Analyze the total solar eclipse
2024-04-08
eclipse.mp4
Import data with Python live task
Transform Pandas Dataframe to MATLAB Table
In this tutorial, we will show how to use MATLAB with the SunPy community Python library for Solar Physics
- https://github.com/yanndebray/matlab-with-python-book
- https://docs.sunpy.org/en/stable/generated/gallery/showcase/eclipse_amount.html
setupWarning: Name is nonexistent or not a directory: /MATLAB Drive/eclipse/pip
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: sunpy in /home/matlab/.local/lib/python3.10/site-packages (5.1.2)
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pipshow("sunpy")Name: sunpy
Version: 5.1.2
Summary: SunPy core package: Python for Solar Physics
Home-page: https://sunpy.org
Author: The SunPy Community
Author-email: sunpy@googlegroups.com
License: BSD 2-Clause
Location: /home/matlab/.local/lib/python3.10/site-packages
Requires: astropy, numpy, packaging, parfive, pyerfa
Required-by:
% Python code input
pycode4 = [...
"import astropy.units as u",...
"from astropy.coordinates import EarthLocation, solar_system_ephemeris",...
"from astropy.time import Time",...
"from sunpy.coordinates import sun",...
"import numpy as np",...
"import pandas as pd",...
"",...
"# Define the geodetic coordinates",...
"latitude = 29.6 * u.deg",...
"longitude = -98.5 * u.deg # Note that the longitude should come first",...
"",...
"# Create the EarthLocation object",...
"location = EarthLocation.from_geodetic(longitude, latitude)",...
"max2023 = Time('2023-10-14 16:54')",...
"max2024 = Time('2024-04-08 18:35')",...
"",...
"time = max2024",...
"",...
"# Define an array of observation times centered around the time of interest",...
"times = time + np.concatenate([np.arange(-120, -5) * u.min,",...
" np.arange(-300, 300) * u.s,",...
" np.arange(5, 121) * u.min])",...
"",...
"# Create an observer coordinate for the time array",...
"observer = location.get_itrs(times)",...
"",...
"# Calculate the eclipse amounts using a JPL ephemeris",...
"with solar_system_ephemeris.set('de440s'):",...
" amount = sun.eclipse_amount(observer)",...
" amount_minimum = sun.eclipse_amount(observer, moon_radius='minimum')",...
"",...
"# Calculate the start/end points of partial/total solar eclipse",...
"partial = np.flatnonzero(amount > 0)",...
"if len(partial) > 0:",...
" print(""Eclipse detected:"")",...
" start_partial, end_partial = times[partial[[0, -1]]]",...
" print(f"" Partial solar eclipse starts at {start_partial} UTC"")",...
"",...
" total = np.flatnonzero(amount_minimum == 1)",...
" if len(total) > 0:",...
" start_total, end_total = times[total[[0, -1]]]",...
" print(f"" Total solar eclipse starts at {start_total} UTC\n""",...
" f"" Total solar eclipse ends at {end_total} UTC"")",...
" print(f"" Partial solar eclipse ends at {end_partial} UTC"")",...
"",...
"df = pd.DataFrame({'time': times.to_datetime(), 'amount': amount, 'amount_minimum': amount_minimum})",...
""...
];
[latitude, longitude, df] = pyrun(pycode4, ...
[ "latitude" "longitude" "df" ])Eclipse detected:
Partial solar eclipse starts at 2024-04-08 17:15:00.000 UTC
Total solar eclipse starts at 2024-04-08 18:33:44.000 UTC
Total solar eclipse ends at 2024-04-08 18:35:33.000 UTC
Partial solar eclipse ends at 2024-04-08 19:55:00.000 UTC
latitude =
Python Quantity:
29.6000
Use details function to view the properties of the Python object.
Use double function to convert to a MATLAB array.
longitude =
Python Quantity:
-98.5000
Use details function to view the properties of the Python object.
Use double function to convert to a MATLAB array.
df =
Python DataFrame with properties:
T: [1x1 py.pandas.core.frame.DataFrame]
at: [1x1 py.pandas.core.indexing._AtIndexer]
attrs: [1x1 py.dict]
axes: [1x2 py.list]
columns: [1x1 py.pandas.core.indexes.base.Index]
dtypes: [1x1 py.pandas.core.series.Series]
empty: 0
flags: [1x1 py.pandas.core.flags.Flags]
iat: [1x1 py.pandas.core.indexing._iAtIndexer]
iloc: [1x1 py.pandas.core.indexing._iLocIndexer]
index: [1x1 py.pandas.core.indexes.range.RangeIndex]
loc: [1x1 py.pandas.core.indexing._LocIndexer]
ndim: [1x1 py.int]
shape: [1x2 py.tuple]
size: [1x1 py.int]
values: [1x1 py.numpy.ndarray]
time amount amount_minimum
0 2024-04-08 16:35:00 0.0 0.0
1 2024-04-08 16:36:00 0.0 0.0
2 2024-04-08 16:37:00 0.0 0.0
3 2024-04-08 16:38:00 0.0 0.0
4 2024-04-08 16:39:00 0.0 0.0
.. ... ... ...
826 2024-04-08 20:31:00 0.0 0.0
827 2024-04-08 20:32:00 0.0 0.0
828 2024-04-08 20:33:00 0.0 0.0
829 2024-04-08 20:34:00 0.0 0.0
830 2024-04-08 20:35:00 0.0 0.0
[831 rows x 3 columns]
% Clean up
clear pycode4;% T = table(df)
TT = timetable(df)| time | amount | amount_minimum | |
|---|---|---|---|
| 1 | 08-Apr-2024 16:35:00 | 0 | 0 |
| 2 | 08-Apr-2024 16:36:00 | 0 | 0 |
| 3 | 08-Apr-2024 16:37:00 | 0 | 0 |
| 4 | 08-Apr-2024 16:38:00 | 0 | 0 |
| 5 | 08-Apr-2024 16:39:00 | 0 | 0 |
| 6 | 08-Apr-2024 16:40:00 | 0 | 0 |
| 7 | 08-Apr-2024 16:41:00 | 0 | 0 |
| 8 | 08-Apr-2024 16:42:00 | 0 | 0 |
| 9 | 08-Apr-2024 16:43:00 | 0 | 0 |
| 10 | 08-Apr-2024 16:44:00 | 0 | 0 |
| 11 | 08-Apr-2024 16:45:00 | 0 | 0 |
| 12 | 08-Apr-2024 16:46:00 | 0 | 0 |
| 13 | 08-Apr-2024 16:47:00 | 0 | 0 |
| 14 | 08-Apr-2024 16:48:00 | 0 | 0 |
% stackedplot(TT)
plot(TT.time,TT.amount)
% San Antonio, Texas
lat = py.numpy.array(latitude); lat = double(lat);
lon = py.numpy.array(longitude); lon = double(lon);
geoscatter(lat,lon, 'filled')
export('livescript.mlx','README.md');
export('livescript.mlx','notebook.ipynb');