WolframAlpha.com
WolframCloud.com
All Sites & Public Resources...
Products & Services
Wolfram|One
Mathematica
Wolfram|Alpha Notebook Edition
Programming Lab
Finance Platform
SystemModeler
Wolfram Player
Wolfram Engine
WolframScript
Enterprise Private Cloud
Enterprise Mathematica
Wolfram|Alpha Appliance
Enterprise Solutions
Corporate Consulting
Technical Consulting
Wolfram|Alpha Business Solutions
Resource System
Data Repository
Neural Net Repository
Function Repository
Wolfram|Alpha
Wolfram|Alpha Pro
Problem Generator
API
Data Drop
Products for Education
Mobile Apps
Wolfram Player
Wolfram Cloud App
Wolfram|Alpha for Mobile
Wolfram|Alpha-Powered Apps
Services
Paid Project Support
Wolfram U
Summer Programs
All Products & Services »
Technologies
Wolfram Language
Revolutionary knowledge-based programming language.
Wolfram Cloud
Central infrastructure for Wolfram's cloud products & services.
Wolfram Science
Technology-enabling science of the computational universe.
Wolfram Notebooks
The preeminent environment for any technical workflows.
Wolfram Engine
Software engine implementing the Wolfram Language.
Wolfram Natural Language Understanding System
Knowledge-based broadly deployed natural language.
Wolfram Data Framework
Semantic framework for real-world data.
Wolfram Universal Deployment System
Instant deployment across cloud, desktop, mobile, and more.
Wolfram Knowledgebase
Curated computable knowledge powering Wolfram|Alpha.
All Technologies »
Solutions
Engineering, R&D
Aerospace & Defense
Chemical Engineering
Control Systems
Electrical Engineering
Image Processing
Industrial Engineering
Mechanical Engineering
Operations Research
More...
Finance, Statistics & Business Analysis
Actuarial Sciences
Bioinformatics
Data Science
Econometrics
Financial Risk Management
Statistics
More...
Education
All Solutions for Education
Trends
Machine Learning
Multiparadigm Data Science
Internet of Things
High-Performance Computing
Hackathons
Software & Web
Software Development
Authoring & Publishing
Interface Development
Web Development
Sciences
Astronomy
Biology
Chemistry
More...
All Solutions »
Learning & Support
Learning
Wolfram Language Documentation
Fast Introduction for Programmers
Wolfram U
Videos & Screencasts
Wolfram Language Introductory Book
Webinars & Training
Summer Programs
Books
Need Help?
Support FAQ
Wolfram Community
Contact Support
Premium Support
Paid Project Support
Technical Consulting
All Learning & Support »
Company
About
Company Background
Wolfram Blog
Events
Contact Us
Work with Us
Careers at Wolfram
Internships
Other Wolfram Language Jobs
Initiatives
Wolfram Foundation
MathWorld
Computer-Based Math
A New Kind of Science
Wolfram Technology for Hackathons
Student Ambassador Program
Wolfram for Startups
Demonstrations Project
Wolfram Innovator Awards
Wolfram + Raspberry Pi
Summer Programs
More...
All Company »
Search
WOLFRAM COMMUNITY
Connect with users of Wolfram technologies to learn, solve problems and share ideas
Join
Sign In
Dashboard
Groups
People
Search
Message Boards
Answer
(
Unmark
)
Mark as an Answer
GROUPS:
Staff Picks
Biological Sciences
Data Science
Music and Sound
Curated Data
External Programs and Systems
Import and Export
Wolfram Language
Machine Learning
Geographic Information System
15
Jofre Espigule-Pons
Biodiversity: The Great Horned Owl (Bubo virginianus)
Jofre Espigule-Pons, Wolfram Research
Posted
1 year ago
4426 Views
|
5 Replies
|
23 Total Likes
Follow this post
|
This is going to be my first post on a series of computational explorations on different animal species and biodiversity related topics. Last week on a late night walk, during the Wolfram Tech Conference, I got lucky and I heard one great horned owl singing on the parking lot of Champaigns football stadium. That’s why I’m going to start the series with this species. Listen to my recorded sound on SoundCloud
here
.
Since this post is actually a cloud embedded notebook, we can easily display a cool intro video using
EmbeddedService
:
E
m
b
e
d
d
e
d
S
e
r
v
i
c
e
[
{
"
Y
o
u
T
u
b
e
"
,
"
b
t
3
X
8
M
J
g
J
W
o
"
}
]
I
n
[
]
:
=
O
u
t
[
]
=
Species Built-in Data
One way of obtaining the species entity of a great horned owl is by using
Interpreter
:
g
H
o
r
n
e
d
O
w
l
=
I
n
t
e
r
p
r
e
t
e
r
[
"
S
p
e
c
i
e
s
"
]
[
"
G
r
e
a
t
H
o
r
n
e
d
O
w
l
"
]
I
n
[
]
:
=
g
r
e
a
t
h
o
r
n
e
d
o
w
l
O
u
t
[
]
=
Once we have the entity of the species, we can check its associated properties, like its, “ScientificName”, “SpeciesAuthority”, and “Image”:
C
o
l
u
m
n
g
r
e
a
t
h
o
r
n
e
d
o
w
l
S
P
E
C
I
E
S
S
P
E
C
I
F
I
C
A
T
I
O
N
[
{
"
S
c
i
e
n
t
i
f
i
c
N
a
m
e
"
,
"
S
p
e
c
i
e
s
A
u
t
h
o
r
i
t
y
"
,
"
I
m
a
g
e
"
}
]
I
n
[
]
:
=
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
G
m
e
l
i
n
,
1
7
8
8
O
u
t
[
]
=
A quick way to check all properties available for a species is by using “Dataset”:
g
r
e
a
t
h
o
r
n
e
d
o
w
l
S
P
E
C
I
E
S
S
P
E
C
I
F
I
C
A
T
I
O
N
[
"
D
a
t
a
s
e
t
"
]
I
n
[
]
:
=
a
l
t
e
r
n
a
t
e
c
o
m
m
o
n
n
a
m
e
s
a
l
t
e
r
n
a
t
e
s
c
i
e
n
t
i
f
i
c
n
a
m
e
s
b
o
d
y
t
e
m
p
e
r
a
t
u
r
e
—
c
l
a
s
s
C
l
a
s
s
:
A
v
e
s
c
o
m
m
o
n
n
a
m
e
g
r
e
a
t
h
o
r
n
e
d
o
w
l
f
a
m
i
l
y
F
a
m
i
l
y
:
S
t
r
i
g
i
d
a
e
g
e
n
u
s
G
e
n
u
s
:
B
u
b
o
i
m
a
g
e
i
n
f
r
a
s
p
e
c
i
e
s
—
k
i
n
g
d
o
m
K
i
n
g
d
o
m
:
A
n
i
m
a
l
i
a
l
e
n
g
t
h
(
1
8
.
1
1
0
2
t
o
2
4
.
8
0
3
1
)
i
n
m
a
x
i
m
u
m
r
e
c
o
r
d
e
d
l
i
f
e
s
p
a
n
2
9
y
r
m
a
x
i
m
u
m
s
p
e
e
d
—
n
a
m
e
g
r
e
a
t
h
o
r
n
e
d
o
w
l
n
u
m
b
e
r
o
f
m
e
m
b
e
r
s
1
3
o
r
d
e
r
O
r
d
e
r
:
S
t
r
i
g
i
f
o
r
m
e
s
p
a
r
e
n
t
e
n
t
i
t
y
G
e
n
u
s
:
B
u
b
o
p
h
y
l
u
m
P
h
y
l
u
m
:
C
h
o
r
d
a
t
a
s
c
i
e
n
t
i
f
i
c
n
a
m
e
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
i
b
l
i
n
g
t
a
x
a
{
…
1
7
}
s
h
o
w
i
n
g
1
–
2
0
o
f
2
7
O
u
t
[
]
=
Alternatively, we can also use
SpeciesData
which is equivalent to
EntityValue
:
l
e
n
g
t
h
=
S
p
e
c
i
e
s
D
a
t
a
g
r
e
a
t
h
o
r
n
e
d
o
w
l
S
P
E
C
I
E
S
S
P
E
C
I
F
I
C
A
T
I
O
N
,
"
L
e
n
g
t
h
"
I
n
[
]
:
=
(
1
8
.
1
1
0
2
t
o
2
4
.
8
0
3
1
)
i
n
O
u
t
[
]
=
E
n
t
i
t
y
V
a
l
u
e
g
r
e
a
t
h
o
r
n
e
d
o
w
l
S
P
E
C
I
E
S
S
P
E
C
I
F
I
C
A
T
I
O
N
,
"
L
e
n
g
t
h
"
I
n
[
]
:
=
(
1
8
.
1
1
0
2
t
o
2
4
.
8
0
3
1
)
i
n
O
u
t
[
]
=
Since I’m not American, I need to convert the units to metric system in order to get a picture of the size of great horned owls:
U
n
i
t
C
o
n
v
e
r
t
[
l
e
n
g
t
h
,
"
C
e
n
t
i
m
e
t
e
r
s
"
]
I
n
[
]
:
=
(
4
6
.
t
o
6
3
.
)
c
m
O
u
t
[
]
=
Oh, Wow! They are actually bigger than I expected.
We can also easily get other eagle owl species using “Siblings”:
o
t
h
e
r
E
a
g
l
e
O
w
l
s
=
S
p
e
c
i
e
s
D
a
t
a
[
g
H
o
r
n
e
d
O
w
l
,
"
S
i
b
l
i
n
g
s
"
]
I
n
[
]
:
=
s
p
o
t
t
e
d
e
a
g
l
e
-
o
w
l
,
p
h
a
r
a
o
h
e
a
g
l
e
-
o
w
l
,
b
l
a
k
i
s
t
o
n
'
s
f
i
s
h
o
w
l
,
E
u
r
a
s
i
a
n
e
a
g
l
e
-
o
w
l
,
B
u
b
o
c
a
p
e
n
s
i
s
,
B
u
b
o
c
i
n
e
r
a
s
c
e
n
s
,
B
u
b
o
c
o
r
o
m
a
n
d
u
s
,
V
e
r
r
e
a
u
x
'
s
e
a
g
l
e
-
o
w
l
,
B
u
b
o
l
e
u
c
o
s
t
i
c
t
u
s
,
B
u
b
o
m
a
g
e
l
l
a
n
i
c
u
s
,
s
p
o
t
-
b
e
l
l
i
e
d
e
a
g
l
e
-
o
w
l
,
p
h
i
l
i
p
p
i
n
e
e
a
g
l
e
-
o
w
l
,
B
u
b
o
p
o
e
n
s
i
s
,
s
n
o
w
y
o
w
l
,
B
u
b
o
s
h
e
l
l
e
y
i
,
B
u
b
o
s
u
m
a
t
r
a
n
u
s
,
B
u
b
o
v
o
s
s
e
l
e
r
i
O
u
t
[
]
=
e
a
g
l
e
O
w
l
s
=
A
p
p
e
n
d
[
o
t
h
e
r
E
a
g
l
e
O
w
l
s
,
g
H
o
r
n
e
d
O
w
l
]
;
I
n
[
]
:
=
Now, we can easily create a MenuView with most of existing eagle owls species around the globe:
M
e
n
u
V
i
e
w
[
D
e
l
e
t
e
M
i
s
s
i
n
g
[
T
h
r
e
a
d
[
R
u
l
e
[
e
a
g
l
e
O
w
l
s
,
S
p
e
c
i
e
s
D
a
t
a
[
e
a
g
l
e
O
w
l
s
,
"
I
m
a
g
e
"
]
]
]
,
2
]
,
1
3
]
I
n
[
]
:
=
g
r
e
a
t
h
o
r
n
e
d
o
w
l
O
u
t
[
]
=
Or what other subspecies of Great Horned Owl do exist using “SubEntities”:
s
u
b
s
p
e
c
i
e
s
=
g
H
o
r
n
e
d
O
w
l
[
"
S
u
b
E
n
t
i
t
i
e
s
"
]
I
n
[
]
:
=
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
a
l
g
i
s
t
u
s
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
e
l
a
c
h
i
s
t
u
s
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
h
e
t
e
r
o
c
n
e
m
i
s
,
N
o
r
t
h
w
e
s
t
e
r
n
H
o
r
n
e
d
O
w
l
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
m
a
y
e
n
s
i
s
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
m
e
s
e
m
b
r
i
n
u
s
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
n
a
c
u
r
u
t
u
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
n
i
g
r
e
s
c
e
n
s
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
p
a
c
i
f
i
c
u
s
,
V
i
r
g
i
n
i
a
g
r
e
a
t
h
o
r
n
e
d
o
w
l
,
S
t
.
M
i
c
h
a
e
l
h
o
r
n
e
d
o
w
l
,
W
e
s
t
e
r
n
h
o
r
n
e
d
o
w
l
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
s
u
b
s
p
.
v
i
r
g
i
n
i
a
n
u
s
O
u
t
[
]
=
Visualizing a Taxonomic Graph for Eagle Owls
We can easily create a customized taxonomic graph for eagle owls using “TaxonomyGraph” and
Graph
directives:
{
t
a
x
o
n
o
m
y
G
r
a
p
h
,
t
a
x
o
n
o
m
i
c
S
e
q
u
e
n
c
e
,
s
i
b
l
i
n
g
s
}
=
S
p
e
c
i
e
s
D
a
t
a
[
g
H
o
r
n
e
d
O
w
l
,
{
"
T
a
x
o
n
o
m
y
G
r
a
p
h
"
,
"
T
a
x
o
n
o
m
i
c
S
e
q
u
e
n
c
e
"
,
"
S
i
b
l
i
n
g
s
"
}
]
;
I
n
[
]
:
=
G
r
a
p
h
t
a
x
o
n
o
m
y
G
r
a
p
h
,
I
m
a
g
e
S
i
z
e
8
0
0
,
V
e
r
t
e
x
L
a
b
e
l
s
T
h
r
e
a
d
[
]
,
V
e
r
t
e
x
S
t
y
l
e
D
i
r
e
c
t
i
v
e
[
B
r
o
w
n
,
O
p
a
c
i
t
y
[
0
.
8
]
]
,
V
e
r
t
e
x
S
i
z
e
2
,
E
d
g
e
S
t
y
l
e
G
r
a
y
,
V
e
r
t
e
x
L
a
b
e
l
S
t
y
l
e
D
i
r
e
c
t
i
v
e
[
F
o
n
t
S
i
z
e
1
3
,
B
o
l
d
]
Map of occurrences of Great Horned Owls using iNaturalist data
Once you create an iNaturalist account (free), it’s fairly easy to import a CVS file with all owl observations from Illinois state and we need to setup the directory where we stored the CSV file:
$
p
a
t
h
=
"
/
U
s
e
r
s
/
j
o
f
r
e
/
s
p
e
c
i
e
s
-
f
u
n
c
t
i
o
n
s
-
a
n
d
-
d
a
t
a
/
i
N
a
t
u
r
a
l
i
s
t
/
I
m
p
o
r
t
e
d
f
i
l
e
s
f
r
o
m
P
l
a
t
f
o
r
m
"
;
S
e
t
D
i
r
e
c
t
o
r
y
[
$
p
a
t
h
]
;
I
n
[
]
:
=
Amazingly, we can automatically import the CSV file as a nice
Dataset
using
SemanticImport
:
d
a
t
a
=
S
e
m
a
n
t
i
c
I
m
p
o
r
t
[
"
o
w
l
s
_
o
b
s
e
r
v
a
t
i
o
n
s
_
I
l
l
i
n
o
i
s
.
c
s
v
"
]
;
I
n
[
]
:
=
Using GeoGraphics creating a map of Illinois with all owls observations is pretty straightforward:
o
w
l
s
D
i
s
t
r
i
b
u
t
i
o
n
=
G
e
o
G
r
a
p
h
i
c
s
{
R
e
d
,
P
o
i
n
t
S
i
z
e
[
0
.
0
1
]
,
P
o
i
n
t
@
G
e
o
P
o
s
i
t
i
o
n
[
V
a
l
u
e
s
@
N
o
r
m
a
l
@
d
a
t
a
[
A
l
l
,
{
"
l
a
t
i
t
u
d
e
"
,
"
l
o
n
g
i
t
u
d
e
"
}
]
]
}
,
G
e
o
R
a
n
g
e
I
l
l
i
n
o
i
s
,
U
n
i
t
e
d
S
t
a
t
e
s
A
D
M
I
N
I
S
T
R
A
T
I
V
E
D
I
V
I
S
I
O
N
,
I
m
a
g
e
S
i
z
e
L
a
r
g
e
I
n
[
]
:
=
O
u
t
[
]
=
Let’s create a MenuView with all different species:
Get list of owl species on the dataset and exclude the sub-species:
i
l
l
i
n
i
S
p
e
c
i
e
s
=
D
e
l
e
t
e
[
U
n
i
o
n
@
N
o
r
m
a
l
@
d
a
t
a
[
A
l
l
,
"
s
c
i
e
n
t
i
f
i
c
_
n
a
m
e
"
]
,
{
{
-
3
}
,
{
-
1
}
}
]
I
n
[
]
:
=
{
A
e
g
o
l
i
u
s
a
c
a
d
i
c
u
s
,
A
s
i
o
f
l
a
m
m
e
u
s
,
A
s
i
o
o
t
u
s
,
A
t
h
e
n
e
c
u
n
i
c
u
l
a
r
i
a
,
B
u
b
o
s
c
a
n
d
i
a
c
u
s
,
B
u
b
o
v
i
r
g
i
n
i
a
n
u
s
,
M
e
g
a
s
c
o
p
s
,
M
e
g
a
s
c
o
p
s
a
s
i
o
,
S
t
r
i
g
i
d
a
e
,
S
t
r
i
g
i
f
o
r
m
e
s
,
S
t
r
i
x
v
a
r
i
a
,
T
y
t
o
a
l
b
a
}
O
u
t
[
]
=
M
e
n
u
V
i
e
w
@
M
a
p
W
i
t
h
{
s
p
e
c
i
e
s
=
#
,
e
n
t
i
t
y
=
I
n
t
e
r
p
r
e
t
e
r
[
"
S
p
e
c
i
e
s
"
]
[
#
]
}
,
e
n
t
i
t
y
L
a
b
e
l
e
d
G
e
o
G
r
a
p
h
i
c
s
{
O
r
a
n
g
e
,
P
o
i
n
t
S
i
z
e
[
0
.
0
2
]
,
P
o
i
n
t
@
G
e
o
P
o
s
i
t
i
o
n
[
V
a
l
u
e
s
@
N
o
r
m
a
l
@
d
a
t
a
[
S
e
l
e
c
t
[
#
[
"
s
c
i
e
n
t
i
f
i
c
_
n
a
m
e
"
]
s
p
e
c
i
e
s
&
]
,
{
"
l
a
t
i
t
u
d
e
"
,
"
l
o
n
g
i
t
u
d
e
"
}
]
]
}
,
G
e
o
R
a
n
g
e
I
l
l
i
n
o
i
s
,
U
n
i
t
e
d
S
t
a
t
e
s
A
D
M
I
N
I
S
T
R
A
T
I
V
E
D
I
V
I
S
I
O
N
,
I
m
a
g
e
S
i
z
e
L
a
r
g
e
,
e
n
t
i
t
y
[
"
I
m
a
g
e
"
]
&
,
i
l
l
i
n
i
S
p
e
c
i
e
s
I
n
[
]
:
=
g
r
e
a
t
h
o
r
n
e
d
o
w
l
O
u
t
[
]
=
Combining data from different Maps:
Here, my original idea was to explore multiple geographical data by combining it on a same map, just to show some of the
GeoGraphics
capabilities that Wolfram Language offers. We will create a density map of Illinois wind farms, a wind vector map and finally combine them with the one from owls occurrences:
Illinois wind farms
Here, I’m going to use some code from Emmanuel Garcés Medina’s WTC19 presentation on Geo Visualizations:
w
i
n
d
f
a
r
m
s
=
w
i
n
d
f
a
r
m
s
;
d
e
n
s
i
t
y
=
G
e
o
S
m
o
o
t
h
H
i
s
t
o
g
r
a
m
w
i
n
d
f
a
r
m
s
,
R
e
g
i
o
n
F
u
n
c
t
i
o
n
-
>
I
l
l
i
n
o
i
s
,
U
n
i
t
e
d
S
t
a
t
e
s
A
D
M
I
N
I
S
T
R
A
T
I
V
E
D
I
V
I
S
I
O
N
,
G
e
o
R
a
n
g
e
I
l
l
i
n
o
i
s
,
U
n
i
t
e
d
S
t
a
t
e
s
A
D
M
I
N
I
S
T
R
A
T
I
V
E
D
I
V
I
S
I
O
N
I
n
[
]
:
=
Wind Vector Map of Illinois
v
e
c
t
o
r
s
=
W
i
n
d
V
e
c
t
o
r
D
a
t
a
R
a
n
d
o
m
G
e
o
P
o
s
i
t
i
o
n
I
l
l
i
n
o
i
s
,
U
n
i
t
e
d
S
t
a
t
e
s
A
D
M
I
N
I
S
T
R
A
T
I
V
E
D
I
V
I
S
I
O
N
,
2
0
0
,
N
o
w
,
"
D
o
w
n
w
i
n
d
G
e
o
V
e
c
t
o
r
"
w
i
n
d
p
l
o
t
=
G
e
o
V
e
c
t
o
r
P
l
o
t
v
e
c
t
o
r
s
,
R
e
g
i
o
n
F
u
n
c
t
i
o
n
-
>
I
l
l
i
n
o
i
s
,
U
n
i
t
e
d
S
t
a
t
e
s
A
D
M
I
N
I
S
T
R
A
T
I
V
E
D
I
V
I
S
I
O
N
I
n
[
]
:
=
Combine all outputs
S
h
o
w
[
d
e
n
s
i
t
y
,
w
i
n
d
p
l
o
t
,
o
w
l
s
D
i
s
t
r
i
b
u
t
i
o
n
]
O
u
t
[
]
=
Identify Great Horned Owl species via
audio
samples
We can try to automatically identify the great horned owl song that I recorded using my phone near Champaign’s Football Stadium:
o
w
l
C
h
a
m
p
a
i
g
n
=
0
0
:
0
0
0
0
:
0
2
;
First, let’s try
AudioIdentify
built-in function, analogue to
ImageIdentify
:
A
u
d
i
o
I
d
e
n
t
i
f
y
[
o
w
l
C
h
a
m
p
a
i
g
n
,
"
a
n
i
m
a
l
"
,
A
c
c
e
p
t
a
n
c
e
T
h
r
e
s
h
o
l
d
.
1
]
I
n
[
]
:
=
b
i
r
d
O
u
t
[
]
=
S
p
e
c
t
r
o
g
r
a
m
[
o
w
l
C
h
a
m
p
a
i
g
n
,
M
e
t
h
o
d
"
M
e
l
F
r
e
q
u
e
n
c
y
"
]
I
n
[
]
:
=
O
u
t
[
]
=
We can use a high and a low pass filter to improve the quality of the audio:
f
i
l
t
e
r
e
d
O
w
l
C
h
a
m
p
a
i
g
n
=
H
i
g
h
p
a
s
s
F
i
l
t
e
r
[
L
o
w
p
a
s
s
F
i
l
t
e
r
[
o
w
l
C
h
a
m
p
a
i
g
n
,
8
0
0
H
z
]
,
3
0
0
H
z
]
I
n
[
]
:
=
0
0
:
0
0
0
0
:
0
2
O
u
t
[
]
=
S
p
e
c
t
r
o
g
r
a
m
[
H
i
g
h
p
a
s
s
F
i
l
t
e
r
[
L
o
w
p
a
s
s
F
i
l
t
e
r
[
o
w
l
C
h
a
m
p
a
i
g
n
,
8
0
0
H
z
]
,
3
0
0
H
z
]
,
M
e
t
h
o
d
"
M
e
l
F
r
e
q
u
e
n
c
y
"
]
I
n
[
]
:
=
O
u
t
[
]
=
Import
audio files
from iNaturalist of the most common
owls found in Illinois
:
As we previously did, it’s fairly easy to import a
CSV file with all owl observations
with audio from North America:
$
p
a
t
h
=
"
/
U
s
e
r
s
/
j
o
f
r
e
/
s
p
e
c
i
e
s
-
f
u
n
c
t
i
o
n
s
-
a
n
d
-
d
a
t
a
/
I
d
e
n
t
i
f
y
S
p
e
c
i
e
s
v
i
a
a
u
d
i
o
/
"
;
S
e
t
D
i
r
e
c
t
o
r
y
[
$
p
a
t
h
]
;
I
n
[
]
:
=
Again, we can automatically import the CSV file as a nice
Dataset
using
SemanticImport
:
d
a
t
a
s
e
t
=
S
e
m
a
n
t
i
c
I
m
p
o
r
t
[
"
U
S
_
O
w
l
s
_
a
u
d
i
o
s
.
c
s
v
"
]
i
d
o
b
s
e
r
v
e
d
_
o
n
_
s
t
r
i
n
g
o
b
s
e
r
v
e
d
_
o
n
t
i
m
e
_
o
b
s
e
r
v
e
d
_
a
t
t
i
m
e
_
z
o
n
e
o
u
t
_
o
f
_
r
a
n
g
e
u
s
e
r
_
i
d
u
s
e
r
_
l
o
g
i
n
c
r
e
a
t
e
d
_
a
t
u
p
d
a
t
e
d
_
a
t
q
u
a
l
i
t
y
_
g
r
a
d
e
l
i
c
e
n
s
e
u
r
l
i
m
a
g
e
_
u
r
l
s
o
u
n
d
_
u
r
l
t
a
g
_
l
i
s
t
d
e
s
c
r
i
p
t
i
o
n
i
d
_
p
l
e
a
s
e
n
u
m
_
i
d
e
n
t
i
f
i
c
a
t
i
o
n
_
a
g
r
e
e
m
e
n
t
s
n
u
m
_
i
d
e
n
t
i
f
i
c
a
t
i
o
n
_
d
i
s
a
g
r
e
e
m
e
n
t
s
c
a
p
t
i
v
e
_
c
u
l
t
i
v
a
t
e
d
o
a
u
t
h
_
a
p
p
l
i
c
a
t
i
o
n
_
i
d
p
l
a
c
e
_
g
u
e
s
s
l
a
t
i
t
u
d
e
l
o
n
g
i
t
u
d
e
p
o
s
i
t
i
o
n
a
l
_
a
c
c
u
r
a
c
y
g
e
o
p
r
i
v
a
c
y
t
a
x
o
n
_
g
e
o
p
r
i
v
a
c
y
c
o
o
r
d
i
n
a
t
e
s
_
o
b
s
c
u
r
e
d
p
o
s
i
t
i
o
n
i
n
g
_
m
e
t
h
o
d
p
o
s
i
t
i
o
n
i
n
g
_
d
e
v
i
c
e
s
p
e
c
i
e
s
_
g
u
e
s
s
s
c
i
e
n
t
i
f
i
c
_
n
a
m
e
c
o
m
m
o
n
_
n
a
m
e
i
c
o
n
i
c
_
t
a
x
o
n
_
n
a
m
e
t
a
x
o
n
_
i
d
3
0
7
7
8
0
2
0
1
3
-
0
6
-
2
0
1
2
:
1
2
a
m
T
h
u
2
0
J
u
n
2
0
1
3
2
0
1
3
-
0
6
-
2
0
0
4
:
1
2
:
0
0
U
T
C
E
a
s
t
e
r
n
T
i
m
e
(
U
S
&
C
a
n
a
d
a
)
f
a
l
s
e
1
0
7
8
7
m
a
r
a
c
t
w
i
n
2
0
1
3
-
0
6
-
2
2
0
3
:
0
2
:
0
5
U
T
C
2
0
1
9
-
0
5
-
3
0
2
2
:
0
4
:
5
0
U
T
C
r
e
s
e
a
r
c
h
C
C
-
B
Y
h
t
t
p
:
/
/
w
w
w
.
i
n
a
t
u
r
a
l
i
s
t
.
o
r
g
/
o
b
s
e
r
v
a
t
i
o
n
s
/
3
0
7
7
8
0
—
—
R
e
c
o
r
d
e
d
f
r
o
m
m
y
b
e
d
r
o
o
m
w
i
n
d
o
w
.
F
i
r
s
t
t
i
m
e
I
'
v
e
h
e
a
r
d
o
r
s
e
e
n
a
n
o
w
l
h
e
r
e
.
I
t
⋱
f
a
l
s
e
2
0
f
a
l
s
e
—
P
o
r
t
e
r
S
q
u
a
r
e
,
C
a
m
b
r
i
d
g
e
M
A
4
2
.
3
8
8
9
-
7
1
.
1
1
9
4
1
6
0
2
o
p
e
n
f
a
l
s
e
E
a
s
t
e
r
n
S
c
r
e
e
c
h
-
O
w
l
M
e
g
a
s
c
o
p
s
a
s
i
o
E
a
s
t
e
r
n
S
c
r
e
e
c
h
-
O
w
l
A
v
e
s
1
9
7
6
5
3
6
1
0
6
4
2
0
1
3
-
0
8
-
0
9
F
r
i
9
A
u
g
2
0
1
3
E
a
s
t
e
r
n
T
i
m
e
(
U
S
&
C
a
n
a
d
a
)
f
a
l
s
e
1
2
0
3
6
z
a
c
c
o
t
a
2
0
1
3
-
0
8
-
1
0
0
2
:
5
5
:
4
3
U
T
C
2
0
1
6
-
0
6
-
2
3
0
3
:
2
5
:
2
6
U
T
C
r
e
s
e
a
r
c
h
C
C
-
B
Y
-
N
C
h
t
t
p
:
/
/
w
w
w
.
i
n
a
t
u
r
a
l
i
s
t
.
o
r
g
/
o
b
s
e
r
v
a
t
i
o
n
s
/
3
6
1
0
6
4
—
h
t
t
p
s
:
/
/
s
t
a
t
i
c
.
i
n
a
t
u
r
a
l
i
s
t
.
o
r
g
/
s
o
u
n
d
s
/
2
9
1
.
m
4
a
?
1
5
0
2
8
2
4
2
0
7
f
a
l
s
e
2
0
f
a
l
s
e
—
S
t
a
r
k
s
b
o
r
o
,
V
T
4
4
.
2
5
7
6
-
7
3
.
0
4
7
9
1
9
0
f
a
l
s
e
B
a
r
r
e
d
O
w
l
(
S
t
r
i
x
v
a
r
i
a
)
S
t
r
i
x
v
a
r
i
a
B
a
r
r
e
d
O
w
l
A
v
e
s
1
9
8
9
3
3
7
3
0
3
2
S
u
n
A
u
g
1
8
2
0
1
3
2
0
:
4
6
:
3
2
G
M
T
-
0
7
0
0
(
P
D
T
)
S
u
n
1