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Analyzing Time Series Data
By
3 authors
Edited
Feb 12, 2022
4 forks
Importers
20 stars
1
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url
=
"https://api.eia.gov/series/?api_key=46a64e6fcd154bec1bb42252060d33ba&series_id=EBA.TEX-ALL.NG.COL.H;EBA.TEX-ALL.NG.NG.H;EBA.TEX-ALL.NG.NUC.H;EBA.TEX-ALL.NG.OTH.H;EBA.TEX-ALL.NG.SUN.H;EBA.TEX-ALL.NG.WAT.H;EBA.TEX-ALL.NG.WND.H&start=20210101T00Z&end=20210228T24Z"
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// data = d3.json(url) // uncomment this line to fetch from the live API
data
=
FileAttachment
(
"texasCrisis2021.json"
)
.
json
(
)
// We cache the API response for futureproofing this article
.
then
(
(
response
)
=>
{
return
(
response
.
series
.
flatMap
(
(
s
)
=>
s
.
data
.
map
(
(
d
)
=>
{
// each element of the data array is a date, value pair like: ["20210228T00Z", 4666]
let
t
=
s
.
name
.
match
(
/Net generation from (.*) for/
)
[
1
]
;
// if (t == "hydro") t = "hydroelectric";
return
{
id
:
s
.
series_id
,
name
:
s
.
name
,
type
:
t
,
// extracts type of energy for better labels
date
:
d3
.
utcParse
(
"%Y%m%dT%HZ"
)
(
d
[
0
]
)
,
// parse the date
value
:
d
[
1
]
}
;
}
)
)
.
sort
(
(
a
,
b
)
=>
+
a
.
date
-
+
b
.
date
)
)
;
}
)
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// match EIA color schemes
color
=
(
{
domain
:
[
'solar'
,
'wind'
,
'coal'
,
'natural gas'
,
'nuclear'
,
'hydro'
,
'other'
]
,
range
:
[
"#FEC704"
,
"#5C9631"
,
"#664F06"
,
"#BD7229"
,
"#A1333F"
,
"#0395D6"
,
"#ADADAD"
]
}
)
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import
{
textcolor
}
from
"@observablehq/text-color-annotations-in-markdown"
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import
{
authorship
,
navigation
,
workshop
}
from
"@observablehq/timeseries-assets"
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