| #!/bin/bash | |
| echo "Installing TextLive" | |
| sudo apt install -y texlive-latex-extra | |
| sudo apt install -y texlive-lang-english texlive-lang-spanish texlive-bibtex-extra | |
| # sudo apt autoremove |
| # Largely a copy of the great gist by Danny Price (https://github.com/telegraphic) | |
| # Minimal modifications to make the example work with Python 3, and to send | |
| # python objects instead of using the msgpack library. | |
| # | |
| # Original gist can be found here: | |
| # https://gist.github.com/telegraphic/2709b7e6edc3a0c39ed9b75452da205 | |
| """ | |
| # app.py - example Dash + ZMQ + msgpack + numpy monitor | |
| This app receives data from zmq_pub.py, and plots it. |
| """Simple demo of argparse in python, see http://zetcode.com/python/argparse/""" | |
| import argparse | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('name') # positional argument, note no dash! | |
| parser.add_argument('-n', type=int, required=True, help="the number") # Required argument, must be a int, eg. -n 4 | |
| parser.add_argument('-e', type=int, default=2, help="defines the value") # Optional argument with a default | |
| parser.add_argument('-o', '--output', action='store_true', help="shows output") # a binary flag |
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """Script to facilitate the import of a Readcube Papers 3 library into Zotero | |
| __Purpose of this script__ | |
| If you export your Readcube (Mekentosj) Papers3 library as a BibTeX file, the file paths to the PDFs are not formatted | |
| correctly for Zotero to import them. |
| """ | |
| Url: https://gist.github.com/wassname/1393c4a57cfcbf03641dbc31886123b8 | |
| """ | |
| import unicodedata | |
| import string | |
| valid_filename_chars = "-_.() %s%s" % (string.ascii_letters, string.digits) | |
| char_limit = 255 | |
| def clean_filename(filename, whitelist=valid_filename_chars, replace=' '): |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman