option(
"--host",
"-h",
required=False,
type=str,
help="Single or list of hosts, separated by comma.",
)
@click.option(
"--ssh-user",
"-U",
required=False,
type=str,
default=None,
help="Username of the SSH user.",
)
@click.option(
"--ssh-key",
"-K",
required=False,
type=str,
default=None,
help="Path to the SSH key file.",
)
@click.option(
"--docker",
"-d",
required=False,
type=str,
default=None,
help="Name of the docker container, if applicable.",
)
@click.option(
"--local",
"-L",
required=False,
type=bool,
is_flag=True,
default=None,
help="Also include information about the local node.",
)
@click.option(
"--output", "-o", required=False, type=str, default=None, help="Output file."
)
@click.option(
"--logs/--no-logs",
is_flag=True,
default=True,
help="Collect logs from ray session dir",
)
@click.option(
"--debug-state/--no-debug-state",
is_flag=True,
default=True,
help="Collect debug_state.txt from ray log dir",
)
@click.option(
"--pip/--no-pip", is_flag=True, default=True, help="Collect installed pip packages"
)
@click.option(
"--processes/--no-processes",
is_flag=True,
default=True,
help="Collect info on running processes",
)
@click.option(
"--processes-verbose/--no-processes-verbose",
is_flag=True,
default=True,
help="Increase process information verbosity",
)
@click.option(
"--tempfile",
"-T",
required=False,
type=str,
default=None,
help="Temporary file to use",
)
def cluster_dump(
cluster_config_file: Optional[str] = None,
host: Optional[str] = None,
ssh_user: Optional[str] = None,
ssh_key: Optional[str] = None,
docker: Optional[str] = None,
local: Optional[bool] = None,
output: Optional[str] = None,
logs: bool = True,
debug_state: bool = True,
pip: bool = True,
processes: bool = True,
processes_verbose: bool = False,
tempfile: Optional[str] = None,
):
"""Get log data from one or more nodes.
Best used with Ray cluster configs:
ray cluster-dump [cluster.yaml]
Include the --local flag to also collect and include data from the
local node.
Missing fields will be tried to be auto-filled.
You can also manually specify a list of hosts using the
``--host <host1,host2,...>`` parameter.
"""
archive_path = get_cluster_dump_archive(
cluster_config_file=cluster_config_file,
host=host,
ssh_user=ssh_user,
ssh_key=ssh_key,
docker=docker,
local=local,
output=output,
logs=logs,
debug_state=debug_state,
pip=pip,
processes=processes,
processes_verbose=processes_verbose,
tempfile=tempfile,
)
if archive_path:
click.echo(f"Created archive: {archive_path}")
else:
click.echo("Could not create archive.")
@cli.command(hidden=True)
@click.option(
"--address", required=False, type=str, help="Override the address to connect to."
)
def global_gc(address):
"""Trigger Python garbage collection on all cluster workers."""
address = services.canonicalize_bootstrap_address(address)
logger.info(f"Connecting to Ray instance at {address}.")
ray.init(address=address)
ray.internal.internal_api.global_gc()
print("Triggered gc.collect() on all workers.")
@cli.command(name="kuberay-autoscaler", hidden=True)
@click.option(
"--cluster-name",
required=True,
type=str,
help="The name of the Ray Cluster.\n"
"Should coincide with the `metadata.name` of the RayCluster CR.",
)
@click.option(
"--cluster-namespace",
required=True,
type=str,
help="The Kubernetes namespace the Ray Cluster lives in.\n"
"Should coincide with the `metadata.namespace` of the RayCluster CR.",
)
@click.option(
"--redis-password",
required=False,
type=str,
default="",
help="The password to use for Redis.\n"
"Ray versions >= 1.11.0 don't use Redis.\n"
"Only set this if you really know what you're doing.",
)
def kuberay_autoscaler(
cluster_name: str, cluster_namespace: str, redis_password: str
) -> None:
"""Runs the autoscaler for a Ray cluster managed by the KubeRay operator.
`ray kuberay-autoscaler` is meant to be used as an entry point in
KubeRay cluster configs.
`ray kuberay-autoscaler` is NOT a public CLI.
"""
run_autoscaler_with_retries(cluster_name, cluster_namespace, redis_password)
@cli.command(name="health-check", hidden=True)
@click.option(
"--address", required=False, type=str, help="Override the address to connect to."
)
@click.option(
"--redis_password",
required=False,
type=str,
default=ray_constants.REDIS_DEFAULT_PASSWORD,
help="Connect to ray with redis_password.",
)
@click.option(
"--component",
required=False,
type=str,
help="Health check for a specific component. Currently supports: "
"[ray_client_server]",
)
def healthcheck(address, redis_password, component):
"""
This is NOT a public api.
Health check a Ray or a specific component. Exit code 0 is healthy.
"""
address = services.canonicalize_bootstrap_address(address)
if use_gcs_for_bootstrap():
gcs_address = address
else:
# If client creation or ping fails, this will exit with a non-zero
# exit code.
redis_client = ray._private.services.create_redis_client(
address, redis_password
)
redis_client.ping()
gcs_address = redis_client.get("GcsServerAddress").decode()
if not component:
try:
options = (("grpc.enable_http_proxy", 0),)
channel = ray._private.utils.init_grpc_channel(gcs_address, options)
stub = gcs_service_pb2_grpc.HeartbeatInfoGcsServiceStub(channel)
request = gcs_service_pb2.CheckAliveRequest()
reply = stub.CheckAlive(
request, timeout=ray.ray_constants.HEALTHCHECK_EXPIRATION_S
)
if reply.status.code == 0:
sys.exit(0)
except Exception:
pass
sys.exit(1)
gcs_client = ray._private.gcs_utils.GcsClient(address=gcs_address)
ray.experimental.internal_kv._initialize_internal_kv(gcs_client)
report_str = ray.experimental.internal_kv._internal_kv_get(
component, namespace=ray_constants.KV_NAMESPACE_HEALTHCHECK
)
if not report_str:
# Status was never updated
sys.exit(1)
report = json.loads(report_str)
# TODO (Alex): We probably shouldn't rely on time here, but cloud providers
# have very well synchronized NTP servers, so this should be fine in
# practice.
cur_time = time.time()
report_time = float(report["time"])
# If the status is too old, the service has probably already died.
delta = cur_time - report_time
time_ok = delta < ray.ray_constants.HEALTHCHECK_EXPIRATION_S
if time_ok:
sys.exit(0)
else:
sys.exit(1)
@cli.command()
@click.option("-v", "--verbose", is_flag=True)
@click.option(
"--dryrun",
is_flag=True,
help="Identifies the wheel but does not execute the installation.",
)
def install_nightly(verbose, dryrun):
"""Install the latest wheels for Ray.
This uses the same python environment as the one that Ray is currently
installed in. Make sure that there is no Ray processes on this
machine (ray stop) when running this command.
"""
raydir = os.path.abspath(os.path.dirname(ray.__file__))
all_wheels_path = os.path.join(raydir, "nightly-wheels.yaml")
wheels = None
if os.path.exists(all_wheels_path):
with open(all_wheels_path) as f:
wheels = yaml.safe_load(f)
if not wheels:
raise click.ClickException(
f"Wheels not found in '{all_wheels_path}'! "
"Please visit https://docs.ray.io/en/master/installation.html to "
"obtain the latest wheels."
)
platform = sys.platform
py_version = "{0}.{1}".format(*sys.version_info[:2])
matching_wheel = None
for target_platform, wheel_map in wheels.items():
if verbose:
print(f"Evaluating os={target_platform}, python={list(wheel_map)}")
if platform.startswith(target_platform):
if py_version in wheel_map:
matching_wheel = wheel_map[py_version]
break
if verbose:
print("Not matched.")
if matching_wheel is None:
raise click.ClickException(
"Unable to identify a matching platform. "
"Please visit https://docs.ray.io/en/master/installation.html to "
"obtain the latest wheels."
)
if dryrun:
print(f"Found wheel: {matching_wheel}")
else:
cmd = [sys.executable, "-m", "pip", "install", "-U", matching_wheel]
print(f"Running: {' '.join(cmd)}.")
subprocess.check_call(cmd)
@cli.command()
@click.option(
"--show-library-path",
"-show",
required=False,
is_flag=True,
help="Show the cpp include path and library path, if provided.",
)
@click.option(
"--generate-bazel-project-template-to",
"-gen",
required=False,
type=str,
help="The directory to generate the bazel project template to," " if provided.",
)
@add_click_logging_options
def cpp(show_library_path, generate_bazel_project_template_to):
"""Show the cpp library path and generate the bazel project template."""
if not show_library_path and not generate_bazel_project_template_to:
raise ValueError(
"Please input at least one option of '--show-library-path'"
" and '--generate-bazel-project-template-to'."
)
raydir = os.path.abspath(os.path.dirname(ray.__file__))
cpp_dir = os.path.join(raydir, "cpp")
cpp_templete_dir = os.path.join(cpp_dir, "example")
include_dir = os.path.join(cpp_dir, "include")
lib_dir = os.path.join(cpp_dir, "lib")
if not os.path.isdir(cpp_dir):
raise ValueError('Please install ray with C++ API by "pip install ray[cpp]".')
if show_library_path:
cli_logger.print("Ray C++ include path {} ", cf.bold(f"{include_dir}"))
cli_logger.print("Ray C++ library path {} ", cf.bold(f"{lib_dir}"))
if generate_bazel_project_template_to:
if not os.path.isdir(generate_bazel_project_template_to):
cli_logger.abort(
"The provided directory "
f"{generate_bazel_project_template_to} doesn't exist."
)
copy_tree(cpp_templete_dir, generate_bazel_project_template_to)
out_include_dir = os.path.join(
generate_bazel_project_template_to, "thirdparty/include"
)
if not os.path.exists(out_include_dir):
os.makedirs(out_include_dir)
copy_tree(include_dir, out_include_dir)
out_lib_dir = os.path.join(generate_bazel_project_template_to, "thirdparty/lib")
if not os.path.exists(out_lib_dir):
os.makedirs(out_lib_dir)
copy_tree(lib_dir, out_lib_dir)
cli_logger.print(
"Project template generated to {}",
cf.bold(f"{os.path.abspath(generate_bazel_project_template_to)}"),
)
cli_logger.print("To build and run this template, run")
cli_logger.print(
cf.bold(
f" cd {os.path.abspath(generate_bazel_project_template_to)}"
" && bash run.sh"
)
)
def add_command_alias(command, name, hidden):
new_command = copy.deepcopy(command)
new_command.hidden = hidden
cli.add_command(new_command, name=name)
cli.add_command(dashboard)
cli.add_command(debug)
cli.add_command(start)
cli.add_command(stop)
cli.add_command(up)
add_command_alias(up, name="create_or_update", hidden=True)
cli.add_command(attach)
cli.add_command(exec)
add_command_alias(exec, name="exec_cmd", hidden=True)
add_command_alias(rsync_down, name="rsync_down", hidden=True)
add_command_alias(rsync_up, name="rsync_up", hidden=True)
cli.add_command(submit)
cli.add_command(down)
add_command_alias(down, name="teardown", hidden=True)
cli.add_command(kill_random_node)
add_command_alias(get_head_ip, name="get_head_ip", hidden=True)
cli.add_command(get_worker_ips)
cli.add_command(microbenchmark)
cli.add_command(stack)
cli.add_command(status)
cli.add_command(memory)
cli.add_command(local_dump)
cli.add_command(cluster_dump)
cli.add_command(global_gc)
cli.add_command(timeline)
cli.add_command(install_nightly)
cli.add_command(cpp)
add_command_alias(job_cli_group, name="job", hidden=True)
try:
from ray.serve.scripts import serve_cli
cli.add_command(serve_cli)
except Exception as e:
logger.debug(f"Integrating ray serve command line tool failed with {e}")
def main():
return cli()
if __name__ == "__main__":
main()
| ray-project/ray | python/ray/scripts/scripts.py | Python | apache-2.0 | 72,026 | [
"VisIt"
] | caf9517f64a6d590b3e0fe5b8a0c6932997576cff487e8bf074d3c3549220768 |
# -*- coding: utf-8 -*-
# Copyright: (c) 2008, Jarek Zgoda <jarek.zgoda@gmail.com>
__revision__ = '$Id: models.py 28 2009-10-22 15:03:02Z jarek.zgoda $'
import datetime
from django.db import models
from django.db.models import CASCADE
from django.urls import reverse
from django.conf import settings
from django.contrib.contenttypes.models import ContentType
from django.contrib.contenttypes.fields import GenericForeignKey
from django.http import HttpRequest, HttpResponse
from django.shortcuts import render
from django.utils.timezone import now as timezone_now
from zerver.lib.utils import generate_random_token
from zerver.models import PreregistrationUser, EmailChangeStatus, MultiuseInvite, \
UserProfile, Realm
from random import SystemRandom
import string
from typing import Any, Dict, Optional, Union
class ConfirmationKeyException(Exception):
WRONG_LENGTH = 1
EXPIRED = 2
DOES_NOT_EXIST = 3
def __init__(self, error_type: int) -> None:
super().__init__()
self.error_type = error_type
def render_confirmation_key_error(request: HttpRequest, exception: ConfirmationKeyException) -> HttpResponse:
if exception.error_type == ConfirmationKeyException.WRONG_LENGTH:
return render(request, 'confirmation/link_malformed.html')
if exception.error_type == ConfirmationKeyException.EXPIRED:
return render(request, 'confirmation/link_expired.html')
return render(request, 'confirmation/link_does_not_exist.html')
def generate_key() -> str:
generator = SystemRandom()
# 24 characters * 5 bits of entropy/character = 120 bits of entropy
return ''.join(generator.choice(string.ascii_lowercase + string.digits) for _ in range(24))
ConfirmationObjT = Union[MultiuseInvite, PreregistrationUser, EmailChangeStatus]
def get_object_from_key(confirmation_key: str,
confirmation_type: int) -> ConfirmationObjT:
# Confirmation keys used to be 40 characters
if len(confirmation_key) not in (24, 40):
raise ConfirmationKeyException(ConfirmationKeyException.WRONG_LENGTH)
try:
confirmation = Confirmation.objects.get(confirmation_key=confirmation_key,
type=confirmation_type)
except Confirmation.DoesNotExist:
raise ConfirmationKeyException(ConfirmationKeyException.DOES_NOT_EXIST)
time_elapsed = timezone_now() - confirmation.date_sent
if time_elapsed.total_seconds() > _properties[confirmation.type].validity_in_days * 24 * 3600:
raise ConfirmationKeyException(ConfirmationKeyException.EXPIRED)
obj = confirmation.content_object
if hasattr(obj, "status"):
obj.status = getattr(settings, 'STATUS_ACTIVE', 1)
obj.save(update_fields=['status'])
return obj
def create_confirmation_link(obj: ContentType, host: str,
confirmation_type: int,
url_args: Optional[Dict[str, str]]=None) -> str:
key = generate_key()
realm = None
if hasattr(obj, 'realm'):
realm = obj.realm
Confirmation.objects.create(content_object=obj, date_sent=timezone_now(), confirmation_key=key,
realm=realm, type=confirmation_type)
return confirmation_url(key, host, confirmation_type, url_args)
def confirmation_url(confirmation_key: str, host: str,
confirmation_type: int,
url_args: Optional[Dict[str, str]]=None) -> str:
if url_args is None:
url_args = {}
url_args['confirmation_key'] = confirmation_key
return '%s%s%s' % (settings.EXTERNAL_URI_SCHEME, host,
reverse(_properties[confirmation_type].url_name, kwargs=url_args))
class Confirmation(models.Model):
content_type = models.ForeignKey(ContentType, on_delete=CASCADE)
object_id = models.PositiveIntegerField() # type: int
content_object = GenericForeignKey('content_type', 'object_id')
date_sent = models.DateTimeField() # type: datetime.datetime
confirmation_key = models.CharField(max_length=40) # type: str
realm = models.ForeignKey(Realm, null=True, on_delete=CASCADE) # type: Optional[Realm]
# The following list is the set of valid types
USER_REGISTRATION = 1
INVITATION = 2
EMAIL_CHANGE = 3
UNSUBSCRIBE = 4
SERVER_REGISTRATION = 5
MULTIUSE_INVITE = 6
REALM_CREATION = 7
REALM_REACTIVATION = 8
type = models.PositiveSmallIntegerField() # type: int
def __str__(self) -> str:
return '<Confirmation: %s>' % (self.content_object,)
class ConfirmationType:
def __init__(self, url_name: str,
validity_in_days: int=settings.CONFIRMATION_LINK_DEFAULT_VALIDITY_DAYS) -> None:
self.url_name = url_name
self.validity_in_days = validity_in_days
_properties = {
Confirmation.USER_REGISTRATION: ConfirmationType('check_prereg_key_and_redirect'),
Confirmation.INVITATION: ConfirmationType('check_prereg_key_and_redirect',
validity_in_days=settings.INVITATION_LINK_VALIDITY_DAYS),
Confirmation.EMAIL_CHANGE: ConfirmationType('zerver.views.user_settings.confirm_email_change'),
Confirmation.UNSUBSCRIBE: ConfirmationType('zerver.views.unsubscribe.email_unsubscribe',
validity_in_days=1000000), # should never expire
Confirmation.MULTIUSE_INVITE: ConfirmationType(
'zerver.views.registration.accounts_home_from_multiuse_invite',
validity_in_days=settings.INVITATION_LINK_VALIDITY_DAYS),
Confirmation.REALM_CREATION: ConfirmationType('check_prereg_key_and_redirect'),
Confirmation.REALM_REACTIVATION: ConfirmationType('zerver.views.realm.realm_reactivation'),
}
def one_click_unsubscribe_link(user_profile: UserProfile, email_type: str) -> str:
"""
Generate a unique link that a logged-out user can visit to unsubscribe from
Zulip e-mails without having to first log in.
"""
return create_confirmation_link(user_profile, user_profile.realm.host,
Confirmation.UNSUBSCRIBE,
url_args = {'email_type': email_type})
# Functions related to links generated by the generate_realm_creation_link.py
# management command.
# Note that being validated here will just allow the user to access the create_realm
# form, where they will enter their email and go through the regular
# Confirmation.REALM_CREATION pathway.
# Arguably RealmCreationKey should just be another ConfirmationObjT and we should
# add another Confirmation.type for this; it's this way for historical reasons.
def validate_key(creation_key: Optional[str]) -> Optional['RealmCreationKey']:
"""Get the record for this key, raising InvalidCreationKey if non-None but invalid."""
if creation_key is None:
return None
try:
key_record = RealmCreationKey.objects.get(creation_key=creation_key)
except RealmCreationKey.DoesNotExist:
raise RealmCreationKey.Invalid()
time_elapsed = timezone_now() - key_record.date_created
if time_elapsed.total_seconds() > settings.REALM_CREATION_LINK_VALIDITY_DAYS * 24 * 3600:
raise RealmCreationKey.Invalid()
return key_record
def generate_realm_creation_url(by_admin: bool=False) -> str:
key = generate_key()
RealmCreationKey.objects.create(creation_key=key,
date_created=timezone_now(),
presume_email_valid=by_admin)
return '%s%s%s' % (settings.EXTERNAL_URI_SCHEME,
settings.EXTERNAL_HOST,
reverse('zerver.views.create_realm',
kwargs={'creation_key': key}))
class RealmCreationKey(models.Model):
creation_key = models.CharField('activation key', max_length=40)
date_created = models.DateTimeField('created', default=timezone_now)
# True just if we should presume the email address the user enters
# is theirs, and skip sending mail to it to confirm that.
presume_email_valid = models.BooleanField(default=False) # type: bool
class Invalid(Exception):
pass
| jackrzhang/zulip | confirmation/models.py | Python | apache-2.0 | 8,199 | [
"VisIt"
] | 6e08fbc80b9f55a453bdb22582cad265d763efab3ec38828e7429785de2dbf5b |
# coding=utf8
#
# Copyright 2013 Dreamlab Onet.pl
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation;
# version 3.0.
# This library 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
# Lesser General Public License for more details.
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, visit
#
# http://www.gnu.org/licenses/lgpl.txt
#
from rmock.runners import SmtpRunner
from .server import LMTPServer
class LmtpRunner(SmtpRunner):
SERVER_CLS = LMTPServer
| tikan/rmock | src/rmock/runners/lmtp/runner.py | Python | lgpl-3.0 | 785 | [
"VisIt"
] | 2678d9a4fb55ff2f5719b77a54a7284e24094e886a08eb03d03c28fdc1bb42f7 |
# Copyright (c) 2009-2021 The Regents of the University of Michigan
# This file is part of the HOOMD-blue project, released under the BSD 3-Clause
# License.
"""Implement parameter dictionaries."""
from abc import abstractmethod
from collections.abc import Mapping, MutableMapping
from itertools import product, combinations_with_replacement
from copy import copy
import numpy as np
from hoomd.util import _to_camel_case, _is_iterable
from hoomd.data.typeconverter import (to_type_converter, RequiredArg,
TypeConverterMapping, OnlyIf, Either)
from hoomd.data.smart_default import (_to_base_defaults, _to_default,
_SmartDefault, _NoDefault)
from hoomd.error import TypeConversionError
def _has_str_elems(obj):
"""Returns True if all elements of iterable are str."""
return all([isinstance(elem, str) for elem in obj])
def _is_good_iterable(obj):
"""Returns True if object is iterable with respect to types."""
return _is_iterable(obj) and _has_str_elems(obj)
def _proper_type_return(val):
"""Expects and requires a dictionary with type keys."""
if len(val) == 0:
return None
elif len(val) == 1:
return list(val.values())[0]
else:
return val
def _raise_if_required_arg(value, current_context=()):
"""Ensure that there are no RequiredArgs in value."""
def _raise_error_with_context(context):
"""Produce a useful error message on missing parameters."""
context_str = " "
for c in context:
if isinstance(c, str):
context_str = context_str + f"in key {c} "
elif isinstance(c, int):
context_str = context_str + f"in index {c} "
raise ValueError(f"Value{context_str}is required")
if value is RequiredArg:
_raise_error_with_context(current_context)
if isinstance(value, Mapping):
for key, item in value.items():
_raise_if_required_arg(item, current_context + (key,))
# _is_good_iterable is required over isinstance(value, Sequence) because a
# str of 1 character is still a sequence and results in infinite recursion.
elif _is_good_iterable(value):
for index, item in enumerate(value):
_raise_if_required_arg(item, current_context + (index,))
class _ValidatedDefaultDict(MutableMapping):
"""Provide support for validating values and multi-type tuple keys.
The class provides the interface for using `hoomd.data.typeconverter` value
validation and processing as well as an infrastructure for setting and
getting multiple mapping values at once.
In addition, default values for all non-existent keys can be set (similar to
default dict) using the `hoomd.data.smart_default` logic. This lets partial
defaults be set.
The constuctor expects either a single positional arugment defining the type
validation for keys, or keyword arguments defining the dictionary of
parameters for each key.
The keyword argument ``_defaults`` is special and is used to specify default
values at object construction.
All keys into this mapping are expected to be str instance is _len_keys is
one, otherwise a tuple of str instances. For tuples, the tuple is sorted
first before accessing or setting any data. This is to prevent needing to
store data for both ``("a", "b")`` and ``("b", "a")`` while preventing the
user from needing to consider tuple item order.
Note:
This class is not directly instantiable due to abstract methods that
must be written for subclasses: `__len__`, `_single_setitem`,
`_single_getitem`, and `__iter__` (yield keys).
"""
def _set_validation_and_defaults(self, *args, **kwargs):
defaults = kwargs.pop('_defaults', _NoDefault)
if len(kwargs) != 0 and len(args) != 0:
raise ValueError("Positional argument(s) and keyword argument(s) "
"cannot both be specified.")
if len(kwargs) == 0 and len(args) == 0:
raise ValueError("Either a positional or keyword "
"argument must be specified.")
if len(args) > 1:
raise ValueError("Only one positional argument allowed.")
if len(kwargs) > 0:
type_spec = kwargs
else:
type_spec = args[0]
self._type_converter = to_type_converter(type_spec)
self._default = _to_default(type_spec, defaults)
@abstractmethod
def _single_getitem(self, key):
pass
def __getitem__(self, keys):
"""Access parameter by key."""
return self.get(keys, self.default)
@abstractmethod
def _single_setitem(self, key, item):
pass
def __setitem__(self, keys, item):
"""Set parameter by key."""
keys = self._yield_keys(keys)
try:
validated_value = self._validate_values(item)
except TypeConversionError as err:
raise TypeConversionError("For types {}, error {}.".format(
list(keys), str(err)))
for key in keys:
self._single_setitem(key, validated_value)
def __delitem__(self, key):
raise NotImplementedError("__delitem__ is not defined for this type.")
def get(self, keys, default=None):
"""Get values for keys with undefined keys returning default.
Args:
keys:
Valid keys specifications (depends on the expected key length).
default (``any``, optional):
The value to default to if a key is not found in the mapping.
If not set, the value defaults to the mapping's default.
Returns:
values:
Returns a dict of the values for the keys asked for if multiple
keys were specified; otherwise, returns the value for the single
key.
"""
# We implement get manually since __getitem__ will always return a value
# for a properly formatted key. This explicit method uses the provided
# default with the benefit that __getitem__ can be defined in terms of
# get.
value = {}
for key in self._yield_keys(keys):
try:
value[key] = self._single_getitem(key)
except KeyError:
value[key] = default
return _proper_type_return(value)
def setdefault(self, keys, default):
"""Set the value for the keys if not already specified.
Args:
keys: Valid keys specifications (depends on the expected key
length).
default (``any``): The value to default to if a key is not found in
the mapping. Must be compatible with the typing specification
specified on construction.
"""
self.__setitem__(filter(self.__contains__, self._yield_keys(keys)),
default)
def _validate_values(self, value):
validated_value = self._type_converter(value)
# We can check the validated_value is a dict here since if it passed the
# type validation it is of a form we expect.
if isinstance(validated_value, dict):
if isinstance(self._type_converter, TypeConverterMapping):
expected_keys = set(self._type_converter.keys())
elif isinstance(self._type_converter.converter, OnlyIf):
expected_keys = set(self._type_converter.converter.cond.keys())
elif isinstance(self._type_converter.converter, Either):
mapping = next(
filter(lambda x: isinstance(x, TypeConverterMapping),
self._type_converter.converter.specs))
expected_keys = set(mapping.keys())
else:
raise ValueError
bad_keys = set(validated_value.keys()) - expected_keys
if len(bad_keys) != 0:
raise ValueError("Keys must be a subset of available keys. "
"Bad keys are {}".format(bad_keys))
# update validated_value with the default (specifically to add dict keys
# that have defaults and were not manually specified).
if isinstance(self._default, _SmartDefault):
return self._default(validated_value)
return validated_value
def _validate_and_split_key(self, key):
"""Validate key given regardless of key length."""
if self._len_keys == 1:
return self._validate_and_split_len_one(key)
else:
return self._validate_and_split_len(key)
def _validate_and_split_len_one(self, key):
"""Validate single type keys.
Accepted input is a string, and arbitrarily nested iterators that
culminate in str types.
"""
if isinstance(key, str):
return [key]
elif _is_iterable(key):
keys = []
for k in key:
keys.extend(self._validate_and_split_len_one(k))
return keys
else:
raise KeyError("The key {} is not valid.".format(key))
def _validate_and_split_len(self, key):
"""Validate all key lengths greater than one, N.
Valid input is an arbitrarily deep series of iterables that culminate
in N length tuples, this includes an iterable depth of zero. The N
length tuples can contain for each member either a type string or an
iterable of type strings.
"""
if isinstance(key, tuple) and len(key) == self._len_keys:
if any([
not _is_good_iterable(v) and not isinstance(v, str)
for v in key
]):
raise KeyError("The key {} is not valid.".format(key))
# convert str to single item list for proper enumeration using
# product
key_types_list = [[v] if isinstance(v, str) else v for v in key]
return list(product(*key_types_list))
elif _is_iterable(key):
keys = []
for k in key:
keys.extend(self._validate_and_split_len(k))
return keys
else:
raise KeyError("The key {} is not valid.".format(key))
def _yield_keys(self, key):
"""Returns the generated keys in proper sorted order.
The order is necessary so ('A', 'B') is equivalent to ('B', A').
"""
if self._len_keys > 1:
keys = self._validate_and_split_key(key)
for key in keys:
yield tuple(sorted(list(key)))
else:
yield from self._validate_and_split_key(key)
def __eq__(self, other):
if not isinstance(other, _ValidatedDefaultDict):
return NotImplemented
if self.default != other.default:
return False
if set(self.keys()) != set(other.keys()):
return False
if isinstance(self.default, dict):
return all(
np.all(self[type_][key] == other[type_][key])
for type_ in self
for key in self[type_])
return all(np.all(self[type_] == other[type_]) for type_ in self)
@property
def default(self):
if isinstance(self._default, _SmartDefault):
return self._default.to_base()
else:
return copy(self._default)
@default.setter
def default(self, new_default):
new_default = self._type_converter(new_default)
if isinstance(self._default, _SmartDefault):
new_default = self._default(new_default)
if isinstance(new_default, dict):
keys = set(self._default.keys())
provided_keys = set(new_default.keys())
if keys.intersection(provided_keys) != provided_keys:
raise ValueError("New default must a subset of current keys.")
self._default = _to_default(new_default)
class TypeParameterDict(_ValidatedDefaultDict):
"""Type parameter dictionary."""
def __init__(self, *args, len_keys, **kwargs):
# Validate proper key constraint
if len_keys < 1 or len_keys != int(len_keys):
raise ValueError("len_keys must be a positive integer.")
self._len_keys = len_keys
self._set_validation_and_defaults(*args, **kwargs)
self._dict = {}
def _single_getitem(self, key):
"""Access parameter by key."""
try:
return self._dict[key]
except KeyError:
return self.default
def _single_setitem(self, key, item):
"""Set parameter by key."""
self._dict[key] = item
def __iter__(self):
"""Get the keys in the mapping."""
if self._len_keys == 1:
yield from self._dict.keys()
else:
for key in self._dict.keys():
yield tuple(sorted(list(key)))
def __len__(self):
"""Return mapping length."""
return len(self._dict)
def to_dict(self):
"""Convert to a `dict`."""
return self._dict
class AttachedTypeParameterDict(_ValidatedDefaultDict):
"""Parameter dictionary synchronized with a C++ class.
This class serves as the "attached" version of the `TypeParameterDict`. The
class performs the same indexing and mutation options as
`TypeParameterDict`, but only allows querying for keys that match the actual
types of the simulation it is attached to.
The interface expects the passed in C++ object to have a getter and setter
that follow the camel case style version of ``param_name``. Likewise
type_kind must be a str of a valid attribute to query types from the state.
Args:
cpp_obj:
A pybind11 wrapped C++ object to set and get the type parameters
from.
param_name (str):
A snake case parameter name (handled automatically by
``TypeParameter``) that when changed to camel case prefixed by get
or set is the str name for the pybind11 exported getter and setter.
type_kind (str):
The str name of the attribute to query the parent simulation's state
for existent types.
type_param_dict (TypeParameterDict):
The `TypeParameterDict` to convert to the "attached" version.
sim (hoomd.Simulation):
The simulation to attach to.
Note:
This class should not be directly instantiated even by developers, but
the `hoomd.data.type_param.TypeParameter` class should be used to
automatically handle this in conjunction with
`hoomd.operation._BaseHOOMDObject` subclasses.
"""
def __init__(self, cpp_obj, param_name, types, type_param_dict):
# store info to communicate with c++
self._cpp_obj = cpp_obj
self._setter = "set" + _to_camel_case(param_name)
self._getter = "get" + _to_camel_case(param_name)
self._len_keys = type_param_dict._len_keys
self._type_keys = self._compute_type_keys(types)
# Get default data
self._default = type_param_dict._default
self._type_converter = type_param_dict._type_converter
# add all types to c++
for key in self:
parameter = type_param_dict._single_getitem(key)
_raise_if_required_arg(parameter)
self._single_setitem(key, parameter)
def to_detached(self):
"""Convert to a detached parameter dict."""
if isinstance(self.default, dict):
type_param_dict = TypeParameterDict(**self.default,
len_keys=self._len_keys)
else:
type_param_dict = TypeParameterDict(self.default,
len_keys=self._len_keys)
type_param_dict._type_converter = self._type_converter
for key in self:
type_param_dict[key] = self[key]
return type_param_dict
def _single_getitem(self, key):
"""Access parameter by key."""
return getattr(self._cpp_obj, self._getter)(key)
def _single_setitem(self, key, item):
"""Set parameter by key."""
getattr(self._cpp_obj, self._setter)(key, item)
def _yield_keys(self, key):
"""Includes key check for existing simulation keys.
Overwritting this means that __getitem__ and __setitem__ plus any
methods that rely on them will error properly even if we don't check for
the key's existence there.
"""
for key in super()._yield_keys(key):
if key not in self._type_keys:
raise KeyError("Type {} does not exist in the "
"system.".format(key))
else:
yield key
def _validate_values(self, val):
val = super()._validate_values(val)
_raise_if_required_arg(val)
return val
def _compute_type_keys(self, types):
"""Compute valid type keys from given types.
We store types as a set since set iteration for ~50 items is marginally
slower to iterate over than a list, but multiple times faster to check
for contained values.
"""
if self._len_keys == 1:
return set(types)
else:
return {
tuple(sorted(key))
for key in combinations_with_replacement(types, self._len_keys)
}
def __iter__(self):
"""Iterate through mapping keys."""
yield from self._type_keys
def __len__(self):
"""Return mapping length."""
return len(self._type_keys)
def to_dict(self):
"""Convert to a `dict`."""
rtn_dict = {}
for key in self:
rtn_dict[key] = getattr(self._cpp_obj, self._getter)(key)
return rtn_dict
class ParameterDict(MutableMapping):
"""Parameter dictionary."""
def __init__(self, _defaults=_NoDefault, **kwargs):
self._type_converter = to_type_converter(kwargs)
self._dict = {**_to_base_defaults(kwargs, _defaults)}
def __setitem__(self, key, value):
"""Set parameter by key."""
if key not in self._type_converter.keys():
self._dict[key] = value
self._type_converter[key] = to_type_converter(value)
else:
self._dict[key] = self._type_converter[key](value)
def __getitem__(self, key):
"""Access parameter by key."""
return self._dict[key]
def __delitem__(self, key):
"""Remove parameter by key."""
del self._dict[key]
del self._type_converter[key]
def __iter__(self):
"""Iterate over keys."""
yield from self._dict
def __len__(self):
"""int: The number of keys."""
return len(self._dict)
def __eq__(self, other):
"""Equality between ParameterDict objects."""
if not isinstance(other, ParameterDict):
return NotImplemented
return (set(self.keys()) == set(other.keys()) and np.all(
[np.all(self[key] == other[key]) for key in self.keys()]))
def update(self, other):
"""Add keys and values to the dictionary."""
if isinstance(other, ParameterDict):
for key, value in other.items():
self._type_converter[key] = other._type_converter[key]
self._dict[key] = value
else:
for key, value in other.items():
self[key] = value
| joaander/hoomd-blue | hoomd/data/parameterdicts.py | Python | bsd-3-clause | 19,595 | [
"HOOMD-blue"
] | 6a36870d7ac62c5b555a388e6ad30f11bf732d2628ce6fd108912bb6f4decd64 |
# Copyright (c) 2006-2013 Regents of the University of Minnesota.
# For licensing terms, see the file LICENSE.
import conf
import g
log = g.log.getLogger('ccp_stp_wrds')
# This isn't a perfect implementation, but we can use different stop words
# depending on what we're searching.
#
# Addy_Stop_Words__Annotation
# Addy_Stop_Words__Tag
# Addy_Stop_Words__Thread
# Addy_Stop_Words__Byway
# Addy_Stop_Words__Region
# Addy_Stop_Words__Waypoint
class Addy_Stop_Words__Item_Versioned(object):
# Here's the vim command to convert debug output to this table.
#
# See the script: statewide_munis_lookup.py
# which writes lines like:
# Oct-25 01:35:19 DEBG stwd_munis_l # cnt: 50280 / ['ve']
#
# .,$s/^[-:_a-zA-Z0-9 ]\+# cnt: \+\(\d\+\) \/ \['\([^']\+\)']/ '\2', # in \1 names/gc
# NOTE: The search fcn. uses this table, which is important for longer
# words: that is, the search fcn. should consider 1-, 2-, and maybe
# even 3-letter words as stop words, unless they're &'ed to a street
# name (e.g., searching just 'ave' returns 0 results, but searching
# 'washington&ave' is ok (but not 'washington|ave')).
#
lookup = set([
'ave', # in 50280 names
'st', # in 34568 names
'via', # in 21863 names
'hwy', # in 17991 names
'rd', # in 16318 names
'trail', # in 11017 names
'dr', # in 10541 names
'la', # in 9952 names
'lake', # in 6754 names
'blvd', # in 5981 names
'park', # in 5642 names
'route', # in 5009 names
'ct', # in 3554 names
'tr', # in 2843 names
'cir', # in 2565 names
'pkwy', # in 2487 names
# BUG nnnn: What if someone really is looking for a nearby river or
# creek? Instead of stop words, can we also search for
# river and creek in the user's viewport? Like, rather
# than consider common words in road names as stop words,
# instead just restrict the bbox: so, do two searches.
# E.g., if I search 'mississippi river', pyserver looks
# for 'mississippi&river', 'mississippi' (since in Minnesota
# it's probably not a common word), and then searches
# for 'river' just encompassing the user's viewport (otherwise
# we'd return way too many results).
# As it stands, not doing bbox searches in general is probably
# bad news for Statewide... not only will we get too many hits,
# those hits will be all over the map...
#
# Argh, maybe what we really need is a LIMIT on the inner
# SQL... meaning we need a way to rank results in the inner...
'river', # in 2462 names
'creek', # in 1892 names
'pl', # in 1672 names
'way', # in 1447 names
'path', # in 1196 names
'road', # in 1099 names
'gateway', # in 844 names
'view', # in 706 names
'ridge', # in 700 names
'summit', # in 663 names
'center', # in 568 names
'ter', # in 487 names
'bluffs', # in 436 names
'hills', # in 420 names
'valley', # in 380 names
'hill', # in 372 names
'bridge', # in 356 names
'street', # in 336 names
'point', # in 280 names
'avenue', # in 276 names
'heights', # in 267 names
'highway', # in 248 names
#'cv', # in 233 names
#'shore', # in 230 names
#'lane', # in 223 names
#'grove', # in 200 names
#'lk', # in 190 names
#'rapids', # in 178 names
#'prairie', # in 171 names
#'ferry', # in 166 names
#'lakes', # in 161 names
#'drive', # in 158 names
#'island', # in 158 names
#'extension', # in 157 names
#'dale', # in 155 names
#'pine', # in 152 names
#'parkway', # in 139 names
#'curve', # in 121 names
#'crossing', # in 120 names
#'meadow', # in 120 names
#'crossroad', # in 107 names
#'fort', # in 104 names
#'loop', # in 102 names
#'ln', # in 101 names
])
# MAYBE: analyze all item names are make stop words out of common words
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__Annotation(object):
# BUG nnnn: The searched words are the street type search words.
# What we really want to do is to ignore other popular
# words that occur hundreds of times, i.e., by analyzing
# all notes and tallying all word usages.
lookup = set([
'is', # in 907 names
'road', # in 490 names
'path', # in 391 names
'trail', # in 360 names
'bridge', # in 299 names
'lane', # in 297 names
'ave', # in 220 names
'street', # in 220 names
'way', # in 201 names
'park', # in 193 names
'route', # in 193 names
'hill', # in 170 names
'st', # in 164 names
'lanes', # in 154 names
'lake', # in 147 names
# ???
'minneapolis',
'mpls',
'minnesota',
'mn',
])
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__Tag(object):
# Rather than use the street type stop words, we need tag counts.
# BUG nnnn: Analyze Tag application counts and make this lookup table.
lookup = set([
])
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__Thread(object):
# MAYBE: Thread stop words? Probably not necessary... yet.
# We could just do the same word-counting as proposed
# for figuring out annotation stop words.
lookup = set([
])
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__Byway(object):
# BUG nnnn: Instead of just searching street types (from
# addressconf.Streets.STREET_TYPES_LIST: see the dev script,
# scripts/setupcp/greatermn/statewide_munis_lookup.py),
# we could count all word usages in byway names.
lookup = set([
'ave', # in 43253 names
'st', # in 32850 names
'hwy', # in 16125 names
'rd', # in 14743 names
'dr', # in 10354 names
'la', # in 9894 names
'park', # in 5474 names
'blvd', # in 5350 names
'lake', # in 4884 names
'trail', # in 4361 names
'ct', # in 3557 names
'tr', # in 2758 names
'cir', # in 2566 names
'pkwy', # in 2102 names
'creek', # in 2043 names
'pl', # in 1671 names
'way', # in 1417 names
'path', # in 1100 names
'river', # in 971 names
'road', # in 958 names
'ridge', # in 773 names
'valley', # in 673 names
'view', # in 652 names
'ter', # in 487 names
'center', # in 477 names
'hill', # in 396 names
'hills', # in 394 names
'highway', # in 379 names
'island', # in 344 names
'street', # in 309 names
#'prairie', # in 284 names
'point', # in 261 names
'pine', # in 249 names
'grove', # in 245 names
'forest', # in 244 names
'shore', # in 236 names
'heights', # in 234 names
'cv', # in 233 names
'bridge', # in 229 names
#'dale', # in 212 names
#'summit', # in 209 names
#'avenue', # in 199 names
#'meadow', # in 182 names
#'orchard', # in 180 names
#'lane', # in 167 names
#'cliff', # in 160 names
#'terrace', # in 157 names
#'curve', # in 147 names
#'lk', # in 144 names
#'parkway', # in 141 names
#'drive', # in 137 names
#'spring', # in 136 names
#'lakes', # in 135 names
#'glen', # in 134 names
#'rapids', # in 129 names
#'bluff', # in 120 names
#'circle', # in 114 names
#'garden', # in 105 names
#'mill', # in 105 names
#'ln', # in 102 names
])
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__Region(object):
# BUG nnnn: See earlier Bug nnnns in this file. We could word-count all
# region names to make this lookup... but so far there aren't many regions
# to begin with, so until there are, this isn't that big of a deal.
lookup = set([
#'park', # in 45 names
#'st', # in 34 names
#'lake', # in 29 names
#'river', # in 11 names
#'creek', # in 10 names
#'trail', # in 10 names
])
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__In_Region(object):
# FIXME: The original CcpV1 search method finds points within regions whose
# names match part of the query. Which is... okay for neighborhoods,
# but pretty ridiculous for large cities.
lookup = set([
'minnesota',
#'minn',
#'mn',
'minneapolis',
'saint paul',
])
#
def __init__(self):
raise # does not instantiate.
# ***
class Addy_Stop_Words__Waypoint(object):
lookup = set([
'park', # in 302 names
'lake', # in 228 names
'center', # in 123 names
'st', # in 104 names
#'trail', # in 75 names
#'station', # in 71 names
#'ave', # in 49 names
#'point', # in 43 names
#'bridge', # in 39 names
#'garden', # in 39 names
#'hwy', # in 11 names
#'island', # in 11 names
#'prairie', # in 11 names
#'course', # in 10 names
#'falls', # in 10 names
#'gateway', # in 10 names
#'green', # in 10 names
#'place', # in 10 names
#'rapids', # in 10 names
])
#
def __init__(self):
raise # does not instantiate.
# ***
class U_S_State_Nicknames(object):
# select state_name, state_abbrev from state_name_abbrev;
# .,$s/\s*\(\w\+\)\s*|\s*\(.*\)/ '\2': '\1',/gc
by_synonym = {
#'minnesota': 'minnesota',
'minn': 'minnesota',
#'gopher state': 'minnesota',
#'land of lakes': 'minnesota',
#'land of 10,000 lakes': 'minnesota',
#'land of 10000 lakes': 'minnesota',
#'land of sky-blue waters': 'minnesota',
#'north star state': 'minnesota',
#'hawkeye state': 'iowa',
#'rough rider state': 'north dakota',
#'mount rushmore state': 'south dakota',
#'sunshine state': 'south dakota',
'wis': 'wisconsin',
#'cheese state': 'wisconsin',
#'badger state': 'wisconsin',
}
#
def __init__(self):
raise # does not instantiate.
# ***
class City_Names_MN(object):
# See the table: state_city_abbrev.
# This table is the exact same...
by_nick_name = {
'alex': 'alexandria',
'app': 'appleton',
'a hills': 'arden hills',
'spamtown': 'austin',
'spamtown usa': 'austin',
'cann': 'cannon falls',
'troit': 'detroit lakes',
'eb': 'east bethel',
'ep': 'eden prairie',
'e.p.': 'eden prairie',
'bubble': 'edina',
'gv': 'golden valley',
'marine': 'marine on saint croix',
'mosc': 'marine on saint croix',
'city of lakes': 'minneapolis',
'mill city': 'minneapolis',
'mini apple': 'minneapolis',
'mpls': 'minneapolis',
'mn city': 'minnesota city',
'mn lake': 'minnesota lake',
'mtka': 'minnetonka',
'mtka beach': 'minnetonka beach',
'mtn iron': 'mountain iron',
'mtn lake': 'mountain lake',
'north stp': 'north saint paul',
'nstp': 'north saint paul',
'nsp': 'north saint paul',
'norwood': 'norwood young america',
'young america': 'norwood young america',
'ny mills': 'new york mills',
'prap': 'park rapids',
'birdtown': 'robbinsdale',
'med town': 'rochester',
'slp': 'saint louis park',
'pigs eye': 'saint paul',
'stp': 'saint paul',
'st p': 'saint paul',
'2harb': 'two harbors',
'hockeytown': 'warroad',
'west stp': 'west saint paul',
'wstp': 'west saint paul',
'wsp': 'west saint paul',
# If we want this silly syn., we'd have to raise n_words_in_longest_name.
'turkey capital of the world': 'worthington',
}
# MAGIC_NUMBER: MN City names follow a nice set of rules: only characters
# (no numbers, punctuation, or symbols), and the longest city
# names are four words long. These facts help us parse a user
# query for the city names.
# 'lake saint croix beach'
# 'marine on saint croix',
#n_words_in_longest_name = 4
# 'turkey capital of the world'
n_words_in_longest_name = 5
# In Python, searching a list is O(n), but a set should be amortized O(1)
# (though worst case O(n)?).
# SELECT municipal_name INTO TEMPORARY TABLE mn_cities
# FROM state_cities ORDER BY municipal_name;
# COPY mn_cities to STDOUT;
city_names = set([
'ada',
'adams',
'adrian',
'afton',
'aitkin',
'akeley',
'albany',
'alberta',
'albert lea',
'albertville',
'alden',
'aldrich',
'alexandria',
'alpha',
'altura',
'alvarado',
'amboy',
'andover',
'annandale',
'anoka',
'appleton',
'apple valley',
'arco',
'arden hills',
'argyle',
'arlington',
'ashby',
'askov',
'atwater',
'audubon',
'aurora',
'austin',
'avoca',
'avon',
'babbitt',
'backus',
'badger',
'bagley',
'balaton',
'barnesville',
'barnum',
'barrett',
'barry',
'battle lake',
'baudette',
'baxter',
'bayport',
'beardsley',
'beaver bay',
'beaver creek',
'becker',
'bejou',
'belgrade',
'bellechester',
'belle plaine',
'bellingham',
'beltrami',
'belview',
'bemidji',
'bena',
'benson',
'bertha',
'bethel',
'bigelow',
'big falls',
'bigfork',
'big lake',
'bingham lake',
'birchwood village',
'bird island',
'biscay',
'biwabik',
'blackduck',
'blaine',
'blomkest',
'blooming prairie',
'bloomington',
'blue earth',
'bluffton',
'bock',
'borup',
'bovey',
'bowlus',
'boyd',
'boy river',
'braham',
'brainerd',
'brandon',
'breckenridge',
'breezy point',
'brewster',
'bricelyn',
'brooklyn center',
'brooklyn park',
'brook park',
'brooks',
'brookston',
'brooten',
'browerville',
'brownsdale',
'browns valley',
'brownsville',
'brownton',
'bruno',
'buckman',
'buffalo',
'buffalo lake',
'buhl',
'burnsville',
'burtrum',
'butterfield',
'byron',
'caledonia',
'callaway',
'calumet',
'cambridge',
'campbell',
'canby',
'cannon falls',
'canton',
'carlos',
'carlton',
'carver',
'cass lake',
'cedar mills',
'center city',
'centerville',
'ceylon',
'champlin',
'chandler',
'chanhassen',
'chaska',
'chatfield',
'chickamaw beach',
'chisago city',
'chisholm',
'chokio',
'circle pines',
'clara city',
'claremont',
'clarissa',
'clarkfield',
'clarks grove',
'clearbrook',
'clear lake',
'clearwater',
'clements',
'cleveland',
'climax',
'clinton',
'clitherall',
'clontarf',
'cloquet',
'coates',
'cobden',
'cohasset',
'cokato',
'cold spring',
'coleraine',
'cologne',
'columbia heights',
'comfrey',
'comstock',
'conger',
'cook',
'coon rapids',
'corcoran',
'correll',
'cosmos',
'cottage grove',
'cottonwood',
'courtland',
'cromwell',
'crookston',
'crosby',
'crosslake',
'crystal',
'currie',
'cuyuna',
'cyrus',
'dakota',
'dalton',
'danube',
'danvers',
'darfur',
'darwin',
'dassel',
'dawson',
'dayton',
'deephaven',
'deer creek',
'deer river',
'deerwood',
'de graff',
'delano',
'delavan',
'delhi',
'dellwood',
'denham',
'dennison',
'dent',
'detroit lakes',
'dexter',
'dilworth',
'dodge center',
'donaldson',
'donnelly',
'doran',
'dover',
'dovray',
'duluth',
'dumont',
'dundas',
'dundee',
'dunnell',
'eagan',
'eagle bend',
'eagle lake',
'east bethel',
'east grand forks',
'east gull lake',
'easton',
'echo',
'eden prairie',
'eden valley',
'edgerton',
'edina',
'effie',
'eitzen',
'elba',
'elbow lake',
'elgin',
'elizabeth',
'elko',
'elk river',
'elkton',
'ellendale',
'ellsworth',
'elmdale',
'elmore',
'elrosa',
'ely',
'elysian',
'emily',
'emmons',
'erhard',
'erskine',
'evan',
'evansville',
'eveleth',
'excelsior',
'eyota',
'fairfax',
'fairmont',
'falcon heights',
'faribault',
'farmington',
'farwell',
'federal dam',
'felton',
'fergus falls',
'fertile',
'fifty lakes',
'finlayson',
'fisher',
'flensburg',
'floodwood',
'florence',
'foley',
'forada',
'forest lake',
'foreston',
'fort ripley',
'fosston',
'fountain',
'foxhome',
'franklin',
'frazee',
'freeborn',
'freeport',
'fridley',
'frost',
'fulda',
'funkley',
'garfield',
'garrison',
'garvin',
'gary',
'gaylord',
'gem lake',
'geneva',
'genola',
'georgetown',
'ghent',
'gibbon',
'gilbert',
'gilman',
'glencoe',
'glenville',
'glenwood',
'glyndon',
'golden valley',
'gonvick',
'goodhue',
'goodridge',
'good thunder',
'goodview',
'graceville',
'granada',
'grand marais',
'grand meadow',
'grand rapids',
'granite falls',
'grant',
'grasston',
'greenbush',
'greenfield',
'green isle',
'greenwald',
'greenwood',
'grey eagle',
'grove city',
'grygla',
'gully',
'hackensack',
'hadley',
'hallock',
'halma',
'halstad',
'hamburg',
'ham lake',
'hammond',
'hampton',
'hancock',
'hanley falls',
'hanover',
'hanska',
'harding',
'hardwick',
'harmony',
'harris',
'hartland',
'hastings',
'hatfield',
'hawley',
'hayfield',
'hayward',
'hazel run',
'hector',
'heidelberg',
'henderson',
'hendricks',
'hendrum',
'henning',
'henriette',
'herman',
'hermantown',
'heron lake',
'hewitt',
'hibbing',
'hill city',
'hillman',
'hills',
'hilltop',
'hinckley',
'hitterdal',
'hoffman',
'hokah',
'holdingford',
'holland',
'hollandale',
'holloway',
'holt',
'hopkins',
'houston',
'howard lake',
'hoyt lakes',
'hugo',
'humboldt',
'hutchinson',
'ihlen',
'independence',
'international falls',
'inver grove heights',
'iona',
'iron junction',
'ironton',
'isanti',
'isle',
'ivanhoe',
'jackson',
'janesville',
'jasper',
'jeffers',
'jenkins',
'johnson',
'jordan',
'kandiyohi',
'karlstad',
'kasota',
'kasson',
'keewatin',
'kelliher',
'kellogg',
'kennedy',
'kenneth',
'kensington',
'kent',
'kenyon',
'kerkhoven',
'kerrick',
'kettle river',
'kiester',
'kilkenny',
'kimball',
'kinbrae',
'kingston',
'kinney',
'la crescent',
'lafayette',
'lake benton',
'lake bronson',
'lake city',
'lake crystal',
'lake elmo',
'lakefield',
'lake henry',
'lakeland',
'lakeland shores',
'lake lillian',
'lake park',
'lake saint croix beach',
'lake shore',
'lakeville',
'lake wilson',
'lamberton',
'lancaster',
'landfall',
'lanesboro',
'laporte',
'la prairie',
'la salle',
'lastrup',
'lauderdale',
'le center',
'lengby',
'leonard',
'leonidas',
'le roy',
'lester prairie',
'le sueur',
'lewiston',
'lewisville',
'lexington',
'lilydale',
'lindstrom',
'lino lakes',
'lismore',
'litchfield',
'little canada',
'little falls',
'littlefork',
'long beach',
'long lake',
'long prairie',
'longville',
'lonsdale',
'loretto',
'louisburg',
'lowry',
'lucan',
'luverne',
'lyle',
'lynd',
'mabel',
'madelia',
'madison',
'madison lake',
'magnolia',
'mahnomen',
'mahtomedi',
'manchester',
'manhattan beach',
'mankato',
'mantorville',
'maple grove',
'maple lake',
'maple plain',
'mapleton',
'mapleview',
'maplewood',
'marble',
'marietta',
'marine on saint croix',
'marshall',
'mayer',
'maynard',
'mazeppa',
'mcgrath',
'mcgregor',
'mcintosh',
'mckinley',
'meadowlands',
'medford',
'medicine lake',
'medina',
'meire grove',
'melrose',
'menahga',
'mendota',
'mendota heights',
'mentor',
'middle river',
'miesville',
'milaca',
'milan',
'millerville',
'millville',
'milroy',
'miltona',
'minneapolis',
'minneiska',
'minneota',
'minnesota city',
'minnesota lake',
'minnetonka',
'minnetonka beach',
'minnetrista',
'mizpah',
'montevideo',
'montgomery',
'monticello',
'montrose',
'moorhead',
'moose lake',
'mora',
'morgan',
'morris',
'morristown',
'morton',
'motley',
'mound',
'mounds view',
'mountain iron',
'mountain lake',
'murdock',
'myrtle',
'nashua',
'nashwauk',
'nassau',
'nelson',
'nerstrand',
'nevis',
'new auburn',
'new brighton',
'newfolden',
'new germany',
'new hope',
'new london',
'new market',
'new munich',
'newport',
'new prague',
'new richland',
'new trier',
'new ulm',
'new york mills',
'nicollet',
'nielsville',
'nimrod',
'nisswa',
'norcross',
'north branch',
'northfield',
'north mankato',
'north oaks',
'northome',
'northrop',
'north saint paul',
'norwood young america',
'oakdale',
'oak grove',
'oak park heights',
'odessa',
'odin',
'ogema',
'ogilvie',
'okabena',
'oklee',
'olivia',
'onamia',
'ormsby',
'orono',
'oronoco',
'orr',
'ortonville',
'osakis',
'oslo',
'osseo',
'ostrander',
'otsego',
'ottertail',
'owatonna',
'palisade',
'parkers prairie',
'park rapids',
'paynesville',
'pease',
'pelican rapids',
'pemberton',
'pennock',
'pequot lakes',
'perham',
'perley',
'peterson',
'pierz',
'pillager',
'pine city',
'pine island',
'pine river',
'pine springs',
'pipestone',
'plainview',
'plato',
'pleasant lake',
'plummer',
'plymouth',
'porter',
'preston',
'princeton',
'prinsburg',
'prior lake',
'proctor',
'quamba',
'racine',
'ramsey',
'randall',
'randolph',
'ranier',
'raymond',
'red lake falls',
'red wing',
'redwood falls',
'regal',
'remer',
'renville',
'revere',
'rice',
'richfield',
'richmond',
'richville',
'riverton',
'robbinsdale',
'rochester',
'rock creek',
'rockford',
'rockville',
'rogers',
'rollingstone',
'ronneby',
'roosevelt',
'roscoe',
'roseau',
'rose creek',
'rosemount',
'roseville',
'rothsay',
'round lake',
'royalton',
'rush city',
'rushford',
'rushford village',
'rushmore',
'russell',
'ruthton',
'rutledge',
'sabin',
'sacred heart',
'saint anthony',
'saint anthony',
'saint augusta',
'saint bonifacius',
'saint charles',
'saint clair',
'saint cloud',
'saint francis',
'saint hilaire',
'saint james',
'saint joseph',
'saint leo',
'saint louis park',
'saint martin',
'saint marys point',
'saint michael',
'saint paul',
'saint paul park',
'saint peter',
'saint rosa',
'saint stephen',
'saint vincent',
'sanborn',
'sandstone',
'sargeant',
'sartell',
'sauk centre',
'sauk rapids',
'savage',
'scanlon',
'seaforth',
'sebeka',
'sedan',
'shafer',
'shakopee',
'shelly',
'sherburn',
'shevlin',
'shoreview',
'shorewood',
'silver bay',
'silver lake',
'skyline',
'slayton',
'sleepy eye',
'sobieski',
'solway',
'south haven',
'south saint paul',
'spicer',
'springfield',
'spring grove',
'spring hill',
'spring lake park',
'spring park',
'spring valley',
'squaw lake',
'stacy',
'staples',
'starbuck',
'steen',
'stephen',
'stewart',
'stewartville',
'stillwater',
'stockton',
'storden',
'strandquist',
'strathcona',
'sturgeon lake',
'sunburg',
'sunfish lake',
'swanville',
'taconite',
'tamarack',
'taopi',
'taunton',
'taylors falls',
'tenney',
'tenstrike',
'thief river falls',
'thomson',
'tintah',
'tonka bay',
'tower',
'tracy',
'trail',
'trimont',
'trommald',
'trosky',
'truman',
'turtle river',
'twin lakes',
'twin valley',
'two harbors',
'tyler',
'ulen',
'underwood',
'upsala',
'urbank',
'utica',
'vadnais heights',
'vergas',
'vermillion',
'verndale',
'vernon center',
'vesta',
'victoria',
'viking',
'villard',
'vining',
'virginia',
'wabasha',
'wabasso',
'waconia',
'wadena',
'wahkon',
'waite park',
'waldorf',
'walker',
'walnut grove',
'walters',
'waltham',
'wanamingo',
'wanda',
'warba',
'warren',
'warroad',
'waseca',
'watertown',
'waterville',
'watkins',
'watson',
'waubun',
'waverly',
'wayzata',
'welcome',
'wells',
'wendell',
'westbrook',
'west concord',
'westport',
'west saint paul',
'west union',
'whalan',
'wheaton',
'white bear lake',
'wilder',
'willernie',
'williams',
'willmar',
'willow river',
'wilmont',
'wilton',
'windom',
'winger',
'winnebago',
'winona',
'winsted',
'winthrop',
'winton',
'wolf lake',
'wolverton',
'woodbury',
'wood lake',
'woodland',
'woodstock',
'worthington',
'wrenshall',
'wright',
'wykoff',
'wyoming',
'zemple',
'zimmerman',
'zumbro falls',
'zumbrota',
])
#
def __init__(self):
raise # does not instantiate.
# ***
# ***
| lbouma/Cyclopath | pyserver/util_/streetaddress/ccp_stop_words.py | Python | apache-2.0 | 28,632 | [
"CRYSTAL",
"Dalton",
"Elk",
"MOOSE"
] | c626dde36283601fb25ca4862a27674ad3a23d3756d5b17bda22e7cd69f7ed2a |
#!/usr/bin/env python
"""Multithreaded interactive interpreter with GTK and Matplotlib support.
WARNING:
As of 2010/06/25, this is not working, at least on Linux.
I have disabled it as a runnable script. - EF
Usage:
pyint-gtk.py -> starts shell with gtk thread running separately
pyint-gtk.py -pylab [filename] -> initializes matplotlib, optionally running
the named file. The shell starts after the file is executed.
Threading code taken from:
http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/65109, by Brian
McErlean and John Finlay.
Matplotlib support taken from interactive.py in the matplotlib distribution.
Also borrows liberally from code.py in the Python standard library."""
from __future__ import print_function
__author__ = "Fernando Perez <Fernando.Perez@colorado.edu>"
import sys
import code
import threading
import gobject
import gtk
try:
import readline
except ImportError:
has_readline = False
else:
has_readline = True
class MTConsole(code.InteractiveConsole):
"""Simple multi-threaded shell"""
def __init__(self,on_kill=None,*args,**kw):
code.InteractiveConsole.__init__(self,*args,**kw)
self.code_to_run = None
self.ready = threading.Condition()
self._kill = False
if on_kill is None:
on_kill = []
# Check that all things to kill are callable:
for _ in on_kill:
if not callable(_):
raise TypeError,'on_kill must be a list of callables'
self.on_kill = on_kill
# Set up tab-completer
if has_readline:
import rlcompleter
try: # this form only works with python 2.3
self.completer = rlcompleter.Completer(self.locals)
except: # simpler for py2.2
self.completer = rlcompleter.Completer()
readline.set_completer(self.completer.complete)
# Use tab for completions
readline.parse_and_bind('tab: complete')
# This forces readline to automatically print the above list when tab
# completion is set to 'complete'.
readline.parse_and_bind('set show-all-if-ambiguous on')
# Bindings for incremental searches in the history. These searches
# use the string typed so far on the command line and search
# anything in the previous input history containing them.
readline.parse_and_bind('"\C-r": reverse-search-history')
readline.parse_and_bind('"\C-s": forward-search-history')
def runsource(self, source, filename="<input>", symbol="single"):
"""Compile and run some source in the interpreter.
Arguments are as for compile_command().
One several things can happen:
1) The input is incorrect; compile_command() raised an
exception (SyntaxError or OverflowError). A syntax traceback
will be printed by calling the showsyntaxerror() method.
2) The input is incomplete, and more input is required;
compile_command() returned None. Nothing happens.
3) The input is complete; compile_command() returned a code
object. The code is executed by calling self.runcode() (which
also handles run-time exceptions, except for SystemExit).
The return value is True in case 2, False in the other cases (unless
an exception is raised). The return value can be used to
decide whether to use sys.ps1 or sys.ps2 to prompt the next
line.
"""
try:
code = self.compile(source, filename, symbol)
except (OverflowError, SyntaxError, ValueError):
# Case 1
self.showsyntaxerror(filename)
return False
if code is None:
# Case 2
return True
# Case 3
# Store code in self, so the execution thread can handle it
self.ready.acquire()
self.code_to_run = code
self.ready.wait() # Wait until processed in timeout interval
self.ready.release()
return False
def runcode(self):
"""Execute a code object.
When an exception occurs, self.showtraceback() is called to display a
traceback."""
self.ready.acquire()
if self._kill:
print('Closing threads...')
sys.stdout.flush()
for tokill in self.on_kill:
tokill()
print('Done.')
if self.code_to_run is not None:
self.ready.notify()
code.InteractiveConsole.runcode(self,self.code_to_run)
self.code_to_run = None
self.ready.release()
return True
def kill (self):
"""Kill the thread, returning when it has been shut down."""
self.ready.acquire()
self._kill = True
self.ready.release()
class GTKInterpreter(threading.Thread):
"""Run gtk.main in the main thread and a python interpreter in a
separate thread.
Python commands can be passed to the thread where they will be executed.
This is implemented by periodically checking for passed code using a
GTK timeout callback.
"""
TIMEOUT = 100 # Millisecond interval between timeouts.
def __init__(self,banner=None):
threading.Thread.__init__(self)
self.banner = banner
self.shell = MTConsole(on_kill=[gtk.main_quit])
def run(self):
self.pre_interact()
self.shell.interact(self.banner)
self.shell.kill()
def mainloop(self):
self.start()
gobject.timeout_add(self.TIMEOUT, self.shell.runcode)
try:
if gtk.gtk_version[0] >= 2:
gtk.gdk.threads_init()
except AttributeError:
pass
gtk.main()
self.join()
def pre_interact(self):
"""This method should be overridden by subclasses.
It gets called right before interact(), but after the thread starts.
Typically used to push initialization code into the interpreter"""
pass
class MatplotLibInterpreter(GTKInterpreter):
"""Threaded interpreter with matplotlib support.
Note that this explicitly sets GTKAgg as the backend, since it has
specific GTK hooks in it."""
def __init__(self,banner=None):
banner = """\nWelcome to matplotlib, a MATLAB-like python environment.
help(matlab) -> help on matlab compatible commands from matplotlib.
help(plotting) -> help on plotting commands.
"""
GTKInterpreter.__init__(self,banner)
def pre_interact(self):
"""Initialize matplotlib before user interaction begins"""
push = self.shell.push
# Code to execute in user's namespace
lines = ["import matplotlib",
"matplotlib.use('GTKAgg')",
"matplotlib.interactive(1)",
"import matplotlib.pylab as pylab",
"from matplotlib.pylab import *\n"]
map(push,lines)
# Execute file if given.
if len(sys.argv)>1:
import matplotlib
matplotlib.interactive(0) # turn off interaction
fname = sys.argv[1]
try:
inFile = file(fname, 'r')
except IOError:
print('*** ERROR *** Could not read file <%s>' % fname)
else:
print('*** Executing file <%s>:' % fname)
for line in inFile:
if line.lstrip().find('show()')==0: continue
print('>>', line)
push(line)
inFile.close()
matplotlib.interactive(1) # turn on interaction
if __name__ == '__main__':
print("This demo is not presently functional, so running")
print("it as a script has been disabled.")
sys.exit()
# Quick sys.argv hack to extract the option and leave filenames in sys.argv.
# For real option handling, use optparse or getopt.
if len(sys.argv) > 1 and sys.argv[1]=='-pylab':
sys.argv = [sys.argv[0]]+sys.argv[2:]
MatplotLibInterpreter().mainloop()
else:
GTKInterpreter().mainloop()
| lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/lib/mpl_examples/user_interfaces/interactive.py | Python | mit | 8,150 | [
"Brian"
] | f4cdc3e9e28deaa53e8d294b38b8335e4235bc12d6c4202e361e107303b45e4f |
#!/usr/bin/env python
########################################
#Globale Karte fuer tests
# from Rabea Amther
########################################
# http://gfesuite.noaa.gov/developer/netCDFPythonInterface.html
import math
import numpy as np
import pylab as pl
import Scientific.IO.NetCDF as IO
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import matplotlib.lines as lines
from mpl_toolkits.basemap import Basemap , addcyclic
from matplotlib.colors import LinearSegmentedColormap
import textwrap
pl.close('all')
########################## for CMIP5 charactors
DIR='/Users/tang/climate/CMIP5/'
VARIABLE='clt'
PRODUCT='Amon'
ENSEMBLE='r1i1p1'
EXPERIMENT='hist'
TIME='195001-200512'
OBS='CRU'
K=0
NonData=['EC-EARTH','CSIRO-Mk3-6-0']
GCMs=['CanESM2',\
'CNRM-CM5',\
'CNRM-CM5',\
'CSIRO-Mk3-6-0',\
'EC-EARTH',\
'EC-EARTH',\
'EC-EARTH',\
'EC-EARTH',\
'IPSL-CM5A-MR',\
'MIROC5',\
'HadGEM2-ES',\
'HadGEM2-ES',\
'MPI-ESM-LR',\
'MPI-ESM-LR',\
'NorESM1-M',\
'GFDL-ESM2M']
ENSEMBLE=['r1i1p1',\
'r3i1p1',\
'r3i1p1',\
'r1i1p1',\
'r12i1p1',\
'r1i1p1',\
'r3i1p1',\
'r12i1p1',\
'r1i1p1',\
'r1i1p1',\
'r2i1p1',\
'r2i1p1',\
'r1i1p1',\
'r1i1p1',\
'r1i1p1',\
'r1i1p1']
COLOR=['darkred','darkblue','darkgreen','deeppink',\
'black','orangered','cyan','magenta']
# read CRU data:
if OBS == 'CRU':
oVar='cld'
obs1='~/climate/GLOBALDATA/OBSDATA/CRU/3.22/cru_ts3.22.2001.2005.cld.summer.mean.AFR.nc'
else:
# read ISCCP data:
oVar='cltisccp'
obs1='/Users/tang/climate/GLOBALDATA/OBSDATA/ISCCP/cltisccp_obs4MIPs_ISCCP_L3_V1.0_200101-200512.summer.mean.AFR.nc'
print obs1
obsfile1=IO.NetCDFFile(obs1,'r')
ObsVar=obsfile1.variables[oVar][0][:][:].copy()
for idx,Model in enumerate(GCMs):
if OBS == 'CRU':
infile1=DIR+EXPERIMENT+'/'+Model+'/'\
'clt_Amon_'+Model+'_historical_'+ENSEMBLE[idx]+\
'_200101-200512.nc.summer.mean.nc.remap.nc'
#GFDL-ESM2M/clt_Amon_GFDL-ESM2M_historical_r1i1p1_200101-200512.nc.summer.mean.nc.remap.nc
else:
infile1=DIR+EXPERIMENT+'/'+Model+'/'\
'clt_AFR-44_'+Model+'_historical_'+ENSEMBLE[idx]+'_'+RCMs[idx]+\
'_mon_200101-200512.nc.summer.mean.nc.remap.nc'
print infile1
if Model in NonData:
infile1=obsfile1
VAR=infile1.variables[oVar][0,:,:].copy()
else:
infile1=IO.NetCDFFile(infile1,'r')
VAR=infile1.variables[VARIABLE][0,:,:].copy()
print 'the variable tas ===============: '
print VAR
#open input files
# read the variables:
lat = infile1.variables['lat'][:].copy()
lon = infile1.variables['lon'][:].copy()
print np.shape(VAR)
print np.shape(ObsVar)
Bias=VAR-ObsVar
print np.shape(Bias)
#quit()
CoLev=10 #number of levels of colorbar
#=================================================== to plot
fig=plt.subplot(4,4,idx+1,aspect='equal')
print "============="
print idx; print Model
map=Basemap(projection='cyl',llcrnrlat=np.min(lat),urcrnrlat=np.max(lat),\
llcrnrlon=np.min(lon),urcrnrlon=np.max(lon),resolution='l')
map.drawcoastlines(linewidth=0.35)
map.drawparallels(np.arange(-90.,91.,15.),labels=[1,0,0,0],linewidth=0.35)
map.drawmeridians(np.arange(-180.,181.,20.),labels=[0,0,0,1],linewidth=0.35)
map.drawmapboundary()
x,y=map(lon,lat)
cmap=plt.get_cmap('bwr')
#cmap=plt.get_cmap('RdBu_r')
pic=map.pcolormesh(x,y,Bias,cmap=cmap)
plt.title(GCMs[idx])
#plt.figtext(0.68,0.73,timestamp, size="small")
#set the same colorbar range
pic.set_clim(vmin=-50,vmax=50)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.01, 0.8])
plt.colorbar(cax=cax)
#if idx > 11:
#plt.colorbar(orientation='horizontal') # draw colorbar
#plt.legend(loc=2)
plt.show()
quit()
#================================================ CMIP5 models
#=================================================== define the Plot:
fig1=plt.figure(figsize=(16,9))
ax = fig1.add_subplot(111)
plt.xlabel('Year',fontsize=16)
plt.ylabel('Global Surface Temperature Change ($^\circ$C)',fontsize=16)
plt.title("Global Surface Tempereture Changes simulated by CMIP5 models",fontsize=18)
plt.ylim(-2,6)
plt.xlim(1950,2100)
plt.grid()
plt.xticks(np.arange(1960, 2100+10, 20))
plt.tick_params(axis='both', which='major', labelsize=14)
plt.tick_params(axis='both', which='minor', labelsize=14)
# vertical at 2005
plt.axvline(x=2005.5,linewidth=2, color='gray')
plt.axhline(y=0,linewidth=2, color='gray')
#plt.plot(x,y,color="blue",linewidth=4)
########################## for historical
########################## for historical
print "========== for hist ==============="
EXPERIMENT='hist'
TIME='185001-200512'
YEAR=range(1850,2006)
Nmonth=1872
SumTemp=np.zeros(Nmonth/12)
K=0
for Model in modelist1:
#define the K-th model input file:
K=K+1 # for average
if Model =='CNRM-CM5':
infile1=DIR+EXPERIMENT+'/'+Model+'/'\
+VARIABLE+'_'+PRODUCT+'_'+Model+'_'+EXPERIMENT+'orical_'+'r3i1p1'+'_'+TIME+'.nc'
else:
infile1=DIR+EXPERIMENT+'/'+Model+'/'\
+VARIABLE+'_'+PRODUCT+'_'+Model+'_'+EXPERIMENT+'orical_'+ENSEMBLE+'_'+TIME+'.nc'
#an example: tas_Amon_CanESM2_rcp85_r1i1p1_200601-210012.nc & \
#this file was copied locally for tests in this book
print('the file is == ' +infile1)
#open input files
infile=IO.NetCDFFile(infile1,'r')
# read the variable tas
TAS=infile.variables[VARIABLE][:,:,:].copy()
print 'the variable tas ===============: '
print TAS
# calculate the annual mean temp:
TEMP=range(0,Nmonth,12)
for j in range(0,Nmonth,12):
TEMP[j/12]=np.mean(TAS[j:j+11][:][:])-AbsTemp
print " temp ======================== absolut"
print TEMP
# reference temp: mean of 1996-2005
RefTemp=np.mean(TEMP[len(TEMP)-10+1:len(TEMP)])
if K==1:
ArrRefTemp=[RefTemp]
else:
ArrRefTemp=ArrRefTemp+[RefTemp]
print 'ArrRefTemp ========== ',ArrRefTemp
TEMP=[t-RefTemp for t in TEMP]
print " temp ======================== relative to mean of 1986-2005"
print TEMP
# get array of temp K*TimeStep
if K==1:
ArrTemp=[TEMP]
else:
ArrTemp=ArrTemp+[TEMP]
SumTemp=SumTemp+TEMP
#print SumTemp
#=================================================== to plot
print "======== to plot =========="
print 'NO. of year:',len(YEAR)
#plot only target models
if Model in TargetModel:
plt.plot(YEAR,TEMP,\
color=COLOR[TargetModel.index(Model)],linewidth=2)
#if Model=='CanESM2':
#plt.plot(YEAR,TEMP,color="red",linewidth=1)
#if Model=='MPI-ESM-LR':
#plt.plot(YEAR,TEMP,color="blue",linewidth=1)
#if Model=='MPI-ESM-MR':
#plt.plot(YEAR,TEMP,color="green",linewidth=1)
#=================================================== for ensemble mean
AveTemp=[e/K for e in SumTemp]
ArrTemp=list(np.array(ArrTemp))
print 'shape of ArrTemp:',np.shape(ArrTemp)
StdTemp=np.std(np.array(ArrTemp),axis=0)
print 'shape of StdTemp:',np.shape(StdTemp)
print "ArrTemp ========================:"
print ArrTemp
print "StdTemp ========================:"
print StdTemp
# 5-95% range ( +-1.64 STD)
StdTemp1=[AveTemp[i]+StdTemp[i]*1.64 for i in range(0,len(StdTemp))]
StdTemp2=[AveTemp[i]-StdTemp[i]*1.64 for i in range(0,len(StdTemp))]
print "Model number for historical is :",K
print "models for historical:";print modelist2
plt.plot(YEAR,AveTemp,label='HIST ensemble mean',color="black",linewidth=4)
plt.plot(YEAR,StdTemp1,color="black",linewidth=0.1)
plt.plot(YEAR,StdTemp2,color="black",linewidth=0.1)
plt.fill_between(YEAR,StdTemp1,StdTemp2,color='black',alpha=0.3)
#plt.show()
# draw NO. of model used:
plt.text(1990,3.2,str(K)+' models',size=16,rotation=0.,
ha="center",va="center",
#bbox = dict(boxstyle="round",
#ec=(1., 0.5, 0.5),
#fc=(1., 0.8, 0.8),
)
########################## for rcp8.5:
########################## for rcp8.5:
EXPERIMENT='rcp85'
TIME='200601-210012'
YEAR=range(2006,2101)
Nmonth=1140
SumTemp=np.zeros(Nmonth/12)
K=0
for Model in modelist2:
#define the K-th model input file:
K=K+1 # for average
infile1=DIR+EXPERIMENT+'/'+Model+'/'\
+VARIABLE+'_'+PRODUCT+'_'+Model+'_'+EXPERIMENT+'_'+ENSEMBLE+'_'+TIME+'.nc'
#an example: tas_Amon_CanESM2_rcp85_r1i1p1_200601-210012.nc & \
#this file was copied locally for tests in this book
print('the file is == ' +infile1)
#open input files
infile=IO.NetCDFFile(infile1,'r')
# read the variable tas
TAS=infile.variables[VARIABLE][:,:,:].copy()
print 'the variable tas ===============: '
print TAS
# calculate the annual mean temp:
TEMP=range(0,Nmonth,12)
for j in range(0,Nmonth,12):
TEMP[j/12]=np.mean(TAS[j:j+11][:][:])-AbsTemp
print " temp ======================== absolut"
print TEMP
# get the reftemp if the model has historical data here
print 'ArrRefTemp in HIST ensembles:',np.shape(ArrRefTemp)
print ArrRefTemp
if Model in modelist1:
print 'model index in HIST: ',modelist1.index(Model)
RefTemp=ArrRefTemp[modelist1.index(Model)]
print 'RefTemp from HIST: ',RefTemp
else:
RefTemp=np.mean(TEMP[0:9])
print 'RefTemp from RCP8.5: ',RefTemp
#RefTemp=np.mean(TEMP[0:4])
# temperature change
TEMP=[t-RefTemp for t in TEMP]
print " temp ======================== relative to mean of 1986-2005"
print TEMP
# get array of temp K*TimeStep
if K==1:
ArrTemp=[TEMP]
else:
ArrTemp=ArrTemp+[TEMP]
SumTemp=SumTemp+TEMP
print 'shape of SumTemp:', np.shape(SumTemp)
#print SumTemp
#=================================================== to plot
print "======== to plot =========="
print 'NO. of year of RCP85 model:',len(YEAR)
#plot only target models
if Model in TargetModel:
plt.plot(YEAR,TEMP,label=Model,\
color=COLOR[TargetModel.index(Model)],linewidth=2)
#RefTemp=ArrRefTemp[modelist1.index(Model)]
#if Model=='CanESM2':
#plt.plot(YEAR,TEMP,label=Model,color="red",linewidth=1)
#if Model=='MPI-ESM-LR':
#plt.plot(YEAR,TEMP,label=Model,color="blue",linewidth=1)
#if Model=='MPI-ESM-MR':
#plt.plot(YEAR,TEMP,label=Model,color="green",linewidth=1)
#=================================================== for ensemble mean
AveTemp=[e/K for e in SumTemp]
ArrTemp=list(np.array(ArrTemp))
print 'shape of ArrTemp:',np.shape(ArrTemp)
StdTemp=np.std(np.array(ArrTemp),axis=0)
print 'shape of StdTemp:',np.shape(StdTemp)
print "ArrTemp ========================:"
print ArrTemp
print "StdTemp ========================:"
print StdTemp
# 5-95% range ( +-1.64 STD)
StdTemp1=[AveTemp[i]+StdTemp[i]*1.64 for i in range(0,len(StdTemp))]
StdTemp2=[AveTemp[i]-StdTemp[i]*1.64 for i in range(0,len(StdTemp))]
print "Model number for historical is :",K
print "models for RCP8.5:";print modelist2
plt.plot(YEAR,AveTemp,label='RCP8.5 ensemble mean',color="red",linewidth=4)
plt.plot(YEAR,StdTemp1,color="red",linewidth=0.1)
plt.plot(YEAR,StdTemp2,color="red",linewidth=0.1)
plt.fill_between(YEAR,StdTemp1,StdTemp2,color='r',alpha=0.3)
# draw NO. of model used:
plt.text(2020,3.2,str(K)+' models',size=16,rotation=0.,
ha="center",va="center",
#bbox = dict(boxstyle="round",
#ec=(1., 0.5, 0.5),
#fc=(1., 0.8, 0.8),
)
print "==============================================="
plt.legend(loc=2)
plt.show()
quit()
| CopyChat/Plotting | Python/bias.TCC.GCMs.py | Python | gpl-3.0 | 12,009 | [
"NetCDF"
] | 27b0b26d52fef5f64565682fa80cb20cb723eb7796a08c77cac6cbf9106922e8 |
# Author: Travis Oliphant
# 1999 -- 2002
import operator
import math
import sys
import timeit
from scipy.spatial import cKDTree
from . import sigtools, dlti
from ._upfirdn import upfirdn, _output_len, _upfirdn_modes
from scipy import linalg, fft as sp_fft
from scipy.fft._helper import _init_nd_shape_and_axes
import numpy as np
from scipy.special import lambertw
from .windows import get_window
from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
from .filter_design import cheby1, _validate_sos
from .fir_filter_design import firwin
from ._sosfilt import _sosfilt
__all__ = ['correlate', 'correlate2d',
'convolve', 'convolve2d', 'fftconvolve', 'oaconvolve',
'order_filter', 'medfilt', 'medfilt2d', 'wiener', 'lfilter',
'lfiltic', 'sosfilt', 'deconvolve', 'hilbert', 'hilbert2',
'cmplx_sort', 'unique_roots', 'invres', 'invresz', 'residue',
'residuez', 'resample', 'resample_poly', 'detrend',
'lfilter_zi', 'sosfilt_zi', 'sosfiltfilt', 'choose_conv_method',
'filtfilt', 'decimate', 'vectorstrength']
_modedict = {'valid': 0, 'same': 1, 'full': 2}
_boundarydict = {'fill': 0, 'pad': 0, 'wrap': 2, 'circular': 2, 'symm': 1,
'symmetric': 1, 'reflect': 4}
def _valfrommode(mode):
try:
return _modedict[mode]
except KeyError:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
def _bvalfromboundary(boundary):
try:
return _boundarydict[boundary] << 2
except KeyError:
raise ValueError("Acceptable boundary flags are 'fill', 'circular' "
"(or 'wrap'), and 'symmetric' (or 'symm').")
def _inputs_swap_needed(mode, shape1, shape2, axes=None):
"""Determine if inputs arrays need to be swapped in `"valid"` mode.
If in `"valid"` mode, returns whether or not the input arrays need to be
swapped depending on whether `shape1` is at least as large as `shape2` in
every calculated dimension.
This is important for some of the correlation and convolution
implementations in this module, where the larger array input needs to come
before the smaller array input when operating in this mode.
Note that if the mode provided is not 'valid', False is immediately
returned.
"""
if mode != 'valid':
return False
if not shape1:
return False
if axes is None:
axes = range(len(shape1))
ok1 = all(shape1[i] >= shape2[i] for i in axes)
ok2 = all(shape2[i] >= shape1[i] for i in axes)
if not (ok1 or ok2):
raise ValueError("For 'valid' mode, one must be at least "
"as large as the other in every dimension")
return not ok1
def correlate(in1, in2, mode='full', method='auto'):
r"""
Cross-correlate two N-dimensional arrays.
Cross-correlate `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the correlation.
``direct``
The correlation is determined directly from sums, the definition of
correlation.
``fft``
The Fast Fourier Transform is used to perform the correlation more
quickly (only available for numerical arrays.)
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See `convolve` Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
correlate : array
An N-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
See Also
--------
choose_conv_method : contains more documentation on `method`.
Notes
-----
The correlation z of two d-dimensional arrays x and y is defined as::
z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])
This way, if x and y are 1-D arrays and ``z = correlate(x, y, 'full')``
then
.. math::
z[k] = (x * y)(k - N + 1)
= \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
where :math:`||x||` is the length of ``x``, :math:`N = \max(||x||,||y||)`,
and :math:`y_m` is 0 when m is outside the range of y.
``method='fft'`` only works for numerical arrays as it relies on
`fftconvolve`. In certain cases (i.e., arrays of objects or when
rounding integers can lose precision), ``method='direct'`` is always used.
When using "same" mode with even-length inputs, the outputs of `correlate`
and `correlate2d` differ: There is a 1-index offset between them.
Examples
--------
Implement a matched filter using cross-correlation, to recover a signal
that has passed through a noisy channel.
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128
>>> import matplotlib.pyplot as plt
>>> clock = np.arange(64, len(sig), 128)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.plot(clock, sig[clock], 'ro')
>>> ax_orig.set_title('Original signal')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_corr.plot(corr)
>>> ax_corr.plot(clock, corr[clock], 'ro')
>>> ax_corr.axhline(0.5, ls=':')
>>> ax_corr.set_title('Cross-correlated with rectangular pulse')
>>> ax_orig.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0:
return in1 * in2.conj()
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
# Don't use _valfrommode, since correlate should not accept numeric modes
try:
val = _modedict[mode]
except KeyError:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
# this either calls fftconvolve or this function with method=='direct'
if method in ('fft', 'auto'):
return convolve(in1, _reverse_and_conj(in2), mode, method)
elif method == 'direct':
# fastpath to faster numpy.correlate for 1d inputs when possible
if _np_conv_ok(in1, in2, mode):
return np.correlate(in1, in2, mode)
# _correlateND is far slower when in2.size > in1.size, so swap them
# and then undo the effect afterward if mode == 'full'. Also, it fails
# with 'valid' mode if in2 is larger than in1, so swap those, too.
# Don't swap inputs for 'same' mode, since shape of in1 matters.
swapped_inputs = ((mode == 'full') and (in2.size > in1.size) or
_inputs_swap_needed(mode, in1.shape, in2.shape))
if swapped_inputs:
in1, in2 = in2, in1
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = tuple(slice(0, i) for i in in1.shape)
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
if swapped_inputs:
# Reverse and conjugate to undo the effect of swapping inputs
z = _reverse_and_conj(z)
return z
else:
raise ValueError("Acceptable method flags are 'auto',"
" 'direct', or 'fft'.")
def _centered(arr, newshape):
# Return the center newshape portion of the array.
newshape = np.asarray(newshape)
currshape = np.array(arr.shape)
startind = (currshape - newshape) // 2
endind = startind + newshape
myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
return arr[tuple(myslice)]
def _init_freq_conv_axes(in1, in2, mode, axes, sorted_axes=False):
"""Handle the axes argument for frequency-domain convolution.
Returns the inputs and axes in a standard form, eliminating redundant axes,
swapping the inputs if necessary, and checking for various potential
errors.
Parameters
----------
in1 : array
First input.
in2 : array
Second input.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output.
See the documentation `fftconvolve` for more information.
axes : list of ints
Axes over which to compute the FFTs.
sorted_axes : bool, optional
If `True`, sort the axes.
Default is `False`, do not sort.
Returns
-------
in1 : array
The first input, possible swapped with the second input.
in2 : array
The second input, possible swapped with the first input.
axes : list of ints
Axes over which to compute the FFTs.
"""
s1 = in1.shape
s2 = in2.shape
noaxes = axes is None
_, axes = _init_nd_shape_and_axes(in1, shape=None, axes=axes)
if not noaxes and not len(axes):
raise ValueError("when provided, axes cannot be empty")
# Axes of length 1 can rely on broadcasting rules for multipy,
# no fft needed.
axes = [a for a in axes if s1[a] != 1 and s2[a] != 1]
if sorted_axes:
axes.sort()
if not all(s1[a] == s2[a] or s1[a] == 1 or s2[a] == 1
for a in range(in1.ndim) if a not in axes):
raise ValueError("incompatible shapes for in1 and in2:"
" {0} and {1}".format(s1, s2))
# Check that input sizes are compatible with 'valid' mode.
if _inputs_swap_needed(mode, s1, s2, axes=axes):
# Convolution is commutative; order doesn't have any effect on output.
in1, in2 = in2, in1
return in1, in2, axes
def _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=False):
"""Convolve two arrays in the frequency domain.
This function implements only base the FFT-related operations.
Specifically, it converts the signals to the frequency domain, multiplies
them, then converts them back to the time domain. Calculations of axes,
shapes, convolution mode, etc. are implemented in higher level-functions,
such as `fftconvolve` and `oaconvolve`. Those functions should be used
instead of this one.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
axes : array_like of ints
Axes over which to compute the FFTs.
shape : array_like of ints
The sizes of the FFTs.
calc_fast_len : bool, optional
If `True`, set each value of `shape` to the next fast FFT length.
Default is `False`, use `axes` as-is.
Returns
-------
out : array
An N-dimensional array containing the discrete linear convolution of
`in1` with `in2`.
"""
if not len(axes):
return in1 * in2
complex_result = (in1.dtype.kind == 'c' or in2.dtype.kind == 'c')
if calc_fast_len:
# Speed up FFT by padding to optimal size.
fshape = [
sp_fft.next_fast_len(shape[a], not complex_result) for a in axes]
else:
fshape = shape
if not complex_result:
fft, ifft = sp_fft.rfftn, sp_fft.irfftn
else:
fft, ifft = sp_fft.fftn, sp_fft.ifftn
sp1 = fft(in1, fshape, axes=axes)
sp2 = fft(in2, fshape, axes=axes)
ret = ifft(sp1 * sp2, fshape, axes=axes)
if calc_fast_len:
fslice = tuple([slice(sz) for sz in shape])
ret = ret[fslice]
return ret
def _apply_conv_mode(ret, s1, s2, mode, axes):
"""Calculate the convolution result shape based on the `mode` argument.
Returns the result sliced to the correct size for the given mode.
Parameters
----------
ret : array
The result array, with the appropriate shape for the 'full' mode.
s1 : list of int
The shape of the first input.
s2 : list of int
The shape of the second input.
mode : str {'full', 'valid', 'same'}
A string indicating the size of the output.
See the documentation `fftconvolve` for more information.
axes : list of ints
Axes over which to compute the convolution.
Returns
-------
ret : array
A copy of `res`, sliced to the correct size for the given `mode`.
"""
if mode == "full":
return ret.copy()
elif mode == "same":
return _centered(ret, s1).copy()
elif mode == "valid":
shape_valid = [ret.shape[a] if a not in axes else s1[a] - s2[a] + 1
for a in range(ret.ndim)]
return _centered(ret, shape_valid).copy()
else:
raise ValueError("acceptable mode flags are 'valid',"
" 'same', or 'full'")
def fftconvolve(in1, in2, mode="full", axes=None):
"""Convolve two N-dimensional arrays using FFT.
Convolve `in1` and `in2` using the fast Fourier transform method, with
the output size determined by the `mode` argument.
This is generally much faster than `convolve` for large arrays (n > ~500),
but can be slower when only a few output values are needed, and can only
output float arrays (int or object array inputs will be cast to float).
As of v0.19, `convolve` automatically chooses this method or the direct
method based on an estimation of which is faster.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
axes : int or array_like of ints or None, optional
Axes over which to compute the convolution.
The default is over all axes.
Returns
-------
out : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
convolve : Uses the direct convolution or FFT convolution algorithm
depending on which is faster.
oaconvolve : Uses the overlap-add method to do convolution, which is
generally faster when the input arrays are large and
significantly different in size.
Examples
--------
Autocorrelation of white noise is an impulse.
>>> from scipy import signal
>>> sig = np.random.randn(1000)
>>> autocorr = signal.fftconvolve(sig, sig[::-1], mode='full')
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('White noise')
>>> ax_mag.plot(np.arange(-len(sig)+1,len(sig)), autocorr)
>>> ax_mag.set_title('Autocorrelation')
>>> fig.tight_layout()
>>> fig.show()
Gaussian blur implemented using FFT convolution. Notice the dark borders
around the image, due to the zero-padding beyond its boundaries.
The `convolve2d` function allows for other types of image boundaries,
but is far slower.
>>> from scipy import misc
>>> face = misc.face(gray=True)
>>> kernel = np.outer(signal.gaussian(70, 8), signal.gaussian(70, 8))
>>> blurred = signal.fftconvolve(face, kernel, mode='same')
>>> fig, (ax_orig, ax_kernel, ax_blurred) = plt.subplots(3, 1,
... figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_kernel.imshow(kernel, cmap='gray')
>>> ax_kernel.set_title('Gaussian kernel')
>>> ax_kernel.set_axis_off()
>>> ax_blurred.imshow(blurred, cmap='gray')
>>> ax_blurred.set_title('Blurred')
>>> ax_blurred.set_axis_off()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0: # scalar inputs
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
elif in1.size == 0 or in2.size == 0: # empty arrays
return np.array([])
in1, in2, axes = _init_freq_conv_axes(in1, in2, mode, axes,
sorted_axes=False)
s1 = in1.shape
s2 = in2.shape
shape = [max((s1[i], s2[i])) if i not in axes else s1[i] + s2[i] - 1
for i in range(in1.ndim)]
ret = _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=True)
return _apply_conv_mode(ret, s1, s2, mode, axes)
def _calc_oa_lens(s1, s2):
"""Calculate the optimal FFT lengths for overlapp-add convolution.
The calculation is done for a single dimension.
Parameters
----------
s1 : int
Size of the dimension for the first array.
s2 : int
Size of the dimension for the second array.
Returns
-------
block_size : int
The size of the FFT blocks.
overlap : int
The amount of overlap between two blocks.
in1_step : int
The size of each step for the first array.
in2_step : int
The size of each step for the first array.
"""
# Set up the arguments for the conventional FFT approach.
fallback = (s1+s2-1, None, s1, s2)
# Use conventional FFT convolve if sizes are same.
if s1 == s2 or s1 == 1 or s2 == 1:
return fallback
if s2 > s1:
s1, s2 = s2, s1
swapped = True
else:
swapped = False
# There cannot be a useful block size if s2 is more than half of s1.
if s2 >= s1/2:
return fallback
# Derivation of optimal block length
# For original formula see:
# https://en.wikipedia.org/wiki/Overlap-add_method
#
# Formula:
# K = overlap = s2-1
# N = block_size
# C = complexity
# e = exponential, exp(1)
#
# C = (N*(log2(N)+1))/(N-K)
# C = (N*log2(2N))/(N-K)
# C = N/(N-K) * log2(2N)
# C1 = N/(N-K)
# C2 = log2(2N) = ln(2N)/ln(2)
#
# dC1/dN = (1*(N-K)-N)/(N-K)^2 = -K/(N-K)^2
# dC2/dN = 2/(2*N*ln(2)) = 1/(N*ln(2))
#
# dC/dN = dC1/dN*C2 + dC2/dN*C1
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + N/(N*ln(2)*(N-K))
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + 1/(ln(2)*(N-K))
# dC/dN = -K*ln(2N)/(ln(2)*(N-K)^2) + (N-K)/(ln(2)*(N-K)^2)
# dC/dN = (-K*ln(2N) + (N-K)/(ln(2)*(N-K)^2)
# dC/dN = (N - K*ln(2N) - K)/(ln(2)*(N-K)^2)
#
# Solve for minimum, where dC/dN = 0
# 0 = (N - K*ln(2N) - K)/(ln(2)*(N-K)^2)
# 0 * ln(2)*(N-K)^2 = N - K*ln(2N) - K
# 0 = N - K*ln(2N) - K
# 0 = N - K*(ln(2N) + 1)
# 0 = N - K*ln(2Ne)
# N = K*ln(2Ne)
# N/K = ln(2Ne)
#
# e^(N/K) = e^ln(2Ne)
# e^(N/K) = 2Ne
# 1/e^(N/K) = 1/(2*N*e)
# e^(N/-K) = 1/(2*N*e)
# e^(N/-K) = K/N*1/(2*K*e)
# N/K*e^(N/-K) = 1/(2*e*K)
# N/-K*e^(N/-K) = -1/(2*e*K)
#
# Using Lambert W function
# https://en.wikipedia.org/wiki/Lambert_W_function
# x = W(y) It is the solution to y = x*e^x
# x = N/-K
# y = -1/(2*e*K)
#
# N/-K = W(-1/(2*e*K))
#
# N = -K*W(-1/(2*e*K))
overlap = s2-1
opt_size = -overlap*lambertw(-1/(2*math.e*overlap), k=-1).real
block_size = sp_fft.next_fast_len(math.ceil(opt_size))
# Use conventional FFT convolve if there is only going to be one block.
if block_size >= s1:
return fallback
if not swapped:
in1_step = block_size-s2+1
in2_step = s2
else:
in1_step = s2
in2_step = block_size-s2+1
return block_size, overlap, in1_step, in2_step
def oaconvolve(in1, in2, mode="full", axes=None):
"""Convolve two N-dimensional arrays using the overlap-add method.
Convolve `in1` and `in2` using the overlap-add method, with
the output size determined by the `mode` argument.
This is generally much faster than `convolve` for large arrays (n > ~500),
and generally much faster than `fftconvolve` when one array is much
larger than the other, but can be slower when only a few output values are
needed or when the arrays are very similar in shape, and can only
output float arrays (int or object array inputs will be cast to float).
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
axes : int or array_like of ints or None, optional
Axes over which to compute the convolution.
The default is over all axes.
Returns
-------
out : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
convolve : Uses the direct convolution or FFT convolution algorithm
depending on which is faster.
fftconvolve : An implementation of convolution using FFT.
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Convolve a 100,000 sample signal with a 512-sample filter.
>>> from scipy import signal
>>> sig = np.random.randn(100000)
>>> filt = signal.firwin(512, 0.01)
>>> fsig = signal.oaconvolve(sig, filt)
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('White noise')
>>> ax_mag.plot(fsig)
>>> ax_mag.set_title('Filtered noise')
>>> fig.tight_layout()
>>> fig.show()
References
----------
.. [1] Wikipedia, "Overlap-add_method".
https://en.wikipedia.org/wiki/Overlap-add_method
.. [2] Richard G. Lyons. Understanding Digital Signal Processing,
Third Edition, 2011. Chapter 13.10.
ISBN 13: 978-0137-02741-5
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if in1.ndim == in2.ndim == 0: # scalar inputs
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
elif in1.size == 0 or in2.size == 0: # empty arrays
return np.array([])
elif in1.shape == in2.shape: # Equivalent to fftconvolve
return fftconvolve(in1, in2, mode=mode, axes=axes)
in1, in2, axes = _init_freq_conv_axes(in1, in2, mode, axes,
sorted_axes=True)
if not axes:
return in1*in2
s1 = in1.shape
s2 = in2.shape
# Calculate this now since in1 is changed later
shape_final = [None if i not in axes else
s1[i] + s2[i] - 1 for i in range(in1.ndim)]
# Calculate the block sizes for the output, steps, first and second inputs.
# It is simpler to calculate them all together than doing them in separate
# loops due to all the special cases that need to be handled.
optimal_sizes = ((-1, -1, s1[i], s2[i]) if i not in axes else
_calc_oa_lens(s1[i], s2[i]) for i in range(in1.ndim))
block_size, overlaps, \
in1_step, in2_step = zip(*optimal_sizes)
# Fall back to fftconvolve if there is only one block in every dimension.
if in1_step == s1 and in2_step == s2:
return fftconvolve(in1, in2, mode=mode, axes=axes)
# Figure out the number of steps and padding.
# This would get too complicated in a list comprehension.
nsteps1 = []
nsteps2 = []
pad_size1 = []
pad_size2 = []
for i in range(in1.ndim):
if i not in axes:
pad_size1 += [(0, 0)]
pad_size2 += [(0, 0)]
continue
if s1[i] > in1_step[i]:
curnstep1 = math.ceil((s1[i]+1)/in1_step[i])
if (block_size[i] - overlaps[i])*curnstep1 < shape_final[i]:
curnstep1 += 1
curpad1 = curnstep1*in1_step[i] - s1[i]
else:
curnstep1 = 1
curpad1 = 0
if s2[i] > in2_step[i]:
curnstep2 = math.ceil((s2[i]+1)/in2_step[i])
if (block_size[i] - overlaps[i])*curnstep2 < shape_final[i]:
curnstep2 += 1
curpad2 = curnstep2*in2_step[i] - s2[i]
else:
curnstep2 = 1
curpad2 = 0
nsteps1 += [curnstep1]
nsteps2 += [curnstep2]
pad_size1 += [(0, curpad1)]
pad_size2 += [(0, curpad2)]
# Pad the array to a size that can be reshaped to the desired shape
# if necessary.
if not all(curpad == (0, 0) for curpad in pad_size1):
in1 = np.pad(in1, pad_size1, mode='constant', constant_values=0)
if not all(curpad == (0, 0) for curpad in pad_size2):
in2 = np.pad(in2, pad_size2, mode='constant', constant_values=0)
# Reshape the overlap-add parts to input block sizes.
split_axes = [iax+i for i, iax in enumerate(axes)]
fft_axes = [iax+1 for iax in split_axes]
# We need to put each new dimension before the corresponding dimension
# being reshaped in order to get the data in the right layout at the end.
reshape_size1 = list(in1_step)
reshape_size2 = list(in2_step)
for i, iax in enumerate(split_axes):
reshape_size1.insert(iax, nsteps1[i])
reshape_size2.insert(iax, nsteps2[i])
in1 = in1.reshape(*reshape_size1)
in2 = in2.reshape(*reshape_size2)
# Do the convolution.
fft_shape = [block_size[i] for i in axes]
ret = _freq_domain_conv(in1, in2, fft_axes, fft_shape, calc_fast_len=False)
# Do the overlap-add.
for ax, ax_fft, ax_split in zip(axes, fft_axes, split_axes):
overlap = overlaps[ax]
if overlap is None:
continue
ret, overpart = np.split(ret, [-overlap], ax_fft)
overpart = np.split(overpart, [-1], ax_split)[0]
ret_overpart = np.split(ret, [overlap], ax_fft)[0]
ret_overpart = np.split(ret_overpart, [1], ax_split)[1]
ret_overpart += overpart
# Reshape back to the correct dimensionality.
shape_ret = [ret.shape[i] if i not in fft_axes else
ret.shape[i]*ret.shape[i-1]
for i in range(ret.ndim) if i not in split_axes]
ret = ret.reshape(*shape_ret)
# Slice to the correct size.
slice_final = tuple([slice(islice) for islice in shape_final])
ret = ret[slice_final]
return _apply_conv_mode(ret, s1, s2, mode, axes)
def _numeric_arrays(arrays, kinds='buifc'):
"""
See if a list of arrays are all numeric.
Parameters
----------
ndarrays : array or list of arrays
arrays to check if numeric.
numeric_kinds : string-like
The dtypes of the arrays to be checked. If the dtype.kind of
the ndarrays are not in this string the function returns False and
otherwise returns True.
"""
if type(arrays) == np.ndarray:
return arrays.dtype.kind in kinds
for array_ in arrays:
if array_.dtype.kind not in kinds:
return False
return True
def _prod(iterable):
"""
Product of a list of numbers.
Faster than np.prod for short lists like array shapes.
"""
product = 1
for x in iterable:
product *= x
return product
def _conv_ops(x_shape, h_shape, mode):
"""
Find the number of operations required for direct/fft methods of
convolution. The direct operations were recorded by making a dummy class to
record the number of operations by overriding ``__mul__`` and ``__add__``.
The FFT operations rely on the (well-known) computational complexity of the
FFT (and the implementation of ``_freq_domain_conv``).
"""
if mode == "full":
out_shape = [n + k - 1 for n, k in zip(x_shape, h_shape)]
elif mode == "valid":
out_shape = [abs(n - k) + 1 for n, k in zip(x_shape, h_shape)]
elif mode == "same":
out_shape = x_shape
else:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full', not mode={}".format(mode))
s1, s2 = x_shape, h_shape
if len(x_shape) == 1:
s1, s2 = s1[0], s2[0]
if mode == "full":
direct_ops = s1 * s2
elif mode == "valid":
direct_ops = (s2 - s1 + 1) * s1 if s2 >= s1 else (s1 - s2 + 1) * s2
elif mode == "same":
direct_ops = s1 * s2 if s1 < s2 else s1 * s2 - (s2 // 2) * ((s2 + 1) // 2)
else:
if mode == "full":
direct_ops = min(_prod(s1), _prod(s2)) * _prod(out_shape)
elif mode == "valid":
direct_ops = min(_prod(s1), _prod(s2)) * _prod(out_shape)
elif mode == "same":
direct_ops = _prod(s1) * _prod(s2)
full_out_shape = [n + k - 1 for n, k in zip(x_shape, h_shape)]
N = _prod(full_out_shape)
fft_ops = 3 * N * np.log(N) # 3 separate FFTs of size full_out_shape
return fft_ops, direct_ops
def _fftconv_faster(x, h, mode):
"""
See if using fftconvolve or convolve is faster.
Parameters
----------
x : np.ndarray
Signal
h : np.ndarray
Kernel
mode : str
Mode passed to convolve
Returns
-------
fft_faster : bool
Notes
-----
See docstring of `choose_conv_method` for details on tuning hardware.
See pull request 11031 for more detail:
https://github.com/scipy/scipy/pull/11031.
"""
fft_ops, direct_ops = _conv_ops(x.shape, h.shape, mode)
offset = -1e-3 if x.ndim == 1 else -1e-4
constants = {
"valid": (1.89095737e-9, 2.1364985e-10, offset),
"full": (1.7649070e-9, 2.1414831e-10, offset),
"same": (3.2646654e-9, 2.8478277e-10, offset)
if h.size <= x.size
else (3.21635404e-9, 1.1773253e-8, -1e-5),
} if x.ndim == 1 else {
"valid": (1.85927e-9, 2.11242e-8, offset),
"full": (1.99817e-9, 1.66174e-8, offset),
"same": (2.04735e-9, 1.55367e-8, offset),
}
O_fft, O_direct, O_offset = constants[mode]
return O_fft * fft_ops < O_direct * direct_ops + O_offset
def _reverse_and_conj(x):
"""
Reverse array `x` in all dimensions and perform the complex conjugate
"""
reverse = (slice(None, None, -1),) * x.ndim
return x[reverse].conj()
def _np_conv_ok(volume, kernel, mode):
"""
See if numpy supports convolution of `volume` and `kernel` (i.e. both are
1D ndarrays and of the appropriate shape). NumPy's 'same' mode uses the
size of the larger input, while SciPy's uses the size of the first input.
Invalid mode strings will return False and be caught by the calling func.
"""
if volume.ndim == kernel.ndim == 1:
if mode in ('full', 'valid'):
return True
elif mode == 'same':
return volume.size >= kernel.size
else:
return False
def _timeit_fast(stmt="pass", setup="pass", repeat=3):
"""
Returns the time the statement/function took, in seconds.
Faster, less precise version of IPython's timeit. `stmt` can be a statement
written as a string or a callable.
Will do only 1 loop (like IPython's timeit) with no repetitions
(unlike IPython) for very slow functions. For fast functions, only does
enough loops to take 5 ms, which seems to produce similar results (on
Windows at least), and avoids doing an extraneous cycle that isn't
measured.
"""
timer = timeit.Timer(stmt, setup)
# determine number of calls per rep so total time for 1 rep >= 5 ms
x = 0
for p in range(0, 10):
number = 10**p
x = timer.timeit(number) # seconds
if x >= 5e-3 / 10: # 5 ms for final test, 1/10th that for this one
break
if x > 1: # second
# If it's macroscopic, don't bother with repetitions
best = x
else:
number *= 10
r = timer.repeat(repeat, number)
best = min(r)
sec = best / number
return sec
def choose_conv_method(in1, in2, mode='full', measure=False):
"""
Find the fastest convolution/correlation method.
This primarily exists to be called during the ``method='auto'`` option in
`convolve` and `correlate`. It can also be used to determine the value of
``method`` for many different convolutions of the same dtype/shape.
In addition, it supports timing the convolution to adapt the value of
``method`` to a particular set of inputs and/or hardware.
Parameters
----------
in1 : array_like
The first argument passed into the convolution function.
in2 : array_like
The second argument passed into the convolution function.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
measure : bool, optional
If True, run and time the convolution of `in1` and `in2` with both
methods and return the fastest. If False (default), predict the fastest
method using precomputed values.
Returns
-------
method : str
A string indicating which convolution method is fastest, either
'direct' or 'fft'
times : dict, optional
A dictionary containing the times (in seconds) needed for each method.
This value is only returned if ``measure=True``.
See Also
--------
convolve
correlate
Notes
-----
Generally, this method is 99% accurate for 2D signals and 85% accurate
for 1D signals for randomly chosen input sizes. For precision, use
``measure=True`` to find the fastest method by timing the convolution.
This can be used to avoid the minimal overhead of finding the fastest
``method`` later, or to adapt the value of ``method`` to a particular set
of inputs.
Experiments were run on an Amazon EC2 r5a.2xlarge machine to test this
function. These experiments measured the ratio between the time required
when using ``method='auto'`` and the time required for the fastest method
(i.e., ``ratio = time_auto / min(time_fft, time_direct)``). In these
experiments, we found:
* There is a 95% chance of this ratio being less than 1.5 for 1D signals
and a 99% chance of being less than 2.5 for 2D signals.
* The ratio was always less than 2.5/5 for 1D/2D signals respectively.
* This function is most inaccurate for 1D convolutions that take between 1
and 10 milliseconds with ``method='direct'``. A good proxy for this
(at least in our experiments) is ``1e6 <= in1.size * in2.size <= 1e7``.
The 2D results almost certainly generalize to 3D/4D/etc because the
implementation is the same (the 1D implementation is different).
All the numbers above are specific to the EC2 machine. However, we did find
that this function generalizes fairly decently across hardware. The speed
tests were of similar quality (and even slightly better) than the same
tests performed on the machine to tune this function's numbers (a mid-2014
15-inch MacBook Pro with 16GB RAM and a 2.5GHz Intel i7 processor).
There are cases when `fftconvolve` supports the inputs but this function
returns `direct` (e.g., to protect against floating point integer
precision).
.. versionadded:: 0.19
Examples
--------
Estimate the fastest method for a given input:
>>> from scipy import signal
>>> img = np.random.rand(32, 32)
>>> filter = np.random.rand(8, 8)
>>> method = signal.choose_conv_method(img, filter, mode='same')
>>> method
'fft'
This can then be applied to other arrays of the same dtype and shape:
>>> img2 = np.random.rand(32, 32)
>>> filter2 = np.random.rand(8, 8)
>>> corr2 = signal.correlate(img2, filter2, mode='same', method=method)
>>> conv2 = signal.convolve(img2, filter2, mode='same', method=method)
The output of this function (``method``) works with `correlate` and
`convolve`.
"""
volume = np.asarray(in1)
kernel = np.asarray(in2)
if measure:
times = {}
for method in ['fft', 'direct']:
times[method] = _timeit_fast(lambda: convolve(volume, kernel,
mode=mode, method=method))
chosen_method = 'fft' if times['fft'] < times['direct'] else 'direct'
return chosen_method, times
# for integer input,
# catch when more precision required than float provides (representing an
# integer as float can lose precision in fftconvolve if larger than 2**52)
if any([_numeric_arrays([x], kinds='ui') for x in [volume, kernel]]):
max_value = int(np.abs(volume).max()) * int(np.abs(kernel).max())
max_value *= int(min(volume.size, kernel.size))
if max_value > 2**np.finfo('float').nmant - 1:
return 'direct'
if _numeric_arrays([volume, kernel], kinds='b'):
return 'direct'
if _numeric_arrays([volume, kernel]):
if _fftconv_faster(volume, kernel, mode):
return 'fft'
return 'direct'
def convolve(in1, in2, mode='full', method='auto'):
"""
Convolve two N-dimensional arrays.
Convolve `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the convolution.
``direct``
The convolution is determined directly from sums, the definition of
convolution.
``fft``
The Fourier Transform is used to perform the convolution by calling
`fftconvolve`.
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
convolve : array
An N-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
See Also
--------
numpy.polymul : performs polynomial multiplication (same operation, but
also accepts poly1d objects)
choose_conv_method : chooses the fastest appropriate convolution method
fftconvolve : Always uses the FFT method.
oaconvolve : Uses the overlap-add method to do convolution, which is
generally faster when the input arrays are large and
significantly different in size.
Notes
-----
By default, `convolve` and `correlate` use ``method='auto'``, which calls
`choose_conv_method` to choose the fastest method using pre-computed
values (`choose_conv_method` can also measure real-world timing with a
keyword argument). Because `fftconvolve` relies on floating point numbers,
there are certain constraints that may force `method=direct` (more detail
in `choose_conv_method` docstring).
Examples
--------
Smooth a square pulse using a Hann window:
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 0.], 100)
>>> win = signal.hann(50)
>>> filtered = signal.convolve(sig, win, mode='same') / sum(win)
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_win, ax_filt) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.set_title('Original pulse')
>>> ax_orig.margins(0, 0.1)
>>> ax_win.plot(win)
>>> ax_win.set_title('Filter impulse response')
>>> ax_win.margins(0, 0.1)
>>> ax_filt.plot(filtered)
>>> ax_filt.set_title('Filtered signal')
>>> ax_filt.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
volume = np.asarray(in1)
kernel = np.asarray(in2)
if volume.ndim == kernel.ndim == 0:
return volume * kernel
elif volume.ndim != kernel.ndim:
raise ValueError("volume and kernel should have the same "
"dimensionality")
if _inputs_swap_needed(mode, volume.shape, kernel.shape):
# Convolution is commutative; order doesn't have any effect on output
volume, kernel = kernel, volume
if method == 'auto':
method = choose_conv_method(volume, kernel, mode=mode)
if method == 'fft':
out = fftconvolve(volume, kernel, mode=mode)
result_type = np.result_type(volume, kernel)
if result_type.kind in {'u', 'i'}:
out = np.around(out)
return out.astype(result_type)
elif method == 'direct':
# fastpath to faster numpy.convolve for 1d inputs when possible
if _np_conv_ok(volume, kernel, mode):
return np.convolve(volume, kernel, mode)
return correlate(volume, _reverse_and_conj(kernel), mode, 'direct')
else:
raise ValueError("Acceptable method flags are 'auto',"
" 'direct', or 'fft'.")
def order_filter(a, domain, rank):
"""
Perform an order filter on an N-D array.
Perform an order filter on the array in. The domain argument acts as a
mask centered over each pixel. The non-zero elements of domain are
used to select elements surrounding each input pixel which are placed
in a list. The list is sorted, and the output for that pixel is the
element corresponding to rank in the sorted list.
Parameters
----------
a : ndarray
The N-dimensional input array.
domain : array_like
A mask array with the same number of dimensions as `a`.
Each dimension should have an odd number of elements.
rank : int
A non-negative integer which selects the element from the
sorted list (0 corresponds to the smallest element, 1 is the
next smallest element, etc.).
Returns
-------
out : ndarray
The results of the order filter in an array with the same
shape as `a`.
Examples
--------
>>> from scipy import signal
>>> x = np.arange(25).reshape(5, 5)
>>> domain = np.identity(3)
>>> x
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
>>> signal.order_filter(x, domain, 0)
array([[ 0., 0., 0., 0., 0.],
[ 0., 0., 1., 2., 0.],
[ 0., 5., 6., 7., 0.],
[ 0., 10., 11., 12., 0.],
[ 0., 0., 0., 0., 0.]])
>>> signal.order_filter(x, domain, 2)
array([[ 6., 7., 8., 9., 4.],
[ 11., 12., 13., 14., 9.],
[ 16., 17., 18., 19., 14.],
[ 21., 22., 23., 24., 19.],
[ 20., 21., 22., 23., 24.]])
"""
domain = np.asarray(domain)
size = domain.shape
for k in range(len(size)):
if (size[k] % 2) != 1:
raise ValueError("Each dimension of domain argument "
" should have an odd number of elements.")
return sigtools._order_filterND(a, domain, rank)
def medfilt(volume, kernel_size=None):
"""
Perform a median filter on an N-dimensional array.
Apply a median filter to the input array using a local window-size
given by `kernel_size`. The array will automatically be zero-padded.
Parameters
----------
volume : array_like
An N-dimensional input array.
kernel_size : array_like, optional
A scalar or an N-length list giving the size of the median filter
window in each dimension. Elements of `kernel_size` should be odd.
If `kernel_size` is a scalar, then this scalar is used as the size in
each dimension. Default size is 3 for each dimension.
Returns
-------
out : ndarray
An array the same size as input containing the median filtered
result.
See also
--------
scipy.ndimage.median_filter
Notes
-------
The more general function `scipy.ndimage.median_filter` has a more
efficient implementation of a median filter and therefore runs much faster.
"""
volume = np.atleast_1d(volume)
if kernel_size is None:
kernel_size = [3] * volume.ndim
kernel_size = np.asarray(kernel_size)
if kernel_size.shape == ():
kernel_size = np.repeat(kernel_size.item(), volume.ndim)
for k in range(volume.ndim):
if (kernel_size[k] % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
domain = np.ones(kernel_size)
numels = np.prod(kernel_size, axis=0)
order = numels // 2
return sigtools._order_filterND(volume, domain, order)
def wiener(im, mysize=None, noise=None):
"""
Perform a Wiener filter on an N-dimensional array.
Apply a Wiener filter to the N-dimensional array `im`.
Parameters
----------
im : ndarray
An N-dimensional array.
mysize : int or array_like, optional
A scalar or an N-length list giving the size of the Wiener filter
window in each dimension. Elements of mysize should be odd.
If mysize is a scalar, then this scalar is used as the size
in each dimension.
noise : float, optional
The noise-power to use. If None, then noise is estimated as the
average of the local variance of the input.
Returns
-------
out : ndarray
Wiener filtered result with the same shape as `im`.
Examples
--------
>>> from scipy.misc import face
>>> from scipy.signal.signaltools import wiener
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> img = np.random.random((40, 40)) #Create a random image
>>> filtered_img = wiener(img, (5, 5)) #Filter the image
>>> f, (plot1, plot2) = plt.subplots(1, 2)
>>> plot1.imshow(img)
>>> plot2.imshow(filtered_img)
>>> plt.show()
Notes
-----
This implementation is similar to wiener2 in Matlab/Octave.
For more details see [1]_
References
----------
.. [1] Lim, Jae S., Two-Dimensional Signal and Image Processing,
Englewood Cliffs, NJ, Prentice Hall, 1990, p. 548.
"""
im = np.asarray(im)
if mysize is None:
mysize = [3] * im.ndim
mysize = np.asarray(mysize)
if mysize.shape == ():
mysize = np.repeat(mysize.item(), im.ndim)
# Estimate the local mean
lMean = correlate(im, np.ones(mysize), 'same') / np.prod(mysize, axis=0)
# Estimate the local variance
lVar = (correlate(im ** 2, np.ones(mysize), 'same') /
np.prod(mysize, axis=0) - lMean ** 2)
# Estimate the noise power if needed.
if noise is None:
noise = np.mean(np.ravel(lVar), axis=0)
res = (im - lMean)
res *= (1 - noise / lVar)
res += lMean
out = np.where(lVar < noise, lMean, res)
return out
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""
Convolve two 2-dimensional arrays.
Convolve `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear convolution
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
A flag indicating how to handle boundaries:
``fill``
pad input arrays with fillvalue. (default)
``wrap``
circular boundary conditions.
``symm``
symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
out : ndarray
A 2-dimensional array containing a subset of the discrete linear
convolution of `in1` with `in2`.
Examples
--------
Compute the gradient of an image by 2D convolution with a complex Scharr
operator. (Horizontal operator is real, vertical is imaginary.) Use
symmetric boundary condition to avoid creating edges at the image
boundaries.
>>> from scipy import signal
>>> from scipy import misc
>>> ascent = misc.ascent()
>>> scharr = np.array([[ -3-3j, 0-10j, +3 -3j],
... [-10+0j, 0+ 0j, +10 +0j],
... [ -3+3j, 0+10j, +3 +3j]]) # Gx + j*Gy
>>> grad = signal.convolve2d(ascent, scharr, boundary='symm', mode='same')
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(3, 1, figsize=(6, 15))
>>> ax_orig.imshow(ascent, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_mag.imshow(np.absolute(grad), cmap='gray')
>>> ax_mag.set_title('Gradient magnitude')
>>> ax_mag.set_axis_off()
>>> ax_ang.imshow(np.angle(grad), cmap='hsv') # hsv is cyclic, like angles
>>> ax_ang.set_title('Gradient orientation')
>>> ax_ang.set_axis_off()
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if not in1.ndim == in2.ndim == 2:
raise ValueError('convolve2d inputs must both be 2-D arrays')
if _inputs_swap_needed(mode, in1.shape, in2.shape):
in1, in2 = in2, in1
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
out = sigtools._convolve2d(in1, in2, 1, val, bval, fillvalue)
return out
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
"""
Cross-correlate two 2-dimensional arrays.
Cross correlate `in1` and `in2` with output size determined by `mode`, and
boundary conditions determined by `boundary` and `fillvalue`.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
boundary : str {'fill', 'wrap', 'symm'}, optional
A flag indicating how to handle boundaries:
``fill``
pad input arrays with fillvalue. (default)
``wrap``
circular boundary conditions.
``symm``
symmetrical boundary conditions.
fillvalue : scalar, optional
Value to fill pad input arrays with. Default is 0.
Returns
-------
correlate2d : ndarray
A 2-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
Notes
-----
When using "same" mode with even-length inputs, the outputs of `correlate`
and `correlate2d` differ: There is a 1-index offset between them.
Examples
--------
Use 2D cross-correlation to find the location of a template in a noisy
image:
>>> from scipy import signal
>>> from scipy import misc
>>> face = misc.face(gray=True) - misc.face(gray=True).mean()
>>> template = np.copy(face[300:365, 670:750]) # right eye
>>> template -= template.mean()
>>> face = face + np.random.randn(*face.shape) * 50 # add noise
>>> corr = signal.correlate2d(face, template, boundary='symm', mode='same')
>>> y, x = np.unravel_index(np.argmax(corr), corr.shape) # find the match
>>> import matplotlib.pyplot as plt
>>> fig, (ax_orig, ax_template, ax_corr) = plt.subplots(3, 1,
... figsize=(6, 15))
>>> ax_orig.imshow(face, cmap='gray')
>>> ax_orig.set_title('Original')
>>> ax_orig.set_axis_off()
>>> ax_template.imshow(template, cmap='gray')
>>> ax_template.set_title('Template')
>>> ax_template.set_axis_off()
>>> ax_corr.imshow(corr, cmap='gray')
>>> ax_corr.set_title('Cross-correlation')
>>> ax_corr.set_axis_off()
>>> ax_orig.plot(x, y, 'ro')
>>> fig.show()
"""
in1 = np.asarray(in1)
in2 = np.asarray(in2)
if not in1.ndim == in2.ndim == 2:
raise ValueError('correlate2d inputs must both be 2-D arrays')
swapped_inputs = _inputs_swap_needed(mode, in1.shape, in2.shape)
if swapped_inputs:
in1, in2 = in2, in1
val = _valfrommode(mode)
bval = _bvalfromboundary(boundary)
out = sigtools._convolve2d(in1, in2.conj(), 0, val, bval, fillvalue)
if swapped_inputs:
out = out[::-1, ::-1]
return out
def medfilt2d(input, kernel_size=3):
"""
Median filter a 2-dimensional array.
Apply a median filter to the `input` array using a local window-size
given by `kernel_size` (must be odd). The array is zero-padded
automatically.
Parameters
----------
input : array_like
A 2-dimensional input array.
kernel_size : array_like, optional
A scalar or a list of length 2, giving the size of the
median filter window in each dimension. Elements of
`kernel_size` should be odd. If `kernel_size` is a scalar,
then this scalar is used as the size in each dimension.
Default is a kernel of size (3, 3).
Returns
-------
out : ndarray
An array the same size as input containing the median filtered
result.
See also
--------
scipy.ndimage.median_filter
Notes
-------
The more general function `scipy.ndimage.median_filter` has a more
efficient implementation of a median filter and therefore runs much faster.
"""
image = np.asarray(input)
if kernel_size is None:
kernel_size = [3] * 2
kernel_size = np.asarray(kernel_size)
if kernel_size.shape == ():
kernel_size = np.repeat(kernel_size.item(), 2)
for size in kernel_size:
if (size % 2) != 1:
raise ValueError("Each element of kernel_size should be odd.")
return sigtools._medfilt2d(image, kernel_size)
def lfilter(b, a, x, axis=-1, zi=None):
"""
Filter data along one-dimension with an IIR or FIR filter.
Filter a data sequence, `x`, using a digital filter. This works for many
fundamental data types (including Object type). The filter is a direct
form II transposed implementation of the standard difference equation
(see Notes).
The function `sosfilt` (and filter design using ``output='sos'``) should be
preferred over `lfilter` for most filtering tasks, as second-order sections
have fewer numerical problems.
Parameters
----------
b : array_like
The numerator coefficient vector in a 1-D sequence.
a : array_like
The denominator coefficient vector in a 1-D sequence. If ``a[0]``
is not 1, then both `a` and `b` are normalized by ``a[0]``.
x : array_like
An N-dimensional input array.
axis : int, optional
The axis of the input data array along which to apply the
linear filter. The filter is applied to each subarray along
this axis. Default is -1.
zi : array_like, optional
Initial conditions for the filter delays. It is a vector
(or array of vectors for an N-dimensional input) of length
``max(len(a), len(b)) - 1``. If `zi` is None or is not given then
initial rest is assumed. See `lfiltic` for more information.
Returns
-------
y : array
The output of the digital filter.
zf : array, optional
If `zi` is None, this is not returned, otherwise, `zf` holds the
final filter delay values.
See Also
--------
lfiltic : Construct initial conditions for `lfilter`.
lfilter_zi : Compute initial state (steady state of step response) for
`lfilter`.
filtfilt : A forward-backward filter, to obtain a filter with linear phase.
savgol_filter : A Savitzky-Golay filter.
sosfilt: Filter data using cascaded second-order sections.
sosfiltfilt: A forward-backward filter using second-order sections.
Notes
-----
The filter function is implemented as a direct II transposed structure.
This means that the filter implements::
a[0]*y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M]
- a[1]*y[n-1] - ... - a[N]*y[n-N]
where `M` is the degree of the numerator, `N` is the degree of the
denominator, and `n` is the sample number. It is implemented using
the following difference equations (assuming M = N)::
a[0]*y[n] = b[0] * x[n] + d[0][n-1]
d[0][n] = b[1] * x[n] - a[1] * y[n] + d[1][n-1]
d[1][n] = b[2] * x[n] - a[2] * y[n] + d[2][n-1]
...
d[N-2][n] = b[N-1]*x[n] - a[N-1]*y[n] + d[N-1][n-1]
d[N-1][n] = b[N] * x[n] - a[N] * y[n]
where `d` are the state variables.
The rational transfer function describing this filter in the
z-transform domain is::
-1 -M
b[0] + b[1]z + ... + b[M] z
Y(z) = -------------------------------- X(z)
-1 -N
a[0] + a[1]z + ... + a[N] z
Examples
--------
Generate a noisy signal to be filtered:
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> t = np.linspace(-1, 1, 201)
>>> x = (np.sin(2*np.pi*0.75*t*(1-t) + 2.1) +
... 0.1*np.sin(2*np.pi*1.25*t + 1) +
... 0.18*np.cos(2*np.pi*3.85*t))
>>> xn = x + np.random.randn(len(t)) * 0.08
Create an order 3 lowpass butterworth filter:
>>> b, a = signal.butter(3, 0.05)
Apply the filter to xn. Use lfilter_zi to choose the initial condition of
the filter:
>>> zi = signal.lfilter_zi(b, a)
>>> z, _ = signal.lfilter(b, a, xn, zi=zi*xn[0])
Apply the filter again, to have a result filtered at an order the same as
filtfilt:
>>> z2, _ = signal.lfilter(b, a, z, zi=zi*z[0])
Use filtfilt to apply the filter:
>>> y = signal.filtfilt(b, a, xn)
Plot the original signal and the various filtered versions:
>>> plt.figure
>>> plt.plot(t, xn, 'b', alpha=0.75)
>>> plt.plot(t, z, 'r--', t, z2, 'r', t, y, 'k')
>>> plt.legend(('noisy signal', 'lfilter, once', 'lfilter, twice',
... 'filtfilt'), loc='best')
>>> plt.grid(True)
>>> plt.show()
"""
a = np.atleast_1d(a)
if len(a) == 1:
# This path only supports types fdgFDGO to mirror _linear_filter below.
# Any of b, a, x, or zi can set the dtype, but there is no default
# casting of other types; instead a NotImplementedError is raised.
b = np.asarray(b)
a = np.asarray(a)
if b.ndim != 1 and a.ndim != 1:
raise ValueError('object of too small depth for desired array')
x = _validate_x(x)
inputs = [b, a, x]
if zi is not None:
# _linear_filter does not broadcast zi, but does do expansion of
# singleton dims.
zi = np.asarray(zi)
if zi.ndim != x.ndim:
raise ValueError('object of too small depth for desired array')
expected_shape = list(x.shape)
expected_shape[axis] = b.shape[0] - 1
expected_shape = tuple(expected_shape)
# check the trivial case where zi is the right shape first
if zi.shape != expected_shape:
strides = zi.ndim * [None]
if axis < 0:
axis += zi.ndim
for k in range(zi.ndim):
if k == axis and zi.shape[k] == expected_shape[k]:
strides[k] = zi.strides[k]
elif k != axis and zi.shape[k] == expected_shape[k]:
strides[k] = zi.strides[k]
elif k != axis and zi.shape[k] == 1:
strides[k] = 0
else:
raise ValueError('Unexpected shape for zi: expected '
'%s, found %s.' %
(expected_shape, zi.shape))
zi = np.lib.stride_tricks.as_strided(zi, expected_shape,
strides)
inputs.append(zi)
dtype = np.result_type(*inputs)
if dtype.char not in 'fdgFDGO':
raise NotImplementedError("input type '%s' not supported" % dtype)
b = np.array(b, dtype=dtype)
a = np.array(a, dtype=dtype, copy=False)
b /= a[0]
x = np.array(x, dtype=dtype, copy=False)
out_full = np.apply_along_axis(lambda y: np.convolve(b, y), axis, x)
ind = out_full.ndim * [slice(None)]
if zi is not None:
ind[axis] = slice(zi.shape[axis])
out_full[tuple(ind)] += zi
ind[axis] = slice(out_full.shape[axis] - len(b) + 1)
out = out_full[tuple(ind)]
if zi is None:
return out
else:
ind[axis] = slice(out_full.shape[axis] - len(b) + 1, None)
zf = out_full[tuple(ind)]
return out, zf
else:
if zi is None:
return sigtools._linear_filter(b, a, x, axis)
else:
return sigtools._linear_filter(b, a, x, axis, zi)
def lfiltic(b, a, y, x=None):
"""
Construct initial conditions for lfilter given input and output vectors.
Given a linear filter (b, a) and initial conditions on the output `y`
and the input `x`, return the initial conditions on the state vector zi
which is used by `lfilter` to generate the output given the input.
Parameters
----------
b : array_like
Linear filter term.
a : array_like
Linear filter term.
y : array_like
Initial conditions.
If ``N = len(a) - 1``, then ``y = {y[-1], y[-2], ..., y[-N]}``.
If `y` is too short, it is padded with zeros.
x : array_like, optional
Initial conditions.
If ``M = len(b) - 1``, then ``x = {x[-1], x[-2], ..., x[-M]}``.
If `x` is not given, its initial conditions are assumed zero.
If `x` is too short, it is padded with zeros.
Returns
-------
zi : ndarray
The state vector ``zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]}``,
where ``K = max(M, N)``.
See Also
--------
lfilter, lfilter_zi
"""
N = np.size(a) - 1
M = np.size(b) - 1
K = max(M, N)
y = np.asarray(y)
if y.dtype.kind in 'bui':
# ensure calculations are floating point
y = y.astype(np.float64)
zi = np.zeros(K, y.dtype)
if x is None:
x = np.zeros(M, y.dtype)
else:
x = np.asarray(x)
L = np.size(x)
if L < M:
x = np.r_[x, np.zeros(M - L)]
L = np.size(y)
if L < N:
y = np.r_[y, np.zeros(N - L)]
for m in range(M):
zi[m] = np.sum(b[m + 1:] * x[:M - m], axis=0)
for m in range(N):
zi[m] -= np.sum(a[m + 1:] * y[:N - m], axis=0)
return zi
def deconvolve(signal, divisor):
"""Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
Returns the quotient and remainder such that
``signal = convolve(divisor, quotient) + remainder``
Parameters
----------
signal : array_like
Signal data, typically a recorded signal
divisor : array_like
Divisor data, typically an impulse response or filter that was
applied to the original signal
Returns
-------
quotient : ndarray
Quotient, typically the recovered original signal
remainder : ndarray
Remainder
Examples
--------
Deconvolve a signal that's been filtered:
>>> from scipy import signal
>>> original = [0, 1, 0, 0, 1, 1, 0, 0]
>>> impulse_response = [2, 1]
>>> recorded = signal.convolve(impulse_response, original)
>>> recorded
array([0, 2, 1, 0, 2, 3, 1, 0, 0])
>>> recovered, remainder = signal.deconvolve(recorded, impulse_response)
>>> recovered
array([ 0., 1., 0., 0., 1., 1., 0., 0.])
See Also
--------
numpy.polydiv : performs polynomial division (same operation, but
also accepts poly1d objects)
"""
num = np.atleast_1d(signal)
den = np.atleast_1d(divisor)
N = len(num)
D = len(den)
if D > N:
quot = []
rem = num
else:
input = np.zeros(N - D + 1, float)
input[0] = 1
quot = lfilter(num, den, input)
rem = num - convolve(den, quot, mode='full')
return quot, rem
def hilbert(x, N=None, axis=-1):
"""
Compute the analytic signal, using the Hilbert transform.
The transformation is done along the last axis by default.
Parameters
----------
x : array_like
Signal data. Must be real.
N : int, optional
Number of Fourier components. Default: ``x.shape[axis]``
axis : int, optional
Axis along which to do the transformation. Default: -1.
Returns
-------
xa : ndarray
Analytic signal of `x`, of each 1-D array along `axis`
Notes
-----
The analytic signal ``x_a(t)`` of signal ``x(t)`` is:
.. math:: x_a = F^{-1}(F(x) 2U) = x + i y
where `F` is the Fourier transform, `U` the unit step function,
and `y` the Hilbert transform of `x`. [1]_
In other words, the negative half of the frequency spectrum is zeroed
out, turning the real-valued signal into a complex signal. The Hilbert
transformed signal can be obtained from ``np.imag(hilbert(x))``, and the
original signal from ``np.real(hilbert(x))``.
Examples
---------
In this example we use the Hilbert transform to determine the amplitude
envelope and instantaneous frequency of an amplitude-modulated signal.
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.signal import hilbert, chirp
>>> duration = 1.0
>>> fs = 400.0
>>> samples = int(fs*duration)
>>> t = np.arange(samples) / fs
We create a chirp of which the frequency increases from 20 Hz to 100 Hz and
apply an amplitude modulation.
>>> signal = chirp(t, 20.0, t[-1], 100.0)
>>> signal *= (1.0 + 0.5 * np.sin(2.0*np.pi*3.0*t) )
The amplitude envelope is given by magnitude of the analytic signal. The
instantaneous frequency can be obtained by differentiating the
instantaneous phase in respect to time. The instantaneous phase corresponds
to the phase angle of the analytic signal.
>>> analytic_signal = hilbert(signal)
>>> amplitude_envelope = np.abs(analytic_signal)
>>> instantaneous_phase = np.unwrap(np.angle(analytic_signal))
>>> instantaneous_frequency = (np.diff(instantaneous_phase) /
... (2.0*np.pi) * fs)
>>> fig = plt.figure()
>>> ax0 = fig.add_subplot(211)
>>> ax0.plot(t, signal, label='signal')
>>> ax0.plot(t, amplitude_envelope, label='envelope')
>>> ax0.set_xlabel("time in seconds")
>>> ax0.legend()
>>> ax1 = fig.add_subplot(212)
>>> ax1.plot(t[1:], instantaneous_frequency)
>>> ax1.set_xlabel("time in seconds")
>>> ax1.set_ylim(0.0, 120.0)
References
----------
.. [1] Wikipedia, "Analytic signal".
https://en.wikipedia.org/wiki/Analytic_signal
.. [2] Leon Cohen, "Time-Frequency Analysis", 1995. Chapter 2.
.. [3] Alan V. Oppenheim, Ronald W. Schafer. Discrete-Time Signal
Processing, Third Edition, 2009. Chapter 12.
ISBN 13: 978-1292-02572-8
"""
x = np.asarray(x)
if np.iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape[axis]
if N <= 0:
raise ValueError("N must be positive.")
Xf = sp_fft.fft(x, N, axis=axis)
h = np.zeros(N)
if N % 2 == 0:
h[0] = h[N // 2] = 1
h[1:N // 2] = 2
else:
h[0] = 1
h[1:(N + 1) // 2] = 2
if x.ndim > 1:
ind = [np.newaxis] * x.ndim
ind[axis] = slice(None)
h = h[tuple(ind)]
x = sp_fft.ifft(Xf * h, axis=axis)
return x
def hilbert2(x, N=None):
"""
Compute the '2-D' analytic signal of `x`
Parameters
----------
x : array_like
2-D signal data.
N : int or tuple of two ints, optional
Number of Fourier components. Default is ``x.shape``
Returns
-------
xa : ndarray
Analytic signal of `x` taken along axes (0,1).
References
----------
.. [1] Wikipedia, "Analytic signal",
https://en.wikipedia.org/wiki/Analytic_signal
"""
x = np.atleast_2d(x)
if x.ndim > 2:
raise ValueError("x must be 2-D.")
if np.iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape
elif isinstance(N, int):
if N <= 0:
raise ValueError("N must be positive.")
N = (N, N)
elif len(N) != 2 or np.any(np.asarray(N) <= 0):
raise ValueError("When given as a tuple, N must hold exactly "
"two positive integers")
Xf = sp_fft.fft2(x, N, axes=(0, 1))
h1 = np.zeros(N[0], 'd')
h2 = np.zeros(N[1], 'd')
for p in range(2):
h = eval("h%d" % (p + 1))
N1 = N[p]
if N1 % 2 == 0:
h[0] = h[N1 // 2] = 1
h[1:N1 // 2] = 2
else:
h[0] = 1
h[1:(N1 + 1) // 2] = 2
exec("h%d = h" % (p + 1), globals(), locals())
h = h1[:, np.newaxis] * h2[np.newaxis, :]
k = x.ndim
while k > 2:
h = h[:, np.newaxis]
k -= 1
x = sp_fft.ifft2(Xf * h, axes=(0, 1))
return x
def cmplx_sort(p):
"""Sort roots based on magnitude.
Parameters
----------
p : array_like
The roots to sort, as a 1-D array.
Returns
-------
p_sorted : ndarray
Sorted roots.
indx : ndarray
Array of indices needed to sort the input `p`.
Examples
--------
>>> from scipy import signal
>>> vals = [1, 4, 1+1.j, 3]
>>> p_sorted, indx = signal.cmplx_sort(vals)
>>> p_sorted
array([1.+0.j, 1.+1.j, 3.+0.j, 4.+0.j])
>>> indx
array([0, 2, 3, 1])
"""
p = np.asarray(p)
indx = np.argsort(abs(p))
return np.take(p, indx, 0), indx
def unique_roots(p, tol=1e-3, rtype='min'):
"""Determine unique roots and their multiplicities from a list of roots.
Parameters
----------
p : array_like
The list of roots.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. Refer to Notes about
the details on roots grouping.
rtype : {'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}, optional
How to determine the returned root if multiple roots are within
`tol` of each other.
- 'max', 'maximum': pick the maximum of those roots
- 'min', 'minimum': pick the minimum of those roots
- 'avg', 'mean': take the average of those roots
When finding minimum or maximum among complex roots they are compared
first by the real part and then by the imaginary part.
Returns
-------
unique : ndarray
The list of unique roots.
multiplicity : ndarray
The multiplicity of each root.
Notes
-----
If we have 3 roots ``a``, ``b`` and ``c``, such that ``a`` is close to
``b`` and ``b`` is close to ``c`` (distance is less than `tol`), then it
doesn't necessarily mean that ``a`` is close to ``c``. It means that roots
grouping is not unique. In this function we use "greedy" grouping going
through the roots in the order they are given in the input `p`.
This utility function is not specific to roots but can be used for any
sequence of values for which uniqueness and multiplicity has to be
determined. For a more general routine, see `numpy.unique`.
Examples
--------
>>> from scipy import signal
>>> vals = [0, 1.3, 1.31, 2.8, 1.25, 2.2, 10.3]
>>> uniq, mult = signal.unique_roots(vals, tol=2e-2, rtype='avg')
Check which roots have multiplicity larger than 1:
>>> uniq[mult > 1]
array([ 1.305])
"""
if rtype in ['max', 'maximum']:
reduce = np.max
elif rtype in ['min', 'minimum']:
reduce = np.min
elif rtype in ['avg', 'mean']:
reduce = np.mean
else:
raise ValueError("`rtype` must be one of "
"{'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}")
p = np.asarray(p)
points = np.empty((len(p), 2))
points[:, 0] = np.real(p)
points[:, 1] = np.imag(p)
tree = cKDTree(points)
p_unique = []
p_multiplicity = []
used = np.zeros(len(p), dtype=bool)
for i in range(len(p)):
if used[i]:
continue
group = tree.query_ball_point(points[i], tol)
group = [x for x in group if not used[x]]
p_unique.append(reduce(p[group]))
p_multiplicity.append(len(group))
used[group] = True
return np.asarray(p_unique), np.asarray(p_multiplicity)
def invres(r, p, k, tol=1e-3, rtype='avg'):
"""Compute b(s) and a(s) from partial fraction expansion.
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(s) b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
a(s) a[0] s**(N) + a[1] s**(N-1) + ... + a[N]
then the partial-fraction expansion H(s) is defined as::
r[0] r[1] r[-1]
= -------- + -------- + ... + --------- + k(s)
(s-p[0]) (s-p[1]) (s-p[-1])
If there are any repeated roots (closer together than `tol`), then H(s)
has terms like::
r[i] r[i+1] r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i]) (s-p[i])**2 (s-p[i])**n
This function is used for polynomials in positive powers of s or z,
such as analog filters or digital filters in controls engineering. For
negative powers of z (typical for digital filters in DSP), use `invresz`.
Parameters
----------
r : array_like
Residues corresponding to the poles. For repeated poles, the residues
must be ordered to correspond to ascending by power fractions.
p : array_like
Poles. Equal poles must be adjacent.
k : array_like
Coefficients of the direct polynomial term.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
b : ndarray
Numerator polynomial coefficients.
a : ndarray
Denominator polynomial coefficients.
See Also
--------
residue, invresz, unique_roots
"""
r = np.atleast_1d(r)
p = np.atleast_1d(p)
k = np.trim_zeros(np.atleast_1d(k), 'f')
unique_poles, multiplicity = _group_poles(p, tol, rtype)
factors, denominator = _compute_factors(unique_poles, multiplicity,
include_powers=True)
if len(k) == 0:
numerator = 0
else:
numerator = np.polymul(k, denominator)
for residue, factor in zip(r, factors):
numerator = np.polyadd(numerator, residue * factor)
return numerator, denominator
def _compute_factors(roots, multiplicity, include_powers=False):
"""Compute the total polynomial divided by factors for each root."""
current = np.array([1])
suffixes = [current]
for pole, mult in zip(roots[-1:0:-1], multiplicity[-1:0:-1]):
monomial = np.array([1, -pole])
for _ in range(mult):
current = np.polymul(current, monomial)
suffixes.append(current)
suffixes = suffixes[::-1]
factors = []
current = np.array([1])
for pole, mult, suffix in zip(roots, multiplicity, suffixes):
monomial = np.array([1, -pole])
block = []
for i in range(mult):
if i == 0 or include_powers:
block.append(np.polymul(current, suffix))
current = np.polymul(current, monomial)
factors.extend(reversed(block))
return factors, current
def _compute_residues(poles, multiplicity, numerator):
denominator_factors, _ = _compute_factors(poles, multiplicity)
numerator = numerator.astype(poles.dtype)
residues = []
for pole, mult, factor in zip(poles, multiplicity,
denominator_factors):
if mult == 1:
residues.append(np.polyval(numerator, pole) /
np.polyval(factor, pole))
else:
numer = numerator.copy()
monomial = np.array([1, -pole])
factor, d = np.polydiv(factor, monomial)
block = []
for _ in range(mult):
numer, n = np.polydiv(numer, monomial)
r = n[0] / d[0]
numer = np.polysub(numer, r * factor)
block.append(r)
residues.extend(reversed(block))
return np.asarray(residues)
def residue(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(s) / a(s).
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(s) b[0] s**(M) + b[1] s**(M-1) + ... + b[M]
H(s) = ------ = ------------------------------------------
a(s) a[0] s**(N) + a[1] s**(N-1) + ... + a[N]
then the partial-fraction expansion H(s) is defined as::
r[0] r[1] r[-1]
= -------- + -------- + ... + --------- + k(s)
(s-p[0]) (s-p[1]) (s-p[-1])
If there are any repeated roots (closer together than `tol`), then H(s)
has terms like::
r[i] r[i+1] r[i+n-1]
-------- + ----------- + ... + -----------
(s-p[i]) (s-p[i])**2 (s-p[i])**n
This function is used for polynomials in positive powers of s or z,
such as analog filters or digital filters in controls engineering. For
negative powers of z (typical for digital filters in DSP), use `residuez`.
See Notes for details about the algorithm.
Parameters
----------
b : array_like
Numerator polynomial coefficients.
a : array_like
Denominator polynomial coefficients.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
r : ndarray
Residues corresponding to the poles. For repeated poles, the residues
are ordered to correspond to ascending by power fractions.
p : ndarray
Poles ordered by magnitude in ascending order.
k : ndarray
Coefficients of the direct polynomial term.
See Also
--------
invres, residuez, numpy.poly, unique_roots
Notes
-----
The "deflation through subtraction" algorithm is used for
computations --- method 6 in [1]_.
The form of partial fraction expansion depends on poles multiplicity in
the exact mathematical sense. However there is no way to exactly
determine multiplicity of roots of a polynomial in numerical computing.
Thus you should think of the result of `residue` with given `tol` as
partial fraction expansion computed for the denominator composed of the
computed poles with empirically determined multiplicity. The choice of
`tol` can drastically change the result if there are close poles.
References
----------
.. [1] J. F. Mahoney, B. D. Sivazlian, "Partial fractions expansion: a
review of computational methodology and efficiency", Journal of
Computational and Applied Mathematics, Vol. 9, 1983.
"""
b = np.asarray(b)
a = np.asarray(a)
if (np.issubdtype(b.dtype, np.complexfloating)
or np.issubdtype(a.dtype, np.complexfloating)):
b = b.astype(complex)
a = a.astype(complex)
else:
b = b.astype(float)
a = a.astype(float)
b = np.trim_zeros(np.atleast_1d(b), 'f')
a = np.trim_zeros(np.atleast_1d(a), 'f')
if a.size == 0:
raise ValueError("Denominator `a` is zero.")
poles = np.roots(a)
if b.size == 0:
return np.zeros(poles.shape), cmplx_sort(poles)[0], np.array([])
if len(b) < len(a):
k = np.empty(0)
else:
k, b = np.polydiv(b, a)
unique_poles, multiplicity = unique_roots(poles, tol=tol, rtype=rtype)
unique_poles, order = cmplx_sort(unique_poles)
multiplicity = multiplicity[order]
residues = _compute_residues(unique_poles, multiplicity, b)
index = 0
for pole, mult in zip(unique_poles, multiplicity):
poles[index:index + mult] = pole
index += mult
return residues / a[0], poles, k
def residuez(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(z) / a(z).
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(z) b[0] + b[1] z**(-1) + ... + b[M] z**(-M)
H(z) = ------ = ------------------------------------------
a(z) a[0] + a[1] z**(-1) + ... + a[N] z**(-N)
then the partial-fraction expansion H(z) is defined as::
r[0] r[-1]
= --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ...
(1-p[0]z**(-1)) (1-p[-1]z**(-1))
If there are any repeated roots (closer than `tol`), then the partial
fraction expansion has terms like::
r[i] r[i+1] r[i+n-1]
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
This function is used for polynomials in negative powers of z,
such as digital filters in DSP. For positive powers, use `residue`.
See Notes of `residue` for details about the algorithm.
Parameters
----------
b : array_like
Numerator polynomial coefficients.
a : array_like
Denominator polynomial coefficients.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
r : ndarray
Residues corresponding to the poles. For repeated poles, the residues
are ordered to correspond to ascending by power fractions.
p : ndarray
Poles ordered by magnitude in ascending order.
k : ndarray
Coefficients of the direct polynomial term.
See Also
--------
invresz, residue, unique_roots
"""
b = np.asarray(b)
a = np.asarray(a)
if (np.issubdtype(b.dtype, np.complexfloating)
or np.issubdtype(a.dtype, np.complexfloating)):
b = b.astype(complex)
a = a.astype(complex)
else:
b = b.astype(float)
a = a.astype(float)
b = np.trim_zeros(np.atleast_1d(b), 'b')
a = np.trim_zeros(np.atleast_1d(a), 'b')
if a.size == 0:
raise ValueError("Denominator `a` is zero.")
elif a[0] == 0:
raise ValueError("First coefficient of determinant `a` must be "
"non-zero.")
poles = np.roots(a)
if b.size == 0:
return np.zeros(poles.shape), cmplx_sort(poles)[0], np.array([])
b_rev = b[::-1]
a_rev = a[::-1]
if len(b_rev) < len(a_rev):
k_rev = np.empty(0)
else:
k_rev, b_rev = np.polydiv(b_rev, a_rev)
unique_poles, multiplicity = unique_roots(poles, tol=tol, rtype=rtype)
unique_poles, order = cmplx_sort(unique_poles)
multiplicity = multiplicity[order]
residues = _compute_residues(1 / unique_poles, multiplicity, b_rev)
index = 0
powers = np.empty(len(residues), dtype=int)
for pole, mult in zip(unique_poles, multiplicity):
poles[index:index + mult] = pole
powers[index:index + mult] = 1 + np.arange(mult)
index += mult
residues *= (-poles) ** powers / a_rev[0]
return residues, poles, k_rev[::-1]
def _group_poles(poles, tol, rtype):
if rtype in ['max', 'maximum']:
reduce = np.max
elif rtype in ['min', 'minimum']:
reduce = np.min
elif rtype in ['avg', 'mean']:
reduce = np.mean
else:
raise ValueError("`rtype` must be one of "
"{'max', 'maximum', 'min', 'minimum', 'avg', 'mean'}")
unique = []
multiplicity = []
pole = poles[0]
block = [pole]
for i in range(1, len(poles)):
if abs(poles[i] - pole) <= tol:
block.append(pole)
else:
unique.append(reduce(block))
multiplicity.append(len(block))
pole = poles[i]
block = [pole]
unique.append(reduce(block))
multiplicity.append(len(block))
return np.asarray(unique), np.asarray(multiplicity)
def invresz(r, p, k, tol=1e-3, rtype='avg'):
"""Compute b(z) and a(z) from partial fraction expansion.
If `M` is the degree of numerator `b` and `N` the degree of denominator
`a`::
b(z) b[0] + b[1] z**(-1) + ... + b[M] z**(-M)
H(z) = ------ = ------------------------------------------
a(z) a[0] + a[1] z**(-1) + ... + a[N] z**(-N)
then the partial-fraction expansion H(z) is defined as::
r[0] r[-1]
= --------------- + ... + ---------------- + k[0] + k[1]z**(-1) ...
(1-p[0]z**(-1)) (1-p[-1]z**(-1))
If there are any repeated roots (closer than `tol`), then the partial
fraction expansion has terms like::
r[i] r[i+1] r[i+n-1]
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
This function is used for polynomials in negative powers of z,
such as digital filters in DSP. For positive powers, use `invres`.
Parameters
----------
r : array_like
Residues corresponding to the poles. For repeated poles, the residues
must be ordered to correspond to ascending by power fractions.
p : array_like
Poles. Equal poles must be adjacent.
k : array_like
Coefficients of the direct polynomial term.
tol : float, optional
The tolerance for two roots to be considered equal in terms of
the distance between them. Default is 1e-3. See `unique_roots`
for further details.
rtype : {'avg', 'min', 'max'}, optional
Method for computing a root to represent a group of identical roots.
Default is 'avg'. See `unique_roots` for further details.
Returns
-------
b : ndarray
Numerator polynomial coefficients.
a : ndarray
Denominator polynomial coefficients.
See Also
--------
residuez, unique_roots, invres
"""
r = np.atleast_1d(r)
p = np.atleast_1d(p)
k = np.trim_zeros(np.atleast_1d(k), 'b')
unique_poles, multiplicity = _group_poles(p, tol, rtype)
factors, denominator = _compute_factors(unique_poles, multiplicity,
include_powers=True)
if len(k) == 0:
numerator = 0
else:
numerator = np.polymul(k[::-1], denominator[::-1])
for residue, factor in zip(r, factors):
numerator = np.polyadd(numerator, residue * factor[::-1])
return numerator[::-1], denominator
def resample(x, num, t=None, axis=0, window=None):
"""
Resample `x` to `num` samples using Fourier method along the given axis.
The resampled signal starts at the same value as `x` but is sampled
with a spacing of ``len(x) / num * (spacing of x)``. Because a
Fourier method is used, the signal is assumed to be periodic.
Parameters
----------
x : array_like
The data to be resampled.
num : int
The number of samples in the resampled signal.
t : array_like, optional
If `t` is given, it is assumed to be the equally spaced sample
positions associated with the signal data in `x`.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : array_like, callable, string, float, or tuple, optional
Specifies the window applied to the signal in the Fourier
domain. See below for details.
Returns
-------
resampled_x or (resampled_x, resampled_t)
Either the resampled array, or, if `t` was given, a tuple
containing the resampled array and the corresponding resampled
positions.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample_poly : Resample using polyphase filtering and an FIR filter.
Notes
-----
The argument `window` controls a Fourier-domain window that tapers
the Fourier spectrum before zero-padding to alleviate ringing in
the resampled values for sampled signals you didn't intend to be
interpreted as band-limited.
If `window` is a function, then it is called with a vector of inputs
indicating the frequency bins (i.e. fftfreq(x.shape[axis]) ).
If `window` is an array of the same length as `x.shape[axis]` it is
assumed to be the window to be applied directly in the Fourier
domain (with dc and low-frequency first).
For any other type of `window`, the function `scipy.signal.get_window`
is called to generate the window.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * len(x) / num``.
If `t` is not None, then it is used solely to calculate the resampled
positions `resampled_t`
As noted, `resample` uses FFT transformations, which can be very
slow if the number of input or output samples is large and prime;
see `scipy.fft.fft`.
Examples
--------
Note that the end of the resampled data rises to meet the first
sample of the next cycle:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f = signal.resample(y, 100)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'go-', xnew, f, '.-', 10, y[0], 'ro')
>>> plt.legend(['data', 'resampled'], loc='best')
>>> plt.show()
"""
x = np.asarray(x)
Nx = x.shape[axis]
# Check if we can use faster real FFT
real_input = np.isrealobj(x)
# Forward transform
if real_input:
X = sp_fft.rfft(x, axis=axis)
else: # Full complex FFT
X = sp_fft.fft(x, axis=axis)
# Apply window to spectrum
if window is not None:
if callable(window):
W = window(sp_fft.fftfreq(Nx))
elif isinstance(window, np.ndarray):
if window.shape != (Nx,):
raise ValueError('window must have the same length as data')
W = window
else:
W = sp_fft.ifftshift(get_window(window, Nx))
newshape_W = [1] * x.ndim
newshape_W[axis] = X.shape[axis]
if real_input:
# Fold the window back on itself to mimic complex behavior
W_real = W.copy()
W_real[1:] += W_real[-1:0:-1]
W_real[1:] *= 0.5
X *= W_real[:newshape_W[axis]].reshape(newshape_W)
else:
X *= W.reshape(newshape_W)
# Copy each half of the original spectrum to the output spectrum, either
# truncating high frequences (downsampling) or zero-padding them
# (upsampling)
# Placeholder array for output spectrum
newshape = list(x.shape)
if real_input:
newshape[axis] = num // 2 + 1
else:
newshape[axis] = num
Y = np.zeros(newshape, X.dtype)
# Copy positive frequency components (and Nyquist, if present)
N = min(num, Nx)
nyq = N // 2 + 1 # Slice index that includes Nyquist if present
sl = [slice(None)] * x.ndim
sl[axis] = slice(0, nyq)
Y[tuple(sl)] = X[tuple(sl)]
if not real_input:
# Copy negative frequency components
if N > 2: # (slice expression doesn't collapse to empty array)
sl[axis] = slice(nyq - N, None)
Y[tuple(sl)] = X[tuple(sl)]
# Split/join Nyquist component(s) if present
# So far we have set Y[+N/2]=X[+N/2]
if N % 2 == 0:
if num < Nx: # downsampling
if real_input:
sl[axis] = slice(N//2, N//2 + 1)
Y[tuple(sl)] *= 2.
else:
# select the component of Y at frequency +N/2,
# add the component of X at -N/2
sl[axis] = slice(-N//2, -N//2 + 1)
Y[tuple(sl)] += X[tuple(sl)]
elif Nx < num: # upsampling
# select the component at frequency +N/2 and halve it
sl[axis] = slice(N//2, N//2 + 1)
Y[tuple(sl)] *= 0.5
if not real_input:
temp = Y[tuple(sl)]
# set the component at -N/2 equal to the component at +N/2
sl[axis] = slice(num-N//2, num-N//2 + 1)
Y[tuple(sl)] = temp
# Inverse transform
if real_input:
y = sp_fft.irfft(Y, num, axis=axis)
else:
y = sp_fft.ifft(Y, axis=axis, overwrite_x=True)
y *= (float(num) / float(Nx))
if t is None:
return y
else:
new_t = np.arange(0, num) * (t[1] - t[0]) * Nx / float(num) + t[0]
return y, new_t
def resample_poly(x, up, down, axis=0, window=('kaiser', 5.0),
padtype='constant', cval=None):
"""
Resample `x` along the given axis using polyphase filtering.
The signal `x` is upsampled by the factor `up`, a zero-phase low-pass
FIR filter is applied, and then it is downsampled by the factor `down`.
The resulting sample rate is ``up / down`` times the original sample
rate. By default, values beyond the boundary of the signal are assumed
to be zero during the filtering step.
Parameters
----------
x : array_like
The data to be resampled.
up : int
The upsampling factor.
down : int
The downsampling factor.
axis : int, optional
The axis of `x` that is resampled. Default is 0.
window : string, tuple, or array_like, optional
Desired window to use to design the low-pass filter, or the FIR filter
coefficients to employ. See below for details.
padtype : string, optional
`constant`, `line`, `mean`, `median`, `maximum`, `minimum` or any of
the other signal extension modes supported by `scipy.signal.upfirdn`.
Changes assumptions on values beyond the boundary. If `constant`,
assumed to be `cval` (default zero). If `line` assumed to continue a
linear trend defined by the first and last points. `mean`, `median`,
`maximum` and `minimum` work as in `np.pad` and assume that the values
beyond the boundary are the mean, median, maximum or minimum
respectively of the array along the axis.
.. versionadded:: 1.4.0
cval : float, optional
Value to use if `padtype='constant'`. Default is zero.
.. versionadded:: 1.4.0
Returns
-------
resampled_x : array
The resampled array.
See Also
--------
decimate : Downsample the signal after applying an FIR or IIR filter.
resample : Resample up or down using the FFT method.
Notes
-----
This polyphase method will likely be faster than the Fourier method
in `scipy.signal.resample` when the number of samples is large and
prime, or when the number of samples is large and `up` and `down`
share a large greatest common denominator. The length of the FIR
filter used will depend on ``max(up, down) // gcd(up, down)``, and
the number of operations during polyphase filtering will depend on
the filter length and `down` (see `scipy.signal.upfirdn` for details).
The argument `window` specifies the FIR low-pass filter design.
If `window` is an array_like it is assumed to be the FIR filter
coefficients. Note that the FIR filter is applied after the upsampling
step, so it should be designed to operate on a signal at a sampling
frequency higher than the original by a factor of `up//gcd(up, down)`.
This function's output will be centered with respect to this array, so it
is best to pass a symmetric filter with an odd number of samples if, as
is usually the case, a zero-phase filter is desired.
For any other type of `window`, the functions `scipy.signal.get_window`
and `scipy.signal.firwin` are called to generate the appropriate filter
coefficients.
The first sample of the returned vector is the same as the first
sample of the input vector. The spacing between samples is changed
from ``dx`` to ``dx * down / float(up)``.
Examples
--------
By default, the end of the resampled data rises to meet the first
sample of the next cycle for the FFT method, and gets closer to zero
for the polyphase method:
>>> from scipy import signal
>>> x = np.linspace(0, 10, 20, endpoint=False)
>>> y = np.cos(-x**2/6.0)
>>> f_fft = signal.resample(y, 100)
>>> f_poly = signal.resample_poly(y, 100, 20)
>>> xnew = np.linspace(0, 10, 100, endpoint=False)
>>> import matplotlib.pyplot as plt
>>> plt.plot(xnew, f_fft, 'b.-', xnew, f_poly, 'r.-')
>>> plt.plot(x, y, 'ko-')
>>> plt.plot(10, y[0], 'bo', 10, 0., 'ro') # boundaries
>>> plt.legend(['resample', 'resamp_poly', 'data'], loc='best')
>>> plt.show()
This default behaviour can be changed by using the padtype option:
>>> import numpy as np
>>> from scipy import signal
>>> N = 5
>>> x = np.linspace(0, 1, N, endpoint=False)
>>> y = 2 + x**2 - 1.7*np.sin(x) + .2*np.cos(11*x)
>>> y2 = 1 + x**3 + 0.1*np.sin(x) + .1*np.cos(11*x)
>>> Y = np.stack([y, y2], axis=-1)
>>> up = 4
>>> xr = np.linspace(0, 1, N*up, endpoint=False)
>>> y2 = signal.resample_poly(Y, up, 1, padtype='constant')
>>> y3 = signal.resample_poly(Y, up, 1, padtype='mean')
>>> y4 = signal.resample_poly(Y, up, 1, padtype='line')
>>> import matplotlib.pyplot as plt
>>> for i in [0,1]:
... plt.figure()
... plt.plot(xr, y4[:,i], 'g.', label='line')
... plt.plot(xr, y3[:,i], 'y.', label='mean')
... plt.plot(xr, y2[:,i], 'r.', label='constant')
... plt.plot(x, Y[:,i], 'k-')
... plt.legend()
>>> plt.show()
"""
x = np.asarray(x)
if up != int(up):
raise ValueError("up must be an integer")
if down != int(down):
raise ValueError("down must be an integer")
up = int(up)
down = int(down)
if up < 1 or down < 1:
raise ValueError('up and down must be >= 1')
if cval is not None and padtype != 'constant':
raise ValueError('cval has no effect when padtype is ', padtype)
# Determine our up and down factors
# Use a rational approximation to save computation time on really long
# signals
g_ = math.gcd(up, down)
up //= g_
down //= g_
if up == down == 1:
return x.copy()
n_in = x.shape[axis]
n_out = n_in * up
n_out = n_out // down + bool(n_out % down)
if isinstance(window, (list, np.ndarray)):
window = np.array(window) # use array to force a copy (we modify it)
if window.ndim > 1:
raise ValueError('window must be 1-D')
half_len = (window.size - 1) // 2
h = window
else:
# Design a linear-phase low-pass FIR filter
max_rate = max(up, down)
f_c = 1. / max_rate # cutoff of FIR filter (rel. to Nyquist)
half_len = 10 * max_rate # reasonable cutoff for our sinc-like function
h = firwin(2 * half_len + 1, f_c, window=window)
h *= up
# Zero-pad our filter to put the output samples at the center
n_pre_pad = (down - half_len % down)
n_post_pad = 0
n_pre_remove = (half_len + n_pre_pad) // down
# We should rarely need to do this given our filter lengths...
while _output_len(len(h) + n_pre_pad + n_post_pad, n_in,
up, down) < n_out + n_pre_remove:
n_post_pad += 1
h = np.concatenate((np.zeros(n_pre_pad, dtype=h.dtype), h,
np.zeros(n_post_pad, dtype=h.dtype)))
n_pre_remove_end = n_pre_remove + n_out
# Remove background depending on the padtype option
funcs = {'mean': np.mean, 'median': np.median,
'minimum': np.amin, 'maximum': np.amax}
upfirdn_kwargs = {'mode': 'constant', 'cval': 0}
if padtype in funcs:
background_values = funcs[padtype](x, axis=axis, keepdims=True)
elif padtype in _upfirdn_modes:
upfirdn_kwargs = {'mode': padtype}
if padtype == 'constant':
if cval is None:
cval = 0
upfirdn_kwargs['cval'] = cval
else:
raise ValueError(
'padtype must be one of: maximum, mean, median, minimum, ' +
', '.join(_upfirdn_modes))
if padtype in funcs:
x = x - background_values
# filter then remove excess
y = upfirdn(h, x, up, down, axis=axis, **upfirdn_kwargs)
keep = [slice(None), ]*x.ndim
keep[axis] = slice(n_pre_remove, n_pre_remove_end)
y_keep = y[tuple(keep)]
# Add background back
if padtype in funcs:
y_keep += background_values
return y_keep
def vectorstrength(events, period):
'''
Determine the vector strength of the events corresponding to the given
period.
The vector strength is a measure of phase synchrony, how well the
timing of the events is synchronized to a single period of a periodic
signal.
If multiple periods are used, calculate the vector strength of each.
This is called the "resonating vector strength".
Parameters
----------
events : 1D array_like
An array of time points containing the timing of the events.
period : float or array_like
The period of the signal that the events should synchronize to.
The period is in the same units as `events`. It can also be an array
of periods, in which case the outputs are arrays of the same length.
Returns
-------
strength : float or 1D array
The strength of the synchronization. 1.0 is perfect synchronization
and 0.0 is no synchronization. If `period` is an array, this is also
an array with each element containing the vector strength at the
corresponding period.
phase : float or array
The phase that the events are most strongly synchronized to in radians.
If `period` is an array, this is also an array with each element
containing the phase for the corresponding period.
References
----------
van Hemmen, JL, Longtin, A, and Vollmayr, AN. Testing resonating vector
strength: Auditory system, electric fish, and noise.
Chaos 21, 047508 (2011);
:doi:`10.1063/1.3670512`.
van Hemmen, JL. Vector strength after Goldberg, Brown, and von Mises:
biological and mathematical perspectives. Biol Cybern.
2013 Aug;107(4):385-96. :doi:`10.1007/s00422-013-0561-7`.
van Hemmen, JL and Vollmayr, AN. Resonating vector strength: what happens
when we vary the "probing" frequency while keeping the spike times
fixed. Biol Cybern. 2013 Aug;107(4):491-94.
:doi:`10.1007/s00422-013-0560-8`.
'''
events = np.asarray(events)
period = np.asarray(period)
if events.ndim > 1:
raise ValueError('events cannot have dimensions more than 1')
if period.ndim > 1:
raise ValueError('period cannot have dimensions more than 1')
# we need to know later if period was originally a scalar
scalarperiod = not period.ndim
events = np.atleast_2d(events)
period = np.atleast_2d(period)
if (period <= 0).any():
raise ValueError('periods must be positive')
# this converts the times to vectors
vectors = np.exp(np.dot(2j*np.pi/period.T, events))
# the vector strength is just the magnitude of the mean of the vectors
# the vector phase is the angle of the mean of the vectors
vectormean = np.mean(vectors, axis=1)
strength = abs(vectormean)
phase = np.angle(vectormean)
# if the original period was a scalar, return scalars
if scalarperiod:
strength = strength[0]
phase = phase[0]
return strength, phase
def detrend(data, axis=-1, type='linear', bp=0, overwrite_data=False):
"""
Remove linear trend along axis from data.
Parameters
----------
data : array_like
The input data.
axis : int, optional
The axis along which to detrend the data. By default this is the
last axis (-1).
type : {'linear', 'constant'}, optional
The type of detrending. If ``type == 'linear'`` (default),
the result of a linear least-squares fit to `data` is subtracted
from `data`.
If ``type == 'constant'``, only the mean of `data` is subtracted.
bp : array_like of ints, optional
A sequence of break points. If given, an individual linear fit is
performed for each part of `data` between two break points.
Break points are specified as indices into `data`. This parameter
only has an effect when ``type == 'linear'``.
overwrite_data : bool, optional
If True, perform in place detrending and avoid a copy. Default is False
Returns
-------
ret : ndarray
The detrended input data.
Examples
--------
>>> from scipy import signal
>>> randgen = np.random.RandomState(9)
>>> npoints = 1000
>>> noise = randgen.randn(npoints)
>>> x = 3 + 2*np.linspace(0, 1, npoints) + noise
>>> (signal.detrend(x) - noise).max() < 0.01
True
"""
if type not in ['linear', 'l', 'constant', 'c']:
raise ValueError("Trend type must be 'linear' or 'constant'.")
data = np.asarray(data)
dtype = data.dtype.char
if dtype not in 'dfDF':
dtype = 'd'
if type in ['constant', 'c']:
ret = data - np.expand_dims(np.mean(data, axis), axis)
return ret
else:
dshape = data.shape
N = dshape[axis]
bp = np.sort(np.unique(np.r_[0, bp, N]))
if np.any(bp > N):
raise ValueError("Breakpoints must be less than length "
"of data along given axis.")
Nreg = len(bp) - 1
# Restructure data so that axis is along first dimension and
# all other dimensions are collapsed into second dimension
rnk = len(dshape)
if axis < 0:
axis = axis + rnk
newdims = np.r_[axis, 0:axis, axis + 1:rnk]
newdata = np.reshape(np.transpose(data, tuple(newdims)),
(N, _prod(dshape) // N))
if not overwrite_data:
newdata = newdata.copy() # make sure we have a copy
if newdata.dtype.char not in 'dfDF':
newdata = newdata.astype(dtype)
# Find leastsq fit and remove it for each piece
for m in range(Nreg):
Npts = bp[m + 1] - bp[m]
A = np.ones((Npts, 2), dtype)
A[:, 0] = np.cast[dtype](np.arange(1, Npts + 1) * 1.0 / Npts)
sl = slice(bp[m], bp[m + 1])
coef, resids, rank, s = linalg.lstsq(A, newdata[sl])
newdata[sl] = newdata[sl] - np.dot(A, coef)
# Put data back in original shape.
tdshape = np.take(dshape, newdims, 0)
ret = np.reshape(newdata, tuple(tdshape))
vals = list(range(1, rnk))
olddims = vals[:axis] + [0] + vals[axis:]
ret = np.transpose(ret, tuple(olddims))
return ret
def lfilter_zi(b, a):
"""
Construct initial conditions for lfilter for step response steady-state.
Compute an initial state `zi` for the `lfilter` function that corresponds
to the steady state of the step response.
A typical use of this function is to set the initial state so that the
output of the filter starts at the same value as the first element of
the signal to be filtered.
Parameters
----------
b, a : array_like (1-D)
The IIR filter coefficients. See `lfilter` for more
information.
Returns
-------
zi : 1-D ndarray
The initial state for the filter.
See Also
--------
lfilter, lfiltic, filtfilt
Notes
-----
A linear filter with order m has a state space representation (A, B, C, D),
for which the output y of the filter can be expressed as::
z(n+1) = A*z(n) + B*x(n)
y(n) = C*z(n) + D*x(n)
where z(n) is a vector of length m, A has shape (m, m), B has shape
(m, 1), C has shape (1, m) and D has shape (1, 1) (assuming x(n) is
a scalar). lfilter_zi solves::
zi = A*zi + B
In other words, it finds the initial condition for which the response
to an input of all ones is a constant.
Given the filter coefficients `a` and `b`, the state space matrices
for the transposed direct form II implementation of the linear filter,
which is the implementation used by scipy.signal.lfilter, are::
A = scipy.linalg.companion(a).T
B = b[1:] - a[1:]*b[0]
assuming `a[0]` is 1.0; if `a[0]` is not 1, `a` and `b` are first
divided by a[0].
Examples
--------
The following code creates a lowpass Butterworth filter. Then it
applies that filter to an array whose values are all 1.0; the
output is also all 1.0, as expected for a lowpass filter. If the
`zi` argument of `lfilter` had not been given, the output would have
shown the transient signal.
>>> from numpy import array, ones
>>> from scipy.signal import lfilter, lfilter_zi, butter
>>> b, a = butter(5, 0.25)
>>> zi = lfilter_zi(b, a)
>>> y, zo = lfilter(b, a, ones(10), zi=zi)
>>> y
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
Another example:
>>> x = array([0.5, 0.5, 0.5, 0.0, 0.0, 0.0, 0.0])
>>> y, zf = lfilter(b, a, x, zi=zi*x[0])
>>> y
array([ 0.5 , 0.5 , 0.5 , 0.49836039, 0.48610528,
0.44399389, 0.35505241])
Note that the `zi` argument to `lfilter` was computed using
`lfilter_zi` and scaled by `x[0]`. Then the output `y` has no
transient until the input drops from 0.5 to 0.0.
"""
# FIXME: Can this function be replaced with an appropriate
# use of lfiltic? For example, when b,a = butter(N,Wn),
# lfiltic(b, a, y=numpy.ones_like(a), x=numpy.ones_like(b)).
#
# We could use scipy.signal.normalize, but it uses warnings in
# cases where a ValueError is more appropriate, and it allows
# b to be 2D.
b = np.atleast_1d(b)
if b.ndim != 1:
raise ValueError("Numerator b must be 1-D.")
a = np.atleast_1d(a)
if a.ndim != 1:
raise ValueError("Denominator a must be 1-D.")
while len(a) > 1 and a[0] == 0.0:
a = a[1:]
if a.size < 1:
raise ValueError("There must be at least one nonzero `a` coefficient.")
if a[0] != 1.0:
# Normalize the coefficients so a[0] == 1.
b = b / a[0]
a = a / a[0]
n = max(len(a), len(b))
# Pad a or b with zeros so they are the same length.
if len(a) < n:
a = np.r_[a, np.zeros(n - len(a))]
elif len(b) < n:
b = np.r_[b, np.zeros(n - len(b))]
IminusA = np.eye(n - 1) - linalg.companion(a).T
B = b[1:] - a[1:] * b[0]
# Solve zi = A*zi + B
zi = np.linalg.solve(IminusA, B)
# For future reference: we could also use the following
# explicit formulas to solve the linear system:
#
# zi = np.zeros(n - 1)
# zi[0] = B.sum() / IminusA[:,0].sum()
# asum = 1.0
# csum = 0.0
# for k in range(1,n-1):
# asum += a[k]
# csum += b[k] - a[k]*b[0]
# zi[k] = asum*zi[0] - csum
return zi
def sosfilt_zi(sos):
"""
Construct initial conditions for sosfilt for step response steady-state.
Compute an initial state `zi` for the `sosfilt` function that corresponds
to the steady state of the step response.
A typical use of this function is to set the initial state so that the
output of the filter starts at the same value as the first element of
the signal to be filtered.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. See `sosfilt` for the SOS filter format
specification.
Returns
-------
zi : ndarray
Initial conditions suitable for use with ``sosfilt``, shape
``(n_sections, 2)``.
See Also
--------
sosfilt, zpk2sos
Notes
-----
.. versionadded:: 0.16.0
Examples
--------
Filter a rectangular pulse that begins at time 0, with and without
the use of the `zi` argument of `scipy.signal.sosfilt`.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> sos = signal.butter(9, 0.125, output='sos')
>>> zi = signal.sosfilt_zi(sos)
>>> x = (np.arange(250) < 100).astype(int)
>>> f1 = signal.sosfilt(sos, x)
>>> f2, zo = signal.sosfilt(sos, x, zi=zi)
>>> plt.plot(x, 'k--', label='x')
>>> plt.plot(f1, 'b', alpha=0.5, linewidth=2, label='filtered')
>>> plt.plot(f2, 'g', alpha=0.25, linewidth=4, label='filtered with zi')
>>> plt.legend(loc='best')
>>> plt.show()
"""
sos = np.asarray(sos)
if sos.ndim != 2 or sos.shape[1] != 6:
raise ValueError('sos must be shape (n_sections, 6)')
n_sections = sos.shape[0]
zi = np.empty((n_sections, 2))
scale = 1.0
for section in range(n_sections):
b = sos[section, :3]
a = sos[section, 3:]
zi[section] = scale * lfilter_zi(b, a)
# If H(z) = B(z)/A(z) is this section's transfer function, then
# b.sum()/a.sum() is H(1), the gain at omega=0. That's the steady
# state value of this section's step response.
scale *= b.sum() / a.sum()
return zi
def _filtfilt_gust(b, a, x, axis=-1, irlen=None):
"""Forward-backward IIR filter that uses Gustafsson's method.
Apply the IIR filter defined by `(b,a)` to `x` twice, first forward
then backward, using Gustafsson's initial conditions [1]_.
Let ``y_fb`` be the result of filtering first forward and then backward,
and let ``y_bf`` be the result of filtering first backward then forward.
Gustafsson's method is to compute initial conditions for the forward
pass and the backward pass such that ``y_fb == y_bf``.
Parameters
----------
b : scalar or 1-D ndarray
Numerator coefficients of the filter.
a : scalar or 1-D ndarray
Denominator coefficients of the filter.
x : ndarray
Data to be filtered.
axis : int, optional
Axis of `x` to be filtered. Default is -1.
irlen : int or None, optional
The length of the nonnegligible part of the impulse response.
If `irlen` is None, or if the length of the signal is less than
``2 * irlen``, then no part of the impulse response is ignored.
Returns
-------
y : ndarray
The filtered data.
x0 : ndarray
Initial condition for the forward filter.
x1 : ndarray
Initial condition for the backward filter.
Notes
-----
Typically the return values `x0` and `x1` are not needed by the
caller. The intended use of these return values is in unit tests.
References
----------
.. [1] F. Gustaffson. Determining the initial states in forward-backward
filtering. Transactions on Signal Processing, 46(4):988-992, 1996.
"""
# In the comments, "Gustafsson's paper" and [1] refer to the
# paper referenced in the docstring.
b = np.atleast_1d(b)
a = np.atleast_1d(a)
order = max(len(b), len(a)) - 1
if order == 0:
# The filter is just scalar multiplication, with no state.
scale = (b[0] / a[0])**2
y = scale * x
return y, np.array([]), np.array([])
if axis != -1 or axis != x.ndim - 1:
# Move the axis containing the data to the end.
x = np.swapaxes(x, axis, x.ndim - 1)
# n is the number of samples in the data to be filtered.
n = x.shape[-1]
if irlen is None or n <= 2*irlen:
m = n
else:
m = irlen
# Create Obs, the observability matrix (called O in the paper).
# This matrix can be interpreted as the operator that propagates
# an arbitrary initial state to the output, assuming the input is
# zero.
# In Gustafsson's paper, the forward and backward filters are not
# necessarily the same, so he has both O_f and O_b. We use the same
# filter in both directions, so we only need O. The same comment
# applies to S below.
Obs = np.zeros((m, order))
zi = np.zeros(order)
zi[0] = 1
Obs[:, 0] = lfilter(b, a, np.zeros(m), zi=zi)[0]
for k in range(1, order):
Obs[k:, k] = Obs[:-k, 0]
# Obsr is O^R (Gustafsson's notation for row-reversed O)
Obsr = Obs[::-1]
# Create S. S is the matrix that applies the filter to the reversed
# propagated initial conditions. That is,
# out = S.dot(zi)
# is the same as
# tmp, _ = lfilter(b, a, zeros(), zi=zi) # Propagate ICs.
# out = lfilter(b, a, tmp[::-1]) # Reverse and filter.
# Equations (5) & (6) of [1]
S = lfilter(b, a, Obs[::-1], axis=0)
# Sr is S^R (row-reversed S)
Sr = S[::-1]
# M is [(S^R - O), (O^R - S)]
if m == n:
M = np.hstack((Sr - Obs, Obsr - S))
else:
# Matrix described in section IV of [1].
M = np.zeros((2*m, 2*order))
M[:m, :order] = Sr - Obs
M[m:, order:] = Obsr - S
# Naive forward-backward and backward-forward filters.
# These have large transients because the filters use zero initial
# conditions.
y_f = lfilter(b, a, x)
y_fb = lfilter(b, a, y_f[..., ::-1])[..., ::-1]
y_b = lfilter(b, a, x[..., ::-1])[..., ::-1]
y_bf = lfilter(b, a, y_b)
delta_y_bf_fb = y_bf - y_fb
if m == n:
delta = delta_y_bf_fb
else:
start_m = delta_y_bf_fb[..., :m]
end_m = delta_y_bf_fb[..., -m:]
delta = np.concatenate((start_m, end_m), axis=-1)
# ic_opt holds the "optimal" initial conditions.
# The following code computes the result shown in the formula
# of the paper between equations (6) and (7).
if delta.ndim == 1:
ic_opt = linalg.lstsq(M, delta)[0]
else:
# Reshape delta so it can be used as an array of multiple
# right-hand-sides in linalg.lstsq.
delta2d = delta.reshape(-1, delta.shape[-1]).T
ic_opt0 = linalg.lstsq(M, delta2d)[0].T
ic_opt = ic_opt0.reshape(delta.shape[:-1] + (M.shape[-1],))
# Now compute the filtered signal using equation (7) of [1].
# First, form [S^R, O^R] and call it W.
if m == n:
W = np.hstack((Sr, Obsr))
else:
W = np.zeros((2*m, 2*order))
W[:m, :order] = Sr
W[m:, order:] = Obsr
# Equation (7) of [1] says
# Y_fb^opt = Y_fb^0 + W * [x_0^opt; x_{N-1}^opt]
# `wic` is (almost) the product on the right.
# W has shape (m, 2*order), and ic_opt has shape (..., 2*order),
# so we can't use W.dot(ic_opt). Instead, we dot ic_opt with W.T,
# so wic has shape (..., m).
wic = ic_opt.dot(W.T)
# `wic` is "almost" the product of W and the optimal ICs in equation
# (7)--if we're using a truncated impulse response (m < n), `wic`
# contains only the adjustments required for the ends of the signal.
# Here we form y_opt, taking this into account if necessary.
y_opt = y_fb
if m == n:
y_opt += wic
else:
y_opt[..., :m] += wic[..., :m]
y_opt[..., -m:] += wic[..., -m:]
x0 = ic_opt[..., :order]
x1 = ic_opt[..., -order:]
if axis != -1 or axis != x.ndim - 1:
# Restore the data axis to its original position.
x0 = np.swapaxes(x0, axis, x.ndim - 1)
x1 = np.swapaxes(x1, axis, x.ndim - 1)
y_opt = np.swapaxes(y_opt, axis, x.ndim - 1)
return y_opt, x0, x1
def filtfilt(b, a, x, axis=-1, padtype='odd', padlen=None, method='pad',
irlen=None):
"""
Apply a digital filter forward and backward to a signal.
This function applies a linear digital filter twice, once forward and
once backwards. The combined filter has zero phase and a filter order
twice that of the original.
The function provides options for handling the edges of the signal.
The function `sosfiltfilt` (and filter design using ``output='sos'``)
should be preferred over `filtfilt` for most filtering tasks, as
second-order sections have fewer numerical problems.
Parameters
----------
b : (N,) array_like
The numerator coefficient vector of the filter.
a : (N,) array_like
The denominator coefficient vector of the filter. If ``a[0]``
is not 1, then both `a` and `b` are normalized by ``a[0]``.
x : array_like
The array of data to be filtered.
axis : int, optional
The axis of `x` to which the filter is applied.
Default is -1.
padtype : str or None, optional
Must be 'odd', 'even', 'constant', or None. This determines the
type of extension to use for the padded signal to which the filter
is applied. If `padtype` is None, no padding is used. The default
is 'odd'.
padlen : int or None, optional
The number of elements by which to extend `x` at both ends of
`axis` before applying the filter. This value must be less than
``x.shape[axis] - 1``. ``padlen=0`` implies no padding.
The default value is ``3 * max(len(a), len(b))``.
method : str, optional
Determines the method for handling the edges of the signal, either
"pad" or "gust". When `method` is "pad", the signal is padded; the
type of padding is determined by `padtype` and `padlen`, and `irlen`
is ignored. When `method` is "gust", Gustafsson's method is used,
and `padtype` and `padlen` are ignored.
irlen : int or None, optional
When `method` is "gust", `irlen` specifies the length of the
impulse response of the filter. If `irlen` is None, no part
of the impulse response is ignored. For a long signal, specifying
`irlen` can significantly improve the performance of the filter.
Returns
-------
y : ndarray
The filtered output with the same shape as `x`.
See Also
--------
sosfiltfilt, lfilter_zi, lfilter, lfiltic, savgol_filter, sosfilt
Notes
-----
When `method` is "pad", the function pads the data along the given axis
in one of three ways: odd, even or constant. The odd and even extensions
have the corresponding symmetry about the end point of the data. The
constant extension extends the data with the values at the end points. On
both the forward and backward passes, the initial condition of the
filter is found by using `lfilter_zi` and scaling it by the end point of
the extended data.
When `method` is "gust", Gustafsson's method [1]_ is used. Initial
conditions are chosen for the forward and backward passes so that the
forward-backward filter gives the same result as the backward-forward
filter.
The option to use Gustaffson's method was added in scipy version 0.16.0.
References
----------
.. [1] F. Gustaffson, "Determining the initial states in forward-backward
filtering", Transactions on Signal Processing, Vol. 46, pp. 988-992,
1996.
Examples
--------
The examples will use several functions from `scipy.signal`.
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
First we create a one second signal that is the sum of two pure sine
waves, with frequencies 5 Hz and 250 Hz, sampled at 2000 Hz.
>>> t = np.linspace(0, 1.0, 2001)
>>> xlow = np.sin(2 * np.pi * 5 * t)
>>> xhigh = np.sin(2 * np.pi * 250 * t)
>>> x = xlow + xhigh
Now create a lowpass Butterworth filter with a cutoff of 0.125 times
the Nyquist frequency, or 125 Hz, and apply it to ``x`` with `filtfilt`.
The result should be approximately ``xlow``, with no phase shift.
>>> b, a = signal.butter(8, 0.125)
>>> y = signal.filtfilt(b, a, x, padlen=150)
>>> np.abs(y - xlow).max()
9.1086182074789912e-06
We get a fairly clean result for this artificial example because
the odd extension is exact, and with the moderately long padding,
the filter's transients have dissipated by the time the actual data
is reached. In general, transient effects at the edges are
unavoidable.
The following example demonstrates the option ``method="gust"``.
First, create a filter.
>>> b, a = signal.ellip(4, 0.01, 120, 0.125) # Filter to be applied.
>>> np.random.seed(123456)
`sig` is a random input signal to be filtered.
>>> n = 60
>>> sig = np.random.randn(n)**3 + 3*np.random.randn(n).cumsum()
Apply `filtfilt` to `sig`, once using the Gustafsson method, and
once using padding, and plot the results for comparison.
>>> fgust = signal.filtfilt(b, a, sig, method="gust")
>>> fpad = signal.filtfilt(b, a, sig, padlen=50)
>>> plt.plot(sig, 'k-', label='input')
>>> plt.plot(fgust, 'b-', linewidth=4, label='gust')
>>> plt.plot(fpad, 'c-', linewidth=1.5, label='pad')
>>> plt.legend(loc='best')
>>> plt.show()
The `irlen` argument can be used to improve the performance
of Gustafsson's method.
Estimate the impulse response length of the filter.
>>> z, p, k = signal.tf2zpk(b, a)
>>> eps = 1e-9
>>> r = np.max(np.abs(p))
>>> approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
>>> approx_impulse_len
137
Apply the filter to a longer signal, with and without the `irlen`
argument. The difference between `y1` and `y2` is small. For long
signals, using `irlen` gives a significant performance improvement.
>>> x = np.random.randn(5000)
>>> y1 = signal.filtfilt(b, a, x, method='gust')
>>> y2 = signal.filtfilt(b, a, x, method='gust', irlen=approx_impulse_len)
>>> print(np.max(np.abs(y1 - y2)))
1.80056858312e-10
"""
b = np.atleast_1d(b)
a = np.atleast_1d(a)
x = np.asarray(x)
if method not in ["pad", "gust"]:
raise ValueError("method must be 'pad' or 'gust'.")
if method == "gust":
y, z1, z2 = _filtfilt_gust(b, a, x, axis=axis, irlen=irlen)
return y
# method == "pad"
edge, ext = _validate_pad(padtype, padlen, x, axis,
ntaps=max(len(a), len(b)))
# Get the steady state of the filter's step response.
zi = lfilter_zi(b, a)
# Reshape zi and create x0 so that zi*x0 broadcasts
# to the correct value for the 'zi' keyword argument
# to lfilter.
zi_shape = [1] * x.ndim
zi_shape[axis] = zi.size
zi = np.reshape(zi, zi_shape)
x0 = axis_slice(ext, stop=1, axis=axis)
# Forward filter.
(y, zf) = lfilter(b, a, ext, axis=axis, zi=zi * x0)
# Backward filter.
# Create y0 so zi*y0 broadcasts appropriately.
y0 = axis_slice(y, start=-1, axis=axis)
(y, zf) = lfilter(b, a, axis_reverse(y, axis=axis), axis=axis, zi=zi * y0)
# Reverse y.
y = axis_reverse(y, axis=axis)
if edge > 0:
# Slice the actual signal from the extended signal.
y = axis_slice(y, start=edge, stop=-edge, axis=axis)
return y
def _validate_pad(padtype, padlen, x, axis, ntaps):
"""Helper to validate padding for filtfilt"""
if padtype not in ['even', 'odd', 'constant', None]:
raise ValueError(("Unknown value '%s' given to padtype. padtype "
"must be 'even', 'odd', 'constant', or None.") %
padtype)
if padtype is None:
padlen = 0
if padlen is None:
# Original padding; preserved for backwards compatibility.
edge = ntaps * 3
else:
edge = padlen
# x's 'axis' dimension must be bigger than edge.
if x.shape[axis] <= edge:
raise ValueError("The length of the input vector x must be greater than "
"padlen, which is %d." % edge)
if padtype is not None and edge > 0:
# Make an extension of length `edge` at each
# end of the input array.
if padtype == 'even':
ext = even_ext(x, edge, axis=axis)
elif padtype == 'odd':
ext = odd_ext(x, edge, axis=axis)
else:
ext = const_ext(x, edge, axis=axis)
else:
ext = x
return edge, ext
def _validate_x(x):
x = np.asarray(x)
if x.ndim == 0:
raise ValueError('x must be at least 1-D')
return x
def sosfilt(sos, x, axis=-1, zi=None):
"""
Filter data along one dimension using cascaded second-order sections.
Filter a data sequence, `x`, using a digital IIR filter defined by
`sos`.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. Each row corresponds to a second-order
section, with the first three columns providing the numerator
coefficients and the last three providing the denominator
coefficients.
x : array_like
An N-dimensional input array.
axis : int, optional
The axis of the input data array along which to apply the
linear filter. The filter is applied to each subarray along
this axis. Default is -1.
zi : array_like, optional
Initial conditions for the cascaded filter delays. It is a (at
least 2D) vector of shape ``(n_sections, ..., 2, ...)``, where
``..., 2, ...`` denotes the shape of `x`, but with ``x.shape[axis]``
replaced by 2. If `zi` is None or is not given then initial rest
(i.e. all zeros) is assumed.
Note that these initial conditions are *not* the same as the initial
conditions given by `lfiltic` or `lfilter_zi`.
Returns
-------
y : ndarray
The output of the digital filter.
zf : ndarray, optional
If `zi` is None, this is not returned, otherwise, `zf` holds the
final filter delay values.
See Also
--------
zpk2sos, sos2zpk, sosfilt_zi, sosfiltfilt, sosfreqz
Notes
-----
The filter function is implemented as a series of second-order filters
with direct-form II transposed structure. It is designed to minimize
numerical precision errors for high-order filters.
.. versionadded:: 0.16.0
Examples
--------
Plot a 13th-order filter's impulse response using both `lfilter` and
`sosfilt`, showing the instability that results from trying to do a
13th-order filter in a single stage (the numerical error pushes some poles
outside of the unit circle):
>>> import matplotlib.pyplot as plt
>>> from scipy import signal
>>> b, a = signal.ellip(13, 0.009, 80, 0.05, output='ba')
>>> sos = signal.ellip(13, 0.009, 80, 0.05, output='sos')
>>> x = signal.unit_impulse(700)
>>> y_tf = signal.lfilter(b, a, x)
>>> y_sos = signal.sosfilt(sos, x)
>>> plt.plot(y_tf, 'r', label='TF')
>>> plt.plot(y_sos, 'k', label='SOS')
>>> plt.legend(loc='best')
>>> plt.show()
"""
x = _validate_x(x)
sos, n_sections = _validate_sos(sos)
x_zi_shape = list(x.shape)
x_zi_shape[axis] = 2
x_zi_shape = tuple([n_sections] + x_zi_shape)
inputs = [sos, x]
if zi is not None:
inputs.append(np.asarray(zi))
dtype = np.result_type(*inputs)
if dtype.char not in 'fdgFDGO':
raise NotImplementedError("input type '%s' not supported" % dtype)
if zi is not None:
zi = np.array(zi, dtype) # make a copy so that we can operate in place
if zi.shape != x_zi_shape:
raise ValueError('Invalid zi shape. With axis=%r, an input with '
'shape %r, and an sos array with %d sections, zi '
'must have shape %r, got %r.' %
(axis, x.shape, n_sections, x_zi_shape, zi.shape))
return_zi = True
else:
zi = np.zeros(x_zi_shape, dtype=dtype)
return_zi = False
axis = axis % x.ndim # make positive
x = np.moveaxis(x, axis, -1)
zi = np.moveaxis(zi, [0, axis + 1], [-2, -1])
x_shape, zi_shape = x.shape, zi.shape
x = np.reshape(x, (-1, x.shape[-1]))
x = np.array(x, dtype, order='C') # make a copy, can modify in place
zi = np.ascontiguousarray(np.reshape(zi, (-1, n_sections, 2)))
sos = sos.astype(dtype, copy=False)
_sosfilt(sos, x, zi)
x.shape = x_shape
x = np.moveaxis(x, -1, axis)
if return_zi:
zi.shape = zi_shape
zi = np.moveaxis(zi, [-2, -1], [0, axis + 1])
out = (x, zi)
else:
out = x
return out
def sosfiltfilt(sos, x, axis=-1, padtype='odd', padlen=None):
"""
A forward-backward digital filter using cascaded second-order sections.
See `filtfilt` for more complete information about this method.
Parameters
----------
sos : array_like
Array of second-order filter coefficients, must have shape
``(n_sections, 6)``. Each row corresponds to a second-order
section, with the first three columns providing the numerator
coefficients and the last three providing the denominator
coefficients.
x : array_like
The array of data to be filtered.
axis : int, optional
The axis of `x` to which the filter is applied.
Default is -1.
padtype : str or None, optional
Must be 'odd', 'even', 'constant', or None. This determines the
type of extension to use for the padded signal to which the filter
is applied. If `padtype` is None, no padding is used. The default
is 'odd'.
padlen : int or None, optional
The number of elements by which to extend `x` at both ends of
`axis` before applying the filter. This value must be less than
``x.shape[axis] - 1``. ``padlen=0`` implies no padding.
The default value is::
3 * (2 * len(sos) + 1 - min((sos[:, 2] == 0).sum(),
(sos[:, 5] == 0).sum()))
The extra subtraction at the end attempts to compensate for poles
and zeros at the origin (e.g. for odd-order filters) to yield
equivalent estimates of `padlen` to those of `filtfilt` for
second-order section filters built with `scipy.signal` functions.
Returns
-------
y : ndarray
The filtered output with the same shape as `x`.
See Also
--------
filtfilt, sosfilt, sosfilt_zi, sosfreqz
Notes
-----
.. versionadded:: 0.18.0
Examples
--------
>>> from scipy.signal import sosfiltfilt, butter
>>> import matplotlib.pyplot as plt
Create an interesting signal to filter.
>>> n = 201
>>> t = np.linspace(0, 1, n)
>>> np.random.seed(123)
>>> x = 1 + (t < 0.5) - 0.25*t**2 + 0.05*np.random.randn(n)
Create a lowpass Butterworth filter, and use it to filter `x`.
>>> sos = butter(4, 0.125, output='sos')
>>> y = sosfiltfilt(sos, x)
For comparison, apply an 8th order filter using `sosfilt`. The filter
is initialized using the mean of the first four values of `x`.
>>> from scipy.signal import sosfilt, sosfilt_zi
>>> sos8 = butter(8, 0.125, output='sos')
>>> zi = x[:4].mean() * sosfilt_zi(sos8)
>>> y2, zo = sosfilt(sos8, x, zi=zi)
Plot the results. Note that the phase of `y` matches the input, while
`y2` has a significant phase delay.
>>> plt.plot(t, x, alpha=0.5, label='x(t)')
>>> plt.plot(t, y, label='y(t)')
>>> plt.plot(t, y2, label='y2(t)')
>>> plt.legend(framealpha=1, shadow=True)
>>> plt.grid(alpha=0.25)
>>> plt.xlabel('t')
>>> plt.show()
"""
sos, n_sections = _validate_sos(sos)
x = _validate_x(x)
# `method` is "pad"...
ntaps = 2 * n_sections + 1
ntaps -= min((sos[:, 2] == 0).sum(), (sos[:, 5] == 0).sum())
edge, ext = _validate_pad(padtype, padlen, x, axis,
ntaps=ntaps)
# These steps follow the same form as filtfilt with modifications
zi = sosfilt_zi(sos) # shape (n_sections, 2) --> (n_sections, ..., 2, ...)
zi_shape = [1] * x.ndim
zi_shape[axis] = 2
zi.shape = [n_sections] + zi_shape
x_0 = axis_slice(ext, stop=1, axis=axis)
(y, zf) = sosfilt(sos, ext, axis=axis, zi=zi * x_0)
y_0 = axis_slice(y, start=-1, axis=axis)
(y, zf) = sosfilt(sos, axis_reverse(y, axis=axis), axis=axis, zi=zi * y_0)
y = axis_reverse(y, axis=axis)
if edge > 0:
y = axis_slice(y, start=edge, stop=-edge, axis=axis)
return y
def decimate(x, q, n=None, ftype='iir', axis=-1, zero_phase=True):
"""
Downsample the signal after applying an anti-aliasing filter.
By default, an order 8 Chebyshev type I filter is used. A 30 point FIR
filter with Hamming window is used if `ftype` is 'fir'.
Parameters
----------
x : array_like
The signal to be downsampled, as an N-dimensional array.
q : int
The downsampling factor. When using IIR downsampling, it is recommended
to call `decimate` multiple times for downsampling factors higher than
13.
n : int, optional
The order of the filter (1 less than the length for 'fir'). Defaults to
8 for 'iir' and 20 times the downsampling factor for 'fir'.
ftype : str {'iir', 'fir'} or ``dlti`` instance, optional
If 'iir' or 'fir', specifies the type of lowpass filter. If an instance
of an `dlti` object, uses that object to filter before downsampling.
axis : int, optional
The axis along which to decimate.
zero_phase : bool, optional
Prevent phase shift by filtering with `filtfilt` instead of `lfilter`
when using an IIR filter, and shifting the outputs back by the filter's
group delay when using an FIR filter. The default value of ``True`` is
recommended, since a phase shift is generally not desired.
.. versionadded:: 0.18.0
Returns
-------
y : ndarray
The down-sampled signal.
See Also
--------
resample : Resample up or down using the FFT method.
resample_poly : Resample using polyphase filtering and an FIR filter.
Notes
-----
The ``zero_phase`` keyword was added in 0.18.0.
The possibility to use instances of ``dlti`` as ``ftype`` was added in
0.18.0.
"""
x = np.asarray(x)
q = operator.index(q)
if n is not None:
n = operator.index(n)
if ftype == 'fir':
if n is None:
half_len = 10 * q # reasonable cutoff for our sinc-like function
n = 2 * half_len
b, a = firwin(n+1, 1. / q, window='hamming'), 1.
elif ftype == 'iir':
if n is None:
n = 8
system = dlti(*cheby1(n, 0.05, 0.8 / q))
b, a = system.num, system.den
elif isinstance(ftype, dlti):
system = ftype._as_tf() # Avoids copying if already in TF form
b, a = system.num, system.den
else:
raise ValueError('invalid ftype')
sl = [slice(None)] * x.ndim
a = np.asarray(a)
if a.size == 1: # FIR case
b = b / a
if zero_phase:
y = resample_poly(x, 1, q, axis=axis, window=b)
else:
# upfirdn is generally faster than lfilter by a factor equal to the
# downsampling factor, since it only calculates the needed outputs
n_out = x.shape[axis] // q + bool(x.shape[axis] % q)
y = upfirdn(b, x, up=1, down=q, axis=axis)
sl[axis] = slice(None, n_out, None)
else: # IIR case
if zero_phase:
y = filtfilt(b, a, x, axis=axis)
else:
y = lfilter(b, a, x, axis=axis)
sl[axis] = slice(None, None, q)
return y[tuple(sl)]
| aeklant/scipy | scipy/signal/signaltools.py | Python | bsd-3-clause | 145,636 | [
"Gaussian"
] | fb47c14af07ff98dfc6f6dac33520725bc9e2617c1130c137bf73fe7fa63400d |
""" Assorted utility functions and classes """
from os import path, listdir
def is_type(typecheck, data):
"""
Generic type checker
typically used to check that a string can be cast to int or float
"""
try:
typecheck(data)
except ValueError:
return False
else:
return True
'''
def walk(topdir, ignore=None):
"""os.walk with an option to ignore directories"""
for dirpath, dirnames, filenames in os.walk(topdir):
dirnames[:] = [
dirname for dirname in dirnames
if os.path.join(dirpath, dirname) not in ignore]
yield dirpath, dirnames, filenames
'''
def walk(top, topdown=True, onerror=None, followlinks=False, ignore=[]):
"""Modified implementation of os.walk with ignore option"""
islink, join, isdir = path.islink, path.join, path.isdir
# We may not have read permission for top, in which case we can't
# get a list of the files the directory contains. os.walk
# always suppressed the exception then, rather than blow up for a
# minor reason when (say) a thousand readable directories are still
# left to visit. That logic is copied here.
try:
names = listdir(top)
except OSError as err:
if onerror is not None:
onerror(err)
return
dirs, nondirs = [], []
for name in names:
if name not in ignore:
if isdir(join(top, name)):
dirs.append(name)
else:
nondirs.append(name)
if topdown:
yield top, dirs, nondirs
for name in dirs:
new_path = join(top, name)
if followlinks or not islink(new_path):
for step in walk(new_path, topdown, onerror, followlinks):
yield step
if not topdown:
yield top, dirs, nondirs
| ben-albrecht/qcl | qcl/utils.py | Python | mit | 1,829 | [
"VisIt"
] | d84eccc8df51cd5be1b3105da48bedb8bec1729e5ebbf4040ac485e8d6eabdc9 |
""" Helpful functions for testing """
from app.models.gym_item import GymItem
from app.models.gym import Gym
from app.models.profile import Profile
from django.contrib.auth.models import User
def create_gym_item(username=None, name=None, location=None, visit=None):
"""
Create a Gym Item instance
:param username: Username for User and Profile objects
:param name: Name for the Gym
:param location: Location for the Gym
:param visit: Visit date for the visit
:return: GymItem object
"""
user = User.objects.create(username=username)
profile = Profile.objects.create(
user=user,
pokemon_go_username=username
)
gym = Gym.objects.create(
name=name,
location=location
)
profile.tracked_gyms.add(gym)
profile.save()
gym_item = GymItem.objects.create(
gym=gym,
profile=profile,
gym_visit_date=visit
)
return gym_item
| Gimpneek/exclusive-raid-gym-tracker | app/tests/common.py | Python | gpl-3.0 | 941 | [
"VisIt"
] | a1cd3f8570cc5ee53632d1f824a94daab12c73105ee9fe3a7afd131438c425f6 |
# coding: utf-8
#
# Copyright 2012 NAMD-EMAP-FGV
#
# This file is part of PyPLN. You can get more information at: http://pypln.org/.
#
# PyPLN is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# PyPLN is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with PyPLN. If not, see <http://www.gnu.org/licenses/>.
import shlex
from HTMLParser import HTMLParser
from tempfile import NamedTemporaryFile
from os import unlink
from subprocess import Popen, PIPE
from mimetypes import guess_type
from re import compile as regexp_compile, DOTALL, escape
import cld
import magic
from pypln.backend.celery_task import PyPLNTask
regexp_tags = regexp_compile(r'(<[ \t]*([a-zA-Z0-9!"./_-]*)[^>]*>)', flags=DOTALL)
regexp_comment = regexp_compile(r'<!--.*?-->', flags=DOTALL)
regexp_spaces_start = regexp_compile('([\n]+)[ \t]*',
flags=DOTALL)
regexp_spaces_end = regexp_compile('[ \t]*\n', flags=DOTALL)
regexp_newlines = regexp_compile('[\n]{3,}', flags=DOTALL)
regexp_spaces = regexp_compile('[ \t]{2,}', flags=DOTALL)
regexp_punctuation = regexp_compile('[ \t]*([' + escape('!,.:;?') + '])',
flags=DOTALL)
breakline_tags = ['table', '/table', 'tr', 'div', '/div', 'h1', '/h1', 'h2',
'/h2', 'h3', '/h3', 'h4', '/h4', 'h5', '/h5', 'h6', '/h6',
'br', 'br/']
double_breakline = ['table', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6']
def clean(text):
text = regexp_spaces_start.sub(r'\1', text)
text = regexp_spaces_end.sub('\n', text)
text = regexp_newlines.sub('\n\n', text)
text = regexp_spaces.sub(' ', text)
text = regexp_punctuation.sub(r'\1', text)
return text.strip()
def parse_html(html, remove_tags=None, remove_inside=None,
replace_space_with=' ', replace_newline_with='\n'):
html = regexp_comment.sub('', html.replace('\n', ''))
data = regexp_tags.split(html)
content_between = data[::3]
complete_tags = data[1::3]
tag_names = [x.lower() for x in data[2::3]]
for index, tag_name in enumerate(tag_names):
if not tag_name.strip():
continue
search_tag = tag_name
if tag_name and tag_name[0] == '/':
search_tag = tag_name[1:]
if remove_tags and search_tag not in remove_inside:
if tag_name in breakline_tags:
if search_tag in double_breakline:
complete_tags[index] = 2 * replace_newline_with
else:
complete_tags[index] = replace_newline_with
else:
complete_tags[index] = replace_space_with
if remove_inside and tag_name in remove_inside:
remove_to = tag_names.index('/' + tag_name, index)
total_to_remove = remove_to - index + 1
complete_tags[index:remove_to + 1] = [''] * total_to_remove
content_between[index + 2:remove_to + 1] = \
[''] * (total_to_remove - 2)
content_between[index + 1] = '\n'
complete_tags.append('')
result = ''.join(sum(zip(content_between, complete_tags), tuple()))
return clean(result)
def get_pdf_metadata(data):
lines = data.strip().splitlines()
metadata = {}
for line in lines:
try:
key, value = line[:line.index(':')], line[line.index(':') + 1:]
except ValueError:
continue
metadata[key.strip()] = value.strip()
return metadata
def extract_pdf(data):
temp = NamedTemporaryFile(delete=False)
filename = temp.name
temp.close()
pdf2html = Popen(shlex.split('pdftohtml -q -i - {}'.format(temp.name)),
stdin=PIPE, stdout=PIPE, stderr=PIPE)
html, html_err = pdf2html.communicate(input=data)
fp = open(filename + 's.html', 'r')
html = fp.read()
fp.close()
unlink(filename + '.html')
unlink(filename + '_ind.html')
unlink(filename + 's.html')
text = parse_html(html.replace(' ', ' '), True, ['script', 'style'])
pdfinfo = Popen(shlex.split('pdfinfo -'), stdin=PIPE, stdout=PIPE,
stderr=PIPE)
meta_out, meta_err = pdfinfo.communicate(input=data)
try:
metadata = get_pdf_metadata(meta_out)
except:
metadata = {}
#TODO: what should I do here?
if not (text and metadata):
return '', {}
elif not html_err:
return text, {} if meta_err else metadata
else:
return '', {}
def trial_decode(text):
"""
Tries to detect text encoding using `magic`. If the detected encoding is
not supported, try utf-8, iso-8859-1 and ultimately falls back to decoding
as utf-8 replacing invalid chars with `U+FFFD` (the replacement character).
This is far from an ideal solution, but the extractor and the rest of the
pipeline need an unicode object.
"""
with magic.Magic(flags=magic.MAGIC_MIME_ENCODING) as m:
content_encoding = m.id_buffer(text)
forced_decoding = False
try:
result = text.decode(content_encoding)
except LookupError:
# If the detected encoding is not supported, we try to decode it as
# utf-8.
try:
result = text.decode('utf-8')
except UnicodeDecodeError:
# Is there a better way of doing this than nesting try/except
# blocks? This smells really bad.
try:
result = text.decode('iso-8859-1')
except UnicodeDecodeError:
# If neither utf-8 nor iso-885901 work are capable of handling
# this text, we just decode it using utf-8 and replace invalid
# chars with U+FFFD.
# Two somewhat arbitrary decisions were made here: use utf-8
# and use 'replace' instead of 'ignore'.
result = text.decode('utf-8', 'replace')
forced_decoding = True
return result, forced_decoding
class Extractor(PyPLNTask):
#TODO: need to verify some exceptions when trying to convert 'evil' PDFs
#TODO: should 'replace_with' be '' when extracting from HTML?
def process(self, file_data):
with magic.Magic(flags=magic.MAGIC_MIME_TYPE) as m:
file_mime_type = m.id_buffer(file_data['contents'])
metadata = {}
if file_mime_type == 'text/plain':
text = file_data['contents']
elif file_mime_type == 'text/html':
text = parse_html(file_data['contents'], True, ['script', 'style'])
elif file_mime_type == 'application/pdf':
text, metadata = extract_pdf(file_data['contents'])
else:
# If we can't detect the mimetype we add a flag that can be read by
# the frontend to provide more information on why the document
# wasn't processed.
# XXX: We're returning an empty text because if we don't the
# pipeline will run indefinitely. The right approach is to make
# pypelinin understand an specific exception (something like
# StopPipeline) as a signal to stop processing this pipeline.
return {'mimetype': 'unknown', 'text': "",
'file_metadata': {}, 'language': ""}
text, forced_decoding = trial_decode(text)
if isinstance(text, unicode):
# HTMLParser only handles unicode objects. We can't pass the text
# through it if we don't know the encoding, and it's possible we
# also shouldn't. There's no way of knowing if it's a badly encoded
# html or a binary blob that happens do have bytes that look liked
# html entities.
text = HTMLParser().unescape(text)
text = clean(text)
if isinstance(text, unicode):
language = cld.detect(text.encode('utf-8'))[1]
else:
language = cld.detect(text)[1]
return {'text': text, 'file_metadata': metadata, 'language': language,
'mimetype': file_mime_type, 'forced_decoding': forced_decoding}
| fccoelho/pypln.backend | pypln/backend/workers/extractor.py | Python | gpl-3.0 | 8,419 | [
"NAMD"
] | f060d359db722e5322c42a92adb01be4abe88574f56313e7b72ea44b53e86663 |
"""Contains the parent class for Scriptlets."""
# Scriptlet.py
# Mission Pinball Framework
# Written by Brian Madden & Gabe Knuth
# Released under the MIT License. (See license info at the end of this file.)
# Documentation and more info at http://missionpinball.com/mpf
import logging
class Scriptlet(object):
def __init__(self, machine, name):
self.machine = machine
self.name = name
self.log = logging.getLogger('Scriptlet.' + name)
self.log.debug("Loading Scriptlet: %s", name)
self.on_load()
def __repr__(self):
return '<Scriptlet.{}>'.format(self.name)
def on_load(self):
"""Automatically called when this Scriptlet loads. It's the intention
that the Scriptlet writer will overwrite this method in the Scriptlet.
"""
pass
| spierepf/mpf | mpf/system/scriptlet.py | Python | mit | 827 | [
"Brian"
] | 9f0ad5ac1c854e7c8bc92c81021e3498ebaa7308ecdbc48fbd22eba1a97edd95 |
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# LICENSE
#
# Copyright (c) 2010-2017, GEM Foundation, G. Weatherill, M. Pagani,
# D. Monelli.
#
# The Hazard Modeller's Toolkit 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.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>
#
# DISCLAIMER
#
# The software Hazard Modeller's Toolkit (openquake.hmtk) provided herein
# is released as a prototype implementation on behalf of
# scientists and engineers working within the GEM Foundation (Global
# Earthquake Model).
#
# It is distributed for the purpose of open collaboration and in the
# hope that it will be useful to the scientific, engineering, disaster
# risk and software design communities.
#
# The software is NOT distributed as part of GEM's OpenQuake suite
# (http://www.globalquakemodel.org/openquake) and must be considered as a
# separate entity. The software provided herein is designed and implemented
# by scientific staff. It is not developed to the design standards, nor
# subject to same level of critical review by professional software
# developers, as GEM's OpenQuake software suite.
#
# Feedback and contribution to the software is welcome, and can be
# directed to the hazard scientific staff of the GEM Model Facility
# (hazard@globalquakemodel.org).
#
# The Hazard Modeller's Toolkit (openquake.hmtk) is therefore distributed WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details.
#
# The GEM Foundation, and the authors of the software, assume no
# liability for use of the software.
'''
Module to test :openquake.hmtk.faults.mfd.characterisric.Characteristic class
'''
import unittest
import numpy as np
from openquake.hazardlib.scalerel import WC1994
from openquake.hmtk.faults.mfd.characteristic import Characteristic
aaae = np.testing.assert_array_almost_equal
class TestSimpleCharacteristic(unittest.TestCase):
'''
Implements the basic set of tests for the simple estimator of the
characteristic earthquake for a fault
:class openquake.hmtk.faults.mfd.characteristic.Characteristic
'''
def setUp(self):
'''
'''
self.model = Characteristic()
self.config = {'MFD_spacing': 0.1,
'Model_Weight': 1.0,
'Maximum_Magnitude': None,
'Maximum_Uncertainty': None,
'Lower_Bound': -2.,
'Upper_Bound': 2.,
'Sigma': None}
self.msr = WC1994()
def test_model_setup(self):
'''
Simple test to ensure model sets up correctly
'''
self.model.setUp(self.config)
expected_dict = {'bin_width': 0.1,
'lower_bound': -2.0,
'mfd_model': 'Characteristic',
'mfd_weight': 1.0,
'mmax': None,
'mmax_sigma': None,
'mmin': None,
'occurrence_rate': None,
'sigma': None,
'upper_bound': 2.0}
self.assertDictEqual(self.model.__dict__, expected_dict)
def test_get_mmax(self):
'''
Tests the function to get Mmax
Values come from WC1994 (tested in openquake.hazardlib) - only
functionality is tested for here!
'''
# Case 1 MMmax and uncertainty specified in config
self.config['Maximum_Magnitude'] = 8.0
self.config['Maximum_Magnitude_Uncertainty'] = 0.2
self.model = Characteristic()
self.model.setUp(self.config)
self.model.get_mmax(self.config, self.msr, 0., 8500.)
self.assertAlmostEqual(self.model.mmax, 8.0)
self.assertAlmostEqual(self.model.mmax_sigma, 0.2)
# Case 2: Mmax and uncertainty not specified in config
self.config['Maximum_Magnitude'] = None
self.config['Maximum_Magnitude_Uncertainty'] = None
self.model = Characteristic()
self.model.setUp(self.config)
self.model.get_mmax(self.config, self.msr, 0., 8500.)
self.assertAlmostEqual(self.model.mmax, 7.9880073)
self.assertAlmostEqual(self.model.mmax_sigma, 0.23)
def test_get_mfd(self):
'''
Tests the calculation of activity rates for the simple
characteristic earthquake distribution.
'''
# Test case 1: Ordinatry fault with Area 8500 km ** 2 (Mmax ~ 8.0),
# and a slip rate of 5 mm/yr. Double truncated Gaussian between [-2, 2]
# standard deviations with sigma = 0.12
self.config = {'MFD_spacing': 0.1,
'Model_Weight': 1.0,
'Maximum_Magnitude': None,
'Maximum_Uncertainty': None,
'Lower_Bound': -2.,
'Upper_Bound': 2.,
'Sigma': 0.12}
self.model = Characteristic()
self.model.setUp(self.config)
self.model.get_mmax(self.config, self.msr, 0., 8500.)
_, _, _ = self.model.get_mfd(5.0, 8500.)
aaae(self.model.occurrence_rate,
np.array([4.20932867e-05, 2.10890168e-04, 3.80422666e-04,
3.56294331e-04, 1.73223702e-04, 2.14781079e-05]))
expected_rate = np.sum(self.model.occurrence_rate)
# Test case 2: Same fault with no standard deviation
self.config['Sigma'] = None
self.model.setUp(self.config)
self.model.get_mmax(self.config, self.msr, 0., 8500.)
_, _, _ = self.model.get_mfd(5.0, 8500.)
aaae(0.0011844, self.model.occurrence_rate)
# As a final check - ensure that the sum of the activity rates from the
# truncated Gaussian model is equal to the rate from the model with no
# variance
aaae(expected_rate, self.model.occurrence_rate, 3)
| gem/oq-hazardlib | openquake/hmtk/tests/faults/mfd/test_characteristic.py | Python | agpl-3.0 | 6,251 | [
"Gaussian"
] | 78b06821cb56a5d69adf4cfcd709edcbe20c37143d1cde3d3a2f0f846d62b3d8 |
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