Soundscapy - Quick Start Guide¶

By Andrew Mitchell, Lecturer, University College London

Background¶

Soundscapy is a python toolbox for analysing quantitative soundscape data. Urban soundscapes are typically assessed through surveys which ask respondents how they perceive the given soundscape. Particularly when collected following the technical specification ISO 12913, these surveys can constitute quantitative data about the soundscape perception. As proposed in How to analyse and represent quantitative soundscape data (Mitchell, Aletta, & Kang, 2022), in order to describe the soundscape perception of a group or of a location, we should consider the distribution of responses. Soundscapy's approach to soundscape analysis follows this approach and makes it simple to process soundscape data and visualise the distribution of responses.

For more information on the theory underlying the assessments and forms of data collection, please see ISO 12913-Part 2, The SSID Protocol (Mitchell, et al., 2020), and How to analyse and represent quantitative soundscape data.

This Notebook¶

The purpose of this notebook is to give a brief overview of how Soundscapy works and how to quickly get started using it to analyse your own soundscape data. The example dataset used is The International Soundscape Database (ISD) (Mitchell, et al., 2021), which is publicly available at Zenodo and is free to use. Soundscapy expects data to follow the format used in the ISD, but can be adapted for similar datasets.

---

Installation¶

To install Soundscapy with pip:

pip install soundscapy

---

Working with Data¶

Loading and Validating Data¶

Let's start by importing Soundscapy and loading the International Soundscape Database (ISD):

In [1]:

# ruff: noqa: T201
# Import Soundscapy
import soundscapy as sspy
from soundscapy.databases import isd

# Load the ISD dataset
data = isd.load()
print(data.shape)

# Validate the dataset with ISD-custom checks
df, excl = isd.validate(data)
print(f"Valid samples: {data.shape[0]}, Excluded samples: {excl.shape[0]}")

# ruff: noqa: T201 # Import Soundscapy import soundscapy as sspy from soundscapy.databases import isd # Load the ISD dataset data = isd.load() print(data.shape) # Validate the dataset with ISD-custom checks df, excl = isd.validate(data) print(f"Valid samples: {data.shape[0]}, Excluded samples: {excl.shape[0]}")

(3589, 142)
Valid samples: 3589, Excluded samples: 56

Calculating ISOPleasant and ISOEventful Coordinates¶

Next, we'll calculate the ISOCoordinate values:

In [2]:

data = sspy.surveys.add_iso_coords(data)
data[["ISOPleasant", "ISOEventful"]].round(2).head()

data = sspy.surveys.add_iso_coords(data) data[["ISOPleasant", "ISOEventful"]].round(2).head()

Out[2]:

	ISOPleasant	ISOEventful
0	0.22	-0.13
1	-0.43	0.53
2	0.68	-0.07
3	0.60	-0.15
4	0.46	-0.15

Soundscapy expects the PAQ values to be Likert scale values ranging from 1 to 5 by default, as specified in ISO 12913 and the SSID Protocol. However, it is possible to use data which, although structured the same way, has a different range of values. For instance this could be a 7-point Likert scale, or a 0 to 100 scale. By passing these numbers both to validate_dataset() and add_paq_coords() as the val_range, Soundscapy will check that the data conforms to what is expected and will automatically scale the ISOCoordinates from -1 to +1 depending on the original value range.

For example:

In [3]:

import pandas as pd

val_range = (0, 100)
sample_transform = {
    "RecordID": ["EX1", "EX2"],
    "pleasant": [40, 25],
    "vibrant": [45, 31],
    "eventful": [41, 54],
    "chaotic": [24, 56],
    "annoying": [8, 52],
    "monotonous": [31, 55],
    "uneventful": [37, 31],
    "calm": [40, 10],
}
sample_transform = pd.DataFrame().from_dict(sample_transform)
sample_transform = sspy.rename_paqs(sample_transform)
sample_transform = sspy.add_iso_coords(sample_transform, val_range=val_range)
sample_transform

import pandas as pd val_range = (0, 100) sample_transform = { "RecordID": ["EX1", "EX2"], "pleasant": [40, 25], "vibrant": [45, 31], "eventful": [41, 54], "chaotic": [24, 56], "annoying": [8, 52], "monotonous": [31, 55], "uneventful": [37, 31], "calm": [40, 10], } sample_transform = pd.DataFrame().from_dict(sample_transform) sample_transform = sspy.rename_paqs(sample_transform) sample_transform = sspy.add_iso_coords(sample_transform, val_range=val_range) sample_transform

Out[3]:

	RecordID	PAQ1	PAQ2	PAQ3	PAQ4	PAQ5	PAQ6	PAQ7	PAQ8	ISOPleasant	ISOEventful
0	EX1	40	45	41	24	8	31	37	40	0.220416	0.010711
1	EX2	25	31	54	56	52	55	31	10	-0.316863	0.159706

Filtering Data¶

Soundscapy includes methods for several filters that are normally needed within the ISD, such as filtering by LocationID or SessionID.

In [4]:

# Filter by location
camden_data = isd.select_location_ids(data, ["CamdenTown"])
print(f"Camden Town samples: {camden_data.shape[0]}")

# Filter by session
regent_data = isd.select_session_ids(data, ["RegentsParkJapan1"])
print(f"Regent's Park Japan session 1 samples: {regent_data.shape[0]}")

# Complex filtering using pandas query
women_over_50 = df.query("gen00 == 'Female' and age00 > 50")
print(f"Women over 50: {women_over_50.shape[0]}")

# Filter by location camden_data = isd.select_location_ids(data, ["CamdenTown"]) print(f"Camden Town samples: {camden_data.shape[0]}") # Filter by session regent_data = isd.select_session_ids(data, ["RegentsParkJapan1"]) print(f"Regent's Park Japan session 1 samples: {regent_data.shape[0]}") # Complex filtering using pandas query women_over_50 = df.query("gen00 == 'Female' and age00 > 50") print(f"Women over 50: {women_over_50.shape[0]}")

Camden Town samples: 105
Regent's Park Japan session 1 samples: 46
Women over 50: 133

All of these filters can also be chained together. So, for instance, to return surveys from women over 50 taken in Camden Town, we would do:

In [5]:

isd.select_location_ids(data, "CamdenTown").query("gen00 == 'Female' and age00 > 50")

isd.select_location_ids(data, "CamdenTown").query("gen00 == 'Female' and age00 > 50")

Out[5]:

	LocationID	SessionID	GroupID	RecordID	start_time	end_time	latitude	longitude	Language	Survey_Version	...	THD_THD_Max	THD_Min_Max	THD_Max_Max	THD_L5_Max	THD_L10_Max	THD_L50_Max	THD_L90_Max	THD_L95_Max	ISOPleasant	ISOEventful
58	CamdenTown	CamdenTown1	CT108	531	2019-05-02 12:10:52	2019-05-02 12:26:45	51.539124	-0.142624	eng	engISO2018	...	-2.12	-4.44	72.28	45.05	37.19	13.40	-1.88	-2.45	-1.464466e-01	0.146447
63	CamdenTown	CamdenTown1	CT111	533	2019-05-02 12:29:42	2019-05-02 12:58:56	51.539124	-0.142624	eng	engISO2018	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-4.142136e-01	-0.457107
104	CamdenTown	CamdenTown3	CT311	593	2019-05-20 12:24:14	2019-05-20 12:28:27	51.539124	-0.142624	eng	engISO2018	...	-4.73	-8.48	76.20	44.21	34.12	11.04	-4.83	-5.99	-1.464466e-01	0.146447
105	CamdenTown	CamdenTown3	CT311	623	2019-05-20 12:25:00	2019-05-20 12:30:00	51.539124	-0.142624	eng	engISO2018	...	-4.73	-8.48	76.20	44.21	34.12	11.04	-4.83	-5.99	1.338835e-01	0.073223
122	CamdenTown	CamdenTown3	CT324	609	2019-05-20 13:51:00	2019-05-20 13:57:00	51.539124	-0.142624	eng	engISO2018	...	-1.53	-9.57	75.83	43.30	33.73	11.88	-5.59	-7.44	1.767767e-01	0.926777
128	CamdenTown	CamdenTown3	CT328	617	2019-05-20 14:13:00	2019-05-20 14:16:00	51.539124	-0.142624	eng	engISO2018	...	-5.89	-1.76	71.56	45.97	38.80	17.59	2.64	1.16	-4.393398e-01	0.250000
132	CamdenTown	CamdenTown4	CT403	1220	2019-07-13 12:31:40	2019-07-13 12:35:30	51.539140	-0.142648	eng	engISO2018	...	-1.32	-3.24	71.78	49.81	42.16	16.53	0.54	-0.65	6.898042e-17	0.603553

7 rows × 144 columns

Plotting¶

Soundscapy offers various plotting functions to visualize soundscape data. Let's explore some of them:

Scatter plots¶

In [6]:

from soundscapy.plotting import ISOPlot

# Basic scatter plot
p1 = (
    ISOPlot(data=isd.select_location_ids(data, ["RussellSq"]), title="Russell Square")
    .create_subplots()
    .add_scatter()
    .style()
)

# Customized scatter plot with multiple locations
p2 = (
    ISOPlot(
        data=isd.select_location_ids(data, ["RussellSq", "EustonTap"]),
        title="Russell Square vs. Euston Tap",
        hue="LocationID",
    )
    .create_subplots()
    .add_scatter()
    .style(diagonal_lines=True, legend_loc="lower left")
)

from soundscapy.plotting import ISOPlot # Basic scatter plot p1 = ( ISOPlot(data=isd.select_location_ids(data, ["RussellSq"]), title="Russell Square") .create_subplots() .add_scatter() .style() ) # Customized scatter plot with multiple locations p2 = ( ISOPlot( data=isd.select_location_ids(data, ["RussellSq", "EustonTap"]), title="Russell Square vs. Euston Tap", hue="LocationID", ) .create_subplots() .add_scatter() .style(diagonal_lines=True, legend_loc="lower left") )

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/3481299990.py:5: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(data=isd.select_location_ids(data, ["RussellSq"]), title="Russell Square")
/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/3481299990.py:13: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(

The CircumplexPlot also allows us to make this set of plots in one go, as a single figure. To do this, we need to set up the subplots slightly differently, then pass the subplot data to each .add_scatter() call separately, rather than passing the whold dataframe to the CircumplexPlot object.

In [8]:

p3 = (
    ISOPlot(title=None)
    .create_subplots(
        2, 1, subplot_titles=["Russell Square", "Russell Square vs. Euston Tap"]
    )
    .add_scatter(data=isd.select_location_ids(data, ["RussellSq"]), on_axis=0)
    .add_scatter(
        data=isd.select_location_ids(data, ["RussellSq", "EustonTap"]),
        on_axis=1,
        hue="LocationID",
    )
    .style()
)
p3.show()

p3 = ( ISOPlot(title=None) .create_subplots( 2, 1, subplot_titles=["Russell Square", "Russell Square vs. Euston Tap"] ) .add_scatter(data=isd.select_location_ids(data, ["RussellSq"]), on_axis=0) .add_scatter( data=isd.select_location_ids(data, ["RussellSq", "EustonTap"]), on_axis=1, hue="LocationID", ) .style() ) p3.show()

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/1865619274.py:2: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(title=None)

Density plots¶

In [9]:

d1 = (
    ISOPlot(
        data=isd.select_location_ids(data, ["CamdenTown"]),
        title="Camden Town Density Plot",
    )
    .create_subplots()
    .add_scatter()
    .add_density()
    .style()
)

d2 = (
    ISOPlot(
        data=isd.select_location_ids(data, ["CamdenTown", "RussellSq", "PancrasLock"]),
        title="Comparison of the soundscapes of three urban spaces",
        hue="LocationID",
        palette="husl",
    )
    .create_subplots(figsize=(8, 8))
    .add_scatter()
    .add_simple_density(hue="LocationID")
    .style(title_fontsize=14)
)

d1 = ( ISOPlot( data=isd.select_location_ids(data, ["CamdenTown"]), title="Camden Town Density Plot", ) .create_subplots() .add_scatter() .add_density() .style() ) d2 = ( ISOPlot( data=isd.select_location_ids(data, ["CamdenTown", "RussellSq", "PancrasLock"]), title="Comparison of the soundscapes of three urban spaces", hue="LocationID", palette="husl", ) .create_subplots(figsize=(8, 8)) .add_scatter() .add_simple_density(hue="LocationID") .style(title_fontsize=14) )

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/3496086766.py:2: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(
/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/3496086766.py:13: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(

Jointplots¶

In [10]:

sspy.jointplot(
    data=isd.select_location_ids(data, ["CamdenTown"]),
    x="ISOEventful",
    y="ISOPleasant",
    title="Camden Town",
    palette="husl",
    kind="kde",
)

sspy.jointplot( data=isd.select_location_ids(data, ["CamdenTown"]), x="ISOEventful", y="ISOPleasant", title="Camden Town", palette="husl", kind="kde", )

Out[10]:

<seaborn.axisgrid.JointGrid at 0x14cedc890>

Creating subplots¶

Soundscapy also provides a method for creating subplots of the circumplex. This is particularly useful when comparing multiple locations.

In [11]:

df["LocationID"].unique()

df["LocationID"].unique()

Out[11]:

array(['CarloV', 'SanMarco', 'PlazaBibRambla', 'CamdenTown', 'EustonTap',
       'Noorderplantsoen', 'MarchmontGarden', 'MonumentoGaribaldi',
       'TateModern', 'PancrasLock', 'TorringtonSq', 'RegentsParkFields',
       'RegentsParkJapan', 'RussellSq', 'StPaulsCross', 'StPaulsRow',
       'CampoPrincipe', 'MiradorSanNicolas', 'LianhuashanParkEntrance',
       'LianhuashanParkForest', 'PingshanPark', 'PingshanStreet',
       'ZhongshanPark', 'OlympicSquare', 'ZhongshanSquare',
       'DadongSquare'], dtype=object)

In [12]:

sub_data = sspy.isd.select_location_ids(
    data, ["PancrasLock", "TorringtonSq", "RegentsParkFields", "RegentsParkJapan"]
)

mp1 = (
    ISOPlot(
        data=sub_data,
        title="Density plots of the first four locations",
    )
    .create_subplots(subplot_by="LocationID", auto_allocate_axes=True)
    .add_scatter()
    .add_density()
    .style()
)

sub_data = sspy.isd.select_location_ids( data, ["PancrasLock", "TorringtonSq", "RegentsParkFields", "RegentsParkJapan"] ) mp1 = ( ISOPlot( data=sub_data, title="Density plots of the first four locations", ) .create_subplots(subplot_by="LocationID", auto_allocate_axes=True) .add_scatter() .add_density() .style() )

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/695190007.py:6: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(
/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/iso_plot.py:503: UserWarning: This is an experimental feature. The number of rows and columns may not be optimal.
  self._allocate_subplot_axes(subplot_titles)

You can also do this manually if you need more control, by creating a figure and axes and then plotting the density plots on the axes.

In [13]:

sub_data2 = sspy.isd.select_location_ids(
    data, ["CarloV", "SanMarco", "PlazaBibRambla", "CamdenTown"]
)

mp2 = (
    ISOPlot(
        data=sub_data2,
        title=None,
        hue="SessionID",
    )
    .create_subplots(
        2,
        2,
        subplot_by="LocationID",
        figsize=(12, 12),
        adjust_figsize=False,
    )
    .add_scatter()
    .add_simple_density(fill=False)
    .style()
)
mp2.show()

sub_data2 = sspy.isd.select_location_ids( data, ["CarloV", "SanMarco", "PlazaBibRambla", "CamdenTown"] ) mp2 = ( ISOPlot( data=sub_data2, title=None, hue="SessionID", ) .create_subplots( 2, 2, subplot_by="LocationID", figsize=(12, 12), adjust_figsize=False, ) .add_scatter() .add_simple_density(fill=False) .style() ) mp2.show()

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/1815054888.py:6: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(
/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/layers.py:218: UserWarning: Density plots are not recommended for small datasets (<30 samples).
  self._valid_density_size(data)

Using Adjusted Angles¶

In Aletta et. al. (2024), we propose a method for adjusting the angles of the circumplex to better represent the perceptual space. These adjusted angles are derived for each language separately, meaning that, once projected, the circumplex coordinates will be comparable across all languages. This ability and the derived angles have been incorporated into Soundscapy.

In [14]:

from soundscapy.surveys import LANGUAGE_ANGLES

adj_data = sspy.surveys.add_iso_coords(
    data,
    names=("AdjustedPleasant", "AdjustedEventful"),
    angles=LANGUAGE_ANGLES["eng"],
    overwrite=True,
)

adj_p = (
    ISOPlot(
        data=isd.select_location_ids(adj_data, ["CamdenTown", "RussellSq"]),
        x="AdjustedPleasant",
        y="AdjustedEventful",
        hue="LocationID",
        title="Adjusted Pleasant vs. Adjusted Eventful",
    )
    .create_subplots()
    .add_scatter()
    .add_simple_density()
    .style()
)
adj_p.show()

from soundscapy.surveys import LANGUAGE_ANGLES adj_data = sspy.surveys.add_iso_coords( data, names=("AdjustedPleasant", "AdjustedEventful"), angles=LANGUAGE_ANGLES["eng"], overwrite=True, ) adj_p = ( ISOPlot( data=isd.select_location_ids(adj_data, ["CamdenTown", "RussellSq"]), x="AdjustedPleasant", y="AdjustedEventful", hue="LocationID", title="Adjusted Pleasant vs. Adjusted Eventful", ) .create_subplots() .add_scatter() .add_simple_density() .style() ) adj_p.show()

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/1321125023.py:11: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(

In [15]:

def apply_iso_coords(row):
    angles = LANGUAGE_ANGLES.get(row["Language"], None)
    if angles:
        return sspy.surveys.add_iso_coords(
            pd.DataFrame([row]),
            angles=angles,
            names=("AdjPleasant", "AdjEventful"),
            overwrite=True,
        ).iloc[0]
    return row


data = data.apply(apply_iso_coords, axis=1)

def apply_iso_coords(row): angles = LANGUAGE_ANGLES.get(row["Language"], None) if angles: return sspy.surveys.add_iso_coords( pd.DataFrame([row]), angles=angles, names=("AdjPleasant", "AdjEventful"), overwrite=True, ).iloc[0] return row data = data.apply(apply_iso_coords, axis=1)

In [16]:

data[["AdjPleasant", "AdjEventful"]].describe()

data[["AdjPleasant", "AdjEventful"]].describe()

Out[16]:

	AdjPleasant	AdjEventful
count	3552.000000	3552.000000
mean	0.216068	0.051510
std	0.330794	0.281739
min	-1.000000	-0.820740
25%	0.000000	-0.138494
50%	0.226012	0.022161
75%	0.440837	0.222739
max	1.000000	0.994374

In [17]:

import matplotlib.pyplot as plt

mp3 = (
    ISOPlot(
        data=data,
        title="Soundscape Density Plots with corrected ISO coordinates",
        x="AdjPleasant",
        y="AdjEventful",
        hue="SessionID",
    )
    .create_subplots(
        subplot_by="LocationID",
        figsize=(4, 4),
        auto_allocate_axes=True,
    )
    .add_scatter()
    .add_simple_density(fill=False)
    .style(title_fontsize=25)
)
plt.show()

import matplotlib.pyplot as plt mp3 = ( ISOPlot( data=data, title="Soundscape Density Plots with corrected ISO coordinates", x="AdjPleasant", y="AdjEventful", hue="SessionID", ) .create_subplots( subplot_by="LocationID", figsize=(4, 4), auto_allocate_axes=True, ) .add_scatter() .add_simple_density(fill=False) .style(title_fontsize=25) ) plt.show()

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/1925802003.py:4: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(
/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/iso_plot.py:503: UserWarning: This is an experimental feature. The number of rows and columns may not be optimal.
  self._allocate_subplot_axes(subplot_titles)
/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/layers.py:218: UserWarning: Density plots are not recommended for small datasets (<30 samples).
  self._valid_density_size(data)

In [18]:

import matplotlib.pyplot as plt
import numpy as np

from soundscapy.spi import DirectParams, MultiSkewNorm

spi = DirectParams(
    xi=np.array([0.5, 0.7]),
    omega=np.array([[0.1, 0.05], [0.05, 0.1]]),
    alpha=np.array([0, -5]),
)
# Create a custom distribution
spi_msn = MultiSkewNorm.from_params(spi)
# Generate random samples
spi_msn.sample(1000)

mp3 = (
    ISOPlot(
        data=data,
        title="Soundscape Density Plots with corrected ISO coordinates",
        x="AdjPleasant",
        y="AdjEventful",
    )
    .create_subplots(
        subplot_by="LocationID",
        figsize=(4, 4),
        auto_allocate_axes=True,
    )
    .add_scatter()
    .add_simple_density(fill=False)
    .add_spi(spi_target_data=spi_msn.sample_data, show_score="on axis")
    .style(legend_loc=False, title_fontsize=30)
)

import matplotlib.pyplot as plt import numpy as np from soundscapy.spi import DirectParams, MultiSkewNorm spi = DirectParams( xi=np.array([0.5, 0.7]), omega=np.array([[0.1, 0.05], [0.05, 0.1]]), alpha=np.array([0, -5]), ) # Create a custom distribution spi_msn = MultiSkewNorm.from_params(spi) # Generate random samples spi_msn.sample(1000) mp3 = ( ISOPlot( data=data, title="Soundscape Density Plots with corrected ISO coordinates", x="AdjPleasant", y="AdjEventful", ) .create_subplots( subplot_by="LocationID", figsize=(4, 4), auto_allocate_axes=True, ) .add_scatter() .add_simple_density(fill=False) .add_spi(spi_target_data=spi_msn.sample_data, show_score="on axis") .style(legend_loc=False, title_fontsize=30) )

/var/folders/6t/7h8wn9n92w5f24ml_bkwck9m0000gn/T/ipykernel_7477/1823870167.py:17: ExperimentalWarning: `ISOPlot` is currently under development and should be considered experimental. `ISOPlot` implements an experimental API for creating layered soundscape circumplex plots. Use with caution.
  ISOPlot(
/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/iso_plot.py:503: UserWarning: This is an experimental feature. The number of rows and columns may not be optimal.
  self._allocate_subplot_axes(subplot_titles)
/Users/mitch/Documents/GitHub/Soundscapy/src/soundscapy/plotting/layers.py:218: UserWarning: Density plots are not recommended for small datasets (<30 samples).
  self._valid_density_size(data)

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