ipashchenko

[
  // Navigation
  {
    "key": "ctrl-h",
    "command": "workbench.action.navigateLeft"
  },
  {
    "key": "ctrl-l",
    "command": "workbench.action.navigateRight"
  },

ipashchenko

How to use clangd C/C++ LSP in any project

tl;dr: If you want to just know the method, skip to How to section

Clangd is a state-of-the-art C/C++ LSP that can be used in every popular text editors like Neovim, Emacs or VS Code. Even CLion uses clangd under the hood. Unfortunately, clangd requires compile_commands.json to work, and the only way to painlessly generate it is to use CMake.

But what if I tell you you can quickly hack your way around that, and generate compile_commands.json for any project, no matter how compilcated? I have used that way at work for years, originaly because I used CLion which supported only CMake projects - but now I use that method succesfully with clangd and Neovim.

Method summary

Basically what we need to achieve is to create a CMake file that will generate a compile_commands.json file with information about:

ipashchenko

Screen Quick Reference

Basic

Description	Command
Start a new session with session name	screen -S <session_name>
List running sessions / screens	screen -ls
Attach to a running session	screen -x
Attach to a running session with name	screen -r <session_name>

ipashchenko

#!/bin/sh

#
# Build GCC with support for offloading to NVIDIA GPUs.
#

work_dir=$HOME/offload/wrk
install_dir=$HOME/offload/install

# Location of the installed CUDA toolkit

ipashchenko

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

ipashchenko

import matplotlib.pyplot as plt
import numpy as np
import seaborn

from keras.layers import Input, Dense, merge, ELU, Dropout
from keras.models import Model
from keras.regularizers import l2
from keras import backend as K
from keras.optimizers import rmsprop, adam

ipashchenko

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

ipashchenko

import joblib
import os

cachedir = 'cache'
if not os.path.isdir(cachedir): os.mkdir(cachedir)
mem = joblib.Memory(cachedir=cachedir, verbose=True)

@mem.cache
def my_long_function(i):
	return i + i

ipashchenko

import numpy as np
import scipy.ndimage as ndimage

# The array you gave above
data = np.array( 
        [
           [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 

ipashchenko

from numpy.fft import fft, ifft, fft2, ifft2, fftshift
import numpy as np

def fft_convolve2d(x,y):
    """ 2D convolution, using FFT"""
    fr = fft2(x)
    fr2 = fft2(np.flipud(np.fliplr(y)))
    m,n = fr.shape
    cc = np.real(ifft2(fr*fr2))
    cc = np.roll(cc, -m/2+1,axis=0)