def test_display_entries():
entries = [
DatabaseEntry(
id=1, source='SOHO', provider='SDAC', physobs='intensity',
fileid='/archive/soho/...',
observation_time_start=datetime(2001, 1, 1, 7, 0, 14),
observation_time_end=datetime(2001, 1, 1, 7, 0, 21),
instrument='EIT', size=259.0, wavemin=171.0,
wavemax=171.0, tags=[Tag('foo'), Tag('bar')]),
DatabaseEntry(
id=2, source='GONG', provider='NSO', physobs='LOS_velocity',
fileid='pptid=11010...',
observation_time_start=datetime(2010, 1, 1, 0, 59),
observation_time_end=datetime(2010, 1, 1, 1),
download_time=datetime(2014, 6, 15, 3, 42, 55, 123456),
instrument='Merged gong', size=944.0,
wavemin=6768.0, wavemax=6768.0, starred=True)]
columns = [
'id', 'source', 'provider', 'physobs', 'fileid', 'download_time',
'observation_time_start', 'instrument', 'size',
'wavemin', 'path', 'starred', 'tags']
table = display_entries(entries, columns)
filedir = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(filedir,'test_table.txt'), 'r') as f:
stored_table = f.read()
assert table.strip() == stored_table.strip()
def test_display_entries():
entries = [
DatabaseEntry(
id=1, source='SOHO', provider='SDAC', physobs='intensity',
fileid='/archive/soho/...',
observation_time_start=datetime(2001, 1, 1, 7, 0, 14),
observation_time_end=datetime(2001, 1, 1, 7, 0, 21),
instrument='EIT', size=259.0, wavemin=171.0,
wavemax=171.0, tags=[Tag('foo'), Tag('bar')]),
DatabaseEntry(
id=2, source='GONG', provider='NSO', physobs='LOS_velocity',
fileid='pptid=11010...',
observation_time_start=datetime(2010, 1, 1, 0, 59),
observation_time_end=datetime(2010, 1, 1, 1),
download_time=datetime(2014, 6, 15, 3, 42, 55, 123456),
instrument='Merged gong', size=944.0,
wavemin=6768.0, wavemax=6768.0, starred=True)]
columns = [
'id', 'source', 'provider', 'physobs', 'fileid', 'download_time',
'observation_time_start', 'instrument', 'size',
'wavemin', 'path', 'starred', 'tags']
table = display_entries(entries, columns)
filedir = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(filedir,'test_table.txt'), 'r') as f:
stored_table = f.read()
assert table.strip() == stored_table.strip()
def test_display_entries():
entries = [
DatabaseEntry(
id=1,
source="SOHO",
provider="SDAC",
physobs="intensity",
fileid="/archive/soho/...",
observation_time_start=datetime(2001, 1, 1, 7, 0, 14),
observation_time_end=datetime(2001, 1, 1, 7, 0, 21),
instrument="EIT",
size=259.0,
wavemin=171.0,
wavemax=171.0,
tags=[Tag("foo"), Tag("bar")],
),
DatabaseEntry(
id=2,
source="GONG",
provider="NSO",
physobs="LOS_velocity",
fileid="pptid=11010...",
observation_time_start=datetime(2010, 1, 1, 0, 59),
observation_time_end=datetime(2010, 1, 1, 1),
download_time=datetime(2014, 6, 15, 3, 42, 55, 123456),
instrument="Merged gong",
size=944.0,
wavemin=6768.0,
wavemax=6768.0,
starred=True,
),
]
columns = [
"id",
"source",
"provider",
"physobs",
"fileid",
"download_time",
"observation_time_start",
"instrument",
"size",
"wavemin",
"path",
"starred",
"tags",
]
table = display_entries(entries, columns)
filedir = os.path.dirname(os.path.realpath(__file__))
with open(os.path.join(filedir, "test_table.txt"), "r") as f:
stored_table = f.read()
assert table.strip() == stored_table.strip()
def test_display_entries():
entries = [
DatabaseEntry(
id=1, source='SOHO', provider='SDAC', physobs='intensity',
fileid='/archive/soho/...',
observation_time_start=datetime(2001, 1, 1, 7, 0, 14),
observation_time_end=datetime(2001, 1, 1, 7, 0, 21),
instrument='EIT', size=259.0, wavemin=171.0,
wavemax=171.0, tags=[Tag('foo'), Tag('bar')]),
DatabaseEntry(
id=2, source='GONG', provider='NSO', physobs='LOS_velocity',
fileid='pptid=11010...',
observation_time_start=datetime(2010, 1, 1, 0, 59),
observation_time_end=datetime(2010, 1, 1, 1),
instrument='Merged gong', size=944.0,
wavemin=6768.0, wavemax=6768.0, starred=True)]
columns = [
'id', 'source', 'provider', 'physobs', 'fileid',
'observation_time_start', 'observation_time_end', 'instrument', 'size',
'wavemin', 'path', 'starred', 'tags']
table = display_entries(entries, columns)
assert table == """id source provider physobs fileid observation_time_start observation_time_end instrument size wavemin path starred tags
def test_display_entries_missing_columns():
with pytest.raises(TypeError):
display_entries([DatabaseEntry()], [])
def test_display_entries_empty_db():
with pytest.raises(TypeError):
display_entries(Database('sqlite:///'), ['id'])
def test_display_entries_missing_entries():
with pytest.raises(TypeError):
display_entries([], ['some', 'columns'])
from sunpy.net import vso
from sunpy.database import Database
database = Database("sqlite:///")
database.download(vso.attrs.Time("2012-08-05", "2012-08-05 00:00:05"),
vso.attrs.Instrument('AIA'))
len(database)
from sunpy.database.tables import display_entries
print display_entries(database,
["id", "observation_time_start", "wavemin", "wavemax"])
def test_display_entries_missing_columns():
with pytest.raises(TypeError):
display_entries([DatabaseEntry()], [])
def test_display_entries_empty_db():
with pytest.raises(TypeError):
display_entries(Database('sqlite:///'), ['id'])
def test_display_entries_missing_entries():
with pytest.raises(TypeError):
display_entries([], ['some', 'columns'])
def test_display_entries_empty_db():
with pytest.raises(TypeError):
display_entries(Database("sqlite:///"), ["id"])
def test_display_entries_missing_entries():
with pytest.raises(TypeError):
display_entries([], ["some", "columns"])
from sunpy.net import vso
from sunpy.database import Database
database = Database("sqlite:///")
database.download(
vso.attrs.Time("2012-08-05", "2012-08-05 00:00:05"),
vso.attrs.Instrument('AIA'))
len(database)
from sunpy.database.tables import display_entries
print display_entries(
database,
["id", "observation_time_start", "wavemin", "wavemax"])
download = Fido.fetch(result,path="./data/")
# Now the data is downloaded you should see a folder called data with a bunch of fits files inside.
# FITS stands for Flexible Interchangeable Transport System. It is the de facto image format for astronomy.
# The image contains a "header" which contains information about the image.
# HMI images have been downloaded for you and are already in the folder (using the online search form https://vso.nascom.nasa.gov/cgi-bin/search).
# Let's add the fits in our folder to the database.
db.add_from_dir("./data/", ignore_already_added=True,time_string_parse_format="%d/%m/%Y")
# Let's see what's now in our database:
for database_entry in db:
if database_entry.observation_time_start is None and database_entry.observation_time_end is None:
db.remove(database_entry)
print(display_entries(db,['id', 'observation_time_start','instrument', 'wavemin']))
# We can now search the database. Here we are searching for images in wavelengths between 1-2 nm or 10-20 Angstrom.
# We have also chosen to sort our results by wavlength!
print(display_entries(db.search(a.Wavelength(1.0*u.nm, 2.0*u.nm)),['id', 'observation_time_start', 'instrument', 'wavemin'], sort=True))
# Another way to fetch data is to use:
# entries = db.fetch(a.Time('2019/05/06 12:00', '2019/05/06 12:01'),a.Instrument('aia'),a.vso.Sample(2*u.minute),a.Wavelength(94*u.angstrom))
# This automatically adds the observations to our database and downloads files checking for duplicates.
# Now we have our images let's plot them!
AIA = db.search(a.Wavelength(.1*u.nm, 60.0*u.nm))
AIAplotDic = {}
for obs in AIA:
