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import tweepy | |
from datetime import datetime, timedelta | |
# options | |
test_mode = False | |
verbose = True | |
delete_tweets = True | |
delete_favs = True | |
days_to_keep = 0 # for whe all of your tweets are really that bad |
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# USAGE: | |
# | |
# This Praat script makes the process of annotating multiple sound files go | |
# faster by automating most of the mouse-clicks that are needed to create and | |
# save new TextGrid files. It takes a folder of sound files. For each sound | |
# file, the script creates a new TextGrid with the annotation tiers that you | |
# list in the settings window, then opens that TextGrid along with its | |
# accompanying sound file. The script pauses while you create annotations for | |
# the sound file. When you are done creating annotations, press "OK" in the | |
# pop-up window to save the TextGrid to a file with the same filename as the |
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# devtools::install_github("thomasp85/transformr") | |
# devtools::install_github("thomasp85/gganimate") | |
library(tidyverse) | |
library(gganimate) | |
library(rstanarm) | |
theme_set(theme_grey()) | |
# Get the mpg value to predict over |
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// Predict from Gaussian Process | |
// All data parameters must be passed as a list to the Stan call | |
// Based on original file from https://code.google.com/p/stan/source/browse/src/models/misc/gaussian-process/ | |
data { | |
int<lower=1> N1; | |
vector[N1] x1; | |
vector[N1] y1; | |
int<lower=1> N2; | |
vector[N2] x2; |