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

library(phonR)
data(indoVowels)

library(sp)     # SpatialPolygons, etc
library(rgeos)  # gIntersection

fem2 <- indo[indo$subj %in% "F02",]
vowels <- unique(as.character(fem2$vowel))
## important! ellipse.conf defaults to 0.6827 (2 s.d.) for plotVowels, but
## defaults internally to 0.95 for phonR:::ellipse.  You must specify the

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

# -*- coding: utf-8 -*-
"""
===============================================================================
Script 'mne logo'
===============================================================================

This script makes the logo for MNE.
"""
# @author: drmccloy
# Created on Mon Jul 20 11:28:16 2015

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