| package main | |
| // To run, first install github.com/google/tiff with: | |
| // go get github.com/google/tiff | |
| // Then run like `go run tiff-dump.go example.tif` | |
| import ( | |
| "fmt" | |
| "github.com/google/tiff" | |
| "os" |
| # -*- coding: utf-8 -*- | |
| ########################################################################## | |
| # YouCompleteMe configuration for ROS # | |
| # Author: Gaël Ecorchard (2015) # | |
| # # | |
| # The file requires the definition of the $ROS_WORKSPACE variable in # | |
| # your shell. # | |
| # Name this file .ycm_extra_conf.py and place it in $ROS_WORKSPACE to # | |
| # use it. # |
| alias youtube-dl-best='youtube-dl -f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/bestvideo+bestaudio" ' | |
| alias youtube-dl-480='youtube-dl -f "bestvideo[height<=480][ext=mp4]+bestaudio[ext=m4a]" ' | |
| alias youtube-dl-720='youtube-dl -f "bestvideo[height<=720][ext=mp4]+bestaudio[ext=m4a]" ' | |
| alias youtube-dl-4k='echo -e "This will transcode the video from webm to h264 which could take a long time\n\n"; youtube-dl -f "bestvideo[ext=webm]+bestaudio[ext=m4a]" --recode-video mp4 ' | |
| alias youtube-dl-mp3='youtube-dl --extract-audio -f bestaudio[ext=mp3] --no-playlist ' |
| ##### Windows | |
| # Windows thumbnail cache files | |
| Thumbs.db | |
| Thumbs.db:encryptable | |
| ehthumbs.db | |
| ehthumbs_vista.db | |
| # Dump file | |
| *.stackdump |
| /*Write a function named coinToss that simulates the tossing of a coin. When you call the function, | |
| it should generate a random number in the range of 1 through 2. If the random number is 1, | |
| the function should display "heads", if it is 2, it should display "tails". Demonstrate the function in a program that asks the user | |
| how many times the coin should be tossed, and then simulates the tossing of the coin that number of times. | |
| */ | |
| #include <iostream> | |
| #include <conio.h> | |
| #include <ctime> | |
| #include <iomanip> |
| import torch | |
| from torch import LongTensor | |
| from torch.nn import Embedding, LSTM | |
| from torch.autograd import Variable | |
| from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
| ## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium'] | |
| # | |
| # Step 1: Construct Vocabulary | |
| # Step 2: Load indexed data (list of instances, where each instance is list of character indices) |
| #!/usr/bin/python3 | |
| # -*- coding: utf-8 -*- | |
| from PyQt5.QtCore import Qt | |
| from PyQt5.QtGui import QImage, QPixmap, QPalette, QPainter | |
| from PyQt5.QtPrintSupport import QPrintDialog, QPrinter | |
| from PyQt5.QtWidgets import QLabel, QSizePolicy, QScrollArea, QMessageBox, QMainWindow, QMenu, QAction, \ | |
| qApp, QFileDialog |
| import matplotlib.pyplot as plt | |
| # Plot model history more easily | |
| # when plotting, smooth out the points by some factor (0.5 = rough, 0.99 = smooth) | |
| # method taken from `Deep Learning with Python` by François Chollet | |
| def smooth_curve(points, factor=0.75): | |
| smoothed_points = [] | |
| for point in points: |
Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma
You can get the list of supported formats with:
ffmpeg -formats
Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and sample rate to 22050 Hz:
| " Background colors for active vs inactive windows | |
| hi ActiveWindow guibg=#17252c | |
| hi InactiveWindow guibg=#0D1B22 | |
| " Call method on window enter | |
| augroup WindowManagement | |
| autocmd! | |
| autocmd WinEnter * call Handle_Win_Enter() | |
| augroup END |
