Vowel plotting in Python

python
vowels
data visualization

A tutorial on normalizing and plotting vowels in python with pandas, seaborn, and matplotlib.

Author
Affiliation

Emily Remirez

UC Berkeley

Published

October 20, 2022

Doi

10.5281/zenodo.7232005

Let’s plot some vowel spaces!

1. Get the code set up

Run everything in this section first!

In these cells, we 1. Import the libraries and packages we’ll be using 2. Define a few functions we’ll use to plot and normalize vowels

Editing these cells will have an impact on the rest of the code in the notebook, so do so carefully!

In the next sections we’ll

  1. Import data
  2. Visualize some examples
  3. Normalize the data
#Import libraries

import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame
import seaborn as sns
import matplotlib.ticker as ticker
from matplotlib.ticker import ScalarFormatter
def vowelplot (vowelcsv, color=None, F1="F1", F2="F2", vowel="Vowel", title="Vowel Plot", unit="Hz", logscale=True):
  
    #Set some parameters for the chart itself
    sns.set(style='ticks', context='notebook')
    plt.figure(figsize=(6,6))
    
    # If there's an argument for color, determine whether it's likely to be categorical
    ## If it's a string (text), use a categorical color palette
    ## If it's a number, use a sequential color palette
    if color != None:
        if type(vowelcsv[color].iloc[0])==str:
            pal = "husl"
        else:
            pal = "viridis"
            
        pl = sns.scatterplot(x = F2,
                             y = F1,
                             hue = color,
                             data = vowelcsv,
                             palette = pal)
        
    # If no color argument is given, don't specify hue, and no palette needed
    else:
        pl = sns.scatterplot(x = F2,
                             y = F1,
                             data = vowelcsv)
    
    
    #Invert axes to correlate with articulatory space!
    pl.invert_yaxis()
    pl.invert_xaxis()
    
    #Add unit to the axis labels
    F1name = str("F1 ("+unit+")")
    F2name = str("F2 ("+unit+")")
    laby = plt.ylabel(F1name)
    labx = plt.xlabel(F2name)

    if logscale == True:
        pl.loglog()
        pl.yaxis.set_major_formatter(ticker.ScalarFormatter())
        pl.yaxis.set_minor_formatter(ticker.ScalarFormatter())
        pl.xaxis.set_major_formatter(ticker.ScalarFormatter())
        pl.xaxis.set_minor_formatter(ticker.ScalarFormatter())
    
    # Add vowel labels
    
    if vowel != None: 
        for line,row in vowelcsv.iterrows():
            pl.text(vowelcsv[F2][line]+0.1,
                    vowelcsv[F1][line],
                    vowelcsv[vowel][line],
                    horizontalalignment = 'left',
                    size = 14, # Edit for larger plots!
                    color = 'black',
                   # weight = 'semibold' # Uncomment for larger plots!
                   )
    
    pl.set_title(title)
    plt.show()
    
    return pl



def barkify (data, formants):
    # For each formant listed, make a copy of the column prefixed with z
    for formant in formants:
        for ch in formant:
            if ch.isnumeric():
                num = ch
        formantchar = (formant.split(num)[0])
        name = str(formant).replace(formantchar,'z')
        # Convert each value from Hz to Bark
        data[name] = 26.81/ (1+ 1960/data[formant]) - 0.53
    # Return the dataframe with the changes
    return data

def Lobify (data, group, formants):
    zscore = lambda x: (x - x.mean()) / x.std()
    for formant in formants:
        name = str("zsc_" + formant)
        col = data.groupby([group])[formant].transform(zscore)
        data.insert(len(data.columns), name, col)
    return data

2. Import data

This expects a CSV file (comma separated values) with the following columns.

  • “F1”: F1 value in Hz
  • “F2”: F2 value in Hz
  • “Vowel”: transcription or label for vowel

If your columns aren’t named exactly like this, that’s okay!
But you’ll need to be sure to provide the exact names of your columns when you make the plot!

You may also have other values, which you can use to label your plot:

  • F3
  • Token
  • Speaker

To import your data

Make sure that the name of the file is correct! When we import the data, we’ll also have to give our dataframe a name, which will go on the left side of the =. When we need to do something to our data, this is the name we’ll use in the code, not the name of our csv! If your csv is in the same folder as this notebook, the code should look like the example below, with the name of your file in quotes within the parentheses.

voweldata = pd.read_csv('NameOfFile.csv')

If the csv is somewhere else, you’ll have to get more specific, for example:

voweldata = pd.read_csv('MainFolder//NameOfFile.csv')

For this example, let’s load in some vowel data from Flege 1994. Thanks to Prof Keith Johnson for suggesting this dataset and doing some pre-processing to make it work with this function! This is a rich dataset that will let us visualize a lot of different features

Import Flege 1994 as F94

Let’s preview the data to confirm we loaded the data and make sure everything is as we expect. We can use df.sample() or df.head(), where df is the name of your dataframe. df.sample() will give you random rows, while df.head() will give you the first rows.

F94 = pd.read_csv('Flege_94_vowels.csv')
F94.sample(5)
lg Vowel Ss point VOT dur F0 F1 F2 F3
53 English a 3 offset 4 110 172 619 1209 1955
64 Spanish i 1 midpoint 16 191 78 354 2238 2576
81 Spanish a 1 onset 17 166 110 517 1195 2591
33 English ɛ 3 onset 2 114 118 452 1466 2395
51 English a 3 onset 4 110 172 551 1136 1857 
F94.head(5)
lg Vowel Ss point VOT dur F0 F1 F2 F3
0 English i 1 onset 10 119 131 312 2205 2726
1 English i 1 midpoint 10 119 131 317 2299 2897
2 English i 1 offset 10 119 131 320 2257 2829
3 English i 2 onset 13 93 130 304 1995 2383
4 English i 2 midpoint 13 93 130 281 2079 2516

3. Plot vowels!

The shortest line of code

That you can use to make a plot only gives the name of your data.

  • This will only work if your F1 column is called exactly “F1”, your F2 column is called exactly “F2”, and your Vowel column is called “Vowel”
vowelplot(voweldata)
  • If your columns have different names be sure to provide them using
vowelplot(voweldata,
          F1 = "YOUR F1 NAME",
          F2 = "YOUR F2 NAME",
          vowel = "YOUR VOWEL NAME")
  • This will plot a your data in F1-F2 space, with all points the same color

The longest line of code

You could use, if you specified every single argument, is:

vowelplot(voweldata, 
          color = "MY COLOR CHOICE", 
          F1 = "MY F1 NAME", 
          F2 = "MY F2 NAME", 
          vowel = "MY VOWEL NAME", 
          title = "MY TITLE", 
          unit = "MY FORMANT UNIT", 
          logscale=True/False)
  • These other arguments are all optional.
    1. color: What would you like color to represent?
      • Don’t forget to put this term in quotes!
      • This needs to be the exact name, including capitalization, of the name of the column in your spreadsheet
    2. title: Add a title for your plot!
      • If you don’t specify anyhing, your plot will be called “Vowel Plot”
      • If you don’t want any title, use title = None
    3. logscale: Do you want the axes to be on a log scale?
      • The log scale is closer to how we perceive sound, so it may be more informative
      • use logscale = True to use the log for your axes
      • use logscale = False to use non-transformed axes
  • The vowel argument adds labels to each point and can also be used in other ways:
    • Use vowel = None to remove all labels from the points (for example, if you want to use only color to distinguish between vowels)
    • Use the name of a different column to label points with that feature instead (for example, if they’re tokens of the same vowel in different contexts)

Demos with Flege 1994

vowelplot(F94,
          color = "lg",
          title = "Vowels by language",
          logscale = True)

<AxesSubplot:title={'center':'Vowels by language'}, xlabel='F2 (Hz)', ylabel='F1 (Hz)'>
vowelplot(F94,
          color = "F0",
          vowel = None,
          title = "Vowels by F0",
          logscale = True)

<AxesSubplot:title={'center':'Vowels by F0'}, xlabel='F2 (Hz)', ylabel='F1 (Hz)'>

4. Import and plot your data!

Remember: - If your data isn’t in the same folder as this notebook, you’ll need to include the path - The function we defined expects some defaults. If your plot is different from that, you need to specify those arguments! For example - If your vowel column is called V, include vowel = "V" - If you want to label it Figure 1, include title = "Figure 1" - If you don’t want the axes to be normalized, include logscale = False - If you want to color code by F0, include color = "F0"

voweldata = pd.read_csv('demo-vowels.csv') # Replace 'demo-vowels.csv'!
voweldata.sample(5)
carrier1 word carrier2 Vowel F1 F2 F3
2 say hayed again e 666 2350 3576
1 say hid again ɪ 484 2271 3304
0 say heed again i 323 2692 3401
7 say hud again ʌ 985 1644 3117
8 say hoed again o 550 1050 3250 
vowelplot(vowelcsv = voweldata,
          color = None,
          F1 = "F1",
          F2 = "F2",
          vowel = "Vowel",
          title = "Vowel Plot",
          unit = "Hz",
          logscale = True)

<AxesSubplot:title={'center':'Vowel Plot'}, xlabel='F2 (Hz)', ylabel='F1 (Hz)'>

5. Normalizing data

When you ran the cells in the “get the code set up” section, you also defined some code for normalizing data.

There are two kinds of normalizing we’ll consider: - Psychoacoustic normalizing to convert Hz to Bark. - This is similar to using the log scale for the axes–I wouldn’t do both! - This psychoacoustic scale is better at capturing the non-linear perception of frequencies. - That is, the perceptual difference between 100 and 200 Hz is perceived as greater than the difference between 2100 and 2200 Hz - The perceptual difference between Bark is more uniform - Using Bark values will produce a plot that looks more like the IPA vowel chart - Lobanov normalizing to compare whole vowel systems - Because everyone’s individual formant values vary so much, it can be hard to compare raw formant values across speakers - Taking the z-score of each formant relative to some category, usually a speaker, lets us focus on how the two vowel systems compare overall - In other words, it gives us a picture of the shape of someone’s vowel space - This is how we can make statements about one person’s [u] being more fronted than another’s, for example - For this to work well, you need to have complete vowel spaces for each speaker

Let’s try normaling the Flege 94 data both ways and visualizing the results

Convert F1 and F2 to Bark using barkify

  • To use barkify, we need to give it the name of our data, and a list of the columns we want to convert
  • In Python, a list goes withins square brackets [] with each item separated by a comma
  • The new columns will have ‘z’ in front of the number, instead of ‘F,’ for example

Let’s try converting F1 and F2 of Flege 94 to Bark and visualizing the results

barkify(voweldata, ["formant","formant"...])
barkify(F94, ["F1","F2"])
F94.head()
lg Vowel Ss point VOT dur F0 F1 F2 F3 z1 z2
0 English i 1 onset 10 119 131 312 2205 2726 3.151655 13.663529
1 English i 1 midpoint 10 119 131 317 2299 2897 3.202442 13.941986
2 English i 1 offset 10 119 131 320 2257 2829 3.232807 13.819104
3 English i 2 onset 13 93 130 304 1995 2383 3.069929 12.993628
4 English i 2 midpoint 13 93 130 281 2079 2516 2.831718 13.269948

Plot the data

Be sure to update your arguments! We’ve changed three things about the data that we need to pay attention to for the code to run and our plot to be accurate: - F1, F2: The name of our F1 and F2 columns are different and need to be specified in order for the code to run - unit: The unit has changed, so the axes need to be updated in order for our plot to be accurate - logscale: The scale has changed, from Hz to Bark, so it no longer makes sense to log transform the axes

vowelplot(F94,
          F1 = "z1",
          F2 = "z2",
          color = "lg",
          unit = "Bark",
          logscale = False)

<AxesSubplot:title={'center':'Vowel Plot'}, xlabel='F2 (Bark)', ylabel='F1 (Bark)'>

Lobanov normalize vowel spaces by Ss using Lobify

Next, lets try normalizing the vowel spaces based on Ss so that we can compare the vowel spaces for each
To use Lobify we need to specify: - data: the data we want to transform - group: the category we want to compare vowel spaces by (usually speaker) - formants: a list of the formant values we want to normalize

Lobify(voweldata, 
       group = "GROUP",
       formants = ["formant1","formant2"...]
      )

This will add a column for each formant with ‘zsc_’ (for ‘z-score’) in front of the formant name.

Subsetting

Before we normalize the data, let’s also subset it so that we’re only looking at English
Let’s also only look at the midpoint of each vowel.
This way we’ll have just one F1 and F2 value for each Ss-vowel pair.

1 condition New_Data = Old_Data[Old_Data.Column=="Category To Keep"]

2 or more conditions
New_Data = Old_Data[(Old_Data.Column=="Category To Keep") & Old_Data.SecondColumn=="Category To Keep"])

F94_2 = F94[(F94.point=="midpoint") & (F94.lg=="English")]
F94_2 = Lobify(F94_2,
       group = "Ss",
       formants = ["z1","z2"]
      )
F94_2.sample(10)
lg Vowel Ss point VOT dur F0 F1 F2 F3 z1 z2 zsc_z1 zsc_z2
58 English ʌ 2 midpoint 19 87 95 599 1200 2367 5.745572 9.651013 0.487282 -1.079360
52 English a 3 midpoint 4 110 172 681 1190 1968 6.383143 9.598222 1.012362 -1.219642
7 English i 3 midpoint 7 109 129 300 2180 2628 3.028850 13.587343 -1.408034 1.461580
4 English i 2 midpoint 13 93 130 281 2079 2516 2.831718 13.269948 -1.569002 1.357100
46 English a 1 midpoint 10 115 155 769 1129 2161 7.024742 9.268799 0.983154 -1.639149
34 English ɛ 3 midpoint 2 114 118 552 1575 2533 5.361369 11.415050 0.275069 0.001508
25 English e 3 midpoint 7 114 195 411 2020 2508 4.117368 13.077085 -0.622579 1.118618
1 English i 1 midpoint 10 119 131 317 2299 2897 3.202442 13.941986 -1.592593 1.330189
37 English æ 1 midpoint 7 116 165 777 1788 2562 7.081023 12.259829 1.021081 0.261349
61 English ʌ 3 midpoint 12 119 96 562 1249 2348 5.444314 9.904930 0.334920 -1.013493 
vowelplot(F94_2,
          F1 = "zsc_z1",
          F2 = "zsc_z2",
          color = "Ss",
          unit = "Lobanov normalized from Bark",
          logscale = False)

<AxesSubplot:title={'center':'Vowel Plot'}, xlabel='F2 (Lobanov normalized from Bark)', ylabel='F1 (Lobanov normalized from Bark)'>

6. Pseudocode recap

To import a csv file (spreadsheet) with pandas

my_data = pd.read_csv('NameOfFile.csv')

To plot your data

vowelplot(my_data,
          color = "Column Name - color coding/hue", # use None for no color
          F1 = "Column Name - Y-axis", # can omit if column is called F1
          F2 = "Column Name - X-axis", # can omit column is called F2
          vowel = "Column Name - point label", # can omit if column is called "Vowel",
                                    # use vowel = None for no label
          title = "Title to go at top of plot", # can omit if title is "Vowel plot",
                                      # use title = None for no title
          unit = "Unit for axis labels", # can omit if unit is Hz
          logscale = True/False # can omit if logscale should be on
         )

To convert Hz to Bark

my_data = barkify(my_data, ["Column Name - formant", "Column Name - formant"...])

To Lobanov normalize by speaker

my_data = lobify(my_data,
                 group = "Column Name - speaker or other grouping",
                 formants = ["Column Name - formant", "Column Name - formant"...]
                )

Reuse

BSD3

Citation

BibTeX citation:
@online{remirez2022,
  author = {Remirez, Emily},
  title = {Vowel Plotting in {Python}},
  series = {Linguistics Methods Hub},
  date = {2022-10-20},
  url = {https://lingmethodshub.github.io/content/python/vowel-plotting-py},
  doi = {10.5281/zenodo.7232005},
  langid = {en}
}
For attribution, please cite this work as:
Remirez, Emily. 2022, October 20. Vowel plotting in Python. Linguistics Methods Hub. (https://lingmethodshub.github.io/content/python/vowel-plotting-py). doi: 10.5281/zenodo.7232005