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Use current NOAA data for aurora example #2180

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merged 2 commits into from
Jun 2, 2023
Merged

Use current NOAA data for aurora example #2180

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0d1cda6
use ovation json data format
lgolston Jun 1, 2023
8ebbfd1
fix code style issue
lgolston Jun 1, 2023
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30 changes: 16 additions & 14 deletions examples/scalar_data/aurora_forecast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,12 +8,12 @@
OVATION-Prime model to forecast the probability of visible aurora at
various locations on Earth. Every five minutes a new forecast is
published for the coming 30 minutes. The data is provided as a
1024 by 512 grid of probabilities in percent of visible aurora. The
data spaced equally in degrees from -180 to 180 and -90 to 90.
360 by 181 grid of probabilities in percent of visible aurora. The
data spaced equally in degrees from 0 to 359 and -90 to 90.

"""
from datetime import datetime
from io import StringIO
import json
from urllib.request import urlopen

from matplotlib.colors import LinearSegmentedColormap
Expand Down Expand Up @@ -45,21 +45,23 @@ def aurora_forecast():
"""

# GitHub gist to download the example data from
url = ('https://gist.githubusercontent.com/belteshassar/'
'c7ea9e02a3e3934a9ddc/raw/aurora-nowcast-map.txt')
url = ('https://gist.githubusercontent.com/lgolston/594c030876c0614d3'
'6d13d03e4f115b6/raw/342ff751419204594180e88d69b3986dbd4fea4a/'
'ovation_aurora_latest.json')
# To plot the current forecast instead, uncomment the following line
# url = 'https://services.swpc.noaa.gov/text/aurora-nowcast-map.txt'
# url = 'https://services.swpc.noaa.gov/json/ovation_aurora_latest.json'

response_text = StringIO(urlopen(url).read().decode('utf-8'))
img = np.loadtxt(response_text)
# Read forecast date and time
response_text.seek(0)
for line in response_text:
if line.startswith('Product Valid At:', 2):
dt = datetime.strptime(line[-17:-1], '%Y-%m-%d %H:%M')
# load data (JSON format)
response = urlopen(url)
aurora = json.loads(response.read().decode('utf-8'))
# parse timestamp
dt = datetime.strptime(aurora['Forecast Time'], '%Y-%m-%dT%H:%M:%SZ')
# convert lists of [lon, lat, value] to 2D array of probability values
aurora_data = np.array(aurora['coordinates'])
img = np.reshape(aurora_data[:, 2], (181, 360), order='F')

img_proj = ccrs.PlateCarree()
img_extent = (-180, 180, -90, 90)
img_extent = (0, 359, -90, 90)
return img, img_proj, img_extent, 'lower', dt


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