Xingjian Du diggerdu

## pseudo_cqt_pytorch.py
cqt_filter_fft = librosa.constantq.__cqt_filter_fft

class PseudoCqt():
    """A class to compute pseudo-CQT with Pytorch.
    Written by Keunwoo Choi
    API (+implementations) follows librosa (https://librosa.github.io/librosa/generated/librosa.core.pseudo_cqt.html)

    Usage:
        src, _ = librosa.load(filename)
        src_tensor = torch.tensor(src)

## client.py
import requests

#http://docs.python-requests.org/en/latest/user/quickstart/#post-a-multipart-encoded-file

url = "http://localhost:5000/"
fin = open('simple_table.pdf', 'rb')
files = {'file': fin}
try:
	r = requests.post(url, files=files)
	print r.text

## pytorch_visualize.py
import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable, Function
from collections import defaultdict
import graphviz

"""
This is a rather distorted implementation of graph visualization in PyTorch.

## blog_tensorflow_scope_decorator.py
# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

## arxiv2kindle.ipynb

      
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                diggerdu
                / arxiv2kindle.ipynb
            
            
              Created
              September 11, 2016 10:34
                — forked from bshillingford/arxiv2kindle.ipynb
            
              
                arxiv2kindle: recompiles an arxiv paper for kindle-sized screens, and sends it to your wifi-enabled kindle
              
          
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	cqt_filter_fft = librosa.constantq.__cqt_filter_fft

	class PseudoCqt():
	"""A class to compute pseudo-CQT with Pytorch.
	Written by Keunwoo Choi
	API (+implementations) follows librosa (https://librosa.github.io/librosa/generated/librosa.core.pseudo_cqt.html)

	Usage:
	src, _ = librosa.load(filename)
	src_tensor = torch.tensor(src)
	import requests

	#http://docs.python-requests.org/en/latest/user/quickstart/#post-a-multipart-encoded-file

	url = "http://localhost:5000/"
	fin = open('simple_table.pdf', 'rb')
	files = {'file': fin}
	try:
	r = requests.post(url, files=files)
	print r.text
	import torch
	import torch.nn as nn
	from torch.nn import Parameter
	from torch.autograd import Variable, Function
	from collections import defaultdict
	import graphviz

	"""
	This is a rather distorted implementation of graph visualization in PyTorch.
	# Working example for my blog post at:
	# https://danijar.github.io/structuring-your-tensorflow-models
	import functools
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data


	def doublewrap(function):
	"""
	A decorator decorator, allowing to use the decorator to be used without