drammock

library(phonR)

# if you want to go straight to PDF, uncomment this line, and the "dev.off()" line at the end:
# cairo_pdf("myfile.pdf", width=6, height=6, pointsize=12)

# assuming that your dataframe is called "mydata":
# first, create columns for the normalized data:
z <- with(mydata, norm.vowels("lobanov", f1=f1, f2=f2, f3=f3, group=speaker))
colnames(z) <- paste(colnames(z), "z", sep="")
mydata <- cbind(mydata, z)

drammock

# install the new version:
download.file("https://raw.githubusercontent.com/drammock/phonR/master/R/phonR.R", "phonR.R")
source("phonR.R")
# or, if you have devtools package, can use
# devtools::source_url("https://raw.githubusercontent.com/drammock/phonR/master/R/phonR.R")

# do the bark normalization before plotting:
BNF$f1bark <- normBark(BNF$f1)
BNF$f2bark <- normBark(BNF$f2)

drammock

## "reduction" is the response variable (ordered factor, none < devoiced < deleted)
table(cleandata$reduction)
##    none  devoiced  deleted 
##   20776       360      420 

## "speaker" is a nuisance predictor, which I intend to model as a random effect
with(cleandata, table(reduction, speaker))
##           speaker
## reduction    F1   F2   F3   M1   M2   M3
##   none     3559 3482 3325 3569 3360 3481

drammock

import expyfun
import numpy as np
import matplotlib.pyplot as plt
plt.ioff()

n_iter = 10000
edges = np.arange(4, 17, dtype=int)
edges = [10, 100, 500]
times = np.zeros(n_iter)
with expyfun.ExperimentController('wait_test', output_dir=None,

drammock

# -*- coding: utf-8 -*-
"""
===============================================================================
Script 'test-eyelink-interactive.py'
===============================================================================

This script tests the connection to the EyeLink eye tracker.
"""
# @author: drmccloy
# Created on Thu Oct 15 09:11:04 2015

drammock

# -*- coding: utf-8 -*-
import numpy as np
from expyfun import ExperimentController

kwargs = dict(exp_name='test', session='000', participant='foo')

with ExperimentController(**kwargs) as ec:
    ec.flip()
    ec.set_background_color('red')
    ec.screen_text('red', color='k')

drammock

# -*- coding: utf-8 -*-

# see:
# http://eeweb.poly.edu/iselesni/lecture_notes/least_squares/
#     LeastSquares_SPdemos/deconvolution/html/deconv_demo.html

import numpy as np
from scipy import linalg, sparse
import matplotlib.pyplot as plt
from pyeparse.utils import pupil_kernel

drammock

# -*- coding: utf-8 -*-

import expyfun as ef
import tdt

circuit = 'C:\\Users\\labuser\\Builds\\expyfun\\expyfun\\data\\expCircuitF32_RM1.rcx'
interface = 'USB'
model = 'RP2'

# approach #1

drammock

# sample data frame
df <- data.frame(zh=c("一", "二", "三", "四", "五", "六", "七", "八", "九", "十"),
                 pinyin=c("yī", "èr", "sān", "sì", "wǔ", "liù", "qī", "bā", "jiǔ", "shí"),
                 ipa=c("ji˥", "aɻ˥˩", "san˥", "sz̩˥˩", "wu˨˩˦", "ljoʊ˥˩", "tɕʰi˥", "pa˥", "tɕjoʊ˨˩˦", "ʂʐ̩˧˥"),
                 numbers=1:10, stringsAsFactors=FALSE)
print(df)
print(df$zh)


save.utf8 <- function(df, file, sep=",", quote=FALSE) {

drammock

tricolor <- function(x, y, z, xcol=NA, ycol=NA, zcol=NA, plot=FALSE) {
    if(length(unique(sapply(list(xcol, ycol, zcol), length))) != 1) {
        stop("All colors (xcol, ycol, zcol) must be the same length")
    }
    cols <- c(xcol, ycol, zcol)
    if(any(is.na(cols))) {
        if(!all(is.na(cols))) warning('NAs detected in user-specified colors; ',
                                      'falling back to defaults.')
        ## defaults
        cols <- matrix(c(0, 120, 240, 100, 100, 100, 50, 50, 50, 1, 1, 1),