| import ast | |
| from timeit import repeat | |
| import threading | |
| from ctypes import pythonapi, c_void_p | |
| import math | |
| import numpy as np | |
| try: | |
| import numexpr as ne | |
| nthreads = ne.ncores |
MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
| import os | |
| from IPython.frontend.qt.console.rich_ipython_widget import RichIPythonWidget | |
| from IPython.frontend.qt.kernelmanager import QtKernelManager | |
| from IPython.frontend.qt.inprocess_kernelmanager import QtInProcessKernelManager | |
| from IPython.kernel.zmq.ipkernel import Kernel | |
| from IPython.kernel.inprocess.ipkernel import InProcessKernel | |
| from IPython.lib import guisupport | |
| import os | |
| from IPython.frontend.qt.console.rich_ipython_widget import RichIPythonWidget | |
| from IPython.frontend.qt.kernelmanager import QtKernelManager | |
| from IPython.frontend.qt.inprocess_kernelmanager import QtInProcessKernelManager | |
| from IPython.kernel.zmq.ipkernel import Kernel | |
| from IPython.kernel.inprocess.ipkernel import InProcessKernel | |
| from IPython.lib import guisupport | |
I've been using the Anaconda python package from continuum.io recently and found it to be a good way to get all the complex compiled libs you need for a scientific python environment. Even better, their conda tool lets you create environments much like virtualenv, but without having to re-compile stuff like numpy, which gets old very very quickly with virtualenv and can be a nightmare to get correctly set up on OSX.
The only thing missing was an easy way to switch environments - their docs suggest running python executables from the install folder, which I find a bit of a pain. Coincidentally I came across this article - Virtualenv's bin/activate is Doing It Wrong - which desribes a simple way to launch a sub-shell with certain environment variables set. Now simple was the key word for me since my bash-fu isn't very strong, but I managed to come up with the script below. Put this in a text file called conda-work
| import sqlalchemy as sa | |
| import sqlalchemy.orm as orm | |
| from sqlalchemy.ext.declarative import declarative_base | |
| from sqlalchemy.ext.declarative import declared_attr | |
| from sqlalchemy.orm import scoped_session, sessionmaker | |
| DBSession = scoped_session(sessionmaker()) | |
| class BaseMixin(object): | |
| query = DBSession.query_property() | |
| id = sa.Column(sa.Integer, primary_key=True) |
| __all__ = ( | |
| ####### Class Objects | |
| #CoGetClassObject - Normal, not wrapped | |
| 'CoDllGetClassObject', #Get ClassObject from a DLL file | |
| ####### ClassFactory::CreateInstance Wrappers | |
| 'CoCreateInstanceFromFactory', #Create an object via IClassFactory::CreateInstance | |
| 'CoCreateInstanceFromFactoryLicenced', #Create a licenced object via IClassFactory2::CreateInstanceLic |
| { | |
| "cmd": ["$file"], | |
| "working_dir": "$file_path", | |
| "selector": "source.dosbatch" | |
| } |
| """ | |
| This module implements the Lowess function for nonparametric regression. | |
| Functions: | |
| lowess Fit a smooth nonparametric regression curve to a scatterplot. | |
| For more information, see | |
| William S. Cleveland: "Robust locally weighted regression and smoothing | |
| scatterplots", Journal of the American Statistical Association, December 1979, |
