Brian Skinn bskinn

Environment (Windows 10)

$ pip list
alabaster (0.7.10)
attrs (17.4.0)
Babel (2.5.1)
certifi (2017.11.5)
chardet (3.0.4)
colorama (0.3.9)
coverage (4.4.2)

decorator (4.1.2)

From here

#!/usr/bin/env python3

import sys
import numpy as np

file_name = sys.argv[1]

Rationale Spillover of temporary variables can be a significant annoyance when working with Jupyter notebooks. Aesthetically and logistically, it's bothersome to have a del var1, var2, var3 hanging around at the bottom of a notebook cell to take care of cleanup of these temp variables. It prevents a Ctrl+End from going to the end of relevant code, and if an Exception is raised in the course of code execution the del cleanup doesn't happen.

A short content manager class takes care of these various problems: when execution leaves the with suite, even due to a raised exception, the indicated variables are deleted.

Images shown are from Win7 and Excel 2013; click any image for a larger version.

Download the .xlam file of the add-in of interest
Save to %USERPROFILE%\AppData\Roaming\Microsoft\AddIns
Usually this will translate to C:\Users\[username]\AppData\Roaming\Microsoft\AddIns
Open Excel
Click File > Options

VBA AddIns

In README.md
- Copyright (update with any changes in a new year)
- Identify the license, and include a link to the LICENSE.txt
- Include 'third-party disclaimer'
- For ones with binary distributables, indicate that the files will be attached to the GitHub release
Create CHANGELOG.md
- Populate with current code status, if posting existing code for the first time
Create standard (sub)set of new labels

Either add directly to local or global .git/config:

alias.logtree=!sh -c 'git log --graph --all --oneline --decorate=full'

Or can use git config:

> git config [--global] alias.logtree '!sh -c '"'"'git log --graph --all --oneline --decorate=full'"'"

Description

There are various situations where it's problematic for intersphinx to always retrieve objects.inv files from their network locations. The major such situations are where network access is unavailable, or very slow. As well, the downloads do always take some time, and thus local caching can usually provide speed benefits on every make.

The below is a Python 3 implementation of an environment variable-controlled switch between remote retrieval of objects.inv files and retrieval from a local cache. As written, if the envvar ISPHX_LOCAL is set to any truthy value, Sphinx will retrieve from local cache. If ISPHX_LOCAL is absent or falsey,

Start with basename.gbw from an ORCA computation. This method has only been tested on Windows 7, primarily because MultiWFN is challenging to execute on Linux and Mac.

Convert to MOLDEN format via orca_2mkl basename -molden
Rename basename.molden.input to basename.molden (this is so MultiWFN will recognize it properly)
Run MultiWFN; load basename.molden
Export the reduced density gradient and "sign(lambda_2)*rho" to CUBE using the following MultiWFN commands:
a. 5 (output property in spatial region)
b. 13 (RDG)
c. 3 (high grid over entire system -- can enter custom grid for better isosurface resolution)
d. 2 (export CUBE)

	ANALYZING SYMMETRY OF NORMAL MODES...

	FREQUENCIES IN CM-1, IR INTENSITIES IN DEBYE2/AMU-ANGSTROM**2,
	REDUCED MASSES IN AMU.

	1 2 3 4 5
	FREQUENCY: 2.81 0.66 0.37 0.17 2.03
	SYMMETRY: A A A A A
	REDUCED MASS: 9.40532 8.22898 8.71590 8.67428 8.49811
	IR INTENSITY: 0.00584 0.00020 0.00001 0.00001 0.00463

	-- TO DO --

	xxx Strip all Optional arguments, to ensure specificity of behavior?

	===THESE MAY BE REDUNDANT===
	Parameterized predictor transforms, eventually to be optimized over, to find, e.g., the optimal exponent for a nonlinear dependence.

	Workflow mini-language? Multi-step transforms from source (predictor transform->residual analysis->...)

	Arbitrary, functional-form definitions of transforms? (<!predname!>, e.g.)