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| using Plots | |
| using SpecialFunctions | |
| function GoS(x) | |
| -(1 .- erf.(x)) | |
| end | |
| function G(x, sigma) | |
| exp.(-x.^2 ./ (2*sigma^2)) | |
| end |
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| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import astra | |
| if 0: | |
| data_corr = np.load('tomo_corr.npy') | |
| vecs = np.load('vectors.npy') | |
| else: | |
| import scipy.io | |
| data_mat = scipy.io.loadmat('../data/Fewer_Plastics_tomo_corr_low_high_vectors') |
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| apt-get upgrade | |
| apt-get install python-setuptools git vim | |
| easy_install pip |
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| sudo echo "su ubuntu -c 'bash /home/ubuntu/start_script.sh'" >> /etc/rc.local | |
| # make ~/start_script.sh |
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| echo "LABEL=cloudimg-rootfs / ext4 defaults 0 0 | |
| /dev/xvdb /home01 ext4 defaults 0 2" >> /etc/fstab |
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| ## STEP1: install tmux-plugin | |
| git clone https://github.com/tmux-plugins/tpm ~/.tmux/plugins/tpm | |
| echo "# List of plugins | |
| set -g @plugin 'tmux-plugins/tpm' | |
| set -g @plugin 'tmux-plugins/tmux-sensible' | |
| # Other examples: | |
| # set -g @plugin 'github_username/plugin_name' | |
| # set -g @plugin '[email protected]/user/plugin' |
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| import re | |
| import requests | |
| import titlecase | |
| # I doubt if we need to go above ten | |
| words_to_numerals =\ | |
| { | |
| 'first': '1', | |
| 'second': '2', | |
| 'third': '3', |
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| /* | |
| JKImage library for communicating with python | |
| Rules | |
| - The input image usually follows the order of [Channel x ny x nx] | |
| - | |
| */ | |
| #include <math.h> | |
| #include <stdio.h> |
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| ########## main.py | |
| import argparse | |
| import time | |
| parser = argparse.ArgumentParser(add_help=False) | |
| parser.add_argument('--niter', type=int, default=100, help='number of iteration') | |
| args, unparsed = parser.parse_known_args() | |
| # make output file name containing the key parameters |
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| import torch.autograd.Variable | |
| max, max_idx = Variable.max(x, 1) | |
| max = Variable.expand_as(max, x) | |
| x = Variable.exp(x - max) | |
| p = Variable(torch.zeros(x.size())) | |
| for i in range(64): | |
| sum = Variable.sum(x[i, :]).data[0] | |
| p[i, :] = x[i, :] / sum |