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OOPS_CONCEPT/OOP Principles/class_program_14.py | abhigyan709/dsalgo | 1 | 56391 | <filename>OOPS_CONCEPT/OOP Principles/class_program_14.py
class Mobile:
def __init__(self, brand, price):
print("Inside Constructor")
self.brand = brand
self.price = price
def purchase(self):
print("Purchasing a mobile")
print("The mobile has brand", self.brand, "and price", self.price)
print("Mobile-1")
mob1 = Mobile("Apple", 20000)
mob1.purchase()
print("Mobile-2")
mob2 = Mobile("Samsung", 8000)
mob2.purchase()
# we can invoke one method from another using self:
class Mobile2:
def display(self):
print("Displaying Details")
def purchase(self):
self.display()
print("Calcuating the price")
Mobile2().purchase() | [
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cx_Freeze/hooks.py | xtreme3d/editor | 22 | 65033 | <filename>cx_Freeze/hooks.py<gh_stars>10-100
import os
import sys
def initialize(finder):
"""upon initialization of the finder, this routine is called to set up some
automatic exclusions for various platforms."""
finder.ExcludeModule("FCNTL")
finder.ExcludeModule("os.path")
finder.ExcludeModule("multiprocessing.Process")
if os.name == "nt":
finder.ExcludeModule("fcntl")
finder.ExcludeModule("grp")
finder.ExcludeModule("pwd")
finder.ExcludeModule("termios")
else:
finder.ExcludeModule("_subprocess")
finder.ExcludeModule("_winreg")
finder.ExcludeModule("msilib")
finder.ExcludeModule("msvcrt")
finder.ExcludeModule("multiprocessing._multiprocessing")
finder.ExcludeModule("nt")
finder.ExcludeModule("nturl2path")
finder.ExcludeModule("pyHook")
finder.ExcludeModule("pythoncom")
finder.ExcludeModule("pywintypes")
finder.ExcludeModule("winerror")
finder.ExcludeModule("winsound")
finder.ExcludeModule("win32api")
finder.ExcludeModule("win32con")
finder.ExcludeModule("win32gui")
finder.ExcludeModule("win32event")
finder.ExcludeModule("win32evtlog")
finder.ExcludeModule("win32evtlogutil")
finder.ExcludeModule("win32file")
finder.ExcludeModule("win32pdh")
finder.ExcludeModule("win32pipe")
finder.ExcludeModule("win32process")
finder.ExcludeModule("win32security")
finder.ExcludeModule("win32service")
finder.ExcludeModule("wx.activex")
if os.name != "posix":
finder.ExcludeModule("posix")
if sys.platform != "darwin":
finder.ExcludeModule("Carbon")
finder.ExcludeModule("gestalt")
finder.ExcludeModule("ic")
finder.ExcludeModule("mac")
finder.ExcludeModule("MacOS")
finder.ExcludeModule("macostools")
finder.ExcludeModule("macpath")
finder.ExcludeModule("macurl2path")
finder.ExcludeModule("_scproxy")
if os.name != "nt":
finder.ExcludeModule("EasyDialogs")
if os.name != "os2":
finder.ExcludeModule("os2")
finder.ExcludeModule("os2emxpath")
finder.ExcludeModule("_emx_link")
if os.name != "ce":
finder.ExcludeModule("ce")
if os.name != "riscos":
finder.ExcludeModule("riscos")
finder.ExcludeModule("riscosenviron")
finder.ExcludeModule("riscospath")
finder.ExcludeModule("rourl2path")
if sys.platform[:4] != "java":
finder.ExcludeModule("java.lang")
finder.ExcludeModule("org.python.core")
if sys.platform[:4] != "OpenVMS":
finder.ExcludeModule("vms_lib")
if sys.version_info[0] >= 3:
finder.ExcludeModule("new")
finder.ExcludeModule("Tkinter")
else:
finder.ExcludeModule("tkinter")
def load_cElementTree(finder, module):
"""the cElementTree module implicitly loads the elementtree.ElementTree
module; make sure this happens."""
finder.IncludeModule("elementtree.ElementTree")
def load_ceODBC(finder, module):
"""the ceODBC module implicitly imports both datetime and decimal; make
sure this happens."""
finder.IncludeModule("datetime")
finder.IncludeModule("decimal")
def load_cx_Oracle(finder, module):
"""the cx_Oracle module implicitly imports datetime; make sure this
happens."""
finder.IncludeModule("datetime")
try:
finder.IncludeModule("decimal")
except ImportError:
pass
def load_datetime(finder, module):
"""the datetime module implicitly imports time; make sure this happens."""
finder.IncludeModule("time")
def load_docutils_frontend(finder, module):
"""The optik module is the old name for the optparse module; ignore the
module if it cannot be found."""
module.IgnoreName("optik")
def load_dummy_threading(finder, module):
"""the dummy_threading module plays games with the name of the threading
module for its own purposes; ignore that here"""
finder.ExcludeModule("_dummy_threading")
def load_email(finder, module):
"""the email package has a bunch of aliases as the submodule names were
all changed to lowercase in Python 2.5; mimic that here."""
if sys.version_info[:2] >= (2, 5):
for name in ("Charset", "Encoders", "Errors", "FeedParser",
"Generator", "Header", "Iterators", "Message", "Parser",
"Utils", "base64MIME", "quopriMIME"):
finder.AddAlias("email.%s" % name, "email.%s" % name.lower())
def load_ftplib(finder, module):
"""the ftplib module attempts to import the SOCKS module; ignore this
module if it cannot be found"""
module.IgnoreName("SOCKS")
def load_GifImagePlugin(finder, module):
"""The GifImagePlugin module optionally imports the _imaging_gif module"""
module.IgnoreName("_imaging_gif")
def load_glib(finder, module):
"""Ignore globals that are imported."""
module.AddGlobalName("GError")
module.AddGlobalName("IOChannel")
module.AddGlobalName("IO_ERR")
module.AddGlobalName("IO_FLAG_APPEND")
module.AddGlobalName("IO_FLAG_GET_MASK")
module.AddGlobalName("IO_FLAG_IS_READABLE")
module.AddGlobalName("IO_FLAG_IS_SEEKABLE")
module.AddGlobalName("IO_FLAG_IS_WRITEABLE")
module.AddGlobalName("IO_FLAG_MASK")
module.AddGlobalName("IO_FLAG_NONBLOCK")
module.AddGlobalName("IO_FLAG_SET_MASK")
module.AddGlobalName("IO_HUP")
module.AddGlobalName("IO_IN")
module.AddGlobalName("IO_NVAL")
module.AddGlobalName("IO_OUT")
module.AddGlobalName("IO_PRI")
module.AddGlobalName("IO_STATUS_AGAIN")
module.AddGlobalName("IO_STATUS_EOF")
module.AddGlobalName("IO_STATUS_ERROR")
module.AddGlobalName("IO_STATUS_NORMAL")
module.AddGlobalName("Idle")
module.AddGlobalName("MainContext")
module.AddGlobalName("MainLoop")
module.AddGlobalName("OPTION_ERROR")
module.AddGlobalName("OPTION_ERROR_BAD_VALUE")
module.AddGlobalName("OPTION_ERROR_FAILED")
module.AddGlobalName("OPTION_ERROR_UNKNOWN_OPTION")
module.AddGlobalName("OPTION_FLAG_FILENAME")
module.AddGlobalName("OPTION_FLAG_HIDDEN")
module.AddGlobalName("OPTION_FLAG_IN_MAIN")
module.AddGlobalName("OPTION_FLAG_NOALIAS")
module.AddGlobalName("OPTION_FLAG_NO_ARG")
module.AddGlobalName("OPTION_FLAG_OPTIONAL_ARG")
module.AddGlobalName("OPTION_FLAG_REVERSE")
module.AddGlobalName("OPTION_REMAINING")
module.AddGlobalName("OptionContext")
module.AddGlobalName("OptionGroup")
module.AddGlobalName("PRIORITY_DEFAULT")
module.AddGlobalName("PRIORITY_DEFAULT_IDLE")
module.AddGlobalName("PRIORITY_HIGH")
module.AddGlobalName("PRIORITY_HIGH_IDLE")
module.AddGlobalName("PRIORITY_LOW")
module.AddGlobalName("Pid")
module.AddGlobalName("PollFD")
module.AddGlobalName("SPAWN_CHILD_INHERITS_STDIN")
module.AddGlobalName("SPAWN_DO_NOT_REAP_CHILD")
module.AddGlobalName("SPAWN_FILE_AND_ARGV_ZERO")
module.AddGlobalName("SPAWN_LEAVE_DESCRIPTORS_OPEN")
module.AddGlobalName("SPAWN_SEARCH_PATH")
module.AddGlobalName("SPAWN_STDERR_TO_DEV_NULL")
module.AddGlobalName("SPAWN_STDOUT_TO_DEV_NULL")
module.AddGlobalName("Source")
module.AddGlobalName("Timeout")
module.AddGlobalName("child_watch_add")
module.AddGlobalName("filename_display_basename")
module.AddGlobalName("filename_display_name")
module.AddGlobalName("filename_from_utf8")
module.AddGlobalName("get_application_name")
module.AddGlobalName("get_current_time")
module.AddGlobalName("get_prgname")
module.AddGlobalName("glib_version")
module.AddGlobalName("idle_add")
module.AddGlobalName("io_add_watch")
module.AddGlobalName("main_context_default")
module.AddGlobalName("main_depth")
module.AddGlobalName("markup_escape_text")
module.AddGlobalName("set_application_name")
module.AddGlobalName("set_prgname")
module.AddGlobalName("source_remove")
module.AddGlobalName("spawn_async")
module.AddGlobalName("timeout_add")
module.AddGlobalName("timeout_add_seconds")
def load_gtk__gtk(finder, module):
"""the gtk._gtk module has a number of implicit imports"""
finder.IncludeModule("atk")
finder.IncludeModule("cairo")
finder.IncludeModule("gio")
finder.IncludeModule("pango")
finder.IncludeModule("pangocairo")
def load_hashlib(finder, module):
"""hashlib's fallback modules don't exist if the equivalent OpenSSL
algorithms are loaded from _hashlib, so we can ignore the error."""
module.IgnoreName("_md5")
module.IgnoreName("_sha")
module.IgnoreName("_sha256")
module.IgnoreName("_sha512")
def load_h5py(finder, module):
"""h5py module has a number of implicit imports"""
finder.IncludeModule('h5py.defs')
finder.IncludeModule('h5py.utils')
finder.IncludeModule('h5py._proxy')
try:
import h5py.api_gen
finder.IncludeModule('h5py.api_gen')
except ImportError:
pass
finder.IncludeModule('h5py._errors')
finder.IncludeModule('h5py.h5ac')
def load_matplotlib(finder, module):
"""the matplotlib module requires data to be found in mpl-data in the
same directory as the frozen executable so oblige it"""
dir = os.path.join(module.path[0], "mpl-data")
finder.IncludeFiles(dir, "mpl-data")
def load_matplotlib_numerix(finder, module):
"""the numpy.numerix module loads a number of modules dynamically"""
for name in ("ma", "fft", "linear_algebra", "random_array", "mlab"):
finder.IncludeModule("%s.%s" % (module.name, name))
def load_Numeric(finder, module):
"""the Numeric module optionally loads the dotblas module; ignore the error
if this modules does not exist."""
module.IgnoreName("dotblas")
def load_numpy_core_multiarray(finder, module):
"""the numpy.core.multiarray module is an extension module and the numpy
module imports * from this module; define the list of global names
available to this module in order to avoid spurious errors about missing
modules"""
module.AddGlobalName("arange")
def load_numpy_core_numerictypes(finder, module):
"""the numpy.core.numerictypes module adds a number of items to itself
dynamically; define these to avoid spurious errors about missing
modules"""
module.AddGlobalName("bool_")
module.AddGlobalName("cdouble")
module.AddGlobalName("complexfloating")
module.AddGlobalName("csingle")
module.AddGlobalName("double")
module.AddGlobalName("float64")
module.AddGlobalName("float_")
module.AddGlobalName("inexact")
module.AddGlobalName("intc")
module.AddGlobalName("int32")
module.AddGlobalName("number")
module.AddGlobalName("single")
def load_numpy_core_umath(finder, module):
"""the numpy.core.umath module is an extension module and the numpy module
imports * from this module; define the list of global names available
to this module in order to avoid spurious errors about missing
modules"""
module.AddGlobalName("add")
module.AddGlobalName("absolute")
module.AddGlobalName("arccos")
module.AddGlobalName("arccosh")
module.AddGlobalName("arcsin")
module.AddGlobalName("arcsinh")
module.AddGlobalName("arctan")
module.AddGlobalName("arctanh")
module.AddGlobalName("bitwise_and")
module.AddGlobalName("bitwise_or")
module.AddGlobalName("bitwise_xor")
module.AddGlobalName("ceil")
module.AddGlobalName("conj")
module.AddGlobalName("conjugate")
module.AddGlobalName("cosh")
module.AddGlobalName("divide")
module.AddGlobalName("fabs")
module.AddGlobalName("floor")
module.AddGlobalName("floor_divide")
module.AddGlobalName("fmod")
module.AddGlobalName("greater")
module.AddGlobalName("hypot")
module.AddGlobalName("invert")
module.AddGlobalName("isfinite")
module.AddGlobalName("isinf")
module.AddGlobalName("isnan")
module.AddGlobalName("less")
module.AddGlobalName("left_shift")
module.AddGlobalName("log")
module.AddGlobalName("logical_and")
module.AddGlobalName("logical_not")
module.AddGlobalName("logical_or")
module.AddGlobalName("logical_xor")
module.AddGlobalName("maximum")
module.AddGlobalName("minimum")
module.AddGlobalName("multiply")
module.AddGlobalName("negative")
module.AddGlobalName("not_equal")
module.AddGlobalName("power")
module.AddGlobalName("remainder")
module.AddGlobalName("right_shift")
module.AddGlobalName("sign")
module.AddGlobalName("sinh")
module.AddGlobalName("sqrt")
module.AddGlobalName("tan")
module.AddGlobalName("tanh")
module.AddGlobalName("true_divide")
def load_numpy_distutils_command_scons(finder, module):
"""the numpy.distutils.command.scons module optionally imports the numscons
module; ignore the error if the module cannot be found."""
module.IgnoreName("numscons")
def load_numpy_distutils_misc_util(finder, module):
"""the numpy.distutils.misc_util module optionally imports the numscons
module; ignore the error if the module cannot be found."""
module.IgnoreName("numscons")
def load_numpy_distutils_system_info(finder, module):
"""the numpy.distutils.system_info module optionally imports the Numeric
module; ignore the error if the module cannot be found."""
module.IgnoreName("Numeric")
def load_numpy_f2py___version__(finder, module):
"""the numpy.f2py.__version__ module optionally imports the __svn_version__
module; ignore the error if the module cannot be found."""
module.IgnoreName("__svn_version__")
def load_numpy_linalg(finder, module):
"""the numpy.linalg module implicitly loads the lapack_lite module; make
sure this happens"""
finder.IncludeModule("numpy.linalg.lapack_lite")
def load_numpy_random_mtrand(finder, module):
"""the numpy.random.mtrand module is an extension module and the numpy
module imports * from this module; define the list of global names
available to this module in order to avoid spurious errors about missing
modules"""
module.AddGlobalName("rand")
module.AddGlobalName("randn")
def load_postgresql_lib(finder, module):
"""the postgresql.lib module requires the libsys.sql file to be included
so make sure that file is included"""
fileName = os.path.join(module.path[0], "libsys.sql")
finder.IncludeFiles(fileName, os.path.basename(fileName))
def load_pty(finder, module):
"""The sgi module is not needed for this module to function."""
module.IgnoreName("sgi")
def load_pydoc(finder, module):
"""The pydoc module will work without the Tkinter module so ignore the
error if that module cannot be found."""
module.IgnoreName("Tkinter")
def load_pythoncom(finder, module):
"""the pythoncom module is actually contained in a DLL but since those
cannot be loaded directly in Python 2.5 and higher a special module is
used to perform that task; simply use that technique directly to
determine the name of the DLL and ensure it is included as a file in
the target directory."""
import pythoncom
finder.IncludeFiles(pythoncom.__file__,
os.path.join("lib", os.path.basename(pythoncom.__file__)),
copyDependentFiles = False)
def load_pywintypes(finder, module):
"""the pywintypes module is actually contained in a DLL but since those
cannot be loaded directly in Python 2.5 and higher a special module is
used to perform that task; simply use that technique directly to
determine the name of the DLL and ensure it is included as a file in the
target directory."""
import pywintypes
finder.IncludeFiles(pywintypes.__file__,
os.path.join("lib", os.path.basename(pywintypes.__file__)),
copyDependentFiles = False)
# PyQt5 and PyQt4 can't both be loaded in the same process, so we cache the
# QtCore module so we can still return something sensible if we try to load
# both.
_qtcore = None
def _qt_implementation(module):
"""Helper function to get name (PyQt4, PyQt5, PySide) and the QtCore module
"""
global _qtcore
name = module.name.split('.')[0]
try:
_qtcore = __import__(name, fromlist=['QtCore']).QtCore
except RuntimeError:
print("WARNING: Tried to load multiple incompatible Qt wrappers. "
"Some incorrect files may be copied.")
return name, _qtcore
def copy_qt_plugins(plugins, finder, QtCore):
"""Helper function to find and copy Qt plugins."""
# Qt Plugins can either be in a plugins directory next to the Qt libraries,
# or in other locations listed by QCoreApplication.libraryPaths()
dir0 = os.path.join(os.path.dirname(QtCore.__file__), "plugins")
for libpath in QtCore.QCoreApplication.libraryPaths() + [dir0]:
sourcepath = os.path.join(str(libpath), plugins)
if os.path.exists(sourcepath):
finder.IncludeFiles(sourcepath, plugins)
def load_PyQt4_phonon(finder, module):
"""In Windows, phonon4.dll requires an additional dll phonon_ds94.dll to
be present in the build directory inside a folder phonon_backend."""
name, QtCore = _qt_implementation(module)
if sys.platform == "win32":
copy_qt_plugins("phonon_backend", finder, QtCore)
load_PySide_phonon = load_PyQt5_phonon = load_PyQt4_phonon
def load_PyQt4_QtCore(finder, module):
"""the PyQt4.QtCore module implicitly imports the sip module and,
depending on configuration, the PyQt4._qt module."""
name, QtCore = _qt_implementation(module)
finder.IncludeModule("sip")
try:
finder.IncludeModule("%s._qt" % name)
except ImportError:
pass
load_PyQt5_QtCore = load_PyQt4_QtCore
def load_PySide_QtCore(finder, module):
"""PySide.QtCore dynamically loads the stdlib atexit module."""
finder.IncludeModule("atexit")
def load_PyQt4_Qt(finder, module):
"""the PyQt4.Qt module is an extension module which imports a number of
other modules and injects their namespace into its own. It seems a
foolish way of doing things but perhaps there is some hidden advantage
to this technique over pure Python; ignore the absence of some of
the modules since not every installation includes all of them."""
name, QtCore = _qt_implementation(module)
finder.IncludeModule("%s.QtCore" % name)
finder.IncludeModule("%s.QtGui" % name)
for mod in ("_qt", "QtSvg", "Qsci", "QtAssistant", "QtNetwork", "QtOpenGL",
"QtScript", "QtSql", "QtSvg", "QtTest", "QtXml"):
try:
finder.IncludeModule(name + '.' + mod)
except ImportError:
pass
load_PyQt5_Qt = load_PyQt4_Qt
def load_PyQt4_uic(finder, module):
"""The uic module makes use of "plugins" that need to be read directly and
cannot be frozen; the PyQt4.QtWebKit and PyQt4.QtNetwork modules are
also implicity loaded."""
name, QtCore = _qt_implementation(module)
dir = os.path.join(module.path[0], "widget-plugins")
finder.IncludeFiles(dir, "%s.uic.widget-plugins" % name)
finder.IncludeModule("%s.QtNetwork" % name)
try:
finder.IncludeModule("%s.QtWebKit" % name)
except ImportError:
pass
load_PyQt5_uic = load_PyQt4_uic
def _QtGui(finder, module, version_str):
name, QtCore = _qt_implementation(module)
finder.IncludeModule("%s.QtCore" % name)
copy_qt_plugins("imageformats", finder, QtCore)
if version_str >= '5':
# On Qt5, we need the platform plugins. For simplicity, we just copy any
# that are installed.
copy_qt_plugins("platforms", finder, QtCore)
def load_PyQt4_QtGui(finder, module):
"""There is a chance that GUI will use some image formats
add the image format plugins
"""
name, QtCore = _qt_implementation(module)
_QtGui(finder, module, QtCore.QT_VERSION_STR)
load_PyQt5_QtGui = load_PyQt4_QtGui
def load_PySide_QtGui(finder, module):
"""There is a chance that GUI will use some image formats
add the image format plugins
"""
from PySide import QtCore
# Pyside.__version* is PySide version, PySide.QtCore.__version* is Qt version
_QtGui(finder, module, QtCore.__version__)
def load_PyQt5_QtWidgets(finder, module):
finder.IncludeModule('PyQt5.QtGui')
def load_PyQt4_QtWebKit(finder, module):
name, QtCore = _qt_implementation(module)
finder.IncludeModule("%s.QtNetwork" % name)
finder.IncludeModule("%s.QtGui" % name)
load_PyQt5_QtWebKit = load_PySide_QtWebKit = load_PyQt4_QtWebKit
def load_PyQt5_QtMultimedia(finder, module):
name, QtCore = _qt_implementation(module)
finder.IncludeModule("%s.QtCore" % name)
finder.IncludeModule("%s.QtMultimediaWidgets" % name)
copy_qt_plugins("mediaservice", finder, QtCore)
def load_reportlab(finder, module):
"""the reportlab module loads a submodule rl_settings via exec so force
its inclusion here"""
finder.IncludeModule("reportlab.rl_settings")
def load_scipy(finder, module):
"""the scipy module loads items within itself in a way that causes
problems without the entire package and a number of other subpackages
being present."""
finder.IncludePackage("scipy._lib")
finder.IncludePackage("scipy.misc")
def load_scipy_linalg(finder, module):
"""the scipy.linalg module loads items within itself in a way that causes
problems without the entire package being present."""
module.AddGlobalName("norm")
finder.IncludePackage("scipy.linalg")
def load_scipy_linalg_interface_gen(finder, module):
"""the scipy.linalg.interface_gen module optionally imports the pre module;
ignore the error if this module cannot be found"""
module.IgnoreName("pre")
def load_scipy_sparse_linalg_dsolve_linsolve(finder, module):
"""the scipy.linalg.dsolve.linsolve optionally loads scikits.umfpack"""
module.IgnoreName("scikits.umfpack")
def load_scipy_special__cephes(finder, module):
"""the scipy.special._cephes is an extension module and the scipy module
imports * from it in places; advertise the global names that are used
in order to avoid spurious errors about missing modules."""
module.AddGlobalName("gammaln")
def load_setuptools_extension(finder, module):
"""the setuptools.extension module optionally loads
Pyrex.Distutils.build_ext but its absence is not considered an error."""
module.IgnoreName("Pyrex.Distutils.build_ext")
def load_site(finder, module):
"""the site module optionally loads the sitecustomize and usercustomize
modules; ignore the error if these modules do not exist."""
module.IgnoreName("sitecustomize")
module.IgnoreName("usercustomize")
def load_tkinter(finder, module):
"""the tkinter module has data files that are required to be loaded so
ensure that they are copied into the directory that is expected at
runtime."""
if sys.platform == "win32":
import tkinter
import _tkinter
tclSourceDir = os.environ["TCL_LIBRARY"]
tkSourceDir = os.environ["TK_LIBRARY"]
finder.IncludeFiles(tclSourceDir, "tcl")
finder.IncludeFiles(tkSourceDir, "tk")
def load_Tkinter(finder, module):
"""the Tkinter module has data files that are required to be loaded so
ensure that they are copied into the directory that is expected at
runtime."""
import Tkinter
import _tkinter
tk = _tkinter.create()
tclDir = os.path.dirname(tk.call("info", "library"))
# on OS X, Tcl and Tk are organized in frameworks, different layout
if sys.platform == 'darwin' and tk.call('tk', 'windowingsystem') == 'aqua':
tclSourceDir=os.path.join(os.path.split(tclDir)[0], 'Tcl')
tkSourceDir = tclSourceDir.replace('Tcl', 'Tk')
else:
tclSourceDir = os.path.join(tclDir, "tcl%s" % _tkinter.TCL_VERSION)
tkSourceDir = os.path.join(tclDir, "tk%s" % _tkinter.TK_VERSION)
finder.IncludeFiles(tclSourceDir, "tcl")
finder.IncludeFiles(tkSourceDir, "tk")
def load_tempfile(finder, module):
"""the tempfile module attempts to load the fcntl and thread modules but
continues if these modules cannot be found; ignore these modules if they
cannot be found."""
module.IgnoreName("fcntl")
module.IgnoreName("thread")
def load_time(finder, module):
"""the time module implicitly loads _strptime; make sure this happens."""
finder.IncludeModule("_strptime")
def load_twisted_conch_ssh_transport(finder, module):
"""the twisted.conch.ssh.transport module uses __import__ builtin to
dynamically load different ciphers at runtime."""
finder.IncludePackage("Crypto.Cipher")
def load_twitter(finder, module):
"""the twitter module tries to load the simplejson, json and django.utils
module in an attempt to locate any module that will implement the
necessary protocol; ignore these modules if they cannot be found."""
module.IgnoreName("json")
module.IgnoreName("simplejson")
module.IgnoreName("django.utils")
def load_win32api(finder, module):
"""the win32api module implicitly loads the pywintypes module; make sure
this happens."""
finder.ExcludeDependentFiles(module.file)
finder.IncludeModule("pywintypes")
def load_win32com(finder, module):
"""the win32com package manipulates its search path at runtime to include
the sibling directory called win32comext; simulate that by changing the
search path in a similar fashion here."""
baseDir = os.path.dirname(os.path.dirname(module.file))
module.path.append(os.path.join(baseDir, "win32comext"))
def load_win32file(finder, module):
"""the win32file module implicitly loads the pywintypes and win32timezone
module; make sure this happens."""
finder.IncludeModule("pywintypes")
finder.IncludeModule("win32timezone")
def load_wx_lib_pubsub_core(finder, module):
"""the wx.lib.pubsub.core module modifies the search path which cannot
be done in a frozen application in the same way; modify the module
search path here instead so that the right modules are found; note
that this only works if the import of wx.lib.pubsub.setupkwargs
occurs first."""
dirName = os.path.dirname(module.file)
module.path.insert(0, os.path.join(dirName, "kwargs"))
def load_Xlib_display(finder, module):
"""the Xlib.display module implicitly loads a number of extension modules;
make sure this happens."""
finder.IncludeModule("Xlib.ext.xtest")
finder.IncludeModule("Xlib.ext.shape")
finder.IncludeModule("Xlib.ext.xinerama")
finder.IncludeModule("Xlib.ext.record")
finder.IncludeModule("Xlib.ext.composite")
finder.IncludeModule("Xlib.ext.randr")
def load_Xlib_support_connect(finder, module):
"""the Xlib.support.connect module implicitly loads a platform specific
module; make sure this happens."""
if sys.platform.split("-")[0] == "OpenVMS":
moduleName = "vms_connect"
else:
moduleName = "unix_connect"
finder.IncludeModule("Xlib.support.%s" % moduleName)
def load_Xlib_XK(finder, module):
"""the Xlib.XK module implicitly loads some keysymdef modules; make sure
this happens."""
finder.IncludeModule("Xlib.keysymdef.miscellany")
finder.IncludeModule("Xlib.keysymdef.latin1")
def load_xml(finder, module):
"""the builtin xml package attempts to load the _xmlplus module to see if
that module should take its role instead; ignore the failure to find
this module, though."""
module.IgnoreName("_xmlplus")
def load_xml_etree_cElementTree(finder, module):
"""the xml.etree.cElementTree module implicitly loads the
xml.etree.ElementTree module; make sure this happens."""
finder.IncludeModule("xml.etree.ElementTree")
def load_xmlrpclib(finder, module):
"""the xmlrpclib optionally imports the _xmlrpclib and sgmlop modules;
ignore the error if these modules cannot be found."""
module.IgnoreName("_xmlrpclib")
module.IgnoreName("sgmlop")
def load_zope(finder, module):
"""the zope package is distributed in multiple packages and they need to be
stitched back together again."""
module.ExtendPath()
def load_zope_component(finder, module):
"""the zope.component package requires the presence of the pkg_resources
module but it uses a dynamic, not static import to do its work."""
finder.IncludeModule("pkg_resources")
def missing_cElementTree(finder, caller):
"""the cElementTree has been incorporated into the standard library in
Python 2.5 so ignore its absence if it cannot found."""
if sys.version_info[:2] >= (2, 5):
caller.IgnoreName("cElementTree")
def missing_EasyDialogs(finder, caller):
"""the EasyDialogs module is not normally present on Windows but it also
may be so instead of excluding it completely, ignore it if it can't be
found"""
if sys.platform == "win32":
caller.IgnoreName("EasyDialogs")
def missing_gdk(finder, caller):
"""the gdk module is buried inside gtk so there is no need to concern
ourselves with an error saying that it cannot be found"""
caller.IgnoreName("gdk")
def missing_ltihooks(finder, caller):
"""this module is not necessairly present so ignore it when it cannot be
found"""
caller.IgnoreName("ltihooks")
def missing_readline(finder, caller):
"""the readline module is not normally present on Windows but it also may
be so instead of excluding it completely, ignore it if it can't be
found"""
if sys.platform == "win32":
caller.IgnoreName("readline")
def missing_xml_etree(finder, caller):
"""the xml.etree package is new for Python 2.5 but it is common practice
to use a try..except.. block in order to support versions earlier than
Python 2.5 transparently; ignore the absence of the package in this
situation."""
if sys.version_info[:2] < (2, 5):
caller.IgnoreName("xml.etree")
def load_zmq(finder, module):
"""the zmq package loads zmq.backend.cython dynamically and links
dynamically to zmq.libzmq."""
finder.IncludePackage("zmq.backend.cython")
if sys.platform == "win32":
# Not sure yet if this is cross platform
import zmq.libzmq
srcFileName = os.path.basename(zmq.libzmq.__file__)
finder.IncludeFiles(
os.path.join(module.path[0], srcFileName), srcFileName)
def load_clr(finder, module):
"""the pythonnet package (imported as 'clr') needs Python.Runtime.dll
in runtime"""
module_dir = os.path.dirname(module.file)
dllname = 'Python.Runtime.dll'
finder.IncludeFiles(os.path.join(module_dir, dllname), dllname)
def _get_base_prefix():
"""Get prefix to site-specific directory prefix where the platform
independent Python files are installed."""
try:
return sys.base_prefix # Python 3.3+
except AttributeError:
# For Python 2, use sys.real_prefix set by virtualenv if set,
# otherwise fall back to sys.prefix
return getattr(sys, "real_prefix", sys.prefix)
def load_sqlite3(finder, module):
"""In Windows, the sqlite3 module requires an additional dll sqlite3.dll to
be present in the build directory."""
if sys.platform == "win32":
dll_name = "sqlite3.dll"
dll_path = os.path.join(_get_base_prefix(), "DLLs", dll_name)
finder.IncludeFiles(dll_path, dll_name)
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27063,
15376,
278,
282,
5693,
524,
7384,
322,
5401,
29941,
29906,
2230,
8028,
13,
539,
3883,
29936,
1207,
1854,
445,
5930,
1213,
15945,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
2272,
29893,
524,
7384,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
5080,
29941,
29906,
2230,
8028,
1159,
13,
13,
13,
1753,
2254,
29918,
23310,
29918,
1982,
29918,
5467,
1491,
29918,
3221,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
26437,
29889,
1982,
29889,
5467,
1491,
29889,
3221,
3883,
878,
11057,
278,
2740,
2224,
607,
2609,
13,
539,
367,
2309,
297,
263,
14671,
2256,
2280,
297,
278,
1021,
982,
29936,
6623,
278,
3883,
13,
539,
2740,
2224,
1244,
2012,
577,
393,
278,
1492,
10585,
526,
1476,
29936,
4443,
13,
539,
393,
445,
871,
1736,
565,
278,
1053,
310,
26437,
29889,
1982,
29889,
5467,
1491,
29889,
14669,
19290,
13,
539,
10008,
937,
1213,
15945,
13,
1678,
4516,
1170,
353,
2897,
29889,
2084,
29889,
25721,
29898,
5453,
29889,
1445,
29897,
13,
1678,
3883,
29889,
2084,
29889,
7851,
29898,
29900,
29892,
2897,
29889,
2084,
29889,
7122,
29898,
3972,
1170,
29892,
376,
19290,
5783,
13,
13,
13,
1753,
2254,
29918,
29990,
1982,
29918,
4990,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
1060,
1982,
29889,
4990,
3883,
27063,
15376,
263,
1353,
310,
6081,
10585,
29936,
13,
539,
1207,
1854,
445,
5930,
1213,
15945,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
1062,
29889,
486,
342,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
1062,
29889,
12181,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
1062,
29889,
29916,
4983,
3304,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
1062,
29889,
11651,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
1062,
29889,
22410,
568,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
1062,
29889,
9502,
29878,
1159,
13,
13,
13,
1753,
2254,
29918,
29990,
1982,
29918,
5924,
29918,
6915,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
1060,
1982,
29889,
5924,
29889,
6915,
3883,
27063,
15376,
263,
7481,
2702,
13,
539,
3883,
29936,
1207,
1854,
445,
5930,
1213,
15945,
13,
1678,
565,
10876,
29889,
12120,
29889,
5451,
703,
29899,
1159,
29961,
29900,
29962,
1275,
376,
6585,
29963,
4345,
1115,
13,
4706,
3883,
1170,
353,
376,
29894,
1516,
29918,
6915,
29908,
13,
1678,
1683,
29901,
13,
4706,
3883,
1170,
353,
376,
24538,
29918,
6915,
29908,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
5924,
29889,
29995,
29879,
29908,
1273,
3883,
1170,
29897,
13,
13,
13,
1753,
2254,
29918,
29990,
1982,
29918,
29990,
29968,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
1060,
1982,
29889,
29990,
29968,
3883,
27063,
15376,
777,
6611,
962,
1753,
10585,
29936,
1207,
1854,
13,
539,
445,
5930,
1213,
15945,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
8149,
962,
1753,
29889,
26737,
3729,
1384,
1159,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
29990,
1982,
29889,
8149,
962,
1753,
29889,
5066,
262,
29896,
1159,
13,
13,
13,
1753,
2254,
29918,
3134,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
4240,
262,
4903,
3577,
14734,
304,
2254,
278,
903,
3134,
11242,
3883,
304,
1074,
565,
13,
539,
393,
3883,
881,
2125,
967,
6297,
2012,
29936,
11455,
278,
10672,
304,
1284,
13,
539,
445,
3883,
29892,
2466,
1213,
15945,
13,
1678,
3883,
29889,
23805,
1170,
703,
29918,
3134,
11242,
1159,
13,
13,
13,
1753,
2254,
29918,
3134,
29918,
300,
929,
29918,
29883,
2642,
9643,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
4903,
29889,
300,
929,
29889,
29883,
2642,
9643,
3883,
27063,
15376,
278,
13,
539,
4903,
29889,
300,
929,
29889,
2642,
9643,
3883,
29936,
1207,
1854,
445,
5930,
1213,
15945,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
3134,
29889,
300,
929,
29889,
2642,
9643,
1159,
13,
13,
13,
1753,
2254,
29918,
3134,
29878,
6739,
1982,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
4903,
29878,
6739,
1982,
2984,
635,
24802,
278,
903,
3134,
29878,
6739,
1982,
322,
269,
29887,
29885,
4757,
10585,
29936,
13,
539,
11455,
278,
1059,
565,
1438,
10585,
2609,
367,
1476,
1213,
15945,
13,
1678,
3883,
29889,
23805,
1170,
703,
29918,
3134,
29878,
6739,
1982,
1159,
13,
1678,
3883,
29889,
23805,
1170,
703,
5311,
29885,
4757,
1159,
13,
13,
13,
1753,
2254,
29918,
29920,
2300,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
503,
2300,
3577,
338,
13235,
297,
2999,
9741,
322,
896,
817,
304,
367,
13,
539,
380,
2335,
287,
1250,
4208,
1449,
1213,
15945,
13,
1678,
3883,
29889,
5647,
355,
2605,
580,
13,
13,
13,
1753,
2254,
29918,
29920,
2300,
29918,
9700,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
503,
2300,
29889,
9700,
3577,
6858,
278,
10122,
310,
278,
282,
9415,
29918,
13237,
13,
539,
3883,
541,
372,
3913,
263,
7343,
29892,
451,
2294,
1053,
304,
437,
967,
664,
1213,
15945,
13,
1678,
1284,
261,
29889,
29419,
7355,
703,
15865,
29918,
13237,
1159,
13,
13,
13,
1753,
4567,
29918,
29883,
2642,
9643,
29898,
2886,
261,
29892,
24959,
1125,
13,
1678,
9995,
1552,
274,
2642,
9643,
756,
1063,
11039,
630,
964,
278,
3918,
3489,
297,
13,
539,
5132,
29871,
29906,
29889,
29945,
577,
11455,
967,
18070,
565,
372,
2609,
1476,
1213,
15945,
13,
1678,
565,
10876,
29889,
3259,
29918,
3888,
7503,
29906,
29962,
6736,
313,
29906,
29892,
29871,
29945,
1125,
13,
4706,
24959,
29889,
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1170,
703,
29883,
2642,
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1159,
13,
13,
13,
1753,
4567,
29918,
29923,
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7647,
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29898,
2886,
261,
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1125,
13,
1678,
9995,
1552,
382,
8995,
7647,
29879,
3883,
338,
451,
12891,
2198,
373,
3852,
541,
372,
884,
13,
539,
1122,
367,
577,
2012,
310,
429,
22368,
372,
6446,
29892,
11455,
372,
565,
372,
508,
29915,
29873,
367,
13,
539,
1476,
15945,
29908,
13,
1678,
565,
10876,
29889,
12120,
1275,
376,
5080,
29941,
29906,
1115,
13,
4706,
24959,
29889,
23805,
1170,
703,
29923,
8995,
7647,
29879,
1159,
13,
13,
13,
1753,
4567,
29918,
29887,
8181,
29898,
2886,
261,
29892,
24959,
1125,
13,
1678,
9995,
1552,
330,
8181,
3883,
338,
18550,
2768,
330,
11178,
577,
727,
338,
694,
817,
304,
5932,
13,
539,
20278,
411,
385,
1059,
5934,
393,
372,
2609,
367,
1476,
15945,
29908,
13,
1678,
24959,
29889,
23805,
1170,
703,
29887,
8181,
1159,
13,
13,
13,
1753,
4567,
29918,
1896,
29875,
1251,
12117,
29898,
2886,
261,
29892,
24959,
1125,
13,
1678,
9995,
1366,
3883,
338,
451,
3520,
1466,
368,
2198,
577,
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372,
746,
372,
2609,
367,
13,
539,
1476,
15945,
29908,
13,
1678,
24959,
29889,
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1170,
703,
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29875,
1251,
12117,
1159,
13,
13,
13,
1753,
4567,
29918,
949,
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261,
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13,
1678,
9995,
1552,
1303,
1220,
3883,
338,
451,
12891,
2198,
373,
3852,
541,
372,
884,
1122,
13,
539,
367,
577,
2012,
310,
429,
22368,
372,
6446,
29892,
11455,
372,
565,
372,
508,
29915,
29873,
367,
13,
539,
1476,
15945,
29908,
13,
1678,
565,
10876,
29889,
12120,
1275,
376,
5080,
29941,
29906,
1115,
13,
4706,
24959,
29889,
23805,
1170,
703,
949,
1220,
1159,
13,
13,
13,
1753,
4567,
29918,
3134,
29918,
300,
929,
29898,
2886,
261,
29892,
24959,
1125,
13,
1678,
9995,
1552,
4903,
29889,
300,
929,
3577,
338,
716,
363,
5132,
29871,
29906,
29889,
29945,
541,
372,
338,
3619,
6944,
13,
539,
304,
671,
263,
1018,
636,
19499,
636,
2908,
297,
1797,
304,
2304,
6910,
8859,
1135,
13,
539,
5132,
29871,
29906,
29889,
29945,
1301,
862,
2705,
29936,
11455,
278,
18070,
310,
278,
3577,
297,
445,
13,
539,
6434,
1213,
15945,
13,
1678,
565,
10876,
29889,
3259,
29918,
3888,
7503,
29906,
29962,
529,
313,
29906,
29892,
29871,
29945,
1125,
13,
4706,
24959,
29889,
23805,
1170,
703,
3134,
29889,
300,
929,
1159,
13,
13,
13,
1753,
2254,
29918,
14018,
29939,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
12162,
29939,
3577,
15376,
12162,
29939,
29889,
27852,
29889,
1270,
386,
265,
11200,
322,
2988,
29871,
13,
1678,
11200,
304,
12162,
29939,
29889,
1982,
14018,
29939,
1213,
15945,
13,
1678,
1284,
261,
29889,
29419,
14459,
703,
14018,
29939,
29889,
27852,
29889,
1270,
386,
265,
1159,
13,
1678,
565,
10876,
29889,
12120,
1275,
376,
5080,
29941,
29906,
1115,
13,
4706,
396,
2216,
1854,
3447,
565,
445,
338,
4891,
7481,
13,
4706,
1053,
12162,
29939,
29889,
1982,
14018,
29939,
13,
4706,
4765,
17020,
353,
2897,
29889,
2084,
29889,
6500,
3871,
29898,
14018,
29939,
29889,
1982,
14018,
29939,
17255,
1445,
1649,
29897,
13,
4706,
1284,
261,
29889,
29419,
10547,
29898,
13,
9651,
2897,
29889,
2084,
29889,
7122,
29898,
5453,
29889,
2084,
29961,
29900,
1402,
4765,
17020,
511,
4765,
17020,
29897,
13,
13,
13,
1753,
2254,
29918,
695,
29878,
29898,
2886,
261,
29892,
3883,
1125,
13,
1678,
9995,
1552,
3017,
1212,
3577,
313,
5215,
287,
408,
525,
695,
29878,
1495,
4225,
5132,
29889,
7944,
29889,
12396,
13,
1678,
297,
10073,
15945,
29908,
13,
1678,
3883,
29918,
3972,
353,
2897,
29889,
2084,
29889,
25721,
29898,
5453,
29889,
1445,
29897,
13,
1678,
24415,
978,
353,
525,
11980,
29889,
7944,
29889,
12396,
29915,
13,
1678,
1284,
261,
29889,
29419,
10547,
29898,
359,
29889,
2084,
29889,
7122,
29898,
5453,
29918,
3972,
29892,
24415,
978,
511,
24415,
978,
29897,
13,
13,
13,
1753,
903,
657,
29918,
3188,
29918,
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13,
1678,
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10944,
304,
3268,
29899,
14940,
3884,
10944,
988,
278,
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13,
1678,
7417,
5132,
2066,
526,
5130,
1213,
15945,
13,
1678,
1018,
29901,
13,
4706,
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10876,
29889,
3188,
29918,
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29871,
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5132,
29871,
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29889,
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13,
1678,
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29901,
13,
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396,
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29918,
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491,
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565,
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13,
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396,
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6416,
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304,
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29889,
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13,
4706,
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679,
5552,
29898,
9675,
29892,
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29918,
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613,
10876,
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13,
13,
13,
1753,
2254,
29918,
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29941,
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2886,
261,
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13,
1678,
9995,
797,
3852,
29892,
278,
21120,
29941,
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385,
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24415,
21120,
29941,
29889,
12396,
304,
13,
539,
367,
2198,
297,
278,
2048,
3884,
1213,
15945,
13,
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565,
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29889,
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1275,
376,
5080,
29941,
29906,
1115,
13,
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24415,
29918,
978,
353,
376,
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29941,
29889,
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29908,
13,
4706,
24415,
29918,
2084,
353,
2897,
29889,
2084,
29889,
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657,
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29918,
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3285,
376,
29928,
2208,
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613,
24415,
29918,
978,
29897,
13,
4706,
1284,
261,
29889,
29419,
10547,
29898,
12396,
29918,
2084,
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resources/views/Absensi/models/DB.py | Ackyras/KP-LabMM | 0 | 157191 | <filename>resources/views/Absensi/models/DB.py
# %%
# import
import pymysql.cursors
from datetime import date
from models.Asprak import Asprak
# %%
class DB:
def __init__(self, DB_HOST, DB_USER, DB_PASS, DB_NAME):
self.host = DB_HOST
self.name = DB_NAME
self.user = DB_USER
self.password = <PASSWORD>
self.cursor = None
self.conn = None
self.conn = pymysql.connect(
host=self.host,
db=self.name,
user=self.user,
passwd=self.password,
)
self.cursor = self.conn.cursor()
# %%
# def absence(NIM, data):
# try :
# sql = "INSERT INTO `absensi` (`asprak_id`) VALUES (`"+data.id+"`)"
# except pymysql.Error as err :
# print("Gagal menambahkan data %d: %s" %(e.args[0], e.args[1]))
# return "success"
def check_absence(self, asprak):
t = time.localtime()
sql = "SELECT `id` FROM `aspraks` WHERE `id` = `" + \
ids+"` AND `tgl_absen` = `"+datenow+"`"
data = self.cursor.fetchfirst(sql)
if data == null:
return True
else :
return False
# def asbence(self, asprak):
# if (check_absence(self.cursor, asprak)):
# try :
# except pymysql.Error as e:
def get_asprak(self, NIM):
sql = "SELECT * FROM `aspraks` WHERE `nim` = '"+NIM+"'"
self.cursor.execute(sql)
data = self.cursor.fetchone()
asprak = Asprak(data[0], data[1], NIM)
return asprak
def check_asprak(self, NIM):
sql = "SELECT * FROM `aspraks` WHERE `nim` = '"+NIM+"'"
data = self.cursor.execute(sql)
if data == 0:
return False
else:
return True
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mylib/web_client.py | mo-han/mo-han-toolbox | 24 | 91452 | <filename>mylib/web_client.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Library for website operation"""
import json
from concurrent.futures.thread import ThreadPoolExecutor
from queue import Queue
from urllib.parse import urlparse, ParseResult
import colorama
import humanize
import lxml.html
import requests.utils
from mylib.easy.stdlibs.typing import JSONType
from mylib.ext import fstk, ostk
from .easy import *
from mylib.ext.http_headers import CURLCookieJar
from .easy.logging import ez_get_logger, LOG_FMT_MESSAGE_ONLY
from .easy.io import SubscriptableFileIO
from .ext.tricks import singleton, iter_factory_retry
from .easy.stdlibs.threading import ez_thread_factory
MAGIC_TXT_NETSCAPE_HTTP_COOKIE_FILE = '# Netscape HTTP Cookie File'
USER_AGENT_FIREFOX_WIN10 = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:64.0) Gecko/20100101 Firefox/64.0'
HTMLElementTree = lxml.html.HtmlElement
def get_html_element_tree(url, **requests_kwargs) -> HTMLElementTree:
r = requests.get(url, **requests_kwargs)
if r.ok:
return lxml.html.document_fromstring(r.text)
else:
raise ConnectionError(r.status_code, r.reason)
def convert_cookies_json_to_netscape(json_data_or_filepath: JSONType or str, disable_filepath: bool = False) -> str:
from mylib.ext.fstk import read_json_file
if not disable_filepath and os.path.isfile(json_data_or_filepath):
json_data = read_json_file(json_data_or_filepath)
else:
json_data = json_data_or_filepath
cookies = ensure_json_cookies(json_data)
tab = '\t'
false_ = 'FALSE' + tab
true_ = 'TRUE' + tab
lines = [MAGIC_TXT_NETSCAPE_HTTP_COOKIE_FILE]
for c in cookies:
http_only_prefix = '#HttpOnly_' if c['httpOnly'] else ''
line = http_only_prefix + c['domain'] + tab
if c['hostOnly']:
line += false_
else:
line += true_
line += c['path'] + tab
if c['secure']:
line += true_
else:
line += false_
line += '{}\t{}\t{}'.format(c.get('expirationDate', 0), c['name'], c['value'])
lines.append(line)
return '\n'.join(lines)
def convert_cookies_file_json_to_netscape(src, dst=None) -> str:
if not os.path.isfile(src):
raise FileNotFoundError(src)
dst = dst or src + '.txt'
with fstk.ensure_open_file(dst, 'w') as f:
f.write(convert_cookies_json_to_netscape(src))
return dst
def ensure_json_cookies(json_data) -> list:
if isinstance(json_data, list):
cookies = json_data
elif isinstance(json_data, dict):
if 'cookies' in json_data:
if isinstance(json_data['cookies'], list):
cookies = json_data['cookies']
else:
raise TypeError("{}['cookies'] is not list".format(json_data))
else:
raise TypeError("dict '{}' has no 'cookies'".format(json_data))
else:
raise TypeError("'{}' is not list or dict".format(json_data))
return cookies
def cookies_dict_from_json(json_data_or_filepath: JSONType or str, disable_filepath: bool = False) -> dict:
from mylib.ext.fstk import read_json_file
if not disable_filepath and os.path.isfile(json_data_or_filepath):
json_data = read_json_file(json_data_or_filepath)
else:
json_data = json_data_or_filepath
d = {}
cookies = ensure_json_cookies(json_data)
for c in cookies:
d[c['name']] = c['value']
return d
def cookies_dict_from_netscape_file(filepath: str, ignore_discard=True, ignore_expires=True) -> dict:
cj = CURLCookieJar(filepath)
cj.load(ignore_discard=ignore_discard, ignore_expires=ignore_expires)
return requests.utils.dict_from_cookiejar(cj)
def cookies_dict_from_file(filepath: str, ignore_discard=True, ignore_expires=True) -> dict:
if not os.path.isfile(filepath):
raise FileNotFoundError(filepath)
if filepath.endswith('.json'):
d = cookies_dict_from_json(filepath)
else:
d = cookies_dict_from_netscape_file(filepath, ignore_discard=ignore_discard, ignore_expires=ignore_expires)
return d
def cookie_str_from_dict(cookies: dict) -> str:
cookies_l = ['{}={}'.format(k, v) for k, v in cookies.items()]
cookie = '; '.join(cookies_l)
return cookie
def headers_from_user_agent(user_agent: str = None, headers: dict = None) -> dict:
from copy import deepcopy
h = deepcopy(headers) or {}
h['User-Agent'] = user_agent or USER_AGENT_FIREFOX_WIN10
return h
def headers_from_cookies(cookies_data: dict or str, headers: dict = None) -> dict:
from copy import deepcopy
h = deepcopy(headers) or headers_from_user_agent()
if isinstance(cookies_data, dict):
cookie = cookie_str_from_dict(cookies_data)
elif isinstance(cookies_data, str):
cookie = cookies_data
else:
raise TypeError('cookies_data', (dict, str))
h['Cookie'] = cookie
return h
def get_phantomjs_splinter(proxy=None, show_image=False, window_size=(1024, 1024)):
import splinter
extra_argv = ['--webdriver-loglevel=WARN']
if proxy:
extra_argv.append('--proxy={}'.format(proxy))
if not show_image:
extra_argv.append('--load-images=no')
b = splinter.Browser(
'phantomjs',
service_log_path=os.path.join(ostk.TEMPDIR, 'ghostdriver.log'),
user_agent=USER_AGENT_FIREFOX_WIN10,
service_args=extra_argv,
)
b.driver.set_window_size(*window_size)
return b
def get_firefox_splinter(headless=True, proxy: str = None, **kwargs):
import splinter
config = {'service_log_path': os.path.join(ostk.TEMPDIR, 'geckodriver.log'),
'headless': headless}
config.update(kwargs)
profile_dict = {}
if proxy:
from urllib.parse import urlparse
prefix = 'network.proxy.'
profile_dict[prefix + 'type'] = 1
proxy_parse = urlparse(proxy)
scheme = proxy_parse.scheme
netloc = proxy_parse.netloc
try:
host, port = netloc.split(':')
port = int(port)
except ValueError:
raise ValueError(proxy)
if scheme in ('http', 'https', ''):
profile_dict[prefix + 'http'] = host
profile_dict[prefix + 'http_port'] = port
profile_dict[prefix + 'https'] = host
profile_dict[prefix + 'https_port'] = port
elif scheme.startswith('socks'):
profile_dict[prefix + 'socks'] = host
profile_dict[prefix + 'socks_port'] = port
else:
raise ValueError(proxy)
browser = splinter.Browser(driver_name='firefox', profile_preferences=profile_dict, **config)
return browser
def get_zope_splinter(**kwargs):
import splinter
return splinter.Browser(driver_name='zope.testbrowser', **kwargs)
get_browser = {
'splinter.phantomjs': get_phantomjs_splinter,
}
def human_filesize(bytes_n: int, no_space=True):
s = humanize.naturalsize(bytes_n, binary=True)
if no_space:
return s.replace(' ', '')
else:
return s
class Download:
def __init__(self, response: requests.Response, filepath: str = None,
content: bytes = None, no_content: bool = False):
content = b'' if no_content else content or response.content
if not response.ok:
raise HTTPResponseInspection(response, content)
self.id = id(response)
self.file = filepath or None
self.code = response.status_code
self.reason = response.reason
self.url = response.request.url
self.data = content
self.size = len(self.data)
content_length = int(response.headers.get('Content-Length', '-1'))
if content_length >= 0 and content_length != self.size:
raise HTTPIncomplete(content_length, self.size)
if self.code == 206:
content_range = response.headers['Content-Range']
start, end, total = [int(s) for s in re.search(r'(\d+)-(\d+)/(\d+)', content_range).groups()]
self.start = start
self.stop = end + 1
if self.stop - self.start != self.size:
raise HTTPIncomplete(self.size, self.stop - self.start)
self.total_size = total
else:
self.start = 0
self.stop = self.size
self.total_size = self.size
@property
def is_complete_data(self):
return self.size == self.total_size
@property
def is_at_end(self):
return self.stop >= self.total_size
class HTTPResponseInspection(Exception):
def __init__(self, response: requests.Response, content: bytes = None, no_content: bool = False, size: int = None):
http_ver = {10: '1.0', 11: '1.1'}
content = b'' if no_content else content or response.content
self.version = http_ver[response.raw.version]
self.code = int(response.status_code)
self.reason = str(response.reason)
self.json = None
self.size = len(content)
if no_content:
self.excerpt = None
elif self.size <= 32:
self.excerpt = content
elif self.size <= 4096:
encoding = response.encoding or response.apparent_encoding
try:
text = str(content, encoding=encoding, errors='replace')
except (LookupError, TypeError):
text = str(content, errors='replace')
h: HTMLElementTree = lxml.html.document_fromstring(text)
self.excerpt = h.body.text_content()
else:
self.excerpt = None
ct = response.headers['content-type']
if 'json' in ct or 'javascript' in ct:
try:
self.json = response.json()
except json.decoder.JSONDecodeError:
pass
if size is not None:
self.size = size
self.excerpt = '{} bytes'.format(size)
def __repr__(self):
t = 'HTTP/{} {} {}'.format(self.version, self.code, self.reason)
if self.json:
t += ', JSON={}'.format(self.json)
elif self.excerpt:
t += ', {}'.format(self.excerpt)
elif self.excerpt is not None:
t += ', {} bytes'.format(self.size)
return t
__str__ = __repr__
class HTTPIncomplete(Exception):
def __init__(self, expect_size: int, recv_size: int):
self.expect_size = expect_size
self.recv_size = recv_size
@singleton
class DownloadPool(ThreadPoolExecutor):
tmpfile_suffix = '.download'
def __init__(self, threads_n: int = 5, timeout: int = 30, name: str = None, show_status: bool = True):
self._max_workers: int = 0
self.queue = Queue()
self.timeout = timeout
self.name = name or self.__class__.__name__
self.logger = ez_get_logger('.'.join((__name__, self.name)), fmt=LOG_FMT_MESSAGE_ONLY)
self.recv_size_queue = Queue()
self.bytes_per_sec = 0
self.emergency_queue = Queue()
self.show_status_interval = 2
self.show_status_enable = show_status
ez_thread_factory(daemon=True)(self.calc_speed).start()
ez_thread_factory(daemon=True)(self.show_status).start()
super().__init__(max_workers=threads_n)
def queue_pipeline(self):
self.logger.debug('queue of {} started'.format(self))
q = self.queue
while True:
args = q.get()
if args is None:
break
url, filepath, retry, kwargs_for_requests = args
self.submit(self.download, url, filepath, retry, **kwargs_for_requests)
self.logger.debug('submit {}'.format(filepath))
self.logger.debug('queue of {} stopped'.format(self))
def show_status(self):
def color(x):
left = colorama.Fore.LIGHTGREEN_EX
right = colorama.Style.RESET_ALL
return left + str(x) + right
colorama.init()
eq = self.emergency_queue
while True:
if self.show_status_enable:
status_msg = f'| {self.name} {color(len(self._threads))}/{self._max_workers} ' \
f'| {color(self.speed):>11} |'
# status_width = len(status_msg)
# preamble = shutil.get_terminal_size()[0] - status_width - 1
# print(' ' * preamble + status_msg, end='\r', file=sys.stderr)
print(status_msg, end='\r', file=sys.stderr)
if not eq.empty():
e = eq.get()
if isinstance(e, Exception):
self.shutdown(wait=False)
raise e
sleep(self.show_status_interval)
@property
def speed(self):
return human_filesize(self.bytes_per_sec, no_space=False) + '/s'
def calc_speed(self):
tl = []
nl = []
q = self.recv_size_queue
while True:
if q.empty():
sleep(0.5)
# print('DEBUG')
continue
t, n = q.get()
tl.append(t)
nl.append(n)
try:
self.bytes_per_sec = sum(nl) // (tl[-1] - tl[0])
except ZeroDivisionError:
self.bytes_per_sec = 0
while time.time() - tl[0] > self.show_status_interval:
tl.pop(0)
nl.pop(0)
def parse_head(self, url, **kwargs_for_requests):
head = requests.head(url, **kwargs_for_requests).headers
split = head.pop('accept-range') == 'bytes'
size = int(head.pop('content-length', '-1'))
self.logger.debug('HEAD: split={}, size={}'.format(split, size))
return {'split': split, 'size': size}
def request_data(self, url, filepath, start=0, stop=0, **kwargs_for_requests) -> Download:
# chunk_size = requests.models.CONTENT_CHUNK_SIZE
chunk_size = 4096 * 1024
kwargs = make_requests_kwargs(**kwargs_for_requests)
if stop:
kwargs['headers']['Range'] = 'bytes={}-{}'.format(start, stop - 1)
elif start > 0:
kwargs['headers']['Range'] = 'bytes={}-'.format(start)
elif start < 0:
kwargs['headers']['Range'] = 'bytes={}'.format(start)
r = requests.get(url, stream=True, timeout=self.timeout, **kwargs)
self.logger.debug(HTTPResponseInspection(r, no_content=True))
content = b''
stop = 0
for chunk in r.iter_content(chunk_size=chunk_size):
self.recv_size_queue.put((time.time(), len(chunk)))
start = stop
stop = start + len(chunk)
content += chunk
total = len(content)
fstk.write_file_chunk(filepath, start, stop, chunk, total)
self.logger.debug(HTTPResponseInspection(r, content=content))
d = Download(r, filepath, content=content)
return d
def write_file(self, dl_obj: Download):
url = dl_obj.url
file = dl_obj.file
start = dl_obj.start
stop = dl_obj.stop
size = dl_obj.size
total = dl_obj.total_size
with SubscriptableFileIO(file, 'rb+') as f:
if f.size != total:
f.truncate(total)
f[start: stop] = dl_obj.data
self.logger.debug('w {} ({}) <- {}'.format(file, human_filesize(size), url))
def download(self, url, filepath, retry, **kwargs_for_requests):
tmpfile = filepath + self.tmpfile_suffix
fstk.touch(tmpfile)
for cnt, x in iter_factory_retry(retry)(self.request_data, url, tmpfile, **kwargs_for_requests):
if isinstance(x, Exception):
self.logger.warning('! <{}> {}'.format(type(x).__name__, x))
# self.logger.warning(''.join(traceback.format_tb(x.__traceback__)))
if cnt:
self.logger.info('++ retry ({}) {} <- {}'.format(cnt, filepath, url))
else:
dl_obj = x
break
else:
return
self.write_file(dl_obj)
os.rename(tmpfile, filepath)
self.log_file_done(filepath, dl_obj.size)
def log_file_done(self, filepath, size):
self.logger.info('* {} ({})'.format(filepath, human_filesize(size)))
def file_already_exists(self, filepath):
if os.path.isfile(filepath):
self.logger.info('# {}'.format(filepath))
return True
else:
return False
def log_new_download(self, url, filepath, retry):
self.logger.info('+ {} <- {} (retry={})'.format(filepath, url, retry))
def submit_download(self, url, filepath, retry, **kwargs_for_requests):
if self.file_already_exists(filepath):
return
future = self.submit(self.download, url, filepath, retry, **kwargs_for_requests)
self.log_new_download(url, filepath, retry)
return future
def put_download_in_queue(self, url, filepath, retry, **kwargs_for_requests):
if self.file_already_exists(filepath):
return
self.queue.put((url, filepath, retry, kwargs_for_requests))
self.log_new_download(url, filepath, retry)
def put_end_of_queue(self):
self.queue.put(None)
def start_queue_loop(self):
ez_thread_factory()(self.queue_pipeline).start()
def parse_https_url(url: str, allow_fragments=True) -> ParseResult:
test_parse = urlparse(url)
if not test_parse.scheme and not test_parse.netloc:
url = 'https://' + url
return urlparse(url, allow_fragments=allow_fragments)
def parse_http_url(url: str, allow_fragments=True) -> ParseResult:
test_parse = urlparse(url)
if not test_parse.scheme and not test_parse.netloc:
url = 'http://' + url
return urlparse(url, allow_fragments=allow_fragments)
def make_requests_kwargs(params=None, cookies=None, headers=None, user_agent=None, proxies=None,
**kwargs):
user_agent = user_agent or USER_AGENT_FIREFOX_WIN10
d = dict(headers=headers_from_user_agent(user_agent=user_agent, headers=headers))
if params:
d.update(params=params)
if cookies:
d.update(cookies=cookies)
if proxies:
d.update(proxies=proxies)
d.update(**kwargs)
return d
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] |
dl_l8s2_uv/satreaders/l8image.py | csaybar/DL-L8S2-UV | 13 | 47882 | <gh_stars>10-100
"""
Classes and functions for reading L8 images and manually annotated cloud masks from the Biome and 38-Cloud
cloud cover dataset.
https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data
"""
import os
from datetime import datetime
from datetime import timezone
import numpy as np
import rasterio
import dl_l8s2_uv.utils as utils
def read_metadata(metadata_file):
assert os.path.exists(metadata_file), "metadata file %s does not exist" % metadata_file
with open(metadata_file, "r") as mf:
lineas = mf.readlines()
dictio_sal = dict()
for l in lineas:
dato = [d.strip() for d in l.strip().split("=")]
if dato[0] == "GROUP":
dictio_sal[dato[1]] = dict()
curr_dict = dictio_sal[dato[1]]
elif (dato[0] == "END_GROUP") or (dato[0] == "END"):
continue
else:
curr_dict[dato[0]] = dato[1].replace('"', "")
return dictio_sal
def compute_toa_single(img, band, metadata, sun_elevation_correction=True):
"""
Readiomatric correction implemented in:
https://www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat-level-1-data-product
:param img:
:param band:
:param metadata:
:param sun_elevation_correction: whether or not to do the sun elevation correction
:return:
"""
band_name = str(band)
if band < 10:
dictio_rescaling = metadata["RADIOMETRIC_RESCALING"]
mult_key = "REFLECTANCE_MULT_BAND_" + band_name
img = img*float(dictio_rescaling[mult_key])
add_key = "REFLECTANCE_ADD_BAND_" + band_name
img += float(dictio_rescaling[add_key])
if sun_elevation_correction:
dictio_rescaling = metadata["IMAGE_ATTRIBUTES"]
sun_elevation_angle_key = "SUN_ELEVATION"
img /= np.sin(float(dictio_rescaling[sun_elevation_angle_key]) / 180. * np.pi)
else:
# (band == 10) or (band == 11)
dictio_rescaling = metadata["RADIOMETRIC_RESCALING"]
mult_key = "RADIANCE_MULT_BAND_" + band_name
img = img * float(dictio_rescaling[mult_key])
add_key = "RADIANCE_ADD_BAND_" + band_name
img += float(dictio_rescaling[add_key])
dictio_rescaling = metadata["TIRS_THERMAL_CONSTANTS"]
k1_key = "K1_CONSTANT_BAND_" + band_name
img = np.log(float(dictio_rescaling[k1_key])/img +1)
k2_key = "K1_CONSTANT_BAND_" + band_name
img = float(dictio_rescaling[k2_key])/img
return img
def load_l8_clouds_fmask(l8bqa):
"""
https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band
https://www.usgs.gov/land-resources/nli/landsat/cfmask-algorithm
:param l8bqa:
:return:
"""
return (l8bqa & (1 << 4)) != 0
SAMPLE_BANDS = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11] # All bands except B8 (pancromatic with different spatial resolution)
ORIENTATION = {"NORTH_UP": "north", "SOUTH_UP": "south"}
class L8Image:
"""
Class to load L1T Landsat-8 image
:param folder_tiffs: folder where the tiffs and metadata stored.
:param slice_rows_cols: list of slices=[slice(100,200),slice(100,200)]
to read only specific locations of the image
"""
def __init__(self, folder_tiffs, slice_rows_cols=None):
if folder_tiffs.endswith('/'):
folder_tiffs = folder_tiffs[:-1]
self.folder_tiffs = folder_tiffs
self.folder = folder_tiffs
self.name = os.path.basename(folder_tiffs)
self.satname = "L8"
self.metadata = self._read_metadata()
dictio_metadata = self.metadata["PRODUCT_METADATA"]
self.polygon = np.array([[float(dictio_metadata["CORNER_LL_LON_PRODUCT"]),
float(dictio_metadata["CORNER_LL_LAT_PRODUCT"])],
[float(dictio_metadata["CORNER_UL_LON_PRODUCT"]),
float(dictio_metadata["CORNER_UL_LAT_PRODUCT"])],
[float(dictio_metadata["CORNER_UR_LON_PRODUCT"]),
float(dictio_metadata["CORNER_UR_LAT_PRODUCT"])],
[float(dictio_metadata["CORNER_LR_LON_PRODUCT"]),
float(dictio_metadata["CORNER_LR_LAT_PRODUCT"])]])
proj_param = self.metadata["PROJECTION_PARAMETERS"]
zone = "" if "UTM_ZONE" not in proj_param else " +zone=%s" % proj_param["UTM_ZONE"]
self.crs_string_biome = "+proj=%s%s +ellps=%s +datum=%s +units=m +%s" % (proj_param["MAP_PROJECTION"].lower(),
zone,
proj_param["ELLIPSOID"],
proj_param["DATUM"],
ORIENTATION[proj_param["ORIENTATION"]])
pm = self.metadata["PRODUCT_METADATA"]
trans = [float(pm["CORNER_UL_PROJECTION_X_PRODUCT"]), float(pm["CORNER_UL_PROJECTION_Y_PRODUCT"])]
self.rasterio_transform = self.src_rasterio().transform
self.transform_numpy = np.array([[30, 0, trans[0]], [0, -30, trans[1]]])
self.nrows = int(pm["REFLECTIVE_LINES"])
self.ncols = int(pm["REFLECTIVE_SAMPLES"])
self.start_date = datetime.strptime(dictio_metadata["DATE_ACQUIRED"] + " " +
dictio_metadata["SCENE_CENTER_TIME"][:8],
"%Y-%m-%d %H:%M:%S").replace(tzinfo=timezone.utc)
self.end_date = self.start_date
if slice_rows_cols is None:
self.slice = (slice(0, self.nrows), slice(0, self.ncols))
else:
self.slice = tuple(slice_rows_cols)
def crs_proj(self):
import rasterio
fileband_name = os.path.join(self.folder_tiffs, self.name + "_B2.TIF")
with rasterio.open(fileband_name, "r") as src:
src_crs = src.crs
return src_crs
@property
def transform(self):
import rasterio
fileband_name = os.path.join(self.folder_tiffs, self.name + "_B2.TIF")
with rasterio.open(fileband_name, "r") as src:
tr = src.transform
return np.array([[tr.a, tr.b, tr.c], [tr.d, tr.e, tr.f]])
def __str__(self):
return self.folder_tiffs
def _read_metadata(self):
metadata_file = os.path.join(self.folder_tiffs, self.name + "_MTL.txt")
return read_metadata(metadata_file)
def load_mask(self, slice_=None):
bqa = self.load_bqa(slice_=slice_)
return bqa == 1
def load_bqa(self, slice_=None):
if slice_ is None:
slice_ = (slice(None), slice(None))
with rasterio.open(os.path.join(self.folder_tiffs, self.name + "_BQA.TIF"), "r") as src:
bqa = src.read(1, window=slice_)
return bqa[slice_]
def load_fmask(self, slice_=None):
bqa = self.load_bqa(slice_=slice_)
return load_l8_clouds_fmask(bqa)
def load_band(self, band, compute_toa_flag=True,
sun_elevation_correction=True,
slice_=None):
"""
https://www.usgs.gov/land-resources/nli/landsat/using-usgs-landsat-level-1-data-product
Lλ = MLQcal + AL
Lλ = TOA spectral radiance (Watts/( m2 * srad * μm))
ML = Band-specific multiplicative rescaling factor from the metadata (RADIANCE_MULT_BAND_x, where x is the band number)
AL = Band-specific additive rescaling factor from the metadata (RADIANCE_ADD_BAND_x, where x is the band number)
Qcal = Quantized and calibrated standard product pixel values (DN)
:param band:
:param compute_toa_flag:
:param sun_elevation_correction:
:param slice_: slice to read
:return:
"""
band_name = str(band)
fileband_name = os.path.join(self.folder_tiffs, self.name + \
"_B" + band_name + ".TIF")
if slice_ is None:
slice_ = self.slice
with rasterio.open(fileband_name, "r") as src:
img = src.read(1, window=slice_).astype(np.float32)
if compute_toa_flag:
return compute_toa_single(img, band, self.metadata,
sun_elevation_correction=sun_elevation_correction)
return img
def src_rasterio(self):
fileband_name = os.path.join(self.folder_tiffs, self.name + \
"_B2.TIF")
return rasterio.open(fileband_name, "r")
def load_bands(self, bands=None, masked=True,
compute_toa_flag=True,
sun_elevation_correction=True,
axis_stack=2,
slice_=None):
"""
load the bands `bands` and stack them over the `axis_stack` axis.
:param bands: Bands to read (band 8 has different spatial resolution)
:param masked: if the bands should be read with the mask
:param compute_toa_flag:
:param axis_stack: axis to stack the bands (0 or 2)
:param sun_elevation_correction: wether or not to apply the sun elevation correction
:param slice_:
:return: the 3D multispectral image
"""
img = None
if bands is None:
bands = SAMPLE_BANDS
assert len(bands) == 1 or (8 not in bands), "Cannot load panchromatic band (B8) together with other bands because it has different resolution"
assert axis_stack in {0, 2}, "Expected to stack on first or last dimension"
for i,k in enumerate(bands):
b = self.load_band(k,compute_toa_flag=compute_toa_flag,
sun_elevation_correction=sun_elevation_correction,
slice_=slice_)
if img is None:
if axis_stack == 2:
img = np.ndarray(b.shape+(len(bands),),dtype=b.dtype)
else:
img = np.ndarray((len(bands),) + b.shape, dtype=b.dtype)
if axis_stack == 2:
img[..., i] = b
else:
img[i] = b
if masked:
mask = self.load_mask(slice_)
mask = utils.mask_2D_to_3D(mask, img.shape[axis_stack])
img = np.ma.masked_array(img, mask)
return img
class Biome(L8Image):
"""
Class to deal with the L8 cloud validation dataset downloaded from https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data
:param folder_tiffs: folder where the tiffs, metadata and envi fixedmask are stored.
"""
def __init__(self, s2_folder):
L8Image.__init__(self, s2_folder)
self.hdr_file = os.path.join(self.folder_tiffs, self.name + "_fixedmask.hdr")
assert os.path.exists(self.hdr_file), "fixedmask not found: {}".format(self.hdr_file)
def _load_fixemask(self, slice_=None):
import spectral.io.envi as envi
if slice_ is None:
slice_ = (slice(None), slice(None))
return envi.open(self.hdr_file)[slice_]
def load_mask(self, slice_=None):
mask_envi = self._load_fixemask(slice_)
return mask_envi[..., 0] == 0
def load_clouds(self, slice_=None):
"""
The interpretation for the bits in each manual mask is as follows:
Value Interpretation
----------------------
0 Fill
64 Cloud Shadow
128 Clear
192 Thin Cloud
255 Cloud
:return:
"""
mask_envi = self._load_fixemask(slice_)
return cloudmask(mask_envi[..., 0])
def cloudmask(fixedmask):
"""
The interpretation for the bits in each manual mask is as follows:
Value Interpretation
----------------------
0 Fill
64 Cloud Shadow
128 Clear
192 Thin Cloud
255 Cloud
:return:
"""
mask = (fixedmask == 0)
mask_envi = np.uint8((fixedmask == 192) | (fixedmask == 255))
return np.ma.masked_array(mask_envi, mask)
def cloud_shadow_mask(fixedmask):
"""
The interpretation for the bits in each manual mask is as follows:
Value Interpretation
----------------------
0 Fill
64 Cloud Shadow
128 Clear
192 Thin Cloud
255 Cloud
:return:
"""
mask = (fixedmask == 0)
mask_envi = np.zeros(fixedmask.shape,dtype=np.uint8)
mask_envi[(fixedmask == 192) | (fixedmask == 255)] = 2
mask_envi[(fixedmask == 64)] = 1
return np.ma.masked_array(mask_envi, mask)
class L8_38Clouds(L8Image):
"""
Class to deal with the 38-Clouds cloud validation dataset.
GT masks downloaded from https://www.kaggle.com/sorour/38cloud-cloud-segmentation-in-satellite-images
(Entire_scene_gts folder)
Landsat-8 images downloaded from the Earth Explorers' portal: https://earthexplorer.usgs.gov/
:param folder_tiffs: folder where the tiffs, metadata and edited_corrected_gts.
"""
def __init__(self, folder_tiffs):
L8Image.__init__(self, folder_tiffs)
self.gt_file = os.path.join(self.folder_tiffs, 'edited_corrected_gts' + self.name + ".TIF")
assert os.path.exists(self.hdf5_file), "edited_corrected_gts not found: {}".format(self.gt_file)
def _load_fixemask(self, slice_=None):
if slice_ is None:
slice_ = (slice(None), slice(None))
with rasterio.open(self.gt_file) as src_gt:
img = src_gt.read(1, window=slice_)
return img
def load_mask(self, slice_=None):
img = self._load_fixemask(slice_)
return img == 0
def load_clouds(self, slice_=None):
"""
The interpretation for the bits in each manual mask is as follows:
Value Interpretation
----------------------
0 Fill
1 Clear
2 Cloud
:return:
"""
img = self._load_fixemask(slice_)
mask = (img == 0)
mask_clouds = np.uint8(img == 2)
return np.ma.masked_array(mask_clouds, mask)
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] |
extract_code.py | sajjad2014/vq-vae-2-pytorch | 1,007 | 199465 | <filename>extract_code.py
import argparse
import pickle
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import lmdb
from tqdm import tqdm
from dataset import ImageFileDataset, CodeRow
from vqvae import VQVAE
def extract(lmdb_env, loader, model, device):
index = 0
with lmdb_env.begin(write=True) as txn:
pbar = tqdm(loader)
for img, _, filename in pbar:
img = img.to(device)
_, _, _, id_t, id_b = model.encode(img)
id_t = id_t.detach().cpu().numpy()
id_b = id_b.detach().cpu().numpy()
for file, top, bottom in zip(filename, id_t, id_b):
row = CodeRow(top=top, bottom=bottom, filename=file)
txn.put(str(index).encode('utf-8'), pickle.dumps(row))
index += 1
pbar.set_description(f'inserted: {index}')
txn.put('length'.encode('utf-8'), str(index).encode('utf-8'))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--size', type=int, default=256)
parser.add_argument('--ckpt', type=str)
parser.add_argument('--name', type=str)
parser.add_argument('path', type=str)
args = parser.parse_args()
device = 'cuda'
transform = transforms.Compose(
[
transforms.Resize(args.size),
transforms.CenterCrop(args.size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
dataset = ImageFileDataset(args.path, transform=transform)
loader = DataLoader(dataset, batch_size=128, shuffle=False, num_workers=4)
model = VQVAE()
model.load_state_dict(torch.load(args.ckpt))
model = model.to(device)
model.eval()
map_size = 100 * 1024 * 1024 * 1024
env = lmdb.open(args.name, map_size=map_size)
extract(env, loader, model, device)
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] |
tests/unit/downloader/test_data_collector.py | ebi-ait/ingest-client | 0 | 62483 | import json
import os
import unittest
from unittest.mock import MagicMock
from ingest.api.ingestapi import IngestApi
from ingest.downloader.data_collector import DataCollector
class DataCollectorTest(unittest.TestCase):
def setUp(self) -> None:
self.maxDiff = None
self.mock_ingest_api = MagicMock(spec=IngestApi)
self.data_collector = DataCollector(self.mock_ingest_api)
self.script_dir = os.path.dirname(__file__)
self.parent_dir = os.path.split(self.script_dir)[0]
self.resources_dir = os.path.join(self.parent_dir, 'resources')
def test_collect_data_by_submission_uuid_returns_correctly(self):
# given
project = self._make_project_data()
self._mock_ingest_api(project)
expected_json = [project['project']] + \
project['biomaterials'] + \
project['processes'] + \
project['protocols'] + \
project['files']
# when
project_uuid = '1234'
entity_dict = self.data_collector.collect_data_by_submission_uuid(project_uuid)
# then
self._assert_all_entities_are_created(entity_dict, expected_json)
self._assert_entities_have_correct_inputs(entity_dict)
def _assert_all_entities_are_created(self, entity_dict, expected_json):
expected_content_list = [entity['content'] for entity in expected_json]
actual_content_list = [entity.content for entity in entity_dict.values()]
self.assertCountEqual(expected_content_list, actual_content_list)
def _assert_entities_have_correct_inputs(self, entity_dict):
specimen = entity_dict['6197380b2807a377aad3a303']
donor = entity_dict['6197380b2807a377aad3a302']
process = entity_dict['6197380b2807a377aad3a30c']
protocols = [entity_dict['6197380b2807a377aad3a307']]
self.assertEqual(specimen.inputs, [donor])
self.assertEqual(specimen.process, process)
self.assertEqual(specimen.protocols, protocols)
self.assertCountEqual([input.id for input in specimen.inputs], [donor.id], 'The specimen has no donor input.')
self.assertEqual(specimen.process.id, process.id,
'The process which links the specimen to the donor is missing.')
self.assertEqual([protocol.id for protocol in specimen.protocols], [protocol.id for protocol in protocols],
'The protocols for the process which links the specimen to the donor are incorrect.')
cell_suspension = entity_dict['6197380b2807a377aad3a304']
file = entity_dict['6197380b2807a377aad3a306']
assay_process = entity_dict['6197380b2807a377aad3a30e']
assay_process_protocols = [entity_dict['6197380b2807a377aad3a30a'], entity_dict['6197380b2807a377aad3a30b']]
self.assertCountEqual([input.id for input in file.inputs], [cell_suspension.id],
'The sequencing file has no cell suspension input.')
self.assertEqual(file.process.id, assay_process.id,
'The process which links the file to the cell suspension is missing.')
self.assertEqual([protocol.id for protocol in file.protocols],
[protocol.id for protocol in assay_process_protocols],
'The protocols for the process which links the file to the cell suspension is incorrect.')
def _mock_ingest_api(self, project):
self.mock_ingest_api.get_submission_by_uuid.return_value = project['submission']
self.mock_ingest_api.get_related_project.return_value = project['project']
response = MagicMock()
response.json.return_value = project['linking_map']
self.mock_ingest_api.get.return_value = response
self.mock_ingest_api.get_related_entities.side_effect = \
[
iter(project['biomaterials']),
iter(project['processes']),
iter(project['protocols']),
iter(project['files'])
]
def _make_project_data(self):
with open(self.resources_dir + '/mock_submission.json') as file:
mock_submission_json = json.load(file)
with open(self.resources_dir + '/mock_project.json') as file:
mock_project_json = json.load(file)
with open(self.resources_dir + '/mock_biomaterials.json') as file:
mock_biomaterials_json = json.load(file)
with open(self.resources_dir + '/mock_processes.json') as file:
mock_processes_json = json.load(file)
with open(self.resources_dir + '/mock_protocols.json') as file:
mock_protocols_json = json.load(file)
with open(self.resources_dir + '/mock_files.json') as file:
mock_files_json = json.load(file)
with open(self.resources_dir + '/linking-map.json') as file:
linking_map = json.load(file)
return {
'submission': mock_submission_json,
'project': mock_project_json,
'biomaterials': mock_biomaterials_json,
'processes': mock_processes_json,
'protocols': mock_protocols_json,
'files': mock_files_json,
'linking_map': linking_map
}
if __name__ == '__main__':
unittest.main()
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insta/forms.py | Samfan01/instagram | 0 | 1606332 | <gh_stars>0
from django import forms
from .models import Image,Profile,Comment
class ImageForm(forms.ModelForm):
class Meta:
model = Image
fields = ('image_name','image_caption','image')
widgets = {
'image_name': forms.TextInput(attrs = {'class': 'form-control'}),
'image_caption': forms.Textarea(attrs = {'class': 'form-control'}),
}
class ProfileForm(forms.ModelForm):
class Meta:
model = Profile
fields = ('first_name','last_name','bio','profile_pic')
widgets = {
'first_name': forms.TextInput(attrs = {'class': 'form-control'}),
'last_name': forms.TextInput(attrs = {'class': 'form-control'}),
'bio': forms.Textarea(attrs = {'class': 'form-control'}),
}
class CommentForm(forms.ModelForm):
class Meta:
model = Comment
fields = ('comment',)
widgets = {
'comment':forms.TextInput(attrs ={'placeholder':'add your comment','rows': 5}),
} | [
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backend/portal/management/commands/load_target_populations.py | pennlabs/student-life | 7 | 156859 | <filename>backend/portal/management/commands/load_target_populations.py
import requests
from django.core.management.base import BaseCommand
from django.utils import timezone
from portal.models import TargetPopulation
class Command(BaseCommand):
def handle(self, *args, **kwargs):
# loads majors, years, schools, and degrees onto TargetPopulations
# runs get_or_create to ensure no duplicates
majors = requests.get("https://platform.pennlabs.org/accounts/majors/").json()
schools = requests.get("https://platform.pennlabs.org/accounts/schools/").json()
degrees = self.get_degrees()
for major in majors:
TargetPopulation.objects.get_or_create(
kind=TargetPopulation.KIND_MAJOR, population=major["name"]
)
for school in schools:
TargetPopulation.objects.get_or_create(
kind=TargetPopulation.KIND_SCHOOL, population=school["name"]
)
for degree in degrees:
TargetPopulation.objects.get_or_create(
kind=TargetPopulation.KIND_DEGREE, population=degree
)
for year in self.get_years():
TargetPopulation.objects.get_or_create(kind=TargetPopulation.KIND_YEAR, population=year)
self.stdout.write("Uploaded Target Populations!")
def get_degrees(self):
return ["BACHELORS", "MASTERS", "PHD", "PROFESSIONAL"]
def get_years(self):
# creates new class year in August in preparation for upcoming school year
return (
[timezone.localtime().year + x for x in range(4)]
if timezone.localtime().month < 8
else [timezone.localtime().year + x for x in range(1, 5)]
)
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app/src/main/python/menu.py | 108360224/watch_video | 0 | 1605327 | <reponame>108360224/watch_video<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Sat May 2 10:47:12 2020
@author: max
"""
from bs4 import BeautifulSoup
import requests
import re
class Menu():
def __init__(self,URL='http://www.99kubo.tv'):
html = requests.get(URL).text
self.soup = BeautifulSoup(html, 'lxml')
self.URL=URL
re_channel=re.compile('mm\d{0,3}')
channel_elem = self.soup.select_one('body > div.top > div.menu > div.mainmenu_top')
menu_href = tuple(a['href'] for a in channel_elem.find_all('a',{'id':re_channel,'target':""}))
menu_string = tuple(a.string for a in channel_elem.find_all('a',{'id':re_channel,'target':""}))
self.menu_list=(menu_string,menu_href)
sort_elem = self.soup.select_one('body > div.top > div.menu > div.mainmenu_bottom')
sort_elem_ul=sort_elem.select('ul')
re_href=re.compile('$(?!html)')
sort_href=tuple(tuple(re.sub(r'^(?!/)','/',a['href']) for a in ul.find_all('a',{'href':re_href}) if re.match(r'(?!\d){1,4}', a.string))[:-10] for ul in sort_elem_ul)+((),)
sort_string=tuple(tuple(a.string for a in ul.find_all('a',{'href':re_href}) if re.match(r'(?!\d){1,4}', a.string))[:-10] for ul in sort_elem_ul)+((),)
self.sort_list=(sort_string,sort_href)
def get_menu_list(self):
return self.menu_list
def get_child_list(self):
return self.sort_list
def get_link(self,menu,sort=''):
URL=self.URL
try:
if sort=='':
URL+=self.menu_list[1][self.menu_list[0].index(menu)]
else:
dex=self.menu_list[0].index(menu)
URL+=self.sort_list[1][dex][self.sort_list[0][dex].index(sort)]
except:
URL+=""
return URL | [
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radvel/basis.py | lgbouma/radvel | 0 | 68067 | <reponame>lgbouma/radvel
import numpy as np
import pandas as pd
from collections import OrderedDict
from radvel.orbit import timeperi_to_timetrans, timetrans_to_timeperi
import radvel.model
BASIS_NAMES = ['per tp e w k', # The synth basis
'per tc secosw sesinw logk',
'per tc secosw sesinw k',
'per tc ecosw esinw k',
'per tc e w k',
'logper tc secosw sesinw k',
'logper tc secosw sesinw logk',
'per tc se w k',
'logper tp e w logk']
ECCENTRICITY_PARAMS_DICT = {
'per tp e w k': 'e w',
'per tc secosw sesinw logk': 'secosw sesinw',
'per tc secosw sesinw k': 'secosw sesinw',
'per tc ecosw esinw k': 'ecosw esinw',
'per tc e w k': 'e w',
'logper tc secosw sesinw k': 'secosw sesinw',
'logper tc secosw sesinw logk': 'secosw sesinw',
'per tc se w k': 'se w',
'logper tp e w logk': 'e w'}
CIRCULAR_PARAMS_DICT = {
'per tp e w k': 'per tp k',
'per tc secosw sesinw logk': 'per tc logk',
'per tc secosw sesinw k': 'per tc k',
'per tc ecosw esinw k': 'per tc k',
'per tc e w k': 'per tc k',
'logper tc secosw sesinw k': 'logper tc k',
'logper tc secosw sesinw logk': 'logper tc logk',
'per tc se w k': 'per tc k',
'logper tp e w logk': 'logper tp logk'}
def _print_valid_basis():
print("Available bases:")
print("\n".join(BASIS_NAMES))
def _copy_params(params_in):
num_planets = params_in.num_planets
basis = params_in.basis.name
planet_letters = params_in.planet_letters
params_out = radvel.model.Parameters(num_planets, basis=basis,
planet_letters=planet_letters)
params_out.update(params_in)
return params_out
class Basis(object):
"""
Object that knows how to convert between the various Keplerian bases
Args:
name (str): basis name
num_planets (int): number of planets
Attributes:
synth_params (str): name of synth basis
Note:
Valid basis functions: \n
'per tp e w k' (The synthesis basis) \n
'per tc secosw sesinw logk' \n
'per tc secosw sesinw k' \n
'per tc ecosw esinw k' \n
'per tc e w k' \n
'logper tc secosw sesinw k'\n
'logper tc secosw sesinw logk'\n
'per tc se w k'
"""
synth_params = 'per tp e w k'.split()
def __init__(self, *args):
self.name = None
self.num_planets = 0
if len(args) == 0:
_print_valid_basis()
# return None
name, num_planets = args
if BASIS_NAMES.count(name) == 0:
print("{} not valid basis".format(name))
_print_valid_basis()
# return None
self.name = name
self.num_planets = num_planets
self.params = name.split()
def __repr__(self):
return "Basis Object <{}>".format(self.name)
def to_any_basis(self, params_in, newbasis):
"""Convenience function for converting Parameters object to an arbitraty basis
Args:
params_in (radvel.Parameters): radvel.Parameters object expressed in current basis
newbasis (string): string corresponding to basis to switch into
Returns:
radvel.Parameters object expressed in the new basis
"""
synth_params = self.to_synth(params_in)
arbbasis_params = self.from_synth(synth_params, newbasis, keep=False)
return arbbasis_params
def to_synth(self, params_in, **kwargs):
"""Convert to synth basis
Convert Parameters object with parameters of a given basis into the
synth basis
Args:
params_in (radvel.Parameters or pandas.DataFrame): radvel.Parameters object or pandas.Dataframe containing
orbital parameters expressed in current basis
noVary (bool [optional]): if True, set the 'vary' attribute of the returned Parameter objects
to '' (used for displaying best fit parameters)
Returns:
Parameters or DataFrame: parameters expressed in the synth basis
"""
basis_name = kwargs.setdefault('basis_name', self.name)
if isinstance(params_in, pd.core.frame.DataFrame):
# Output by emcee
params_out = params_in.copy()
else:
params_out = _copy_params(params_in)
for num_planet in range(1, 1+self.num_planets):
def _getpar(key):
if isinstance(params_in, pd.core.frame.DataFrame):
return params_in['{}{}'.format(key, num_planet)]
else:
return params_in['{}{}'.format(key, num_planet)].value
def _setpar(key, new_value):
key_name = '{}{}'.format(key, num_planet)
if isinstance(params_in, pd.core.frame.DataFrame):
params_out[key_name] = new_value
else:
if key_name in params_in:
local_vary = params_in[key_name].vary
local_mcmcscale = params_in[key_name].mcmcscale
elif kwargs.get('noVary', True):
local_vary = ''
local_mcmcscale = None
else:
local_vary = True
local_mcmcscale = None
params_out[key_name] = radvel.model.Parameter(value=new_value,
vary=local_vary,
mcmcscale=local_mcmcscale)
# transform into synth basis
if basis_name == 'per tp e w k':
# already in the synth basis
per = _getpar('per')
tp = _getpar('tp')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if basis_name == 'per tc e w k':
per = _getpar('per')
tc = _getpar('tc')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'per tc se w k':
# pull out parameters
per = _getpar('per')
tc = _getpar('tc')
se = _getpar('se')
w = _getpar('w')
k = _getpar('k')
e = se**2
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'per tc secosw sesinw logk':
# pull out parameters
per = _getpar('per')
tc = _getpar('tc')
secosw = _getpar('secosw')
sesinw = _getpar('sesinw')
logk = _getpar('logk')
k = np.exp(logk)
e = secosw**2 + sesinw**2
w = np.arctan2(sesinw, secosw)
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'per tc secosw sesinw k':
# pull out parameters
per = _getpar('per')
tc = _getpar('tc')
secosw = _getpar('secosw')
sesinw = _getpar('sesinw')
k = _getpar('k')
# transform into synth basis
e = secosw**2 + sesinw**2
w = np.arctan2(sesinw, secosw)
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'logper tc secosw sesinw k':
# pull out parameters
logper = _getpar('logper')
tc = _getpar('tc')
secosw = _getpar('secosw')
sesinw = _getpar('sesinw')
k = _getpar('k')
# transform into synth basis
per = np.exp(logper)
e = secosw**2 + sesinw**2
w = np.arctan2(sesinw, secosw)
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'logper tc secosw sesinw logk':
# pull out parameters
logper = _getpar('logper')
tc = _getpar('tc')
secosw = _getpar('secosw')
sesinw = _getpar('sesinw')
k = _getpar('logk')
# transform into synth basis
per = np.exp(logper)
e = secosw ** 2 + sesinw ** 2
k = np.exp(k)
w = np.arctan2(sesinw, secosw)
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'per tc ecosw esinw k':
# pull out parameters
per = _getpar('per')
tc = _getpar('tc')
ecosw = _getpar('ecosw')
esinw = _getpar('esinw')
k = _getpar('k')
# transform into synth basis
e = np.sqrt(ecosw**2 + esinw**2)
w = np.arctan2(esinw, ecosw)
tp = timetrans_to_timeperi(tc, per, e, w)
if basis_name == 'logper tp e w logk':
# pull out parameters
logper = _getpar('logper')
tp = _getpar('tp')
e = _getpar('e')
w = _getpar('w')
k = _getpar('logk')
# transform into synth basis
per = np.exp(logper)
k = np.exp(k)
# shoves synth parameters from namespace into param_out
_setpar('per', per)
_setpar('tp', tp)
_setpar('e', e)
_setpar('w', w)
_setpar('k', k)
if isinstance(params_out, radvel.model.Parameters):
params_out.basis = Basis('per tp e w k', self.num_planets)
return params_out
def from_synth(self, params_in, newbasis, **kwargs):
"""Convert from synth basis into another basis
Convert instance of Parameters with parameters of a given basis into the synth basis
Args:
params_in (radvel.Parameters or pandas.DataFrame): radvel.Parameters object or pandas.Dataframe containing
orbital parameters expressed in current basis
newbasis (string): string corresponding to basis to switch into
keep (bool [optional]): keep the parameters expressed in
the old basis, else remove them from the output
dictionary/DataFrame
Returns:
dict or dataframe with the parameters converted into the new basis
"""
if newbasis not in BASIS_NAMES:
print("{} not valid basis".format(newbasis))
_print_valid_basis()
return None
if isinstance(params_in, pd.core.frame.DataFrame):
# Output by emcee
params_out = params_in.copy()
else:
params_out = _copy_params(params_in)
for num_planet in range(1, 1+self.num_planets):
def _getpar(key):
if isinstance(params_in, pd.core.frame.DataFrame):
return params_in['{}{}'.format(key, num_planet)]
else:
return params_in['{}{}'.format(key, num_planet)].value
def _setpar(key, new_value):
key_name = '{}{}'.format(key, num_planet)
if isinstance(params_in, pd.core.frame.DataFrame):
params_out[key_name] = new_value
else:
if key_name in params_in:
local_vary = params_in[key_name].vary
local_mcmcscale = params_in[key_name].mcmcscale
else:
local_vary = True
local_mcmcscale = None
params_out[key_name] = radvel.model.Parameter(value=new_value,
vary=local_vary,
mcmcscale=local_mcmcscale)
def _delpar(key):
if isinstance(params_in, OrderedDict):
del params_out['{}{}'.format(key, num_planet)]
elif isinstance(params_in, pd.core.frame.DataFrame):
params_out.drop('{}{}'.format(key, num_planet))
if newbasis == 'per tc e w k':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
tp = _getpar('tp')
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
_setpar('w', w)
if not kwargs.get('keep', True):
_delpar('tp')
if newbasis == 'per tc se w k':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
tp = _getpar('tp')
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
_setpar('w', w)
_setpar('se', np.sqrt(e))
if not kwargs.get('keep', True):
_delpar('tp')
_delpar('e')
if newbasis == 'per tc secosw sesinw logk':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if 'tp' in params_in.planet_parameters:
tp = _getpar('tp')
else:
tc = _getpar('tc')
tp = timetrans_to_timeperi(tc, per, e, w)
_setpar('tp', tp)
_setpar('secosw', np.sqrt(e)*np.cos(w))
_setpar('sesinw', np.sqrt(e)*np.sin(w))
_setpar('logk', np.log(k))
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
if not kwargs.get('keep', True):
_delpar('tp')
_delpar('e')
_delpar('w')
_delpar('k')
# basis_name = newbasis
self.params = newbasis.split()
if newbasis == 'per tc secosw sesinw k':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if 'tp' in params_in.planet_parameters:
tp = _getpar('tp')
else:
tc = _getpar('tc')
tp = timetrans_to_timeperi(tc, per, e, w)
_setpar('tp', tp)
_setpar('secosw', np.sqrt(e)*np.cos(w))
_setpar('sesinw', np.sqrt(e)*np.sin(w))
_setpar('k', k)
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
if not kwargs.get('keep', True):
_delpar('tp')
_delpar('e')
_delpar('w')
self.name = newbasis
self.params = newbasis.split()
if newbasis == 'logper tc secosw sesinw k':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if 'tp' in params_in.planet_parameters:
tp = _getpar('tp')
else:
tc = _getpar('tc')
tp = timetrans_to_timeperi(tc, per, e, w)
_setpar('tp', tp)
_setpar('logper', np.log(per))
_setpar('secosw', np.sqrt(e)*np.cos(w))
_setpar('sesinw', np.sqrt(e)*np.sin(w))
_setpar('k', k)
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
if not kwargs.get('keep', True):
_delpar('per')
_delpar('tp')
_delpar('e')
_delpar('w')
self.name = newbasis
self.params = newbasis.split()
if newbasis == 'logper tc secosw sesinw logk':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if 'tp' in params_in.planet_parameters:
tp = _getpar('tp')
else:
tc = _getpar('tc')
tp = timetrans_to_timeperi(tc, per, e, w)
_setpar('tp', tp)
_setpar('logper', np.log(per))
_setpar('secosw', np.sqrt(e)*np.cos(w))
_setpar('sesinw', np.sqrt(e)*np.sin(w))
_setpar('logk', np.log(k))
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
if not kwargs.get('keep', True):
_delpar('per')
_delpar('tp')
_delpar('e')
_delpar('w')
_delpar('k')
self.name = newbasis
self.params = newbasis.split()
if newbasis == 'per tc ecosw esinw k':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if 'tp' in params_in.planet_parameters:
tp = _getpar('tp')
else:
tc = _getpar('tc')
tp = timetrans_to_timeperi(tc, per, e, w)
_setpar('tp', tp)
_setpar('ecosw', e*np.cos(w))
_setpar('esinw', e*np.sin(w))
_setpar('k', k)
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
if not kwargs.get('keep', True):
_delpar('tp')
_delpar('e')
_delpar('w')
self.name = newbasis
self.params = newbasis.split()
if newbasis == 'logper tp e w logk':
per = _getpar('per')
e = _getpar('e')
w = _getpar('w')
k = _getpar('k')
if 'tp' in params_in.planet_parameters:
tp = _getpar('tp')
else:
tc = _getpar('tc')
tp = timetrans_to_timeperi(tc, per, e, w)
_setpar('tp', tp)
_setpar('logper', np.log(per))
_setpar('logk', np.log(k))
_setpar('tc', timeperi_to_timetrans(tp, per, e, w))
if not kwargs.get('keep', True):
_delpar('per')
_delpar('k')
self.name = newbasis
self.params = newbasis.split()
params_out.basis = Basis(newbasis, self.num_planets)
return params_out
def get_eparams(self):
"""Return the eccentricity parameters for the object's basis
Returns:
the params which have to do with eccentricity
"""
assert BASIS_NAMES.count(self.name) == 1, "Invalid basis"
eparamstring = ECCENTRICITY_PARAMS_DICT[self.name]
eparamlist = eparamstring.split()
assert len(eparamlist) == 2
return eparamlist
def get_circparams(self):
"""Return the 3 parameters for a circular orbit of a plent in the object's basis
Returns:
the params for a circular orbit
"""
assert BASIS_NAMES.count(self.name) == 1, "Invalid basis"
circparamstring = CIRCULAR_PARAMS_DICT[self.name]
circparamlist = circparamstring.split()
assert len(circparamlist) == 3
return circparamlist
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] |
pyScripts/crawlers_and_extraction/extraction_of_domain_doi.py | felipecuetor/WebSemantica_Proyecto | 0 | 72507 | <filename>pyScripts/crawlers_and_extraction/extraction_of_domain_doi.py
from selenium import webdriver
import time
import re
#from selenium.webdriver.common.keys import keys
def clean_string(str_to_clean):
clean_str = str_to_clean
clean_str = clean_str.replace(":", "<#><#>")
clean_str = clean_str.replace(";", "<$<$<")
clean_str = clean_str.replace(",", ">*>*>")
clean_str = clean_str.replace("\n", "")
return clean_str
def return_string(str_to_clean):
clean_str = str_to_clean
clean_str = clean_str.replace( "<#><#>",":")
clean_str = clean_str.replace("<$<$<",";")
clean_str = clean_str.replace(">*>*>",",")
return clean_str
driver = webdriver.Chrome()
with open('../../../data_final/doi_list.csv', 'w') as file:
file.close()
doi_iter_list = []
with open('../../../data_final/unified_doaj_arxiv_ceur_dblp.csv', 'r') as file:
for line in file:
split_doc = line.split(",")
external_link_split = split_doc[4].split(";")
for link in external_link_split:
if link.startswith("doi:"):
try:
doi_value = link.split(":")[1]
driver.get("https://doi.org/"+doi_value)
time.sleep(1)
#doi_iter_list.append(driver.current_url)
with open('../../../data_final/doi_list.csv', 'a') as file:
file.write(doi_value+" "+driver.current_url+"\n")
file.close()
except Exception as e:
print e
file.close()
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bwgpt2/workspace.py | daemon/bungeeweb-gpt2 | 0 | 75474 | <gh_stars>0
UUID_RESOLVE_FILENAME = 'uuid-resolv.json'
CHAT_LOG_FILENAME = 'chat-log.tsv' | [
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pymtl3/passes/rtlir/behavioral/BehavioralRTLIRTypeCheckL2Pass.py | mondO/pymtl3 | 0 | 1613979 | <reponame>mondO/pymtl3
#=========================================================================
# BehavioralRTLIRTypeCheckL2Pass.py
#=========================================================================
# Author : <NAME>
# Date : March 29, 2019
"""Provide L2 behavioral RTLIR type check pass."""
from collections import OrderedDict
from pymtl3 import Bits32
from pymtl3.passes.BasePass import BasePass, PassMetadata
from pymtl3.passes.rtlir.errors import PyMTLTypeError
from pymtl3.passes.rtlir.rtype import RTLIRDataType as rdt
from pymtl3.passes.rtlir.rtype import RTLIRType as rt
from . import BehavioralRTLIR as bir
from .BehavioralRTLIRTypeCheckL1Pass import BehavioralRTLIRTypeCheckVisitorL1
class BehavioralRTLIRTypeCheckL2Pass( BasePass ):
def __call__( s, m ):
"""Perform type checking on all RTLIR in rtlir_upblks."""
if not hasattr( m, '_pass_behavioral_rtlir_type_check' ):
m._pass_behavioral_rtlir_type_check = PassMetadata()
m._pass_behavioral_rtlir_type_check.rtlir_freevars = OrderedDict()
m._pass_behavioral_rtlir_type_check.rtlir_tmpvars = OrderedDict()
m._pass_behavioral_rtlir_type_check.rtlir_accessed = set()
visitor = BehavioralRTLIRTypeCheckVisitorL2(
m,
m._pass_behavioral_rtlir_type_check.rtlir_freevars,
m._pass_behavioral_rtlir_type_check.rtlir_accessed,
m._pass_behavioral_rtlir_type_check.rtlir_tmpvars
)
for blk in m.get_update_block_order():
visitor.enter( blk, m._pass_behavioral_rtlir_gen.rtlir_upblks[ blk ] )
class BehavioralRTLIRTypeCheckVisitorL2( BehavioralRTLIRTypeCheckVisitorL1 ):
def __init__( s, component, freevars, accessed, tmpvars ):
super().__init__(component, freevars, accessed)
s.tmpvars = tmpvars
s.BinOp_max_nbits = (bir.Add, bir.Sub, bir.Mult, bir.Div, bir.Mod, bir.Pow,
bir.BitAnd, bir.BitOr, bir.BitXor)
s.BinOp_left_nbits = ( bir.ShiftLeft, bir.ShiftRightLogic )
s.type_expect = {}
lhs_types = ( rt.Port, rt.Wire, rt.NetWire, rt.NoneType )
s.type_expect[ 'Assign' ] = {
'targets' : ( lhs_types, 'lhs of assignment must be signal/tmpvar!' ),
'value' : ( rt.Signal, 'rhs of assignment should be signal/const!' )
}
s.type_expect[ 'BinOp' ] = {
'left' : ( rt.Signal, 'lhs of binop should be signal/const!' ),
'right' : ( rt.Signal, 'rhs of binop should be signal/const!' ),
}
s.type_expect[ 'UnaryOp' ] = {
'operand' : ( rt.Signal, 'unary op only applies to signals and consts!' )
}
s.type_expect[ 'For' ] = {
'start' : ( rt.Const, 'the start of a for-loop must be a constant expression!' ),
'end':( rt.Const, 'the end of a for-loop must be a constant expression!' ),
'step':( rt.Const, 'the step of a for-loop must be a constant expression!' )
}
s.type_expect[ 'If' ] = {
'cond' : ( rt.Signal, 'the condition of if must be a signal!' )
}
s.type_expect[ 'IfExp' ] = {
'cond' : ( rt.Signal, 'the condition of if-exp must be a signal!' ),
'body' : ( rt.Signal, 'the body of if-exp must be a signal!' ),
'orelse' : ( rt.Signal, 'the else branch of if-exp must be a signal!' )
}
def eval_const_binop( s, l, op, r ):
"""Evaluate ( l op r ) and return the result as an integer."""
assert type( l ) == int or isinstance( l, pymtl3_datatype.Bits )
assert type( r ) == int or isinstance( r, pymtl3_datatype.Bits )
op_dict = {
bir.And : 'and', bir.Or : 'or',
bir.Add : '+', bir.Sub : '-', bir.Mult : '*', bir.Div : '/',
bir.Mod : '%', bir.Pow : '**',
bir.ShiftLeft : '<<', bir.ShiftRightLogic : '>>',
bir.BitAnd : '&', bir.BitOr : '|', bir.BitXor : '^',
}
_op = op_dict[ type( op ) ]
return eval( f'l{_op}r' )
def _visit_Assign_single_target( s, node, target, i ):
rhs_type = node.value.Type
lhs_type = target.Type
if isinstance( target, bir.TmpVar ):
tmpvar_id = (target.name, target.upblk_name)
if lhs_type != rt.NoneType() and lhs_type.get_dtype() != rhs_type.get_dtype():
raise PyMTLTypeError( s.blk, node.ast,
f'conflicting type {rhs_type} for temporary variable {node.targets[i].name}(LHS target#{i} of {lhs_type})!' )
# Creating a temporaray variable
# Reminder that a temporary variable is essentially a wire. So we use
# rt.Wire here instead of rt.NetWire
target.Type = rt.Wire( rhs_type.get_dtype() )
s.tmpvars[ tmpvar_id ] = rt.Wire( rhs_type.get_dtype() )
else:
# non-temporary assignment is an L1 thing
super()._visit_Assign_single_target( node, target, i )
def visit_Assign( s, node ):
# lhs_types = [ target.Type.get_dtype() for target in node.targets ]
# RHS should have the same type as LHS
for i, target in enumerate( node.targets ):
s._visit_Assign_single_target( node, target, i )
node.Type = None
def visit_If( s, node ):
# Can the type of condition be cast into bool?
dtype = node.cond.Type.get_dtype()
if not rdt.Bool()( dtype ):
raise PyMTLTypeError( s.blk, node.ast,
'the condition of "if" cannot be converted to bool!'
)
node.Type = None
def visit_For( s, node ):
try:
step = node.step._value
if step == 0:
raise PyMTLTypeError( s.blk, node.ast,
'the step of for-loop cannot be zero!' )
except AttributeError:
raise PyMTLTypeError( s.blk, node.ast,
'the step of for-loop must be a constant!' )
node.Type = None
def visit_LoopVar( s, node ):
node.Type = rt.Const( rdt.Vector( 32 ), None )
def visit_TmpVar( s, node ):
tmpvar_id = (node.name, node.upblk_name)
if tmpvar_id not in s.tmpvars:
# This tmpvar is being created. Later when it is used, its type can
# be read from the tmpvar type environment.
node.Type = rt.NoneType()
else:
node.Type = s.tmpvars[ tmpvar_id ]
def visit_IfExp( s, node ):
# Can the type of condition be cast into bool?
if not rdt.Bool()( node.cond.Type.get_dtype() ):
raise PyMTLTypeError( s.blk, node.ast,
'the condition of "if-exp" cannot be converted to bool!' )
# body and orelse must have the same type
# if node.body.Type != node.orelse.Type:
if not node.body.Type.get_dtype()( node.orelse.Type.get_dtype() ):
raise PyMTLTypeError( s.blk, node.ast,
'the body and orelse of "if-exp" must have the same type!' )
node.Type = node.body.Type
def visit_UnaryOp( s, node ):
if isinstance( node.op, bir.Not ):
dtype = node.operand.Type.get_dtype()
if not rdt.Bool()( dtype ):
raise PyMTLTypeError( s.blk, node.ast,
'the operand of "Logic-not" cannot be cast to bool!' )
if dtype.get_length() != 1:
raise PyMTLTypeError( s.blk, node.ast,
'the operand of "Logic-not" is not a single bit!' )
node.Type = rt.NetWire( rdt.Bool() )
else:
node.Type = node.operand.Type
if hasattr( node.operand, '_value' ):
opmap = {
bir.Invert : '~',
bir.Not : 'not',
bir.UAdd : '+',
bir.USub : '-',
}
try:
op = opmap[node.op.__class__]
operand = node.operand._value.uint()
node._value = eval(f"Bits32({op}{operand})")
except:
pass
def visit_BoolOp( s, node ):
for value in node.values:
if not isinstance(value.Type, rt.Signal) or not rdt.Bool()(value.Type.get_dtype()):
raise PyMTLTypeError( s.blk, node.ast,
f"{value} of {value.Type} cannot be cast into bool!")
node.Type = rt.NetWire( rdt.Bool() )
def visit_BinOp( s, node ):
op = node.op
l_type = node.left.Type.get_dtype()
r_type = node.right.Type.get_dtype()
if not( rdt.Vector(1)( l_type ) and rdt.Vector(1)( r_type ) ):
raise PyMTLTypeError( s.blk, node.ast,
f"both sides of {op.__class__.__name__} should be of vector type!" )
if not isinstance( op, s.BinOp_left_nbits ) and l_type != r_type:
raise PyMTLTypeError( s.blk, node.ast,
f"LHS and RHS of {op.__class__.__name__} should have the same type ({l_type} vs {r_type})!" )
l_nbits = l_type.get_length()
r_nbits = r_type.get_length()
# Enforcing Verilog bitwidth inference rules
res_nbits = 0
if isinstance( op, s.BinOp_max_nbits ):
res_nbits = max( l_nbits, r_nbits )
elif isinstance( op, s.BinOp_left_nbits ):
res_nbits = l_nbits
else:
raise Exception( 'RTLIRTypeCheck internal error: unrecognized op!' )
try:
# Both sides are constant expressions
l_val = node.left._value
r_val = node.rigth._value
node._value = s.eval_const_binop( l_val, op, r_val )
node.Type = rt.Const( rdt.Vector( res_nbits ) )
except AttributeError:
# Both sides are constant but the value cannot be determined statically
if isinstance(node.left.Type, rt.Const) and isinstance(node.right.Type, rt.Const):
node.Type = rt.Const( rdt.Vector( res_nbits ), None )
# Variable
else:
node.Type = rt.NetWire( rdt.Vector( res_nbits ) )
def visit_Compare( s, node ):
l_type = node.left.Type.get_dtype()
r_type = node.right.Type.get_dtype()
if l_type != r_type:
raise PyMTLTypeError( s.blk, node.ast,
f"LHS and RHS of {node.op.__class__.__name__} have different types ({l_type} vs {r_type})!" )
node.Type = rt.NetWire( rdt.Bool() )
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platform_disk_api/utils.py | neuro-inc/platform-disk-api | 0 | 155720 | from datetime import datetime, timedelta, timezone
def utc_now() -> datetime:
return datetime.now(timezone.utc)
def datetime_dump(dt: datetime) -> str:
return str(dt.timestamp())
def datetime_load(raw: str) -> datetime:
return datetime.fromtimestamp(float(raw), timezone.utc)
def timedelta_dump(td: timedelta) -> str:
return str(td.total_seconds())
def timedelta_load(raw: str) -> timedelta:
return timedelta(seconds=float(raw))
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wongkar_selling/wongkar_selling/doctype/rule/rule.py | Wahyulutfi97/wongkar_selling | 0 | 59067 | # -*- coding: utf-8 -*-
# Copyright (c) 2021, Wongkar and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
class Rule(Document):
def validate(self):
frappe.msgprint("after_insert")
# manufacture = frappe.get_value("Rule",{"item_code": self.item_code,"type": "Manufacture"}, "item_code")
# if manufacture:
# frappe.throw("Disconut Manufature Item "+manufacture+" sudah ada !")
# dealer = frappe.get_value("Rule",{"item_code": self.item_code,"type": "Dealer"}, "item_code")
# if dealer:
# frappe.throw("Disconut Dealer Item "+dealer+" sudah ada !")
# main_dealer = frappe.get_value("Rule",{"item_code": self.item_code,"type": "Main Dealer"}, "item_code")
# if main_dealer:
# frappe.throw("Disconut Main Dealer Item "+main_dealer+" sudah ada !")
leasing = frappe.db.get_value("Rule",{"item_code": self.item_code, "besar_dp" : self.besar_dp, "tenor": self.tenor}, "item_code")
if leasing:
frappe.throw("Disconut Item "+leasing+" sudah ada !")
if self.type == "Leasing" and self.besar_dp == "":
frappe.throw("Masukkan besad DP !")
if self.type == "Leasing" and self.tenor == "":
frappe.throw("Masukkan besad Tenor !")
# biaya_penjualan_kendaraan = frappe.get_value("Rule",{"item_code": self.item_code,"type": "Biaya Penjualan Kendaraan"}, "item_code")
# if biaya_penjualan_kendaraan:
# frappe.throw("Disconut Biaya Penjualan Kendaraan Item "+biaya_penjualan_kendaraan+" sudah ada !")
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] |
tensorflow/python/util/tf_should_use_test.py | npow/tensorflow | 0 | 4904 | # Copyright 2017 The TensorFlow 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.
# ==============================================================================
"""Unit tests for tf_should_use."""
# pylint: disable=unused-import
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
import gc
import sys
from tensorflow.python.framework import constant_op
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
from tensorflow.python.util import tf_should_use
@contextlib.contextmanager
def reroute_error():
"""Temporarily reroute errors written to tf_logging.error into `captured`."""
with test.mock.patch.object(tf_should_use.tf_logging, 'error') as error:
with test.mock.patch.object(tf_should_use.tf_logging, 'fatal') as fatal:
yield error, fatal
class TfShouldUseTest(test.TestCase):
def testAddShouldUseWarningWhenNotUsed(self):
c = constant_op.constant(0, name='blah0')
def in_this_function():
h = tf_should_use._add_should_use_warning(c)
del h
with reroute_error() as (error, _):
in_this_function()
error.assert_called()
msg = '\n'.join(error.call_args[0])
self.assertIn('Object was never used', msg)
self.assertIn('blah0:0', msg)
self.assertIn('in_this_function', msg)
self.assertFalse(gc.garbage)
def testAddShouldUseFatalWhenNotUsed(self):
c = constant_op.constant(0, name='blah0')
def in_this_function():
h = tf_should_use._add_should_use_warning(c, fatal_error=True)
del h
with reroute_error() as (_, fatal):
in_this_function()
fatal.assert_called()
msg = '\n'.join(fatal.call_args[0])
self.assertIn('Object was never used', msg)
self.assertIn('blah0:0', msg)
self.assertIn('in_this_function', msg)
self.assertFalse(gc.garbage)
def _testAddShouldUseWarningWhenUsed(self, fn, name):
c = constant_op.constant(0, name=name)
with reroute_error() as (error, fatal):
h = tf_should_use._add_should_use_warning(c)
fn(h)
del h
error.assert_not_called()
fatal.assert_not_called()
def testAddShouldUseWarningWhenUsedWithAdd(self):
def add(h):
_ = h + 1
self._testAddShouldUseWarningWhenUsed(add, name='blah_add')
gc.collect()
self.assertFalse(gc.garbage)
def testAddShouldUseWarningWhenUsedWithGetName(self):
def get_name(h):
_ = h.name
self._testAddShouldUseWarningWhenUsed(get_name, name='blah_get_name')
gc.collect()
self.assertFalse(gc.garbage)
def testShouldUseResult(self):
@tf_should_use.should_use_result
def return_const(value):
return constant_op.constant(value, name='blah2')
with reroute_error() as (error, _):
return_const(0.0)
error.assert_called()
msg = '\n'.join(error.call_args[0])
self.assertIn('Object was never used', msg)
self.assertIn('blah2:0', msg)
self.assertIn('return_const', msg)
gc.collect()
self.assertFalse(gc.garbage)
def testShouldUseResultWhenNotReallyUsed(self):
@tf_should_use.should_use_result
def return_const(value):
return constant_op.constant(value, name='blah3')
with reroute_error() as (error, _):
with self.test_session():
return_const(0.0)
# Creating another op and executing it does not mark the
# unused op as being "used".
v = constant_op.constant(1.0, name='meh')
v.eval()
error.assert_called()
msg = '\n'.join(error.call_args[0])
self.assertIn('Object was never used', msg)
self.assertIn('blah3:0', msg)
self.assertIn('return_const', msg)
gc.collect()
self.assertFalse(gc.garbage)
# Tests that mark_used is available in the API.
def testMarkUsed(self):
@tf_should_use.should_use_result
def return_const(value):
return constant_op.constant(value, name='blah3')
with self.test_session():
return_const(0.0).mark_used()
if __name__ == '__main__':
test.main()
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src/evaluators/parser_evaluator.py | shigashiyama/seikanlp | 4 | 114323 | import sys
from evaluators.common import AccuracyEvaluator, DoubleAccuracyEvaluator
class ParserEvaluator(AccuracyEvaluator):
def __init__(self, ignore_head=True):
super().__init__(ignore_head=ignore_head, ignored_labels=set())
class TypedParserEvaluator(DoubleAccuracyEvaluator):
def __init__(self, ignore_head=True, ignored_labels=set()):
super().__init__(ignore_head=ignore_head, ignored_labels=ignored_labels)
def report_results(self, sen_counter, counts, loss, file=sys.stderr):
ave_loss = loss / counts.l1.total
uas = 1.*counts.l1.correct / counts.l1.total
las = 1.*counts.l2.correct / counts.l2.total
print('ave loss: %.5f'% ave_loss, file=file)
print('sen, token, correct head, correct label: {} {} {} {}'.format(
sen_counter, counts.l1.total, counts.l1.correct, counts.l2.correct), file=file)
print('UAS, LAS:%6.2f %6.2f' % ((100.*uas), (100.*las)), file=file)
res = '%.2f\t%.2f\t%.4f' % ((100.*uas), (100.*las), ave_loss)
return res
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graphql_subscription_manager/__init__.py | Danielhiversen/PyGraphqlWebsocketManager | 0 | 26288 | """Subscription manager for Graph QL websocket."""
import asyncio
import json
import logging
import socket
import sys
from time import time
import pkg_resources
import websockets
_LOGGER = logging.getLogger(__name__)
STATE_STARTING = "starting"
STATE_RUNNING = "running"
STATE_STOPPED = "stopped"
try:
VERSION = pkg_resources.require("graphql-subscription-manager")[0].version
except Exception: # pylint: disable=broad-except
VERSION = "dev"
class SubscriptionManager:
"""Subscription manager."""
# pylint: disable=too-many-instance-attributes
def __init__(self, init_payload, url):
"""Create resources for websocket communication."""
try:
self.loop = asyncio.get_running_loop()
except RuntimeError:
self.loop = asyncio.get_event_loop()
self.subscriptions = {}
self._url = url
self._state = None
self.websocket = None
self._retry_timer = None
self._client_task = None
self._wait_time_before_retry = 15
self._session_id = 0
self._init_payload = init_payload
self._show_connection_error = True
self._is_running = False
self._user_agent = "Python/{0[0]}.{0[1]} PyGraphqlWebsocketManager/{1}".format(
sys.version_info, VERSION
)
def start(self):
"""Start websocket."""
_LOGGER.debug("Start state %s.", self._state)
if self._state == STATE_RUNNING:
return
self._state = STATE_STARTING
self._cancel_client_task()
self._client_task = self.loop.create_task(self.running())
for subscription in self.subscriptions.copy():
callback, sub_query = self.subscriptions.pop(subscription, (None, None))
_LOGGER.debug("Removed, %s", subscription)
if callback is None:
continue
_LOGGER.debug("Add subscription %s", callback)
self.loop.create_task(self.subscribe(sub_query, callback))
@property
def is_running(self):
"""Return if client is running or not."""
return self._is_running
async def running(self):
"""Start websocket connection."""
# pylint: disable=too-many-branches, too-many-statements
await self._close_websocket()
_LOGGER.debug("Starting")
try:
self.websocket = await asyncio.wait_for(
websockets.connect(
self._url,
subprotocols=["graphql-subscriptions"],
extra_headers={"User-Agent": self._user_agent},
),
timeout=30,
)
except Exception: # pylint: disable=broad-except
_LOGGER.debug("Failed to connect. Reconnecting... ", exc_info=True)
self._state = STATE_STOPPED
self.retry()
return
self._state = STATE_RUNNING
_LOGGER.debug("Running")
await self.websocket.send(
json.dumps({"type": "init", "payload": self._init_payload})
)
try:
k = 0
while self._state == STATE_RUNNING:
try:
msg = await asyncio.wait_for(self.websocket.recv(), timeout=30)
except asyncio.TimeoutError:
k += 1
if k > 10:
if self._show_connection_error:
_LOGGER.error("No data, reconnecting.")
self._show_connection_error = False
else:
_LOGGER.warning("No data, reconnecting.")
self._is_running = False
_LOGGER.debug("Reconnecting")
self._state = STATE_STOPPED
self.retry()
_LOGGER.debug(
"No websocket data in 30 seconds, checking the connection."
)
try:
pong_waiter = await self.websocket.ping()
await asyncio.wait_for(pong_waiter, timeout=10)
except asyncio.TimeoutError:
if self._show_connection_error:
_LOGGER.error(
"No response to ping in 10 seconds, reconnecting."
)
self._show_connection_error = False
else:
_LOGGER.warning(
"No response to ping in 10 seconds, reconnecting."
)
self._is_running = False
_LOGGER.debug("Reconnecting")
self._state = STATE_STOPPED
self.retry()
continue
k = 0
self._is_running = True
await self._process_msg(msg)
self._show_connection_error = True
except (websockets.exceptions.InvalidStatusCode, socket.gaierror):
if self._show_connection_error:
_LOGGER.error("Connection error", exc_info=True)
self._show_connection_error = False
else:
_LOGGER.debug("Connection error", exc_info=True)
except websockets.exceptions.ConnectionClosed:
if self._show_connection_error and self._state != STATE_STOPPED:
_LOGGER.error("Connection error", exc_info=True)
self._show_connection_error = False
else:
_LOGGER.debug("Connection error", exc_info=True)
except Exception: # pylint: disable=broad-except
_LOGGER.error("Unexpected error", exc_info=True)
finally:
await self._close_websocket()
if self._state != STATE_STOPPED:
_LOGGER.debug("Reconnecting")
self._state = STATE_STOPPED
self.retry()
_LOGGER.debug("Closing running task.")
async def stop(self, timeout=10):
"""Close websocket connection."""
_LOGGER.debug("Stopping client.")
start_time = time()
self._cancel_retry_timer()
for subscription_id in range(len(self.subscriptions)):
_LOGGER.debug("Sending unsubscribe: %s", subscription_id)
await self.unsubscribe(subscription_id)
while (
timeout > 0
and self.websocket is not None
and not self.subscriptions
and (time() - start_time) < timeout / 2
):
await asyncio.sleep(0.1)
self._state = STATE_STOPPED
await self._close_websocket()
while (
timeout > 0
and self.websocket is not None
and not self.websocket.closed
and (time() - start_time) < timeout
):
await asyncio.sleep(0.1)
self._cancel_client_task()
_LOGGER.debug("Server connection is stopped")
def retry(self):
"""Retry to connect to websocket."""
_LOGGER.debug("Retry, state: %s", self._state)
if self._state in [STATE_STARTING, STATE_RUNNING]:
_LOGGER.debug("Skip retry since state: %s", self._state)
return
_LOGGER.debug("Cancel retry timer")
self._cancel_retry_timer()
self._state = STATE_STARTING
_LOGGER.debug("Restart")
self._retry_timer = self.loop.call_later(
self._wait_time_before_retry, self.start
)
_LOGGER.debug(
"Reconnecting to server in %i seconds.", self._wait_time_before_retry
)
async def subscribe(self, sub_query, callback, timeout=3):
"""Add a new subscription."""
current_session_id = self._session_id
self._session_id += 1
subscription = {
"query": sub_query,
"type": "subscription_start",
"id": current_session_id,
}
json_subscription = json.dumps(subscription)
self.subscriptions[current_session_id] = (callback, sub_query)
start_time = time()
while time() - start_time < timeout:
if (
self.websocket is None
or not self.websocket.open
or not self._state == STATE_RUNNING
):
await asyncio.sleep(1)
continue
await self.websocket.send(json_subscription)
_LOGGER.debug("New subscription %s", current_session_id)
return current_session_id
async def unsubscribe(self, subscription_id):
"""Unsubscribe."""
if self.websocket is None or not self.websocket.open:
_LOGGER.warning("Websocket is closed.")
return
await self.websocket.send(
json.dumps({"id": subscription_id, "type": "subscription_end"})
)
if self.subscriptions and subscription_id in self.subscriptions:
self.subscriptions.pop(subscription_id)
async def _close_websocket(self):
if self.websocket is None:
return
try:
await self.websocket.close()
finally:
self.websocket = None
async def _process_msg(self, msg):
"""Process received msg."""
result = json.loads(msg)
_LOGGER.debug("Recv, %s", result)
if result.get("type") == "init_fail":
if (
result.get("payload", {}).get("error")
== "Too many concurrent sockets for token"
):
self._wait_time_before_retry = self._wait_time_before_retry * 2
if self._wait_time_before_retry >= 120:
_LOGGER.error(
"Connection is closed, too many concurrent sockets for token"
)
self._wait_time_before_retry = min(self._wait_time_before_retry, 600)
return
_LOGGER.error(result.get("payload", {}).get("error"))
return
subscription_id = result.get("id")
if subscription_id is None:
return
callback, _ = self.subscriptions.get(subscription_id, (None, None))
if callback is None:
_LOGGER.debug("Unknown id %s.", subscription_id)
return
if result.get("type", "") == "complete":
_LOGGER.debug("Unsubscribe %s successfully.", subscription_id)
return
data = result.get("payload")
if data is None:
return
self._wait_time_before_retry = 15
try:
await callback(data)
except TypeError as exp:
if "object NoneType can't be used in 'await' expression" in str(exp):
callback(data)
return
raise exp
def _cancel_retry_timer(self):
if self._retry_timer is None:
return
try:
self._retry_timer.cancel()
finally:
self._retry_timer = None
def _cancel_client_task(self):
if self._client_task is None:
return
try:
self._client_task.cancel()
finally:
self._client_task = None
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] |
livestyled/tests/test_resource_screens.py | livestyled/python-sdk | 0 | 1600662 | import os
import time
from livestyled.models import Screen, Widget, WidgetVariation
from livestyled.resource_client import LiveStyledResourceClient
from livestyled.tests.utils import configure_mock_responses
FIXTURES_DIR = os.path.join(os.path.dirname(__file__), 'fixtures')
TEST_API_DOMAIN = 'test.livestyled.com'
CONTENT_TYPE = 'application/ld+json'
REQUEST_BASE_URL = 'https://' + TEST_API_DOMAIN + '/v4/{}'
def test_get_screens(requests_mock):
mock_responses = (
('GET', REQUEST_BASE_URL.format('canvas/screens'), 'mock_responses/ls_api/canvas/screens.json', 200),
)
configure_mock_responses(requests_mock, mock_responses, FIXTURES_DIR, CONTENT_TYPE)
resource_client = LiveStyledResourceClient(TEST_API_DOMAIN, 'bar')
screens = resource_client.get_screens()
assert isinstance(next(screens), Screen)
def test_get_screens_widget_by_audience(requests_mock):
collection_endpoint = REQUEST_BASE_URL.format('canvas/screens/1/widgets_by_audience?audience=1')
mock_responses = (
('GET', collection_endpoint, 'mock_responses/ls_api/canvas/widgets_by_audience.json', 200),
)
configure_mock_responses(requests_mock, mock_responses, FIXTURES_DIR, CONTENT_TYPE)
resource_client = LiveStyledResourceClient(TEST_API_DOMAIN, 'bar')
widgets = resource_client.get_widgets_by_audience(screen_id=1, audience_id=1, timestamp=time.time())
current_widget = next(widgets)
assert isinstance(current_widget, Widget)
assert isinstance(current_widget.variation, WidgetVariation)
def test_get_screens_item(requests_mock):
item_endpoint = REQUEST_BASE_URL.format('canvas/screens/1')
mock_responses = (
('GET', item_endpoint, 'mock_responses/ls_api/canvas/item_screen.json', 200),
)
configure_mock_responses(requests_mock, mock_responses, FIXTURES_DIR, CONTENT_TYPE)
resource_client = LiveStyledResourceClient(TEST_API_DOMAIN, 'bar')
widget = resource_client.get_screen(id=1)
assert isinstance(widget, Screen)
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text_util.py | liuzl/nlp4econ | 3 | 113669 | #encoding=utf-8
import re
RE_SEN_SPLITER = u"(.+?(\r|\n|。|!|！|\?|？|;|；|……))"
def get_sentences(text):
if not isinstance(text, (unicode,)): return []
s = re.sub(RE_SEN_SPLITER, lambda x: x.group(0)+"\t", text)
return [x.strip() for x in s.split('\t') if x.strip() != '']
if __name__ == "__main__":
text = u'''在上市之后，京东的股价虽然也有突出的表现，但是也曾在上市第二天险些破发，不过随后，股价一路上涨，至6月12日，创出了29.6美元/股的新高，不过当日股价仍以跌幅6.31%收盘。在随后的价格交易日中，股价出现涨跌轮转的情况，截至6月30日，公司股价收于28.51美元/股，当日涨幅为1.79%。
虽然京东是迄今为止中国在美上市公司规模最大的IPO，但是华尔街正等待着今年下半年阿里巴巴集团里程碑式的股票发行交易，据信阿里巴巴的募资规模可能高达200亿美元。
迅雷“新贵上市”'''
sentences = get_sentences(text)
for sen in sentences:
print sen
print '='*10
| [
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fibonacci_sphere.py | GRASBOCK/fibonacci-sphere-section | 0 | 184594 | from math import ceil, floor
import numpy as np
from BoundingBox import BoundingBox
def fibonacci_sphere_section(num_points_whole_sphere: int, bbox: BoundingBox):
lat_min, lat_max, lon_min, lon_max = bbox.lat_min, bbox.lat_max, bbox.lon_min, bbox.lon_max
#print("lat_min: {}, lat_max: {}, lon_min: {}, lon_max: {}".format(lat_min, lat_max, lon_min, lon_max))
ga = (3 - np.sqrt(5)) * np.pi # golden angle
repeat = np.pi*2/ga # after how many indices the 2 pi is reached
z_step = 2.0/num_points_whole_sphere
z_min_bound = z_step-1.0 # minimum z
z_max_bound = 1.0 - z_step # maximum z
z_min = np.sin(lat_min)
z_max = np.sin(lat_max)
if z_min < z_min_bound:
z_min = z_min_bound
if z_max > z_max_bound:
z_max = z_max_bound
# linear interpolation
linInterp = lambda x1, x2, y1, y2, x: (y2-y1)/(x2-x1)*(x-x1)
i_min = linInterp(z_min_bound, z_max_bound, 0.0, float(num_points_whole_sphere), z_min) # smallest i where z is within bounding box latitude
i_max = linInterp(z_min_bound, z_max_bound, 0.0, float(num_points_whole_sphere), z_max) # biggest i where z is within bounding box latitude
i_min_0 = np.floor(i_min / repeat) * repeat # smallest i at 0° degrees
circulations = ceil((i_max - i_min_0)/repeat) # number of circulations
relative_begin = linInterp(0.0, np.pi*2, 0.0, repeat, lon_min) # relative index to start of bounding box longitude
relative_end = linInterp(0.0, np.pi*2, 0.0, repeat, lon_max) # relative index to end of bounding box longitude
theta = []
z = []
for r in range(circulations):
offset = repeat*r + i_min_0
i_start = ceil(offset + relative_begin)
i_end = int(offset + relative_end)
if i_end >= num_points_whole_sphere: # prevent overflow
i_end = num_points_whole_sphere -1
indices = range(i_start, i_end+1) # all indices within the bounding box for the current circulation. can be empty
#print("r: {r:4d}, o: {o:.3f}, s: {s:.3f}, e: {e:.3f}".format(r=r, o=offset, s=offset + relative_begin, e=offset + relative_end), indices, list(indices))
for i in indices:
theta.append(ga*i)
z.append(z_step-1.0 + z_step*i)
theta = np.array(theta)
z = np.array(z)
# a list of the radii at each height step of the unit circle
radius = np.sqrt(1 - z * z)
# Determine where xy fall on the sphere, given the azimuthal and polar angles
y = radius * np.sin(theta)
x = radius * np.cos(theta)
return x, y, z | [
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] |
unittests/test_cases.py | arieltrevisan/python-3-from-scratch-practices | 0 | 112294 | <reponame>arieltrevisan/python-3-from-scratch-practices
import unittest as ut
"""
https://docs.python.org/3/library/unittest.html#test-discovery
"""
class TestClassOne(ut.TestCase):
@classmethod
def setUpClass(cls):
# print("setupClass")
pass
@classmethod
def tearDownClass(cls):
# print("tearDownClass")
pass
# setup of each method
def setUp(self):
# print("setUp")
pass
# teardown of each method
def tearDown(self):
# print("tearDown")
pass
def test_bool(self):
value = True
self.assertTrue(value, "Not True")
def test_equals(self):
value_1 = 1
value_2 = 1
self.assertEqual(value_1, value_2)
def test_raise(self):
with self.assertRaises(ZeroDivisionError):
1 / 0
class TestClassTwo(ut.TestCase):
def test_1(self):
self.assertEqual(1, 1)
def test_2(self):
list_1 = [1, 2, 3]
list_2 = [2, 3, 1]
self.assertCountEqual(list_1, list_2)
if __name__ == '__main__':
ut.main(verbosity=2)
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src/metemcyber/core/bc/catalog.py | soum-kazuaki/metemcyber | 11 | 51262 | #
# Copyright 2021, NTT Communications Corp.
#
# 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 __future__ import annotations
from typing import Dict, Optional
from uuid import UUID
from eth_typing import ChecksumAddress
from .account import Account
from .cti_catalog import CTICatalog
class TokenInfo():
def __init__(self, address, token_id, owner, uuid, title, price, operator, like_count):
self.address = address
self.token_id = token_id
self.owner = owner
self.uuid = uuid
self.title = title
self.price = price
self.operator = operator
self.like_count = like_count
class CatalogInfo():
def __init__(self, address, catalog_id, owner, private, tokens):
self.address = address
self.catalog_id = catalog_id
self.owner = owner
self.private = private
self.tokens: Dict[ChecksumAddress, TokenInfo] = tokens
class Catalog():
__addressed_catalogs: Dict[ChecksumAddress, CatalogInfo] = {}
@property
def _catalogs_by_address(self):
return Catalog.__addressed_catalogs
@property
def _catalogs_by_id(self):
return {info['catalog_id']: info
for info in Catalog.__addressed_catalogs.values()}
def __init__(self, account: Account) -> None:
self.account: Account = account
self.address: Optional[ChecksumAddress] = None
self.catalog_id: int = self._gen_catalog_id()
self.owner: Optional[ChecksumAddress] = None
self.private: Optional[bool] = None
self.tokens: Dict[ChecksumAddress, TokenInfo] = {}
def get(self, address: ChecksumAddress) -> Catalog:
self.address = address
self._sync_catalog()
return self
def get_by_id(self, catalog_id: int) -> Catalog:
return self.get(self._catalogs_by_id()[catalog_id].address)
def new(self, group: ChecksumAddress) -> Catalog:
cti_catalog = CTICatalog(self.account).new(group)
return self.get(cti_catalog.address)
def uncache(self, entire: bool = False) -> None:
if entire:
del Catalog.__addressed_catalogs
# pylint: disable=unused-private-member
Catalog.__addressed_catalogs = {}
else:
assert self.address
if self.address in Catalog.__addressed_catalogs:
del Catalog.__addressed_catalogs[self.address]
@staticmethod
def _gen_catalog_id() -> int:
return max([val.catalog_id for val
in Catalog.__addressed_catalogs.values()] + [0]
) + 1
def _sync_catalog(self) -> None:
assert self.address
cinfo = self._catalogs_by_address.get(self.address)
if not cinfo:
cinfo = CatalogInfo(self.address, self.catalog_id, None, None, {})
Catalog.__addressed_catalogs[self.address] = cinfo
cti_catalog = CTICatalog(self.account).get(self.address)
cinfo.owner = cti_catalog.get_owner()
cinfo.private = cti_catalog.is_private()
for taddr in cti_catalog.list_token_uris():
tid, owner, uuid, title, price, operator, lcount = \
cti_catalog.get_cti_info(taddr)
cinfo.tokens[taddr] = TokenInfo(
taddr, tid, owner, uuid, title, price, operator, lcount)
self.catalog_id = cinfo.catalog_id
self.owner = cinfo.owner
self.private = cinfo.private
self.tokens = cinfo.tokens
def get_tokeninfo(self, address: ChecksumAddress) -> TokenInfo:
if address not in self.tokens.keys():
raise Exception(f'No such token({address}) on catalog({self.address})')
return self.tokens[address]
def get_tokeninfo_by_id(self, token_id: int) -> TokenInfo:
return self.get_tokeninfo(self.id2address(token_id))
def id2address(self, token_id: int) -> ChecksumAddress:
cinfo = self._catalogs_by_address.get(self.address)
tmp = [tinfo.address for tinfo in cinfo.tokens.values() if tinfo.token_id == token_id]
if not tmp:
raise Exception(f'No such token id({token_id}) on catalog({self.address})')
assert len(tmp) == 1
return tmp[0]
def register_cti(
self,
token: ChecksumAddress,
uuid: UUID,
title: str,
price: int,
operator: ChecksumAddress) -> None:
if price < 0:
raise Exception(f'Invalid price: {price}')
assert self.address
cti_catalog = CTICatalog(self.account).get(self.address)
cti_catalog.register_cti(token, uuid, title, price, operator)
self.uncache()
def publish_cti(self, producer: ChecksumAddress, token: ChecksumAddress) -> None:
assert self.address
cti_catalog = CTICatalog(self.account).get(self.address)
cti_catalog.publish_cti(producer, token)
self.uncache()
def modify_cti(self, token: ChecksumAddress, uuid: UUID, title: str, price: int) -> None:
if price < 0:
raise Exception(f'Invalid price: {price}')
assert self.address
cti_catalog = CTICatalog(self.account).get(self.address)
cti_catalog.modify_cti(token, uuid, title, price, '')
self.uncache()
def unregister_cti(self, token: ChecksumAddress) -> None:
assert self.address
cti_catalog = CTICatalog(self.account).get(self.address)
cti_catalog.unregister_cti(token)
self.uncache()
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greengraph/commands.py | ernestwcl/greengraph | 0 | 116560 | from argparse import ArgumentParser
from matplotlib import pyplot as plt
from greengraph import Greengraph
from map import Map
def process():
parser = ArgumentParser(description = "Create a graph showing number of green pixels between two locations on a satellite image")
parser.add_argument('--origin', help="origin" , default = 'London')
parser.add_argument('--dest', help = "destination", default = 'Oxford')
parser.add_argument('--nsteps', help = 'number of points inbetween, default 10', type=int, default = 10 )
parser.add_argument('--output', help = 'Output file name, use "*.png"', default = False)
arguments= parser.parse_args()
plt.close()
figure = plt.figure()
figure_axes = figure.add_subplot(1,1,1)
figure_axes.plot(Greengraph(arguments.origin ,arguments.dest).green_between(arguments.nsteps))
if arguments.output:
figure.savefig(arguments.output)
plt.show()
if __name__ == "__main__":
process() | [
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Semester 6/MA 322 (Scientific Computing Theory)/Labs/Lab 7/Code/q7.py | Imperial-lord/IITG | 4 | 42848 | # Question 07, Lab 07
# AB Satyaprakash, 180123062
# imports
import pandas as pd
import numpy as np
# functions
def f(t, y):
return y - t**2 + 1
def F(t):
return (t+1)**2 - 0.5*np.exp(t)
def RungeKutta4(t, y, h):
k1 = f(t, y)
k2 = f(t+h/2, y+h*k1/2)
k3 = f(t+h/2, y+h*k2/2)
k4 = f(t+h, y+h*k3)
return y + h*(k1 + 2*k2 + 2*k3 + k4)/6
def AdamsBashforth(t, y, h):
return y[-1] + h*(55*f(t[-1], y[-1]) - 59*f(t[-2], y[-2]) + 37*f(t[-3], y[-3]) - 9*f(t[-4], y[-4]))/24
def AdasmMoulton(t, y, h):
t1 = t[-1]+h
y1 = AdamsBashforth(t, y, h)
return y[-1] + h*(9*f(t1, y1) + 19*f(t[-1], y[-1]) - 5*f(t[-2], y[-2]) + f(t[-3], y[-3]))/24
# program body
t = [0]
y = [0.5]
h = 0.2
t.append(round(t[-1]+h, 1))
y.append(RungeKutta4(t[-1], y[-1], h))
t.append(round(t[-1]+h, 1))
y.append(RungeKutta4(t[-1], y[-1], h))
t.append(round(t[-1]+h, 1))
y.append(RungeKutta4(t[-1], y[-1], h))
yact = []
while t[-1] < 2:
y.append(AdasmMoulton(t, y, h))
t.append(round(t[-1]+h, 1))
for T in t:
yact.append(F(T))
df = pd.DataFrame()
df["Adam's Predictor-Corrector Method"] = pd.Series(y)
df['Actual Value'] = pd.Series(yact)
df.set_index(pd.Series(t), inplace=True)
print(df)
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python-algorithm/leetcode/problem_69.py | isudox/nerd-algorithm | 5 | 139975 | <reponame>isudox/nerd-algorithm
"""69. Sqrt(x)
https://leetcode.com/problems/sqrtx/
Implement int sqrt(int x).
Compute and return the square root of x, where x is guaranteed to be a
non-negative integer.
Since the return type is an integer, the decimal digits are truncated and
only the integer part of the result is returned.
Example 1:
Input: 4
Output: 2
Example 2:
Input: 8
Output: 2
Explanation: The square root of 8 is 2.82842..., and since
the decimal part is truncated, 2 is returned.
"""
class Solution:
def my_sqrt(self, x: int) -> int:
"""
It takes much time.
"""
if x == 0:
return 0
if x == 1:
return 1
for i in range(1, x):
temp = x // i
square = temp ** 2
if square == x:
return temp
elif square < x:
return temp
def my_sqrt_1(self, x: int) -> int:
def divide(l: int, r: int) -> int:
if l ** 2 == x:
return l
if r ** 2 == x:
return r
if r - l == 1:
return l
temp = (l + r) // 2
square = temp ** 2
if square < x:
return divide(temp, r)
if square > x:
return divide(l, temp)
else:
return temp
return divide(0, x)
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esp/4_digit_7_segment.py | steve9164/micropython_quokka | 3 | 82235 | import machine
spi = machine.SPI(1)
spi.init(baudrate=1000000)
rclk = machine.Pin(15, machine.Pin.OUT)
rclk.off()
ZERO = 0b00111111
ONE = 0b00000110
TWO = 0b01011011
THREE = 0b01001111
FOUR = 0b01100110
FIVE = 0b01101101
SIX = 0b01111101
SEVEN = 0b00000111
EIGHT = 0b01111111
NINE = 0b01101111
digits = [ZERO, ONE, TWO, THREE, FOUR, FIVE, SIX, SEVEN, EIGHT, NINE]
def display(digitBytes):
rclk.off()
spi.write(bytes(digitBytes))
rclk.on()
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today/models.py | wwj718/xiguaforvps | 1 | 167185 | <gh_stars>1-10
#coding=utf-8
from time import time
from urlparse import urlparse
from django.db import models
from django.db.models.signals import post_save
from django.dispatch import receiver
from mezzanine.core.models import Displayable, Ownable
from mezzanine.generic.models import Rating
from mezzanine.generic.fields import RatingField, CommentsField
class Link(Displayable, Ownable):
c=(('hc','清真餐厅'),('yc','一餐厅'),('ec','二餐厅'),('sc','三餐厅'),('jby','聚博园'),('other','未分类'))
canteen=models.CharField(max_length=20,choices=c,default='ec')
link = models.URLField(blank=True) #这个根本不需要，不要删除吧，免得麻烦，只要不让它出现就行,完成
rating = RatingField()
comments = CommentsField()
solved = models.BooleanField(default=False)
@models.permalink
def get_absolute_url(self):
return ("link_detail", (), {"slug": self.slug})
@property
def domain(self):
return urlparse(self.link).netloc
class Profile(models.Model):
user = models.OneToOneField("auth.User")
website = models.URLField(blank=True)
bio = models.TextField(blank=True)
karma = models.IntegerField(default=0, editable=False)
def __unicode__(self):
return "%s (%s)" % (self.user, self.karma)
@receiver(post_save, sender=Rating)
def karma(sender, **kwargs):
"""
Each time a rating is saved, check its value and modify the
profile karma for the related object's user accordingly.
Since ratings are either +1/-1, if a rating is being edited,
we can assume that the existing rating is in the other direction,
so we multiply the karma modifier by 2.
"""
rating = kwargs["instance"]
value = int(rating.value)
if not kwargs["created"]:
value *= 2
content_object = rating.content_object
if rating.user != content_object.user:
queryset = Profile.objects.filter(user=content_object.user)
queryset.update(karma=models.F("karma") + value)
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29889,
19206,
29889,
9621,
1053,
390,
1218,
3073,
29892,
461,
29879,
3073,
13,
13,
1990,
6645,
29898,
9323,
519,
29892,
438,
1233,
519,
1125,
13,
1678,
274,
7607,
877,
29882,
29883,
3788,
30989,
30848,
236,
167,
147,
232,
145,
136,
5477,
877,
11078,
3788,
30287,
236,
167,
147,
232,
145,
136,
5477,
877,
687,
3788,
30685,
236,
167,
147,
232,
145,
136,
5477,
877,
1557,
3788,
30457,
236,
167,
147,
232,
145,
136,
5477,
877,
29926,
1609,
3788,
235,
132,
157,
31196,
232,
158,
176,
5477,
877,
1228,
3788,
31295,
30748,
30832,
8785,
13,
1678,
508,
9404,
29922,
9794,
29889,
27890,
29898,
3317,
29918,
2848,
29922,
29906,
29900,
29892,
1859,
1575,
29922,
29883,
29892,
4381,
2433,
687,
1495,
13,
1678,
1544,
353,
4733,
29889,
4219,
3073,
29898,
19465,
29922,
5574,
29897,
259,
396,
30810,
30502,
31393,
30346,
30413,
31383,
30698,
30214,
30413,
30698,
31916,
31152,
232,
147,
170,
30214,
232,
136,
144,
31050,
31846,
234,
134,
169,
30214,
31557,
30698,
30413,
235,
177,
172,
232,
177,
134,
30544,
31424,
31238,
30448,
29892,
31366,
30494,
13,
1678,
21700,
353,
390,
1218,
3073,
580,
13,
1678,
6589,
353,
461,
29879,
3073,
580,
13,
1678,
7484,
29871,
353,
4733,
29889,
18146,
3073,
29898,
4381,
29922,
8824,
29897,
13,
268,
13,
1678,
732,
9794,
29889,
546,
5156,
682,
13,
1678,
822,
679,
29918,
23552,
29918,
2271,
29898,
1311,
1125,
13,
4706,
736,
4852,
2324,
29918,
16432,
613,
313,
511,
8853,
29517,
1115,
1583,
29889,
29517,
1800,
13,
13,
1678,
732,
6799,
13,
1678,
822,
5354,
29898,
1311,
1125,
13,
4706,
736,
3142,
5510,
29898,
1311,
29889,
2324,
467,
1212,
2029,
13,
13,
13,
1990,
20802,
29898,
9794,
29889,
3195,
1125,
13,
13,
1678,
1404,
353,
4733,
29889,
6716,
1762,
6716,
3073,
703,
5150,
29889,
2659,
1159,
13,
1678,
4700,
353,
4733,
29889,
4219,
3073,
29898,
19465,
29922,
5574,
29897,
13,
1678,
17799,
353,
4733,
29889,
15778,
29898,
19465,
29922,
5574,
29897,
13,
1678,
10856,
655,
353,
4733,
29889,
7798,
3073,
29898,
4381,
29922,
29900,
29892,
3863,
519,
29922,
8824,
29897,
13,
13,
1678,
822,
4770,
2523,
356,
12035,
1311,
1125,
13,
4706,
736,
11860,
29879,
313,
29995,
29879,
5513,
1273,
313,
1311,
29889,
1792,
29892,
1583,
29889,
5689,
655,
29897,
13,
13,
13,
29992,
13556,
2147,
29898,
2490,
29918,
7620,
29892,
10004,
29922,
29934,
1218,
29897,
13,
1753,
10856,
655,
29898,
15452,
29892,
3579,
19290,
1125,
13,
1678,
9995,
13,
1678,
7806,
931,
263,
21700,
338,
7160,
29892,
1423,
967,
995,
322,
6623,
278,
13,
1678,
8722,
10856,
655,
363,
278,
4475,
1203,
29915,
29879,
1404,
16205,
29889,
13,
1678,
4001,
26838,
526,
2845,
718,
29896,
24028,
29896,
29892,
565,
263,
21700,
338,
1641,
8788,
29892,
13,
1678,
591,
508,
5251,
393,
278,
5923,
21700,
338,
297,
278,
916,
5305,
29892,
13,
1678,
577,
591,
22932,
278,
10856,
655,
878,
3709,
491,
29871,
29906,
29889,
13,
1678,
9995,
13,
1678,
21700,
353,
9049,
5085,
3366,
8758,
3108,
13,
1678,
995,
353,
938,
29898,
29741,
29889,
1767,
29897,
13,
1678,
565,
451,
9049,
5085,
3366,
11600,
3108,
29901,
13,
4706,
995,
334,
29922,
29871,
29906,
13,
1678,
2793,
29918,
3318,
353,
21700,
29889,
3051,
29918,
3318,
13,
1678,
565,
21700,
29889,
1792,
2804,
2793,
29918,
3318,
29889,
1792,
29901,
13,
4706,
2346,
842,
353,
20802,
29889,
12650,
29889,
4572,
29898,
1792,
29922,
3051,
29918,
3318,
29889,
1792,
29897,
13,
4706,
2346,
842,
29889,
5504,
29898,
5689,
655,
29922,
9794,
29889,
29943,
703,
5689,
655,
1159,
718,
995,
29897,
13,
2
] |
Problem 59.py | mmax5/project-euler | 0 | 60211 | import sys
__author__ = 'mikemax'
text = open('p059_cipher.txt').read(-1)
weights = {}
sums = {}
string = {}
for a in range(97, 122):
for b in range(97, 122):
for c in range(97, 122):
weights[(a << 16) + (b << 8) + c] = 0
sums[(a << 16) + (b << 8) + c] = 0
string[(a << 16) + (b << 8) + c] = ''
chars = [int(char) for char in text.split(',')]
for i in range(0, len(chars), 3):
for w in weights.keys():
first = w & 0xFF ^ chars[i]
if(len(chars)> i+1):
second = (w >> 8) & 0xFF ^ chars[i+1]
third = (w >> 16) & 0xFF ^ chars[i+2]
j = 0
for c in [first, second, third]:
string[w] += chr(c)
if (65 <= c <= 91) or (97 <= c <= 122):
weights[w] += 1
if(len(chars)> i +j):
sums[w] += c
j += 1
max = 0
max_w = 0
for key, value in weights.iteritems():
if value > max:
max = value
max_w = key
print string[max_w], sums[max_w] | [
1,
1053,
10876,
13,
13,
1649,
8921,
1649,
353,
525,
29885,
638,
331,
1165,
29915,
13,
726,
353,
1722,
877,
29886,
29900,
29945,
29929,
29918,
455,
8096,
29889,
3945,
2824,
949,
6278,
29896,
29897,
13,
13,
705,
5861,
353,
6571,
13,
2083,
29879,
353,
6571,
13,
1807,
353,
6571,
13,
13,
1454,
263,
297,
3464,
29898,
29929,
29955,
29892,
29871,
29896,
29906,
29906,
1125,
13,
1678,
363,
289,
297,
3464,
29898,
29929,
29955,
29892,
29871,
29896,
29906,
29906,
1125,
13,
4706,
363,
274,
297,
3464,
29898,
29929,
29955,
29892,
29871,
29896,
29906,
29906,
1125,
13,
9651,
18177,
15625,
29874,
3532,
29871,
29896,
29953,
29897,
718,
313,
29890,
3532,
29871,
29947,
29897,
718,
274,
29962,
353,
29871,
29900,
13,
9651,
25470,
15625,
29874,
3532,
29871,
29896,
29953,
29897,
718,
313,
29890,
3532,
29871,
29947,
29897,
718,
274,
29962,
353,
29871,
29900,
13,
9651,
1347,
15625,
29874,
3532,
29871,
29896,
29953,
29897,
718,
313,
29890,
3532,
29871,
29947,
29897,
718,
274,
29962,
353,
6629,
13,
13,
305,
1503,
353,
518,
524,
29898,
3090,
29897,
363,
1373,
297,
1426,
29889,
5451,
29317,
1495,
29962,
13,
1454,
474,
297,
3464,
29898,
29900,
29892,
7431,
29898,
305,
1503,
511,
29871,
29941,
1125,
13,
1678,
363,
281,
297,
18177,
29889,
8149,
7295,
13,
4706,
937,
353,
281,
669,
29871,
29900,
29916,
4198,
6228,
22524,
29961,
29875,
29962,
13,
4706,
565,
29898,
2435,
29898,
305,
1503,
15410,
474,
29974,
29896,
1125,
13,
9651,
1473,
353,
313,
29893,
5099,
29871,
29947,
29897,
669,
29871,
29900,
29916,
4198,
6228,
22524,
29961,
29875,
29974,
29896,
29962,
13,
9651,
4654,
353,
313,
29893,
5099,
29871,
29896,
29953,
29897,
669,
29871,
29900,
29916,
4198,
6228,
22524,
29961,
29875,
29974,
29906,
29962,
13,
13,
4706,
432,
353,
29871,
29900,
13,
4706,
363,
274,
297,
518,
4102,
29892,
1473,
29892,
4654,
5387,
13,
9651,
1347,
29961,
29893,
29962,
4619,
18460,
29898,
29883,
29897,
13,
9651,
565,
313,
29953,
29945,
5277,
274,
5277,
29871,
29929,
29896,
29897,
470,
313,
29929,
29955,
5277,
274,
5277,
29871,
29896,
29906,
29906,
1125,
13,
18884,
18177,
29961,
29893,
29962,
4619,
29871,
29896,
13,
9651,
565,
29898,
2435,
29898,
305,
1503,
15410,
474,
718,
29926,
1125,
13,
18884,
25470,
29961,
29893,
29962,
4619,
274,
13,
9651,
432,
4619,
29871,
29896,
13,
13,
3317,
353,
29871,
29900,
13,
3317,
29918,
29893,
353,
29871,
29900,
13,
1454,
1820,
29892,
995,
297,
18177,
29889,
1524,
7076,
7295,
13,
1678,
565,
995,
1405,
4236,
29901,
13,
4706,
4236,
353,
995,
13,
4706,
4236,
29918,
29893,
353,
1820,
13,
13,
2158,
1347,
29961,
3317,
29918,
29893,
1402,
25470,
29961,
3317,
29918,
29893,
29962,
2
] |
google/home/enterprise/sdm/v1/enterprise-sdm-v1-py/google/home/enterprise/sdm_v1/services/smart_device_management_service/client.py | googleapis/googleapis-gen | 7 | 94883 | <gh_stars>1-10
# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
from distutils import util
import os
import re
from typing import Dict, Optional, Sequence, Tuple, Type, Union
import pkg_resources
from google.api_core import client_options as client_options_lib # type: ignore
from google.api_core import exceptions as core_exceptions # type: ignore
from google.api_core import gapic_v1 # type: ignore
from google.api_core import retry as retries # type: ignore
from google.auth import credentials as ga_credentials # type: ignore
from google.auth.transport import mtls # type: ignore
from google.auth.transport.grpc import SslCredentials # type: ignore
from google.auth.exceptions import MutualTLSChannelError # type: ignore
from google.oauth2 import service_account # type: ignore
from google.home.enterprise.sdm_v1.services.smart_device_management_service import pagers
from google.home.enterprise.sdm_v1.types import device
from google.home.enterprise.sdm_v1.types import site
from google.home.enterprise.sdm_v1.types import smart_device_management_service
from google.protobuf import struct_pb2 # type: ignore
from .transports.base import SmartDeviceManagementServiceTransport, DEFAULT_CLIENT_INFO
from .transports.grpc import SmartDeviceManagementServiceGrpcTransport
from .transports.grpc_asyncio import SmartDeviceManagementServiceGrpcAsyncIOTransport
class SmartDeviceManagementServiceClientMeta(type):
"""Metaclass for the SmartDeviceManagementService client.
This provides class-level methods for building and retrieving
support objects (e.g. transport) without polluting the client instance
objects.
"""
_transport_registry = OrderedDict() # type: Dict[str, Type[SmartDeviceManagementServiceTransport]]
_transport_registry["grpc"] = SmartDeviceManagementServiceGrpcTransport
_transport_registry["grpc_asyncio"] = SmartDeviceManagementServiceGrpcAsyncIOTransport
def get_transport_class(cls,
label: str = None,
) -> Type[SmartDeviceManagementServiceTransport]:
"""Returns an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use.
"""
# If a specific transport is requested, return that one.
if label:
return cls._transport_registry[label]
# No transport is requested; return the default (that is, the first one
# in the dictionary).
return next(iter(cls._transport_registry.values()))
class SmartDeviceManagementServiceClient(metaclass=SmartDeviceManagementServiceClientMeta):
"""A service that allows API consumers to provision and manage
Google Home structures and devices for enterprise use cases.
"""
@staticmethod
def _get_default_mtls_endpoint(api_endpoint):
"""Converts api endpoint to mTLS endpoint.
Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to
"*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively.
Args:
api_endpoint (Optional[str]): the api endpoint to convert.
Returns:
str: converted mTLS api endpoint.
"""
if not api_endpoint:
return api_endpoint
mtls_endpoint_re = re.compile(
r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?"
)
m = mtls_endpoint_re.match(api_endpoint)
name, mtls, sandbox, googledomain = m.groups()
if mtls or not googledomain:
return api_endpoint
if sandbox:
return api_endpoint.replace(
"sandbox.googleapis.com", "mtls.sandbox.googleapis.com"
)
return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com")
DEFAULT_ENDPOINT = "smartdevicemanagement.googleapis.com"
DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore
DEFAULT_ENDPOINT
)
@classmethod
def from_service_account_info(cls, info: dict, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
info.
Args:
info (dict): The service account private key info.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
SmartDeviceManagementServiceClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_info(info)
kwargs["credentials"] = credentials
return cls(*args, **kwargs)
@classmethod
def from_service_account_file(cls, filename: str, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
SmartDeviceManagementServiceClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_file(
filename)
kwargs["credentials"] = credentials
return cls(*args, **kwargs)
from_service_account_json = from_service_account_file
@property
def transport(self) -> SmartDeviceManagementServiceTransport:
"""Returns the transport used by the client instance.
Returns:
SmartDeviceManagementServiceTransport: The transport used by the client
instance.
"""
return self._transport
@staticmethod
def device_path(enterprise: str,device: str,) -> str:
"""Returns a fully-qualified device string."""
return "enterprises/{enterprise}/devices/{device}".format(enterprise=enterprise, device=device, )
@staticmethod
def parse_device_path(path: str) -> Dict[str,str]:
"""Parses a device path into its component segments."""
m = re.match(r"^enterprises/(?P<enterprise>.+?)/devices/(?P<device>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def room_path(enterprise: str,structure: str,room: str,) -> str:
"""Returns a fully-qualified room string."""
return "enterprises/{enterprise}/structures/{structure}/rooms/{room}".format(enterprise=enterprise, structure=structure, room=room, )
@staticmethod
def parse_room_path(path: str) -> Dict[str,str]:
"""Parses a room path into its component segments."""
m = re.match(r"^enterprises/(?P<enterprise>.+?)/structures/(?P<structure>.+?)/rooms/(?P<room>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def structure_path(enterprise: str,structure: str,) -> str:
"""Returns a fully-qualified structure string."""
return "enterprises/{enterprise}/structures/{structure}".format(enterprise=enterprise, structure=structure, )
@staticmethod
def parse_structure_path(path: str) -> Dict[str,str]:
"""Parses a structure path into its component segments."""
m = re.match(r"^enterprises/(?P<enterprise>.+?)/structures/(?P<structure>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def common_billing_account_path(billing_account: str, ) -> str:
"""Returns a fully-qualified billing_account string."""
return "billingAccounts/{billing_account}".format(billing_account=billing_account, )
@staticmethod
def parse_common_billing_account_path(path: str) -> Dict[str,str]:
"""Parse a billing_account path into its component segments."""
m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def common_folder_path(folder: str, ) -> str:
"""Returns a fully-qualified folder string."""
return "folders/{folder}".format(folder=folder, )
@staticmethod
def parse_common_folder_path(path: str) -> Dict[str,str]:
"""Parse a folder path into its component segments."""
m = re.match(r"^folders/(?P<folder>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def common_organization_path(organization: str, ) -> str:
"""Returns a fully-qualified organization string."""
return "organizations/{organization}".format(organization=organization, )
@staticmethod
def parse_common_organization_path(path: str) -> Dict[str,str]:
"""Parse a organization path into its component segments."""
m = re.match(r"^organizations/(?P<organization>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def common_project_path(project: str, ) -> str:
"""Returns a fully-qualified project string."""
return "projects/{project}".format(project=project, )
@staticmethod
def parse_common_project_path(path: str) -> Dict[str,str]:
"""Parse a project path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)$", path)
return m.groupdict() if m else {}
@staticmethod
def common_location_path(project: str, location: str, ) -> str:
"""Returns a fully-qualified location string."""
return "projects/{project}/locations/{location}".format(project=project, location=location, )
@staticmethod
def parse_common_location_path(path: str) -> Dict[str,str]:
"""Parse a location path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path)
return m.groupdict() if m else {}
def __init__(self, *,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Union[str, SmartDeviceManagementServiceTransport, None] = None,
client_options: Optional[client_options_lib.ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the smart device management service client.
Args:
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
transport (Union[str, SmartDeviceManagementServiceTransport]): The
transport to use. If set to None, a transport is chosen
automatically.
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. It won't take effect if a ``transport`` instance is provided.
(1) The ``api_endpoint`` property can be used to override the
default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
environment variable can also be used to override the endpoint:
"always" (always use the default mTLS endpoint), "never" (always
use the default regular endpoint) and "auto" (auto switch to the
default mTLS endpoint if client certificate is present, this is
the default value). However, the ``api_endpoint`` property takes
precedence if provided.
(2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
is "true", then the ``client_cert_source`` property can be used
to provide client certificate for mutual TLS transport. If
not provided, the default SSL client certificate will be used if
present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
set, no client certificate will be used.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
Raises:
google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
creation failed for any reason.
"""
if isinstance(client_options, dict):
client_options = client_options_lib.from_dict(client_options)
if client_options is None:
client_options = client_options_lib.ClientOptions()
# Create SSL credentials for mutual TLS if needed.
use_client_cert = bool(util.strtobool(os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")))
client_cert_source_func = None
is_mtls = False
if use_client_cert:
if client_options.client_cert_source:
is_mtls = True
client_cert_source_func = client_options.client_cert_source
else:
is_mtls = mtls.has_default_client_cert_source()
if is_mtls:
client_cert_source_func = mtls.default_client_cert_source()
else:
client_cert_source_func = None
# Figure out which api endpoint to use.
if client_options.api_endpoint is not None:
api_endpoint = client_options.api_endpoint
else:
use_mtls_env = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
if use_mtls_env == "never":
api_endpoint = self.DEFAULT_ENDPOINT
elif use_mtls_env == "always":
api_endpoint = self.DEFAULT_MTLS_ENDPOINT
elif use_mtls_env == "auto":
if is_mtls:
api_endpoint = self.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = self.DEFAULT_ENDPOINT
else:
raise MutualTLSChannelError(
"Unsupported GOOGLE_API_USE_MTLS_ENDPOINT value. Accepted "
"values: never, auto, always"
)
# Save or instantiate the transport.
# Ordinarily, we provide the transport, but allowing a custom transport
# instance provides an extensibility point for unusual situations.
if isinstance(transport, SmartDeviceManagementServiceTransport):
# transport is a SmartDeviceManagementServiceTransport instance.
if credentials or client_options.credentials_file:
raise ValueError("When providing a transport instance, "
"provide its credentials directly.")
if client_options.scopes:
raise ValueError(
"When providing a transport instance, provide its scopes "
"directly."
)
self._transport = transport
else:
Transport = type(self).get_transport_class(transport)
self._transport = Transport(
credentials=credentials,
credentials_file=client_options.credentials_file,
host=api_endpoint,
scopes=client_options.scopes,
client_cert_source_for_mtls=client_cert_source_func,
quota_project_id=client_options.quota_project_id,
client_info=client_info,
always_use_jwt_access=True,
)
def get_device(self,
request: Union[smart_device_management_service.GetDeviceRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> device.Device:
r"""Gets a device managed by the enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.GetDeviceRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.GetDevice
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.types.Device:
Device resource represents an
instance of enterprise managed device in
the property.
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.GetDeviceRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.GetDeviceRequest):
request = smart_device_management_service.GetDeviceRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.get_device]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("name", request.name),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def list_devices(self,
request: Union[smart_device_management_service.ListDevicesRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListDevicesPager:
r"""Lists devices managed by the enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.ListDevicesRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.ListDevices
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.services.smart_device_management_service.pagers.ListDevicesPager:
Response message for
SmartDeviceManagementService.ListDevices
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.ListDevicesRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.ListDevicesRequest):
request = smart_device_management_service.ListDevicesRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_devices]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("parent", request.parent),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListDevicesPager(
method=rpc,
request=request,
response=response,
metadata=metadata,
)
# Done; return the response.
return response
def execute_device_command(self,
request: Union[smart_device_management_service.ExecuteDeviceCommandRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> smart_device_management_service.ExecuteDeviceCommandResponse:
r"""Executes a command to device managed by the
enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.ExecuteDeviceCommandRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.ExecuteDeviceCommand
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.types.ExecuteDeviceCommandResponse:
Response message for
SmartDeviceManagementService.ExecuteDeviceCommand
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.ExecuteDeviceCommandRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.ExecuteDeviceCommandRequest):
request = smart_device_management_service.ExecuteDeviceCommandRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.execute_device_command]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("name", request.name),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def get_structure(self,
request: Union[smart_device_management_service.GetStructureRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> site.Structure:
r"""Gets a structure managed by the enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.GetStructureRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.GetStructure
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.types.Structure:
Structure resource represents an
instance of enterprise managed home or
hotel room.
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.GetStructureRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.GetStructureRequest):
request = smart_device_management_service.GetStructureRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.get_structure]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("name", request.name),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def list_structures(self,
request: Union[smart_device_management_service.ListStructuresRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListStructuresPager:
r"""Lists structures managed by the enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.ListStructuresRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.ListStructures
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.services.smart_device_management_service.pagers.ListStructuresPager:
Response message for
SmartDeviceManagementService.ListStructures
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.ListStructuresRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.ListStructuresRequest):
request = smart_device_management_service.ListStructuresRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_structures]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("parent", request.parent),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListStructuresPager(
method=rpc,
request=request,
response=response,
metadata=metadata,
)
# Done; return the response.
return response
def get_room(self,
request: Union[smart_device_management_service.GetRoomRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> site.Room:
r"""Gets a room managed by the enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.GetRoomRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.GetRoom
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.types.Room:
Room resource represents an instance
of sub-space within a structure such as
rooms in a hotel suite or rental
apartment.
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.GetRoomRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.GetRoomRequest):
request = smart_device_management_service.GetRoomRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.get_room]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("name", request.name),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def list_rooms(self,
request: Union[smart_device_management_service.ListRoomsRequest, dict] = None,
*,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListRoomsPager:
r"""Lists rooms managed by the enterprise.
Args:
request (Union[google.home.enterprise.sdm_v1.types.ListRoomsRequest, dict]):
The request object. Request message for
SmartDeviceManagementService.ListRooms
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.home.enterprise.sdm_v1.services.smart_device_management_service.pagers.ListRoomsPager:
Response message for
SmartDeviceManagementService.ListRooms
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# Minor optimization to avoid making a copy if the user passes
# in a smart_device_management_service.ListRoomsRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, smart_device_management_service.ListRoomsRequest):
request = smart_device_management_service.ListRoomsRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_rooms]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((
("parent", request.parent),
)),
)
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListRoomsPager(
method=rpc,
request=request,
response=response,
metadata=metadata,
)
# Done; return the response.
return response
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
"""Releases underlying transport's resources.
.. warning::
ONLY use as a context manager if the transport is NOT shared
with other clients! Exiting the with block will CLOSE the transport
and may cause errors in other clients!
"""
self.transport.close()
try:
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=pkg_resources.get_distribution(
"google-home-enterprise-sdm",
).version,
)
except pkg_resources.DistributionNotFound:
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()
__all__ = (
"SmartDeviceManagementServiceClient",
)
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7761,
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3896,
7734,
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13,
13,
4706,
826,
3174,
29901,
13,
9651,
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978,
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978,
511,
13,
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13,
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2009,
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13,
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2009,
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13,
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13,
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826,
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13,
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313,
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3608,
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4928,
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10729,
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2009,
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13,
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313,
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552,
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851,
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886,
607,
881,
367,
13,
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2665,
3412,
411,
278,
2009,
408,
15562,
29889,
13,
13,
4706,
16969,
29901,
13,
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5386,
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7734,
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4928,
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29918,
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3844,
442,
29918,
10141,
29918,
21895,
29918,
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13573,
414,
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19560,
1973,
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13,
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2643,
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13,
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442,
11501,
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1973,
13,
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20504,
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1203,
674,
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13,
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2582,
322,
8814,
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13,
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13,
13,
4706,
9995,
13,
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396,
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470,
1302,
261,
346,
263,
17814,
9721,
2009,
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13,
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396,
21987,
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304,
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3907,
263,
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565,
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9026,
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4984,
1973,
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13,
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4852,
3560,
613,
2009,
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3560,
511,
13,
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1723,
511,
13,
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13,
13,
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396,
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278,
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13,
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2009,
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414,
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1973,
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13,
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1158,
29922,
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6739,
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13,
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2009,
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3827,
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2933,
13,
13,
1678,
822,
679,
29918,
8345,
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1311,
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13,
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2009,
29901,
7761,
29961,
3844,
442,
29918,
10141,
29918,
21895,
29918,
5509,
29889,
2577,
9588,
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gslib/tests/test_stet_util.py | ttobisawa/gsutil | 0 | 8863 | <reponame>ttobisawa/gsutil<filename>gslib/tests/test_stet_util.py
# -*- coding: utf-8 -*-
# Copyright 2021 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for stet_util.py."""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import os
import shutil
from gslib import storage_url
from gslib.tests import testcase
from gslib.tests import util
from gslib.tests.util import unittest
from gslib.utils import execution_util
from gslib.utils import stet_util
import mock
class TestStetUtil(testcase.GsUtilUnitTestCase):
"""Test STET utils."""
@mock.patch.object(execution_util, 'ExecuteExternalCommand')
def test_stet_upload_uses_binary_and_config_from_boto(
self, mock_execute_external_command):
fake_config_path = self.CreateTempFile()
mock_execute_external_command.return_value = ('stdout', 'stderr')
mock_logger = mock.Mock()
source_url = storage_url.StorageUrlFromString('in')
destination_url = storage_url.StorageUrlFromString('gs://bucket/obj')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', 'fake_binary_path'),
('GSUtil', 'stet_config_path', fake_config_path),
]):
out_file_url = stet_util.encrypt_upload(source_url, destination_url,
mock_logger)
self.assertEqual(out_file_url,
storage_url.StorageUrlFromString('in_.stet_tmp'))
mock_execute_external_command.assert_called_once_with([
'fake_binary_path',
'encrypt',
'--config-file={}'.format(fake_config_path),
'--blob-id=gs://bucket/obj',
'in',
'in_.stet_tmp',
])
mock_logger.debug.assert_called_once_with('stderr')
@mock.patch.object(execution_util, 'ExecuteExternalCommand')
def test_stet_upload_runs_with_binary_from_path_with_correct_settings(
self, mock_execute_external_command):
fake_config_path = self.CreateTempFile()
temporary_path_directory = self.CreateTempDir()
fake_stet_binary_path = self.CreateTempFile(tmpdir=temporary_path_directory,
file_name='stet')
previous_path = os.getenv('PATH')
os.environ['PATH'] += os.path.pathsep + temporary_path_directory
mock_execute_external_command.return_value = ('stdout', 'stderr')
mock_logger = mock.Mock()
source_url = storage_url.StorageUrlFromString('in')
destination_url = storage_url.StorageUrlFromString('gs://bucket/obj')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', None),
('GSUtil', 'stet_config_path', fake_config_path),
]):
out_file_url = stet_util.encrypt_upload(source_url, destination_url,
mock_logger)
self.assertEqual(out_file_url,
storage_url.StorageUrlFromString('in_.stet_tmp'))
mock_execute_external_command.assert_called_once_with([
fake_stet_binary_path,
'encrypt',
'--config-file={}'.format(fake_config_path),
'--blob-id=gs://bucket/obj',
'in',
'in_.stet_tmp',
])
mock_logger.debug.assert_called_once_with('stderr')
os.environ['PATH'] = previous_path
@mock.patch.object(execution_util, 'ExecuteExternalCommand')
def test_stet_upload_uses_config_from_default_path_with_correct_settings(
self, mock_execute_external_command):
mock_execute_external_command.return_value = ('stdout', 'stderr')
mock_logger = mock.Mock()
source_url = storage_url.StorageUrlFromString('in')
destination_url = storage_url.StorageUrlFromString('gs://bucket/obj')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', 'fake_binary_path'),
('GSUtil', 'stet_config_path', None),
]):
with mock.patch.object(os.path,
'exists',
new=mock.Mock(return_value=True)):
out_file_url = stet_util.encrypt_upload(source_url, destination_url,
mock_logger)
self.assertEqual(out_file_url,
storage_url.StorageUrlFromString('in_.stet_tmp'))
mock_execute_external_command.assert_called_once_with([
'fake_binary_path',
'encrypt',
'--config-file={}'.format(
os.path.expanduser(stet_util.DEFAULT_STET_CONFIG_PATH)),
'--blob-id=gs://bucket/obj',
'in',
'in_.stet_tmp',
])
mock_logger.debug.assert_called_once_with('stderr')
@mock.patch.object(shutil, 'move')
@mock.patch.object(execution_util, 'ExecuteExternalCommand')
def test_stet_download_runs_binary_and_replaces_temp_file(
self, mock_execute_external_command, mock_move):
fake_config_path = self.CreateTempFile()
mock_execute_external_command.return_value = ('stdout', 'stderr')
mock_logger = mock.Mock()
source_url = storage_url.StorageUrlFromString('gs://bucket/obj')
destination_url = storage_url.StorageUrlFromString('out')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', 'fake_binary_path'),
('GSUtil', 'stet_config_path', fake_config_path),
]):
stet_util.decrypt_download(source_url, destination_url, mock_logger)
mock_execute_external_command.assert_called_once_with([
'fake_binary_path', 'decrypt',
'--config-file={}'.format(fake_config_path),
'--blob-id=gs://bucket/obj', 'out', 'out_.stet_tmp'
])
mock_logger.debug.assert_called_once_with('stderr')
mock_move.assert_called_once_with('out_.stet_tmp', 'out')
@mock.patch.object(stet_util,
'_get_stet_binary_from_path',
new=mock.Mock(return_value=None))
def test_stet_util_errors_if_no_binary(self):
fake_config_path = self.CreateTempFile()
source_url = storage_url.StorageUrlFromString('in')
destination_url = storage_url.StorageUrlFromString('gs://bucket/obj')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', None),
('GSUtil', 'stet_config_path', fake_config_path),
]):
with self.assertRaises(KeyError):
stet_util.encrypt_upload(source_url, destination_url, None)
def test_stet_util_errors_if_no_config(self):
source_url = storage_url.StorageUrlFromString('in')
destination_url = storage_url.StorageUrlFromString('gs://bucket/obj')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', 'fake_binary_path'),
('GSUtil', 'stet_config_path', None),
]):
with mock.patch.object(os.path,
'exists',
new=mock.Mock(return_value=False)):
with self.assertRaises(KeyError):
stet_util.encrypt_upload(source_url, destination_url, None)
@mock.patch.object(os.path, 'expanduser', autospec=True)
@mock.patch.object(execution_util,
'ExecuteExternalCommand',
new=mock.Mock(return_value=('stdout', 'stderr')))
def test_stet_util_expands_home_directory_symbol(self, mock_expanduser):
fake_config_path = self.CreateTempFile()
source_url = storage_url.StorageUrlFromString('in')
destination_url = storage_url.StorageUrlFromString('gs://bucket/obj')
with util.SetBotoConfigForTest([
('GSUtil', 'stet_binary_path', 'fake_binary_path'),
('GSUtil', 'stet_config_path', fake_config_path),
]):
stet_util.encrypt_upload(source_url, destination_url, mock.Mock())
mock_expanduser.assert_has_calls(
[mock.call('fake_binary_path'),
mock.call(fake_config_path)])
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] |
app.py | Terrence-Cummings/sqlalchemy-challenge | 0 | 38799 | #Dependencies, libraries, and imports
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
#SQLalchemy libraries and functions
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, inspect, MetaData
from sqlalchemy import Column, Integer, String, Float
from sqlalchemy.ext.declarative import declarative_base
#VROOM, VROOM!
engine = create_engine("sqlite:///Resources/hawaii.sqlite")
#Use automap to get table structures and reflect into classes
Base = automap_base()
Base.prepare(engine, reflect=True)
#See what classes have been created. Classes created should match tables found by Inspector
classes_created = Base.classes.keys()
#Single variable to represent each Class associated with the automapped Base
Measurement = Base.classes.measurement
Station = Base.classes.station
#Classes are now all setup. Start query session.
session = Session(engine)
# Design a query to retrieve the last 12 months of precipitation data and plot the results
#Find the earliest date in the Measurement table by query. Convert to python dictionary, read date as text, convert to datetime.
earliest_date_query = session.query(Measurement.date).order_by(Measurement.date).first()
ed_dict=earliest_date_query._asdict()
earliest_date = ed_dict['date']
earliest_date_dt = dt.datetime.strptime(earliest_date, "%Y-%m-%d")
#Find the latest date in the Measurement table by query. Convert to python dictionary, read date as text, convert to datetime.
latest_date_query = session.query(Measurement.date).order_by(Measurement.date.desc()).first()
ld_dict=latest_date_query._asdict()
latest_date = ld_dict['date']
latest_date_dt = dt.datetime.strptime(latest_date, "%Y-%m-%d")
# Calculate the date 1 year ago from the latest data point in the database
year_ago_latest_dt = latest_date_dt - dt.timedelta(days=365)
year_ago_latest = dt.datetime.strftime(year_ago_latest_dt, "%Y-%m-%d")
# What are the most active stations? (i.e. what stations have the most rows)?
stat_freq = session.query(Measurement.station, func.count(Measurement.station)).group_by(Measurement.station).order_by(func.count(Measurement.station).desc()).all()
max_stat_freq = stat_freq[0][0]
session.close()
#BEGIN FLASK APP
from flask import Flask, jsonify
app = Flask(__name__)
@app.route("/")
def welcome():
print("Server received request for 'Home' page...")
return (
f"Welcome to Surf's Up weather API!<br>"
f"We collect precipitation and temperature data from weather stations on the island of Oahu in Hawaii.<br><br>"
f"Earliest date of data = {earliest_date}<br>"
f"Latest date of data = {latest_date}<br><br>"
f"Available URL Routes:<br><br>"
f"Below URL returns JSON of precipitation on Oahu on each day between {year_ago_latest} and {latest_date}.<br>"
f"Copy this URL to browser:<br>"
f"/api/v1.0/precipitation<br><br>"
f"Below URL returns JSON of temperature at station {max_stat_freq} on Oahu on each day between {year_ago_latest} and {latest_date}.<br>"
f"Copy this URL to browser:<br>"
f"/api/v1.0/temperature<br><br>"
f"Below URL returns JSON of the weather stations on Oahu.<br>"
f"Copy this URL to browser:<br>"
f"/api/v1.0/stations<br><br>"
f"Below URL returns the max, min, and avg temperature on Oahu encompassing the START and END dates provided by the user in the URL.<br>"
f"If no END date provided in the URL then END date is assume to be {latest_date}<br>"
f"Copy this URL to browser and replace START/END with dates in YYYY-MM-DD format:<br>"
f"/api/v1.0/START/END"
)
@app.route("/api/v1.0/precipitation/")
def precipitation():
print("Server received request for 'Precipitation' page...")
session = Session(engine)
#Query precipitation observations for last year
date_prcp_query = session.query(Measurement.date, Measurement.prcp).\
filter(Measurement.date<=latest_date_dt).filter(Measurement.date>=year_ago_latest_dt)
session.close()
#Save as df and grab only the max precip observation for each day in the last year
date_prcp_df = pd.DataFrame(date_prcp_query, columns=['Date', 'Precipitation'])
date_prcp_df.set_index('Date', inplace=True)
date_prcp_df.dropna(inplace=True)
date_prcp_df.sort_index(inplace=True)
date_prcp_max = date_prcp_df.groupby('Date')[['Precipitation']].max()
#Turn into dictionary for jsonification
prcp_query_dict = date_prcp_max.to_dict()
return jsonify(prcp_query_dict)
@app.route("/api/v1.0/stations/")
def stations():
print("Server received request for 'Stations' page...")
session = Session(engine)
#Query all the weather station details
station_query = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation)
station_df = pd.DataFrame(station_query, columns = ['station', 'name', 'latitude', 'longitude', 'elevation'])
station_df.set_index('station', inplace=True)
station_df.dropna(inplace=True)
session.close()
#Make a dictionary of weather station characteristics for jsonification
station_dict = station_df.to_dict(orient='index')
return jsonify(station_dict)
@app.route("/api/v1.0/temperature/")
def temperatures():
print("Server received request for 'Temperatures' page...")
session = Session(engine)
#Query temperature observations for the last year at the station with the most observations
tobs_date_query = session.query(Measurement.date, Measurement.tobs).\
filter(Measurement.date<=latest_date_dt).filter(Measurement.date>=year_ago_latest_dt).\
filter(Measurement.station==max_stat_freq)
session.close()
#Save query as df
tobs_date_df = pd.DataFrame(tobs_date_query, columns=['Date','Temperature'])
tobs_date_df.set_index('Date', inplace=True)
tobs_date_df.dropna(inplace=True)
#Transform df into dictionary for jsonification
tobs_date_dict = tobs_date_df.to_dict()
return jsonify(tobs_date_dict)
@app.route("/api/v1.0/<start>/")
def temp_start(start):
#Control on START date within database date range
if start<earliest_date or start>latest_date:
return (
f"START must be between {earliest_date} and {latest_date}.<br>"
f"/api/v1.0/START"
)
print("Server received request for 'Min, Max, Avg Start End' page...")
session = Session(engine)
#Query max, min, and avg temperature between START date and last date in database
TMAX = session.query(func.max(Measurement.tobs)).\
filter(Measurement.date<=latest_date).filter(Measurement.date>=start).all()
TMIN = session.query(func.min(Measurement.tobs)).\
filter(Measurement.date<=latest_date).filter(Measurement.date>=start).all()
TAVG = session.query(func.avg(Measurement.tobs)).\
filter(Measurement.date<=latest_date).filter(Measurement.date>=start).all()
session.close()
#Round TAVG for presentation
TAVG = round(TAVG[0][0],1)
#Calc number of days in the query for information
days_obs = latest_date_dt - dt.datetime.strptime(start, "%Y-%m-%d")
days_obs = days_obs.days
return (
f"The maximum temperature on Oahu for the {days_obs} days between {start} and {latest_date} was {TMAX[0][0]}.<br>"
f"The minimum temperature on Oahu for the {days_obs} days between {start} and {latest_date} was {TMIN[0][0]}.<br>"
f"The average temperature on Oahu for the {days_obs} days between {start} and {latest_date} was {TAVG}.<br>"
)
@app.route("/api/v1.0/<start>/<end>/")
def temp_start_end(start, end):
#Check START and END dates are within the range of database dates
if start<earliest_date or start>latest_date or end<earliest_date or end>latest_date:
return (
f"START and END must be between {earliest_date} and {latest_date}.<br>"
f"/api/v1.0/START/END"
)
#Allow for START and END interchanged in URL
if end<start:
start_temp = start
start = end
end = start_temp
print("Server received request for 'Min, Max, Avg Start End' page...")
session = Session(engine)
#Query max, min, and avg temperature between START date and END date in database
TMAX = session.query(func.max(Measurement.tobs)).\
filter(Measurement.date<=end).filter(Measurement.date>=start).all()
TMIN = session.query(func.min(Measurement.tobs)).\
filter(Measurement.date<=end).filter(Measurement.date>=start).all()
TAVG = session.query(func.avg(Measurement.tobs)).\
filter(Measurement.date<=end).filter(Measurement.date>=start).all()
session.close()
#Round TAVG for presentation
TAVG = round(TAVG[0][0],1)
#Calc number of days in the query for information
days_obs = dt.datetime.strptime(end, "%Y-%m-%d") - dt.datetime.strptime(start, "%Y-%m-%d")
days_obs = days_obs.days
return (
f"The maximum temperature on Oahu for the {days_obs} days between {start} and {end} was {TMAX[0][0]}.<br>"
f"The minimum temperature on Oahu for the {days_obs} days between {start} and {end} was {TMIN[0][0]}.<br>"
f"The average temperature on Oahu for the {days_obs} days between {start} and {end} was {TAVG}.<br>"
)
if __name__ == "__main__":
app.run(debug=True)
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legacy_code/tf_cnn_siamese/model.py | PerryXDeng/project_punyslayer | 2 | 7297 | import legacy_code.tf_cnn_siamese.configurations as conf
import tensorflow as tf
import numpy as np
def construct_cnn(x, conv_weights, conv_biases, fc_weights, fc_biases,
dropout = False):
"""
constructs the convolution graph for one image
:param x: input node
:param conv_weights: convolution weights
:param conv_biases: relu biases for each convolution
:param fc_weights: fully connected weights, only one set should be used here
:param fc_biases: fully connected biases, only one set should be used here
:param dropout: whether to add a dropout layer for the fully connected layer
:return: output node
"""
k = conf.NUM_POOL
for i in range(conf.NUM_CONVS):
x = tf.nn.conv2d(x, conv_weights[i], strides=[1, 1, 1, 1], padding='SAME',
data_format=conf.DATA_FORMAT)
x = tf.nn.relu(tf.nn.bias_add(x, conv_biases[i],
data_format=conf.DATA_FORMAT))
if k > 0:
x = tf.nn.max_pool(x, ksize=conf.POOL_KDIM,strides=conf.POOL_KDIM,
padding='VALID', data_format=conf.DATA_FORMAT)
k -= 1
# Reshape the feature map cuboids into vectors for fc layers
features_shape = x.get_shape().as_list()
n = features_shape[0]
m = features_shape[1] * features_shape[2] * features_shape[3]
features = tf.reshape(x, [n, m])
# last fc_weights determine output dimensions
fc = tf.nn.sigmoid(tf.matmul(features, fc_weights[0]) + fc_biases[0])
# for actual training
if dropout:
fc = tf.nn.dropout(fc, conf.DROP_RATE)
return fc
def construct_logits_model(x_1, x_2, conv_weights, conv_biases, fc_weights,
fc_biases, dropout=False):
"""
constructs the logit node before the final sigmoid activation
:param x_1: input image node 1
:param x_2: input image node 2
:param conv_weights: nodes for convolution weights
:param conv_biases: nodes for convolution relu biases
:param fc_weights: nodes for fully connected weights
:param fc_biases: nodes for fully connected biases
:param dropout: whether to include dropout layers
:return: logit node
"""
with tf.name_scope("twin_1"):
twin_1 = construct_cnn(x_1, conv_weights, conv_biases,
fc_weights, fc_biases, dropout)
with tf.name_scope("twin_2"):
twin_2 = construct_cnn(x_2, conv_weights, conv_biases,
fc_weights, fc_biases, dropout)
# logits on squared difference
sq_diff = tf.squared_difference(twin_1, twin_2)
logits = tf.matmul(sq_diff, fc_weights[1]) + fc_biases[1]
return logits
def construct_full_model(x_1, x_2, conv_weights, conv_biases,fc_weights,
fc_biases):
"""
constructs the graph for the neural network without loss node or optimizer
:param x_1: input image node 1
:param x_2: input image node 2
:param conv_weights: nodes for convolution weights
:param conv_biases: nodes for convolution relu biases
:param fc_weights: nodes for fully connected weights
:param fc_biases: nodes for fully connected biases
:return: sigmoid output node
"""
logits = construct_logits_model(x_1, x_2, conv_weights, conv_biases,
fc_weights, fc_biases, dropout=False)
return tf.nn.sigmoid(logits)
def construct_loss_optimizer(x_1, x_2, labels, conv_weights, conv_biases,
fc_weights, fc_biases, dropout=False,
lagrange=False):
"""
constructs the neural network graph with the loss and optimizer node
:param x_1: input image node 1
:param x_2: input image node 2
:param labels: expected output
:param conv_weights: nodes for convolution weights
:param conv_biases: nodes for convolution relu biases
:param fc_weights: nodes for fully connected weights
:param fc_biases: nodes for fully connected biases
:param dropout: whether to use dropout
:param lagrange: whether to apply constraints
:return: the node for the optimizer as well as the loss
"""
logits = construct_logits_model(x_1, x_2, conv_weights, conv_biases,
fc_weights, fc_biases, dropout)
# cross entropy loss on sigmoids of joined output and labels
loss_vec = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels,
logits=logits)
loss = tf.reduce_mean(loss_vec)
if lagrange:
# constraints on sigmoid layers
regularizers = (tf.nn.l2_loss(fc_weights[0]) + tf.nn.l2_loss(fc_biases[0]) +
tf.nn.l2_loss(fc_weights[1]) + tf.nn.l2_loss(fc_biases[1]))
loss += conf.LAMBDA * regularizers
# setting up the optimization
batch = tf.Variable(0, dtype=conf.DTYPE)
# vanilla momentum optimizer
# accumulation = momentum * accumulation + gradient
# every epoch: variable -= learning_rate * accumulation
# batch_total = labels.shape[0]
# learning_rate = tf.train.exponential_decay(
# conf.BASE_LEARNING_RATE,
# batch * conf.BATCH_SIZE, # Current index into the dataset.
# batch_total,
# conf.DECAY_RATE, # Decay rate.
# staircase=True)
# trainer = tf.train.MomentumOptimizer(learning_rate, conf.MOMENTUM)\
# .minimize(loss, global_step=batch)
# adaptive momentum estimation optimizer
# default params: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08
trainer = tf.train.AdamOptimizer().minimize(loss, global_step=batch)
return trainer, loss
def construct_joined_model(twin_1, twin_2, fc_weights, fc_biases):
"""
constructs joined model for two sets of extracted features
:param twin_1: features node extracted from first image
:param twin_2: features node extracted from second image
:param fc_weights: nodes for fully connected weights
:param fc_biases: nodes for fully connected biases
:return: logit node
"""
# logits on squared difference
sq_diff = tf.squared_difference(twin_1, twin_2)
logits = tf.matmul(sq_diff, fc_weights[1]) + fc_biases[1]
return tf.nn.sigmoid(logits)
def initialize_weights():
"""
initializes the variable tensors to be trained in the neural network, decides
network dimensions
:return: nodes for the variables
"""
# twin network convolution and pooling variables
conv_weights = []
conv_biases = []
fc_weights = []
fc_biases = []
for i in range(conf.NUM_CONVS):
if i == 0:
inp = conf.NUM_CHANNELS
else:
inp = conf.NUM_FILTERS[i - 1]
out = conf.NUM_FILTERS[i]
conv_dim = [conf.FILTER_LEN, conf.FILTER_LEN, inp, out]
weight_name = "twin_conv" + str(i + 1) + "_weights"
bias_name = "twin_conv" + str(i + 1) + "_biases"
conv_weights.append(tf.Variable(tf.truncated_normal(conv_dim, stddev=0.1,
seed=conf.SEED, dtype=conf.DTYPE),
name=weight_name))
conv_biases.append(tf.Variable(tf.zeros([out], dtype=conf.DTYPE),
name=bias_name))
# twin network fullly connected variables
inp = conf.FEATURE_MAP_SIZE
out = conf.NUM_FC_NEURONS
fc_weights.append(tf.Variable(tf.truncated_normal([inp, out], stddev=0.1,
seed=conf.SEED, dtype=conf.DTYPE),
name="twin_fc_weights"))
fc_biases.append(tf.Variable(tf.constant(0.1, shape=[out], dtype=conf.DTYPE),
name="twin_fc_biases"))
# joined network fully connected variables
inp = conf.NUM_FC_NEURONS
out = 1
fc_weights.append(tf.Variable(tf.truncated_normal([inp, out], stddev=0.1,
seed=conf.SEED, dtype=conf.DTYPE),
name="joined_fc_weights"))
fc_biases.append(tf.Variable(tf.constant(0.1, shape=[out], dtype=conf.DTYPE),
name="joined_fc_biases"))
return conv_weights, conv_biases, fc_weights, fc_biases
def num_params():
"""
calculates the number of parameters in the model
:return: m, number of parameters
"""
m = 0
for i in range(conf.NUM_CONVS):
if i == 0:
inp = conf.NUM_CHANNELS
else:
inp = conf.NUM_FILTERS[i - 1]
out = conf.NUM_FILTERS[i]
conv_dim = [conf.FILTER_LEN, conf.FILTER_LEN, inp, out]
m += np.prod(conv_dim) + np.prod(out)
inp = conf.FEATURE_MAP_SIZE
out = conf.NUM_FC_NEURONS
m += inp * out + out
inp = conf.NUM_FC_NEURONS
out = 1
m += inp * out + out
return m
if __name__ == "__main__":
print("Number of Parameters: " + str(num_params()))
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] |
src/temp2.py | FabBrolMons/frbayart | 0 | 238 | from w1thermsensor import W1ThermSensor
sensor = W1ThermSensor()
temperature_in_celsius = sensor.get_temperature()
temperature_in_fahrenheit = sensor.get_temperature(W1ThermSensor.DEGREES_F)
temperature_in_all_units = sensor.get_temperatures([W1ThermSensor.DEGREES_C, W1ThermSensor.DEGREES_F, W1ThermSensor.KELVIN])
print("Sensor id:" + sensor.id)
print(temperature_in_celsius)
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cms/migrations/0006_auto_20170122_1545.py | josemlp91/django-landingcms | 0 | 10310 | # -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-01-22 15:45
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('content', '0002_auto_20170122_1509'),
('cms', '0005_auto_20170122_1534'),
]
operations = [
migrations.AddField(
model_name='paginahome',
name='posts1_imagen',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts1_imagen', to='content.ImageContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts1_texto',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts1_texto', to='content.TextContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts1_titulo',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts1_titulo', to='content.TitleContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts2_imagen',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts2_imagen', to='content.ImageContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts2_texto',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts2_texto', to='content.TextContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts2_titulo',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts2_titulo', to='content.TitleContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts3_imagen',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts3_imagen', to='content.ImageContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts3_texto',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts3_texto', to='content.TextContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts3_titulo',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts3_titulo', to='content.TitleContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts4_imagen',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts4_imagen', to='content.ImageContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts4_texto',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts4_texto', to='content.TextContent'),
),
migrations.AddField(
model_name='paginahome',
name='posts4_titulo',
field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='posts4_titulo', to='content.TitleContent'),
),
]
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pypeflow/plugins/contrib/__init__.py | pypeflow/pypeflow | 2 | 59721 | # coding: utf-8
from __future__ import unicode_literals
__author__ = 'gleber'
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src/main/9.tuning_parameters.py | Peter-32/bank_customer | 0 | 154066 | # Add this project to the path
import os; import sys; currDir = os.path.dirname(os.path.realpath("__file__"))
rootDir = os.path.abspath(os.path.join(currDir, '..')); sys.path.insert(1, rootDir)
# Warnings
import warnings
warnings.filterwarnings("ignore")
# My modules
from features.build_features import *
# Public modules
from sklearn.model_selection import GridSearchCV
import numpy as np
from pandas import read_csv
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, learning_curve
from sklearn.metrics import precision_recall_curve, confusion_matrix, \
precision_score, recall_score
from sklearn.model_selection import cross_val_predict
from numpy.random import seed
import lightgbm as lgb
# Inputs
SHOW_ERROR_ANALYSIS = True
# Extract
seed(40)
train = read_csv("../../data/interim/train.csv")
train_y = train[["y"]].values
dev = read_csv("../../data/interim/dev.csv")
dev_y = dev[["y"]].values
# Data parameters
features_pipeline = data_preparation()
# Model parameters
full_pipeline = Pipeline([
("features", features_pipeline),
("clf", lgb.LGBMClassifier(class_weight='balanced')),
])
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from sklearn.model_selection import train_test_split
# Set seed
np.random.seed(40)
# Initialize
max_score = 0.0
initial_n_estimators = 50
best_params = {'n_estimators':initial_n_estimators, 'learning_rate':0.1, 'max_depth':6, 'subsample':1.00, 'colsample_bytree':1.0, 'reg_lambda':1}
def get_score(params):
model = Pipeline([
("features", features_pipeline),
("clf", lgb.LGBMClassifier(class_weight='balanced', **params)),
])
print("Fitting model")
print(params)
model.fit(train, train_y)
prob_y = model.predict_proba(dev)[:, 1]
precision, recall, _ = precision_recall_curve(dev_y, prob_y, pos_label=1)
score = max([y for (x,y) in zip(precision, recall) if x >= 0.20])
return score
# 1) Tune max depth
params = best_params.copy()
for max_depth in [1, 2, 4, 6, 8]:
params['max_depth'] = max_depth
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned max depth and have a new max score: %.3f" % score)
# 2) Tune subsample
params = best_params.copy()
for subsample in [0.40, 0.60, 0.80, 0.90]:
params["subsample"] = subsample
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned subsample and have a new max score: %.3f" % score)
# 3) Tune n_estimators
params = best_params.copy()
for n_estimators in [1.1, 1.3]:
params["n_estimators"] = int(initial_n_estimators*n_estimators)
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned n_estimators and have a new max score: %.3f" % score)
# 4) Tune learning rate
params = best_params.copy()
for n_estimators, learning_rate in zip([initial_n_estimators*1.4, int(initial_n_estimators/1.4)], [0.07, 0.15]):
params["n_estimators"] = int(n_estimators)
params["learning_rate"] = learning_rate
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned learning rate and have a new max score: %.3f" % score)
# 5) Tune n_estimators again
params = best_params.copy()
for n_estimators in [int(1.1*initial_n_estimators), int(1.2*initial_n_estimators)]:
params["n_estimators"] = n_estimators
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned n_estimators again and have a new max score: %.3f" % score)
# 6) Tune sampling by tree
params = best_params.copy()
for colsample_bytree in [0.6, 0.8, 0.9]:
params["colsample_bytree"] = colsample_bytree
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned colsample_bytree and have a new max score: %.3f" % score)
# 7) Tune subsample again
params = best_params.copy()
for subsample in [0.6, 0.75, 0.9]:
params["subsample"] = subsample
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned subsample again and have a new max score: %.3f" % score)
# 9) Tune sampling fields
params = best_params.copy()
subsample_ = 0.9 if best_params["subsample"] == 1.0 else best_params["subsample"]
colsample_bytree_ = 0.6 if best_params["colsample_bytree"] == 0.5 else best_params["colsample_bytree"]
for subsample in [subsample_, subsample_ + 0.1]:
for colsample_bytree in [colsample_bytree_ - 0.1, colsample_bytree_]:
params["subsample"] = subsample
params["colsample_bytree"] = colsample_bytree
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned sampling fields and have a new max score: %.3f" % score)
# 10) Tune alpha
params = best_params.copy()
for reg_lambda in [3, 10, 33, 100, 300]:
params["reg_lambda"] = reg_lambda
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned alpha and have a new max score: %.3f" % score)
# 11) Tune trees
params = best_params.copy()
up = 1
for i in range(5):
params["n_estimators"] = int(params["n_estimators"] * (1.4 - 0.05*i) if up == 1 else params["n_estimators"] / (1.4 - 0.05*i))
score = get_score(params)
if score > max_score:
max_score = score
best_params = params.copy()
print("Tuned n_estimators and have a new max score: %.3f" % score)
else:
up = 0 if up == 1 else 1
print("Max score: %.3f" % max_score)
print("Best params: %s" % best_params)
full_pipeline = Pipeline([
("features", features_pipeline),
("clf", lgb.LGBMClassifier(class_weight='balanced', **best_params)),
])
full_pipeline = Pipeline([
("features", features_pipeline),
("clf", lgb.LGBMClassifier(class_weight='balanced')),
])
# Tune features pipeline parameters
parameters = {
'features__num_pipeline__new_numeric_attribs_adder__add_age_booleans': [True, False],
'features__hybrid_pipeline__new_hybrid_attribs_adder__add_income': [True, False],
'features__cat_pipeline__drop_unimportant_category_values__drop': [True, False],
'features__cat_pipeline__cat_encoder__drop': ['first', None]
}
gs = GridSearchCV(full_pipeline, parameters, cv=5)
gs.fit(train, train_y)
print("Best params: %s" % gs.best_params_)
full_pipeline = Pipeline([
("features", features_pipeline),
("clf", lgb.LGBMClassifier(class_weight='balanced', **best_params, **gs.best_params_)),
])
# Fit
full_pipeline.fit(train, train_y)
# Predict
precision_threshold = 0.20
prob_y = full_pipeline.predict_proba(dev)[:, 1]
precision, recall, thresholds = precision_recall_curve(dev_y, prob_y, pos_label=1)
print(precision, recall, thresholds)
score = max([y for (x,y) in zip(precision, recall) if x >= precision_threshold])
print('Recall score: %.3f' % score)
# Error analysis
if SHOW_ERROR_ANALYSIS:
precision_threshold_index = min([i for (x,i) in zip(precision, range(len(precision))) if x >= precision_threshold])
dev["prob_y"] = prob_y
prob_y_threshold = (list(thresholds) + [1.1])[precision_threshold_index]
pred_y = (prob_y >= prob_y_threshold).astype(bool)
print("Prob y Threshold: %.1f" % (prob_y_threshold*100))
print(confusion_matrix(dev_y, pred_y))
print("Recall: %.1f" % (recall_score(dev_y, pred_y)*100))
print("Precision: %.1f" % (precision_score(dev_y, pred_y)*100))
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2
] |
images.py | tikurahul/Inkplate-6-micropython | 21 | 123007 | <gh_stars>10-100
# https://javl.github.io/image2cpp/
# 40x40
CALENDAR_40_40 = bytearray([
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0x38, 0x00, 0x1c, 0x00, 0x00, 0x3c, 0x00, 0x1c, 0x00, 0x01, 0xff, 0xff, 0xff, 0x80, 0x03, 0xff,
0xff, 0xff, 0xc0, 0x07, 0xff, 0xff, 0xff, 0xe0, 0x07, 0xff, 0xff, 0xff, 0xe0, 0x07, 0xff, 0xff,
0xff, 0xe0, 0x07, 0xff, 0xff, 0xff, 0xe0, 0x07, 0xff, 0xff, 0xff, 0xe0, 0x07, 0xff, 0xff, 0xff,
0xe0, 0x07, 0x80, 0x00, 0x01, 0xe0, 0x07, 0x00, 0x00, 0x00, 0xe0, 0x07, 0x00, 0x00, 0x00, 0xe0,
0x07, 0x00, 0x00, 0x00, 0xe0, 0x07, 0x00, 0x00, 0x00, 0xe0, 0x07, 0x00, 0x00, 0x00, 0xe0, 0x07,
0x00, 0x00, 0x00, 0xe0, 0x07, 0x00, 0x0f, 0xf0, 0xe0, 0x07, 0x00, 0x0f, 0xf0, 0xe0, 0x07, 0x00,
0x0f, 0xf0, 0xe0, 0x07, 0x00, 0x0f, 0xf0, 0xe0, 0x07, 0x00, 0x0f, 0xf0, 0xe0, 0x07, 0x00, 0x0f,
0xf0, 0xe0, 0x07, 0x00, 0x0f, 0xf0, 0xe0, 0x07, 0x00, 0x0f, 0xf0, 0xe0, 0x07, 0x00, 0x00, 0x00,
0xe0, 0x07, 0x00, 0x00, 0x00, 0xe0, 0x07, 0x00, 0x00, 0x00, 0xe0, 0x07, 0x80, 0x00, 0x01, 0xe0,
0x07, 0xff, 0xff, 0xff, 0xe0, 0x03, 0xff, 0xff, 0xff, 0xc0, 0x01, 0xff, 0xff, 0xff, 0x80, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00
])
| [
1,
529,
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python/displaywidget.py | karlssonper/gpuip | 10 | 31259 | <reponame>karlssonper/gpuip
from PySide import QtGui, QtOpenGL, QtCore
from OpenGL import GL
from OpenGL import GL
from OpenGL.GL import shaders
from OpenGL.arrays import vbo
from OpenGL.GL.ARB import texture_rg
from OpenGL.GL.ARB import half_float_vertex
from ctypes import c_void_p
import numpy
import math
vert_src = """#version 120
attribute vec2 positionIn;
attribute vec2 texIn;
varying vec2 texcoord;
void main()
{
gl_Position= vec4(positionIn * 2.0 - vec2(1),0,1);
texcoord = texIn;
}
"""
frag_src = """#version 120
uniform sampler2D texture;
uniform int hdr_mode;
uniform float g;
uniform float m;
uniform float s;
varying vec2 texcoord;
float convert(float x)
{
return clamp(pow(x*m,g) *s, 0.0, 1.0);
}
void main()
{
vec2 coords = vec2(texcoord.x, 1.0 - texcoord.y);
vec3 tex = texture2D(texture, coords).xyz;
if (hdr_mode == 1) {
gl_FragColor = vec4(convert(tex.x), convert(tex.y), convert(tex.z), 1);
} else {
gl_FragColor = vec4(tex,1);
}
}
"""
class DisplayWidget(QtGui.QWidget):
def __init__(self, parent):
super(DisplayWidget, self).__init__(parent)
self.buffers = None
self.glWidget = GLWidget(self)
self.bufferComboBox = QtGui.QComboBox(self)
policy = QtGui.QSizePolicy()
policy.setHorizontalPolicy(QtGui.QSizePolicy.Expanding)
self.bufferComboBox.setSizePolicy(policy)
label = QtGui.QLabel("Buffers:")
label.setBuddy(self.bufferComboBox)
self.bufferComboBox.currentIndexChanged["QString"].connect(
self.onBufferSelectChange)
self.interactiveCheckBox = QtGui.QCheckBox("Interactive", self)
midLayout = QtGui.QHBoxLayout()
midLayout.addWidget(label)
midLayout.addWidget(self.bufferComboBox)
midLayout.addWidget(self.interactiveCheckBox)
self.label = QtGui.QLabel("Exposure: 0", self)
self.slider = QtGui.QSlider(QtCore.Qt.Horizontal, self)
self.slider.setRange(-100,100)
self.slider.setValue(0)
self.slider.valueChanged.connect(self.onExposureChange)
bottomLayout = QtGui.QHBoxLayout()
bottomLayout.addWidget(self.label)
bottomLayout.addWidget(self.slider)
bottomLayout.setSizeConstraint(QtGui.QLayout.SetMinimumSize)
layout = QtGui.QVBoxLayout()
layout.addWidget(self.glWidget)
layout.addLayout(midLayout)
layout.addLayout(bottomLayout)
self.setLayout(layout)
def setBuffers(self, buffers):
for i in xrange(self.bufferComboBox.count()):
self.bufferComboBox.removeItem(0)
self.buffers = buffers
buffersList = buffers.keys()
buffersList.sort()
self.bufferComboBox.addItems(buffersList)
def setActiveBuffer(self, bufferName):
idx = self.bufferComboBox.findText(bufferName)
if idx == self.bufferComboBox.currentIndex():
self.refreshDisplay()
else:
self.bufferComboBox.setCurrentIndex(idx)
def onBufferSelectChange(self, value):
if str(value) in self.buffers:
ndarray = self.buffers[str(value)].data
self.glWidget.copyDataToTexture(ndarray)
if ndarray.dtype == numpy.float32 or ndarray.dtype == numpy.float16:
self.slider.setEnabled(True)
else:
self.slider.setEnabled(False)
self.glWidget.glDraw()
def onExposureChange(self):
value = 0.1 * self.slider.value()
self.glWidget.exposure = value
self.label.setText("Exposure: " + str(value))
self.glWidget.glDraw()
def refreshDisplay(self):
self.onBufferSelectChange(self.bufferComboBox.currentText())
def sizeHint(self):
return QtCore.QSize(400,400)
class GLWidget(QtOpenGL.QGLWidget):
def __init__(self, parent):
super(GLWidget, self).__init__(parent)
self.w = 440
self.h = 440
self.rightBtnDown = False
self.texture = None
self.texturedata = None
self.shader = None
self.hdr_mode = 0
self.vbo = None
self.scale = 0.5
self.steps = 0
self.cx = 0.5
self.cy = 0.5
self.gamma = 1.0/2.2
self.exposure = 0
self.zoomFactor = 1.35
self.panFactor = 0.002
def initializeGL(self):
pass
def copyDataToTexture(self, ndarray):
# Update dimensions of widget
self.texturedata = ndarray
self.w = ndarray.shape[0]
self.h = ndarray.shape[1]
self.updateGeometry()
# Generate new texture
if not self.texture:
try:
self.texture = GL.glGenTextures(1)
except Exception:
return
target = GL.GL_TEXTURE_2D
GL.glBindTexture(target, self.texture)
GL.glTexParameterf(target, GL.GL_TEXTURE_MAG_FILTER, GL.GL_NEAREST)
GL.glTexParameterf(target, GL.GL_TEXTURE_MIN_FILTER, GL.GL_NEAREST)
GL.glTexParameterf(target, GL.GL_TEXTURE_WRAP_S, GL.GL_CLAMP_TO_EDGE)
GL.glTexParameterf(target, GL.GL_TEXTURE_WRAP_T, GL.GL_CLAMP_TO_EDGE)
GL.glTexParameteri(target, GL.GL_GENERATE_MIPMAP, GL.GL_FALSE);
# Get texture format
channels = ndarray.shape[2] if ndarray.ndim == 3 else 1
if channels == 1:
glFormat = GL.GL_RED
elif channels == 2:
glFormat = GL.GL_RG
elif channels == 3:
glFormat = GL.GL_RGB
elif channels == 4:
glFormat = GL.GL_RGBA
glInternalFormat = glFormat
# Get texture type
if ndarray.dtype == numpy.float32:
glType = GL.GL_FLOAT
# Need to use the exposure shader if floating point
self.hdr_mode = 1
# The internal format changes with floating point textures
if channels == 1:
glInternalFormat = texture_rg.GL_R32F
elif channels == 2:
glInternalFormat = texture_rg.GL_RG32F
elif channels == 3:
glInternalFormat = GL.GL_RGB32F
elif channels == 4:
glInternalFormat = GL.GL_RGBA32F
elif ndarray.dtype == numpy.float16:
glType = GL.GL_FLOAT
# Need to use the exposure shader if floating point
self.hdr_mode = 1
# The internal format changes with floating point textures
if channels == 1:
glInternalFormat = texture_rg.GL_R16F
elif channels == 2:
glInternalFormat = texture_rg.GL_RG16F
elif channels == 3:
glInternalFormat = GL.GL_RGB16F
elif channels == 4:
glInternalFormat = GL.GL_RGBA16F
else:
glType = GL.GL_UNSIGNED_BYTE
self.hdr_mode = 0
# Copy data to texture
GL.glTexImage2D(target, 0, glInternalFormat, self.w, self.h,
0, glFormat, glType, ndarray)
GL.glBindTexture(target, 0)
def resizeGL(self, width, height):
GL.glViewport(0,0,width,height)
GL.glMatrixMode(GL.GL_PROJECTION)
GL.glLoadIdentity()
GL.glOrtho(0,1,0,1,0,1)
GL.glMatrixMode(GL.GL_MODELVIEW)
def compileShaders(self):
# Build shaders
vert_shader = shaders.compileShader(vert_src, GL.GL_VERTEX_SHADER)
frag_shader = shaders.compileShader(frag_src, GL.GL_FRAGMENT_SHADER)
self.shader = shaders.compileProgram(vert_shader, frag_shader)
def paintGL(self):
if GL.glCheckFramebufferStatus(GL.GL_FRAMEBUFFER) == 33305:
return
GL.glClear(GL.GL_COLOR_BUFFER_BIT)
if not self.texture:
return
if not self.shader:
self.compileShaders()
if not self.vbo:
self.vbo = GL.glGenBuffers(1)
shaders.glUseProgram(self.shader)
GL.glBindBuffer(GL.GL_ARRAY_BUFFER, self.vbo)
vertices = numpy.array(
[-self.scale + self.cx, -self.scale + self.cy ,
self.scale + self.cx, -self.scale + self.cy,
self.scale + self.cx, self.scale + self.cy,
-self.scale + self.cx, self.scale + self.cy,
0,0,1,0,1,1,0,1], dtype = numpy.float32)
GL.glBufferData(GL.GL_ARRAY_BUFFER, 64, vertices, GL.GL_STATIC_DRAW)
loc = GL.glGetAttribLocation(self.shader, "positionIn")
GL.glEnableVertexAttribArray(loc)
GL.glVertexAttribPointer(loc, 2, GL.GL_FLOAT, 0, 8, c_void_p(0))
loc = GL.glGetAttribLocation(self.shader, "texIn")
GL.glEnableVertexAttribArray(loc)
GL.glVertexAttribPointer(loc, 2, GL.GL_FLOAT, 0, 8, c_void_p(32))
def _uniformLoc(name):
return GL.glGetUniformLocation(self.shader,name)
GL.glUniform1f(_uniformLoc("g"), self.gamma);
GL.glUniform1f(_uniformLoc("m"), math.pow(2, self.exposure + 2.47393))
GL.glUniform1f(_uniformLoc("s"), math.pow(2, -3.5 * self.gamma))
GL.glUniform1i(_uniformLoc("hdr_mode"), self.hdr_mode);
GL.glUniform1i(_uniformLoc("texture"), 0);
GL.glActiveTexture(GL.GL_TEXTURE0);
GL.glBindTexture(GL.GL_TEXTURE_2D, self.texture)
GL.glDrawArrays(GL.GL_QUADS, 0, 4);
GL.glBindTexture(GL.GL_TEXTURE_2D, 0)
loc = GL.glGetAttribLocation(self.shader, "positionIn")
GL.glDisableVertexAttribArray(loc)
loc = GL.glGetAttribLocation(self.shader, "texIn")
GL.glDisableVertexAttribArray(loc)
GL.glBindBuffer(GL.GL_ARRAY_BUFFER, 0)
shaders.glUseProgram(0)
if self.rightBtnDown:
self.renderPixelInfo()
def mousePressEvent(self, event):
self.lastPos = event.pos()
if event.button()== QtCore.Qt.RightButton:
self.rightBtnDown = True
self.glDraw()
def mouseReleaseEvent(self, event):
if event.button() == QtCore.Qt.RightButton:
self.rightBtnDown = False
self.glDraw()
def mouseMoveEvent(self, event):
dx = event.x() - self.lastPos.x()
dy = event.y() - self.lastPos.y()
if event.buttons() & QtCore.Qt.LeftButton:
self.cx += self.panFactor*dx
self.cy -= self.panFactor*dy
self.correctCenterCoordinates()
self.lastPos = event.pos()
self.glDraw()
def wheelEvent(self, event):
if event.delta() > 0:
self.steps += 1
else:
self.steps -= 1
# Only allow inital zoom (not smaller)
if self.steps < 0:
self.steps = 0
self.scale = 0.5 * math.pow(self.zoomFactor, self.steps)
self.correctCenterCoordinates()
self.glDraw()
def correctCenterCoordinates(self):
if -self.scale + self.cx > 0:
self.cx = self.scale
if self.scale + self.cx < 1:
self.cx = 1 - self.scale
if -self.scale + self.cy > 0:
self.cy = self.scale
if self.scale + self.cy < 1:
self.cy = 1 - self.scale
def sizeHint(self):
return QtCore.QSize(self.w,self.h)
def renderPixelInfo(self):
# Get pixel positions px and py
size = 2.0*(self.scale)
offx = self.w * (self.scale - self.cx) / size
offy = self.h * (self.scale - self.cy) / size
px = int(offx + (self.lastPos.x() * self.w) / (self.width() * size))
py = int(offy + (self.lastPos.y() * self.h) / (self.height()* size))
py = self.h - py
px = min(max(px,0), self.w - 1)
py = min(max(py,0), self.h - 1)
val = [None, None, None, None]
for i in xrange(self.texturedata.shape[2]):
val[i] = self.texturedata[px][py][i]
texts = ["x:%i y:%i" % (px,py),
"R:%f" % val[0] if val[0] else "n/a",
"G:%f" % val[1] if val[1] else "n/a",
"B:%f" % val[2] if val[2] else "n/a"]
font = QtGui.QFont()
font.setFamily("Monospace")
#font.setFixedPitch(True);
metrics = QtGui.QFontMetrics(font)
sx = 20 # spacing variable
w,h = metrics.width(texts[1]), metrics.height()
metrics.width(" ")
x,y = self.lastPos.x(), self.height() - self.lastPos.y() - sx
dx,dy = 1.0/self.width(), 1.0/self.height()
# Calculate pixel info position
# Swap position if outside screen
if x + 1.5*sx + w < self.width():
x0 = x + 0.75*sx
x1 = x + 1.5*sx + w + 10
tx = x + sx
else:
x0 = x - 0.75*sx
x1 = x - 1.5*sx - w
tx = x - sx - w
if y + sx - 5 * h > 0:
y0 = y + sx
y1 = y + sx - 5 * h
ty = self.height()-y
else:
y0 = y - sx + 3 * h
y1 = y - sx + 8 * h
ty = self.height()-y - 5 * h - 0.5*sx
# Draw transparent quad
GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA);
GL.glEnable(GL.GL_BLEND)
GL.glBegin(GL.GL_QUADS)
GL.glColor4f(0,0,0,0.8)
for x,y in zip([x0,x1,x1,x0],[y0,y0,y1,y1]):
GL.glVertex2f(x * dx, y * dy)
GL.glEnd()
GL.glDisable(GL.GL_BLEND)
# Render text
GL.glColor4f(1,1,1,1)
for i,text in enumerate(texts):
self.renderText(tx, ty + i*h, text, font)
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taburu/event.py | TRI-AMDD/taburu | 0 | 60727 | <reponame>TRI-AMDD/taburu
# Copyright (c) 2019 Toyota Research Institute
import abc
from datetime import datetime
from monty.json import MSONable
class Event(abc.ABC):
def __init__(self, time=None):
self._time = time or datetime.utcnow().isoformat()
# TODO: more descriptive name, populate space?
class ParametersAdded(Event, MSONable):
def __init__(self, table_name, parameters, time=None):
self.table_name = table_name
self.parameters = parameters
super(ParametersAdded, self).__init__(time)
def as_dict(self):
return {
"@class": self.__class__.__name__,
"@module": self.__class__.__module__,
"table_name": self.table_name,
"parameters": self.parameters,
"time": self._time
}
class MethodAdded(Event, MSONable):
def __init__(self, name, parameter_indices, time=None):
self.name = name
self.parameter_indices = parameter_indices
super(MethodAdded, self).__init__(time)
def as_dict(self):
return {
"@class": self.__class__.__name__,
"@module": self.__class__.__module__,
"name": self.name,
"parameters": self.parameter_indices,
"time": self._time
}
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odoo/addons/base/tests/test_ir_http.py | jjiege/odoo | 0 | 78159 | # -*- coding: utf-8 -*-
from odoo.tests import common
import odoo
GIF = b"R0lGODdhAQABAIAAAP///////ywAAAAAAQABAAACAkQBADs="
class test_ir_http_mimetype(common.TransactionCase):
def test_ir_http_mimetype_attachment(self):
""" Test mimetype for attachment """
attachment = self.env['ir.attachment'].create({
'datas': GIF,
'name': 'Test mimetype gif',
'datas_fname': 'file.gif'})
status, headers, content = self.env['ir.http'].binary_content(
id=attachment.id,
mimetype=None,
default_mimetype='application/octet-stream',
env=self.env
)
mimetype = dict(headers).get('Content-Type')
self.assertEqual(mimetype, 'image/gif')
def test_ir_http_mimetype_attachment_name(self):
""" Test mimetype for attachment with bad name"""
attachment = self.env['ir.attachment'].create({
'datas': GIF,
'name': 'Test mimetype gif with png name',
'datas_fname': 'file.png'})
status, headers, content = self.env['ir.http'].binary_content(
id=attachment.id,
mimetype=None,
default_mimetype='application/octet-stream',
env=self.env
)
mimetype = dict(headers).get('Content-Type')
# TODO: fix and change it in master, should be image/gif
self.assertEqual(mimetype, 'image/png')
def test_ir_http_mimetype_basic_field(self):
""" Test mimetype for classic field """
partner = self.env['res.partner'].create({
'image': GIF,
'name': 'Test mimetype basic field',
})
status, headers, content = self.env['ir.http'].binary_content(
model='res.partner',
id=partner.id,
field='image',
default_mimetype='application/octet-stream',
env=self.env
)
mimetype = dict(headers).get('Content-Type')
self.assertEqual(mimetype, 'image/gif')
def test_ir_http_mimetype_computed_field(self):
""" Test mimetype for computed field wich resize picture"""
prop = self.env['ir.property'].create({
'fields_id': self.env['ir.model.fields'].search([], limit=1).id,
'name': "Property binary",
'value_binary': GIF,
'type': 'binary',
})
resized = odoo.tools.image_get_resized_images(prop.value_binary, return_big=True, avoid_resize_medium=True)['image_small']
# Simul computed field which resize and that is not attachement=True (E.G. on product)
prop.write({'value_binary': resized})
status, headers, content = self.env['ir.http'].binary_content(
model='ir.property',
id=prop.id,
field='value_binary',
default_mimetype='application/octet-stream',
env=self.env
)
mimetype = dict(headers).get('Content-Type')
self.assertEqual(mimetype, 'image/gif')
def test_ir_http_attachment_access(self):
""" Test attachment access with and without access token """
public_user = self.env.ref('base.public_user')
attachment = self.env['ir.attachment'].create({
'datas': GIF,
'name': 'Test valid access token with image',
'datas_fname': 'image.gif'
})
defaults = {
'id': attachment.id,
'default_mimetype': 'image/gif',
'env': public_user.sudo(public_user.id).env,
}
def test_access(**kwargs):
status, _, _ = self.env['ir.http'].binary_content(
**dict(defaults, **kwargs)
)
return status
status = test_access()
self.assertEqual(status, 403, "no access")
status = test_access(access_token=u'Secret')
self.assertEqual(status, 403,
"no access if access token for attachment without access token")
attachment.access_token = u'Secret'
status = test_access(access_token=u'Secret')
self.assertEqual(status, 200, "access for correct access token")
status = test_access(access_token=u'Wrong')
self.assertEqual(status, 403, "no access for wrong access token")
attachment.public = True
status = test_access()
self.assertEqual(status, 200, "access for attachment with access")
status = test_access(access_token=u'Wrong')
self.assertEqual(status, 403,
"no access for wrong access token for attachment with access")
attachment.unlink()
status = test_access()
self.assertEqual(status, 404, "no access for deleted attachment")
status = test_access(access_token=u'Secret')
self.assertEqual(status, 404,
"no access with access token for deleted attachment")
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TwitterModule/tweet_complaint.py | CFGIndia20/team-19 | 0 | 176088 | <reponame>CFGIndia20/team-19<filename>TwitterModule/tweet_complaint.py
class TweetComplaint:
def __init__(self, complain_text, city, state, tweet_id, username, image_url):
self.complain_text = complain_text
self.tweet_id = tweet_id
self.username = username
self.city = city
self.state = state
self.image_url = image_url
self.status = "reported"
def __str__(self):
return self.complain_text +'\n'+ str(self.tweet_id) +'\n'+ self.username +'\n'+ self.city +'\n'+ self.state +'\n'+ self.image_url
def to_dict(self):
return {'category': '', 'city': self.city, 'state': self.state, 'image_url': self.image_url,
'status': self.status, 'text': self.complain_text, 'tweet_id': self.tweet_id, 'username': self.username} | [
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funcoes.py | ZezaoDev/Circtrigo | 0 | 3294 | <reponame>ZezaoDev/Circtrigo
import turtle as t
import math
class circTrigo:
def __init__(self):
self.raio = 0
self.grau = 0
self.seno = 0
self.cosseno = 0
self.tangente = 0
self.quadrante = 0
self.tema = ''
t.bgcolor("black")
t.pencolor("white")
def seta(self):
# DESENHA UMA SETA
t.left(90)
t.forward(5)
t.right(120)
t.forward(10)
t.right(120)
t.forward(10)
t.right(120)
t.forward(5)
t.right(90)
def linha(self, pxls):
# DESENHA UMA LINHA PONTILHADA
pixels = int(pxls//1)
if pixels % 2 == 0:
pixels = pixels + 1
for x in range(0, pixels//10):
t.pendown()
t.forward(5)
t.penup()
t.forward(5)
t.pendown()
t.forward(pixels%10)
def reset(self):
# RETORNA PRA POSICAO INICIAL
t.penup()
t.home()
t.pendown()
t.speed(0)
t.pensize(2)
t.pencolor("white")
def circulo(self, raio):
# DESENHA O CIRCULO
self.raio = raio
t.right(90)
t.penup()
t.forward(self.raio)
t.left(90)
t.pendown()
t.circle(self.raio)
self.reset()
def eixos(self):
# EIXO X
t.penup()
t.backward(self.raio + 50)
t.pendown()
self.linha((self.raio*2)+100)
self.seta()
self.reset()
# EIXO Y
t.left(90)
t.penup()
t.backward(self.raio + 50)
t.pendown()
self.linha((self.raio*2)+100)
self.seta()
self.reset()
def angulo(self, grau):
# DESENHA O ANGULO
self.grau = grau % 360
t.left(self.grau)
t.forward(self.raio)
self.reset()
# DEFINE O VALOR DO SENO, COSSENO E TANGENTE.
self.seno = math.sin(math.radians(self.grau))
self.cosseno = math.cos(math.radians(self.grau))
self.tangente = math.tan(math.radians(self.grau))
# DEFINE O QUADRANTE DO ANGULO
vquad = self.grau
if 0 < vquad < 90:
self.quadrante = 1
elif 90 < vquad < 180:
self.quadrante = 2
elif 180 < vquad < 270:
self.quadrante = 3
elif 270 < vquad < 360:
self.quadrante = 4
if vquad == 0 or vquad == 90 or vquad == 180 or vquad == 270 or vquad == 360: # Quadrante 0 representa os angulos de resultados indefinidos
self.quadrante = 0
def sen(self):
# DESENHA O SENO
t.left(self.grau)
t.forward(self.raio)
t.pencolor("red")
if self.quadrante == 1:
t.left(180 - self.grau)
self.linha(self.cosseno * self.raio)
t.left(90)
t.forward(self.seno * self.raio)
print (self.seno)
elif self.quadrante == 2:
t.right(self.grau)
self.linha((self.cosseno * self.raio) * -1)
t.right(90)
t.forward(self.seno * self.raio)
print (self.seno)
elif self.quadrante == 3:
t.right(self.grau)
self.linha(self.cosseno * self.raio * -1)
t.left(90)
t.forward(self.seno * self.raio * -1)
print (self.seno)
elif self.quadrante == 4:
t.left(180 - self.grau)
self.linha(self.cosseno * self.raio)
t.left(90)
t.forward(self.seno * self.raio)
print (self.seno)
else:
print("Erro: angulo invalido")
self.reset()
def csen(self):
# DESENHA O COSSENO
t.left(self.grau)
t.forward(self.raio)
t.pencolor("green")
if self.quadrante == 1:
t.right(self.grau + 90)
self.linha(self.seno * self.raio)
t.right(90)
t.forward(self.cosseno * self.raio)
print (self.cosseno)
elif self.quadrante == 2:
t.right(self.grau + 90)
self.linha(self.seno * self.raio)
t.right(90)
t.forward(self.cosseno * self.raio)
print (self.cosseno)
elif self.quadrante == 3:
t.right(self.grau - 90)
self.linha(self.seno * self.raio * -1)
t.right(90)
t.forward(self.cosseno * self.raio * -1)
print (self.cosseno)
elif self.quadrante == 4:
t.right(self.grau - 90)
self.linha(self.seno * self.raio * -1)
t.left(90)
t.forward(self.cosseno * self.raio)
print (self.cosseno)
else:
print("Erro: angulo invalido")
self.reset()
def tan(self):
# DESENHA A TANGENTE
t.left(self.grau)
t.penup()
t.pencolor("blue")
if self.quadrante == 1:
t.forward(self.raio)
t.pendown()
self.linha(math.sqrt(((self.tangente*self.raio)**2) + (self.raio**2)) - self.raio)
t.right(self.grau + 90)
t.forward(self.tangente * self.raio)
print (self.tangente)
elif self.quadrante == 2:
t.left(180)
t.forward(self.raio)
t.pendown()
self.linha(math.sqrt(((self.tangente*self.raio)**2) + (self.raio**2)) - self.raio)
t.left(90 - self.grau)
t.forward(self.tangente * self.raio)
print (self.tangente)
elif self.quadrante == 3:
t.left(180)
t.forward(self.raio)
t.pendown()
self.linha(math.sqrt(((self.tangente*self.raio)**2) + (self.raio**2)) - self.raio)
t.right(self.grau - 90)
t.forward(self.tangente * self.raio)
print (self.tangente)
elif self.quadrante == 4:
t.forward(self.raio)
t.pendown()
self.linha(math.sqrt(((self.tangente*self.raio)**2) + (self.raio**2)) - self.raio)
t.right(90 + self.grau)
t.forward(self.tangente * self.raio)
print (self.tangente)
else:
print("Erro: angulo invalido")
self.reset()
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] |
Notatki/Obliczenia-naukowe/NumPy/NumPy.py | bzglinicki/python-trainin | 1 | 150909 | # Programowanie I R
# Pakiet NumPy
import numpy as np
arr_list = np.array([1, 2, 3, 4, 5])
print(arr_list)
print(type(arr_list))
print()
arr_tuple = np.array((1, 2, 3, 4, 5))
print(arr_tuple)
print(type(arr_tuple))
print()
arr = np.array(42)
print(arr)
print(arr.ndim)
print()
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)
print(arr.ndim)
print()
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])
print(arr)
print(arr.ndim)
print()
arr = np.array([1, 2, 3, 4], ndmin = 5)
print(arr)
print(arr.ndim)
print()
#***********************************************************************************
arr = np.array([1, 2, 3, 4])
print(arr[0])
print(arr[2] + arr[3])
print()
arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])
print(arr[1, 3])
print(arr[1, 2])
print(arr[1, -1])
print()
arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(arr[0, 1, 2])
print()
#***********************************************************************************
arr = np.array([1, 2, 3, 4, 5, 6, 7])
print(arr[1:5])
print(arr[4:])
print(arr[:4])
print(arr[1:5:2])
print()
arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
print(arr[1, 1:4])
print(arr[0:2, 1:4])
#***********************************************************************************
arr1 = np.array([10, 11, 12, 13, 14, 15])
arr2 = np.array([20, 21, 22, 23, 24, 25])
newarr = np.add(arr1, arr2)
print(newarr)
print()
newarr = np.subtract(arr1, arr2)
print(newarr)
print()
newarr = np.multiply(arr1, arr2)
print(newarr)
print()
newarr = np.divide(arr1, arr2)
print(newarr)
print() | [
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setup.py | srini009/reducer | 0 | 1616943 | <gh_stars>0
import os
import shutil
import re
import sys
def replace_in_file(filename, mapping):
print('==> Editing '+filename)
infile = open(filename)
outfile = open(filename+'.tmp', 'w+')
for line in infile:
for k, v in mapping.items():
line = line.replace(k, v)
outfile.write(line)
infile.close()
outfile.close()
shutil.copyfile(filename+'.tmp', filename)
os.remove(filename+'.tmp')
def list_files_to_edit(root, extensions,
exclude_directories=[],
exclude_files=[]):
result = []
for dirname, dirnames, filenames in os.walk(root):
for filename in filenames:
if filename in exclude_files:
continue
for ext in extensions:
if filename.endswith(ext):
result.append(os.path.join(dirname, filename))
break
for d in exclude_directories:
if d in dirnames:
dirnames.remove(d)
return result
def rename_files_and_directories(root, extensions,
mapping,
exclude_directories=[],
exclude_files=[]):
for dirname, dirnames, filenames in os.walk(root):
# exclude directories
for d in exclude_directories:
if d in dirnames:
dirnames.remove(d)
# rename folders
names_to_changes = {}
for subdirname in dirnames:
new_name = subdirname
for k, v in mapping.items():
new_name = new_name.replace(k, v)
if new_name != subdirname:
print("==> Renaming "+os.path.join(dirname, subdirname)+" into "+os.path.join(dirname, new_name))
shutil.move(os.path.join(dirname, subdirname),
os.path.join(dirname, new_name))
dirnames.remove(subdirname)
dirnames.append(new_name)
# rename files
for filename in filenames:
if filename in exclude_files:
continue
for ext in extensions:
if filename.endswith(ext):
new_name = filename
for k, v in mapping.items():
new_name = new_name.replace(k, v)
if new_name != filename:
print("==> Renaming "+os.path.join(dirname, filename)+" into "+os.path.join(dirname, new_name))
os.rename(os.path.join(dirname, filename),
os.path.join(dirname, new_name))
break # don't try the next extension for this file
if __name__ == '__main__':
service_name = input('Enter the name of your service: ')
if(not re.match('[a-zA-Z_][a-zA-Z\d_]*', service_name)):
print("Error: service name must start with a letter and consist of letters, digits, or underscores")
sys.exit(-1)
service_name = service_name.rstrip()
resource_name = input('Enter the name of the resources (e.g., database): ')
if(not re.match('[a-zA-Z_][a-zA-Z\d_]*', resource_name)):
print("Error: resource name must start with a letter and consist of letters, digits, or underscores")
sys.exit(-1)
mapping = {
'symbiomon' : service_name,
'SYMBIOMON' : service_name.upper(),
'metric' : resource_name,
'METRIC' : resource_name.upper()
}
files_to_edit = list_files_to_edit('.',
extensions=['.c', '.h', '.txt', '.in'],
exclude_directories=['.git','build', '.spack-env', 'munit'],
exclude_files=['uthash.h'])
for f in files_to_edit:
replace_in_file(f, mapping)
rename_files_and_directories('.',
extensions=['.c', '.h', '.txt', '.in'],
mapping=mapping,
exclude_directories=['.git','build', '.spack-env', 'munit'],
exclude_files=['uthash.h'])
| [
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] |
core/management/commands/gendoc.py | klebed/esdc-ce | 97 | 44962 | import os
import re
import shutil
from ._base import DanubeCloudCommand, CommandOption, CommandError, lcd
class Command(DanubeCloudCommand):
help = 'Generate documentation files displayed in GUI.'
DOC_REPO = 'https://github.com/erigones/esdc-docs.git'
DOC_TMP_DIR = '/var/tmp/esdc-docs'
options = (
CommandOption('--api', '--api-only', action='store_true', dest='api_only', default=False,
help='Generate only the API documentation.'),
CommandOption('--user-guide', '--user-guide-only', action='store_true', dest='user_guide_only', default=False,
help='Generate only the User Guide.'),
)
def gendoc_api(self):
"""Generate api documentation"""
with lcd(self.PROJECT_DIR):
doc_dir = self._path(self.PROJECT_DIR, 'doc', 'api')
doc_dst = self._path(self.PROJECT_DIR, 'api', 'static', 'api', 'doc')
bin_dst = self._path(self.PROJECT_DIR, 'api', 'static', 'api', 'bin')
# Build sphinx docs
with lcd(doc_dir):
self.local('make esdc-clean; make esdc ESDOCDIR="%s"' % doc_dst)
# Create es script suitable for download
es_src = self._path(self.PROJECT_DIR, 'bin', 'es')
es_dst = self._path(bin_dst, 'es')
es_current = os.path.join(self.settings.PROJECT_DIR, 'var', 'www', 'static', 'api', 'bin', 'es')
api_url = "API_URL = '%s'" % (self.settings.SITE_LINK + '/api')
if os.path.isfile(es_current):
with open(es_current, 'r') as es0:
for line in es0:
if line.startswith("API_URL = '"):
api_url = line
break
with open(es_src) as es1:
with os.fdopen(os.open(es_dst, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o644), 'w') as es2:
es2.write(es1.read().replace("API_URL = 'http://127.0.0.1:8000/api'", api_url))
# Copy es_bash_completion.sh to download location
es_bc_src = self._path(doc_dir, 'es_bash_completion.sh')
self.local('cp %s %s' % (es_bc_src, bin_dst))
self.display('API documentation built successfully.', color='green')
def gendoc_user_guide(self, fallback_branch='master'):
"""Generate user guide"""
doc_dst = self._path(self.PROJECT_DIR, 'gui', 'static', 'user-guide')
with lcd(self.PROJECT_DIR):
try:
branch = self.get_git_version()[0] # Git tag or branch name
except CommandError:
self.display('Could not determine our branch or tag', color='yellow')
branch = fallback_branch
self.display('Falling back to "%s" branch' % branch, color='yellow')
else:
self.display('We are on branch "%s"' % branch)
if self._path_exists(self.DOC_TMP_DIR, 'user-guide', 'conf.py'):
existing_repo = True
self.display('%s already exists in %s' % (self.DOC_REPO, self.DOC_TMP_DIR), color='yellow')
with lcd(self.DOC_TMP_DIR):
self.local('git fetch')
self.display('%s has been successfully updated.' % self.DOC_REPO, color='green')
else:
if self._path_exists(self.DOC_TMP_DIR):
self.display('Removing stale %s', self.DOC_TMP_DIR, color='yellow')
shutil.rmtree(self.DOC_TMP_DIR)
existing_repo = False
self.local('git clone %s %s' % (self.DOC_REPO, self.DOC_TMP_DIR))
self.display('%s has been successfully cloned.' % self.DOC_TMP_DIR, color='green')
with lcd(self.DOC_TMP_DIR):
if self.local('git checkout %s' % branch, raise_on_error=False) != 0:
self.display('Could not checkout esdc-docs branch "%s"' % branch, color='yellow')
branch = fallback_branch
self.display('Falling back to "%s" branch' % branch, color='yellow')
self.local('git checkout %s' % branch)
self.display('Checked out esdc-docs branch "%s"' % branch, color='green')
# If the branch is no a tag name, then we need to merge/pull
if existing_repo and not re.search('^v[0-9]', branch):
self.local('git merge --ff-only origin/%s' % branch)
self.display('Merged esdc-docs branch "%s"' % branch, color='green')
# Build sphinx docs
with lcd(self._path(self.DOC_TMP_DIR, 'user-guide')):
self.local('make esdc-clean; make esdc ESDOCDIR="%s"' % doc_dst)
self.display('User guide built successfully.', color='green')
def handle(self, api_only=False, user_guide_only=False, **options):
if api_only and user_guide_only:
pass
elif api_only:
self.gendoc_api()
return
elif user_guide_only:
self.gendoc_user_guide()
return
self.gendoc_api()
self.display('\n\n', stderr=True)
self.gendoc_user_guide()
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13075,
580,
13,
2
] |
arc004_a.py | Lockdef/kyopro-code | 0 | 167238 | import math
n = int(input())
s = [list(map(int, input().split())) for _ in range(n)]
ans = 0
for i in range(n):
for j in range(i+1, n):
x_i = s[i][0]
y_i = s[i][1]
x_j = s[j][0]
y_j = s[j][1]
ans = max(ans, math.sqrt((x_i - x_j)**2 + (y_i - y_j)**2))
print(ans)
| [
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] |
783-minimum-distance-between-bst-nodes/783-minimum-distance-between-bst-nodes.py | hyeseonko/LeetCode | 2 | 19880 | # Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def minDiffInBST(self, root: Optional[TreeNode]) -> int:
output=[]
stack=[root]
while(stack):
cur = stack.pop(0)
output.append(cur.val)
if cur.left:
stack.append(cur.left)
if cur.right:
stack.append(cur.right)
sorted_output=sorted(output)
diff = sorted_output[1]-sorted_output[0]
for i in range(2,len(sorted_output)):
if sorted_output[i]-sorted_output[i-1]<diff:
diff=sorted_output[i]-sorted_output[i-1]
return diff | [
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4905,
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29875,
29899,
29896,
29962,
13,
4706,
736,
2923,
2
] |
nodes/swagger_server/models/job_simulator_opts.py | rdbox-intec/r2s2_for_rostest | 0 | 44990 | <filename>nodes/swagger_server/models/job_simulator_opts.py<gh_stars>0
# coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from swagger_server.models.base_model_ import Model
from swagger_server import util
class JobSimulatorOpts(Model):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, display_no: int=None, screent_no: int=None, vnc_password: str=None, no_vnc_port: int=None): # noqa: E501
"""JobSimulatorOpts - a model defined in Swagger
:param display_no: The display_no of this JobSimulatorOpts. # noqa: E501
:type display_no: int
:param screent_no: The screent_no of this JobSimulatorOpts. # noqa: E501
:type screent_no: int
:param vnc_password: The vnc_password of this JobSimulatorOpts. # noqa: E501
:type vnc_password: str
:param no_vnc_port: The no_vnc_port of this JobSimulatorOpts. # noqa: E501
:type no_vnc_port: int
"""
self.swagger_types = {
'display_no': int,
'screent_no': int,
'vnc_password': str,
'no_vnc_port': int
}
self.attribute_map = {
'display_no': 'display_no',
'screent_no': 'screent_no',
'vnc_password': '<PASSWORD>',
'no_vnc_port': 'no_vnc_port'
}
self._display_no = display_no
self._screent_no = screent_no
self._vnc_password = <PASSWORD>
self._no_vnc_port = no_vnc_port
@classmethod
def from_dict(cls, dikt) -> 'JobSimulatorOpts':
"""Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The Job_simulator_opts of this JobSimulatorOpts. # noqa: E501
:rtype: JobSimulatorOpts
"""
return util.deserialize_model(dikt, cls)
@property
def display_no(self) -> int:
"""Gets the display_no of this JobSimulatorOpts.
:return: The display_no of this JobSimulatorOpts.
:rtype: int
"""
return self._display_no
@display_no.setter
def display_no(self, display_no: int):
"""Sets the display_no of this JobSimulatorOpts.
:param display_no: The display_no of this JobSimulatorOpts.
:type display_no: int
"""
self._display_no = display_no
@property
def screent_no(self) -> int:
"""Gets the screent_no of this JobSimulatorOpts.
:return: The screent_no of this JobSimulatorOpts.
:rtype: int
"""
return self._screent_no
@screent_no.setter
def screent_no(self, screent_no: int):
"""Sets the screent_no of this JobSimulatorOpts.
:param screent_no: The screent_no of this JobSimulatorOpts.
:type screent_no: int
"""
self._screent_no = screent_no
@property
def vnc_password(self) -> str:
"""Gets the vnc_password of this JobSimulatorOpts.
:return: The vnc_password of this JobSimulatorOpts.
:rtype: str
"""
return self._vnc_password
@vnc_password.setter
def vnc_password(self, vnc_password: str):
"""Sets the vnc_password of this JobSimulatorOpts.
:param vnc_password: The vnc_password of this JobSimulatorOpts.
:type vnc_password: str
"""
self._vnc_password = vnc_password
@property
def no_vnc_port(self) -> int:
"""Gets the no_vnc_port of this JobSimulatorOpts.
:return: The no_vnc_port of this JobSimulatorOpts.
:rtype: int
"""
return self._no_vnc_port
@no_vnc_port.setter
def no_vnc_port(self, no_vnc_port: int):
"""Sets the no_vnc_port of this JobSimulatorOpts.
:param no_vnc_port: The no_vnc_port of this JobSimulatorOpts.
:type no_vnc_port: int
"""
self._no_vnc_port = no_vnc_port
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] |
src/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py | Bpowers4/turicreate | 1 | 145383 | <reponame>Bpowers4/turicreate<filename>src/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py
# -*- coding: utf-8 -*-
# Copyright © 2019 Apple Inc. All rights reserved.
#
# Use of this source code is governed by a BSD-3-clause license that can
# be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
from __future__ import print_function as _
from __future__ import division as _
from __future__ import absolute_import as _
import turicreate as _tc
from turicreate.toolkits import _coreml_utils
from turicreate.toolkits._model import Model as _Model
import turicreate.toolkits._internal_utils as _tkutl
from turicreate.toolkits._main import ToolkitError as _ToolkitError
from ..image_classifier._evaluation import Evaluation as _Evaluation
from .. import _pre_trained_models
BITMAP_WIDTH = 28
BITMAP_HEIGHT = 28
TRAIN_VALIDATION_SPLIT = 0.95
def _raise_error_if_not_drawing_classifier_input_sframe(dataset, feature, target):
"""
Performs some sanity checks on the SFrame provided as input to
`turicreate.drawing_classifier.create` and raises a ToolkitError
if something in the dataset is missing or wrong.
"""
from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
_raise_error_if_not_sframe(dataset)
if feature not in dataset.column_names():
raise _ToolkitError("Feature column '%s' does not exist" % feature)
if target not in dataset.column_names():
raise _ToolkitError("Target column '%s' does not exist" % target)
if dataset[feature].dtype != _tc.Image and dataset[feature].dtype != list:
raise _ToolkitError(
"Feature column must contain images"
+ " or stroke-based drawings encoded as lists of strokes"
+ " where each stroke is a list of points and"
+ " each point is stored as a dictionary"
)
if dataset[target].dtype != int and dataset[target].dtype != str:
raise _ToolkitError(
"Target column contains "
+ str(dataset[target].dtype)
+ " but it must contain strings or integers to represent"
+ " labels for drawings."
)
if len(dataset) == 0:
raise _ToolkitError("Input Dataset is empty!")
def create(
input_dataset,
target,
feature=None,
validation_set="auto",
warm_start="auto",
batch_size=256,
max_iterations=500,
verbose=True,
random_seed=None,
**kwargs
):
"""
Create a :class:`DrawingClassifier` model.
Parameters
----------
dataset : SFrame
Input data. The columns named by the ``feature`` and ``target``
parameters will be extracted for training the drawing classifier.
target : string
Name of the column containing the target variable. The values in this
column must be of string or integer type.
feature : string optional
Name of the column containing the input drawings.
The feature column can contain either bitmap-based drawings or
stroke-based drawings. Bitmap-based drawing input can be a grayscale
tc.Image of any size.
Stroke-based drawing input must be in the following format:
Every drawing must be represented by a list of strokes, where each
stroke must be a list of points in the order in which they were drawn
on the canvas.
Each point must be a dictionary with two keys, "x" and "y", and their
respective values must be numerical, i.e. either integer or float.
validation_set : SFrame optional
A dataset for monitoring the model's generalization performance.
The format of this SFrame must be the same as the training set.
By default this argument is set to 'auto' and a validation set is
automatically sampled and used for progress printing. If
validation_set is set to None, then no additional metrics
are computed. The default value is 'auto'.
warm_start : string optional
A string to denote which pretrained model to use. Set to "auto"
by default which uses a model trained on 245 of the 345 classes in the
Quick, Draw! dataset. To disable warm start, pass in None to this
argument. Here is a list of all the pretrained models that
can be passed in as this argument:
"auto": Uses quickdraw_245_v0
"quickdraw_245_v0": Uses a model trained on 245 of the 345 classes in the
Quick, Draw! dataset.
None: No Warm Start
batch_size: int optional
The number of drawings per training step. If not set, a default
value of 256 will be used. If you are getting memory errors,
try decreasing this value. If you have a powerful computer, increasing
this value may improve performance.
max_iterations : int optional
The maximum number of allowed passes through the data. More passes over
the data can result in a more accurately trained model.
verbose : bool optional
If True, print progress updates and model details.
random_seed : int, optional
The results can be reproduced when given the same seed.
Returns
-------
out : DrawingClassifier
A trained :class:`DrawingClassifier` model.
See Also
--------
DrawingClassifier
Examples
--------
.. sourcecode:: python
# Train a drawing classifier model
>>> model = turicreate.drawing_classifier.create(data)
# Make predictions on the training set and as column to the SFrame
>>> data['predictions'] = model.predict(data)
"""
accepted_values_for_warm_start = ["auto", "quickdraw_245_v0", None]
if warm_start is not None:
if type(warm_start) is not str:
raise TypeError(
"'warm_start' must be a string or None. "
+ "'warm_start' can take in the following values: "
+ str(accepted_values_for_warm_start)
)
if warm_start not in accepted_values_for_warm_start:
raise _ToolkitError(
"Unrecognized value for 'warm_start': "
+ warm_start
+ ". 'warm_start' can take in the following "
+ "values: "
+ str(accepted_values_for_warm_start)
)
# Replace 'auto' with name of current default Warm Start model.
warm_start = warm_start.replace("auto", "quickdraw_245_v0")
if "_advanced_parameters" in kwargs:
# XXX: this code will not work. Tracking in https://github.com/apple/turicreate/issues/3007
# Make sure no additional parameters are provided
new_keys = set(kwargs["_advanced_parameters"].keys())
set_keys = set(params.keys())
unsupported = new_keys - set_keys
if unsupported:
raise _ToolkitError("Unknown advanced parameters: {}".format(unsupported))
params.update(kwargs["_advanced_parameters"])
# @TODO: Should be able to automatically choose number of iterations
# based on data size: Tracked in Github Issue #1576
if not isinstance(input_dataset, _tc.SFrame):
raise TypeError('"input_dataset" must be of type SFrame.')
# automatically infer feature column
if feature is None:
feature = _tkutl._find_only_drawing_column(input_dataset)
_raise_error_if_not_drawing_classifier_input_sframe(input_dataset, feature, target)
if batch_size is not None and not isinstance(batch_size, int):
raise TypeError("'batch_size' must be an integer >= 1")
if batch_size is not None and batch_size < 1:
raise ValueError("'batch_size' must be >= 1")
if max_iterations is not None and not isinstance(max_iterations, int):
raise TypeError("'max_iterations' must be an integer >= 1")
if max_iterations is not None and max_iterations < 1:
raise ValueError("'max_iterations' must be >= 1")
import turicreate.toolkits.libtctensorflow
model = _tc.extensions.drawing_classifier()
options = dict()
options["batch_size"] = batch_size
options["max_iterations"] = max_iterations
options["verbose"] = verbose
options["_show_loss"] = False
if validation_set is None:
validation_set = _tc.SFrame()
if warm_start:
# Load CoreML warmstart model
pretrained_mlmodel = _pre_trained_models.DrawingClassifierPreTrainedMLModel()
options["mlmodel_path"] = pretrained_mlmodel.get_model_path()
if random_seed is not None:
options["random_seed"] = random_seed
options["warm_start"] = "" if warm_start is None else warm_start
model.train(input_dataset, target, feature, validation_set, options)
return DrawingClassifier(model_proxy=model, name="drawing_classifier")
class DrawingClassifier(_Model):
"""
A trained model using C++ implementation that is ready to use for classification or export to
CoreML.
This model should not be constructed directly.
"""
_CPP_DRAWING_CLASSIFIER_VERSION = 1
def __init__(self, model_proxy=None, name=None):
self.__proxy__ = model_proxy
self.__name__ = name
@classmethod
def _native_name(cls):
return "drawing_classifier"
def __str__(self):
"""
Return a string description of the model to the ``print`` method.
Returns
-------
out : string
A description of the DrawingClassifier.
"""
return self.__repr__()
def __repr__(self):
"""
Returns a string description of the model, including (where relevant)
the schema of the training data, description of the training data,
training statistics, and model hyperparameters.
Returns
-------
out : string
A description of the model.
"""
width = 40
sections, section_titles = self._get_summary_struct()
out = _tkutl._toolkit_repr_print(self, sections, section_titles, width=width)
return out
def _get_version(self):
return self._CPP_DRAWING_CLASSIFIER_VERSION
def export_coreml(self, filename):
"""
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel")
"""
additional_user_defined_metadata = _coreml_utils._get_tc_version_info()
short_description = _coreml_utils._mlmodel_short_description(
"Drawing Classifier"
)
self.__proxy__.export_to_coreml(
filename, short_description, additional_user_defined_metadata
)
def predict(self, dataset, output_type="class"):
"""
Predict on an SFrame or SArray of drawings, or on a single drawing.
Parameters
----------
data : SFrame | SArray | tc.Image
The drawing(s) on which to perform drawing classification.
If dataset is an SFrame, it must have a column with the same name
as the feature column during training. Additional columns are
ignored.
If the data is a single drawing, it can be either of type tc.Image,
in which case it is a bitmap-based drawing input,
or of type list, in which case it is a stroke-based drawing input.
output_type : {'probability', 'class', 'probability_vector'}, optional
Form of the predictions which are one of:
- 'class': Class prediction. For multi-class classification, this
returns the class with maximum probability.
- 'probability': Prediction probability associated with the True
class (not applicable for multi-class classification)
- 'probability_vector': Prediction probability associated with each
class as a vector. Label ordering is dictated by the ``classes``
member variable.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve
performance.
verbose : bool, optional
If True, prints prediction progress.
Returns
-------
out : SArray
An SArray with model predictions. Each element corresponds to
a drawing and contains a single value corresponding to the
predicted label. Each prediction will have type integer or string
depending on the type of the classes the model was trained on.
If `data` is a single drawing, the return value will be a single
prediction.
See Also
--------
evaluate
Examples
--------
.. sourcecode:: python
# Make predictions
>>> pred = model.predict(data)
# Print predictions, for a better overview
>>> print(pred)
dtype: int
Rows: 10
[3, 4, 3, 3, 4, 5, 8, 8, 8, 4]
"""
if isinstance(dataset, _tc.SArray):
dataset = _tc.SFrame({self.feature: dataset})
return self.__proxy__.predict(dataset, output_type)
def predict_topk(self, dataset, output_type="probability", k=3):
"""
Return top-k predictions for the ``dataset``, using the trained model.
Predictions are returned as an SFrame with three columns: `id`,
`class`, and `probability` or `rank`, depending on the ``output_type``
parameter.
Parameters
----------
dataset : SFrame | SArray | turicreate.Image
Drawings to be classified.
If dataset is an SFrame, it must include columns with the same
names as the features used for model training, but does not require
a target column. Additional columns are ignored.
output_type : {'probability', 'rank'}, optional
Choose the return type of the prediction:
- `probability`: Probability associated with each label in the
prediction.
- `rank` : Rank associated with each label in the prediction.
k : int, optional
Number of classes to return for each input example.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve
performance.
Returns
-------
out : SFrame
An SFrame with model predictions.
See Also
--------
predict, evaluate
Examples
--------
>>> pred = m.predict_topk(validation_data, k=3)
>>> print(pred)
+----+-------+-------------------+
| id | class | probability |
+----+-------+-------------------+
| 0 | 4 | 0.995623886585 |
| 0 | 9 | 0.0038311756216 |
| 0 | 7 | 0.000301006948575 |
| 1 | 1 | 0.928708016872 |
| 1 | 3 | 0.0440889261663 |
| 1 | 2 | 0.0176190119237 |
| 2 | 3 | 0.996967732906 |
| 2 | 2 | 0.00151345680933 |
| 2 | 7 | 0.000637513934635 |
| 3 | 1 | 0.998070061207 |
| .. | ... | ... |
+----+-------+-------------------+
[35688 rows x 3 columns]
"""
if isinstance(dataset, _tc.SArray):
dataset = _tc.SFrame({self.feature: dataset})
return self.__proxy__.predict_topk(dataset, output_type, k)
def evaluate(self, dataset, metric="auto"):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include columns with the same
names as the session_id, target and features used for model training.
Additional columns are ignored.
metric : str, optional
Name of the evaluation metric. Possible values are:
- 'auto' : Returns all available metrics.
- 'accuracy' : Classification accuracy (micro average).
- 'auc' : Area under the ROC curve (macro average)
- 'precision' : Precision score (macro average)
- 'recall' : Recall score (macro average)
- 'f1_score' : F1 score (macro average)
- 'log_loss' : Log loss
- 'confusion_matrix' : An SFrame with counts of possible
prediction/true label combinations.
- 'roc_curve' : An SFrame containing information needed for an
ROC curve
Returns
-------
out : dict
Dictionary of evaluation results where the key is the name of the
evaluation metric (e.g. `accuracy`) and the value is the evaluation
score.
See Also
----------
create, predict
Examples
----------
.. sourcecode:: python
>>> results = model.evaluate(data)
>>> print results['accuracy']
"""
evaluation_result = self.__proxy__.evaluate(dataset, metric)
class_label = evaluation_result["prediction_class"]
probability_vector = evaluation_result["prediction_prob"]
del evaluation_result["prediction_class"]
del evaluation_result["prediction_prob"]
predicted = _tc.SFrame(
{"label": class_label, "probability": probability_vector}
)
labels = self.classes
from .._evaluate_utils import (
entropy,
confidence,
relative_confidence,
get_confusion_matrix,
hclusterSort,
l2Dist,
)
evaluation_result["num_test_examples"] = len(dataset)
for k in ["num_classes", "num_examples", "training_time", "max_iterations"]:
evaluation_result[k] = getattr(self, k)
# evaluation_result['input_image_shape'] = getattr(self, 'input_image_shape')
evaluation_result["model_name"] = "Drawing Classifier"
extended_test = dataset.add_column(predicted["probability"], "probs")
extended_test["label"] = dataset[self.target]
extended_test = extended_test.add_columns(
[
extended_test.apply(
lambda d: labels[d["probs"].index(confidence(d["probs"]))]
),
extended_test.apply(lambda d: entropy(d["probs"])),
extended_test.apply(lambda d: confidence(d["probs"])),
extended_test.apply(lambda d: relative_confidence(d["probs"])),
],
["predicted_label", "entropy", "confidence", "relative_confidence"],
)
extended_test = extended_test.add_column(
extended_test.apply(lambda d: d["label"] == d["predicted_label"]), "correct"
)
sf_conf_mat = get_confusion_matrix(extended_test, labels)
confidence_threshold = 0.5
hesitant_threshold = 0.2
evaluation_result["confidence_threshold"] = confidence_threshold
evaluation_result["hesitant_threshold"] = hesitant_threshold
evaluation_result["confidence_metric_for_threshold"] = "relative_confidence"
evaluation_result["conf_mat"] = list(sf_conf_mat)
vectors = map(
lambda l: {
"name": l,
"pos": list(
sf_conf_mat[sf_conf_mat["target_label"] == l].sort(
"predicted_label"
)["norm_prob"]
),
},
labels,
)
evaluation_result["sorted_labels"] = hclusterSort(vectors, l2Dist)[0][
"name"
].split("|")
per_l = extended_test.groupby(
["label"],
{
"count": _tc.aggregate.COUNT,
"correct_count": _tc.aggregate.SUM("correct"),
},
)
per_l["recall"] = per_l.apply(lambda l: l["correct_count"] * 1.0 / l["count"])
per_pl = extended_test.groupby(
["predicted_label"],
{
"predicted_count": _tc.aggregate.COUNT,
"correct_count": _tc.aggregate.SUM("correct"),
},
)
per_pl["precision"] = per_pl.apply(
lambda l: l["correct_count"] * 1.0 / l["predicted_count"]
)
per_pl = per_pl.rename({"predicted_label": "label"})
evaluation_result["label_metrics"] = list(
per_l.join(per_pl, on="label", how="outer").select_columns(
[
"label",
"count",
"correct_count",
"predicted_count",
"recall",
"precision",
]
)
)
evaluation_result["labels"] = labels
extended_test = extended_test.add_row_number("__idx").rename(
{"label": "target_label"}
)
evaluation_result["test_data"] = extended_test
evaluation_result["feature"] = self.feature
return _Evaluation(evaluation_result)
def _get_summary_struct(self):
"""
Returns a structured description of the model, including (where
relevant) the schema of the training data, description of the training
data, training statistics, and model hyperparameters.
Returns
-------
sections : list (of list of tuples)
A list of summary sections.
Each section is a list.
Each item in a section list is a tuple of the form:
('<label>','<field>')
section_titles: list
A list of section titles.
The order matches that of the 'sections' object.
"""
model_fields = [
("Number of classes", "num_classes"),
("Feature column", "feature"),
("Target column", "target"),
]
training_fields = [
("Training Iterations", "max_iterations"),
("Training Accuracy", "training_accuracy"),
("Validation Accuracy", "validation_accuracy"),
("Training Time", "training_time"),
("Number of Examples", "num_examples"),
]
section_titles = ["Schema", "Training summary"]
return ([model_fields, training_fields], section_titles)
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29918,
3018,
1312,
29918,
9794,
29889,
16327,
2385,
3709,
6572,
5323,
1312,
1988,
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13,
1990,
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13,
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9995,
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319,
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1904,
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671,
363,
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1402,
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29918,
23545,
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13,
2
] |
main/models.py | LeonDante-ctrl/danterev | 0 | 1617187 | from django.db import models
class Award(models.Model):
name=models.CharField(max_length=300)
description=models.TextField(max_length=5000)
developer=models.CharField(max_length=300)
created_date=models.DateField()
averangeRating=models.FloatField(default=0)
image=models.URLField(default=None, null=True)
linktosite=models.URLField(default=None, null=True)
def __str__(self):
return self.name
# Create your models here.
| [
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2
] |
python/course/leetcode/leetcode.py | TimVan1596/ACM-ICPC | 1 | 30951 | # -*- coding:utf-8 -*-
# @Time:2020/6/15 11:38
# @Author:TimVan
# @File:leetcode.py
# @Software:PyCharm
# Definition for singly-linked list.
# for j in range(10, 5, -1):
# print(j)
print('a'.find(' '))
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] |
bigml/tests/create_external_steps.py | pertinkoira/python | 0 | 59950 | <filename>bigml/tests/create_external_steps.py<gh_stars>0
# -*- coding: utf-8 -*-
#
# Copyright 2020-2022 BigML
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import time
import json
import csv
import sys
from datetime import datetime
from .world import world, res_filename
from nose.tools import eq_, assert_less
from bigml.api import HTTP_CREATED, HTTP_ACCEPTED
from bigml.api import FINISHED
from bigml.api import FAULTY
from bigml.api import UPLOADING
from bigml.api import get_status
from .read_resource_steps import wait_until_status_code_is
#@step(r'I create an external connector$')
def i_create_external_connector(step):
resource = world.api.create_external_connector(None, \
{'project': world.project_id})
# update status
world.status = resource['code']
world.location = resource['location']
world.external_connector = resource['object']
# save reference
world.external_connectors.append(resource['resource'])
#@step(r'I wait until the external connector status code is either (\d) or (\d) less than (\d+)')
def wait_until_external_connector_status_code_is(step, code1, code2, secs):
world.external_connector = wait_until_status_code_is(
code1, code2, secs, world.external_connector)
#@step(r'I wait until the external_connector is ready less than (\d+)')
def the_external_connector_is_finished(step, secs):
wait_until_external_connector_status_code_is(step, FINISHED, FAULTY, secs)
#@step(r'I update the external_connector with params "(.*)"')
def i_update_external_connector_with(step, data="{}"):
resource = world.api.update_external_connector( \
world.external_connector.get('resource'), json.loads(data))
world.status = resource['code']
eq_(world.status, HTTP_ACCEPTED)
#@step(r'the external connector exists and has args "(.*)"')
def external_connector_has_args(step, args="{}"):
args = json.loads(args)
for key, value in list(args.items()):
if key in world.external_connector:
eq_(world.external_connector[key], value,
"Expected key %s: %s. Found %s" % (key, value, world.external_connector[key]))
else:
assert False, "No key %s in external connector." % key
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2
] |
MRC/q.py | alinghi/AI-Portfolio-Hub | 0 | 79867 | <gh_stars>0
from datasets import (
Dataset,
load_from_disk,
concatenate_datasets,
)
org_dataset = load_from_disk("data/test_dataset")
tt=org_dataset["validation"]
f=open("./question.csv","w")
for idx,i in enumerate(tt):
f.write("\t ".join([str(idx+1),i["id"],i["question"]])+"\n")
f.close()
| [
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Chapter 13/stableSpiral.py | hsauro/PathwayModelingBook | 2 | 137270 | <reponame>hsauro/PathwayModelingBook
import numpy as np
import matplotlib.pyplot as plt
import tellurium as te
Y, X = np.mgrid[-4:4:100j, -4:4:100j]
r = te.loada('''
x' = -0.5*x - y;
y' = x - y;
x = 4; y = -4;
''')
m = r.simulate (0, 8, 100)
U = -0.5*X - Y
V = X - Y
plt.subplots(1,2, figsize=(10,4))
plt.subplot(121)
plt.xlabel('x', fontsize='16')
plt.ylabel('y', fontsize='16')
plt.streamplot(X, Y, U, V, density=[1, 1])
plt.ylim((-4,4))
plt.xlim((-4,4))
plt.axhline(0, color='black')
plt.axvline(0, color='black')
plt.subplot(122)
plt.ylim((-5,5))
plt.xlim((0,8))
plt.xlabel('Time', fontsize='13')
plt.plot (m[:,0], m[:,1], color='r', linewidth=2, label='x')
plt.plot (m[:,0], m[:,2], color='b', linewidth=2, label='y')
plt.legend()
plt.savefig ('c:\\tmp\\phase.pdf')
plt.show()
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GPBlend/dct_module.py | GuardSkill/ImageBlend | 0 | 92083 | <filename>GPBlend/dct_module.py
import time
import torch
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numbers
import numpy as np
class dctmodule(nn.Module):
def __init__(self, shape):
super(dctmodule, self).__init__()
N = shape[-1]
k = torch.nn.Parameter(- torch.arange(N, dtype=float)[None, :] * np.pi / (2 * N))
self.W_r = torch.cos(k)
self.W_i = torch.sin(k)
def forward(self, x, norm=None):
x_shape = x.shape
N = x_shape[-1]
x = x.contiguous().view(-1, N)
v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)
# Vc = torch.rfft(v, 1, onesided=False) #
Vc = torch.view_as_real(torch.fft.fft(v, dim=1)) # pytoch 1.9
V = Vc[:, :, 0] * self.W_r - Vc[:, :, 1] * self.W_i
if norm == 'ortho':
V[:, 0] /= np.sqrt(N) * 2
V[:, 1:] /= np.sqrt(N / 2) * 2
V = 2 * V.view(*x_shape)
return V
class dctmodule2D(nn.Module):
def __init__(self, shape):
super(dctmodule2D, self).__init__()
N_weight = shape[-1]
k_weight = - torch.arange(N_weight, dtype=float)[None, :] * np.pi / (2 * N_weight)
self.W_r_weight = torch.nn.Parameter(torch.cos(k_weight))
self.W_i_weight = torch.nn.Parameter(torch.sin(k_weight))
self.inverted_index_weight = torch.arange(N_weight - 1, 0, -2)
self.index_weight = torch.arange(0, N_weight, 2)
N_height = shape[-2]
k_height = - torch.arange(N_height, dtype=float)[None, :] * np.pi / (2 * N_height)
self.W_r_height = torch.nn.Parameter(torch.cos(k_height))
self.W_i_height = torch.nn.Parameter(torch.sin(k_height))
self.inverted_index_height = torch.arange(N_height - 1, 0, -2)
self.index_height = torch.arange(0, N_height, 2)
self.N_weight = N_weight
self.N_height = N_height
def _apply(self, fn):
super(dctmodule2D, self)._apply(fn)
self.inverted_index_weight = fn(self.inverted_index_weight)
self.inverted_index_height = fn(self.inverted_index_height)
self.W_r_weight = fn(self.W_r_weight)
self.W_i_weight = fn(self.W_i_weight)
self.W_r_height = fn(self.W_r_height)
self.W_i_height = fn(self.W_i_height)
return self
def forward(self, x, norm='ortho'):
x_shape = x.shape
N = self.N_weight
x = x.contiguous().view(-1, N)
# v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)
v = torch.cat([x[:, self.index_weight], x[:, self.inverted_index_weight]], dim=1)
# Vc = torch.rfft(v, 1, onesided=False) #
Vc = torch.view_as_real(torch.fft.fft(v.float(), dim=1)) # pytoch 1.9
# Vc = torch.view_as_real(torch.fft.fft(v, dim=1))
V = Vc[:, :, 0] * self.W_r_weight - Vc[:, :, 1] * self.W_i_weight
if norm == 'ortho':
V[:, 0] /= np.sqrt(N) * 2
V[:, 1:] /= np.sqrt(N / 2) * 2
V = 2 * V.view(*x_shape)
x = V.transpose(-1, -2)
x_shape = x.shape
N = self.N_height
x = x.contiguous().view(-1, N)
# v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)
v = torch.cat([x[:, self.index_height], x[:, self.inverted_index_height]], dim=1)
# Vc = torch.rfft(v, 1, onesided=False)
# torch.cuda.synchronize()
# TEST_TIME = time.time()
Vc = torch.view_as_real(torch.fft.fft(v, dim=1)) # pytoch 1.9 第二次fft最耗时
# Vc = torch.view_as_real(torch.fft.fft(v, dim=1))
# Vc = Vc
# torch.cuda.synchronize()
# print('TEST TIME', (time.time() - TEST_TIME) * 1000)
V = Vc[:, :, 0] * self.W_r_height - Vc[:, :, 1] * self.W_i_height
if norm == 'ortho':
V[:, 0] /= np.sqrt(N) * 2
V[:, 1:] /= np.sqrt(N / 2) * 2
V = 2 * V.view(*x_shape)
V = V.transpose(-1, -2)
return V
class idctmodule2D(nn.Module):
def __init__(self, shape):
super(idctmodule2D, self).__init__()
N_weight = shape[-1]
k_weight = torch.arange(N_weight, dtype=float)[None, :] * np.pi / (2 * N_weight)
self.W_r_weight = torch.nn.Parameter(torch.cos(k_weight))
self.W_i_weight = torch.nn.Parameter(torch.sin(k_weight))
self.inverted_index_weight = torch.arange(N_weight - 1, 0, -2)
self.index_weight = torch.arange(0, N_weight, 2)
N_height = shape[-2]
k_height = torch.arange(N_height, dtype=float)[None, :] * np.pi / (2 * N_height)
self.W_r_height = torch.nn.Parameter(torch.cos(k_height))
self.W_i_height = torch.nn.Parameter(torch.sin(k_height))
self.inverted_index_height = torch.arange(N_height - 1, 0, -2)
self.index_height = torch.arange(0, N_height, 2)
self.N_weight = N_weight
self.N_height = N_height
def _apply(self, fn):
super(idctmodule2D, self)._apply(fn)
self.inverted_index_weight = fn(self.inverted_index_weight)
self.inverted_index_height = fn(self.inverted_index_height)
self.W_r_weight = fn(self.W_r_weight)
self.W_i_weight = fn(self.W_i_weight)
self.W_r_height = fn(self.W_r_height)
self.W_i_height = fn(self.W_i_height)
return self
def forward(self, X, norm='ortho'):
x_shape = X.shape
N = self.N_weight
X_v = X.contiguous().view(-1, x_shape[-1]) / 2
if norm == 'ortho':
X_v[:, 0] *= np.sqrt(N) * 2
X_v[:, 1:] *= np.sqrt(N / 2) * 2
V_t_r = X_v
V_t_i = torch.cat([X_v[:, :1] * 0, -X_v.flip([1])[:, :-1]], dim=1)
V_r = V_t_r * self.W_r_weight - V_t_i * self.W_i_weight
V_i = V_t_r * self.W_i_weight + V_t_i * self.W_r_weight
V = torch.cat([V_r.unsqueeze(2), V_i.unsqueeze(2)], dim=2)
# v = torch.irfft(V, 1, onesided=False)
v = torch.fft.irfft(torch.view_as_complex(V), n=V.shape[1], dim=1) # torch 1.9
# v = torch.fft.irfft(torch.view_as_complex(V.float()), n=V.shape[1], dim=1).half() # torch 1.9
x = v.new_zeros(v.shape)
x[:, ::2] += v[:, :N - (N // 2)]
x[:, 1::2] += v.flip([1])[:, :N // 2]
x = x.view(*x_shape)
X = x.transpose(-1, -2)
x_shape = X.shape
N = self.N_height
X_v = X.contiguous().view(-1, x_shape[-1]) / 2
if norm == 'ortho':
X_v[:, 0] *= np.sqrt(N) * 2
X_v[:, 1:] *= np.sqrt(N / 2) * 2
V_t_r = X_v
V_t_i = torch.cat([X_v[:, :1] * 0, -X_v.flip([1])[:, :-1]], dim=1)
V_r = V_t_r * self.W_r_height - V_t_i * self.W_i_height
V_i = V_t_r * self.W_i_height + V_t_i * self.W_r_height
V = torch.cat([V_r.unsqueeze(2), V_i.unsqueeze(2)], dim=2)
# v = torch.irfft(V, 1, onesided=
v = torch.fft.irfft(torch.view_as_complex(V), n=V.shape[1], dim=1)
# v = torch.fft.irfft(torch.view_as_complex(V.float()), n=V.shape[1], dim=1).half() # torch 1.9
x = v.new_zeros(v.shape)
x[:, ::2] += v[:, :N - (N // 2)]
x[:, 1::2] += v.flip([1])[:, :N // 2]
x = x.view(*x_shape)
return x.transpose(-1, -2)
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sets-add.py | limeonion/Python-Programming | 0 | 19550 | <reponame>limeonion/Python-Programming
'''
f we want to add a single element to an existing set, we can use the .add() operation.
It adds the element to the set and returns 'None'.
Example
>>> s = set('HackerRank')
>>> s.add('H')
>>> print s
set(['a', 'c', 'e', 'H', 'k', 'n', 'r', 'R'])
>>> print s.add('HackerRank')
None
>>> print s
set(['a', 'c', 'e', 'HackerRank', 'H', 'k', 'n', 'r', 'R'])
The first line contains an integer N, the total number of country stamps.
The next N lines contains the name of the country where the stamp is from.
Output Format
Output the total number of distinct country stamps on a single line.
'''
n = int(input())
countries = set()
for i in range(n):
countries.add(input())
print(len(countries))
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] |
scripts/testSmurfProcessor.py | bopopescu/smurf-processor | 1 | 111094 | <filename>scripts/testSmurfProcessor.py
#!/usr/bin/env python3
#-----------------------------------------------------------------------------
# Title : Test file for MceTransmit
#-----------------------------------------------------------------------------
# File : exoTest.py
# Created : 2018-02-28
#-----------------------------------------------------------------------------
# This file is part of the rogue_example software. It is subject to
# the license terms in the LICENSE.txt file found in the top-level directory
# of this distribution and at:
# https://confluence.slac.stanford.edu/display/ppareg/LICENSE.html.
# No part of the rogue_example software, including this file, may be
# copied, modified, propagated, or distributed except according to the terms
# contained in the LICENSE.txt file.
#-----------------------------------------------------------------------------
import rogue.utilities
import Smurf
import pyrogue
import time
# Data generator
prbsTx = rogue.utilities.Prbs()
# Our receiver
rx = Smurf.SmurfProcessor()
# Connect the stream
pyrogue.streamConnect(prbsTx,rx)
# Generate Data
#prbsTx.enable(1000)
try:
while (True):
prbsTx.genFrame(2176)
#print(" Rx: Count {}, Bytes {}, Last {}".format(rx.getCount(), rx.getBytes(), rx.getLast()))
time.sleep(.0025)
rx.printTransmitStatistic()
except KeyboardInterrupt:
pass
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MultiObjectiveModel_YMPNet_Pavan/depth_predictor.py | eva5covergence/EVA5_AI_Projects | 0 | 195190 | <filename>MultiObjectiveModel_YMPNet_Pavan/depth_predictor.py
"""Compute depth maps for images in the input folder.
"""
import os
import glob
import torch
import midas_utils
import cv2
from midas_processing import pre_processing
from midas_processing import post_processing
from utils.model_utils import load_model
cfg = 'cfg/yolov3-custom.cfg'
def write_depth_outs(input_path, output_path, model_path, device):
print("initialize")
model = load_model(model_path, device,cfg)
model.eval()
image_gen = pre_processing(input_path, output_path, device)
count=0
for sample,img, img_name,num_images in image_gen:
# print(sample.shape, img.shape)
f=os.path.basename(img_name)
f=os.path.splitext(f)[0]
print(" Writing {} ({}/{})".format(output_path+f+".png", count + 1, num_images))
prediction,(_,_) = model(sample)
prediction, filename = post_processing(prediction, output_path, img, img_name)
# print(prediction.shape)
midas_utils.write_depth(filename, prediction, bits=2)
count+=1
print("DONE")
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unittests/testing.py | DNPLab/dnpLab | 0 | 154398 | import numpy as np
import dnplab as dnp
def get_gauss_3d(std_noise=0.0):
x = np.r_[0:100]
y = np.r_[0:100]
z = np.r_[0:100]
noise = std_noise * np.random.randn(len(x), len(y), len(z))
gauss = np.exp(-1.0 * (x - 50) ** 2.0 / (10.0 ** 2))
gauss_3d = (
gauss.reshape(-1, 1, 1) * gauss.reshape(1, -1, 1) * gauss.reshape(1, 1, -1)
)
gauss_3d += noise
return x, y, z, gauss_3d
def test3d(std_noise=0.0):
x, y, z, gauss_3d = get_gauss_3d(std_noise)
test_data = dnp.DNPData(gauss_3d, ["x", "y", "z"], [x, y, z])
return test_data
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] |
Weather/Citi_Weather_Impact.py | sakshiseth/Citi_Hack | 5 | 120957 | <filename>Weather/Citi_Weather_Impact.py
from Weather.WeatherAlert import getWeatherForecastDetails
import matplotlib.pyplot as plt
import random
class makeWeatherConclusion():
def getWeatherConclusionAsImage(self):
metro_city = ["Delhi", "Bangalore", "Pune", "Hyderabad", "Chennai", "Mumbai", "Kolkata", "Ahmedabad",
"Amaravati", "Visakhapatnam", "Jaipur", "Surat", "Jamshedpur", "Nagpur"]
WeatherFore = getWeatherForecastDetails()
State_pair = dict()
for city in metro_city:
response = WeatherFore.getDetails(city)
if response in State_pair:
State_pair[response] += ", "+ city
else:
State_pair[response] = city
Labels = []
Sizes = []
for condition in State_pair:
label = str(condition).upper()+ " : " +State_pair[condition]
Labels.append(label)
Sizes.append(len(label.split(', ')))
print(Labels)
print(Sizes)
explode = []
for i in range(0,len(Sizes)):
if i%2 == 0:
explode.append(0.0)
else:
explode.append(0.1)
plt.subplots(figsize =(15, 12))
plt.pie(Sizes, labels = Labels,explode=explode)
plt.savefig('./template/Weather.jpg') | [
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scripts/watch_continuous.py | drozzy/autonomous-learning-library | 584 | 128820 | # pylint: disable=unused-import
import argparse
from all.bodies import TimeFeature
from all.environments import GymEnvironment, PybulletEnvironment
from all.experiments import load_and_watch
from .continuous import ENVS
def main():
parser = argparse.ArgumentParser(description="Watch a continuous agent.")
parser.add_argument("env", help="ID of the Environment")
parser.add_argument("filename", help="File where the model was saved.")
parser.add_argument(
"--device",
default="cuda",
help="The name of the device to run the agent on (e.g. cpu, cuda, cuda:0)",
)
parser.add_argument(
"--fps",
default=120,
help="Playback speed",
)
args = parser.parse_args()
if args.env in ENVS:
env = GymEnvironment(args.env, device=args.device)
elif 'BulletEnv' in args.env or args.env in PybulletEnvironment.short_names:
env = PybulletEnvironment(args.env, device=args.device)
else:
env = GymEnvironment(args.env, device=args.device)
load_and_watch(args.filename, env, fps=args.fps)
if __name__ == "__main__":
main()
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hy-data-analysis-with-python-spring-2020/part05-e12_coefficient_of_determination/test/test_coefficient_of_determination.py | Melimet/DAP2020 | 0 | 95329 | <reponame>Melimet/DAP2020
#!/usr/bin/python3
import unittest
from tmc import points
from tmc.utils import load, get_out
coefficient_of_determination = load('src.coefficient_of_determination', 'coefficient_of_determination')
class CoefficientOfDetermination(unittest.TestCase):
@points('p05-12.1')
def test_all_features(self):
scores = coefficient_of_determination()
self.assertAlmostEqual(scores[0], 1.0, msg="Incorrect coefficient of determination!")
@points('p05-12.2')
def test_individual_features(self):
scores = coefficient_of_determination()
sums=[0.0258828579115,0.0968186306153,0.0881564161891,0.868276772892]
for i in range(1,5):
self.assertAlmostEqual(sums[i-1], sum(scores[i:i+2]),
msg="Incorrect individual coefficients of determination!")
if __name__ == '__main__':
unittest.main()
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lsc.py | yubeic/Lie-Group-Sparse-Coding | 9 | 128549 | <reponame>yubeic/Lie-Group-Sparse-Coding<filename>lsc.py
import torch
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as f
import utils
from IPython import display
import time
device = torch.device("cuda")
def combine_variance(avg_a, count_a, var_a, avg_b, count_b, var_b):
"""
Compute variance of X given mean and variances of A and B, where X = A union B.
Reference: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#cite_note-:0-10
"""
if count_a + count_b <= 1:
return torch.zeros(var_a.size()).cuda()
delta = avg_b - avg_a
M2_a = var_a * (count_a - 1)
M2_b = var_b * (count_b - 1)
M2 = M2_a + M2_b + delta ** 2 * count_a * count_b / (count_a + count_b)
return M2 / (count_a + count_b - 1)
def combine_mean(avg_a, count_a, avg_b, count_b):
"""
Compute variance of X given mean and variances of A and B, where X = A union B.
"""
if count_a + count_b == 0:
return torch.zeros(avg_a.size()).cuda()
return (count_a * avg_a + count_b * avg_b)/(count_a + count_b)
def statistics(data):
"""
Compute mean and variance of data along first axis
"""
B = data.size()[0]
avg = data.sum(dim=0)/B
if B == 1:
variance = torch.zeros(avg.size()).cuda()
else:
variance = ((data - avg)**2).sum(dim=0)/(B-1)
return (avg, variance)
class RunningStatistics:
"""
Compute running mean and variance
"""
def __init__(self, size):
self.avg = torch.zeros(size).cuda()
self.variance = torch.zeros(size).cuda()
self.stderr = torch.zeros(size).cuda()
self.count = 0
def update(self, data):
"""
data - (batch_size, ...(any number of dimensions here)...)
"""
new_avg, new_variance = statistics(data)
new_count = data.size()[0]
updated_avg = combine_mean(self.avg, self.count, new_avg, new_count)
updated_variance = combine_variance(self.avg, self.count, self.variance, new_avg, new_count, new_variance)
self.avg, self.variance = updated_avg, updated_variance
self.count = self.count + new_count
self.stderr = (self.variance/self.count)**0.5
def print_stats(self):
ratio = torch.abs(self.stderr/self.avg)
print("Standard error ratio range: {} - {}".format(ratio.min(), ratio.max()))
def orthogonalize(w):
u, s, v = torch.svd(w)
return torch.mm(u, v.t())
def eta(T, eta_0=0.25):
return eta_0/np.sqrt(T)
def to_cartesian(r, theta):
return (r*torch.cos(theta)).float(), (r*torch.sin(theta)).float()
def to_polar(a, b):
return (a**2 + b**2).sqrt().float(), torch.atan2(b, a).float()
def rot(delta_theta, a, b):
r, theta = to_polar(a, b)
return to_cartesian(r, theta+delta_theta)
def reconstruct(x, w, s_hat, omega):
"""
x - (B,D)
w - (B,L*2)
s_hat - (B,n)
omega - (L,n)
returns y_hat - (B,D)
"""
w1, w2 = w[:,::2], w[:,1::2] # (D,L), (D,L)
u1 = torch.einsum('dl, bd -> bl', w1, x) # (B,L)
u2 = torch.einsum('dl, bd -> bl', w2, x) # (B,L)
m_hat = torch.einsum('ln,bn->bl', omega.float(), s_hat.float()) # (B,L)
B, L = u1.size()[0], u1.size()[1]
ru = torch.zeros((B,L*2)).cuda() # (B,L*2)
ru1, ru2 = rot(m_hat, u1, u2) # (B,L), (B,L)
ru[:,::2], ru[:,1::2] = ru1, ru2
y_hat = torch.einsum('dj, bj -> bd', w, ru) # (B,D)
return y_hat
def posterior_phi(u, v, omega, s, k=None, m=None):
"""
Inputs:
u, v - (B,L*2)
omega - (L,n)
k, m - (B,J)
Returns:
k_hat, m_hat - (B,J), where J = number of unique omegas excluding omega = (0,0)
"""
u1, u2 = u[:,::2], u[:,1::2] # (B,L)
v1, v2 = v[:,::2], v[:,1::2] # (B,L)
B, L, n = u1.size()[0], u1.size()[1], omega.size()[1]
assert isinstance(omega, torch.cuda.IntTensor)
unique_omega, inverse_idx = torch.unique(omega, dim=0, return_inverse=True) # (J,n), (L)
J = unique_omega.size()[0]
a = (u1*v1+u2*v2)/s**2 # (B,L)
b = (u1*v2-u2*v1)/s**2 # (B,L)
graph1, graph2 = torch.zeros(B,L,J,device=device), torch.zeros(B,L,J,device=device)
graph1[:,torch.arange(L,device=device),inverse_idx] = a
graph2[:,torch.arange(L,device=device),inverse_idx] = b
eta_posterior_1, eta_posterior_2 = graph1.sum(dim=1), graph2.sum(dim=1) # (B,J)
zero_inds = (unique_omega == 0).all(dim=1)
eta_posterior_1[:,zero_inds], eta_posterior_2[:,zero_inds] = 0, 0
if k is not None and m is not None:
eta_prior_1, eta_prior_2 = to_cartesian(k, m) # (B,J), (B,J)
eta_posterior_1 += eta_prior_1
eta_posterior_2 += eta_prior_2
k_hat, m_hat = to_polar(eta_posterior_1, eta_posterior_2) # (B,J), (B,J)
return k_hat, m_hat
def compute_q(u, v, omega, k_hat, m_hat, N=100, map_est=False):
"""
Inputs:
u, v - (B,L*2)
omega - (L,n)
k_hat, m_hat - (B,J)
"""
B, L = u.size()[0], int(u.size()[1]/2)
unique_omega, inverse_idx = torch.unique(omega, dim=0, return_inverse=True) # (J,n), (L)
c, s = utils.circular_moment_numint_multi(k_hat, m_hat, unique_omega, unique_omega, N=N, map_est=map_est) # (B,J), (B,J) (0.0013s)
c, s = c[:,inverse_idx], s[:,inverse_idx] # (B,L), (B,L)
qc, qs = torch.empty(B,L*2,device=device), torch.empty(B,L*2,device=device)
qc[:,::2], qc[:,1::2] = c.clone(), c.clone()
qs[:,::2], qs[:,1::2] = s.clone(), s.clone()
return qc, qs # (B,L*2),(B,L*2),((B,L*2),(B,L*2))
def compute_aux_var(y, psi, w, alpha, omega, sigma, k=None, m=None, N=100, map_est=False):
x = torch.einsum('dk,bk->bd',psi,alpha) # (B,D)
u = torch.einsum('dl,bd->bl',w,x) # (B,L*2)
v = torch.einsum('dl,bd->bl',w,y) # (B,L*2)
k_hat, m_hat = posterior_phi(u, v, omega, sigma, k=k, m=m) # (B,J), (B,J) (0.0013s)
q = compute_q(u, v, omega, k_hat, m_hat, N=N, map_est=map_est) # (0.0017s)
res = y - torch.einsum('dl,bl->bd',w,mul_q(q, u)) # (B,D)
tres = torch.einsum('dl,bl->bd',w,mul_q(q, torch.einsum('dl,bd->bl',w,res), transpose=True))
return x, u, v, k_hat, m_hat, q, res, tres
def mul_q(q, x, transpose=False):
qc, qs = q[0], q[1]
xc = x.clone()
xs = torch.zeros(x.size(),device=device)
xs[:,::2], xs[:,1::2] = -x[:,1::2].clone(), x[:,::2].clone()
if transpose:
xs = -xs
result = qc*xc + qs*xs
return result
def grad_w(x, res, w, q, sigma):
B = x.size()[0]
wres, wx = torch.einsum('dl,bd->bl',w,res), torch.einsum('dl,bd->bl',w,x)
qwres, qwx = mul_q(q, wres, transpose=True), mul_q(q, wx, transpose=False)
return (torch.einsum('bd,bl->dl',x,qwres) + torch.einsum('bd,bl->dl',res,qwx)) / (sigma**2*B)
def grad_w_modified(x, res, w, q, sigma):
B = x.size()[0]
wres, wx = torch.einsum('dl,bd->bl',w,res), torch.einsum('dl,bd->bl',w,x)
qwres, qwx = mul_q(q, wres, transpose=True), mul_q(q, wx, transpose=False)
return (torch.einsum('bd,bl->dl',x,qwres) + torch.einsum('bd,bl->dl',res,qwx)) / (sigma**2*B)
def grad_alpha(tres, psi, alpha, prev_alpha, sigma, lamb, lamb2):
return torch.einsum('dk,bd->bk',psi,tres) / (sigma**2) - lamb*torch.sign(alpha) - 2*lamb2*(alpha - prev_alpha)
def grad_alpha_modified(tres, psi, alpha, prev_alpha, sigma, lamb, lamb2):
return torch.einsum('dk,bd->bk',psi,tres) / (sigma**2) - lamb*torch.sign(alpha) - 2*lamb2*(alpha - prev_alpha)
def grad_psi(tres, alpha, sigma):
B = tres.size()[0]
return torch.einsum('bd,bk->dk',tres,alpha) / (sigma**2 * B)
def grad_psi_modified(psi, tres, alpha, sigma, lamb):
B = tres.size()[0]
reg_term = torch.einsum('dk,k->dk',psi,torch.diag(torch.einsum('dk,dl->kl',psi,psi))-1)
return torch.einsum('bd,bk->dk',tres,alpha) / (sigma**2 * B) - lamb*reg_term
def alpha_update_FISTA(y, psi, w, omega, alpha, sigma, lamb, lamb2, steps, k=None, m=None, eta_alpha=0.001, N=100, modified=False, plot=False, map_est=False, adaptive=True, adaptive_steps=False):
D, K = psi.size()[0], psi.size()[1]
tk_n, tk = 1.0, 1.0
prev_alpha, alpha_y = alpha.clone(), alpha.clone()
if adaptive:
wpsi = torch.mm(w.t(),psi)
lipschitz = 1.5*torch.max(torch.symeig(torch.mm(wpsi.t(),wpsi),eigenvectors=False)[0])/sigma**2
eta_alpha = 1.0/lipschitz
# print(1/eta_alpha)
if adaptive_steps:
steps = int(21*torch.sqrt(0.001/eta_alpha))-1
# print(eta_alpha, int(21*torch.sqrt(0.001/eta_alpha))-1)
if plot:
fig = plt.figure(figsize=(10,5))
for t in range(steps):
# From here till end excluding compute_aux_var (0.0033s)
tk = tk_n
tk_n = (1+np.sqrt(1+4*tk**2))/2
alpha_pre = alpha.clone()
x, u, v, k_hat, m_hat, q, res, tres = compute_aux_var(y, psi, w, alpha_y, omega, sigma, k=k, m=m, N=N, map_est=map_est) # (0.0032s)
if modified:
dalpha = eta(1, eta_0=eta_alpha)*grad_alpha_modified(tres, psi, alpha_y, prev_alpha, sigma, lamb, lamb2)
else:
dalpha = eta(1, eta_0=eta_alpha)*grad_alpha(tres, psi, alpha_y, prev_alpha, sigma, lamb, lamb2)
alpha = (alpha_y + dalpha).clamp(min=0.)
alpha_y = alpha + (tk-1)/tk_n * (alpha - alpha_pre)
# Plot loss and alpha
if plot and t % 1 == 0:
nll = -log_likelihood(x, y, u, v, psi, alpha, k_hat, m_hat, omega, lamb, lamb2, sigma, mean=True, N=N, modified=modified)
fig.add_subplot(1,2,1)
plt.scatter(t, nll)
fig.add_subplot(1,2,2)
plt.scatter(np.ones(alpha.size()[1])*t, alpha[0].cpu().numpy(), c=np.arange(K))
display.clear_output(wait=True)
display.display(plt.gcf())
if plot:
display.clear_output()
return alpha
def psi_update(psi, alpha, tres, sigma, eta_psi, lamb, modified=False):
if modified:
dpsi = eta(1, eta_0=eta_psi)*grad_psi_modified(psi, tres, alpha, sigma, lamb) # (D,K)
new_psi = psi + dpsi
else:
dpsi = eta(1, eta_0=eta_psi)*grad_psi(tres, alpha, sigma) # (D,K)
new_psi = f.normalize(psi + dpsi, dim=0)
# if torch.isnan(new_psi).any():
# print("Infinity in new_psi. Setting dpsi = 0.")
# new_psi = psi.clone()
dpsi_length = torch.mean(torch.norm(new_psi - psi, dim=0))
psi = new_psi.clone()
return psi, dpsi_length
def w_update_riemann(optimizer, x, w, q, res, sigma, modified=False):
"""
optimizer - geoopt.optim.RiemannianAdam/geoopt.optim.RiemannianSGD
psi, w - geoopt.ManifoldParameter
"""
w_old = w.clone()
optimizer.zero_grad()
if modified:
w.grad = -grad_w_modified(x, res, w, q, sigma)
else:
w.grad = -grad_w(x, res, w, q, sigma)
optimizer.step()
dw_norm = torch.norm(w - w_old)
return w, dw_norm
def log_likelihood(x, y, u, v, psi, alpha, k_hat, m_hat, omega, lamb, lamb2, s, mean=True, N=100, modified=False, map_est=False):
"""
Computes log likelihood.
x, y - (B,D)
u, v - (B,L*2)
omega - (L,n)
k_hat, m_hat - (B,J)
Assume k = 0, m = 0
"""
D = x.size()[1]
u1, u2 = u[:,::2], u[:,1::2]
v1, v2 = v[:,::2], v[:,1::2]
k, m = torch.zeros(k_hat.size(),device=device), torch.zeros(m_hat.size(),device=device) # (B,J), (B,J)
unique_omega = torch.unique(omega, dim=0) # (J)
Z_term = utils.log_Z_numint_multi(k_hat, m_hat, unique_omega, N=N, map_est=map_est) - utils.log_Z_numint_multi(k, m, unique_omega, N=N, map_est=map_est) # (B)
constant_term = - ((u**2).sum(dim=1)+(y**2).sum(dim=1))/(2*s**2) # (B)
zero_inds = (omega == 0).all(dim=1)
u10, u20 = u1[:,zero_inds], u2[:,zero_inds]
v10, v20 = v1[:,zero_inds], v2[:,zero_inds]
if len(zero_inds) > 0:
zero_term = (torch.einsum('bn,bn->b',u10,v10) + torch.einsum('bn,bn->b',u20,v20))/s**2 # (B)
else:
zero_term = torch.zeros(x.size()[0],device=device) # (B)
batch_log_likelihood = Z_term + constant_term + zero_term - lamb*alpha.norm(p=1,dim=1) # (B)
if modified:
batch_log_likelihood -= lamb2*torch.norm(torch.diag(torch.einsum('dk,dl->kl',psi,psi))-1)**2 # Psi regularization term
if mean:
return torch.mean(batch_log_likelihood).cpu().numpy() # (1)
else:
return batch_log_likelihood.cpu().numpy() # (B) | [
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setup.py | olgabot/NouGAT | 5 | 94298 | #!/usr/bin/env python
from nougat import __version__
from setuptools import setup
version = __version__
try:
with open("requirements.txt", "r") as f:
install_requires = [x.strip() for x in f.readlines()]
except IOError:
install_requires = []
setup(name='nougat',
version=version,
description= 'An automated analysis pipeline for de novo assembly',
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/SciLifeLab/NouGAT',
license='MIT',
scripts=['nougat/deNovo_pipeline.py'],
packages=['nougat'],
namespace_packages=['nougat'],
entry_points={
'console_scripts': [
'scilifelab_denovo = sciLifeLab_utils.run_denovo:main',
],
},
install_requires=install_requires
)
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tools/lint/lib/checks/negative.py | maiermic/test262 | 0 | 1616291 | <reponame>maiermic/test262
import re
from ..check import Check
_THROW_STMT = re.compile(
r'^\$DONOTEVALUATE\(\);$',
re.MULTILINE)
_THROW_STMT_LEGACY = re.compile(
r'^throw "Test262: This statement should not be evaluated\.";$',
re.MULTILINE)
class CheckNegative(Check):
'''Ensure tests have the expected YAML-formatted metadata.'''
ID = 'NEGATIVE'
def run(self, name, meta, source):
if meta is None or meta.get('negative') is None:
return
negative = meta['negative']
if not isinstance(negative, dict):
return '"negative" must be a dictionary with fields "type" and "phase"'
if not 'type' in negative:
return '"negative" must specify a "type" field'
if not 'phase' in negative:
return '"negative" must specify a "phase" field'
if negative["phase"] in ["parse", "resolution"]:
if meta.get('flags') and 'raw' in meta['flags']:
if not _THROW_STMT_LEGACY.search(source):
return 'Negative tests of type "early" must include a `throw` statement'
elif not _THROW_STMT.search(source):
return 'Negative tests of type "early" must include a $DONOTEVALUATE() call'
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pyaud_plugins/__init__.py | jshwi/pyaud | 2 | 102256 | """
plugins
=======
"""
from . import modules
__all__ = ["modules"]
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fiftyone/core/patches.py | SNeugber/fiftyone | 0 | 8282 | """
Patches views.
| Copyright 2017-2021, Voxel51, Inc.
| `voxel51.com <https://voxel51.com/>`_
|
"""
from copy import deepcopy
import eta.core.utils as etau
import fiftyone.core.aggregations as foa
import fiftyone.core.dataset as fod
import fiftyone.core.fields as fof
import fiftyone.core.labels as fol
import fiftyone.core.media as fom
import fiftyone.core.sample as fos
import fiftyone.core.view as fov
_SINGLE_TYPES_MAP = {
fol.Detections: fol.Detection,
fol.Polylines: fol.Polyline,
}
_PATCHES_TYPES = (fol.Detections, fol.Polylines)
_NO_MATCH_ID = ""
class _PatchView(fos.SampleView):
@property
def _sample_id(self):
return self._doc.sample_id
def save(self):
super().save()
self._view._sync_source_sample(self)
class PatchView(_PatchView):
"""A patch in a :class:`PatchesView`.
:class:`PatchView` instances should not be created manually; they are
generated by iterating over :class:`PatchesView` instances.
Args:
doc: a :class:`fiftyone.core.odm.DatasetSampleDocument`
view: the :class:`PatchesView` that the patch belongs to
selected_fields (None): a set of field names that this view is
restricted to
excluded_fields (None): a set of field names that are excluded from
this view
filtered_fields (None): a set of field names of list fields that are
filtered in this view
"""
pass
class EvaluationPatchView(_PatchView):
"""A patch in an :class:`EvaluationPatchesView`.
:class:`EvaluationPatchView` instances should not be created manually; they
are generated by iterating over :class:`EvaluationPatchesView` instances.
Args:
doc: a :class:`fiftyone.core.odm.DatasetSampleDocument`
view: the :class:`EvaluationPatchesView` that the patch belongs to
selected_fields (None): a set of field names that this view is
restricted to
excluded_fields (None): a set of field names that are excluded from
this view
filtered_fields (None): a set of field names of list fields that are
filtered in this view
"""
pass
class _PatchesView(fov.DatasetView):
def __init__(
self, source_collection, patches_stage, patches_dataset, _stages=None
):
if _stages is None:
_stages = []
self._source_collection = source_collection
self._patches_stage = patches_stage
self._patches_dataset = patches_dataset
self.__stages = _stages
def __copy__(self):
return self.__class__(
self._source_collection,
deepcopy(self._patches_stage),
self._patches_dataset,
_stages=deepcopy(self.__stages),
)
@property
def _base_view(self):
return self.__class__(
self._source_collection,
self._patches_stage,
self._patches_dataset,
)
@property
def _dataset(self):
return self._patches_dataset
@property
def _root_dataset(self):
return self._source_collection._root_dataset
@property
def _stages(self):
return self.__stages
@property
def _all_stages(self):
return (
self._source_collection.view()._all_stages
+ [self._patches_stage]
+ self.__stages
)
@property
def _label_fields(self):
raise NotImplementedError("subclass must implement _label_fields")
@property
def _element_str(self):
return "patch"
@property
def _elements_str(self):
return "patches"
@property
def name(self):
return self.dataset_name + "-patches"
@classmethod
def _get_default_sample_fields(
cls, include_private=False, use_db_fields=False
):
fields = super()._get_default_sample_fields(
include_private=include_private, use_db_fields=use_db_fields
)
if use_db_fields:
return fields + ("_sample_id",)
return fields + ("sample_id",)
def set_values(self, field_name, *args, **kwargs):
field = field_name.split(".", 1)[0]
must_sync = field in self._label_fields
# The `set_values()` operation could change the contents of this view,
# so we first record the sample IDs that need to be synced
if must_sync and self._stages:
ids = self.values("_id")
else:
ids = None
super().set_values(field_name, *args, **kwargs)
if must_sync:
self._sync_source_field(field, ids=ids)
def save(self, fields=None):
"""Overwrites the object patches in the source dataset with the
contents of the view.
If this view contains any additional fields that were not extracted
from the source dataset, these fields are not saved.
.. warning::
This will permanently delete any omitted, filtered, or otherwise
modified patches from the source dataset.
Args:
fields (None): an optional field or list of fields to save. If
specified, only these fields are overwritten
"""
if etau.is_str(fields):
fields = [fields]
super().save(fields=fields)
if fields is None:
fields = self._label_fields
else:
fields = [l for l in fields if l in self._label_fields]
#
# IMPORTANT: we sync the contents of `_patches_dataset`, not `self`
# here because the `save()` call above updated the dataset, which means
# this view may no longer have the same contents (e.g., if `skip()` is
# involved)
#
self._sync_source_root(fields)
def reload(self):
self._root_dataset.reload()
#
# Regenerate the patches dataset
#
# This assumes that calling `load_view()` when the current patches
# dataset has been deleted will cause a new one to be generated
#
self._patches_dataset.delete()
_view = self._patches_stage.load_view(self._source_collection)
self._patches_dataset = _view._patches_dataset
def _sync_source_sample(self, sample):
for field in self._label_fields:
self._sync_source_sample_field(sample, field)
def _sync_source_sample_field(self, sample, field):
label_type = self._patches_dataset._get_label_field_type(field)
is_list_field = issubclass(label_type, fol._LABEL_LIST_FIELDS)
doc = sample._doc.field_to_mongo(field)
if is_list_field:
doc = doc[label_type._LABEL_LIST_FIELD]
self._source_collection._set_labels_by_id(
field, [sample.sample_id], [doc]
)
def _sync_source_field(self, field, ids=None):
_, label_path = self._patches_dataset._get_label_field_path(field)
if ids is not None:
view = self._patches_dataset.mongo(
[{"$match": {"_id": {"$in": ids}}}]
)
else:
view = self._patches_dataset
sample_ids, docs = view.aggregate(
[foa.Values("sample_id"), foa.Values(label_path, _raw=True)]
)
self._source_collection._set_labels_by_id(field, sample_ids, docs)
def _sync_source_root(self, fields):
for field in fields:
self._sync_source_root_field(field)
def _sync_source_root_field(self, field):
_, id_path = self._get_label_field_path(field, "id")
label_path = id_path.rsplit(".", 1)[0]
#
# Sync label updates
#
sample_ids, docs, label_ids = self._patches_dataset.aggregate(
[
foa.Values("sample_id"),
foa.Values(label_path, _raw=True),
foa.Values(id_path, unwind=True),
]
)
self._source_collection._set_labels_by_id(field, sample_ids, docs)
#
# Sync label deletions
#
_, src_id_path = self._source_collection._get_label_field_path(
field, "id"
)
src_ids = self._source_collection.values(src_id_path, unwind=True)
delete_ids = set(src_ids) - set(label_ids)
if delete_ids:
self._source_collection._dataset.delete_labels(
ids=delete_ids, fields=field
)
def _get_ids_map(self, field):
label_type = self._patches_dataset._get_label_field_type(field)
is_list_field = issubclass(label_type, fol._LABEL_LIST_FIELDS)
_, id_path = self._get_label_field_path(field, "id")
sample_ids, label_ids = self.values(["id", id_path])
ids_map = {}
if is_list_field:
for sample_id, _label_ids in zip(sample_ids, label_ids):
if not _label_ids:
continue
for label_id in _label_ids:
ids_map[label_id] = sample_id
else:
for sample_id, label_id in zip(sample_ids, label_ids):
if not label_id:
continue
ids_map[label_id] = sample_id
return ids_map
class PatchesView(_PatchesView):
"""A :class:`fiftyone.core.view.DatasetView` of patches from a
:class:`fiftyone.core.dataset.Dataset`.
Patches views contain an ordered collection of patch samples, each of which
contains a subset of a sample of the parent dataset corresponding to a
single object or logical grouping of of objects.
Patches retrieved from patches views are returned as :class:`PatchView`
objects.
Args:
source_collection: the
:class:`fiftyone.core.collections.SampleCollection` from which this
view was created
patches_stage: the :class:`fiftyone.core.stages.ToPatches` stage that
defines how the patches were extracted
patches_dataset: the :class:`fiftyone.core.dataset.Dataset` that serves
the patches in this view
"""
_SAMPLE_CLS = PatchView
def __init__(
self, source_collection, patches_stage, patches_dataset, _stages=None
):
super().__init__(
source_collection, patches_stage, patches_dataset, _stages=_stages
)
self._patches_field = patches_stage.field
@property
def _label_fields(self):
return [self._patches_field]
@property
def patches_field(self):
"""The field from which the patches in this view were extracted."""
return self._patches_field
class EvaluationPatchesView(_PatchesView):
"""A :class:`fiftyone.core.view.DatasetView` containing evaluation patches
from a :class:`fiftyone.core.dataset.Dataset`.
Evalation patches views contain an ordered collection of evaluation
examples, each of which contains the ground truth and/or predicted labels
for a true positive, false positive, or false negative example from an
evaluation run on the underlying dataset.
Patches retrieved from patches views are returned as
:class:`EvaluationPatchView` objects.
Args:
source_collection: the
:class:`fiftyone.core.collections.SampleCollection` from which this
view was created
patches_stage: the :class:`fiftyone.core.stages.ToEvaluationPatches`
stage that defines how the patches were extracted
patches_dataset: the :class:`fiftyone.core.dataset.Dataset` that serves
the patches in this view
"""
_SAMPLE_CLS = EvaluationPatchView
def __init__(
self, source_collection, patches_stage, patches_dataset, _stages=None
):
super().__init__(
source_collection, patches_stage, patches_dataset, _stages=_stages
)
eval_key = patches_stage.eval_key
eval_info = source_collection.get_evaluation_info(eval_key)
self._gt_field = eval_info.config.gt_field
self._pred_field = eval_info.config.pred_field
@property
def _label_fields(self):
return [self._gt_field, self._pred_field]
@property
def gt_field(self):
"""The ground truth field for the evaluation patches in this view."""
return self._gt_field
@property
def pred_field(self):
"""The predictions field for the evaluation patches in this view."""
return self._pred_field
def make_patches_dataset(
sample_collection, field, keep_label_lists=False, name=None
):
"""Creates a dataset that contains one sample per object patch in the
specified field of the collection.
Fields other than ``field`` and the default sample fields will not be
included in the returned dataset. A ``sample_id`` field will be added that
records the sample ID from which each patch was taken.
Args:
sample_collection: a
:class:`fiftyone.core.collections.SampleCollection`
field: the patches field, which must be of type
:class:`fiftyone.core.labels.Detections` or
:class:`fiftyone.core.labels.Polylines`
keep_label_lists (False): whether to store the patches in label list
fields of the same type as the input collection rather than using
their single label variants
name (None): a name for the returned dataset
Returns:
a :class:`fiftyone.core.dataset.Dataset`
"""
if keep_label_lists:
field_type = sample_collection._get_label_field_type(field)
else:
field_type = _get_single_label_field_type(sample_collection, field)
dataset = fod.Dataset(name, _patches=True)
dataset.media_type = fom.IMAGE
dataset.add_sample_field(
"sample_id", fof.ObjectIdField, db_field="_sample_id"
)
dataset.add_sample_field(
field, fof.EmbeddedDocumentField, embedded_doc_type=field_type
)
patches_view = _make_patches_view(
sample_collection, field, keep_label_lists=keep_label_lists
)
_write_samples(dataset, patches_view)
return dataset
def _get_single_label_field_type(sample_collection, field):
label_type = sample_collection._get_label_field_type(field)
if label_type not in _SINGLE_TYPES_MAP:
raise ValueError("Unsupported label field type %s" % label_type)
return _SINGLE_TYPES_MAP[label_type]
def make_evaluation_dataset(sample_collection, eval_key, name=None):
"""Creates a dataset based on the results of the evaluation with the given
key that contains one sample for each true positive, false positive, and
false negative example in the input collection, respectively.
True positive examples will result in samples with both their ground truth
and predicted fields populated, while false positive/negative examples will
only have one of their corresponding predicted/ground truth fields
populated, respectively.
If multiple predictions are matched to a ground truth object (e.g., if the
evaluation protocol includes a crowd attribute), then all matched
predictions will be stored in the single sample along with the ground truth
object.
The returned dataset will also have top-level ``type`` and ``iou`` fields
populated based on the evaluation results for that example, as well as a
``sample_id`` field recording the sample ID of the example, and a ``crowd``
field if the evaluation protocol defines a crowd attribute.
.. note::
The returned dataset will contain patches for the contents of the input
collection, which may differ from the view on which the ``eval_key``
evaluation was performed. This may exclude some labels that were
evaluated and/or include labels that were not evaluated.
If you would like to see patches for the exact view on which an
evaluation was performed, first call
:meth:`load_evaluation_view() <fiftyone.core.collections.SampleCollection.load_evaluation_view>`
to load the view and then convert to patches.
Args:
sample_collection: a
:class:`fiftyone.core.collections.SampleCollection`
eval_key: an evaluation key that corresponds to the evaluation of
ground truth/predicted fields that are of type
:class:`fiftyone.core.labels.Detections` or
:class:`fiftyone.core.labels.Polylines`
name (None): a name for the returned dataset
Returns:
a :class:`fiftyone.core.dataset.Dataset`
"""
# Parse evaluation info
eval_info = sample_collection.get_evaluation_info(eval_key)
pred_field = eval_info.config.pred_field
gt_field = eval_info.config.gt_field
if hasattr(eval_info.config, "iscrowd"):
crowd_attr = eval_info.config.iscrowd
else:
crowd_attr = None
pred_type = sample_collection._get_label_field_type(pred_field)
gt_type = sample_collection._get_label_field_type(gt_field)
# Setup dataset with correct schema
dataset = fod.Dataset(name, _patches=True)
dataset.media_type = fom.IMAGE
dataset.add_sample_field(
pred_field, fof.EmbeddedDocumentField, embedded_doc_type=pred_type
)
dataset.add_sample_field(
gt_field, fof.EmbeddedDocumentField, embedded_doc_type=gt_type
)
dataset.add_sample_field(
"sample_id", fof.ObjectIdField, db_field="_sample_id"
)
dataset.add_sample_field("type", fof.StringField)
dataset.add_sample_field("iou", fof.FloatField)
if crowd_attr is not None:
dataset.add_sample_field("crowd", fof.BooleanField)
# Add ground truth patches
gt_view = _make_eval_view(
sample_collection, eval_key, gt_field, crowd_attr=crowd_attr
)
_write_samples(dataset, gt_view)
# Merge matched predictions
_merge_matched_labels(dataset, sample_collection, eval_key, pred_field)
# Add unmatched predictions
unmatched_pred_view = _make_eval_view(
sample_collection, eval_key, pred_field, skip_matched=True
)
_add_samples(dataset, unmatched_pred_view)
return dataset
def _make_patches_view(sample_collection, field, keep_label_lists=False):
if sample_collection._is_frames:
raise ValueError(
"Creating patches views into frame views is not yet supported"
)
if sample_collection._is_frame_field(field):
raise ValueError(
"Frame label patches cannot be directly extracted; you must first "
"convert your video dataset to frames via `to_frames()`"
)
label_type = sample_collection._get_label_field_type(field)
if issubclass(label_type, _PATCHES_TYPES):
list_field = field + "." + label_type._LABEL_LIST_FIELD
else:
raise ValueError(
"Invalid label field type %s. Extracting patches is only "
"supported for the following types: %s"
% (label_type, _PATCHES_TYPES)
)
pipeline = [
{
"$project": {
"_id": True,
"_sample_id": "$_id",
"_media_type": True,
"filepath": True,
"metadata": True,
"tags": True,
field + "._cls": True,
list_field: True,
}
},
{"$unwind": "$" + list_field},
{"$set": {"_rand": {"$rand": {}}}},
{"$set": {"_id": "$" + list_field + "._id"}},
]
if keep_label_lists:
pipeline.append({"$set": {list_field: ["$" + list_field]}})
else:
pipeline.append({"$set": {field: "$" + list_field}})
return sample_collection.mongo(pipeline)
def _make_eval_view(
sample_collection, eval_key, field, skip_matched=False, crowd_attr=None
):
eval_type = field + "." + eval_key
eval_id = field + "." + eval_key + "_id"
eval_iou = field + "." + eval_key + "_iou"
view = _make_patches_view(sample_collection, field)
if skip_matched:
view = view.mongo(
[
{
"$match": {
"$expr": {
"$or": [
{"$eq": ["$" + eval_id, _NO_MATCH_ID]},
{"$not": {"$gt": ["$" + eval_id, None]}},
]
}
}
}
]
)
view = view.mongo(
[{"$set": {"type": "$" + eval_type, "iou": "$" + eval_iou}}]
)
if crowd_attr is not None:
crowd_path1 = "$" + field + "." + crowd_attr
# @todo remove Attributes usage
crowd_path2 = "$" + field + ".attributes." + crowd_attr + ".value"
view = view.mongo(
[
{
"$set": {
"crowd": {
"$cond": {
"if": {"$gt": [crowd_path1, None]},
"then": {"$toBool": crowd_path1},
"else": {
"$cond": {
"if": {"$gt": [crowd_path2, None]},
"then": {"$toBool": crowd_path2},
"else": None,
}
},
}
}
}
}
]
)
return _upgrade_labels(view, field)
def _upgrade_labels(view, field):
tmp_field = "_" + field
label_type = view._get_label_field_type(field)
return view.mongo(
[
{"$set": {tmp_field: "$" + field}},
{"$unset": field},
{
"$set": {
field: {
"_cls": label_type.__name__,
label_type._LABEL_LIST_FIELD: ["$" + tmp_field],
}
}
},
{"$unset": tmp_field},
]
)
def _merge_matched_labels(dataset, src_collection, eval_key, field):
field_type = src_collection._get_label_field_type(field)
list_field = field + "." + field_type._LABEL_LIST_FIELD
eval_id = eval_key + "_id"
eval_field = list_field + "." + eval_id
pipeline = src_collection._pipeline(detach_frames=True)
pipeline.extend(
[
{"$project": {list_field: True}},
{"$unwind": "$" + list_field},
{
"$match": {
"$expr": {
"$and": [
{"$gt": ["$" + eval_field, None]},
{"$ne": ["$" + eval_field, _NO_MATCH_ID]},
]
}
}
},
{
"$group": {
"_id": {"$toObjectId": "$" + eval_field},
"_labels": {"$push": "$" + list_field},
}
},
{
"$project": {
field: {
"_cls": field_type.__name__,
field_type._LABEL_LIST_FIELD: "$_labels",
}
},
},
{
"$merge": {
"into": dataset._sample_collection_name,
"on": "_id",
"whenMatched": "merge",
"whenNotMatched": "discard",
}
},
]
)
src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)
def _write_samples(dataset, src_collection):
pipeline = src_collection._pipeline(detach_frames=True)
pipeline.append({"$out": dataset._sample_collection_name})
src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)
def _add_samples(dataset, src_collection):
pipeline = src_collection._pipeline(detach_frames=True)
pipeline.append(
{
"$merge": {
"into": dataset._sample_collection_name,
"on": "_id",
"whenMatched": "keepExisting",
"whenNotMatched": "insert",
}
}
)
src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)
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349,
905,
267,
1043,
7373,
29925,
905,
267,
1043,
1125,
13,
1678,
9995,
29909,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
1493,
29889,
16390,
24541,
1043,
29952,
310,
13261,
267,
515,
263,
13,
1678,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
24713,
29889,
16390,
24541,
1412,
13,
13,
1678,
349,
905,
267,
8386,
1712,
385,
10372,
4333,
310,
13261,
11916,
29892,
1269,
310,
607,
13,
1678,
3743,
263,
11306,
310,
263,
4559,
310,
278,
3847,
8783,
6590,
304,
263,
13,
1678,
2323,
1203,
470,
16667,
27270,
310,
310,
3618,
29889,
13,
13,
1678,
349,
905,
267,
27387,
515,
13261,
267,
8386,
526,
4133,
408,
584,
1990,
18078,
29925,
905,
1043,
29952,
13,
1678,
3618,
29889,
13,
13,
1678,
826,
3174,
29901,
13,
4706,
2752,
29918,
10855,
29901,
278,
13,
9651,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
29027,
29889,
17708,
7196,
29952,
515,
607,
445,
13,
9651,
1776,
471,
2825,
13,
4706,
13261,
267,
29918,
19190,
29901,
278,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
303,
1179,
29889,
1762,
29925,
905,
267,
29952,
7408,
393,
13,
9651,
17645,
920,
278,
13261,
267,
892,
23892,
13,
4706,
13261,
267,
29918,
24713,
29901,
278,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
24713,
29889,
16390,
24541,
29952,
393,
19700,
13,
9651,
278,
13261,
267,
297,
445,
1776,
13,
1678,
9995,
13,
13,
1678,
903,
8132,
3580,
1307,
29918,
6154,
29903,
353,
349,
905,
1043,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
4706,
1583,
29892,
2752,
29918,
10855,
29892,
13261,
267,
29918,
19190,
29892,
13261,
267,
29918,
24713,
29892,
903,
303,
1179,
29922,
8516,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
13,
9651,
2752,
29918,
10855,
29892,
13261,
267,
29918,
19190,
29892,
13261,
267,
29918,
24713,
29892,
903,
303,
1179,
29922,
29918,
303,
1179,
13,
4706,
1723,
13,
13,
4706,
1583,
3032,
5041,
267,
29918,
2671,
353,
13261,
267,
29918,
19190,
29889,
2671,
13,
13,
1678,
732,
6799,
13,
1678,
822,
903,
1643,
29918,
9621,
29898,
1311,
1125,
13,
4706,
736,
518,
1311,
3032,
5041,
267,
29918,
2671,
29962,
13,
13,
1678,
732,
6799,
13,
1678,
822,
13261,
267,
29918,
2671,
29898,
1311,
1125,
13,
4706,
9995,
1576,
1746,
515,
607,
278,
13261,
267,
297,
445,
1776,
892,
23892,
1213,
15945,
13,
4706,
736,
1583,
3032,
5041,
267,
29918,
2671,
13,
13,
13,
1990,
382,
4387,
362,
29925,
905,
267,
1043,
7373,
29925,
905,
267,
1043,
1125,
13,
1678,
9995,
29909,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
1493,
29889,
16390,
24541,
1043,
29952,
6943,
17983,
13261,
267,
13,
1678,
515,
263,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
24713,
29889,
16390,
24541,
1412,
13,
13,
1678,
382,
791,
362,
13261,
267,
8386,
1712,
385,
10372,
4333,
310,
17983,
13,
1678,
6455,
29892,
1269,
310,
607,
3743,
278,
5962,
8760,
322,
29914,
272,
25383,
11073,
13,
1678,
363,
263,
1565,
6374,
29892,
2089,
6374,
29892,
470,
2089,
8178,
1342,
515,
385,
13,
1678,
17983,
1065,
373,
278,
14407,
8783,
29889,
13,
13,
1678,
349,
905,
267,
27387,
515,
13261,
267,
8386,
526,
4133,
408,
13,
1678,
584,
1990,
18078,
29923,
4387,
362,
29925,
905,
1043,
29952,
3618,
29889,
13,
13,
1678,
826,
3174,
29901,
13,
4706,
2752,
29918,
10855,
29901,
278,
13,
9651,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
29027,
29889,
17708,
7196,
29952,
515,
607,
445,
13,
9651,
1776,
471,
2825,
13,
4706,
13261,
267,
29918,
19190,
29901,
278,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
303,
1179,
29889,
1762,
29923,
4387,
362,
29925,
905,
267,
29952,
13,
9651,
7408,
393,
17645,
920,
278,
13261,
267,
892,
23892,
13,
4706,
13261,
267,
29918,
24713,
29901,
278,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
24713,
29889,
16390,
24541,
29952,
393,
19700,
13,
9651,
278,
13261,
267,
297,
445,
1776,
13,
1678,
9995,
13,
13,
1678,
903,
8132,
3580,
1307,
29918,
6154,
29903,
353,
382,
4387,
362,
29925,
905,
1043,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
4706,
1583,
29892,
2752,
29918,
10855,
29892,
13261,
267,
29918,
19190,
29892,
13261,
267,
29918,
24713,
29892,
903,
303,
1179,
29922,
8516,
13,
268,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
13,
9651,
2752,
29918,
10855,
29892,
13261,
267,
29918,
19190,
29892,
13261,
267,
29918,
24713,
29892,
903,
303,
1179,
29922,
29918,
303,
1179,
13,
4706,
1723,
13,
13,
4706,
19745,
29918,
1989,
353,
13261,
267,
29918,
19190,
29889,
14513,
29918,
1989,
13,
4706,
19745,
29918,
3888,
353,
2752,
29918,
10855,
29889,
657,
29918,
24219,
362,
29918,
3888,
29898,
14513,
29918,
1989,
29897,
13,
4706,
1583,
3032,
4141,
29918,
2671,
353,
19745,
29918,
3888,
29889,
2917,
29889,
4141,
29918,
2671,
13,
4706,
1583,
3032,
11965,
29918,
2671,
353,
19745,
29918,
3888,
29889,
2917,
29889,
11965,
29918,
2671,
13,
13,
1678,
732,
6799,
13,
1678,
822,
903,
1643,
29918,
9621,
29898,
1311,
1125,
13,
4706,
736,
518,
1311,
3032,
4141,
29918,
2671,
29892,
1583,
3032,
11965,
29918,
2671,
29962,
13,
13,
1678,
732,
6799,
13,
1678,
822,
330,
29873,
29918,
2671,
29898,
1311,
1125,
13,
4706,
9995,
1576,
5962,
8760,
1746,
363,
278,
17983,
13261,
267,
297,
445,
1776,
1213,
15945,
13,
4706,
736,
1583,
3032,
4141,
29918,
2671,
13,
13,
1678,
732,
6799,
13,
1678,
822,
4450,
29918,
2671,
29898,
1311,
1125,
13,
4706,
9995,
1576,
27303,
1746,
363,
278,
17983,
13261,
267,
297,
445,
1776,
1213,
15945,
13,
4706,
736,
1583,
3032,
11965,
29918,
2671,
13,
13,
13,
1753,
1207,
29918,
5041,
267,
29918,
24713,
29898,
13,
1678,
4559,
29918,
10855,
29892,
1746,
29892,
3013,
29918,
1643,
29918,
21513,
29922,
8824,
29892,
1024,
29922,
8516,
13,
1125,
13,
1678,
9995,
9832,
1078,
263,
8783,
393,
3743,
697,
4559,
639,
1203,
13261,
297,
278,
13,
1678,
6790,
1746,
310,
278,
4333,
29889,
13,
13,
1678,
8989,
29879,
916,
1135,
4954,
2671,
16159,
322,
278,
2322,
4559,
4235,
674,
451,
367,
13,
1678,
5134,
297,
278,
4133,
8783,
29889,
319,
4954,
11249,
29918,
333,
16159,
1746,
674,
367,
2715,
393,
13,
1678,
6475,
278,
4559,
3553,
515,
607,
1269,
13261,
471,
4586,
29889,
13,
13,
1678,
826,
3174,
29901,
13,
4706,
4559,
29918,
10855,
29901,
263,
13,
9651,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
29027,
29889,
17708,
7196,
29952,
13,
4706,
1746,
29901,
278,
13261,
267,
1746,
29892,
607,
1818,
367,
310,
1134,
13,
9651,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
21134,
29889,
29928,
2650,
1953,
29952,
470,
13,
9651,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
21134,
29889,
7713,
2904,
1475,
29952,
13,
4706,
3013,
29918,
1643,
29918,
21513,
313,
8824,
1125,
3692,
304,
3787,
278,
13261,
267,
297,
3858,
1051,
13,
9651,
4235,
310,
278,
1021,
1134,
408,
278,
1881,
4333,
3265,
1135,
773,
13,
9651,
1009,
2323,
3858,
29161,
13,
4706,
1024,
313,
8516,
1125,
263,
1024,
363,
278,
4133,
8783,
13,
13,
1678,
16969,
29901,
13,
4706,
263,
584,
1990,
18078,
28491,
1017,
650,
29889,
3221,
29889,
24713,
29889,
16390,
24541,
29952,
13,
1678,
9995,
13,
1678,
565,
3013,
29918,
1643,
29918,
21513,
29901,
13,
4706,
1746,
29918,
1853,
353,
4559,
29918,
10855,
3032,
657,
29918,
1643,
29918,
2671,
29918,
1853,
29898,
2671,
29897,
13,
1678,
1683,
29901,
13,
4706,
1746,
29918,
1853,
353,
903,
657,
29918,
14369,
29918,
1643,
29918,
2671,
29918,
1853,
29898,
11249,
29918,
10855,
29892,
1746,
29897,
13,
13,
1678,
8783,
353,
285,
397,
29889,
16390,
24541,
29898,
978,
29892,
903,
5041,
267,
29922,
5574,
29897,
13,
1678,
8783,
29889,
9799,
29918,
1853,
353,
285,
290,
29889,
2382,
13,
1678,
8783,
29889,
1202,
29918,
11249,
29918,
2671,
29898,
13,
4706,
376,
11249,
29918,
333,
613,
285,
974,
29889,
2061,
1204,
3073,
29892,
4833,
29918,
2671,
543,
29918,
11249,
29918,
333,
29908,
13,
1678,
1723,
13,
1678,
8783,
29889,
1202,
29918,
11249,
29918,
2671,
29898,
13,
4706,
1746,
29892,
285,
974,
29889,
6026,
2580,
7176,
6268,
3073,
29892,
15685,
29918,
1514,
29918,
1853,
29922,
2671,
29918,
1853,
13,
1678,
1723,
13,
13,
1678,
13261,
267,
29918,
1493,
353,
903,
5675,
29918,
5041,
267,
29918,
1493,
29898,
13,
4706,
4559,
29918,
10855,
29892,
1746,
29892,
3013,
29918,
1643,
29918,
21513,
29922,
17462,
29918,
1643,
29918,
21513,
13,
1678,
1723,
13,
1678,
903,
3539,
29918,
27736,
29898,
24713,
29892,
13261,
267,
29918,
1493,
29897,
13,
13,
1678,
736,
8783,
13,
13,
13,
1753,
903,
657,
29918,
14369,
29918,
1643,
29918,
2671,
29918,
1853,
29898,
11249,
29918,
10855,
29892,
1746,
1125,
13,
1678,
3858,
29918,
1853,
353,
4559,
29918,
10855,
3032,
657,
29918,
1643,
29918,
2671,
29918,
1853,
29898,
2671,
29897,
13,
13,
1678,
565,
3858,
29918,
1853,
451,
297,
903,
29903,
4214,
1307,
29918,
15631,
29925,
2890,
29918,
23827,
29901,
13,
4706,
12020,
7865,
2392,
703,
25807,
29884,
3016,
287,
3858,
1746,
1134,
1273,
29879,
29908,
1273,
3858,
29918,
1853,
29897,
13,
13,
1678,
736,
903,
29903,
4214,
1307,
29918,
15631,
29925,
2890,
29918,
23827,
29961,
1643,
29918,
1853,
29962,
13,
13,
13,
1753,
1207,
29918,
24219,
362,
29918,
24713,
29898,
11249,
29918,
10855,
29892,
19745,
29918,
1989,
29892,
1024,
29922,
8516,
1125,
13,
1678,
9995,
9832,
1078,
263,
8783,
2729,
373,
278,
2582,
310,
278,
17983,
411,
278,
2183,
13,
1678,
1820,
393,
3743,
697,
4559,
363,
1269,
1565,
6374,
29892,
2089,
6374,
29892,
322,
13,
1678,
2089,
8178,
1342,
297,
278,
1881,
4333,
29892,
8307,
29889,
13,
13,
1678,
5852,
6374,
6455,
674,
1121,
297,
11916,
411,
1716,
1009,
5962,
8760,
13,
1678,
322,
25383,
4235,
24146,
29892,
1550,
2089,
6374,
29914,
22198,
6455,
674,
13,
1678,
871,
505,
697,
310,
1009,
6590,
25383,
29914,
2057,
8760,
4235,
13,
1678,
24146,
29892,
8307,
29889,
13,
13,
1678,
960,
2999,
27303,
526,
19228,
304,
263,
5962,
8760,
1203,
313,
29872,
29889,
29887,
1696,
565,
278,
13,
1678,
17983,
9608,
7805,
263,
19174,
5352,
511,
769,
599,
19228,
13,
1678,
27303,
674,
367,
6087,
297,
278,
2323,
4559,
3412,
411,
278,
5962,
8760,
13,
1678,
1203,
29889,
13,
13,
1678,
450,
4133,
8783,
674,
884,
505,
2246,
29899,
5563,
4954,
1853,
16159,
322,
4954,
29875,
283,
16159,
4235,
13,
1678,
24146,
2729,
373,
278,
17983,
2582,
363,
393,
1342,
29892,
408,
1532,
408,
263,
13,
1678,
4954,
11249,
29918,
333,
16159,
1746,
16867,
278,
4559,
3553,
310,
278,
1342,
29892,
322,
263,
4954,
29883,
798,
29881,
16159,
13,
1678,
1746,
565,
278,
17983,
9608,
17645,
263,
19174,
5352,
29889,
13,
13,
1678,
6317,
4443,
1057,
13,
13,
4706,
450,
4133,
8783,
674,
1712,
13261,
267,
363,
278,
8118,
310,
278,
1881,
13,
4706,
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preprocess.py | Antonioxv/Transformer-Pytorch | 3 | 82679 | <reponame>Antonioxv/Transformer-Pytorch
# -*- coding: utf-8 -*-
"""
Copied from https://www.github.com/kyubyong/transformer.
"""
from __future__ import print_function
import os
import regex
import codecs
import argparse
from collections import Counter
parser = argparse.ArgumentParser(description='Preprocessing')
parser.add_argument('--train_src', required=True, type=str, help='Path to training source data')
parser.add_argument('--train_tgt', required=True, type=str, help='Path to training target data')
def make_vocab(file_path, file_name):
"""Constructs vocabulary.
Args:
file_path: A string. Input file path.
file_name: A string. Output file name.
Writes vocabulary line by line to `preprocessed/file_name`
"""
text = codecs.open(file_path, 'r', 'utf-8').read()
text = regex.sub("[^\s\p{Latin}']", "", text)
words = text.split()
word2cnt = Counter(words)
os.makedirs('preprocessed', exist_ok=True)
with codecs.open('preprocessed/{}'.format(file_name), 'w', 'utf-8') as fout:
fout.write("{}\t1000000000\n{}\t1000000000\n{}\t1000000000\n{}\t1000000000\n".format("<PAD>", "<UNK>", "<S>", "</S>"))
for word, cnt in word2cnt.most_common(len(word2cnt)):
fout.write(u"{}\t{}\n".format(word, cnt))
def main():
args = parser.parse_args()
print(args)
make_vocab(args.train_src, "de.vocab.tsv")
make_vocab(args.train_tgt, "en.vocab.tsv")
print('Terminated')
if __name__ == '__main__':
main()
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db_conn.py | achhetr/Library-book-store-app | 0 | 16901 | import sqlite3
class Database:
# create book always if not exists
def __init__(self,db):
self.conn = sqlite3.connect(db)
self.cur = self.conn.execute("CREATE TABLE IF NOT EXISTS book (id INTEGER PRIMARY KEY, " +
"title TEXT, author TEXT, year INTEGER, isbn INTEGER)")
self.conn.commit()
def insert(self,title,author,year,isbn):
self.cur.execute("INSERT INTO book VALUES (NULL,?,?,?,?)",(title,author,year,isbn))
self.conn.commit()
def view(self):
self.cur.execute("SELECT * FROM book")
rows = self.cur.fetchall()
return rows
def search(self,title="",author="",year="",isbn=""):
self.cur.execute("SELECT * FROM book WHERE title=? OR author=? " +
"OR year=? OR isbn=?",(title,author,year,isbn))
rows = self.cur.fetchall()
return rows
def delete(self,id):
self.cur.execute("DELETE FROM book WHERE id=?",(id,))
self.conn.commit()
def update(self,id,title,author,year,isbn):
self.cur.execute("UPDATE book SET title=?, author=?, " +
"year=?,isbn=? WHERE id=?", (title,author,year,isbn,id))
self.conn.commit()
def __del__(self):
self.conn.close() | [
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src/data/make_weather.py | sreejithkrishnanr/powerlaws | 1 | 181307 | <filename>src/data/make_weather.py<gh_stars>1-10
# -*- coding: utf-8 -*-
import os
import sys
import click
import logging
from dotenv import find_dotenv, load_dotenv
from collections import OrderedDict
import pandas as pd
@click.command()
@click.argument('input_filepath', type=click.Path(exists=True))
@click.argument('output_filepath', type=click.Path())
def main(input_filepath, output_filepath):
""" Process weather.csv
"""
from src.utils.data import ensure_no_na
logger = logging.getLogger(__name__)
logger.info('Reading %s' % (input_filepath,))
dataset = pd.read_csv(input_filepath, parse_dates=[1])
logger.info("Processing weather data")
dataset['Weight'] = 1 / dataset['Distance']
dataset['Weighted_Temperature'] = dataset['Temperature'] * dataset['Weight']
groups = dataset.groupby(['SiteId', 'Timestamp'])
dataset = pd.DataFrame(data=OrderedDict([
('DistanceMean', groups['Distance'].mean()),
('DistanceMin', groups['Distance'].min()),
('DistanceMax', groups['Distance'].max()),
('DistanceVariance', groups['Distance'].std().fillna(0)),
('NumStations', groups['Distance'].count()),
('TemperatureMean', groups['Weighted_Temperature'].sum() / groups['Weight'].sum()),
('TemperatureVariance', groups['Temperature'].std().fillna(0)),
('TemperatureMin', groups['Temperature'].min()),
('TemperatureMax', groups['Temperature'].max()),
]))
dataset = dataset.reset_index()
ensure_no_na(dataset)
logger.info("Saving to %s" % (output_filepath,))
dataset.to_hdf(output_filepath, "data", index=False)
if __name__ == '__main__':
log_fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
logging.basicConfig(level=logging.INFO, format=log_fmt)
# not used in this stub but often useful for finding various files
project_dir = os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)
sys.path.insert(0, project_dir)
# find .env automagically by walking up directories until it's found, then
# load up the .env entries as environment variables
load_dotenv(find_dotenv())
main()
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venv1/Lib/site-packages/tensorflow/contrib/saved_model/python/saved_model/signature_def_utils.py | Soum-Soum/Tensorflow_Face_Finder | 0 | 90099 | # Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""SignatureDef utility functions implementation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
def get_signature_def_by_key(meta_graph_def, signature_def_key):
"""Utility function to get a SignatureDef protocol buffer by its key.
Args:
meta_graph_def: MetaGraphDef protocol buffer with the SignatureDefMap to
look up.
signature_def_key: Key of the SignatureDef protocol buffer to find in the
SignatureDefMap.
Returns:
A SignatureDef protocol buffer corresponding to the supplied key, if it
exists.
Raises:
ValueError: If no entry corresponding to the supplied key is found in the
SignatureDefMap of the MetaGraphDef.
"""
if signature_def_key not in meta_graph_def.signature_def:
raise ValueError("No SignatureDef with key '%s' found in MetaGraphDef." %
signature_def_key)
return meta_graph_def.signature_def[signature_def_key]
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] |
datatube/test/coerce_dtypes_test.py | eerkela/archivetube | 0 | 37701 | from datetime import datetime, timedelta, timezone
import random
import unittest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
import pytz
if __name__ == "__main__":
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from datatube.dtype import coerce_dtypes
unittest.TestCase.maxDiff = None
class CoerceDtypeBasicTests(unittest.TestCase):
def test_coerce_dtypes_returns_copy(self):
# series
in_series = pd.Series([1, 2, 3])
out_series = coerce_dtypes(in_series, float)
self.assertNotEqual(id(in_series), id(out_series))
# dataframe
in_df = pd.DataFrame({"copy": [1, 2, 3]})
out_df = coerce_dtypes(in_df, {"copy": float})
self.assertNotEqual(id(in_df), id(out_df))
class CoerceIntegerDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
size = 3 # minimum 3
cls.integers = [-1 * size // 2 + i + 1 for i in range(size)]
# integers = [..., -1, 0, 1, ...]
cls.bool_flags = [(i + 1) % 2 for i in range(size)]
# bool_flags = [1, 0, 1, 0, 1, ...]
cls.col_name = "integers"
def test_coerce_from_integer_to_integer_no_na(self):
in_data = self.integers
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_integer_with_na(self):
in_data = self.integers + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_float_no_na(self):
in_data = self.integers
out_data = [float(i) for i in self.integers]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_float_with_na(self):
in_data = self.integers + [None]
out_data = [float(i) for i in self.integers] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_complex_no_na(self):
in_data = self.integers
out_data = [complex(i, 0) for i in self.integers]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_complex_with_na(self):
in_data = self.integers + [None]
out_data = [complex(i, 0) for i in self.integers] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_string_no_na(self):
in_data = self.integers
out_data = [str(i) for i in self.integers]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_string_with_na(self):
in_data = self.integers + [None]
out_data = [str(i) for i in self.integers] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_generic_integer_to_boolean_no_na(self):
in_data = self.integers
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_generic_integer_to_boolean_with_na(self):
in_data = self.integers + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_integer_bool_flag_to_boolean_no_na(self):
in_data = self.bool_flags
out_data = [bool(i) for i in self.bool_flags]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_bool_flag_to_boolean_with_na(self):
in_data = self.bool_flags + [None]
out_data = [bool(i) for i in self.bool_flags] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_datetime_no_na(self):
in_data = self.integers
out_data = [datetime.fromtimestamp(i, tz=timezone.utc)
for i in self.integers]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_datetime_with_na(self):
in_data = self.integers + [None]
out_data = [datetime.fromtimestamp(i, tz=timezone.utc)
for i in self.integers] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_timedelta_no_na(self):
in_data = self.integers
out_data = [timedelta(seconds=i) for i in self.integers]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_timedelta_with_na(self):
in_data = self.integers + [None]
out_data = [timedelta(seconds=i) for i in self.integers] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_object_no_na(self):
in_series = pd.Series(self.integers)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_to_object_with_na(self):
in_series = pd.Series(self.integers + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceFloatDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
random.seed(12345)
size = 3 # minimum 3
cls.whole_floats = [-1 * size // 2 + i + 1.0 for i in range(size)]
# whole_flats = [..., -1.0, 0.0, 1.0, ...]
cls.decimal_floats = [-1 * size // 2 + i + 1 + random.random()
for i in range(size)]
# decimal_floats = [..., -1.0 + e, 0.0 + e, 1.0 + e, ...]
cls.decimal_floats_between_0_and_1 = [random.random()
for _ in range(size)]
# decimal_floats_between_0_and_1 = [0.xxxx, 0.xxxx, 0.xxxx, ...]
cls.bool_flags = [(i + 1.0) % 2 for i in range(size)]
# bool_flags = [1.0, 0.0, 1.0, 0.0, 1.0, ...]
cls.col_name = "floats"
def test_coerce_from_whole_float_to_integer_no_na(self):
in_data = self.whole_floats
out_data = [int(f) for f in self.whole_floats]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_whole_float_to_integer_with_na(self):
in_data = self.whole_floats + [None]
out_data = [int(f) for f in self.whole_floats] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_decimal_float_to_integer_no_na(self):
in_data = self.decimal_floats
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_decimal_float_to_integer_with_na(self):
in_data = self.decimal_floats + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_float_to_float_no_na(self):
in_data = self.decimal_floats
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_float_with_na(self):
in_data = self.decimal_floats + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_complex_no_na(self):
in_data = self.decimal_floats
out_data = [complex(f, 0) for f in self.decimal_floats]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_complex_with_na(self):
in_data = self.decimal_floats + [None]
out_data = [complex(f, 0) for f in self.decimal_floats] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_string_no_na(self):
in_data = self.decimal_floats
out_data = [str(f) for f in self.decimal_floats]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_string_with_na(self):
in_data = self.decimal_floats + [None]
out_data = [str(f) for f in self.decimal_floats] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_generic_float_to_boolean_no_na(self):
in_data = self.decimal_floats
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_generic_float_to_boolean_with_na(self):
in_data = self.decimal_floats + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_float_bool_flag_to_boolean_no_na(self):
in_data = self.bool_flags
out_data = [bool(f) for f in self.bool_flags]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_float_bool_flag_to_boolean_with_na(self):
in_data = self.bool_flags + [None]
out_data = [bool(f) for f in self.bool_flags] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_decimal_float_between_0_and_1_to_boolean_no_na(self):
in_data = self.decimal_floats_between_0_and_1
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_decimal_float_between_0_and_1_to_boolean_with_na(self):
in_data = self.decimal_floats_between_0_and_1 + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_float_to_datetime_no_na(self):
in_data = self.decimal_floats
out_data = [datetime.fromtimestamp(f, tz=timezone.utc)
for f in self.decimal_floats]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_datetime_with_na(self):
in_data = self.decimal_floats + [None]
out_data = [datetime.fromtimestamp(f, tz=timezone.utc)
for f in self.decimal_floats] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_timedelta_no_na(self):
in_data = self.decimal_floats
out_data = [timedelta(seconds=f) for f in self.decimal_floats]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_timedelta_with_na(self):
in_data = self.decimal_floats + [None]
out_data = [timedelta(seconds=f) for f in self.decimal_floats] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_object_no_na(self):
in_series = pd.Series(self.decimal_floats)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_float_to_object_with_na(self):
in_series = pd.Series(self.decimal_floats + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceComplexDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
random.seed(12345)
size = 3
cls.real_whole_complex = [complex(-1 * size // 2 + i + 1.0, 0)
for i in range(size)]
# ^ = [..., complex(-1, 0), complex(0, 0), complex(1, 0), ...]
cls.real_complex = [complex(-1 * size // 2 + i + 1 + random.random(), 0)
for i in range(size)]
# ^ = [..., complex(-1+e, 0), complex(0+e, 0), complex(1+e, 0), ...]
cls.real_complex_between_0_and_1 = [complex(random.random(), 0)
for _ in range(size)]
# ^ = [complex(0.xxxx, 0), complex(0.xxxx, 0), complex(0.xxxx, 0), ...]
cls.imag_complex = [complex(-1 * size // 2 + i + 1 + random.random(),
-1 * size // 2 + i + 1 + random.random())
for i in range(size)]
# ^ = [..., complex(-1+e,-1+e), complex(0+e,0+e), complex(1+e,1+e), ...]
cls.bool_flags = [complex((i + 1) % 2, 0) for i in range(size)]
# ^ = [complex(1, 0), complex(0, 0), complex(1, 0), complex(0, 0), ...]
cls.col_name = "complex"
def test_coerce_from_real_whole_complex_to_integer_no_na(self):
in_data = self.real_whole_complex
out_data = [int(c.real) for c in self.real_whole_complex]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_real_whole_complex_to_integer_with_na(self):
in_data = self.real_whole_complex + [None]
out_data = [int(c.real) for c in self.real_whole_complex] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_real_decimal_complex_to_integer_no_na(self):
in_data = self.real_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_decimal_complex_to_integer_with_na(self):
in_data = self.real_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_integer_no_na(self):
in_data = self.imag_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_integer_with_na(self):
in_data = self.imag_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_complex_to_float_no_na(self):
in_data = self.real_complex
out_data = [c.real for c in self.real_complex]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_real_complex_to_float_with_na(self):
in_data = self.real_complex + [None]
out_data = [c.real for c in self.real_complex] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_imaginary_complex_to_float_no_na(self):
in_data = self.imag_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {float} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, float)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {float} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: float})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_float_with_na(self):
in_data = self.imag_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {float} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, float)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {float} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: float})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_complex_to_complex_no_na(self):
in_data = self.imag_complex
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_to_complex_with_na(self):
in_data = self.imag_complex + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_to_string_no_na(self):
in_data = self.imag_complex
out_data = [str(c) for c in self.imag_complex]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_to_string_with_na(self):
in_data = self.imag_complex + [None]
out_data = [str(c) for c in self.imag_complex] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_bool_flag_to_boolean_no_na(self):
in_data = self.bool_flags
out_data = [bool(c.real) for c in self.bool_flags]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_bool_flag_to_boolean_with_na(self):
in_data = self.bool_flags + [None]
out_data = [bool(c.real) for c in self.bool_flags] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_real_complex_to_boolean_no_na(self):
in_data = self.real_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_complex_to_boolean_with_na(self):
in_data = self.real_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_whole_complex_to_boolean_no_na(self):
in_data = self.real_whole_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_whole_complex_to_boolean_with_na(self):
in_data = self.real_whole_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_complex_between_0_and_1_to_boolean_no_na(self):
in_data = self.real_complex_between_0_and_1
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_complex_between_0_and_1_to_boolean_with_na(self):
in_data = self.real_complex_between_0_and_1 + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_boolean_no_na(self):
in_data = self.imag_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_boolean_with_na(self):
in_data = self.imag_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_complex_to_datetime_no_na(self):
in_data = self.real_complex
out_data = [datetime.fromtimestamp(c.real, tz=timezone.utc)
for c in self.real_complex]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_real_complex_to_datetime_with_na(self):
in_data = self.real_complex + [None]
out_data = [datetime.fromtimestamp(c.real, tz=timezone.utc)
for c in self.real_complex] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_imaginary_complex_to_datetime_no_na(self):
in_data = self.imag_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {datetime} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, datetime)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {datetime} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: datetime})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_datetime_with_na(self):
in_data = self.imag_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {datetime} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, datetime)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {datetime} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: datetime})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_real_complex_to_timedelta_no_na(self):
in_data = self.real_complex
out_data = [timedelta(seconds=c.real) for c in self.real_complex]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_real_complex_to_timedelta_with_na(self):
in_data = self.real_complex + [None]
out_data = ([timedelta(seconds=c.real) for c in self.real_complex] +
[None])
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_imaginary_complex_to_timedelta_no_na(self):
in_data = self.imag_complex
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {timedelta} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, timedelta)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {timedelta} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: timedelta})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_imaginary_complex_to_timedelta_with_na(self):
in_data = self.imag_complex + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {timedelta} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, timedelta)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {timedelta} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: timedelta})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_complex_to_object_no_na(self):
in_series = pd.Series(self.imag_complex)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_to_object_wth_na(self):
in_series = pd.Series(self.imag_complex + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceStringDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
random.seed(12345)
size = 3
cls.integers = [-1 * size // 2 + i + 1 for i in range(size)]
# ^ = [..., -1, 0, 1, ...]
cls.floats = [i + random.random() for i in cls.integers]
# ^ = [..., -1+e, 0+e, 1+e, ...]
cls.complex = [complex(f, f) for f in cls.floats]
# ^ = [..., complex(-1+e,-1+e), complex(0+e,0+e), complex(1+e,1+e), ...]
cls.characters = [chr((i % 26) + ord("a")) for i in range(size)]
# ^ = ["a", "b", "c", ..., "a", "b", "c", ...]
cls.booleans = [bool((i + 1) % 2) for i in range(size)]
# ^ = [True, False, True, False, ...]
cls.naive_datetimes = [datetime.utcfromtimestamp(f) for f in cls.floats]
# ^ = [..., utc time -1+e, utc time 0+e, utc_time 1+e, ...] (no tz)
cls.aware_datetimes = [datetime.fromtimestamp(f, tz=timezone.utc)
for f in cls.floats]
# ^ = [..., utc time -1+e, utc time 0+e, utc_time 1+e, ...] (with tz)
cls.aware_naive_datetimes = []
for index, f in enumerate(cls.floats):
if index % 2: # naive
cls.aware_naive_datetimes.append(datetime.utcfromtimestamp(f))
else: # aware
val = datetime.fromtimestamp(f, tz=timezone.utc)
cls.aware_naive_datetimes.append(val)
# ^ = [aware, naive, aware, naive, aware, ...]
cls.mixed_timezones = []
for index, f in enumerate(cls.floats):
tz_name = pytz.all_timezones[index % len(pytz.all_timezones)]
tz = pytz.timezone(tz_name)
val = datetime.fromtimestamp(f, tz=tz)
cls.mixed_timezones.append(val)
# ^ = ["Africa/Abidjan", "Africa/Accra", "Africa/Addis_Ababa", ...]
cls.timedeltas = [timedelta(seconds=f) for f in cls.floats]
# ^ = [..., -1+e seconds, 0+e seconds, 1+e seconds, ...]
cls.col_name = "strings"
def test_coerce_from_integer_string_to_integer_no_na(self):
in_data = [str(i) for i in self.integers]
out_data = self.integers
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_integer_string_to_integer_with_na(self):
in_data = [str(i) for i in self.integers] + [None]
out_data = self.integers + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_float_string_to_float_no_na(self):
in_data = [str(f) for f in self.floats]
out_data = self.floats
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_float_string_to_float_with_na(self):
in_data = [str(f) for f in self.floats] + [None]
out_data = self.floats + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_string_to_complex_no_na(self):
in_data = [str(c) for c in self.complex]
out_data = self.complex
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_complex_string_to_complex_with_na(self):
in_data = [str(c) for c in self.complex] + [None]
out_data = self.complex + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_character_string_to_string_no_na(self):
in_data = self.characters
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_character_string_to_string_with_na(self):
in_data = self.characters + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_string_to_boolean_no_na(self):
in_data = [str(b) for b in self.booleans]
out_data = self.booleans
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_string_to_boolean_with_na(self):
in_data = [str(b) for b in self.booleans] + [None]
out_data = self.booleans + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_naive_datetime_string_to_datetime_no_na(self):
in_data = [str(d) for d in self.naive_datetimes]
out_data = self.naive_datetimes
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_naive_datetime_string_to_datetime_with_na(self):
in_data = [str(d) for d in self.naive_datetimes] + [None]
out_data = self.naive_datetimes + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_naive_ISO_8601_string_to_datetime_no_na(self):
in_data = [d.isoformat() for d in self.naive_datetimes]
out_data = self.naive_datetimes
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_naive_ISO_8601_string_to_datetime_with_na(self):
in_data = [d.isoformat() for d in self.naive_datetimes] + [None]
out_data = self.naive_datetimes + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_aware_datetime_string_to_datetime_no_na(self):
in_data = [str(d) for d in self.aware_datetimes]
out_data = self.aware_datetimes
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_aware_datetime_string_to_datetime_with_na(self):
in_data = [str(d) for d in self.aware_datetimes] + [None]
out_data = self.aware_datetimes + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_aware_ISO_8601_string_to_datetime_no_na(self):
in_data = [d.isoformat() for d in self.aware_datetimes]
out_data = self.aware_datetimes
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_aware_ISO_8601_string_to_datetime_with_na(self):
in_data = [d.isoformat() for d in self.aware_datetimes] + [None]
out_data = self.aware_datetimes + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_aware_naive_datetime_string_to_datetime_no_na(self):
raise NotImplementedError()
def test_coerce_from_aware_naive_datetime_string_to_datetime_with_na(self):
raise NotImplementedError()
def test_coerce_from_aware_naive_ISO_8601_string_to_datetime_no_na(self):
raise NotImplementedError()
def test_coerce_from_aware_naive_ISO_8601_string_to_datetime_with_na(self):
raise NotImplementedError()
def test_coerce_from_mixed_tz_datetime_string_to_datetime_no_na(self):
raise NotImplementedError()
def test_coerce_from_mixed_tz_datetime_string_to_datetime_with_na(self):
raise NotImplementedError()
def test_coerce_from_mixed_tz_ISO_8601_string_to_datetime_no_na(self):
raise NotImplementedError()
def test_coerce_from_mixed_tz_ISO_8601_string_to_datetime_with_na(self):
raise NotImplementedError()
def test_coerce_from_timedelta_string_to_timedelta_no_na(self):
in_data = [str(t) for t in self.timedeltas]
out_data = self.timedeltas
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_string_to_timedelta_with_na(self):
in_data = [str(t) for t in self.timedeltas] + [None]
out_data = self.timedeltas + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_string_to_object_no_na(self):
in_series = pd.Series(self.timedeltas)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_string_to_object_with_na(self):
in_series = pd.Series(self.timedeltas + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceBooleanDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
size = 3
cls.booleans = [bool((i + 1) % 2) for i in range(size)]
# ^ = [True, False, True, False, ...]
cls.col_name = "booleans"
def test_coerce_from_boolean_to_integer_no_na(self):
in_data = self.booleans
out_data = [int(b) for b in self.booleans]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_integer_with_na(self):
in_data = self.booleans + [None]
out_data = [int(b) for b in self.booleans] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_float_no_na(self):
in_data = self.booleans
out_data = [float(b) for b in self.booleans]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_float_with_na(self):
in_data = self.booleans + [None]
out_data = [float(b) for b in self.booleans] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_complex_no_na(self):
in_data = self.booleans
out_data = [complex(b, 0) for b in self.booleans]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_complex_with_na(self):
in_data = self.booleans + [None]
out_data = [complex(b, 0) for b in self.booleans] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_string_no_na(self):
in_data = self.booleans
out_data = [str(b) for b in self.booleans]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_string_with_na(self):
in_data = self.booleans + [None]
out_data = [str(b) for b in self.booleans] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_boolean_no_na(self):
in_data = self.booleans
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_boolean_with_na(self):
in_data = self.booleans + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_datetime_no_na(self):
in_data = self.booleans
out_data = [datetime.fromtimestamp(b, tz=timezone.utc)
for b in self.booleans]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_datetime_with_na(self):
in_data = self.booleans + [None]
out_data = [datetime.fromtimestamp(b, tz=timezone.utc)
for b in self.booleans] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_timedelta_no_na(self):
in_data = self.booleans
out_data = [timedelta(seconds=b) for b in self.booleans]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_timedelta_with_na(self):
in_data = self.booleans + [None]
out_data = [timedelta(seconds=b) for b in self.booleans] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_object_no_na(self):
in_series = pd.Series(self.booleans)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: in_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_boolean_to_object_with_na(self):
in_series = pd.Series(self.booleans + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: in_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceDatetimeDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
random.seed(12345)
size = 3
integers = [-1 * size // 2 + i + 1 for i in range(size)]
floats = [i + random.random() for i in integers]
cls.whole_datetimes = [datetime.fromtimestamp(i, tz=timezone.utc)
for i in integers]
# ^ = [..., utc time -1, utc time 0, utc time 1, ...]
cls.datetimes_between_0_and_1 = [datetime.fromtimestamp(random.random(),
tz=timezone.utc)
for _ in range(size)]
# ^ = [utc time 0+e, utc time 0+e, utc time 0+e, ...]
cls.bool_flags = [datetime.fromtimestamp((i + 1) % 2, tz=timezone.utc)
for i in range(size)]
# ^ = [utc time 1, utc time 0, utc time 1, utc time 0, ...]
cls.naive_datetimes = [datetime.utcfromtimestamp(f) for f in floats]
# ^ = [..., utc time -1+e, utc time 0+e, utc time 1+e, ...] (no tz)
cls.aware_datetimes = [datetime.fromtimestamp(f, tz=timezone.utc)
for f in floats]
# ^ = [..., utc time -1+e, utc time 0+e, utc_time 1+e, ...] (with tz)
cls.aware_naive_datetimes = []
for index, f in enumerate(floats):
if index % 2: # naive
cls.aware_naive_datetimes.append(datetime.utcfromtimestamp(f))
else: # aware
val = datetime.fromtimestamp(f, tz=timezone.utc)
cls.aware_naive_datetimes.append(val)
# ^ = [aware, naive, aware, naive, aware, ...]
cls.mixed_timezones = []
for index, f in enumerate(floats):
tz_name = pytz.all_timezones[index % len(pytz.all_timezones)]
tz = pytz.timezone(tz_name)
val = datetime.fromtimestamp(f, tz=tz)
cls.mixed_timezones.append(val)
# ^ = ["Africa/Abidjan", "Africa/Accra", "Africa/Addis_Ababa", ...]
cls.col_name = "datetimes"
def test_coerce_from_whole_datetime_to_integer_no_na(self):
in_data = self.whole_datetimes
out_data = [int(d.timestamp()) for d in self.whole_datetimes]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_whole_datetime_to_integer_with_na(self):
in_data = self.whole_datetimes + [None]
out_data = [int(d.timestamp()) for d in self.whole_datetimes] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_random_datetime_to_integer_no_na(self):
in_data = self.aware_datetimes
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_random_datetime_to_integer_with_na(self):
in_data = self.aware_datetimes + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_datetime_to_float_no_na(self):
in_data = self.aware_datetimes
out_data = [d.timestamp() for d in self.aware_datetimes]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_datetime_to_float_with_na(self):
in_data = self.aware_datetimes + [None]
out_data = [d.timestamp() for d in self.aware_datetimes] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_complex_no_na(self):
in_data = self.aware_datetimes
out_data = [complex(d.timestamp(), 0) for d in self.aware_datetimes]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_complex_with_na(self):
in_data = self.aware_datetimes + [None]
out_data = ([complex(d.timestamp(), 0) for d in self.aware_datetimes] +
[None])
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_string_no_na(self):
in_data = self.aware_datetimes
out_data = [d.isoformat() for d in self.aware_datetimes]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_string_with_na(self):
in_data = self.aware_datetimes + [None]
out_data = [d.isoformat() for d in self.aware_datetimes] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_bool_flag_to_boolean_no_na(self):
in_data = self.bool_flags
out_data = [bool(d.timestamp()) for d in self.bool_flags]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_bool_flag_to_boolean_with_na(self):
in_data = self.bool_flags + [None]
out_data = [bool(d.timestamp()) for d in self.bool_flags] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_random_datetime_to_boolean_no_na(self):
in_data = self.aware_datetimes
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_random_datetime_to_boolean_with_na(self):
in_data = self.aware_datetimes + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_datetime_between_0_and_1_to_boolean_no_na(self):
in_data = self.datetimes_between_0_and_1
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_datetime_between_0_and_1_to_boolean_with_na(self):
in_data = self.datetimes_between_0_and_1 + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_datetime_to_datetime_no_na(self):
in_data = self.aware_datetimes
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_datetime_with_na(self):
in_data = self.aware_datetimes + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_timedelta_no_na(self):
in_data = self.aware_datetimes
out_data = [timedelta(seconds=d.timestamp())
for d in self.aware_datetimes]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_timedelta_with_na(self):
in_data = self.aware_datetimes + [None]
out_data = [timedelta(seconds=d.timestamp())
for d in self.aware_datetimes] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_object_no_na(self):
in_series = pd.Series(self.aware_datetimes)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_datetime_to_object_with_na(self):
in_series = pd.Series(self.aware_datetimes + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceTimedeltaDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
random.seed(1<PASSWORD>)
size = 3
integers = [-1 * size // 2 + i + 1 for i in range(size)]
floats = [i + random.random() for i in integers]
cls.whole_timedeltas = [timedelta(seconds=i) for i in integers]
# ^ = [..., timedelta(-1), timedelta(0), timedelta(1), ...]
cls.timedeltas = [timedelta(seconds=f) for f in floats]
# ^ = [..., timedelta(-1+e), timedelta(0+e), timedelta(1+e), ...]
cls.timedeltas_between_0_and_1 = [timedelta(seconds=random.random())
for _ in range(size)]
# ^ = [timedelta(0+e), timedelta(0+e), timedelta(0+e), ...]
cls.bool_flags = [timedelta(seconds=(i + 1) % 2) for i in range(size)]
# ^ = [timedelta(1), timedelta(0), timedelta(1), timedelta(0), ...]
cls.col_name = "timedeltas"
def test_coerce_from_whole_timedelta_to_integer_no_na(self):
in_data = self.whole_timedeltas
out_data = [int(t.total_seconds()) for t in self.whole_timedeltas]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_whole_timedelta_to_integer_with_na(self):
in_data = self.whole_timedeltas + [None]
out_data = ([int(t.total_seconds()) for t in self.whole_timedeltas] +
[None])
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, int)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: int})
assert_frame_equal(result, out_df)
def test_coerce_from_random_timedelta_to_integer_no_na(self):
in_data = self.timedeltas
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_random_timedelta_to_integer_with_na(self):
in_data = self.timedeltas + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {int} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, int)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {int} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: int})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_timedelta_to_float_no_na(self):
in_data = self.timedeltas
out_data = [t.total_seconds() for t in self.timedeltas]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_float_with_na(self):
in_data = self.timedeltas + [None]
out_data = [t.total_seconds() for t in self.timedeltas] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, float)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: float})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_complex_no_na(self):
in_data = self.timedeltas
out_data = [complex(t.total_seconds(), 0) for t in self.timedeltas]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_complex_with_na(self):
in_data = self.timedeltas + [None]
out_data = ([complex(t.total_seconds(), 0) for t in self.timedeltas] +
[None])
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, complex)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: complex})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_string_no_na(self):
in_data = self.timedeltas
out_data = [str(pd.Timedelta(t)) for t in self.timedeltas]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_string_with_na(self):
in_data = self.timedeltas + [None]
out_data = [str(pd.Timedelta(t)) for t in self.timedeltas] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, str)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: str})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_bool_flag_to_boolean_no_na(self):
in_data = self.bool_flags
out_data = [bool(d.total_seconds()) for d in self.bool_flags]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_bool_flag_to_boolean_with_na(self):
in_data = self.bool_flags + [None]
out_data = [bool(d.total_seconds()) for d in self.bool_flags] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, bool)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: bool})
assert_frame_equal(result, out_df)
def test_coerce_from_random_timedelta_to_boolean_no_na(self):
in_data = self.timedeltas
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_random_timedelta_to_boolean_with_na(self):
in_data = self.timedeltas + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_timedelta_between_0_and_1_to_boolean_no_na(self):
in_data = self.timedeltas_between_0_and_1
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_timedelta_between_0_and_1_to_boolean_with_na(self):
in_data = self.timedeltas_between_0_and_1 + [None]
# series
in_series = pd.Series(in_data)
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce series "
f"values to {bool} without losing information (head: "
f"{list(in_series.head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_series, bool)
self.assertEqual(str(err.exception), err_msg)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
err_msg = (f"[datatube.dtype.coerce_dtypes] cannot coerce column "
f"{repr(self.col_name)} to {bool} without losing "
f"information (head: {list(in_df[self.col_name].head())})")
with self.assertRaises(ValueError) as err:
coerce_dtypes(in_df, {self.col_name: bool})
self.assertEqual(str(err.exception), err_msg)
def test_coerce_from_timedelta_to_datetime_no_na(self):
in_data = self.timedeltas
out_data = [datetime.fromtimestamp(t.total_seconds(), tz=timezone.utc)
for t in self.timedeltas]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_datetime_with_na(self):
in_data = self.timedeltas + [None]
out_data = [datetime.fromtimestamp(t.total_seconds(), tz=timezone.utc)
for t in self.timedeltas] + [None]
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, datetime)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: datetime})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_timedelta_no_na(self):
in_data = self.timedeltas
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_timedelta_with_na(self):
in_data = self.timedeltas + [None]
out_data = in_data.copy()
# series
in_series = pd.Series(in_data)
out_series = pd.Series(out_data)
result = coerce_dtypes(in_series, timedelta)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_data})
out_df = pd.DataFrame({self.col_name: out_data})
result = coerce_dtypes(in_df, {self.col_name: timedelta})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_object_no_na(self):
in_series = pd.Series(self.timedeltas)
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
def test_coerce_from_timedelta_to_object_with_na(self):
in_series = pd.Series(self.timedeltas + [None])
out_series = in_series.astype(np.dtype("O"))
# series
result = coerce_dtypes(in_series, object)
assert_series_equal(result, out_series)
# dataframe
in_df = pd.DataFrame({self.col_name: in_series})
out_df = pd.DataFrame({self.col_name: out_series})
result = coerce_dtypes(in_df, {self.col_name: object})
assert_frame_equal(result, out_df)
class CoerceObjectDtypeTests(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
class NonCastableObject:
pass
class CastableObject:
def to_datetime(self) -> datetime:
return datetime.fromtimestamp(random.randint(0, 86400),
tz=timezone.utc)
def to_timedelta(self) -> timedelta:
return timedelta(seconds=random.randint(0, 86400))
def __int__(self) -> int:
return random.randint(0, 10)
def __float__(self) -> float:
return random.random()
def __complex__(self) -> complex:
return complex(random.random(), random.random())
def __str__(self) -> str:
return chr(random.randint(0, 26) + ord("a"))
def __bool__(self) -> bool:
return bool(random.randint(0, 1))
size = 3
cls.non_castable_objects = [NonCastableObject() for _ in range(size)]
cls.castable_objects = [CastableObject() for _ in range(size)]
cls.nones = [None for _ in range(size)]
cls.col_name = "objects"
def test_coerce_from_object_to_integer(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_float(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_complex(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_string(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_boolean(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_datetime(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_timedelta(self):
pass
# raise NotImplementedError()
def test_coerce_from_object_to_object(self):
pass
# raise NotImplementedError()
# def test_check_dtypes_datetime_mixed_timezones(self):
# test_df = pd.DataFrame({"timestamp": [datetime.now(timezone.utc),
# datetime.now()]})
# self.assertTrue(check_dtypes(test_df, timestamp=datetime))
# def test_coerce_dtypes_kwargless_error(self):
# atomics = [t.__name__ if isinstance(t, type) else str(t)
# for t in AVAILABLE_DTYPES]
# err_msg = (f"[datatube.stats.coerce_dtypes] `coerce_dtypes` must be "
# f"invoked with at least one keyword argument mapping a "
# f"column in `data` to an atomic data type: "
# f"{tuple(atomics)}")
# with self.assertRaises(RuntimeError) as err:
# coerce_dtypes(self.no_na)
# self.assertEqual(str(err.exception), err_msg)
# def test_coerce_dtypes_kwargs_no_na_no_errors(self):
# for col_name, expected in self.conversions.items():
# for conv in expected:
# coerce_dtypes(self.no_na, **{col_name: conv})
# def test_coerce_dtypes_kwargs_with_na_no_errors(self):
# for col_name, expected in self.conversions.items():
# for conv in expected:
# coerce_dtypes(self.with_na, **{col_name: conv})
# def test_coerce_dtypes_matches_check_dtypes(self):
# # This does not work for coercion to <class 'object'> because of the
# # automatic convert_dtypes() step of check_dtypes. These columns will
# # always be better represented by some other data type, unless it was
# # an object to begin with.
# for col_name, expected in self.conversions.items():
# for conv in expected:
# result = coerce_dtypes(self.no_na, **{col_name: conv})
# na_result = coerce_dtypes(self.with_na, **{col_name: conv})
# check_result = check_dtypes(result, **{col_name: conv})
# check_na_result = check_dtypes(na_result, **{col_name: conv})
# if conv != object:
# try:
# self.assertTrue(check_result)
# self.assertTrue(check_na_result)
# except AssertionError as exc:
# err_msg = (f"col_name: {repr(col_name)}, typespec: "
# f"{conv}, expected: {expected}")
# raise AssertionError(err_msg) from exc
# def test_coerce_dtypes_returns_copy(self):
# result = coerce_dtypes(self.with_na, a=float)
# self.assertNotEqual(list(result.dtypes), list(self.with_na.dtypes))
# def test_coerce_dtypes_datetime_preserves_timezone(self):
# raise NotImplementedError()
if __name__ == "__main__":
unittest.main() | [
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helper_functions_class.py | lucaschatham/lambdata | 0 | 14078 | <reponame>lucaschatham/lambdata<filename>helper_functions_class.py
"""
Here are two different functions used for common data cleaning tasks.
You can use these functions to load data into a pandas Dataframe.
"""
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
class CleanData:
def __init__(self):
"""
This init function instantiates objects
"""
return
# This function randomizes variables
def randomize(self, df, seed):
random_this = shuffle(df,random_state=seed)
return random_this
# This function determines if there are missing values in the specified DataFrame
def null_count(self, df):
num_nulls = df.isnull().sum().sum()
return num_nulls
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resources/lib/database_tv.py | bradyemerson/plugin.video.showtimeanytime | 0 | 22285 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os.path
from datetime import date, datetime
from sqlite3 import dbapi2 as sqlite
from bs4 import BeautifulSoup
import simplejson as json
import xbmcvfs
import xbmcgui
import common
import connection
import database_common as db_common
def create():
c = _database.cursor()
c.execute('''CREATE TABLE series
(series_id INTEGER PRIMARY KEY,
title TEXT,
title_sort TEXT,
plot TEXT,
directors TEXT,
actors TEXT,
thumb TEXT,
total_seasons INTEGER,
total_episodes INTEGER,
favor BOOLEAN DEFAULT 0,
in_last_update BOOLEAN DEFAULT 1,
last_updated timestamp);''')
c.execute('''CREATE TABLE season
(season_id INTEGER PRIMARY KEY,
season_no INTEGER,
series_id INTEGER,
plot TEXT,
FOREIGN KEY(series_id) REFERENCES series(series_id) ON DELETE CASCADE);''')
c.execute('''CREATE TABLE episode
(episode_id INTEGER PRIMARY KEY,
season_id INTEGER,
episode_no INTEGER,
title TEXT,
title_sort TEXT,
plot TEXT,
duration INTEGER,
year INTEGER,
studio TEXT,
mpaa TEXT,
advisories TEXT,
aired_date timestamp,
thumb TEXT,
play_count INTEGER DEFAULT 0,
FOREIGN KEY(season_id) REFERENCES season(season_id) ON DELETE CASCADE);''')
_database.commit()
c.close()
def insert_series(series_id, title=None, title_sort=None, plot=None, directors=None, actors=None, thumb=None,
total_seasons=None, total_episodes=None):
c = _database.cursor()
c.execute('''INSERT OR REPLACE INTO series (
series_id,
title,
title_sort,
plot,
directors,
actors,
thumb,
total_seasons,
total_episodes,
favor,
in_last_update,
last_updated) VALUES (
:series_id,
:title,
:title_sort,
:plot,
:directors,
:actors,
:thumb,
:total_seasons,
:total_episodes,
(SELECT favor FROM series WHERE series_id = :series_id),
:in_last_update,
(SELECT last_updated FROM series WHERE series_id = :series_id))''', {
'series_id': series_id,
'title': title,
'title_sort': title_sort,
'plot': plot,
'directors': directors,
'actors': actors,
'thumb': thumb,
'total_seasons': total_seasons,
'total_episodes': total_episodes,
'in_last_update': True
})
_database.commit()
c.close()
def insert_season(series_id, season_no, plot=None):
c = _database.cursor()
row = lookup_season(series_id=series_id, season_no=season_no, fields='season_id').fetchone()
if row:
c.execute('''UPDATE season SET plot = :plot WHERE season_id = :season_id''', {
'season_id': row['season_id'],
'plot': plot
})
else:
c.execute('''INSERT INTO season (series_id, season_no, plot) VALUES (
:series_id,
:season_no,
:plot
)''', {
'series_id': series_id,
'season_no': season_no,
'plot': plot
})
_database.commit()
c.close()
def insert_episode(episode_id, season_id, episode_no=None, title=None, title_sort=None, plot=None,
duration=None, year=None, studio=None, mpaa=None, advisories=None, aired_date=None, thumb=None):
c = _database.cursor()
c.execute('''INSERT OR REPLACE INTO episode (
episode_id,
season_id,
episode_no,
title,
title_sort,
plot,
duration,
year,
studio,
mpaa,
advisories,
aired_date,
thumb,
play_count) VALUES (
:episode_id,
:season_id,
:episode_no,
:title,
:title_sort,
:plot,
:duration,
:year,
:studio,
:mpaa,
:advisories,
:aired_date,
:thumb,
COALESCE((SELECT play_count FROM episode WHERE episode_id = :episode_id), 0))''', {
'episode_id': episode_id,
'season_id': season_id,
'episode_no': episode_no,
'title': title,
'title_sort': title_sort,
'plot': plot,
'duration': duration,
'year': year,
'studio': studio,
'mpaa': mpaa,
'advisories': advisories,
'aired_date': aired_date,
'thumb': thumb
})
_database.commit()
c.close()
def lookup_series(content_id, fields='*'):
c = _database.cursor()
return c.execute('SELECT DISTINCT {0} FROM series WHERE series_id = (?)'.format(fields), (content_id,))
def lookup_season(season_id=None, series_id=None, season_no=None, fields='*'):
c = _database.cursor()
if season_id:
return c.execute('SELECT {0} FROM season WHERE season_id = (?)'.format(fields), (season_id,))
elif series_id and season_no:
return c.execute('SELECT {0} FROM season WHERE series_id = (?) AND season_no = (?)'.format(fields),
(series_id, season_no))
def lookup_episode(content_id):
c = _database.cursor()
return c.execute('SELECT DISTINCT * FROM episode WHERE episode_id = (?)', (content_id,))
def delete_series(content_id):
c = _database.cursor()
c.execute('DELETE FROM series WHERE series_id = (?)', (content_id,))
c.close()
def watch_episode(content_id):
# TODO make this actually increment
c = _database.cursor()
c.execute("UPDATE episode SET play_count = play_count + 1 WHERE episode_id = (?)", (content_id,))
_database.commit()
c.close()
return c.rowcount
def unwatch_episode(content_id):
c = _database.cursor()
c.execute("UPDATE episode SET play_count=? WHERE episode_id = (?)", (0, content_id))
_database.commit()
c.close()
return c.rowcount
def favor_series(content_id):
c = _database.cursor()
c.execute("UPDATE series SET favor=? WHERE series_id=?", (True, content_id))
_database.commit()
c.close()
return c.rowcount
def unfavor_series(content_id):
c = _database.cursor()
c.execute("UPDATE series SET favor=? WHERE series_id=?", (False, content_id))
_database.commit()
c.close()
return c.rowcount
def get_series(directorfilter=False, watchedfilter=False, favorfilter=False, actorfilter=False,
alphafilter=False, studiofilter=False):
c = _database.cursor()
if actorfilter:
actorfilter = '%' + actorfilter + '%'
return c.execute('SELECT DISTINCT * FROM series WHERE actors LIKE (?)',
(actorfilter,))
elif directorfilter:
return c.execute('SELECT DISTINCT * FROM series WHERE directors LIKE (?)',
(directorfilter,))
elif studiofilter:
return c.execute('SELECT DISTINCT * FROM series WHERE studio = (?)', (studiofilter,))
elif watchedfilter:
return c.execute('SELECT DISTINCT * FROM series WHERE playcount > 0')
elif favorfilter:
return c.execute('SELECT DISTINCT * FROM series WHERE favor = 1')
elif alphafilter:
return c.execute('SELECT DISTINCT * FROM series WHERE title REGEXP (?)',
(alphafilter + '*',))
else:
return c.execute('SELECT DISTINCT * FROM series')
def get_series_season_count(series_id):
c = _database.cursor()
row = c.execute('''SELECT MAX(sea.content_id) AS total_seasons
FROM season AS sea
JOIN series AS ser ON ser.content_id = sea.series_content_id
WHERE ser.content_id = (?)
GROUP BY ser.content_id''', (series_id,)).fetchone()
c.close()
if row:
return row['total_seasons']
else:
return 0
def get_series_episode_count(series_id, filter=None):
c = _database.cursor()
if filter == 'watched':
row = c.execute('''SELECT COUNT(e.episode_id) AS total_episodes
FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
JOIN series AS ser ON ser.series_id = sea.series_id
WHERE ser.series_id = (?) AND e.play_count > 0
GROUP BY ser.series_id''', (series_id,)).fetchone()
else:
row = c.execute('''SELECT COUNT(e.episode_id) AS total_episodes
FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
JOIN series AS ser ON ser.series_id = sea.series_id
WHERE ser.series_id = (?)
GROUP BY ser.series_id''', (series_id,)).fetchone()
c.close()
if row:
return row['total_episodes']
else:
return 0
def get_series_year(series_id):
c = _database.cursor()
row = c.execute('''SELECT e.year FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
JOIN series AS ser ON ser.series_id = sea.series_id
WHERE ser.series_id = (?)
ORDER BY e.year ASC LIMIT 1''', (series_id,)).fetchone()
c.close()
if row:
return row['year']
else:
return None
def _update_series_last_update(series_id, time=datetime.now()):
c = _database.cursor()
c.execute('UPDATE series SET last_updated = :last_update WHERE series_id = :series_id', {
'last_update': time,
'series_id': series_id
})
c.close()
def get_seasons(series_id):
c = _database.cursor()
return c.execute('''SELECT DISTINCT sea.*,ser.title AS series_title
FROM season AS sea
JOIN series AS ser ON ser.series_id = sea.series_id
WHERE ser.series_id = (?)''', (series_id,))
def get_season_episode_count(season_id, filter=None):
c = _database.cursor()
if filter == 'watched':
row = c.execute('''SELECT COUNT(e.episode_id) AS total_episodes
FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
WHERE sea.season_id = (?) AND e.play_count > 0
GROUP BY sea.season_id''', (season_id,)).fetchone()
else:
row = c.execute('''SELECT COUNT(e.episode_id) AS total_episodes
FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
WHERE sea.season_id = (?)
GROUP BY sea.season_id''', (season_id,)).fetchone()
c.close()
if row:
return row['total_episodes']
else:
return 0
def get_season_year(season_id):
c = _database.cursor()
row = c.execute('''SELECT e.year FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
WHERE sea.season_id = (?)
ORDER BY e.year ASC LIMIT 1''', (season_id,)).fetchone()
c.close()
if row:
return row['year']
else:
return None
def get_episodes(season_id):
c = _database.cursor()
return c.execute('''SELECT DISTINCT e.*, sea.season_no AS season_no, ser.title AS series_title, ser.series_id AS series_id
FROM episode AS e
JOIN season AS sea ON sea.season_id = e.season_id
JOIN series AS ser ON ser.series_id = sea.series_id
WHERE e.season_id = (?)''', (season_id,))
def get_types(col):
c = _database.cursor()
items = c.execute('select distinct %s from series' % col)
list = []
for data in items:
data = data[0]
if type(data) == type(str()):
if 'Rated' in data:
item = data.split('for')[0]
if item not in list and item <> '' and item <> 0 and item <> 'Inc.' and item <> 'LLC.':
list.append(item)
else:
data = data.decode('utf-8').encode('utf-8').split(',')
for item in data:
item = item.replace('& ', '').strip()
if item not in list and item <> '' and item <> 0 and item <> 'Inc.' and item <> 'LLC.':
list.append(item)
elif data <> 0:
if data is not None:
list.append(str(data))
c.close()
return list
def update_tv(force=False):
# Check if we've recently updated and skip
if not force and not _needs_update():
return
dialog = xbmcgui.DialogProgress()
dialog.create('Refreshing TV Database')
dialog.update(0, 'Initializing TV Scan')
xml_series_url = '{0}/tve/xml/category?categoryid=101'.format(db_common.API_DOMAIN)
data = connection.get_url(xml_series_url)
soup = BeautifulSoup(data)
series_list = soup.find('subcategory', recursive=False).find('series', recursive=False).find_all('series', recursive=False)
# Mark all series as unfound. This will be updated as we go through
c = _database.cursor()
c.execute("UPDATE series SET in_last_update = 0")
_database.commit()
c.close()
total = len(series_list)
count = 0
for series in series_list:
count += 1
dialog.update(0, 'Scanned {0} of {1} TV series'.format(count, total))
print 'series: '
print series
series_json_url = '{0}/api/series/{1}'.format(db_common.API_DOMAIN, series['seriesid'])
json_data = json.loads(connection.get_url(series_json_url))
series_id = series['seriesid']
title = common.string_unicode(json_data['name'])
title_sort = common.string_unicode(json_data['sortName'])
plot = common.string_unicode(json_data['description']['long'])
total_seasons = json_data['totalSeasons']
total_episodes = json_data['totalEpisodes']
thumb = None
for image in series.find_all('Image'):
if image['width'] == '1920' and image['height'] == '1080':
thumb = image.find('url').string
break
insert_series(series_id, title, title_sort, plot, None, None, thumb, total_seasons, total_episodes)
# Season Children
if 'seasons' in json_data:
_json_process_seasons(json_data['seasons'], series_id)
_set_last_update()
# Remove unfound movies
c = _database.cursor()
c.execute("DELETE FROM series WHERE in_last_update = 0")
c.close()
def _json_process_seasons(season_data, series_id):
for season in season_data:
insert_season(series_id, season['seasonNum'], season['description']['long'])
def update_series(series_id, force=False):
# Check for new episodes every 12 hours
row = lookup_series(series_id, 'last_updated').fetchone()
if force is False and row['last_updated']:
last_update = common.parse_date(row['last_updated'], '%Y-%m-%d %H:%M:%S.%f')
if (datetime.now() - last_update).seconds < 43200:
# No update needed
return
xml_series_url = '{0}/tve/xml/series?seriesid={1}'.format(db_common.API_DOMAIN, series_id)
data = connection.get_url(xml_series_url)
series = BeautifulSoup(data).find('series', recursive=False)
for episode in series.find_all('title', attrs={'type': 'Episode'}):
episode_id = episode['titleid']
title = common.string_unicode(episode.find('title', recursive=False).string)
title_sort = common.string_unicode(episode.find('sorttitle', recursive=False).string)
plot = common.string_unicode(episode.find('description', recursive=False).string)
year = episode.find('releaseyear', recursive=False).string
duration = episode.find('duration', recursive=False).string
mpaa = episode.find('rating', recursive=False).string
advisories = episode.find('advisories', recursive=False).string
air_date = None
try:
air_date = common.parse_date(episode.find('originalairdate', recursive=False).string, '%m/%d/%Y %I:%M%p')
except:
pass
thumb = None
for image in episode.find_all('image'):
if image['width'] == '866' and image['height'] == '487':
thumb = image.find('url').string
break
series_tag = episode.find('series', recursive=False)
episode_no = series_tag['episode']
season_no = series_tag['season']
season = lookup_season(series_id=series_id, season_no=season_no, fields='season_id').fetchone()
if not season:
insert_season(series_tag['seriesid'], season_no)
season = lookup_season(series_id=series_id, season_no=season_no, fields='season_id').fetchone()
season_id = season['season_id']
insert_episode(episode_id, season_id, episode_no, title, title_sort, plot, duration, year, None,
mpaa, advisories, air_date, thumb)
_update_series_last_update(series_id)
def _needs_update():
# Update every 15 days
if 'last_update' in _database_meta:
last_update = common.parse_date(_database_meta['last_update'], '%Y-%m-%d')
return (date.today() - last_update.date()).days > 15
return True
def _set_last_update():
_database_meta['last_update'] = date.today().strftime('%Y-%m-%d')
_write_meta_file()
def _write_meta_file():
f = open(DB_META_FILE, 'w')
json.dump(_database_meta, f)
f.close()
DB_META_FILE = os.path.join(common.__addonprofile__, 'tv.meta')
_database_meta = False
if xbmcvfs.exists(DB_META_FILE):
f = open(DB_META_FILE, 'r')
_database_meta = json.load(f)
f.close()
else:
_database_meta = {}
DB_FILE = os.path.join(common.__addonprofile__, 'tv.db')
if not xbmcvfs.exists(DB_FILE):
_database = sqlite.connect(DB_FILE)
_database.text_factory = str
_database.row_factory = sqlite.Row
create()
else:
_database = sqlite.connect(DB_FILE)
_database.text_factory = str
_database.row_factory = sqlite.Row
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] |
emmaa/analyze_tests_results.py | pagreene/emmaa | 0 | 1616644 | <reponame>pagreene/emmaa
import logging
import jsonpickle
from collections import defaultdict
from emmaa.model import load_config_from_s3, get_model_stats, \
load_stmts_from_s3
from emmaa.model_tests import load_model_manager_from_s3
from emmaa.util import find_latest_s3_file, find_nth_latest_s3_file, \
strip_out_date, EMMAA_BUCKET_NAME, load_json_from_s3, save_json_to_s3, \
get_credentials, update_status
from indra.statements.statements import Statement
from indra.assemblers.english.assembler import EnglishAssembler
from indra.literature import pubmed_client, crossref_client, pmc_client
from indra_db import get_db
from indra_db.client.principal.curation import get_curations
from indra_db.util import unpack
logger = logging.getLogger(__name__)
CONTENT_TYPE_FUNCTION_MAPPING = {
'statements': 'get_stmt_hashes',
'applied_tests': 'get_applied_test_hashes',
'passed_tests': 'get_passed_test_hashes',
'paths': 'get_passed_test_hashes',
'raw_papers': 'get_all_raw_paper_ids',
'assembled_papers': 'get_all_assembled_paper_ids'}
TWITTER_MODEL_TYPES = {'pysb': '@PySysBio',
'pybel': '@pybelbio',
'signed_graph': 'Signed Graph',
'unsigned_graph': 'Unsigned Graph'}
class Round(object):
"""Parent class for classes analyzing one round of something (model or
tests).
Parameters
----------
date_str : str
Time when ModelManager responsible for this round was created.
Attributes
----------
function_mapping : dict
A dictionary of strings mapping a type of content to a tuple of
functions necessary to find delta for this type of content. First
function in a tuple gets a list of all hashes for a given content type,
while the second returns an English description of a given content type
for a single hash.
"""
def __init__(self, date_str):
self.date_str = date_str
self.function_mapping = CONTENT_TYPE_FUNCTION_MAPPING
@classmethod
def load_from_s3_key(cls, key):
raise NotImplementedError("Method must be implemented in child class.")
def get_english_statement(self, stmt):
ea = EnglishAssembler([stmt])
sentence = ea.make_model()
return ('', sentence, '')
def find_delta_hashes(self, other_round, content_type, **kwargs):
"""Return a dictionary of changed hashes of a given content type. This
method makes use of self.function_mapping dictionary.
Parameters
----------
other_round : emmaa.analyze_tests_results.TestRound
A different instance of a TestRound
content_type : str
A type of the content to find delta. Accepted values:
- statements
- applied_tests
- passed_tests
- paths
**kwargs : dict
For some of content types, additional arguments must be
provided sych as mc_type.
Returns
-------
hashes : dict
A dictionary containing lists of added and removed hashes of a
given content type between two test rounds.
"""
logger.info(f'Finding a hashes delta for {content_type}.')
latest_hashes = getattr(
self, self.function_mapping[content_type])(**kwargs)
logger.info(f'Found {len(latest_hashes)} hashes in current round.')
previous_hashes = getattr(
other_round,
other_round.function_mapping[content_type])(**kwargs)
logger.info(f'Found {len(previous_hashes)} hashes in other round.')
# Find hashes unique for each of the rounds - this is delta
added_hashes = list(set(latest_hashes) - set(previous_hashes))
removed_hashes = list(set(previous_hashes) - set(latest_hashes))
hashes = {'added': added_hashes, 'removed': removed_hashes}
return hashes
class ModelRound(Round):
"""Analyzes the results of one model update round.
Parameters
----------
statements : list[indra.statements.Statement]
A list of INDRA Statements used to assemble a model.
date_str : str
Time when ModelManager responsible for this round was created.
paper_ids : list(str)
A list of paper IDs used to get raw statements for this round.
paper_id_type : str
Type of paper ID used.
Attributes
----------
stmts_by_papers : dict
A dictionary mapping the paper IDs to sets of hashes of assembled
statements with evidences retrieved from these papers.
"""
def __init__(self, statements, date_str, paper_ids=None,
paper_id_type='TRID', emmaa_statements=None):
super().__init__(date_str)
self.statements = statements
self.paper_ids = paper_ids if paper_ids else []
self.paper_id_type = paper_id_type
self.emmaa_statements = emmaa_statements if emmaa_statements else []
self.stmts_by_papers = self.get_assembled_stmts_by_paper(paper_id_type)
@classmethod
def load_from_s3_key(cls, key, bucket=EMMAA_BUCKET_NAME,
load_estmts=False):
mm = load_model_manager_from_s3(key=key, bucket=bucket)
if not mm:
return
statements = mm.model.assembled_stmts
date_str = mm.date_str
try:
paper_ids = list(mm.model.paper_ids)
except AttributeError:
paper_ids = None
paper_id_type = mm.model.reading_config.get('main_id_type', 'TRID')
estmts = None
if load_estmts:
estmts, _ = load_stmts_from_s3(mm.model.name, bucket)
return cls(statements, date_str, paper_ids, paper_id_type, estmts)
def get_total_statements(self):
"""Return a total number of statements in a model."""
total = len(self.statements)
logger.info(f'An assembled model has {total} statements.')
return total
def get_stmt_hashes(self):
"""Return a list of hashes for all statements in a model."""
return [str(stmt.get_hash(refresh=True)) for stmt in self.statements]
def get_statement_types(self):
"""Return a sorted list of tuples containing a statement type and a
number of times a statement of this type occured in a model.
"""
statement_types = defaultdict(int)
logger.info('Finding a distribution of statements types.')
for stmt in self.statements:
statement_types[type(stmt).__name__] += 1
return sorted(statement_types.items(), key=lambda x: x[1], reverse=True)
def get_agent_distribution(self):
"""Return a sorted list of tuples containing an agent name and a number
of times this agent occured in statements of a model."""
logger.info('Finding agent distribution among model statements.')
agent_count = defaultdict(int)
for stmt in self.statements:
for agent in stmt.agent_list():
if agent is not None:
agent_count[agent.name] += 1
return sorted(agent_count.items(), key=lambda x: x[1], reverse=True)
def get_statements_by_evidence(self):
"""Return a sorted list of tuples containing a statement hash and a
number of times this statement occured in a model."""
stmts_evidence = {}
for stmt in self.statements:
stmts_evidence[str(stmt.get_hash(refresh=True))] = len(stmt.evidence)
logger.info('Sorting statements by evidence count.')
return sorted(stmts_evidence.items(), key=lambda x: x[1], reverse=True)
def get_english_statements_by_hash(self):
"""Return a dictionary mapping a statement and its English description."""
stmts_by_hash = {}
for stmt in self.statements:
stmts_by_hash[str(stmt.get_hash(refresh=True))] = (
self.get_english_statement(stmt))
return stmts_by_hash
def get_sources_distribution(self):
logger.info('Finding distribution of sources of statement evidences.')
sources_count = defaultdict(int)
for stmt in self.statements:
for evid in stmt.evidence:
if evid.source_api:
sources_count[evid.source_api] += 1
return sorted(sources_count.items(), key=lambda x: x[1], reverse=True)
def get_all_raw_paper_ids(self):
"""Return all paper IDs used in this round."""
return self.paper_ids
def get_number_raw_papers(self):
"""Return a total number of papers in this round."""
return len(self.paper_ids)
def get_assembled_stmts_by_paper(self, id_type='TRID'):
"""Get a mapping of paper IDs (TRID or PII) to assembled statements."""
logger.info('Mapping papers to statements')
stmts_by_papers = {}
for stmt in self.statements:
stmt_hash = stmt.get_hash()
for evid in stmt.evidence:
paper_id = None
if id_type == 'pii':
paper_id = evid.annotations.get('pii')
if evid.text_refs:
paper_id = evid.text_refs.get(id_type)
if not paper_id:
paper_id = evid.text_refs.get(id_type.lower())
if paper_id:
if paper_id in stmts_by_papers:
stmts_by_papers[paper_id].add(stmt_hash)
else:
stmts_by_papers[paper_id] = {stmt_hash}
for k, v in stmts_by_papers.items():
stmts_by_papers[k] = list(v)
return stmts_by_papers
def get_all_assembled_paper_ids(self):
return list(self.stmts_by_papers.keys())
def get_number_assembled_papers(self):
return len(self.stmts_by_papers)
def get_papers_distribution(self):
"""Return a sorted list of tuples containing a paper ID and a number
of unique statements extracted from that paper."""
logger.info('Finding paper distribution')
paper_stmt_count = {paper_id: len(stmts) for (paper_id, stmts) in
self.stmts_by_papers.items()}
return sorted(paper_stmt_count.items(), key=lambda x: x[1],
reverse=True)
def get_raw_paper_counts(self):
logger.info('Finding raw statement count per paper')
if not self.emmaa_statements:
logger.info('Did not load raw EMMAA statements')
return {}
raw_by_papers = defaultdict(int)
for estmt in self.emmaa_statements:
for evid in estmt.stmt.evidence:
paper_id = None
id_type = self.paper_id_type
if id_type == 'pii':
paper_id = evid.annotations.get('pii')
if evid.text_refs:
paper_id = evid.text_refs.get(id_type)
if not paper_id:
paper_id = evid.text_refs.get(id_type.lower())
if paper_id:
raw_by_papers[paper_id] += 1
return raw_by_papers
def get_paper_titles_and_links(self, trids):
"""Return a dictionary mapping paper IDs to their titles."""
if self.paper_id_type == 'pii':
return {}, {}
db = get_db('primary')
trs = db.select_all(db.TextRef, db.TextRef.id.in_(trids))
ref_dicts = [tr.get_ref_dict() for tr in trs]
trid_to_title = {}
trid_to_link = {}
trid_to_pmids = {}
trid_to_pmcids = {}
trid_to_dois = {}
check_in_db = []
# Map TRIDs to available PMIDs, DOIs, PMCIDs in this order
for ref_dict in ref_dicts:
if 'PMID' in ref_dict:
trid_to_pmids[ref_dict['TRID']] = ref_dict['PMID']
elif 'PMCID' in ref_dict:
trid_to_pmcids[ref_dict['TRID']] = ref_dict['PMCID']
elif 'DOI' in ref_dict:
trid_to_dois[ref_dict['TRID']] = ref_dict['DOI']
logger.info(f'From {len(trids)} TRIDs got {len(trid_to_pmids)} PMIDs,'
f' {len(trid_to_pmcids)} PMCIDs, {len(trid_to_dois)} DOIs')
# First get titles and links for available PMIDs
if trid_to_pmids:
logger.info(f'Getting titles for {len(trid_to_pmids)} PMIDs')
pmids = list(trid_to_pmids.values())
pmids_to_titles = _get_pmid_titles(pmids)
for trid, pmid in trid_to_pmids.items():
if pmid in pmids_to_titles:
trid_to_title[str(trid)] = pmids_to_titles[pmid]
else:
check_in_db.append(trid)
link = _get_publication_link(pmid, 'PMID')
trid_to_link[str(trid)] = link
# Then get titles and links for available PMCIDs
if trid_to_pmcids:
logger.info(f'Getting titles for {len(trid_to_pmcids)} PMCIDs')
for trid, pmcid in trid_to_pmcids.items():
title = _get_pmcid_title(pmcid)
if title:
trid_to_title[str(trid)] = title
else:
check_in_db.append(trid)
link = _get_publication_link(pmcid, 'PMCID')
trid_to_link[str(trid)] = link
# Then get titles and links for available DOIs
if trid_to_dois:
logger.info(f'Getting titles for {len(trid_to_dois)} DOIs')
for trid, doi in trid_to_dois.items():
title = _get_doi_title(doi)
if title:
trid_to_title[str(trid)] = title
else:
check_in_db.append(trid)
link = _get_publication_link(doi, 'DOI')
trid_to_link[str(trid)] = link
# Try getting remaining titles from db
if check_in_db:
logger.info(f'Getting titles for {len(check_in_db)} remaining '
'TRIDs from DB')
tcs = db.select_all(db.TextContent,
db.TextContent.text_ref_id.in_(check_in_db),
db.TextContent.text_type == 'title')
for tc in tcs:
title = unpack(tc.content)
trid_to_title[str(tc.text_ref_id)] = title
return trid_to_title, trid_to_link
def get_curation_stats(self):
if not self.emmaa_statements:
logger.info('Did not load raw EMMAA statements')
return
curations = get_curations()
curators_ev = defaultdict(set)
curators_stmt = defaultdict(set)
curators_ev_counts = {}
curators_stmt_counts = {}
curs_by_tags = defaultdict(int)
curs_by_hash = defaultdict(list)
cur_ev_dates = defaultdict(set)
cur_stmt_dates = defaultdict(set)
cur_ev_date_sum = []
cur_stmt_date_sum = []
for cur in curations:
curs_by_hash[cur['source_hash']].append(cur)
df = '%Y-%m-%d-00-00-00'
for estmt in self.emmaa_statements:
for ev in estmt.stmt.evidence:
source_hash = ev.get_source_hash()
curs_for_hash = curs_by_hash.get(source_hash)
if curs_for_hash:
for cur in curs_for_hash:
curators_ev[cur['curator']].add(cur['source_hash'])
curators_stmt[cur['curator']].add(cur['pa_hash'])
curs_by_tags[cur['tag']] += 1
cur_ev_dates[cur['date'].strftime(df)].add(
cur['source_hash'])
cur_stmt_dates[cur['date'].strftime(df)].add(
cur['pa_hash'])
for cur, entries in curators_ev.items():
curators_ev_counts[cur] = len(entries)
for cur, entries in curators_stmt.items():
curators_stmt_counts[cur] = len(entries)
current_ev_sum = 0
current_stmt_sum = 0
for date, entries in sorted(cur_ev_dates.items()):
current_ev_sum += len(entries)
cur_ev_date_sum.append((date, current_ev_sum))
for date, entries in sorted(cur_stmt_dates.items()):
current_stmt_sum += len(entries)
cur_stmt_date_sum.append((date, current_stmt_sum))
cur_stats = {
'curators_ev_counts': sorted(
curators_ev_counts.items(), key=lambda x: x[1], reverse=True),
'curators_stmt_counts': sorted(
curators_stmt_counts.items(), key=lambda x: x[1], reverse=True),
'curs_by_tags': sorted(
curs_by_tags.items(), key=lambda x: x[1], reverse=True),
'cur_ev_dates': cur_ev_date_sum,
'cur_stmt_dates': cur_stmt_date_sum
}
return cur_stats
class TestRound(Round):
"""Analyzes the results of one test round.
Parameters
----------
json_results : list[dict]
A list of JSON formatted dictionaries to store information about the
test results. The first dictionary contains information about the
model. Each consecutive dictionary contains information about a single
test applied to the model and test results.
date_str : str
Time when ModelManager responsible for this round was created.
Attributes
----------
mc_types_results : dict
A dictionary mapping a type of a ModelChecker to a list of test
results generated by this ModelChecker
tests : list[indra.statements.Statement]
A list of INDRA Statements used to make EMMAA tests.
english_test_results : dict
A dictionary mapping a test hash and a list containing its English
description, result in Pass/Fail/n_a form and either a path if it
was found or a result code if it was not.
"""
def __init__(self, json_results, date_str):
super().__init__(date_str)
self.json_results = json_results
mc_types = self.json_results[0].get('mc_types', ['pysb'])
self.mc_types_results = {}
for mc_type in mc_types:
self.mc_types_results[mc_type] = self._get_results(mc_type)
self.tests = self._get_tests()
self.english_test_results = self._get_applied_tests_results()
@classmethod
def load_from_s3_key(cls, key, bucket=EMMAA_BUCKET_NAME):
logger.info(f'Loading json from {key}')
json_results = load_json_from_s3(bucket, key)
date_str = json_results[0].get('date_str', strip_out_date(key))
return cls(json_results, date_str)
def get_applied_test_hashes(self):
"""Return a list of hashes for all applied tests."""
return list(self.english_test_results.keys())
def get_passed_test_hashes(self, mc_type='pysb'):
"""Return a list of hashes for passed tests."""
return [test_hash for test_hash in self.english_test_results.keys() if
self.english_test_results[test_hash][mc_type][0] == 'Pass']
def get_total_applied_tests(self):
"""Return a number of all applied tests."""
total = len(self.tests)
logger.info(f'{total} tests were applied.')
return total
def get_number_passed_tests(self, mc_type='pysb'):
"""Return a number of all passed tests."""
total = len(self.get_passed_test_hashes(mc_type))
logger.info(f'{total} tests passed.')
return total
def passed_over_total(self, mc_type='pysb'):
"""Return a ratio of passed over total tests."""
return self.get_number_passed_tests(mc_type)/self.get_total_applied_tests()
def _get_applied_tests_results(self):
"""Return a dictionary mapping a test hash and a list containing its
English description, result in Pass/Fail form and either a path if it
was found or a result code if it was not."""
tests_by_hash = {}
logger.info('Retrieving test hashes, english tests and test results.')
def get_pass_fail(res):
# Here use result.path_found because we care if the path was found
# and do not care about path length
if res.path_found:
return 'Pass'
elif res.result_code == 'STATEMENT_TYPE_NOT_HANDLED':
return 'n_a'
else:
return 'Fail'
def get_path_or_code(ix, res, mc_type):
path_or_code = None
# Here use result.paths because we care about actual path (i.e.
# we can't get a path exceeding max path length)
if res.paths:
try:
path_or_code = (
self.json_results[ix+1][mc_type]['path_json'])
# if json doesn't contain some of the fields
except KeyError:
pass
# If path wasn't found or presented in json
if not path_or_code:
try:
path_or_code = (
self.json_results[ix+1][mc_type]['result_code'])
except KeyError:
pass
# Couldn't get either path or code description from json
if not path_or_code:
path_or_code = res.result_code
return path_or_code
for ix, test in enumerate(self.tests):
test_hash = str(test.get_hash(refresh=True))
tests_by_hash[test_hash] = {
'test': self.get_english_statement(test)}
for mc_type in self.mc_types_results:
result = self.mc_types_results[mc_type][ix]
tests_by_hash[test_hash][mc_type] = [
get_pass_fail(result),
get_path_or_code(ix, result, mc_type)]
return tests_by_hash
def get_path_stmt_counts(self):
path_stmt_counts = self.json_results[0].get('path_stmt_counts')
if path_stmt_counts:
return sorted(
path_stmt_counts.items(), key=lambda x: x[1], reverse=True)
return []
def _get_results(self, mc_type):
unpickler = jsonpickle.unpickler.Unpickler()
test_results = [unpickler.restore(result[mc_type]['result_json'])
for result in self.json_results[1:]]
return test_results
def _get_tests(self):
tests = [Statement._from_json(res['test_json'])
for res in self.json_results[1:]]
return tests
class StatsGenerator(object):
"""Parent class for classes generating statistic for a given round of
tests or model update.
Parameters
----------
model_name : str
A name of a model the tests were run against.
latest_round : ModelRound or TestRound or None
An instance of a ModelRound or TestRound to generate statistics for.
If not given, will be generated by loading json from s3.
previous_round : ModelRound or TestRound or None
A different instance of a ModelRound or TestRound to find delta
between two rounds. If not given, will be generated by loading json
from s3.
previous_json_stats : dict
A JSON-formatted dictionary containing model or test statistics for
the previous round.
Attributes
----------
json_stats : dict
A JSON-formatted dictionary containing model or test statistics.
"""
def __init__(self, model_name, latest_round=None, previous_round=None,
previous_json_stats=None, bucket=EMMAA_BUCKET_NAME):
self.model_name = model_name
self.bucket = bucket
self.previous_date_str = None
if not latest_round:
self.latest_round = self._get_latest_round()
else:
self.latest_round = latest_round
if not previous_json_stats:
self.previous_json_stats = self._get_previous_json_stats()
else:
self.previous_json_stats = previous_json_stats
if not previous_round:
self.previous_round = self._get_previous_round()
else:
self.previous_round = previous_round
self.json_stats = {}
def make_changes_over_time(self):
"""Add changes to model and tests over time to json_stats."""
raise NotImplementedError("Method must be implemented in child class.")
def get_over_time(self, section, metrics, **kwargs):
raise NotImplementedError("Method must be implemented in child class.")
def get_dates(self):
if not self.previous_json_stats:
previous_dates = []
else:
previous_dates = (
self.previous_json_stats['changes_over_time']['dates'])
previous_dates.append(self.latest_round.date_str)
return previous_dates
def save_to_s3_key(self, stats_key):
if self.json_stats:
logger.info(f'Uploading statistics to {stats_key}')
save_json_to_s3(self.json_stats, self.bucket, stats_key)
def save_to_s3(self):
raise NotImplementedError("Method must be implemented in child class.")
def _get_latest_round(self):
raise NotImplementedError("Method must be implemented in child class.")
def _get_previous_round(self):
raise NotImplementedError("Method must be implemented in child class.")
def _get_previous_json_stats(self):
raise NotImplementedError("Method must be implemented in child class.")
class ModelStatsGenerator(StatsGenerator):
"""Generates statistic for a given model update round.
Parameters
----------
model_name : str
A name of a model the tests were run against.
latest_round : emmaa.analyze_tests_results.ModelRound
An instance of a ModelRound to generate statistics for. If not given,
will be generated by loading model data from s3.
previous_round : emmaa.analyze_tests_results.ModelRound
A different instance of a ModelRound to find delta between two rounds.
If not given, will be generated by loading model data from s3.
previous_json_stats : list[dict]
A JSON-formatted dictionary containing model statistics for previous
update round.
Attributes
----------
json_stats : dict
A JSON-formatted dictionary containing model statistics.
"""
def __init__(self, model_name, latest_round=None, previous_round=None,
previous_json_stats=None, bucket=EMMAA_BUCKET_NAME):
super().__init__(model_name, latest_round, previous_round,
previous_json_stats, bucket)
def make_stats(self):
"""Check if two latest model rounds were found and add statistics to
json_stats dictionary. If both latest round and previous round
were passed or found on s3, a dictionary will have three key-value
pairs: model_summary, model_delta, and changes_over_time.
"""
if not self.latest_round:
logger.info(f'Latest round for {self.model_name} is not found.')
return
if self.previous_json_stats and not self.previous_round:
logger.info(f'Latest stats are found but latest round is not.')
return
logger.info(f'Generating stats for {self.model_name}.')
self.make_model_summary()
self.make_model_delta()
self.make_paper_delta()
self.make_paper_summary()
self.make_curation_summary()
self.make_changes_over_time()
def make_model_summary(self):
"""Add latest model state summary to json_stats."""
logger.info(f'Generating model summary for {self.model_name}.')
self.json_stats['model_summary'] = {
'model_name': self.model_name,
'number_of_statements': self.latest_round.get_total_statements(),
'stmts_type_distr': self.latest_round.get_statement_types(),
'agent_distr': self.latest_round.get_agent_distribution(),
'stmts_by_evidence': self.latest_round.get_statements_by_evidence(),
'sources': self.latest_round.get_sources_distribution(),
'all_stmts': self.latest_round.get_english_statements_by_hash()
}
def make_model_delta(self):
"""Add model delta between two latest model states to json_stats."""
logger.info(f'Generating model delta for {self.model_name}.')
if not self.previous_round:
self.json_stats['model_delta'] = {
'statements_hashes_delta': {'added': [], 'removed': []}}
else:
stmts_delta = self.latest_round.find_delta_hashes(
self.previous_round, 'statements')
self.json_stats['model_delta'] = {
'statements_hashes_delta': stmts_delta}
msg = _make_twitter_msg(self.model_name, 'stmts', stmts_delta,
self.latest_round.date_str[:10])
if msg:
logger.info(msg)
def make_paper_summary(self):
"""Add latest paper summary to json_stats."""
logger.info(f'Generating model summary for {self.model_name}.')
self.json_stats['paper_summary'] = {
'raw_paper_ids': self.latest_round.get_all_raw_paper_ids(),
'number_of_raw_papers': self.latest_round.get_number_raw_papers(),
'assembled_paper_ids': (
self.latest_round.get_all_assembled_paper_ids()),
'number_of_assembled_papers': (
self.latest_round.get_number_assembled_papers()),
'stmts_by_paper': self.latest_round.stmts_by_papers,
'paper_distr': self.latest_round.get_papers_distribution(),
'raw_paper_counts': self.latest_round.get_raw_paper_counts()
}
freq_trids = [pair[0] for pair in
self.json_stats['paper_summary']['paper_distr'][:10]]
new_trids = self.json_stats['paper_delta']['raw_paper_ids_delta'][
'added']
trids = list(set(freq_trids).union(set(new_trids)))
titles, links = self.latest_round.get_paper_titles_and_links(trids)
self.json_stats['paper_summary']['paper_titles'] = titles
self.json_stats['paper_summary']['paper_links'] = links
def make_paper_delta(self):
"""Add paper delta between two latest model states to json_stats."""
logger.info(f'Generating paper delta for {self.model_name}.')
if not self.previous_round or not self.previous_round.paper_ids:
self.json_stats['paper_delta'] = {
'raw_paper_ids_delta': {'added': [], 'removed': []},
'assembled_paper_ids_delta': {'added': [], 'removed': []}}
else:
raw_paper_delta = self.latest_round.find_delta_hashes(
self.previous_round, 'raw_papers')
assembled_paper_delta = self.latest_round.find_delta_hashes(
self.previous_round, 'assembled_papers')
self.json_stats['paper_delta'] = {
'raw_paper_ids_delta': raw_paper_delta,
'assembled_paper_ids_delta': assembled_paper_delta}
logger.info(f'Read {len(raw_paper_delta["added"])} new papers.')
logger.info(f'Got assembled statements from '
f'{len(assembled_paper_delta["added"])} new papers.')
def make_curation_summary(self):
"""Add latest curation summary to json_stats."""
logger.info(f'Generating curation summary for { self.model_name}.')
cur_stats = self.latest_round.get_curation_stats()
self.json_stats['curation_summary'] = cur_stats
def make_changes_over_time(self):
"""Add changes to model over time to json_stats."""
logger.info(f'Comparing changes over time for {self.model_name}.')
self.json_stats['changes_over_time'] = {
'number_of_statements': self.get_over_time(
'model_summary', 'number_of_statements'),
'number_of_raw_papers': self.get_over_time(
'paper_summary', 'number_of_raw_papers'),
'number_of_assembled_papers': self.get_over_time(
'paper_summary', 'number_of_assembled_papers'),
'dates': self.get_dates()}
def get_over_time(self, section, metrics, mc_type='pysb'):
logger.info(f'Getting changes over time in {metrics} '
f'for {self.model_name}.')
# First available stats
if not self.previous_json_stats:
previous_data = []
else:
previous_data = (
self.previous_json_stats['changes_over_time'].get(metrics, []))
previous_data.append(self.json_stats[section][metrics])
return previous_data
def save_to_s3(self):
date_str = self.latest_round.date_str
stats_key = (
f'model_stats/{self.model_name}/model_stats_{date_str}.json')
super().save_to_s3_key(stats_key)
def _get_latest_round(self):
latest_key = find_latest_s3_file(
self.bucket, f'results/{self.model_name}/model_manager_',
extension='.pkl')
if latest_key is None:
logger.info(f'Could not find a key to the latest model manager '
f'for {self.model_name} model.')
return
logger.info(f'Loading latest round from {latest_key}')
mr = ModelRound.load_from_s3_key(latest_key, bucket=self.bucket,
load_estmts=True)
return mr
def _get_previous_round(self):
if not self.previous_json_stats:
logger.info('Not loading previous round without previous stats')
return
previous_key = (f'results/{self.model_name}/model_manager_'
f'{self.previous_date_str}.pkl')
if previous_key is None:
logger.info(f'Could not find a key to the previous model manager '
f'for {self.model_name} model.')
return
logger.info(f'Loading previous round from {previous_key}')
mr = ModelRound.load_from_s3_key(previous_key, bucket=self.bucket)
return mr
def _get_previous_json_stats(self):
key = find_latest_s3_file(
self.bucket, f'model_stats/{self.model_name}/model_stats_', '.json')
# This is the first time statistics is generated for this model
if key is None:
logger.info(f'Could not find a key to the previous statistics ')
return
# If stats for this date exists, previous stats is the second latest
if strip_out_date(key) == self.latest_round.date_str:
logger.info(f'Statistics for latest round already exists')
key = find_nth_latest_s3_file(
1, self.bucket, f'model_stats/{self.model_name}/model_stats_',
'.json')
# Store the date string to find previous round with it
self.previous_date_str = strip_out_date(key)
logger.info(f'Loading earlier statistics from {key}')
previous_json_stats = load_json_from_s3(self.bucket, key)
return previous_json_stats
class TestStatsGenerator(StatsGenerator):
"""Generates statistic for a given test round.
Parameters
----------
model_name : str
A name of a model the tests were run against.
test_corpus_str : str
A name of a test corpus the model was tested against.
latest_round : emmaa.analyze_tests_results.TestRound
An instance of a TestRound to generate statistics for. If not given,
will be generated by loading test results from s3.
previous_round : emmaa.analyze_tests_results.TestRound
A different instance of a TestRound to find delta between two rounds.
If not given, will be generated by loading test results from s3.
previous_json_stats : list[dict]
A JSON-formatted dictionary containing test statistics for previous
test round.
Attributes
----------
json_stats : dict
A JSON-formatted dictionary containing test statistics.
"""
def __init__(self, model_name, test_corpus_str='large_corpus_tests',
latest_round=None, previous_round=None,
previous_json_stats=None, bucket=EMMAA_BUCKET_NAME):
self.test_corpus = test_corpus_str
super().__init__(model_name, latest_round, previous_round,
previous_json_stats, bucket)
def make_stats(self):
"""Check if two latest test rounds were found and add statistics to
json_stats dictionary. If both latest round and previous round
were passed or found on s3, a dictionary will have three key-value
pairs: test_round_summary, tests_delta, and changes_over_time.
"""
if not self.latest_round:
logger.info(f'Latest round for {self.model_name} is not found.')
return
if self.previous_json_stats and not self.previous_round:
logger.info(f'Latest stats are found but latest round is not.')
return
logger.info(f'Generating stats for {self.model_name}.')
self.make_test_summary()
self.make_tests_delta()
self.make_changes_over_time()
def make_test_summary(self):
"""Add latest test round summary to json_stats."""
logger.info(f'Generating test summary for {self.model_name}.')
self.json_stats['test_round_summary'] = {
'test_data': self.latest_round.json_results[0].get('test_data'),
'number_applied_tests': self.latest_round.get_total_applied_tests(),
'all_test_results': self.latest_round.english_test_results,
'path_stmt_counts': self.latest_round.get_path_stmt_counts()}
for mc_type in self.latest_round.mc_types_results:
self.json_stats['test_round_summary'][mc_type] = {
'number_passed_tests': (
self.latest_round.get_number_passed_tests(mc_type)),
'passed_ratio': self.latest_round.passed_over_total(mc_type)}
def make_tests_delta(self):
"""Add tests delta between two latest test rounds to json_stats."""
logger.info(f'Generating tests delta for {self.model_name}.')
date = self.latest_round.date_str[:10]
test_name = None
test_data = self.latest_round.json_results[0].get('test_data')
if test_data:
test_name = test_data.get('name')
if not self.previous_round:
tests_delta = {
'applied_hashes_delta': {'added': [], 'removed': []}}
else:
applied_delta = self.latest_round.find_delta_hashes(
self.previous_round, 'applied_tests')
tests_delta = {
'applied_hashes_delta': applied_delta}
msg = _make_twitter_msg(
self.model_name, 'applied_tests', applied_delta, date,
test_corpus=self.test_corpus, test_name=test_name)
if msg:
logger.info(msg)
for mc_type in self.latest_round.mc_types_results:
if not self.previous_round or mc_type not in \
self.previous_round.mc_types_results:
tests_delta[mc_type] = {
'passed_hashes_delta': {'added': [], 'removed': []}}
else:
passed_delta = self.latest_round.find_delta_hashes(
self.previous_round, 'passed_tests', mc_type=mc_type)
tests_delta[mc_type] = {
'passed_hashes_delta': passed_delta}
msg = _make_twitter_msg(
self.model_name, 'passed_tests', passed_delta, date,
mc_type, test_corpus=self.test_corpus, test_name=test_name)
if msg:
logger.info(msg)
self.json_stats['tests_delta'] = tests_delta
def make_changes_over_time(self):
"""Add changes to tests over time to json_stats."""
logger.info(f'Comparing changes over time for {self.model_name}.')
self.json_stats['changes_over_time'] = {
'number_applied_tests': self.get_over_time(
'test_round_summary', 'number_applied_tests'),
'dates': self.get_dates()}
for mc_type in self.latest_round.mc_types_results:
self.json_stats['changes_over_time'][mc_type] = {
'number_passed_tests': self.get_over_time(
'test_round_summary', 'number_passed_tests', mc_type),
'passed_ratio': self.get_over_time(
'test_round_summary', 'passed_ratio', mc_type)}
def get_over_time(self, section, metrics, mc_type='pysb'):
logger.info(f'Getting changes over time in {metrics} '
f'for {self.model_name}.')
# Not mc_type relevant data
if metrics == 'number_applied_tests':
# First available stats
if not self.previous_json_stats:
previous_data = []
else:
previous_data = (
self.previous_json_stats['changes_over_time'][metrics])
previous_data.append(self.json_stats[section][metrics])
# Mc_type relevant data
else:
# First available stats
if not self.previous_json_stats:
previous_data = []
else:
# This mc_type wasn't available in previous stats
if mc_type not in \
self.previous_json_stats['changes_over_time']:
previous_data = []
else:
previous_data = (
self.previous_json_stats[
'changes_over_time'][mc_type][metrics])
previous_data.append(self.json_stats[section][mc_type][metrics])
return previous_data
def save_to_s3(self):
date_str = self.latest_round.date_str
stats_key = (f'stats/{self.model_name}/test_stats_{self.test_corpus}_'
f'{date_str}.json')
super().save_to_s3_key(stats_key)
def _get_latest_round(self):
latest_key = find_latest_s3_file(
self.bucket,
f'results/{self.model_name}/results_{self.test_corpus}',
extension='.json')
if latest_key is None:
logger.info(f'Could not find a key to the latest test results '
f'for {self.model_name} model.')
return
logger.info(f'Loading latest round from {latest_key}')
tr = TestRound.load_from_s3_key(latest_key, bucket=self.bucket)
return tr
def _get_previous_round(self):
if not self.previous_json_stats:
logger.info('Not loading previous round without previous stats')
return
previous_key = (f'results/{self.model_name}/results_{self.test_corpus}'
f'_{self.previous_date_str}.json')
if previous_key is None:
logger.info(f'Could not find a key to the previous test results '
f'for {self.model_name} model.')
return
logger.info(f'Loading previous round from {previous_key}')
tr = TestRound.load_from_s3_key(previous_key, bucket=self.bucket)
return tr
def _get_previous_json_stats(self):
key = find_latest_s3_file(
self.bucket,
f'stats/{self.model_name}/test_stats_{self.test_corpus}_', '.json')
# This is the first time statistics is generated for this model
if key is None:
logger.info(f'Could not find a key to the previous statistics ')
return
# If stats for this date exists, previous stats is the second latest
if strip_out_date(key) == self.latest_round.date_str:
logger.info(f'Statistics for latest round already exists')
key = find_nth_latest_s3_file(
1, self.bucket,
f'stats/{self.model_name}/test_stats_{self.test_corpus}_',
'.json')
# Store the date string to find previous round with it
self.previous_date_str = strip_out_date(key)
logger.info(f'Loading earlier statistics from {key}')
previous_json_stats = load_json_from_s3(self.bucket, key)
return previous_json_stats
def generate_stats_on_s3(
model_name, mode, test_corpus_str='large_corpus_tests',
upload_stats=True, bucket=EMMAA_BUCKET_NAME):
"""Generate statistics for latest round of model update or tests.
Parameters
----------
model_name : str
A name of EmmaaModel.
mode : str
Type of stats to generate (model or tests)
test_corpus_str : str
A name of a test corpus.
upload_stats : Optional[bool]
Whether to upload latest statistics about model and a test.
Default: True
"""
if mode == 'model':
sg = ModelStatsGenerator(model_name, bucket=bucket)
elif mode == 'tests':
sg = TestStatsGenerator(model_name, test_corpus_str, bucket=bucket)
else:
raise TypeError('Mode must be either model or tests')
sg.make_stats()
# Optionally upload stats to S3
if upload_stats:
sg.save_to_s3()
return sg
def _make_twitter_msg(model_name, msg_type, delta, date, mc_type=None,
test_corpus=None, test_name=None, new_papers=None):
if len(delta['added']) == 0:
logger.info(f'No {msg_type} delta found')
return
if not test_name:
test_name = test_corpus
plural = 's' if len(delta['added']) > 1 else ''
if msg_type == 'stmts':
if not new_papers:
logger.info(f'No new papers found')
return
else:
paper_plural = 's' if new_papers > 1 else ''
msg = (f'Today I read {new_papers} new publication{paper_plural} '
f'and learned {len(delta["added"])} new mechanism{plural}. '
f'See https://emmaa.indra.bio/dashboard/{model_name}'
f'?tab=model&date={date}#addedStmts for more '
'details.')
elif msg_type == 'applied_tests':
msg = (f'Today I applied {len(delta["added"])} new test{plural} in '
f'the {test_name}. See '
f'https://emmaa.indra.bio/dashboard/{model_name}?tab=tests'
f'&test_corpus={test_corpus}&date={date}#newAppliedTests for '
'more details.')
elif msg_type == 'passed_tests' and mc_type:
msg = (f'Today I explained {len(delta["added"])} new '
f'observation{plural} in the {test_name} with my '
f'{TWITTER_MODEL_TYPES[mc_type]} model. See '
f'https://emmaa.indra.bio/dashboard/{model_name}?tab=tests'
f'&test_corpus={test_corpus}&date={date}#newPassedTests for '
'more details.')
else:
raise TypeError(f'Invalid message type: {msg_type}.')
return msg
def tweet_deltas(model_name, test_corpora, date, bucket=EMMAA_BUCKET_NAME):
model_stats, _ = get_model_stats(model_name, 'model', date=date)
test_stats_by_corpus = {}
for test_corpus in test_corpora:
test_stats, _ = get_model_stats(model_name, 'test', tests=test_corpus,
date=date)
if not test_stats:
logger.info(f'Could not find test stats for {test_corpus}')
test_stats_by_corpus[test_corpus] = test_stats
if not model_stats or not test_stats_by_corpus:
logger.warning('Stats are not found, not tweeting')
return
config = load_config_from_s3(model_name, bucket)
twitter_key = config.get('twitter')
twitter_cred = get_credentials(twitter_key)
if not twitter_cred:
logger.warning('Twitter credentials are not found, not tweeting')
# Model message
stmts_delta = model_stats['model_delta']['statements_hashes_delta']
paper_delta = model_stats['paper_delta']['raw_paper_ids_delta']
new_papers = len(paper_delta['added'])
stmts_msg = _make_twitter_msg(model_name, 'stmts', stmts_delta, date,
new_papers=new_papers)
if stmts_msg:
logger.info(stmts_msg)
if twitter_cred:
update_status(stmts_msg, twitter_cred)
# Tests messages
for test_corpus, test_stats in test_stats_by_corpus.items():
test_name = None
test_data = test_stats['test_round_summary'].get('test_data')
if test_data:
test_name = test_data.get('name')
for k, v in test_stats['tests_delta'].items():
if k == 'applied_hashes_delta':
applied_delta = v
applied_msg = _make_twitter_msg(
model_name, 'applied_tests', applied_delta, date,
test_corpus=test_corpus, test_name=test_name)
if applied_msg:
logger.info(applied_msg)
if twitter_cred:
update_status(applied_msg, twitter_cred)
else:
mc_type = k
passed_delta = v['passed_hashes_delta']
passed_msg = _make_twitter_msg(
model_name, 'passed_tests', passed_delta,
date, mc_type, test_corpus=test_corpus,
test_name=test_name)
if passed_msg:
logger.info(passed_msg)
if twitter_cred:
update_status(passed_msg, twitter_cred)
logger.info('Done tweeting')
def _get_pmid_titles(pmids):
pmids_to_titles = {}
n = 200
n_batches = len(pmids) // n
if len(pmids) % n:
n_batches += 1
for i in range(n_batches):
start = n * i
end = start + n
batch = pmids[start: end]
m = pubmed_client.get_metadata_for_ids(batch)
for pmid, metadata in m.items():
pmids_to_titles[pmid] = metadata['title']
return pmids_to_titles
def _get_doi_title(doi):
m = crossref_client.get_metadata(doi)
if m:
title = m.get('title')
if title:
return title[0]
def _get_pmcid_title(pmcid):
title = pmc_client.get_title(pmcid)
return title
def _get_publication_link(paper_id, paper_id_type):
if paper_id_type == 'PMID':
name = 'PubMed'
link = f'https://pubmed.ncbi.nlm.nih.gov/{paper_id}'
elif paper_id_type == 'PMCID':
name = 'PMC'
link = f'https://www.ncbi.nlm.nih.gov/pmc/articles/{paper_id}'
elif paper_id_type == 'DOI':
name = 'DOI'
link = f'https://dx.doi.org/{paper_id}'
return (link, name)
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13,
13,
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679,
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21556,
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286,
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13,
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13,
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517,
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313,
13,
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285,
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648,
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13,
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6081,
2433,
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13,
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565,
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338,
6213,
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13,
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17927,
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3888,
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451,
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263,
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17927,
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4513,
515,
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1495,
13,
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286,
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353,
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618,
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1989,
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20968,
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21454,
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13,
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308,
2254,
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342,
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5574,
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9902,
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13,
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1256,
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710,
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13,
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565,
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338,
6213,
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13,
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17927,
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3888,
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451,
1284,
263,
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304,
278,
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17927,
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4513,
515,
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1495,
13,
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286,
29878,
353,
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29934,
618,
29889,
1359,
29918,
3166,
29918,
29879,
29941,
29918,
1989,
29898,
24957,
29918,
1989,
29892,
20968,
29922,
1311,
29889,
21454,
29897,
13,
4706,
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] |
randomizer/iso/constants.py | soulweaver91/pokemon-gc-randomizer | 0 | 122067 | <reponame>soulweaver91/pokemon-gc-randomizer<filename>randomizer/iso/constants.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from enum import Enum, IntEnum
class PokemonSpecies(IntEnum):
NONE = 0x000
BULBASAUR = 0x001
IVYSAUR = 0x002
VENUSAUR = 0x003
CHARMANDER = 0x004
CHARMELEON = 0x005
CHARIZARD = 0x006
SQUIRTLE = 0x007
WARTORTLE = 0x008
BLASTOISE = 0x009
CATERPIE = 0x00A
METAPOD = 0x00B
BUTTERFREE = 0x00C
WEEDLE = 0x00D
KAKUNA = 0x00E
BEEDRILL = 0x00F
PIDGEY = 0x010
PIDGEOTTO = 0x011
PIDGEOT = 0x012
RATTATA = 0x013
RATICATE = 0x014
SPEAROW = 0x015
FEAROW = 0x016
EKANS = 0x017
ARBOK = 0x018
PIKACHU = 0x019
RAICHU = 0x01A
SANDSHREW = 0x01B
SANDSLASH = 0x01C
NIDORAN_F = 0x01D
NIDORINA = 0x01E
NIDOQUEEN = 0x01F
NIDORAN_M = 0x020
NIDORINO = 0x021
NIDOKING = 0x022
CLEFAIRY = 0x023
CLEFABLE = 0x024
VULPIX = 0x025
NINETALES = 0x026
JIGGLYPUFF = 0x027
WIGGLYTUFF = 0x028
ZUBAT = 0x029
GOLBAT = 0x02A
ODDISH = 0x02B
GLOOM = 0x02C
VILEPLUME = 0x02D
PARAS = 0x02E
PARASECT = 0x02F
VENONAT = 0x030
VENOMOTH = 0x031
DIGLETT = 0x032
DUGTRIO = 0x033
MEOWTH = 0x034
PERSIAN = 0x035
PSYDUCK = 0x036
GOLDUCK = 0x037
MANKEY = 0x038
PRIMEAPE = 0x039
GROWLITHE = 0x03A
ARCANINE = 0x03B
POLIWAG = 0x03C
POLIWHIRL = 0x03D
POLIWRATH = 0x03E
ABRA = 0x03F
KADABRA = 0x040
ALAKAZAM = 0x041
MACHOP = 0x042
MACHOKE = 0x043
MACHAMP = 0x044
BELLSPROUT = 0x045
WEEPINBELL = 0x046
VICTREEBEL = 0x047
TENTACOOL = 0x048
TENTACRUEL = 0x049
GEODUDE = 0x04A
GRAVELER = 0x04B
GOLEM = 0x04C
PONYTA = 0x04D
RAPIDASH = 0x04E
SLOWPOKE = 0x04F
SLOWBRO = 0x050
MAGNEMITE = 0x051
MAGNETON = 0x052
FARFETCH_D = 0x053
DODUO = 0x054
DODRIO = 0x055
SEEL = 0x056
DEWGONG = 0x057
GRIMER = 0x058
MUK = 0x059
SHELLDER = 0x05A
CLOYSTER = 0x05B
GASTLY = 0x05C
HAUNTER = 0x05D
GENGAR = 0x05E
ONIX = 0x05F
DROWZEE = 0x060
HYPNO = 0x061
KRABBY = 0x062
KINGLER = 0x063
VOLTORB = 0x064
ELECTRODE = 0x065
EXEGGCUTE = 0x066
EXEGGUTOR = 0x067
CUBONE = 0x068
MAROWAK = 0x069
HITMONLEE = 0x06A
HITMONCHAN = 0x06B
LICKITUNG = 0x06C
KOFFING = 0x06D
WEEZING = 0x06E
RHYHORN = 0x06F
RHYDON = 0x070
CHANSEY = 0x071
TANGELA = 0x072
KANGASKHAN = 0x073
HORSEA = 0x074
SEADRA = 0x075
GOLDEEN = 0x076
SEAKING = 0x077
STARYU = 0x078
STARMIE = 0x079
MR_MIME = 0x07A
SCYTHER = 0x07B
JYNX = 0x07C
ELECTABUZZ = 0x07D
MAGMAR = 0x07E
PINSIR = 0x07F
TAUROS = 0x080
MAGIKARP = 0x081
GYARADOS = 0x082
LAPRAS = 0x083
DITTO = 0x084
EEVEE = 0x085
VAPOREON = 0x086
JOLTEON = 0x087
FLAREON = 0x088
PORYGON = 0x089
OMANYTE = 0x08A
OMASTAR = 0x08B
KABUTO = 0x08C
KABUTOPS = 0x08D
AERODACTYL = 0x08E
SNORLAX = 0x08F
ARTICUNO = 0x090
ZAPDOS = 0x091
MOLTRES = 0x092
DRATINI = 0x093
DRAGONAIR = 0x094
DRAGONITE = 0x095
MEWTWO = 0x096
MEW = 0x097
CHIKORITA = 0x098
BAYLEEF = 0x099
MEGANIUM = 0x09A
CYNDAQUIL = 0x09B
QUILAVA = 0x09C
TYPHLOSION = 0x09D
TOTODILE = 0x09E
CROCONAW = 0x09F
FERALIGATR = 0x0A0
SENTRET = 0x0A1
FURRET = 0x0A2
HOOTHOOT = 0x0A3
NOCTOWL = 0x0A4
LEDYBA = 0x0A5
LEDIAN = 0x0A6
SPINARAK = 0x0A7
ARIADOS = 0x0A8
CROBAT = 0x0A9
CHINCHOU = 0x0AA
LANTURN = 0x0AB
PICHU = 0x0AC
CLEFFA = 0x0AD
IGGLYBUFF = 0x0AE
TOGEPI = 0x0AF
TOGETIC = 0x0B0
NATU = 0x0B1
XATU = 0x0B2
MAREEP = 0x0B3
FLAAFFY = 0x0B4
AMPHAROS = 0x0B5
BELLOSSOM = 0x0B6
MARILL = 0x0B7
AZUMARILL = 0x0B8
SUDOWOODO = 0x0B9
POLITOED = 0x0BA
HOPPIP = 0x0BB
SKIPLOOM = 0x0BC
JUMPLUFF = 0x0BD
AIPOM = 0x0BE
SUNKERN = 0x0BF
SUNFLORA = 0x0C0
YANMA = 0x0C1
WOOPER = 0x0C2
QUAGSIRE = 0x0C3
ESPEON = 0x0C4
UMBREON = 0x0C5
MURKROW = 0x0C6
SLOWKING = 0x0C7
MISDREAVUS = 0x0C8
UNOWN = 0x0C9
WOBBUFFET = 0x0CA
GIRAFARIG = 0x0CB
PINECO = 0x0CC
FORRETRESS = 0x0CD
DUNSPARCE = 0x0CE
GLIGAR = 0x0CF
STEELIX = 0x0D0
SNUBBULL = 0x0D1
GRANBULL = 0x0D2
QWILFISH = 0x0D3
SCIZOR = 0x0D4
SHUCKLE = 0x0D5
HERACROSS = 0x0D6
SNEASEL = 0x0D7
TEDDIURSA = 0x0D8
URSARING = 0x0D9
SLUGMA = 0x0DA
MAGCARGO = 0x0DB
SWINUB = 0x0DC
PILOSWINE = 0x0DD
CORSOLA = 0x0DE
REMORAID = 0x0DF
OCTILLERY = 0x0E0
DELIBIRD = 0x0E1
MANTINE = 0x0E2
SKARMORY = 0x0E3
HOUNDOUR = 0x0E4
HOUNDOOM = 0x0E5
KINGDRA = 0x0E6
PHANPY = 0x0E7
DONPHAN = 0x0E8
PORYGON2 = 0x0E9
STANTLER = 0x0EA
SMEARGLE = 0x0EB
TYROGUE = 0x0EC
HITMONTOP = 0x0ED
SMOOCHUM = 0x0EE
ELEKID = 0x0EF
MAGBY = 0x0F0
MILTANK = 0x0F1
BLISSEY = 0x0F2
RAIKOU = 0x0F3
ENTEI = 0x0F4
SUICUNE = 0x0F5
LARVITAR = 0x0F6
PUPITAR = 0x0F7
TYRANITAR = 0x0F8
LUGIA = 0x0F9
HO_OH = 0x0FA
CELEBI = 0x0FB
UNUSED_252 = 0x0FC
UNUSED_253 = 0x0FD
UNUSED_254 = 0x0FE
UNUSED_255 = 0x0FF
UNUSED_256 = 0x100
UNUSED_257 = 0x101
UNUSED_258 = 0x102
UNUSED_259 = 0x103
UNUSED_260 = 0x104
UNUSED_261 = 0x105
UNUSED_262 = 0x106
UNUSED_263 = 0x107
UNUSED_264 = 0x108
UNUSED_265 = 0x109
UNUSED_266 = 0x10A
UNUSED_267 = 0x10B
UNUSED_268 = 0x10C
UNUSED_269 = 0x10D
UNUSED_270 = 0x10E
UNUSED_271 = 0x10F
UNUSED_272 = 0x110
UNUSED_273 = 0x111
UNUSED_274 = 0x112
UNUSED_275 = 0x113
UNUSED_276 = 0x114
TREECKO = 0x115
GROVYLE = 0x116
SCEPTILE = 0x117
TORCHIC = 0x118
COMBUSKEN = 0x119
BLAZIKEN = 0x11A
MUDKIP = 0x11B
MARSHTOMP = 0x11C
SWAMPERT = 0x11D
POOCHYENA = 0x11E
MIGHTYENA = 0x11F
ZIGZAGOON = 0x120
LINOONE = 0x121
WURMPLE = 0x122
SILCOON = 0x123
BEAUTIFLY = 0x124
CASCOON = 0x125
DUSTOX = 0x126
LOTAD = 0x127
LOMBRE = 0x128
LUDICOLO = 0x129
SEEDOT = 0x12A
NUZLEAF = 0x12B
SHIFTRY = 0x12C
NINCADA = 0x12D
NINJASK = 0x12E
SHEDINJA = 0x12F
TAILLOW = 0x130
SWELLOW = 0x131
SHROOMISH = 0x132
BRELOOM = 0x133
SPINDA = 0x134
WINGULL = 0x135
PELIPPER = 0x136
SURSKIT = 0x137
MASQUERAIN = 0x138
WAILMER = 0x139
WAILORD = 0x13A
SKITTY = 0x13B
DELCATTY = 0x13C
KECLEON = 0x13D
BALTOY = 0x13E
CLAYDOL = 0x13F
NOSEPASS = 0x140
TORKOAL = 0x141
SABLEYE = 0x142
BARBOACH = 0x143
WHISCASH = 0x144
LUVDISC = 0x145
CORPHISH = 0x146
CRAWDAUNT = 0x147
FEEBAS = 0x148
MILOTIC = 0x149
CARVANHA = 0x14A
SHARPEDO = 0x14B
TRAPINCH = 0x14C
VIBRAVA = 0x14D
FLYGON = 0x14E
MAKUHITA = 0x14F
HARIYAMA = 0x150
ELECTRIKE = 0x151
MANECTRIC = 0x152
NUMEL = 0x153
CAMERUPT = 0x154
SPHEAL = 0x155
SEALEO = 0x156
WALREIN = 0x157
CACNEA = 0x158
CACTURNE = 0x159
SNORUNT = 0x15A
GLALIE = 0x15B
LUNATONE = 0x15C
SOLROCK = 0x15D
AZURILL = 0x15E
SPOINK = 0x15F
GRUMPIG = 0x160
PLUSLE = 0x161
MINUN = 0x162
MAWILE = 0x163
MEDITITE = 0x164
MEDICHAM = 0x165
SWABLU = 0x166
ALTARIA = 0x167
WYNAUT = 0x168
DUSKULL = 0x169
DUSCLOPS = 0x16A
ROSELIA = 0x16B
SLAKOTH = 0x16C
VIGOROTH = 0x16D
SLAKING = 0x16E
GULPIN = 0x16F
SWALOT = 0x170
TROPIUS = 0x171
WHISMUR = 0x172
LOUDRED = 0x173
EXPLOUD = 0x174
CLAMPERL = 0x175
HUNTAIL = 0x176
GOREBYSS = 0x177
ABSOL = 0x178
SHUPPET = 0x179
BANETTE = 0x17A
SEVIPER = 0x17B
ZANGOOSE = 0x17C
RELICANTH = 0x17D
ARON = 0x17E
LAIRON = 0x17F
AGGRON = 0x180
CASTFORM = 0x181
VOLBEAT = 0x182
ILLUMISE = 0x183
LILEEP = 0x184
CRADILY = 0x185
ANORITH = 0x186
ARMALDO = 0x187
RALTS = 0x188
KIRLIA = 0x189
GARDEVOIR = 0x18A
BAGON = 0x18B
SHELGON = 0x18C
SALAMENCE = 0x18D
BELDUM = 0x18E
METANG = 0x18F
METAGROSS = 0x190
REGIROCK = 0x191
REGICE = 0x192
REGISTEEL = 0x193
KYOGRE = 0x194
GROUDON = 0x195
RAYQUAZA = 0x196
LATIAS = 0x197
LATIOS = 0x198
JIRACHI = 0x199
DEOXYS = 0x19A
CHIMECHO = 0x19B
EGG = 0x19C
BONSLY = 0x19D
MUNCHLAX = 0x19E
class Type(Enum):
NORMAL = 0x00
FIGHTING = 0x01
FLYING = 0x02
POISON = 0x03
GROUND = 0x04
ROCK = 0x05
BUG = 0x06
GHOST = 0x07
STEEL = 0x08
CURSE = 0x09
FIRE = 0x0A
WATER = 0x0B
GRASS = 0x0C
ELECTRIC = 0x0D
PSYCHIC = 0x0E
ICE = 0x0F
DRAGON = 0x10
DARK = 0x11
VALID_POKEMON_TYPES = [a for a in list(Type) if a not in [
Type.CURSE
]]
class Ability(IntEnum):
NONE = 0x00
STENCH = 0x01
DRIZZLE = 0x02
SPEED_BOOST = 0x03
BATTLE_ARMOR = 0x04
STURDY = 0x05
DAMP = 0x06
LIMBER = 0x07
SAND_VEIL = 0x08
STATIC = 0x09
VOLT_ABSORB = 0x0A
WATER_ABSORB = 0x0B
OBLIVIOUS = 0x0C
CLOUD_NINE = 0x0D
COMPOUND_EYES = 0x0E
INSOMNIA = 0x0F
COLOR_CHANGE = 0x10
IMMUNITY = 0x11
FLASH_FIRE = 0x12
SHIELD_DUST = 0x13
OWN_TEMPO = 0x14
SUCTION_CUPS = 0x15
INTIMIDATE = 0x16
SHADOW_TAG = 0x17
ROUGH_SKIN = 0x18
WONDER_GUARD = 0x19
LEVITATE = 0x1A
EFFECT_SPORE = 0x1B
SYNCHRONIZE = 0x1C
CLEAR_BODY = 0x1D
NATURAL_CURE = 0x1E
LIGHTING_ROD = 0x1F
SERENE_GRACE = 0x20
SWIFT_SWIM = 0x21
CHLOROPHYLL = 0x22
ILLUMINATE = 0x23
TRACE = 0x24
HUGE_POWER = 0x25
POISON_POINT = 0x26
INNER_FOCUS = 0x27
MAGMA_ARMOR = 0x28
WATER_VEIL = 0x29
MAGNET_PULL = 0x2A
SOUNDPROOF = 0x2B
RAIN_DISH = 0x2C
SAND_STREAM = 0x2D
PRESSURE = 0x2E
THICK_FAT = 0x2F
EARLY_BIRD = 0x30
FLAME_BODY = 0x31
RUN_AWAY = 0x32
KEEN_EYE = 0x33
HYPER_CUTTER = 0x34
PICKUP = 0x35
TRUANT = 0x36
HUSTLE = 0x37
CUTE_CHARM = 0x38
PLUS = 0x39
MINUS = 0x3A
FORECAST = 0x3B
STICKY_HOLD = 0x3C
SHED_SKIN = 0x3D
GUTS = 0x3E
MARVEL_SCALE = 0x3F
LIQUID_OOZE = 0x40
OVERGROW = 0x41
BLAZE = 0x42
TORRENT = 0x43
SWARM = 0x44
ROCK_HEAD = 0x45
DROUGHT = 0x46
ARENA_TRAP = 0x47
VITAL_SPIRIT = 0x48
WHITE_SMOKE = 0x49
PURE_POWER = 0x4A
SHELL_ARMOR = 0x4B
CACOPHONY = 0x4C
AIR_LOCK = 0x4D
UNIQUE_DAMAGING_MOVE = -1
STATUS_MOVE = 0
OHKO_MOVE = 1000
class Move(Enum):
NONE = 0x00
POUND = 0x01
KARATE_CHOP = 0x02
DOUBLESLAP = 0x03
DOUBLE_SLAP = DOUBLESLAP
COMET_PUNCH = 0x04
MEGA_PUNCH = 0x05
PAY_DAY = 0x06
FIRE_PUNCH = 0x07
ICE_PUNCH = 0x08
THUNDERPUNCH = 0x09
THUNDER_PUNCH = THUNDERPUNCH
SCRATCH = 0x0A
VICEGRIP = 0x0B
VICE_GRIP = VICEGRIP
GUILLOTINE = 0x0C
RAZOR_WIND = 0x0D
SWORDS_DANCE = 0x0E
CUT = 0x0F
GUST = 0x10
WING_ATTACK = 0x11
WHIRLWIND = 0x12
FLY = 0x13
BIND = 0x14
SLAM = 0x15
VINE_WHIP = 0x16
STOMP = 0x17
DOUBLE_KICK = 0x18
MEGA_KICK = 0x19
JUMP_KICK = 0x1A
ROLLING_KICK = 0x1B
SAND_ATTACK = 0x1C
HEADBUTT = 0x1D
HORN_ATTACK = 0x1E
FURY_ATTACK = 0x1F
HORN_DRILL = 0x20
TACKLE = 0x21
BODY_SLAM = 0x22
WRAP = 0x23
TAKE_DOWN = 0x24
THRASH = 0x25
DOUBLE_EDGE = 0x26
TAIL_WHIP = 0x27
POISON_STING = 0x28
TWINEEDLE = 0x29
PIN_MISSILE = 0x2A
LEER = 0x2B
BITE = 0x2C
GROWL = 0x2D
ROAR = 0x2E
SING = 0x2F
SUPERSONIC = 0x30
SONICBOOM = 0x31
SONIC_BOOM = SONICBOOM
DISABLE = 0x32
ACID = 0x33
EMBER = 0x34
FLAMETHROWER = 0x35
MIST = 0x36
WATER_GUN = 0x37
HYDRO_PUMP = 0x38
SURF = 0x39
ICE_BEAM = 0x3A
BLIZZARD = 0x3B
PSYBEAM = 0x3C
BUBBLEBEAM = 0x3D
BUBBLE_BEAM = BUBBLEBEAM
AURORA_BEAM = 0x3E
HYPER_BEAM = 0x3F
PECK = 0x40
DRILL_PECK = 0x41
SUBMISSION = 0x42
LOW_KICK = 0x43
COUNTER = 0x44
SEISMIC_TOSS = 0x45
STRENGTH = 0x46
ABSORB = 0x47
MEGA_DRAIN = 0x48
LEECH_SEED = 0x49
GROWTH = 0x4A
RAZOR_LEAF = 0x4B
SOLARBEAM = 0x4C
SOLAR_BEAM = SOLARBEAM
POISONPOWDER = 0x4D
POISON_POWDER = POISONPOWDER
STUN_SPORE = 0x4E
SLEEP_POWDER = 0x4F
PETAL_DANCE = 0x50
STRING_SHOT = 0x51
DRAGON_RAGE = 0x52
FIRE_SPIN = 0x53
THUNDERSHOCK = 0x54
THUNDER_SHOCK = THUNDERSHOCK
THUNDERBOLT = 0x55
THUNDER_BOLT = THUNDERBOLT
THUNDER_WAVE = 0x56
THUNDER = 0x57
ROCK_THROW = 0x58
EARTHQUAKE = 0x59
FISSURE = 0x5A
DIG = 0x5B
TOXIC = 0x5C
CONFUSION = 0x5D
PSYCHIC = 0x5E
HYPNOSIS = 0x5F
MEDITATE = 0x60
AGILITY = 0x61
QUICK_ATTACK = 0x62
RAGE = 0x63
TELEPORT = 0x64
NIGHT_SHADE = 0x65
MIMIC = 0x66
SCREECH = 0x67
DOUBLE_TEAM = 0x68
RECOVER = 0x69
HARDEN = 0x6A
MINIMIZE = 0x6B
SMOKESCREEN = 0x6C
SMOKE_SCREEN = SMOKESCREEN
CONFUSE_RAY = 0x6D
WITHDRAW = 0x6E
DEFENSE_CURL = 0x6F
BARRIER = 0x70
LIGHT_SCREEN = 0x71
HAZE = 0x72
REFLECT = 0x73
FOCUS_ENERGY = 0x74
BIDE = 0x75
METRONOME = 0x76
MIRROR_MOVE = 0x77
SELFDESTRUCT = 0x78
SELF_DESTRUCT = SELFDESTRUCT
EGG_BOMB = 0x79
LICK = 0x7A
SMOG = 0x7B
SLUDGE = 0x7C
BONE_CLUB = 0x7D
FIRE_BLAST = 0x7E
WATERFALL = 0x7F
CLAMP = 0x80
SWIFT = 0x81
SKULL_BASH = 0x82
SPIKE_CANNON = 0x83
CONSTRICT = 0x84
AMNESIA = 0x85
KINESIS = 0x86
SOFTBOILED = 0x87
SOFT_BOILED = SOFTBOILED
HI_JUMP_KICK = 0x88
HIGH_JUMP_KICK = HI_JUMP_KICK
GLARE = 0x89
DREAM_EATER = 0x8A
POISON_GAS = 0x8B
BARRAGE = 0x8C
LEECH_LIFE = 0x8D
LOVELY_KISS = 0x8E
SKY_ATTACK = 0x8F
TRANSFORM = 0x90
BUBBLE = 0x91
DIZZY_PUNCH = 0x92
SPORE = 0x93
FLASH = 0x94
PSYWAVE = 0x95
SPLASH = 0x96
ACID_ARMOR = 0x97
CRABHAMMER = 0x98
EXPLOSION = 0x99
FURY_SWIPES = 0x9A
BONEMERANG = 0x9B
REST = 0x9C
ROCK_SLIDE = 0x9D
HYPER_FANG = 0x9E
SHARPEN = 0x9F
CONVERSION = 0xA0
TRI_ATTACK = 0xA1
SUPER_FANG = 0xA2
SLASH = 0xA3
SUBSTITUTE = 0xA4
STRUGGLE = 0xA5
SKETCH = 0xA6
TRIPLE_KICK = 0xA7
THIEF = 0xA8
SPIDER_WEB = 0xA9
MIND_READER = 0xAA
NIGHTMARE = 0xAB
FLAME_WHEEL = 0xAC
SNORE = 0xAD
CURSE = 0xAE
FLAIL = 0xAF
CONVERSION_2 = 0xB0
AEROBLAST = 0xB1
COTTON_SPORE = 0xB2
REVERSAL = 0xB3
SPITE = 0xB4
POWDER_SNOW = 0xB5
PROTECT = 0xB6
MACH_PUNCH = 0xB7
SCARY_FACE = 0xB8
FAINT_ATTACK = 0xB9
FEINT_ATTACK = FAINT_ATTACK
SWEET_KISS = 0xBA
BELLY_DRUM = 0xBB
SLUDGE_BOMB = 0xBC
MUD_SLAP = 0xBD
OCTAZOOKA = 0xBE
SPIKES = 0xBF
ZAP_CANNON = 0xC0
FORESIGHT = 0xC1
DESTINY_BOND = 0xC2
PERISH_SONG = 0xC3
ICY_WIND = 0xC4
DETECT = 0xC5
BONE_RUSH = 0xC6
LOCK_ON = 0xC7
OUTRAGE = 0xC8
SANDSTORM = 0xC9
GIGA_DRAIN = 0xCA
ENDURE = 0xCB
CHARM = 0xCC
ROLLOUT = 0xCD
FALSE_SWIPE = 0xCE
SWAGGER = 0xCF
MILK_DRINK = 0xD0
SPARK = 0xD1
FURY_CUTTER = 0xD2
STEEL_WING = 0xD3
MEAN_LOOK = 0xD4
ATTRACT = 0xD5
SLEEP_TALK = 0xD6
HEAL_BELL = 0xD7
RETURN = 0xD8
PRESENT = 0xD9
FRUSTRATION = 0xDA
SAFEGUARD = 0xDB
PAIN_SPLIT = 0xDC
SACRED_FIRE = 0xDD
MAGNITUDE = 0xDE
DYNAMICPUNCH = 0xDF
DYNAMIC_PUNCH = DYNAMICPUNCH
MEGAHORN = 0xE0
DRAGONBREATH = 0xE1
DRAGON_BREATH = DRAGONBREATH
BATON_PASS = 0xE2
ENCORE = 0xE3
PURSUIT = 0xE4
RAPID_SPIN = 0xE5
SWEET_SCENT = 0xE6
IRON_TAIL = 0xE7
METAL_CLAW = 0xE8
VITAL_THROW = 0xE9
MORNING_SUN = 0xEA
SYNTHESIS = 0xEB
MOONLIGHT = 0xEC
HIDDEN_POWER = 0xED
CROSS_CHOP = 0xEE
TWISTER = 0xEF
RAIN_DANCE = 0xF0
SUNNY_DAY = 0xF1
CRUNCH = 0xF2
MIRROR_COAT = 0xF3
PSYCH_UP = 0xF4
EXTREMESPEED = 0xF5
EXTREME_SPEED = EXTREMESPEED
ANCIENTPOWER = 0xF6
ANCIENT_POWER = ANCIENTPOWER
SHADOW_BALL = 0xF7
FUTURE_SIGHT = 0xF8
ROCK_SMASH = 0xF9
WHIRLPOOL = 0xFA
BEAT_UP = 0xFB
FAKE_OUT = 0xFC
UPROAR = 0xFD
STOCKPILE = 0xFE
SPIT_UP = 0xFF
SWALLOW = 0x100
HEAT_WAVE = 0x101
HAIL = 0x102
TORMENT = 0x103
FLATTER = 0x104
WILL_O_WISP = 0x105
MEMENTO = 0x106
FACADE = 0x107
FOCUS_PUNCH = 0x108
SMELLINGSALT = 0x109
SMELLING_SALTS = SMELLINGSALT
FOLLOW_ME = 0x10A
NATURE_POWER = 0x10B
CHARGE = 0x10C
TAUNT = 0x10D
HELPING_HAND = 0x10E
TRICK = 0x10F
ROLE_PLAY = 0x110
WISH = 0x111
ASSIST = 0x112
INGRAIN = 0x113
SUPERPOWER = 0x114
MAGIC_COAT = 0x115
RECYCLE = 0x116
REVENGE = 0x117
BRICK_BREAK = 0x118
YAWN = 0x119
KNOCK_OFF = 0x11A
ENDEAVOR = 0x11B
ERUPTION = 0x11C
SKILL_SWAP = 0x11D
IMPRISON = 0x11E
REFRESH = 0x11F
GRUDGE = 0x120
SNATCH = 0x121
SECRET_POWER = 0x122
DIVE = 0x123
ARM_THRUST = 0x124
CAMOUFLAGE = 0x125
TAIL_GLOW = 0x126
LUSTER_PURGE = 0x127
MIST_BALL = 0x128
FEATHERDANCE = 0x129
FEATHER_DANCE = FEATHERDANCE
TEETER_DANCE = 0x12A
BLAZE_KICK = 0x12B
MUD_SPORT = 0x12C
ICE_BALL = 0x12D
NEEDLE_ARM = 0x12E
SLACK_OFF = 0x12F
HYPER_VOICE = 0x130
POISON_FANG = 0x131
CRUSH_CLAW = 0x132
BLAST_BURN = 0x133
HYDRO_CANNON = 0x134
METEOR_MASH = 0x135
ASTONISH = 0x136
WEATHER_BALL = 0x137
AROMATHERAPY = 0x138
FAKE_TEARS = 0x139
AIR_CUTTER = 0x13A
OVERHEAT = 0x13B
ODOR_SLEUTH = 0x13C
ROCK_TOMB = 0x13D
SILVER_WIND = 0x13E
METAL_SOUND = 0x13F
GRASSWHISTLE = 0x140
GRASS_WHISTLE = GRASSWHISTLE
TICKLE = 0x141
COSMIC_POWER = 0x142
WATER_SPOUT = 0x143
SIGNAL_BEAM = 0x144
SHADOW_PUNCH = 0x145
EXTRASENSORY = 0x146
SKY_UPPERCUT = 0x147
SAND_TOMB = 0x148
SHEER_COLD = 0x149
MUDDY_WATER = 0x14A
BULLET_SEED = 0x14B
AERIAL_ACE = 0x14C
ICICLE_SPEAR = 0x14D
IRON_DEFENSE = 0x14E
BLOCK = 0x14F
HOWL = 0x150
DRAGON_CLAW = 0x151
FRENZY_PLANT = 0x152
BULK_UP = 0x153
BOUNCE = 0x154
MUD_SHOT = 0x155
POISON_TAIL = 0x156
COVET = 0x157
VOLT_TACKLE = 0x158
MAGICAL_LEAF = 0x159
WATER_SPORT = 0x15A
CALM_MIND = 0x15B
LEAF_BLADE = 0x15C
DRAGON_DANCE = 0x15D
ROCK_BLAST = 0x15E
SHOCK_WAVE = 0x15F
WATER_PULSE = 0x160
DOOM_DESIRE = 0x161
PSYCHO_BOOST = 0x162
UNUSED_0x163 = 0x163
SHADOW_BLITZ = 0x164
SHADOW_RUSH_COLO = 0x164
SHADOW_RUSH_XD = 0x165
SHADOW_BREAK = 0x166
SHADOW_END = 0x167
SHADOW_WAVE = 0x168
SHADOW_RAVE = 0x169
SHADOW_STORM = 0x16A
SHADOW_FIRE = 0x16B
SHADOW_BOLT = 0x16C
SHADOW_CHILL = 0x16D
SHADOW_BLAST = 0x16E
SHADOW_SKY = 0x16F
SHADOW_HOLD = 0x170
SHADOW_MIST = 0x171
SHADOW_PANIC = 0x172
SHADOW_DOWN = 0x173
SHADOW_SHED = 0x174
SHADOW_HALF = 0x175
@classmethod
def _missing_(cls, value):
return Move.NONE
class Item(Enum):
NONE = 0x0000
MASTER_BALL = 0x0001
ULTRA_BALL = 0x0002
GREAT_BALL = 0x0003
POKE_BALL = 0x0004
SAFARI_BALL = 0x0005
NET_BALL = 0x0006
DIVE_BALL = 0x0007
NEST_BALL = 0x0008
REPEAT_BALL = 0x0009
TIMER_BALL = 0x000A
LUXURY_BALL = 0x000B
PREMIER_BALL = 0x000C
POTION = 0x000D
ANTIDOTE = 0x000E
BURN_HEAL = 0x000F
ICE_HEAL = 0x0010
AWAKENING = 0x0011
PARLYZ_HEAL = 0x0012
FULL_RESTORE = 0x0013
MAX_POTION = 0x0014
HYPER_POTION = 0x0015
SUPER_POTION = 0x0016
FULL_HEAL = 0x0017
REVIVE = 0x0018
MAX_REVIVE = 0x0019
FRESH_WATER = 0x001A
SODA_POP = 0x001B
LEMONADE = 0x001C
MOOMOO_MILK = 0x001D
ENERGYPOWDER = 0x001E
ENERGY_ROOT = 0x001F
HEAL_POWDER = 0x0020
REVIVAL_HERB = 0x0021
ETHER = 0x0022
MAX_ETHER = 0x0023
ELIXIR = 0x0024
MAX_ELIXIR = 0x0025
LAVA_COOKIE = 0x0026
BLUE_FLUTE = 0x0027
YELLOW_FLUTE = 0x0028
RED_FLUTE = 0x0029
BLACK_FLUTE = 0x002A
WHITE_FLUTE = 0x002B
BERRY_JUICE = 0x002C
SACRED_ASH = 0x002D
SHOAL_SALT = 0x002E
SHOAL_SHELL = 0x002F
RED_SHARD = 0x0030
BLUE_SHARD = 0x0031
YELLOW_SHARD = 0x0032
GREEN_SHARD = 0x0033
HP_UP = 0x003F
PROTEIN = 0x0040
IRON = 0x0041
CARBOS = 0x0042
CALCIUM = 0x0043
RARE_CANDY = 0x0044
PP_UP = 0x0045
ZINC = 0x0046
PP_MAX = 0x0047
GUARD_SPEC_ = 0x0049
DIRE_HIT = 0x004A
X_ATTACK = 0x004B
X_DEFEND = 0x004C
X_SPEED = 0x004D
X_ACCURACY = 0x004E
X_SPECIAL = 0x004F
POKE_DOLL = 0x0050
FLUFFY_TAIL = 0x0051
SUPER_REPEL = 0x0053
MAX_REPEL = 0x0054
ESCAPE_ROPE = 0x0055
REPEL = 0x0056
SUN_STONE = 0x005D
MOON_STONE = 0x005E
FIRE_STONE = 0x005F
THUNDERSTONE = 0x0060
WATER_STONE = 0x0061
LEAF_STONE = 0x0062
TINYMUSHROOM = 0x0067
BIG_MUSHROOM = 0x0068
PEARL = 0x006A
BIG_PEARL = 0x006B
STARDUST = 0x006C
STAR_PIECE = 0x006D
NUGGET = 0x006E
HEART_SCALE = 0x006F
CHERI_BERRY = 0x0085
CHESTO_BERRY = 0x0086
PECHA_BERRY = 0x0087
RAWST_BERRY = 0x0088
ASPEAR_BERRY = 0x0089
LEPPA_BERRY = 0x008A
ORAN_BERRY = 0x008B
PERSIM_BERRY = 0x008C
LUM_BERRY = 0x008D
SITRUS_BERRY = 0x008E
FIGY_BERRY = 0x008F
WIKI_BERRY = 0x0090
MAGO_BERRY = 0x0091
AGUAV_BERRY = 0x0092
IAPAPA_BERRY = 0x0093
RAZZ_BERRY = 0x0094
BLUK_BERRY = 0x0095
NANAB_BERRY = 0x0096
WEPEAR_BERRY = 0x0097
PINAP_BERRY = 0x0098
POMEG_BERRY = 0x0099
KELPSY_BERRY = 0x009A
QUALOT_BERRY = 0x009B
HONDEW_BERRY = 0x009C
GREPA_BERRY = 0x009D
TAMATO_BERRY = 0x009E
CORNN_BERRY = 0x009F
MAGOST_BERRY = 0x00A0
RABUTA_BERRY = 0x00A1
NOMEL_BERRY = 0x00A2
SPELON_BERRY = 0x00A3
PAMTRE_BERRY = 0x00A4
WATMEL_BERRY = 0x00A5
DURIN_BERRY = 0x00A6
BELUE_BERRY = 0x00A7
LIECHI_BERRY = 0x00A8
GANLON_BERRY = 0x00A9
SALAC_BERRY = 0x00AA
PETAYA_BERRY = 0x00AB
APICOT_BERRY = 0x00AC
LANSAT_BERRY = 0x00AD
STARF_BERRY = 0x00AE
BRIGHTPOWDER = 0x00B3
WHITE_HERB = 0x00B4
MACHO_BRACE = 0x00B5
EXP_SHARE = 0x00B6
QUICK_CLAW = 0x00B7
SOOTHE_BELL = 0x00B8
MENTAL_HERB = 0x00B9
CHOICE_BAND = 0x00BA
KINGS_ROCK = 0x00BB
SILVERPOWDER = 0x00BC
AMULET_COIN = 0x00BD
CLEANSE_TAG = 0x00BE
SOUL_DEW = 0x00BF
DEEPSEATOOTH = 0x00C0
DEEPSEASCALE = 0x00C1
SMOKE_BALL = 0x00C2
EVERSTONE = 0x00C3
FOCUS_BAND = 0x00C4
LUCKY_EGG = 0x00C5
SCOPE_LENS = 0x00C6
METAL_COAT = 0x00C7
LEFTOVERS = 0x00C8
DRAGON_SCALE = 0x00C9
LIGHT_BALL = 0x00CA
SOFT_SAND = 0x00CB
HARD_STONE = 0x00CC
MIRACLE_SEED = 0x00CD
BLACKGLASSES = 0x00CE
BLACK_BELT = 0x00CF
MAGNET = 0x00D0
MYSTIC_WATER = 0x00D1
SHARP_BEAK = 0x00D2
POISON_BARB = 0x00D3
NEVERMELTICE = 0x00D4
SPELL_TAG = 0x00D5
TWISTEDSPOON = 0x00D6
CHARCOAL = 0x00D7
DRAGON_FANG = 0x00D8
SILK_SCARF = 0x00D9
UP_GRADE = 0x00DA
SHELL_BELL = 0x00DB
SEA_INCENSE = 0x00DC
LAX_INCENSE = 0x00DD
LUCKY_PUNCH = 0x00DE
METAL_POWDER = 0x00DF
THICK_CLUB = 0x00E0
STICK = 0x00E1
RED_SCARF = 0x00FE
BLUE_SCARF = 0x00FF
PINK_SCARF = 0x0100
GREEN_SCARF = 0x0101
YELLOW_SCARF = 0x0102
TM01 = 0x0121
TM02 = 0x0122
TM03 = 0x0123
TM04 = 0x0124
TM05 = 0x0125
TM06 = 0x0126
TM07 = 0x0127
TM08 = 0x0128
TM09 = 0x0129
TM10 = 0x012A
TM11 = 0x012B
TM12 = 0x012C
TM13 = 0x012D
TM14 = 0x012E
TM15 = 0x012F
TM16 = 0x0130
TM17 = 0x0131
TM18 = 0x0132
TM19 = 0x0133
TM20 = 0x0134
TM21 = 0x0135
TM22 = 0x0136
TM23 = 0x0137
TM24 = 0x0138
TM25 = 0x0139
TM26 = 0x013A
TM27 = 0x013B
TM28 = 0x013C
TM29 = 0x013D
TM30 = 0x013E
TM31 = 0x013F
TM32 = 0x0140
TM33 = 0x0141
TM34 = 0x0142
TM35 = 0x0143
TM36 = 0x0144
TM37 = 0x0145
TM38 = 0x0146
TM39 = 0x0147
TM40 = 0x0148
TM41 = 0x0149
TM42 = 0x014A
TM43 = 0x014B
TM44 = 0x014C
TM45 = 0x014D
TM46 = 0x014E
TM47 = 0x014F
TM48 = 0x0150
TM49 = 0x0151
TM50 = 0x0152
# Colosseum key items
JAIL_KEY = 0x01F4
ELEVATOR_KEY_COLO = 0x01F5
SMALL_TABLET = 0x01F6
F_DISK = 0x01F7
R_DISK = 0x01F8
L_DISK = 0x01F9
D_DISK = 0x01FA
U_DISK = 0x01FB
SUBWAY_KEY = 0x01FC
MAINGATE_KEY = 0x01FD
CARD_KEY = 0x01FE
DOWN_ST_KEY = 0x01FF
DNA_SAMPLE_1 = 0x0200
DNA_SAMPLE_2 = 0x0201
DNA_SAMPLE_3 = 0x0202
DNA_SAMPLE_4 = 0x0203
DNA_SAMPLE_5 = 0x0204
DNA_SAMPLE_6 = 0x0205
DNA_SAMPLE_7 = 0x0206
DNA_SAMPLE_8 = 0x0207
DNA_SAMPLE_9 = 0x0208
DNA_SAMPLE_10 = 0x0209
DNA_SAMPLE_11 = 0x020A
DNA_SAMPLE_12 = 0x020B
DNA_SAMPLE_13 = 0x020C
DNA_SAMPLE_14 = 0x020D
DNA_SAMPLE_15 = 0x020E
DNA_SAMPLE_16 = 0x020F
DNA_SAMPLE_17 = 0x0210
DNA_SAMPLE_18 = 0x0211
DATA_ROM_COLO = 0x0212
STEEL_TEETH = 0x0213
GEAR = 0x0214
RED_ID_BADGE = 0x0215
GRN_ID_BADGE = 0x0216
BLU_ID_BADGE = 0x0217
YLW_ID_BADGE = 0x0218
TIME_FLUTE = 0x0219
EIN_FILE_S = 0x021A
EIN_FILE_H = 0x021B
EIN_FILE_C = 0x021C
EIN_FILE_P = 0x021D
COLOGNE_CASE_COLO = 0x021E
JOY_SCENT_COLO = 0x021F
EXCITE_SCENT_COLO = 0x0220
VIVID_SCENT_COLO = 0x0221
POWERUP_PART = 0x0222
EIN_FILE_F = 0x0223
# XD key items
SAFE_KEY = 0x01F4
ELEVATOR_KEY_XD = 0x01F5
BONSLY_CARD = 0x01F6
MACHINE_PART = 0x01F7
GONZAPS_KEY = 0x01F8
DATA_ROM_XD = 0x01F9
ID_CARD = 0x01FA
MUSIC_DISC = 0x01FB
SYSTEM_LEVER = 0x01FC
MAYORS_NOTE = 0x01FD
MIROR_RADAR = 0x01FE
POKE_SNACK = 0x01FF
COLOGNE_CASE_XD = 0x0200
JOY_SCENT_XD = 0x0201
EXCITE_SCENT_XD = 0x0202
VIVID_SCENT_XD = 0x0203
SUN_SHARD = 0x0204
MOON_SHARD = 0x0205
BONSLY_PHOTO = 0x0206
CRY_ANALYZER = 0x0207
KRANE_MEMO_1 = 0x020B
KRANE_MEMO_2 = 0x020C
KRANE_MEMO_3 = 0x020D
KRANE_MEMO_4 = 0x020E
KRANE_MEMO_5 = 0x020F
VOICE_CASE_1 = 0x0210
VOICE_CASE_2 = 0x0211
VOICE_CASE_3 = 0x0212
VOICE_CASE_4 = 0x0213
VOICE_CASE_5 = 0x0214
DISC_CASE = 0x0215
BATTLE_CD_01 = 0x0216
BATTLE_CD_02 = 0x0217
BATTLE_CD_03 = 0x0218
BATTLE_CD_04 = 0x0219
BATTLE_CD_05 = 0x021A
BATTLE_CD_06 = 0x021B
BATTLE_CD_07 = 0x021C
BATTLE_CD_08 = 0x021D
BATTLE_CD_09 = 0x021E
BATTLE_CD_10 = 0x021F
BATTLE_CD_11 = 0x0220
BATTLE_CD_12 = 0x0221
BATTLE_CD_13 = 0x0222
BATTLE_CD_14 = 0x0223
BATTLE_CD_15 = 0x0224
BATTLE_CD_16 = 0x0225
BATTLE_CD_17 = 0x0226
BATTLE_CD_18 = 0x0227
BATTLE_CD_19 = 0x0228
BATTLE_CD_20 = 0x0229
BATTLE_CD_21 = 0x022A
BATTLE_CD_22 = 0x022B
BATTLE_CD_23 = 0x022C
BATTLE_CD_24 = 0x022D
BATTLE_CD_25 = 0x022E
BATTLE_CD_26 = 0x022F
BATTLE_CD_27 = 0x0230
BATTLE_CD_28 = 0x0231
BATTLE_CD_29 = 0x0232
BATTLE_CD_30 = 0x0233
BATTLE_CD_31 = 0x0234
BATTLE_CD_32 = 0x0235
BATTLE_CD_33 = 0x0236
BATTLE_CD_34 = 0x0237
BATTLE_CD_35 = 0x0238
BATTLE_CD_36 = 0x0239
BATTLE_CD_37 = 0x023A
BATTLE_CD_38 = 0x023B
BATTLE_CD_39 = 0x023C
BATTLE_CD_40 = 0x023D
BATTLE_CD_41 = 0x023E
BATTLE_CD_42 = 0x023F
BATTLE_CD_43 = 0x0240
BATTLE_CD_44 = 0x0241
BATTLE_CD_45 = 0x0242
BATTLE_CD_46 = 0x0243
BATTLE_CD_47 = 0x0244
BATTLE_CD_48 = 0x0245
BATTLE_CD_49 = 0x0246
BATTLE_CD_50 = 0x0247
BATTLE_CD_51 = 0x0248
BATTLE_CD_52 = 0x0249
BATTLE_CD_53 = 0x024A
BATTLE_CD_54 = 0x024B
BATTLE_CD_55 = 0x024C
BATTLE_CD_56 = 0x024D
BATTLE_CD_57 = 0x024E
BATTLE_CD_58 = 0x024F
BATTLE_CD_59 = 0x0250
BATTLE_CD_60 = 0x0251
@classmethod
def _missing_(cls, value):
return Item.NONE
class ExpClass(Enum):
ERRATIC = 0x01
FAST = 0x04
MEDIUM_FAST = 0x00
MEDIUM_SLOW = 0x03
SLOW = 0x05
FLUCTUATING = 0x02
class EvolutionType(Enum):
NONE = 0x00
HAPPINESS = 0x01
HAPPINESS_DAY = 0x02
HAPPINESS_NIGHT = 0x03
LEVEL_UP = 0x04
TRADE_ALWAYS = 0x05
TRADE_WITH_ITEM = 0x06
STONE_EVOLUTION = 0x07
ATTACK_GT_DEFENSE = 0x08
ATTACK_EQ_DEFENSE = 0x09
ATTACK_LT_DEFENSE = 0x0A
PERSONALITY_VALUE_MODULO_LOW = 0x0B
PERSONALITY_VALUE_MODULO_HIGH = 0x0C
UNUSED_13 = 0x0D
SHEDINJA = 0x0E
HIGH_BEAUTY = 0x0F
HAPPINESS_WITH_ITEM_IN_BAG = 0x10
@property
def param_is_level(self):
return self in [self.LEVEL_UP, self.ATTACK_EQ_DEFENSE, self.ATTACK_GT_DEFENSE, self.ATTACK_LT_DEFENSE,
self.PERSONALITY_VALUE_MODULO_LOW, self.PERSONALITY_VALUE_MODULO_HIGH, self.SHEDINJA]
@property
def param_is_item(self):
return self in [self.TRADE_WITH_ITEM, self.STONE_EVOLUTION, self.HAPPINESS_WITH_ITEM_IN_BAG]
class Nature(IntEnum):
HARDY = 0x00
LONELY = 0x01
BRAVE = 0x02
ADAMANT = 0x03
NAUGHTY = 0x04
BOLD = 0x05
DOCILE = 0x06
RELAXED = 0x07
IMPISH = 0x08
LAX = 0x09
TIMID = 0x0A
HASTY = 0x0B
SERIOUS = 0x0C
JOLLY = 0x0D
NAIVE = 0x0E
MODEST = 0x0F
MILD = 0x10
QUIET = 0x11
BASHFUL = 0x12
RASH = 0x13
CALM = 0x14
GENTLE = 0x15
SASSY = 0x16
CAREFUL = 0x17
QUIRKY = 0x18
# used in Colosseum party data
RANDOM = 0xFF
@classmethod
def _missing_(cls, value):
return -1
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src/examples/cli.py | wdstar/python-examples | 0 | 70289 | #!/usr/bin/env python3
import argparse
import coloredlogs
import logging
import time
import sys
logger = logging.getLogger(__name__)
def log_sample(args: argparse.Namespace):
logger.debug("debug message.")
logger.info("info message.")
logger.warning("warning message.")
logger.error("error message.")
def node_list(args: argparse.Namespace):
logger.info("Execute node list command")
logger.info(args)
def pod_list(args: argparse.Namespace):
logger.info("Execute pod list command")
logger.info(args)
def pod_delete(args: argparse.Namespace):
logger.info("Execute pod delete command")
logger.info(args)
def main():
logging.raiseExceptions = False
logging.Formatter.converter = time.localtime
# logging.Formatter.converter = time.gmtime
"""
logging.basicConfig(
# filename="sample.log",
handlers=[logging.StreamHandler()],
level=logging.DEBUG,
format="%(asctime)s %(name)s %(levelname)s %(message)s",
datefmt="%Y-%m-%d %H:%M:%S %Z %z",
)
"""
coloredlogs.install(
stream=sys.stdout, # default: sys.stderr
level="DEBUG",
fmt="%(asctime)s %(name)s %(levelname)s %(message)s",
datefmt="%Y-%m-%d %H:%M:%S %Z %z",
)
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers(dest="resource", help="target resource")
# Note: In advance `dest` key word arg. of add_subparsers() must be set.
subparsers.required = True
# Alternative: (Python 3.7 or later)
# subparsers = parser.add_subparsers(
# dest="resource", required=True, help="target resource"
# )
parser_log = subparsers.add_parser("log", help="manage logs")
subparser_log = parser_log.add_subparsers(dest="verb", help="operation")
subparser_log.required = True
parser_log_sample = subparser_log.add_parser("sample", help="sample logs.")
parser_log_sample.set_defaults(cmd_func=log_sample)
parser_node = subparsers.add_parser("node", help="manage nodes")
subparser_node = parser_node.add_subparsers(dest="verb", help="operation")
subparser_node.required = True
parser_node_list = subparser_node.add_parser("list", help="list nodes.")
parser_node_list.set_defaults(cmd_func=node_list)
parser_pod = subparsers.add_parser("pod", help="manage pods")
subparser_pod = parser_pod.add_subparsers(dest="verb", help="operation")
subparser_pod.required = True
parser_pod_list = subparser_pod.add_parser("list", help="list pods")
parser_pod_list.set_defaults(cmd_func=pod_list)
parser_pod_delete = subparser_pod.add_parser("delete", help="delete a pod")
parser_pod_delete.add_argument("name", help="pod name")
parser_pod_delete.set_defaults(cmd_func=pod_delete)
args = parser.parse_args()
if hasattr(args, "cmd_func"):
args.cmd_func(args)
else:
args.cmd_help()
if __name__ == "__main__":
main()
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] |
tests/functional/test_owsproxy_app.py | bird-house/twitcher | 13 | 177655 | """
Run tests OWSProxy tests with external WPS.
Please start `Emu WPS <https://emu.readthedocs.io/en/latest/>`_ on port 5000:
http://localhost:5000/wps
"""
import pytest
from .base import FunctionalTest
class OWSProxyAppTest(FunctionalTest):
def setUp(self):
super(OWSProxyAppTest, self).setUp()
self.init_database()
self.init_store()
self.config.include('twitcher.owsproxy')
self.app = self.test_app()
@pytest.mark.online
def test_getcaps(self):
resp = self.app.get('/ows/proxy/wps?service=wps&request=getcapabilities')
assert resp.status_code == 200
assert resp.content_type == 'text/xml'
resp.mustcontain('</wps:Capabilities>')
@pytest.mark.online
def test_describeprocess(self):
resp = self.app.get(
'/ows/proxy/wps?service=wps&request=describeprocess&version=1.0.0&identifier=dummyprocess')
assert resp.status_code == 200
assert resp.content_type == 'text/xml'
resp.mustcontain('</wps:ProcessDescriptions>')
@pytest.mark.online
def test_execute_allowed(self):
access_token = self.create_token()
url = "/ows/proxy/wps_secured?service=wps&request=execute&version=1.0.0&identifier=hello&DataInputs=name=tux&access_token={}".format(access_token) # noqa
resp = self.app.get(url)
assert resp.status_code == 200
assert resp.content_type == 'text/xml'
print(resp.body)
resp.mustcontain(
'<wps:ProcessSucceeded>PyWPS Process Say Hello finished</wps:ProcessSucceeded>')
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backend/ohq/permissions.py | pennlabs/office-hours-queue | 8 | 152144 | from django.db.models import Q
from rest_framework import permissions
from ohq.models import Course, Membership, Question
# Hierarchy of permissions is usually:
# Professor > Head TA > TA > Student > User
# Anonymous Users can't do anything
# Note: everything other than IsSuperuser and CoursePermission is
# scoped to a specific course.
class IsSuperuser(permissions.BasePermission):
"""
Grants permission if the current user is a superuser.
"""
def has_object_permission(self, request, view, obj):
return request.user.is_authenticated and request.user.is_superuser
def has_permission(self, request, view):
return request.user.is_authenticated and request.user.is_superuser
class CoursePermission(permissions.BasePermission):
"""
Anyone can get or list courses.
Only head TAs or professors should be able to modify a course.
Current Head TAs+ or faculty members can create courses.
No one can delete courses.
"""
def has_object_permission(self, request, view, obj):
# Anyone can get a single course
if view.action == "retrieve":
return True
membership = Membership.objects.filter(course=obj, user=request.user).first()
# Non members can't do anything other than retrieve a course
if membership is None:
return False
# No one can delete a course
if view.action == "destroy":
return False
# Course leadership can make changes
if view.action in ["update", "partial_update"]:
return membership.is_leadership and not obj.archived
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
# Members can list courses
if view.action == "list":
return True
if view.action == "create":
return (
request.user.groups.filter(name="platform_faculty").exists()
or Membership.objects.filter(
user=request.user, kind=Membership.KIND_HEAD_TA
).exists()
or Membership.objects.filter(
user=request.user, kind=Membership.KIND_PROFESSOR
).exists()
)
return True
class QueuePermission(permissions.BasePermission):
"""
TAs+ should be able to modify a course.
Only head TAs or professors can create or delete queues.
Students+ can get or list queues
"""
def has_object_permission(self, request, view, obj):
membership = Membership.objects.get(course=view.kwargs["course_pk"], user=request.user)
# Students+ can get a single queue
if view.action == "retrieve":
return True
# TAs+ can make changes
if view.action in ["update", "partial_update", "clear"]:
return membership.is_ta and not obj.archived
# Head TAs+ can create or delete a queue
if view.action == "destroy":
return membership.is_leadership
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
# TAs+ can make changes
if view.action in ["update", "partial_update"]:
return membership.is_ta
# Head TAs+ can create or delete a queue
if view.action == "create":
return membership.is_leadership
return True
class QuestionPermission(permissions.BasePermission):
"""
Students can create questions
Students can get, list, or modify their own questions.
TAs+ can list questions and modify any question.
No one can delete questions.
"""
def has_object_permission(self, request, view, obj):
membership = Membership.objects.get(course=view.kwargs["course_pk"], user=request.user)
# Students can get or modify their own question
# TAs+ can get or modify any questions
if view.action in ["retrieve", "update", "partial_update", "position"]:
return obj.asked_by == request.user or membership.is_ta
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
# No one can delete questions
if view.action == "destroy":
return False
# Students can view their last asked question:
if view.action in ["last", "quota_count"]:
return membership.kind == Membership.KIND_STUDENT
# Students can only create 1 question per queue
if view.action == "create":
existing_question = Question.objects.filter(
Q(queue=view.kwargs["queue_pk"])
& Q(asked_by=request.user)
& (Q(status=Question.STATUS_ASKED) | Q(status=Question.STATUS_ACTIVE))
).first()
return membership.kind == Membership.KIND_STUDENT and existing_question is None
# Students+ can get, list, or modify questions
# With restrictions defined in has_object_permission
return True
class QuestionSearchPermission(permissions.BasePermission):
"""
TAs+ can list questions.
"""
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
# TAs+ can list questions
return membership.is_ta
class MembershipPermission(permissions.BasePermission):
"""
Students can get their own membership.
TAs+ can list memberships.
Users can create a Student membership with open courses.
Head TAs+ can modify and delete memberships.
Students can delete their own memberships.
"""
def has_object_permission(self, request, view, obj):
membership = Membership.objects.get(course=view.kwargs["course_pk"], user=request.user)
# Students can get their own memberships
# TAs+ can get any memberships
if view.action == "retrieve":
return obj.user == request.user or membership.is_ta
# Students can delete their own memberships
# Head TAs+ can delete any memberships
# TODO: make sure Head TAs+ can't delete professors
# and professors can't delete themselves
if view.action == "destroy":
return obj.user == request.user or membership.is_leadership
# Head TAs+ can modify any membership
# TODO: make sure Head TAs+ can't delete professors
# and professors can't delete themselves
if view.action in ["update", "partial_update"]:
return membership.is_leadership
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# No one can create a membership
if view.action == "create":
course = Course.objects.get(id=view.kwargs["course_pk"])
return membership is None and not course.invite_only
# Non-Students can't do anything besides create a membership
if membership is None:
return False
# Students+ can get, modify, and delete memberships
# can list memberships of leaders.
# With restrictions defined in has_object_permission
return True
class MembershipInvitePermission(permissions.BasePermission):
"""
TAs+ can get and list membership invites.
Head TAs+ can create, modify, and delete memberships.
"""
def has_object_permission(self, request, view, obj):
membership = Membership.objects.get(course=view.kwargs["course_pk"], user=request.user)
# TAs+ can get any membership invite
if view.action == "retrieve":
return membership.is_ta
# Head TAs+ can modify or delete any memberships
if view.action in ["destroy", "update", "partial_update"]:
return membership.is_leadership
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
# Head TAs+ can create membership invites
if view.action == "create":
return membership.is_leadership
# TAs+ can list membership invites
if view.action == "list":
return membership.is_ta
# TAs+ can get, modify, and delete memberships
# With restrictions defined in has_object_permission
return membership.is_ta
class MassInvitePermission(permissions.BasePermission):
"""
Head TAs+ can create mass membership invites.
"""
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
return membership.is_leadership
class QueueStatisticPermission(permissions.BasePermission):
"""
Students+ can access queue related statistics
"""
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# anyone who is a member of the class can see queue related statistics
return membership is not None
class AnnouncementPermission(permissions.BasePermission):
"""
TAs+ can create/update/delete announcements
Students can get/list announcements
"""
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
# Students+ can get/list announcements
if view.action in ["list", "retrieve"]:
return True
# TAs+ can create, modify, and delete announcements
return membership.is_ta
class TagPermission(permissions.BasePermission):
"""
Head TAs+ should be able to create, modify and delete a tag.
Students+ can get or list tags.
"""
def has_object_permission(self, request, view, obj):
membership = Membership.objects.get(course=view.kwargs["course_pk"], user=request.user)
# Students+ can get a single tag
if view.action == "retrieve":
return True
# Head TAs+ can make changes
if view.action in ["destroy", "partial_update", "update"]:
return membership.is_leadership
def has_permission(self, request, view):
# Anonymous users can't do anything
if not request.user.is_authenticated:
return False
membership = Membership.objects.filter(
course=view.kwargs["course_pk"], user=request.user
).first()
# Non-Students can't do anything
if membership is None:
return False
if view.action in ["list", "retrieve"]:
return True
# Head TAs+ can make changes
if view.action in ["create", "destroy", "update", "partial_update"]:
return membership.is_leadership
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] |
src/clcore.py | ShepardPower/PyMCBuilder | 1 | 9655 | <filename>src/clcore.py
# I'm just the one that executes the instructions!
import sys, math, json, operator, time
import mcpi.minecraft as minecraft
from PIL import Image as pillow
from blockid import get_block
import mcpi.block as block
import functions as pymc
from tqdm import tqdm
import tkinter as tk
# Functions
# Main code
mc = minecraft.Minecraft.create()
try:
json_file = open("blocks.json")
json_put = json.load(json_file)
except:
pymc.chat(mc, "blocks.json not found, exiting!", 0)
sys.exit(1)
try:
rim = pillow.open(sys.argv[1])
except:
pymc.chat(mc, "bad image, exiting!", 0)
sys.exit(1)
orders = []
used = []
imwid, imhei = rim.size
if imhei > 200:
maxheight = 200
rim.thumbnail((200, maxheight), pillow.ANTIALIAS)
imwid, imhei = rim.size
pymc.chat(mc, "image is over 200 pixels, reducing height.", 1)
rim.convert('RGB')
im = rim.load()
pbar = tqdm(total=imhei*imwid)
for hei in range(imhei):
for wid in range(imwid):
smal = pymc.comp_pixel((im[wid, hei][0], im[wid, hei][1], im[wid, hei][2]), json_put)
im[wid, hei] = smal[1]
used.append(str(smal[2]))
pbar.update(1)
pbar.close()
rim.save("result.GIF") # The result
json_file.close()
oldPos = mc.player.getPos()
playerPos = [round(oldPos.x), round(oldPos.y), round(oldPos.z)]
pymc.chat(mc, "Ready!")
pbar = tqdm(total=imhei*imwid)
num_temp = imhei*imwid-1
for hei in range(imhei):
for wid in range(imwid):
#print(used[wid + (imhei * hei)])
gblock = get_block(used[num_temp])
mc.setBlock(playerPos[0]+wid, playerPos[1]+hei, playerPos[2], gblock)
num_temp -= 1
pbar.update(1)
pbar.close()
pymc.chat(mc, "Done!!")
pymc.chat(mc, "Please star us on github if you like the result!", 2)
| [
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373,
18546,
565,
366,
763,
278,
1121,
29991,
613,
29871,
29906,
29897,
13,
2
] |
09.Printing Stars in C Shape.py | Kushal997-das/Pattern_Printing | 27 | 66831 | <filename>09.Printing Stars in C Shape.py
#Printing Stars in 'C' Shape !
'''
****
*
*
*
*
*
****
'''
for row in range(7):
for col in range(5):
if (col==0 and (row!=0 and row!=6)) or (row==0 or row==6) and (col>0):
print('*',end='')
else:
print(end=' ')
print() | [
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2
] |
tools/build_rules/gtk_dependent.bzl | Ewpratten/frc_971_mirror | 39 | 13383 | disable_gtk_binaries = True
def gtk_dependent_cc_library(**kwargs):
if not disable_gtk_binaries:
native.cc_library(**kwargs)
def gtk_dependent_cc_binary(**kwargs):
if not disable_gtk_binaries:
native.cc_binary(**kwargs)
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python/paddle/fluid/tests/unittests/test_rnn_cell_api.py | L-Net-1992/Paddle | 11 | 31523 | # Copyright (c) 2019 PaddlePaddle 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.
from __future__ import print_function
import unittest
import numpy
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid.framework import program_guard, Program
from paddle.fluid.executor import Executor
from paddle.fluid import framework
from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell
from paddle.fluid.layers import rnn as dynamic_rnn
from paddle.fluid import contrib
from paddle.fluid.contrib.layers import basic_lstm
import paddle.fluid.layers.utils as utils
import numpy as np
class TestLSTMCellError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
batch_size, input_size, hidden_size = 4, 16, 16
inputs = fluid.data(name='inputs',
shape=[None, input_size],
dtype='float32')
pre_hidden = fluid.data(name='pre_hidden',
shape=[None, hidden_size],
dtype='float32')
pre_cell = fluid.data(name='pre_cell',
shape=[None, hidden_size],
dtype='float32')
cell = LSTMCell(hidden_size)
def test_input_Variable():
np_input = np.random.random(
(batch_size, input_size)).astype("float32")
cell(np_input, [pre_hidden, pre_cell])
self.assertRaises(TypeError, test_input_Variable)
def test_pre_hidden_Variable():
np_pre_hidden = np.random.random(
(batch_size, hidden_size)).astype("float32")
cell(inputs, [np_pre_hidden, pre_cell])
self.assertRaises(TypeError, test_pre_hidden_Variable)
def test_pre_cell_Variable():
np_pre_cell = np.random.random(
(batch_size, input_size)).astype("float32")
cell(inputs, [pre_hidden, np_pre_cell])
self.assertRaises(TypeError, test_pre_cell_Variable)
def test_input_type():
error_inputs = fluid.data(name='error_inputs',
shape=[None, input_size],
dtype='int32')
cell(error_inputs, [pre_hidden, pre_cell])
self.assertRaises(TypeError, test_input_type)
def test_pre_hidden_type():
error_pre_hidden = fluid.data(name='error_pre_hidden',
shape=[None, hidden_size],
dtype='int32')
cell(inputs, [error_pre_hidden, pre_cell])
self.assertRaises(TypeError, test_pre_hidden_type)
def test_pre_cell_type():
error_pre_cell = fluid.data(name='error_pre_cell',
shape=[None, hidden_size],
dtype='int32')
cell(inputs, [pre_hidden, error_pre_cell])
self.assertRaises(TypeError, test_pre_cell_type)
def test_dtype():
# the input type must be Variable
LSTMCell(hidden_size, dtype="int32")
self.assertRaises(TypeError, test_dtype)
class TestLSTMCell(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
def test_run(self):
inputs = fluid.data(name='inputs',
shape=[None, self.input_size],
dtype='float32')
pre_hidden = fluid.data(name='pre_hidden',
shape=[None, self.hidden_size],
dtype='float32')
pre_cell = fluid.data(name='pre_cell',
shape=[None, self.hidden_size],
dtype='float32')
cell = LSTMCell(self.hidden_size)
lstm_hidden_new, lstm_states_new = cell(inputs, [pre_hidden, pre_cell])
lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit(
"basicLSTM", self.hidden_size, None, None, None, None, 1.0,
"float32")
lstm_hidden, lstm_cell = lstm_unit(inputs, pre_hidden, pre_cell)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
inputs_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
pre_cell_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
param_names = [[
"LSTMCell/BasicLSTMUnit_0.w_0", "basicLSTM/BasicLSTMUnit_0.w_0"
], ["LSTMCell/BasicLSTMUnit_0.b_0", "basicLSTM/BasicLSTMUnit_0.b_0"]]
for names in param_names:
param = np.array(fluid.global_scope().find_var(
names[0]).get_tensor())
param = np.random.uniform(-0.1, 0.1,
size=param.shape).astype('float32')
fluid.global_scope().find_var(names[0]).get_tensor().set(
param, place)
fluid.global_scope().find_var(names[1]).get_tensor().set(
param, place)
out = exe.run(feed={
'inputs': inputs_np,
'pre_hidden': pre_hidden_np,
'pre_cell': pre_cell_np
},
fetch_list=[lstm_hidden_new, lstm_hidden])
self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
class TestGRUCellError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
batch_size, input_size, hidden_size = 4, 16, 16
inputs = fluid.data(name='inputs',
shape=[None, input_size],
dtype='float32')
pre_hidden = layers.data(name='pre_hidden',
shape=[None, hidden_size],
append_batch_size=False,
dtype='float32')
cell = GRUCell(hidden_size)
def test_input_Variable():
np_input = np.random.random(
(batch_size, input_size)).astype("float32")
cell(np_input, pre_hidden)
self.assertRaises(TypeError, test_input_Variable)
def test_pre_hidden_Variable():
np_pre_hidden = np.random.random(
(batch_size, hidden_size)).astype("float32")
cell(inputs, np_pre_hidden)
self.assertRaises(TypeError, test_pre_hidden_Variable)
def test_input_type():
error_inputs = fluid.data(name='error_inputs',
shape=[None, input_size],
dtype='int32')
cell(error_inputs, pre_hidden)
self.assertRaises(TypeError, test_input_type)
def test_pre_hidden_type():
error_pre_hidden = fluid.data(name='error_pre_hidden',
shape=[None, hidden_size],
dtype='int32')
cell(inputs, error_pre_hidden)
self.assertRaises(TypeError, test_pre_hidden_type)
def test_dtype():
# the input type must be Variable
GRUCell(hidden_size, dtype="int32")
self.assertRaises(TypeError, test_dtype)
class TestGRUCell(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
def test_run(self):
inputs = fluid.data(name='inputs',
shape=[None, self.input_size],
dtype='float32')
pre_hidden = layers.data(name='pre_hidden',
shape=[None, self.hidden_size],
append_batch_size=False,
dtype='float32')
cell = GRUCell(self.hidden_size)
gru_hidden_new, _ = cell(inputs, pre_hidden)
gru_unit = contrib.layers.rnn_impl.BasicGRUUnit("basicGRU",
self.hidden_size, None,
None, None, None,
"float32")
gru_hidden = gru_unit(inputs, pre_hidden)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
inputs_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
param_names = [
["GRUCell/BasicGRUUnit_0.w_0", "basicGRU/BasicGRUUnit_0.w_0"],
["GRUCell/BasicGRUUnit_0.w_1", "basicGRU/BasicGRUUnit_0.w_1"],
["GRUCell/BasicGRUUnit_0.b_0", "basicGRU/BasicGRUUnit_0.b_0"],
["GRUCell/BasicGRUUnit_0.b_1", "basicGRU/BasicGRUUnit_0.b_1"]
]
for names in param_names:
param = np.array(fluid.global_scope().find_var(
names[0]).get_tensor())
param = np.random.uniform(-0.1, 0.1,
size=param.shape).astype('float32')
fluid.global_scope().find_var(names[0]).get_tensor().set(
param, place)
fluid.global_scope().find_var(names[1]).get_tensor().set(
param, place)
out = exe.run(feed={
'inputs': inputs_np,
'pre_hidden': pre_hidden_np
},
fetch_list=[gru_hidden_new, gru_hidden])
self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
class TestRnnError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
batch_size = 4
input_size = 16
hidden_size = 16
seq_len = 4
inputs = fluid.data(name='inputs',
shape=[None, input_size],
dtype='float32')
pre_hidden = layers.data(name='pre_hidden',
shape=[None, hidden_size],
append_batch_size=False,
dtype='float32')
inputs_basic_lstm = fluid.data(name='inputs_basic_lstm',
shape=[None, None, input_size],
dtype='float32')
sequence_length = fluid.data(name="sequence_length",
shape=[None],
dtype='int64')
inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm,
perm=[1, 0, 2])
cell = LSTMCell(hidden_size, name="LSTMCell_for_rnn")
np_inputs_dynamic_rnn = np.random.random(
(seq_len, batch_size, input_size)).astype("float32")
def test_input_Variable():
dynamic_rnn(cell=cell,
inputs=np_inputs_dynamic_rnn,
sequence_length=sequence_length,
is_reverse=False)
self.assertRaises(TypeError, test_input_Variable)
def test_input_list():
dynamic_rnn(cell=cell,
inputs=[np_inputs_dynamic_rnn],
sequence_length=sequence_length,
is_reverse=False)
self.assertRaises(TypeError, test_input_list)
def test_initial_states_type():
cell = GRUCell(hidden_size, name="GRUCell_for_rnn")
error_initial_states = np.random.random(
(batch_size, hidden_size)).astype("float32")
dynamic_rnn(cell=cell,
inputs=inputs_dynamic_rnn,
initial_states=error_initial_states,
sequence_length=sequence_length,
is_reverse=False)
self.assertRaises(TypeError, test_initial_states_type)
def test_initial_states_list():
error_initial_states = [
np.random.random(
(batch_size, hidden_size)).astype("float32"),
np.random.random(
(batch_size, hidden_size)).astype("float32")
]
dynamic_rnn(cell=cell,
inputs=inputs_dynamic_rnn,
initial_states=error_initial_states,
sequence_length=sequence_length,
is_reverse=False)
self.assertRaises(TypeError, test_initial_states_type)
def test_sequence_length_type():
np_sequence_length = np.random.random(
(batch_size)).astype("float32")
dynamic_rnn(cell=cell,
inputs=inputs_dynamic_rnn,
sequence_length=np_sequence_length,
is_reverse=False)
self.assertRaises(TypeError, test_sequence_length_type)
class TestRnn(unittest.TestCase):
def setUp(self):
self.batch_size = 4
self.input_size = 16
self.hidden_size = 16
self.seq_len = 4
def test_run(self):
inputs_basic_lstm = fluid.data(name='inputs_basic_lstm',
shape=[None, None, self.input_size],
dtype='float32')
sequence_length = fluid.data(name="sequence_length",
shape=[None],
dtype='int64')
inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2])
cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn")
output, final_state = dynamic_rnn(cell=cell,
inputs=inputs_dynamic_rnn,
sequence_length=sequence_length,
is_reverse=False)
output_new = layers.transpose(output, perm=[1, 0, 2])
rnn_out, last_hidden, last_cell = basic_lstm(inputs_basic_lstm, None, None, self.hidden_size, num_layers=1, \
batch_first = False, bidirectional=False, sequence_length=sequence_length, forget_bias = 1.0)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
inputs_basic_lstm_np = np.random.uniform(
-0.1, 0.1,
(self.seq_len, self.batch_size, self.input_size)).astype('float32')
sequence_length_np = np.ones(self.batch_size,
dtype='int64') * self.seq_len
inputs_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
pre_hidden_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
pre_cell_np = np.random.uniform(
-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
param_names = [[
"LSTMCell_for_rnn/BasicLSTMUnit_0.w_0",
"basic_lstm_layers_0/BasicLSTMUnit_0.w_0"
],
[
"LSTMCell_for_rnn/BasicLSTMUnit_0.b_0",
"basic_lstm_layers_0/BasicLSTMUnit_0.b_0"
]]
for names in param_names:
param = np.array(fluid.global_scope().find_var(
names[0]).get_tensor())
param = np.random.uniform(-0.1, 0.1,
size=param.shape).astype('float32')
fluid.global_scope().find_var(names[0]).get_tensor().set(
param, place)
fluid.global_scope().find_var(names[1]).get_tensor().set(
param, place)
out = exe.run(feed={
'inputs_basic_lstm': inputs_basic_lstm_np,
'sequence_length': sequence_length_np,
'inputs': inputs_np,
'pre_hidden': pre_hidden_np,
'pre_cell': pre_cell_np
},
fetch_list=[output_new, rnn_out])
self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4))
class TestRnnUtil(unittest.TestCase):
"""
Test cases for rnn apis' utility methods for coverage.
"""
def test_case(self):
inputs = {"key1": 1, "key2": 2}
func = lambda x: x + 1
outputs = utils.map_structure(func, inputs)
utils.assert_same_structure(inputs, outputs)
try:
inputs["key3"] = 3
utils.assert_same_structure(inputs, outputs)
except ValueError as identifier:
pass
class EncoderCell(RNNCell):
"""Encoder Cell"""
def __init__(
self,
num_layers,
hidden_size,
dropout_prob=0.,
init_scale=0.1,
):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(LSTMCell(hidden_size))
def call(self, step_input, states):
new_states = []
for i in range(self.num_layers):
out, new_state = self.lstm_cells[i](step_input, states[i])
step_input = layers.dropout(
out,
self.dropout_prob,
) if self.dropout_prob else out
new_states.append(new_state)
return step_input, new_states
@property
def state_shape(self):
return [cell.state_shape for cell in self.lstm_cells]
class DecoderCell(RNNCell):
"""Decoder Cell"""
def __init__(self, num_layers, hidden_size, dropout_prob=0.):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(LSTMCell(hidden_size))
def call(self, step_input, states):
new_lstm_states = []
for i in range(self.num_layers):
out, new_lstm_state = self.lstm_cells[i](step_input, states[i])
step_input = layers.dropout(
out,
self.dropout_prob,
) if self.dropout_prob else out
new_lstm_states.append(new_lstm_state)
return step_input, new_lstm_states
def def_seq2seq_model(num_layers, hidden_size, dropout_prob, src_vocab_size,
trg_vocab_size):
"vanilla seq2seq model"
# data
source = fluid.data(name="src", shape=[None, None], dtype="int64")
source_length = fluid.data(name="src_sequence_length",
shape=[None],
dtype="int64")
target = fluid.data(name="trg", shape=[None, None], dtype="int64")
target_length = fluid.data(name="trg_sequence_length",
shape=[None],
dtype="int64")
label = fluid.data(name="label", shape=[None, None, 1], dtype="int64")
# embedding
src_emb = fluid.embedding(source, (src_vocab_size, hidden_size))
tar_emb = fluid.embedding(target, (src_vocab_size, hidden_size))
# encoder
enc_cell = EncoderCell(num_layers, hidden_size, dropout_prob)
enc_output, enc_final_state = dynamic_rnn(cell=enc_cell,
inputs=src_emb,
sequence_length=source_length)
# decoder
dec_cell = DecoderCell(num_layers, hidden_size, dropout_prob)
dec_output, dec_final_state = dynamic_rnn(cell=dec_cell,
inputs=tar_emb,
initial_states=enc_final_state)
logits = layers.fc(dec_output,
size=trg_vocab_size,
num_flatten_dims=len(dec_output.shape) - 1,
bias_attr=False)
# loss
loss = layers.softmax_with_cross_entropy(logits=logits,
label=label,
soft_label=False)
loss = layers.unsqueeze(loss, axes=[2])
max_tar_seq_len = layers.shape(target)[1]
tar_mask = layers.sequence_mask(target_length,
maxlen=max_tar_seq_len,
dtype="float32")
loss = loss * tar_mask
loss = layers.reduce_mean(loss, dim=[0])
loss = layers.reduce_sum(loss)
# optimizer
optimizer = fluid.optimizer.Adam(0.001)
optimizer.minimize(loss)
return loss
class TestSeq2SeqModel(unittest.TestCase):
"""
Test cases to confirm seq2seq api training correctly.
"""
def setUp(self):
np.random.seed(123)
self.model_hparams = {
"num_layers": 2,
"hidden_size": 128,
"dropout_prob": 0.1,
"src_vocab_size": 100,
"trg_vocab_size": 100
}
self.iter_num = iter_num = 2
self.batch_size = batch_size = 4
src_seq_len = 10
trg_seq_len = 12
self.data = {
"src":
np.random.randint(
2, self.model_hparams["src_vocab_size"],
(iter_num * batch_size, src_seq_len)).astype("int64"),
"src_sequence_length":
np.random.randint(1, src_seq_len,
(iter_num * batch_size, )).astype("int64"),
"trg":
np.random.randint(
2, self.model_hparams["src_vocab_size"],
(iter_num * batch_size, trg_seq_len)).astype("int64"),
"trg_sequence_length":
np.random.randint(1, trg_seq_len,
(iter_num * batch_size, )).astype("int64"),
"label":
np.random.randint(
2, self.model_hparams["src_vocab_size"],
(iter_num * batch_size, trg_seq_len, 1)).astype("int64"),
}
place = core.CUDAPlace(
0) if core.is_compiled_with_cuda() else core.CPUPlace()
self.exe = Executor(place)
def test_seq2seq_model(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
cost = def_seq2seq_model(**self.model_hparams)
self.exe.run(startup_program)
for iter_idx in range(self.iter_num):
cost_val = self.exe.run(feed={
"src":
self.data["src"][iter_idx * self.batch_size:(iter_idx + 1) *
self.batch_size, :],
"src_sequence_length":
self.data["src_sequence_length"][iter_idx *
self.batch_size:(iter_idx +
1) *
self.batch_size],
"trg":
self.data["trg"][iter_idx * self.batch_size:(iter_idx + 1) *
self.batch_size, :],
"trg_sequence_length":
self.data["trg_sequence_length"][iter_idx *
self.batch_size:(iter_idx +
1) *
self.batch_size],
"label":
self.data["label"][iter_idx *
self.batch_size:(iter_idx + 1) *
self.batch_size]
},
fetch_list=[cost])[0]
print("iter_idx: %d, cost: %f" % (iter_idx, cost_val))
if __name__ == '__main__':
unittest.main()
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] |
ovirt-guest-agent/GuestAgentLinux2.py | edwardbadboy/ovirtagent-ubuntu | 0 | 67318 | #
# Copyright 2010-2012 Red Hat, Inc. and/or its affiliates.
#
# 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.
#
# Refer to the README and COPYING files for full details of the license.
#
import logging
import os
import socket
import subprocess
import threading
import time
from OVirtAgentLogic import AgentLogicBase, DataRetriverBase
CredServer = None
try:
from CredServer import CredServer as CredServerImported
CredServer = CredServerImported
except ImportError:
# The CredServer doesn't exist in RHEL-5. So we provide a
# fake server that do nothing.
class CredServerFake(threading.Thread):
def user_authenticated(self, credentials):
pass
CredServer = CredServerFake
class PkgMgr(object):
def rpm_list_packages(self, app_list):
""" Implementes the package retrieval for rpm based environments """
apps = set()
for name in app_list.split():
ts = self.rpm.TransactionSet()
for app in ts.dbMatch('name', name):
apps.add("%s-%s-%s" %
(app['name'], app['version'], app['release']))
return apps
def apt_list_packages(self, app_list):
""" Implementes the package retrieval for apt based environments """
INSTALLED_STATE = self.apt_pkg.CURSTATE_INSTALLED
apps = set()
cache = self.apt_pkg.Cache()
for app in app_list.split():
if app in cache:
pkg = cache[app]
# Normal package
if pkg.current_state == INSTALLED_STATE:
detail = (app, pkg.current_ver.ver_str)
apps.add("%s-%s" % (detail))
# virtual package
elif len(pkg.provides_list) > 0:
for _, _, pkg in pkg.provides_list:
if pkg.parent_pkg.current_state == INSTALLED_STATE:
detail = (app, pkg.parent_pkg.current_ver.ver_str)
apps.add("%s-%s" % (detail))
return apps
def list_pkgs(self, app_list):
""" Implements the package retrieval for apt and rpm if present and
returns a joined list of packages installed on the system. """
apps = set()
if self.rpm:
apps.update(self.rpm_list_packages(app_list))
if self.apt_pkg:
apps.update(self.apt_list_packages(app_list))
apps = list(apps)
logging.debug("PkgMgr: list_pkgs returns [%s]" % (str(apps)))
return apps
def __init__(self):
self.rpm = None
self.apt_pkg = None
try:
import rpm
self.rpm = rpm
except ImportError:
pass
try:
from apt import apt_pkg
apt_pkg.init()
self.apt_pkg = apt_pkg
except ImportError:
pass
if not self.rpm and not self.apt_pkg:
logging.info("Unknown package management. Application list "
"report is disabled.")
class NicMgr(object):
def __init__(self):
try:
import ethtool
except ImportError:
raise NotImplementedError
self.ethtool = ethtool
self.list_nics = self.ethtool_list_nics
def _get_ipv4_addresses(self, dev):
if hasattr(dev, 'get_ipv4_addresses'):
ipv4_addrs = []
for ip in dev.get_ipv4_addresses():
ipv4_addrs.append(ip.address)
return ipv4_addrs
if dev.ipv4_address is not None:
return [dev.ipv4_address]
else:
return []
def _get_ipv6_addresses(self, dev):
ipv6_addrs = []
for ip in dev.get_ipv6_addresses():
ipv6_addrs.append(ip.address)
return ipv6_addrs
def ethtool_list_nics(self):
interfaces = list()
try:
for dev in self.ethtool.get_devices():
flags = self.ethtool.get_flags(dev)
if flags & self.ethtool.IFF_UP and \
not(flags & self.ethtool.IFF_LOOPBACK):
devinfo = self.ethtool.get_interfaces_info(dev)[0]
interfaces.append(
{'name': dev,
'inet': self._get_ipv4_addresses(devinfo),
'inet6': self._get_ipv6_addresses(devinfo),
'hw': self.ethtool.get_hwaddr(dev)})
except:
logging.exception("Error retrieving network interfaces.")
return interfaces
class CommandHandlerLinux:
def __init__(self, agent):
self.agent = agent
def lock_screen(self):
cmd = ['/usr/share/ovirt-guest-agent/ovirt-locksession']
logging.debug("Executing lock session command: '%s'", cmd)
subprocess.call(cmd)
def login(self, credentials):
self.agent.cred_server.user_authenticated(credentials)
def logoff(self):
pass
def shutdown(self, timeout, msg):
# The shutdown command works with minutes while vdsm send value in
# seconds, so we round up the value to minutes.
delay = (int(timeout) + 59) / 60
cmd = ['/usr/share/ovirt-guest-agent/ovirt-shutdown', '-h',
"+%d" % (delay), "\"%s\"" % (msg)]
logging.debug("Executing shutdown command: %s", cmd)
subprocess.call(cmd)
def hibernate(self, state):
cmd = ['/usr/share/ovirt-guest-agent/ovirt-hibernate', state]
logging.debug("Executing hibernate command: %s", cmd)
subprocess.call(cmd)
class LinuxDataRetriver(DataRetriverBase):
def __init__(self):
try:
pkgmgr = PkgMgr()
except NotImplementedError:
self.list_pkgs = lambda app_list: []
else:
self.list_pkgs = pkgmgr.list_pkgs
try:
nicmgr = NicMgr()
except NotImplementedError:
self.list_nics = lambda: []
else:
self.list_nics = nicmgr.list_nics
self.app_list = ""
self.ignored_fs = ""
self._init_vmstat()
DataRetriverBase.__init__(self)
def getMachineName(self):
return socket.getfqdn()
def getOsVersion(self):
return os.uname()[2]
def getAllNetworkInterfaces(self):
return self.list_nics()
def getApplications(self):
return self.list_pkgs(self.app_list)
def getAvailableRAM(self):
free = 0
for line in open('/proc/meminfo'):
var, value = line.strip().split()[0:2]
if var in ('MemFree:', 'Buffers:', 'Cached:'):
free += long(value)
return str(free / 1024)
def getUsers(self):
users = ''
try:
cmdline = '/usr/bin/users | /usr/bin/tr " " "\n" | /usr/bin/uniq'
users = ' '.join(os.popen(cmdline).read().split())
except:
logging.exception("Error retrieving logged in users.")
return users
def getActiveUser(self):
users = os.popen('/usr/bin/users').read().split()
try:
user = users[0]
except:
user = 'None'
return user
def getDisksUsage(self):
usages = list()
with open('/proc/mounts') as mounts:
for mount in mounts:
try:
(device, path, fs) = mount.split()[:3]
if not fs in self.ignored_fs:
# path might include spaces.
path = path.decode("string-escape")
statvfs = os.statvfs(path)
total = statvfs.f_bsize * statvfs.f_blocks
used = total - statvfs.f_bsize * statvfs.f_bfree
usages.append({'path': path, 'fs': fs, 'total': total,
'used': used})
except:
logging.exception("Error retrieving disks usages.")
return usages
def getMemoryStats(self):
try:
self._get_meminfo()
self._get_vmstat()
except:
logging.exception("Error retrieving memory stats.")
return self.memStats
def _init_vmstat(self):
self.vmstat = {}
self.vmstat['timestamp_prev'] = time.time()
fields = ['swap_in', 'swap_out', 'pageflt', 'majflt']
for field in fields:
self.vmstat[field + '_prev'] = None
self.vmstat[field + '_cur'] = None
def _get_meminfo(self):
fields = {'MemTotal:': 0, 'MemFree:': 0, 'Buffers:': 0,
'Cached:': 0}
free = 0
for line in open('/proc/meminfo'):
(key, value) = line.strip().split()[0:2]
if key in fields.keys():
fields[key] = int(value)
if key in ('MemFree:', 'Buffers:', 'Cached:'):
free += int(value)
self.memStats['mem_total'] = fields['MemTotal:']
self.memStats['mem_unused'] = fields['MemFree:']
self.memStats['mem_free'] = free
def _get_vmstat(self):
"""
/proc/vmstat reports cumulative statistics so we must subtract the
previous values to get the difference since the last collection.
"""
fields = {'pswpin': 'swap_in', 'pswpout': 'swap_out',
'pgfault': 'pageflt', 'pgmajfault': 'majflt'}
self.vmstat['timestamp_cur'] = time.time()
interval = self.vmstat['timestamp_cur'] - self.vmstat['timestamp_prev']
self.vmstat['timestamp_prev'] = self.vmstat['timestamp_cur']
for line in open('/proc/vmstat'):
(key, value) = line.strip().split()[0:2]
if key in fields.keys():
name = fields[key]
self.vmstat[name + '_prev'] = self.vmstat[name + '_cur']
self.vmstat[name + '_cur'] = int(value)
if self.vmstat[name + '_prev'] is None:
self.vmstat[name + '_prev'] = self.vmstat[name + '_cur']
self.memStats[name] = int((self.vmstat[name + '_cur'] -
self.vmstat[name + '_prev']) /
interval)
class LinuxVdsAgent(AgentLogicBase):
def __init__(self, config):
AgentLogicBase.__init__(self, config)
self.dr = LinuxDataRetriver()
self.dr.app_list = config.get("general", "applications_list")
self.dr.ignored_fs = set(config.get("general", "ignored_fs").split())
self.commandHandler = CommandHandlerLinux(self)
self.cred_server = CredServer()
def run(self):
self.cred_server.start()
AgentLogicBase.run(self)
def stop(self):
self.cred_server.join()
AgentLogicBase.stop(self)
def test():
import pprint
dr = LinuxDataRetriver()
dr.app_list = "kernel kernel-headers aspell"
dr.ignored_fs = set("rootfs tmpfs autofs cgroup selinuxfs udev mqueue "
"nfsd proc sysfs devtmpfs hugetlbfs rpc_pipefs devpts "
"securityfs debugfs binfmt_misc fuse.gvfsd-fuse "
"fuse.gvfs-fuse-daemon fusectl usbfs".split())
print "Machine Name:", dr.getMachineName()
print "OS Version:", dr.getOsVersion()
print "Network Interfaces:", dr.getAllNetworkInterfaces()
print "Installed Applications:", dr.getApplications()
print "Available RAM:", dr.getAvailableRAM()
print "Logged in Users:", dr.getUsers()
print "Active User:", dr.getActiveUser()
print "Disks Usage:", pprint.pprint(dr.getDisksUsage())
print "Memory Stats:", dr.getMemoryStats()
if __name__ == '__main__':
test()
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] |
python-lambda-simple/app.py | hemanthkolla/cdk-templates | 117 | 1609370 | <gh_stars>100-1000
#!/usr/bin/env python3
import os
from aws_cdk import core
from python_lambda_simple.python_lambda_simple_stack import PythonLambdaSimpleStack
app = core.App()
# for production set your Environment to specify the target region and account
# Prod_Env__Ire = env=core.Environment(region="eu-west-1",account="<YOUR_ACCOUNT-ID>")
PythonLambdaSimpleStack(app, "python-lambda-simple", env=core.Environment(
account=os.environ["CDK_DEFAULT_ACCOUNT"],
region=os.environ["CDK_DEFAULT_REGION"]))
app.synth()
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Penrose/generate - 1808.py | kart324/MultiNLP | 0 | 120192 | <filename>Penrose/generate - 1808.py
import re
import string
inp = '''A is a set.
A, B, C are sets.
B is contained in A.
C is contained in B.
C is a set.
C is contained in A.
P is a point.
P is contained in A.
P is not contained in C.
X is a vectorspace.
U is a vector.
V is a vector.
U and V belong to the vectorspace X.
X is the vectorspace to which U and V belong.
U and V are orthogonal vectors.
U and V are equal vectors.
U and V have equal lengths.
U and V make an angle of 45 degrees with each other.
W=U+V.
W is equal to the sum of U and V.
W is the dot product of U and V.
P is a point.
P,Q,R,S are points.
A is a segment.
A,B,C,D are segments.
T is an angle.
T,U,V are angles.
T is a triangle.
W is a ray.
W is a bisector to T.
W is a perpendicular bisector to A.
A and B are sets.
C and D are sets.
P and Q are sets.
F is a function.
F,G,H are functions.
F is an injective function from A to B.
G is a surjective function from C to D.
H is a bijective function from P to Q.
F is an injective function from A to B.
G is a surjective function from B to C.
H is a bijective function from C to D.
S is a square.
R is a rectangle.
K is a right angled triangle.
K is a triangle with angles 60,40,80.'''
strs = inp.split('\n')
ent_keys=['set','sets','vector','vectorspace','point','points','segment','segments','angle','angles'
,'triangle','ray','function','functions','square','rectangle']
oper = ['dot product','sum','+','=','equal','orthogonal','right angled','angle of 45 degrees with each other','injective','bijective','surjective']
for idx,st in enumerate(strs):
g= open(str(idx+1)+".txt", mode='w', encoding='utf-8')
g.write(st)
st_sub = re.sub(r"[,.;@#?!&$]+", ' ', st)
f = open(str(idx+1)+".ann", mode='w', encoding='utf-8')
t_count=1
for obj in re.finditer('[0-9A-Z]+',st_sub):
f.write('T'+str(t_count)+'\t'+'VAR'+' '+str(obj.start())+' '+str(obj.end())+'\t'+obj.group()+'\n')
t_count+=1
for ent in ent_keys:
for obj in re.finditer(ent,st_sub):
f.write('T'+str(t_count)+'\t'+'ATTR'+' '+str(obj.start())+' '+str(obj.end())+'\t'+ent+'\n')
t_count+=1
for op in oper:
for obj in re.finditer(re.escape(op),st_sub):
f.write('T'+str(t_count)+'\t'+'OPER'+' '+str(obj.start())+' '+str(obj.end())+'\t'+op+'\n')
t_count+=1
f.close()
g.close()
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pykotor/resource/formats/ssf/io_binary.py | NickHugi/PyKotor | 1 | 184673 | <reponame>NickHugi/PyKotor<gh_stars>1-10
from __future__ import annotations
from typing import Optional
from pykotor.resource.formats.ssf.data import SSF, SSFSound
from pykotor.resource.type import TARGET_TYPES, SOURCE_TYPES, ResourceReader, ResourceWriter
class SSFBinaryReader(ResourceReader):
def __init__(self, source: SOURCE_TYPES, offset: int = 0, size: int = 0):
super().__init__(source, offset, size)
self._ssf: Optional[SSF] = None
def load(self, auto_close: bool = True) -> SSF:
self._ssf = SSF()
file_type = self._reader.read_string(4)
file_version = self._reader.read_string(4)
if file_type != "SSF ":
raise TypeError("Attempted to load an invalid SSF was loaded.")
if file_version != "V1.1":
raise TypeError("The supplied SSF file version is not supported.")
sounds_offset = self._reader.read_uint32()
self._reader.seek(sounds_offset)
self._ssf.set(SSFSound.BATTLE_CRY_1, self._reader.read_uint32())
self._ssf.set(SSFSound.BATTLE_CRY_2, self._reader.read_uint32())
self._ssf.set(SSFSound.BATTLE_CRY_3, self._reader.read_uint32())
self._ssf.set(SSFSound.BATTLE_CRY_4, self._reader.read_uint32())
self._ssf.set(SSFSound.BATTLE_CRY_5, self._reader.read_uint32())
self._ssf.set(SSFSound.BATTLE_CRY_6, self._reader.read_uint32())
self._ssf.set(SSFSound.SELECT_1, self._reader.read_uint32())
self._ssf.set(SSFSound.SELECT_2, self._reader.read_uint32())
self._ssf.set(SSFSound.SELECT_3, self._reader.read_uint32())
self._ssf.set(SSFSound.ATTACK_GRUNT_1, self._reader.read_uint32())
self._ssf.set(SSFSound.ATTACK_GRUNT_2, self._reader.read_uint32())
self._ssf.set(SSFSound.ATTACK_GRUNT_3, self._reader.read_uint32())
self._ssf.set(SSFSound.PAIN_GRUNT_1, self._reader.read_uint32())
self._ssf.set(SSFSound.PAIN_GRUNT_2, self._reader.read_uint32())
self._ssf.set(SSFSound.LOW_HEALTH, self._reader.read_uint32())
self._ssf.set(SSFSound.DEAD, self._reader.read_uint32())
self._ssf.set(SSFSound.CRITICAL_HIT, self._reader.read_uint32())
self._ssf.set(SSFSound.TARGET_IMMUNE, self._reader.read_uint32())
self._ssf.set(SSFSound.LAY_MINE, self._reader.read_uint32())
self._ssf.set(SSFSound.DISARM_MINE, self._reader.read_uint32())
self._ssf.set(SSFSound.BEGIN_STEALTH, self._reader.read_uint32())
self._ssf.set(SSFSound.BEGIN_SEARCH, self._reader.read_uint32())
self._ssf.set(SSFSound.BEGIN_UNLOCK, self._reader.read_uint32())
self._ssf.set(SSFSound.UNLOCK_FAILED, self._reader.read_uint32())
self._ssf.set(SSFSound.UNLOCK_SUCCESS, self._reader.read_uint32())
self._ssf.set(SSFSound.SEPARATED_FROM_PARTY, self._reader.read_uint32())
self._ssf.set(SSFSound.REJOINED_PARTY, self._reader.read_uint32())
self._ssf.set(SSFSound.POISONED, self._reader.read_uint32())
if auto_close:
self._reader.close()
return self._ssf
class SSFBinaryWriter(ResourceWriter):
def __init__(self, ssf: SSF, target: TARGET_TYPES):
super().__init__(target)
self._ssf: SSF = ssf
def write(self, auto_close: bool = True) -> None:
self._writer.write_string("SSF ")
self._writer.write_string("V1.1")
self._writer.write_uint32(12)
self._writer.write_uint32(self._ssf.get(SSFSound.BATTLE_CRY_1), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BATTLE_CRY_2), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BATTLE_CRY_3), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BATTLE_CRY_4), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BATTLE_CRY_5), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BATTLE_CRY_6), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.SELECT_1), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.SELECT_2), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.SELECT_3), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.ATTACK_GRUNT_1), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.ATTACK_GRUNT_2), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.ATTACK_GRUNT_3), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.PAIN_GRUNT_1), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.PAIN_GRUNT_2), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.LOW_HEALTH), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.DEAD), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.CRITICAL_HIT), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.TARGET_IMMUNE), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.LAY_MINE), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.DISARM_MINE), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BEGIN_STEALTH), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BEGIN_SEARCH), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.BEGIN_UNLOCK), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.UNLOCK_FAILED), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.UNLOCK_SUCCESS), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.SEPARATED_FROM_PARTY), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.REJOINED_PARTY), max_neg1=True)
self._writer.write_uint32(self._ssf.get(SSFSound.POISONED), max_neg1=True)
for i in range(8):
self._writer.write_uint32(0xFFFFFFFF)
if auto_close:
self._writer.close()
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ngraph/frontends/neon/data/ptb.py | NervanaSystems/ngraph-python | 18 | 1608799 | # ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
from ngraph.util.persist import valid_path_append, fetch_file
import os
import numpy as np
class PTB(object):
"""
Penn Treebank data set from http://arxiv.org/pdf/1409.2329v5.pdf
Arguments:
path (string): Data directory to find the data, if not existing, will
download the data
shift_target (boolean): Set the target to be the same sequence of shifted
version of the sequence. Default to be True, for
language models.
"""
def __init__(self, path='.', use_words=False, shift_target=True):
self.path = path
self.url = 'https://raw.githubusercontent.com/wojzaremba/lstm/master/data'
self.filemap = dict(train=dict(filename='ptb.train.txt', size=5101618),
test=dict(filename='ptb.test.txt', size=449945),
valid=dict(filename='ptb.valid.txt', size=399782))
self.shift_target = shift_target
self.use_words = use_words
def load_data(self):
self.data_dict = {}
self.vocab = None
for phase in ['train', 'test', 'valid']:
filename, filesize = self.filemap[phase]['filename'], self.filemap[phase]['size']
workdir, filepath = valid_path_append(self.path, '', filename)
if not os.path.exists(filepath):
fetch_file(self.url, filename, filepath, filesize)
tokens = open(filepath).read() # add tokenization here if necessary
if self.use_words:
tokens = tokens.strip().split()
self.vocab = sorted(set(tokens)) if self.vocab is None else self.vocab
# vocab dicts
self.token_to_index = dict((t, i) for i, t in enumerate(self.vocab))
self.index_to_token = dict((i, t) for i, t in enumerate(self.vocab))
# map tokens to indices
X = np.asarray([self.token_to_index[t] for t in tokens], dtype=np.uint32)
if self.shift_target:
y = np.concatenate((X[1:], X[:1]))
else:
y = X.copy()
self.data_dict[phase] = {'inp_txt': X, 'tgt_txt': y}
return self.data_dict
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ServerInitialisation/vmpackage/serverCreationInitialisation.py | flexiops/Server-Initialisation | 0 | 1601040 | import requests
import json
import time
from serverapi.serverMethods import get_server_state
from serverapi.serverMethods import change_server_status
WAIT_TIME = 30 #seconds
MAX_NO_ATTEMPTS = 5
serverUUID = ""
isVerbose = False
ENDPOINT = "https://cp.sd1.flexiant.net:4442/"
#Login UUID/username/password
customerUUID=""
username=""
password=""
#Method used to authenticate a user with platform and create a token
def getToken(endpoint, username, cust_uuid, password):
tokenURL = "%srest/user/current/authentication" % endpoint
apiUserName = username + "/" + cust_uuid
tokenPayload = {'automaticallyRenew': 'True'}
tokenRequest = requests.get(tokenURL, params=tokenPayload,
auth=(apiUserName, password))
retry = True
count = 1
#Try the request multiple times if failure occurs
while ((count <= MAX_NO_ATTEMPTS) and (retry == True)):
#Get response from reserver
tokenRequest = requests.get(tokenURL, params=tokenPayload,
auth=(apiUserName, password))
#If the token is returned correctly, return it
if tokenRequest.ok:
token = tokenRequest.content
tokenObj = json.loads(token)
return tokenObj['publicToken']
#If the server is busy, increment the count and try the request again
if (tokenRequest.status_code == 429):
print "Server busy - received 429 response, wait and retry. Attempt number: ", count
time.sleep(WAIT_TIME)
count = count + 1
else:
#If the server can not be reached, raise an exception
raise Exception("Failed contacting %s with %s (%s)" % (
tokenURL, tokenRequest.reason, tokenRequest.status_code))
#If max amount of attempts have been reached, raise an exception
if ((retry == True) and (count == MAX_NO_ATTEMPTS)):
raise Exception("HTTP 429 ERROR, Maximum unsuccessful attempts made to send request to the server")
#Method used to create a new server instance on the platform
def rest_create_server(auth_parms, server_name, server_po_uuid, image_uuid, cluster_uuid, vdc_uuid, cpu_count,
ram_amount, nics, boot_disk_po_uuid, context_script):
createURL = auth_parms['endpoint'] + "rest/user/current/resources/server"
#Get image size
img_ret = list_image(auth_parms, image_uuid)
size = img_ret['size']
#Set up parameters as required
server_json = {
"resourceName": server_name,
"productOfferUUID": server_po_uuid,
"imageUUID": image_uuid,
"clusterUUID": cluster_uuid,
"vdcUUID": vdc_uuid,
"nics" : nics,
"cpu": cpu_count,
"ram": ram_amount,
"disks": [{"iso": False,
# "resourceName": "the disk"
"resourceType": "DISK",
"resourceUUID": boot_disk_po_uuid,
"size": size,
"vdcUUID": vdc_uuid,
# "productOfferUUID": disk_po_uuid
}],
"resourceType": "SERVER",
"resourceMetadata": {"publicMetadata": context_script},
# "serverCapabilities": ["CLONE", "CHILDREN_PERSIST_ON_DELETE", "CHILDREN_PERSIST_ON_REVERT"],
}
payload = server_json
print(payload)
payload_as_string = json.JSONEncoder().encode(payload)
# Need to set the content type, because if we don't the payload is just silently ignored
headers = {'content-type': 'application/json'}
#Submit server create request
result = rest_submit_postrequest(createURL, payload_as_string, headers, auth_parms)
return result
def list_image(auth_params, uuid):
""" Get Image details """
# Setup serach filter
sf = { "searchFilter" :
{ "filterConditions": [{"condition": "IS_EQUAL_TO",
"field": "resourceUUID",
"value": [uuid]
}
]
}
}
if (isVerbose):
print("sf=")
print sf
print("---")
#Check if image can be accessed properly
result_set = rest_list_resource(auth_params, "image", sf)
if result_set['totalCount'] == 0:
raise RuntimeError("Image " + uuid + " not found or you do not have permissions to use it")
print("==== Image Result ====")
print result_set
print("=========");
# return just the first element (there was only one, right ?), otheriwse we end up doing e.g. img_ret['list'][0]['vdcUUID']
# all over the place
return result_set['list'][0]
#Method used to submit a REST POST request
def rest_submit_postrequest(theURL, payload, headers, auth_parms):
retry = True
count = 1
#Attempt the request multiple times if failure occurs
while ((count <= MAX_NO_ATTEMPTS) and (retry == True)):
res = requests.post(theURL, payload, auth=(auth_parms['token'], ''), headers=headers)
print("==============================================================")
print "Request submitted, response URL and contents:"
print(res.url)
print res.content
#jsonDump = json.dumps(res.content)
jsonContent = json.loads(res.content)
#Take the newly created serverUUID which is used later to create the server
global serverUUID
serverUUID = jsonContent["itemUUID"]
print("HTTP response code: ", res.status_code)
# Status 202 (Accepted) is good
if ((res.status_code == 202) or (res.status_code == 200)):
response = json.loads(res.content)
retry = False
return response
#If server is busy, attempt the request again
if (res.status_code == 429):
print "Server busy - received 429 response, wait and retry. Attempt number: ", count
time.sleep(WAIT_TIME)
count = count + 1
else:
# Something else went wrong. Pick out the status code and message
response = json.loads(res.content)
retry = False
return ""
print("==============================================================")
#If max amount of requests has been reached, print an error message
if ((retry == True) and (count == MAX_NO_ATTEMPTS)):
print "HTTP 429 ERROR, Maximum unsuccessful attempts made to send request to the server"
# print(response['message'] + " (error code: " + response['errorCode'] + ")")
return ""
#Use a REST request to list resources
def rest_list_resource(auth_parms, res_type, payload):
print auth_parms
theURL = auth_parms['endpoint'] + "rest/user/current/resources/" + res_type + "/list"
print theURL
if payload != None:
payload_as_string = json.JSONEncoder().encode(payload);
print("payload_as_string=" + payload_as_string)
# Note we use data= and not params= here
# See: http://requests.readthedocs.org/en/v1.0.1/user/quickstart/
#
# Also, we need to set the content type, because if we don't the payload is just silently ignored
headers = {'content-type': 'application/json'}
print("theURL=" + theURL)
retry = True
count = 1
#If the request failed, retry the request a specified number of times
while ((count <= MAX_NO_ATTEMPTS) and (retry == True)):
if payload != None:
res = requests.get(theURL, data=payload_as_string, auth=(auth_parms['token'], ''), headers=headers)
else:
res = requests.get(theURL, auth=(auth_parms['token'], ''), headers=headers)
#Output the result from the request
print("==============================================================")
print(res.url)
print("res=" + str(res))
print res.content
# Status 202 (Accepted) is good
if (res.status_code == 200):
response = json.loads(res.content)
retry = False
return response
#If the server is busy, retry the request
if (res.status_code == 429):
print "Server busy - received 429 response, wait and retry. Attempt number: ", count
time.sleep(WAIT_TIME)
count = count + 1
else:
# Something else went wrong. Pick out the status code and message
response = json.loads(res.content)
print("HTTP response code: ", res.status_code)
retry = False
return ""
#Max number of requests has been reached
if ((retry == True) and (count == MAX_NO_ATTEMPTS)):
raise RuntimeError("HTTP 429 ERROR, Maximum unsuccessful attempts made to send request to the server")
return ""
#Method used to create the server and deploy it to the platform
def create_server():
hostname = "https://cp.sd1.flexiant.net:4442/"
#Get token to authenticate the user
token = getToken(hostname, username,
customerUUID, password)
auth = dict(endpoint=hostname, token=token)
#Parameters that are to be sent to the platform. (Add/remove as required for target platform)
auth_parms = auth
server_name = ""
server_productoffer_uuid = ""
image_uuid = ""
cluster_uuid = ""
vdc_uuid = ""
cpu_count = 1
ram_amount = 512
boot_disk_po_uuid = ""
context_script = ""
networkUUID = ""
networkType = ""
resourceName = ""
resourceType = ""
#NIC is a virtual resource that stands for Network Interface Card
nic = {
"clusterUUID": cluster_uuid,
"networkUUID": networkUUID,
"networkType": networkType,
"resourceName": resourceName,
"resourceType": resourceType,
"vdcUUID": vdc_uuid,
}
nics = [
nic
]
#Call the create server method. Modify parameters as required for the target platform
rest_create_server(auth_parms, server_name, server_productoffer_uuid,
image_uuid, cluster_uuid,
vdc_uuid, cpu_count,
ram_amount, nics, boot_disk_po_uuid, context_script)
#Helper method used to change the state of a server into running mode
def start_server(auth_parms, server_uuid):
"""Function to start server, uuid in server_data"""
server_state = get_server_state(auth_parms, server_uuid)
if server_state == 'STOPPED':
rc = change_server_status(auth_parms=auth_parms, server_uuid=server_uuid, state='RUNNING')
if (rc != 0):
raise Exception("Failed to put server " + server_uuid + " in to running state")
#The main method used to launch a server instance
def StartVM(customerUUID, customerUsername, customerPassword, serverUUID):
#Authenticate the api connection
auth_client = api_session(customerUsername, customerUUID, customerPassword)
#Get the current server state
server_state = get_server_state(auth_client, serverUUID)
if (server_state == 'RUNNING'):
print "Server is already running"
return
if (server_state == 'STOPPED' or server_state == 'STOPPING'):
start_server(auth_client, serverUUID)
print "Server is now RUNNING "
else:
print "Server could not be started because it is - %s " % server_state
#Method used to get api session
def api_session(customerUsername, customerUUID, customerPassword):
"""Function to set up api session, import credentials etc."""
token = getToken(ENDPOINT, customerUsername, customerUUID, customerPassword)
auth_client = dict(endpoint=ENDPOINT, token=token)
return auth_client
create_server()
time.sleep(20)
StartVM(customerUUID,username,password,serverUUID)
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climpy/utils/avhrr_utils.py | SeregaOsipov/ClimPy | 6 | 1616699 | from climpy.utils.file_path_utils import get_root_storage_path_on_hpc
import netCDF4
from climpy.utils.diag_decorators import time_interval_selection
# from climpy.utils.time_utils import process_time_range_impl
from climpy.utils.netcdf_utils import generate_netcdf_uniform_time_data
import numpy as np
__author__ = '<NAME> <<EMAIL>>'
@time_interval_selection
def prepare_avhrr_aod(zonal_mean=False):
file_name = 'aot_avhrr_1989-1992'
lat_name = 'latitude'
lon_name = 'longitude'
if zonal_mean:
file_name += '_zonal_mean'
lat_name = 'lat'
lon_name = 'lon'
file_path = get_root_storage_path_on_hpc() + 'Data/AVHRR/AOT/' + file_name + '.nc'
nc = netCDF4.Dataset(file_path)
time_data = generate_netcdf_uniform_time_data(nc.variables['time'])
# t_slice, time_data = process_time_range_impl(time_data[:], time_range_vo)
aod_data = nc.variables['aot1'][:] # [t_slice]
if zonal_mean:
aod_data = np.squeeze(aod_data)
lat_data = nc.variables[lat_name][:]
lon_data = nc.variables[lon_name][:]
vo = {}
vo['data'] = aod_data
vo['time'] = time_data
vo['lat'] = lat_data
vo['lon'] = lon_data
return vo
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app/main.py | guhuajun/azwebapp-demo | 0 | 184443 | <filename>app/main.py<gh_stars>0
# -*- coding: utf-8 -*-
# pylint: disable=
from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Route
import uvicorn
async def index(request):
return JSONResponse({'hello': 'world'})
routes = [
Route('/', index),
]
app = Starlette(debug=False, routes=routes)
if __name__ == "__main__":
uvicorn.run(app, host='0.0.0.0', port=8000)
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Lectures_Codes/examples-05/lecture05/lenghts_of_words_example/lengths_of_words.py | MichalKyjovsky/NPRG065_Programing_in_Python | 0 | 46054 | <reponame>MichalKyjovsky/NPRG065_Programing_in_Python
from collections import Counter, defaultdict
from stats import print_histogram_2d
# This is a list of characters that are interpreted as punctuations and thus ignored.
punctuation_chars = ",;:.?!()[]<>/-'\""
trtrable = str.maketrans(punctuation_chars, " " * len(punctuation_chars))
def get_word_lengths(filename):
'''
Counts the number of occurrences of word lengths
:param filename: Filename to process
:return: Returns a Counter (i.e. dictionary of ints). They keys are integers representing different word lenghts. The values are number of occurences. E.g.:
{
1: 5,
2: 4,
4: 7
}
'''
counter = Counter()
with open(filename, 'rt') as fin:
for line in fin:
for word in line.translate(trtrable).split():
counter[len(word)] += 1
return counter
def get_word_lengths_multiple(filenames):
'''
Counts the number of occurrences of word lengths in multiple files
:param filenames: List of files to process
:return: Returns a dictionary of dictionaries. The top-level keys are integers representing different word lenghts. Second-level keys are filesnames. Values are number of occurences. E.g.:
{
1: {
'filename1': 5,
'filename2': 3
},
2: {
'filename1': 4,
'filename2': 6
}
4: {
'filename1': 7,
'filename2': 2
}
}
'''
output = defaultdict(dict)
for filename in filenames:
counts = get_word_lengths(filename)
for key, count in counts.items():
output[key][filename] = count
return output
if __name__ == '__main__':
import sys
counts = get_word_lengths_multiple(sys.argv[1:])
print_histogram_2d(counts)
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recommonmark/parser.py | vale981/recommonmark | 3 | 82835 | <reponame>vale981/recommonmark<gh_stars>1-10
"""Docutils CommonMark parser"""
import sys
from os.path import splitext
from docutils import parsers, nodes
from sphinx import addnodes
from commonmark import Parser
from warnings import warn
if sys.version_info < (3, 0):
from urlparse import urlparse, unquote
else:
from urllib.parse import urlparse, unquote
__all__ = ['CommonMarkParser']
class CommonMarkParser(parsers.Parser):
"""Docutils parser for CommonMark"""
supported = ('md', 'markdown')
translate_section_name = None
default_config = {
'known_url_schemes': None,
}
def __init__(self):
self._level_to_elem = {}
def parse(self, inputstring, document):
self.document = document
self.current_node = document
self.config = self.default_config.copy()
try:
new_cfg = self.document.settings.env.config.recommonmark_config
self.config.update(new_cfg)
except AttributeError:
pass
self.setup_parse(inputstring, document)
self.setup_sections()
parser = Parser()
ast = parser.parse(inputstring + '\n')
self.convert_ast(ast)
self.finish_parse()
def convert_ast(self, ast):
for (node, entering) in ast.walker():
fn_prefix = "visit" if entering else "depart"
fn_name = "{0}_{1}".format(fn_prefix, node.t.lower())
fn_default = "default_{0}".format(fn_prefix)
fn = getattr(self, fn_name, None)
if fn is None:
fn = getattr(self, fn_default)
fn(node)
# Node type enter/exit handlers
def default_visit(self, mdnode):
pass
def default_depart(self, mdnode):
"""Default node depart handler
If there is a matching ``visit_<type>`` method for a container node,
then we should make sure to back up to it's parent element when the node
is exited.
"""
if mdnode.is_container():
fn_name = 'visit_{0}'.format(mdnode.t)
if not hasattr(self, fn_name):
warn("Container node skipped: type={0}".format(mdnode.t))
else:
self.current_node = self.current_node.parent
def visit_heading(self, mdnode):
# Test if we're replacing a section level first
if isinstance(self.current_node, nodes.section):
if self.is_section_level(mdnode.level, self.current_node):
self.current_node = self.current_node.parent
title_node = nodes.title()
title_node.line = mdnode.sourcepos[0][0]
new_section = nodes.section()
new_section.line = mdnode.sourcepos[0][0]
new_section.append(title_node)
self.add_section(new_section, mdnode.level)
# Set the current node to the title node to accumulate text children/etc
# for heading.
self.current_node = title_node
def depart_heading(self, _):
"""Finish establishing section
Wrap up title node, but stick in the section node. Add the section names
based on all the text nodes added to the title.
"""
assert isinstance(self.current_node, nodes.title)
# The title node has a tree of text nodes, use the whole thing to
# determine the section id and names
text = self.current_node.astext()
if self.translate_section_name:
text = self.translate_section_name(text)
name = nodes.fully_normalize_name(text)
section = self.current_node.parent
section['names'].append(name)
self.document.note_implicit_target(section, section)
self.current_node = section
def visit_text(self, mdnode):
self.current_node.append(nodes.Text(mdnode.literal, mdnode.literal))
def visit_softbreak(self, _):
self.current_node.append(nodes.Text('\n'))
def visit_linebreak(self, _):
self.current_node.append(nodes.raw('', '<br />', format='html'))
def visit_paragraph(self, mdnode):
p = nodes.paragraph(mdnode.literal)
p.line = mdnode.sourcepos[0][0]
self.current_node.append(p)
self.current_node = p
def visit_emph(self, _):
n = nodes.emphasis()
self.current_node.append(n)
self.current_node = n
def visit_strong(self, _):
n = nodes.strong()
self.current_node.append(n)
self.current_node = n
def visit_code(self, mdnode):
n = nodes.literal(mdnode.literal, mdnode.literal)
self.current_node.append(n)
def visit_link(self, mdnode):
ref_node = nodes.reference()
# Check destination is supported for cross-linking and remove extension
destination = mdnode.destination
_, ext = splitext(destination)
# TODO check for other supported extensions, such as those specified in
# the Sphinx conf.py file but how to access this information?
# TODO this should probably only remove the extension for local paths,
# i.e. not uri's starting with http or other external prefix.
if ext.replace('.', '') in self.supported:
destination = destination.replace(ext, '')
ref_node['refuri'] = destination
# TODO okay, so this is acutally not always the right line number, but
# these mdnodes won't have sourcepos on them for whatever reason. This
# is better than 0 though.
ref_node.line = self._get_line(mdnode)
if mdnode.title:
ref_node['title'] = mdnode.title
next_node = ref_node
url_check = urlparse(destination)
# If there's not a url scheme (e.g. 'https' for 'https:...' links),
# or there is a scheme but it's not in the list of known_url_schemes,
# then assume it's a cross-reference and pass it to Sphinx as an `:any:` ref.
known_url_schemes = self.config.get('known_url_schemes')
if known_url_schemes:
scheme_known = url_check.scheme in known_url_schemes
else:
scheme_known = bool(url_check.scheme)
if not url_check.fragment and not scheme_known:
wrap_node = addnodes.pending_xref(
reftarget=unquote(destination),
reftype='any',
refdomain=None, # Added to enable cross-linking
refexplicit=True,
refwarn=True
)
# TODO also not correct sourcepos
wrap_node.line = self._get_line(mdnode)
if mdnode.title:
wrap_node['title'] = mdnode.title
wrap_node.append(ref_node)
next_node = wrap_node
self.current_node.append(next_node)
self.current_node = ref_node
def depart_link(self, mdnode):
if isinstance(self.current_node.parent, addnodes.pending_xref):
self.current_node = self.current_node.parent.parent
else:
self.current_node = self.current_node.parent
def visit_image(self, mdnode):
img_node = nodes.image()
img_node['uri'] = mdnode.destination
if mdnode.first_child and mdnode.first_child.literal:
content = [mdnode.first_child.literal]
n = mdnode.first_child
mdnode.first_child.literal = ''
mdnode.first_child = mdnode.last_child = None
while getattr(n, 'nxt'):
n.nxt, n = None, n.nxt
content.append(n.literal)
img_node['alt'] = ''.join(content)
self.current_node.append(img_node)
self.current_node = img_node
def visit_list(self, mdnode):
list_node = None
if (mdnode.list_data['type'] == "bullet"):
list_node_cls = nodes.bullet_list
else:
list_node_cls = nodes.enumerated_list
list_node = list_node_cls()
list_node.line = mdnode.sourcepos[0][0]
self.current_node.append(list_node)
self.current_node = list_node
def visit_item(self, mdnode):
node = nodes.list_item()
node.line = mdnode.sourcepos[0][0]
self.current_node.append(node)
self.current_node = node
def visit_code_block(self, mdnode):
kwargs = {}
if mdnode.is_fenced and mdnode.info:
kwargs['language'] = mdnode.info
text = ''.join(mdnode.literal)
if text.endswith('\n'):
text = text[:-1]
node = nodes.literal_block(text, text, **kwargs)
self.current_node.append(node)
def visit_block_quote(self, mdnode):
q = nodes.block_quote()
q.line = mdnode.sourcepos[0][0]
self.current_node.append(q)
self.current_node = q
def visit_html(self, mdnode):
raw_node = nodes.raw(mdnode.literal,
mdnode.literal, format='html')
if mdnode.sourcepos is not None:
raw_node.line = mdnode.sourcepos[0][0]
self.current_node.append(raw_node)
def visit_html_inline(self, mdnode):
self.visit_html(mdnode)
def visit_html_block(self, mdnode):
self.visit_html(mdnode)
def visit_thematic_break(self, _):
self.current_node.append(nodes.transition())
# Section handling
def setup_sections(self):
self._level_to_elem = {0: self.document}
def add_section(self, section, level):
parent_level = max(
section_level for section_level in self._level_to_elem
if level > section_level
)
parent = self._level_to_elem[parent_level]
parent.append(section)
self._level_to_elem[level] = section
# Prune level to limit
self._level_to_elem = dict(
(section_level, section)
for section_level, section in self._level_to_elem.items()
if section_level <= level
)
def is_section_level(self, level, section):
return self._level_to_elem.get(level, None) == section
def _get_line(self, mdnode):
while mdnode:
if mdnode.sourcepos:
return mdnode.sourcepos[0][0]
mdnode = mdnode.parent
return 0
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scripts/perf/model_size.py | cclauss/archai | 344 | 70711 | <filename>scripts/perf/model_size.py
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from archai.nas.model_desc import ModelDesc
from archai.common.common import common_init
from archai.nas.model import Model
from archai.algos.petridish.petridish_model_desc_builder import PetridishModelBuilder
from archai.common.model_summary import summary
conf = common_init(config_filepath='confs/petridish_cifar.yaml',
param_args=['--common.experiment_name', 'petridish_run2_seed42_eval'])
conf_eval = conf['nas']['eval']
conf_model_desc = conf_eval['model_desc']
conf_model_desc['n_cells'] = 14
template_model_desc = ModelDesc.load('$expdir/final_model_desc.yaml')
model_builder = PetridishModelBuilder()
model_desc = model_builder.build(conf_model_desc, template=template_model_desc)
mb = PetridishModelBuilder()
model = Model(model_desc, droppath=False, affine=False)
summary(model, [64, 3, 32, 32])
exit(0) | [
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htbapi/machines.py | zachhanson94/htb | 0 | 1600064 | """Methods pertaining to machines on HTB.
This module contains classes and methods for working with machines
on HTB.
"""
from . import session
from .models import HTBObject
from typing import List, Optional
import json
class HTBMachine(HTBObject):
"""A machine on HTB
Attributes:
id (int): The machine ID.
name (str): The name of the machine.
os (str): The OS of the machine.
active (bool): Whether the machine is active.
retired (bool): Whether the machine is retired.
points (int): The number of points the machine is currently worth.
static_points (int): The number of points the machine was worth.
release (str): The timestamp of when the machine was released.
user_owns_count (int): The number of user owns.
root_owns_count (int): The number of root owns.
free (bool): Whether the machine is available to non-vip users.
authUserInUserOwns (bool): Whether the user has owned user.
authUserInRootOwns (bool): Whether the user has owned root.
authUserHasReviewed (bool): Whether the user has left a review.
stars (float): Number of stars (rating) the box has.
difficulty (int): The machine difficulty.
avatar (str): The path to the machines avatar image.
feedbackForChart (DifficultyChart): The difficulty chart data.
difficultyText (str): The difficulty (Easy, Medium, Hard, Insane).
isCompleted (bool): Whether the user has completed the box.
last_reset_time (str): Time since last reset.
playInfo (LiveMachineInfo): The live info of the machine.
maker (HTBProfile): The profile of the maker.
maker2 (HTBProfile): The profile of the second maker.
authUserFirstUserTime (str): The time it took the user to own user.
authUserFirstRootTime (str): The time it took the user to own root.
userBlood (MachineBlood): The blood object for user blood.
userBloodAvatar (str): The user blood user's avatar.
rootBlood (MachineBlood): The blood object for root blood.
rootBloodAvatar (str): The root blood user's avatar.
firstUserBloodTime (str): The time it took for user blood to fall.
firstRootBloodTime (str): The time it took for root blood to fall.
recommended (bool): Whether this machine is recommended.
"""
objectendpoint = "/machine/profile/"
objectkey = "info"
def findmachines(name: str) -> List[HTBMachine]:
"""Searches for machines matchine :name.
Searches HTB for any machines matching the name :name.
Args:
name: A partial or full name to search for.
Returns:
A list of matching machines.
"""
resp = session.get(
"/search/fetch",
params={"query": name, "tags": json.dumps(["machines"])})
results = resp.json()
searchresults = results["machines"] if "machines" in results else []
matches = [HTBMachine(res) for res in searchresults]
return matches
def findmachine(name: str) -> Optional[HTBMachine]:
"""Finds a specific machine by name.
Searches HTB for a specific machine matching the specified name
and returns it if one is found. Otherwise returns None.
Args:
name: An exact box name to lookup.
Returns:
A HTBMachine matching the requested name.
"""
boxes = findmachines(name)
for box in boxes:
if box.name == name:
return box
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] |
facemask_detector/python_func_test.py | acassemi/socialdistance | 0 | 1600207 | from detect_mask_image_function import detect_mask_complete
from detect_mask_image_function import detect_mask_simplified
# To use the complete version make sure you have the directories created
#if you download the whole git hub structure you are good to go.
#just add the image that you want to analyze in the images_to_be_analized directory
# before running it
#uncomment it to test it
#result_complete = detect_mask_complete("images_to_be_analyzed/example_03.png")
#print (result_complete)
#this is the simplified version that won't move or create new images
result_simplified = detect_mask_simplified("images_to_be_analyzed/example_01.png")
print (result_simplified)
| [
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] |
tests/compiler/dataflow/test_model_root_cause.py | inmanta/inmanta-core | 6 | 198697 | """
Copyright 2020 Inmanta
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.
Contact: <EMAIL>
"""
from typing import List, Set
import pytest
from compiler.dataflow.conftest import DataflowTestHelper, get_dataflow_node
from inmanta.ast import MultiException
from inmanta.execute.dataflow import AssignableNode, AssignableNodeReference, AttributeNode, DataflowGraph
from inmanta.execute.dataflow.root_cause import UnsetRootCauseAnalyzer
def get_attribute_node(graph: DataflowGraph, attr: str) -> AttributeNode:
node_ref: AssignableNodeReference = get_dataflow_node(graph, attr)
node: AssignableNode = next(node_ref.nodes())
assert isinstance(node, AttributeNode)
return node
@pytest.mark.parametrize("attribute_equivalence", [True, False])
@pytest.mark.parametrize("variable_equivalence", [True, False])
def test_dataflow_model_root_cause(
dataflow_test_helper: DataflowTestHelper, attribute_equivalence: bool, variable_equivalence: bool
) -> None:
dataflow_test_helper.compile(
"""
entity C:
number i
end
entity V:
number n
number i
end
index V(i)
entity U:
end
U.v [1] -- V
entity X:
number n
end
implement C using std::none
implement V using std::none
implement U using std::none
implement X using std::none
c = C()
%s
u = U()
x = X()
u.v = V(n = 42, i = c.i)
x.n = u.v.n
%s
"""
% (
"""
c.i = cc.i
cc = C(i = c.i)
"""
if attribute_equivalence
else "",
"""
c.i = i
i = c.i
"""
if variable_equivalence
else "",
),
MultiException,
)
graph: DataflowGraph = dataflow_test_helper.get_graph()
x_n: AttributeNode = get_attribute_node(graph, "x.n")
c_i: AttributeNode = get_attribute_node(graph, "c.i")
u_v: AttributeNode = get_attribute_node(graph, "u.v")
attributes: List[AttributeNode] = [x_n, c_i, u_v]
root_causes: Set[AttributeNode] = {c_i}
if attribute_equivalence:
cc_i: AttributeNode = get_attribute_node(graph, "cc.i")
attributes.append(cc_i)
root_causes.add(cc_i)
assert UnsetRootCauseAnalyzer(attributes).root_causes() == root_causes
def test_cyclic_model_a(dataflow_test_helper: DataflowTestHelper):
dataflow_test_helper.compile(
"""
entity A:
number n
end
implement A using std::none
x = A()
y = A()
z = A()
x.n = y.n
y.n = x.n
x.n = z.n
""",
MultiException,
)
graph: DataflowGraph = dataflow_test_helper.get_graph()
x_n: AttributeNode = get_attribute_node(graph, "x.n")
y_n: AttributeNode = get_attribute_node(graph, "y.n")
z_n: AttributeNode = get_attribute_node(graph, "z.n")
attributes: List[AttributeNode] = [x_n, y_n, z_n]
root_causes: Set[AttributeNode] = {z_n}
assert UnsetRootCauseAnalyzer(attributes).root_causes() == root_causes
def test_cyclic_model_b(dataflow_test_helper: DataflowTestHelper):
"""
This model has an equivalence that
1. is to be ignored as a root
2. cause two things (that now become roots)
"""
dataflow_test_helper.compile(
"""
entity A:
number n
end
implement A using std::none
x = A()
y = A()
y.n = n
x.n = n
n = m
m = n
""",
MultiException,
)
graph: DataflowGraph = dataflow_test_helper.get_graph()
x_n: AttributeNode = get_attribute_node(graph, "x.n")
y_n: AttributeNode = get_attribute_node(graph, "y.n")
attributes: List[AttributeNode] = [x_n, y_n]
root_causes: Set[AttributeNode] = {x_n, y_n}
assert UnsetRootCauseAnalyzer(attributes).root_causes() == root_causes
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29920,
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29913,
13,
13,
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29907,
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2744,
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13,
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4299,
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29890,
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393,
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304,
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313,
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376,
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29962,
353,
518,
29916,
29918,
29876,
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29918,
29876,
29962,
13,
1678,
3876,
29918,
1113,
6394,
29901,
3789,
29961,
6708,
4247,
29962,
353,
426,
29916,
29918,
29876,
29892,
343,
29918,
29876,
29913,
13,
13,
1678,
4974,
853,
842,
10303,
29907,
1071,
2744,
14997,
3298,
29898,
15697,
467,
4632,
29918,
1113,
6394,
580,
1275,
3876,
29918,
1113,
6394,
13,
2
] |
Python/05 - Math/Triangle Quest 2.py | sohammanjrekar/HackerRank | 0 | 84707 | """
Problem:https://www.hackerrank.com/challenges/triangle-quest-2/problem
Author: <NAME>
"""
for i in range(1, int(input()) + 1):
print(((10 ** i) // 9) ** 2)
| [
1,
9995,
30004,
13,
26604,
29901,
991,
597,
1636,
29889,
29882,
28940,
10003,
29889,
510,
29914,
305,
16047,
267,
29914,
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29899,
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29906,
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13,
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29901,
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474,
29897,
849,
29871,
29929,
29897,
3579,
29871,
29906,
8443,
13,
2
] |
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