Hinrich B. Winther liob
liob
/ BibTexCiteKeyForMarkdown.js
Last active
April 13, 2021 13:59
Zotero translator - BibTeX key for Markdown
based on the work of Peter O'Brien, Simon Kornblith and Richard Karnesky
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| { | |
| "translatorID": "7cb70025-a888-4a29-a210-93ec52da40d5", | |
| "translatorType": 3, | |
| "label": "BibTeX key for Markdown", | |
| "creator": "created by liob based on the works of Peter O'Brien, Simon Kornblith and Richard Karnesky", | |
| "target": "bib", | |
| "minVersion": "2.1.9", | |
| "maxVersion": null, | |
| "priority": 200, | |
| "inRepository": true, |
liob
/ config.php
Created
August 8, 2013 10:36
Example Configuration of Z-Push-2 for the Z-Push-OX Backend
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| <?php | |
| /*********************************************** | |
| * File : config.php | |
| * Project : Z-Push | |
| * Descr : Main configuration file | |
| * | |
| * Created : 01.10.2007 | |
| * | |
| * Copyright 2007 - 2013 Zarafa Deutschland GmbH | |
| * |
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| BRANCH = r1.7 | |
| ANDROIDSDK = /usr/lib/android-sdk | |
| ANDROIDNDK = /usr/lib/android-ndk | |
| OSMSRC = osmand | |
| RESOURCESDIR = resources/rendering_styles/style-icons resources/routing resources/obf_creation resources/countries-info | |
| REPO = https://github.com/osmandapp/Osmand.git | |
| GIT = git | |
| ANT = ant | |
| NDKBUILD = $(ANDROIDNDK)/ndk-build |
liob
/ bland_altman_plot.py
Last active
July 8, 2017 12:29
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| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from matplotlib.transforms import blended_transform_factory | |
| def bland_altman(x, y, axis=None, sd_offset=1.96, | |
| n_format='{0:.2f}', *args, **kwargs): |
liob
/ n dimensional gaus kernel
Last active
October 25, 2017 08:26
create a n dimensional gaussian kernel for the given shape
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| import numpy as np | |
| def gaussian(shape, sigma=1.0, mu=0.0): | |
| """ create a n dimensional gaussian kernel for the given shape """ | |
| m = np.meshgrid(*[np.linspace(-1,1,s) for s in shape]) | |
| d = np.sqrt(np.sum([x*x for x in m], axis=0)) | |
| g = np.exp(-( (d-mu)**2 / ( 2.0 * sigma**2 ) ) ) | |
| return g / np.sum(g) |