- Open Automator
- Create a new document
- Select Quick Action
- Set “Service receives selected” to
files or folders
inany application
- Add a
Run Shell Script
action- your default shell should already be selected, otherwise use
/bin/zsh
for macOS 10.15 (”Catalina”) or later - older versions of macOS use
/bin/bash
- your default shell should already be selected, otherwise use
- if you're using something else, you probably know what to do 😉
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# Python program to create Blockchain | |
# For timestamp | |
import datetime | |
# Calculating the hash | |
# in order to add digital | |
# fingerprints to the blocks | |
import hashlib |
네이버 나눔 글꼴(https://zetawiki.com/wiki/%EB%82%98%EB%88%94%EA%B8%80%EA%BC%B4)
Download and Install
# download and extract font files(.ttf)
wget http://cdn.naver.com/naver/NanumFont/fontfiles/NanumFont_TTF_ALL.zip
wget https://github.com/naver/nanumfont/releases/download/VER2.5/NanumGothicCoding-2.5.zip
unzip NanumFont_TTF_ALL.zip
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diskutil list | |
diskutil unmountDisk /dev/disk2 | |
diskutil eraseDisk FAT32 SANDISK /dev/disk2 |
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# ported from https://github.com/pvigier/perlin-numpy/blob/master/perlin2d.py | |
import torch | |
import math | |
def rand_perlin_2d(shape, res, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3): | |
delta = (res[0] / shape[0], res[1] / shape[1]) | |
d = (shape[0] // res[0], shape[1] // res[1]) | |
grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])), dim = -1) % 1 |
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yalmip('clear') | |
T = [0 1 0 1; 1 0 1 0; 0 1 0 1; 1 0 1 0]; | |
x = sdpvar(4, 1); | |
y = sdpvar(4, 1); | |
assign(x, randn(4, 1)); | |
assign(y, randn(4, 1)); | |
const = [x'*x <= 1; y'*y <=1]; | |
% const = []; |
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{ | |
"Statement": [ | |
{ | |
"Action": [ | |
"apigateway:*", | |
"cloudformation:CancelUpdateStack", | |
"cloudformation:ContinueUpdateRollback", | |
"cloudformation:CreateChangeSet", | |
"cloudformation:CreateStack", | |
"cloudformation:CreateUploadBucket", |
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# This example shows how to use keras TensorBoard callback | |
# with model.train_on_batch | |
import tensorflow.keras as keras | |
# Setup the model | |
model = keras.models.Sequential() | |
model.add(...) # Add your layers | |
model.compile(...) # Compile as usual |
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def tf_pca(x): | |
''' | |
Compute PCA on the bottom two dimensions of x, | |
eg assuming dims = [..., observations, features] | |
''' | |
# Center | |
x -= tf.reduce_mean(x, -2, keepdims=True) | |
# Currently, the GPU implementation of SVD is awful. | |
# It is slower than moving data back to CPU to SVD there |
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