Shark Deng sharkdeng

## Hilbert Curve 2D
<html>

<script type="text/javascript">
	function d2xy(n, d) {
		var rx, ry, s, t, x, y;

		t = d;
		x = y = 0;
		for (s = 1; s < n; s *= 2) {
			rx = 1 & (t / 2);

## get_abs_path_for_immediate_children.py
def get_abs_paths_for_immediate_children (tree, input_paths_string):

    input_paths = input_paths_string.strip().split(".")
    tmp_tree = tree
    for input_path in input_paths:
        tmp_tree = tmp_tree.n(input_path)

    output_paths = []

    children = list(tmp_tree.children.keys())

## useHexo.md

      
              1 file
            
          
              57 forks
            
          
              30 comments
            
          
              241 stars
            
          
                btfak
                / useHexo.md
            
            
              Created
              May 26, 2016 09:41
            
              
                How to use Hexo and deploy to GitHub Pages
              
          
    How to use Hexo and deploy to GitHub Pages


https://github.com/hexojs/hexo
https://hexo.io/docs/

1. Install Hexo

$ sudo npm install -g hexo-cli

$ hexo -v
hexo-cli: 0.1.9


## make_nb.py
"""Create a notebook containing code from a script.

Run as:  python make_nb.py my_script.py
"""
import sys

import nbformat
from nbformat.v4 import new_notebook, new_code_cell

nb = new_notebook()

## react-text-file-reader.js
import React from "react";


/*
	Read a text file and out put the content.

	Example Usage:

	var myTxt = require("./myTxt.txt");
	...

## gradient_accumulation.py
model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

## wodpress2txt.py
## this snippet allows you to fastly convert post from wordpress xml format to txt

from xml.dom.minidom import parse, parseString
import os

dom = parse('jobyme88.xml')  # parse an XML file by name

os.path.makedirs('blogs')

items = dom.getElementsByTagName('item')

## pdf2jpg.py
# extract pdf to jpg
from pdf2image import convert_from_path
from tqdm import tqdm
import os
import uuid


input_path = 'corridor.pdf'
uid = uuid.uuid4().hex[:4]
output_path = uid + '-' + input_path.split('.')[0]

## is_nan.py
def is_nan(a):
    import math
    import numpy as np

    if isinstance(a, float):
        return math.isnan(a)
    elif isinstance(a, np.float64):
        return np.isnan(a)
    else:
        return False

## code.tex
%Source: https://www.overleaf.com/learn/latex/code_listing%

\usepackage{listings}
\usepackage{xcolor}

\definecolor{codegreen}{rgb}{0,0.6,0}
\definecolor{codegray}{rgb}{0.5,0.5,0.5}
\definecolor{codepurple}{rgb}{0.58,0,0.82}
\definecolor{backcolour}{rgb}{0.95,0.95,0.92}
	<html>

	<script type="text/javascript">
	function d2xy(n, d) {
	var rx, ry, s, t, x, y;

	t = d;
	x = y = 0;
	for (s = 1; s < n; s *= 2) {
	rx = 1 & (t / 2);
	def get_abs_paths_for_immediate_children (tree, input_paths_string):

	input_paths = input_paths_string.strip().split(".")
	tmp_tree = tree
	for input_path in input_paths:
	tmp_tree = tmp_tree.n(input_path)

	output_paths = []

	children = list(tmp_tree.children.keys())
	"""Create a notebook containing code from a script.

	Run as: python make_nb.py my_script.py
	"""
	import sys

	import nbformat
	from nbformat.v4 import new_notebook, new_code_cell

	nb = new_notebook()
	import React from "react";


	/*
	Read a text file and out put the content.

	Example Usage:

	var myTxt = require("./myTxt.txt");
	...
	model.zero_grad() # Reset gradients tensors
	for i, (inputs, labels) in enumerate(training_set):
	predictions = model(inputs) # Forward pass
	loss = loss_function(predictions, labels) # Compute loss function
	loss = loss / accumulation_steps # Normalize our loss (if averaged)
	loss.backward() # Backward pass
	if (i+1) % accumulation_steps == 0: # Wait for several backward steps
	optimizer.step() # Now we can do an optimizer step
	model.zero_grad() # Reset gradients tensors
	if (i+1) % evaluation_steps == 0: # Evaluate the model when we...
	## this snippet allows you to fastly convert post from wordpress xml format to txt

	from xml.dom.minidom import parse, parseString
	import os

	dom = parse('jobyme88.xml') # parse an XML file by name

	os.path.makedirs('blogs')

	items = dom.getElementsByTagName('item')
	# extract pdf to jpg
	from pdf2image import convert_from_path
	from tqdm import tqdm
	import os
	import uuid


	input_path = 'corridor.pdf'
	uid = uuid.uuid4().hex[:4]
	output_path = uid + '-' + input_path.split('.')[0]
	def is_nan(a):
	import math
	import numpy as np

	if isinstance(a, float):
	return math.isnan(a)
	elif isinstance(a, np.float64):
	return np.isnan(a)
	else:
	return False
	%Source: https://www.overleaf.com/learn/latex/code_listing%

	\usepackage{listings}
	\usepackage{xcolor}

	\definecolor{codegreen}{rgb}{0,0.6,0}
	\definecolor{codegray}{rgb}{0.5,0.5,0.5}
	\definecolor{codepurple}{rgb}{0.58,0,0.82}
	\definecolor{backcolour}{rgb}{0.95,0.95,0.92}