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local ts_utils = require("nvim-treesitter.ts_utils")

local M = {}

-- Credit: https://www.reddit.com/r/neovim/comments/tge2ty/python_toggle_fstring_using_treesitter/
M.toggle_fstring = function()
  local winnr = 0
  local cursor = vim.api.nvim_win_get_cursor(winnr)
  local node = ts_utils.get_node_at_cursor()  ---@type TSNode?

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Displaying Configurations in TensorBoard

This is a simple demonstration of displaying training configuration in a TensorBoard, using text summaries.

Screenshot

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-- Make sure TS syntax tree is updated when needed by plugin (with some throttling)
-- even if the `highlight` module is not enabled.
-- See https://github.com/nvim-treesitter/nvim-treesitter/issues/2492
_G.TreesitterParse = function()
  local lang = ts_parsers.ft_to_lang(vim.bo.filetype)
  local parser = ts_parsers.get_parser(vim.fn.bufnr(), lang)
  if parser then
    parser:parse()
    return true
  else

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# in a courtesy of Caffe's filter visualization example
# http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb

def imshow_grid(data, height=None, width=None, normalize=False, padsize=1, padval=0):
    '''
    Take an array of shape (N, H, W) or (N, H, W, C)
    and visualize each (H, W) image in a grid style (height x width).
    '''
    if normalize:
        data -= data.min()

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from matplotlib import animation
from IPython.display import display_html
from IPython.display import HTML

def display_frames_as_gif(frames):
    """
    Displays a list of frames as a gif, with controls
    """
    fig, ax = plt.subplots()
    patch = ax.imshow(frames[0])

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#!/bin/bash

function print_osc() {
    if [[ -n $TMUX ]] ; then
        printf "\033Ptmux;\033\033]";
    else printf "\033]"; fi
}

function print_st() {
    if [[ -n $TMUX ]] ; then

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# use the following snippet in your ipython notebook shell
import argparse
import tensorflow as tf

tf.app.flags.FLAGS = tf.python.platform.flags._FlagValues()
tf.app.flags._global_parser = argparse.ArgumentParser()

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import tensorflow as tf
import tensorflow.contrib.layers as layers
from datetime import datetime

# MNIST input data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

def multilayer_perceptron(x):
    fc1 = layers.fully_connected(x, 256, activation_fn=tf.nn.relu, scope='fc1')

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brew install poppler

# 현재 디렉토리의 모든 *.pdf 패턴을 page1 만 추출해서 page1.*.pdf 로 저장
for source_pdf in ./*.pdf; do
    pdfseparate -f 1 -l 1 ${source_pdf} page1.${source_pdf}
done
pdfunite page1.* page1.merged.pdf