def _read_file(fname, **kwargs):
"""
Reading a file with `sunpy.io` for automatic source detection.
Parameters
----------
fname : `str`
The file path to parse.
Returns
-------
parsed : `bool`
`True` if file has been read.
pairs : `list` or `str`
List of ``(data, header)`` pairs if ``parsed`` is `True`, ``fname`` if ``parsed`` is `False`.
`False` if the file is not supported or incorrect.
"""
if 'source' not in kwargs.keys() or not kwargs['source']:
try:
pairs = read_file(fname, **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
if isinstance(filemeta, FileHeader):
data = filedata
meta = MetaDict(filemeta)
new_pairs.append(HDPair(data, meta))
return True, new_pairs
except UnrecognizedFileTypeError:
return False, fname
else:
return False, fname
def _read_file(self, fname, **kwargs):
"""
Read in a file name and return the list of (data, meta) pairs in that file.
"""
# File gets read here. This needs to be generic enough to seamlessly
# call a fits file or a jpeg2k file, etc
# NOTE: use os.fspath so that fname can be either a str or pathlib.Path
# This can be removed once read_file supports pathlib.Path
log.debug(f'Reading {fname}')
try:
pairs = read_file(os.fspath(fname), **kwargs)
except Exception as e:
msg = f"Failed to read {fname}."
raise IOError(msg) from e
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
assert isinstance(filemeta, FileHeader)
# This tests that the data is more than 1D
if len(np.shape(filedata)) > 1:
data = filedata
meta = MetaDict(filemeta)
new_pairs.append((data, meta))
if not new_pairs:
raise NoMapsInFileError(
f"Found no HDUs with >= 2D data in '{fname}'.")
return new_pairs
def meta(self, dfile, fname):
data, header = read_file(dfile[0])[0]
# Add the missing meta information to the header
header['CUNIT1'] = 'arcsec'
header['CUNIT2'] = 'arcsec'
ax = sunpy.map.Map(data, header)
fig = plt.figure()
ax.plot()
instr = str(header['TELESCOP'] + "-" + header['INSTRUME'] + "-" +
header['DETECTOR'])
date = str(header['DATE-OBS'])
time = str(header['TIME-OBS'])
filter = str(header['FILTER'])
ylabel = str(header['CTYPE2'])
xlabel = str(header['CTYPE1'])
img_name = fname.split(".")[0] + ".png"
plt.savefig(img_name)
write(img_name, instr, date, time, xlabel, ylabel, "arcsec", "arcsec",
filter)
plt.close()
def _read_file(fname, **kwargs):
"""
Reading a file with `sunpy.io` for automatic source detection.
Parameters
----------
fname : `str`
The file path to parse.
Returns
-------
parsed : `bool`
`True` if file has been read.
pairs : `list` or `str`
List of ``(data, header)`` pairs if ``parsed`` is `True`, ``fname`` if ``parsed`` is `False`.
`False` if the file is not supported or incorrect.
"""
if 'source' not in kwargs.keys() or not kwargs['source']:
try:
pairs = read_file(fname, **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
if isinstance(filemeta, FileHeader):
data = filedata
meta = MetaDict(filemeta)
new_pairs.append(HDPair(data, meta))
return True, new_pairs
except UnrecognizedFileTypeError:
return False, fname
else:
return False, fname
def _read_file(self, fname, **kwargs):
""" Read in a file name and return the list of (data, meta) pairs in
that file. """
# File gets read here. This needs to be generic enough to seamlessly
# call a fits file or a jpeg2k file, etc
pairs = read_file(fname, **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
assert isinstance(filemeta, FileHeader)
# This tests that the data is more than 1D
if len(np.shape(filedata)) > 1:
data = filedata
meta = MetaDict(filemeta)
new_pairs.append((data, meta))
return new_pairs
def _read_file(self, fname, **kwargs):
""" Read in a file name and return the list of (data, meta) pairs in
that file. """
# File gets read here. This needs to be generic enough to seamlessly
#call a fits file or a jpeg2k file, etc
pairs = read_file(fname, **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
assert isinstance(filemeta, FileHeader)
#This tests that the data is more than 1D
if len(np.shape(filedata)) > 1:
data = filedata
meta = MapMeta(filemeta)
new_pairs.append((data, meta))
return new_pairs
def _read_file(self, fname, **kwargs):
""" Read in a file name and return the list of (data, meta) pairs in
that file. """
# File gets read here. This needs to be generic enough to seamlessly
# call a fits file or a jpeg2k file, etc
# NOTE: use os.fspath so that fname can be either a str or pathlib.Path
# This can be removed once read_file supports pathlib.Path
log.debug(f'Reading {fname}')
pairs = read_file(os.fspath(fname), **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
assert isinstance(filemeta, FileHeader)
# This tests that the data is more than 1D
if len(np.shape(filedata)) > 1:
data = filedata
meta = MetaDict(filemeta)
new_pairs.append((data, meta))
return new_pairs
def _read_file(fname, **kwargs):
"""
Reading a file with `sunpy.io` for automatic source detection.
Parameters
----------
fname : `str`
The file path to parse.
Returns
-------
pairs : `list` or `str`
List of ``(data, header)`` pairs or ``fname`` if the file is not supported or incorrect.
"""
if 'source' not in kwargs.keys() or not kwargs['source']:
try:
if detect_filetype(fname) == 'cdf':
# Put import here to ensure there is no import dependency
# on cdflib for TimeSeries
from sunpy.io.cdf import read_cdf
return read_cdf(os.fspath(fname), **kwargs)
except UnrecognizedFileTypeError:
pass
try:
pairs = read_file(os.fspath(fname), **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
if isinstance(filemeta, FileHeader):
data = filedata
meta = MetaDict(filemeta)
new_pairs.append(HDPair(data, meta))
return [new_pairs]
except UnrecognizedFileTypeError:
return [fname]
else:
return [fname]
def _read_file(self, fname, **kwargs):
"""
Test reading a file with sunpy.io for automatic source detection.
Parameters
----------
fname : filename
kwargs
Returns
-------
parsed : bool
True if file has been reading
pairs : list or string
List of (data, header) pairs if ``parsed`` is ``True`` or ``fname``
if ``False``
"""
if 'source' not in kwargs.keys() or not kwargs['source']:
try:
pairs = read_file(fname, **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
if isinstance(filemeta, FileHeader):
data = filedata
meta = MetaDict(filemeta)
new_pairs.append(HDPair(data, meta))
return True, new_pairs
except UnrecognizedFileTypeError:
return False, fname
else:
return False, fname
def _read_file(self, fname, **kwargs):
"""
Test reading a file with sunpy.io for automatic source detection.
Parameters
----------
fname : filename
kwargs
Returns
-------
parsed : bool
True if file has been reading
pairs : list or string
List of (data, header) pairs if ``parsed`` is ``True`` or ``fname``
if ``False``
"""
if 'source' not in kwargs.keys() or not kwargs['source']:
try:
pairs = read_file(fname, **kwargs)
new_pairs = []
for pair in pairs:
filedata, filemeta = pair
if isinstance(filemeta, FileHeader):
data = filedata
meta = MetaDict(filemeta)
new_pairs.append(HDPair(data, meta))
return True, new_pairs
except UnrecognizedFileTypeError:
return False, fname
else:
return False, fname
result = Fido.search(timerange, instrument) ############################################################################### # Let's inspect the result print(result) ############################################################################### # The following shows how to download the results. If we # don't provide a path it will download the file into the sunpy data directory. # The output provides the path of the downloaded files. downloaded_files = Fido.fetch(result) print(downloaded_files) ############################################################################### # The downloaded file lacks the correct meta. We want to open the file and # access both the data and the header information. data, header = read_file(downloaded_files[0])[0] # Add the missing meta information to the header header['CUNIT1'] = 'arcsec' header['CUNIT2'] = 'arcsec' ############################################################################### # With this fix we can load it into a map and plot the results. # Please note that there is no plot displayed below as this example is skipped # due to timeouts that can occur when you try to download LASCO C3 data. lascomap = sunpy.map.Map(data, header) fig = plt.figure() lascomap.plot() plt.show()
result = Fido.search(timerange, instrument) # ``result`` contains the return from the online search. # In this case, we have found 4 files that correspond to our search parameters. print(result) ############################################################################### # The next step is to download the search results and `Fido.fetch` will be used. # We will pass in the ``result`` and ``downloaded_files`` will contain # a list of the location of each of the downloaded files. downloaded_files = Fido.fetch(result) print(downloaded_files) ############################################################################### # Finally we can pass in the first file we downloaded into `sunpy.map.Map` # to create a SunPy Map object which allows us to easily plot the image. # However, there is a problem here. The downloaded file lacks the correct meta # information needed to create a `sunpy.map.Map`. # We want to open the file and access both the data and the header information. data, header = read_file(downloaded_files[0])[0] # Add the missing meta information to the header header['CUNIT1'] = 'arcsec' header['CUNIT2'] = 'arcsec' # Now we can pass in the data and header directly into `sunpy.map.Map` lascomap = sunpy.map.Map(data, header) lascomap.peek()
# The three images we see here are a continuum image (like the intensitygram we plotted at the start) of the photosphere, a map of the photosphere's magnetic field, and a map of the solar surface velocity.
# We had to rotate these images to align them with AIA ones because the two instruments are orientated differently on the spacecraft.
## Solar Corona
# We can also use SunPy to look at pictures of the solar corona.
# The SOHO telescope has three detectors (C1,C2,C3) on its LASCO instrument which image the corona in different wavelengths and distances from the photosphere.
# Let's get an image from the LASCO C1 detector:
coronaSOHO = Fido.search(a.Time('2000/02/27 07:42', '2000/02/27 07:43'), a.Instrument('LASCO'), a.Detector('C3'))
# I is a good idea to print this search before attempting to download it
# This will help you check that you're not downloading tons of files or no files
coronaSOHOdata = Fido.fetch(coronaSOHO[0],path="./lascoData/")
data, header = read_file(coronaSOHOdata[0])[0]
header['CUNIT1'] = 'arcsec'
header['CUNIT2'] = 'arcsec'
coronamap = sunpy.map.Map(data, header)
fig = plt.figure(4)
axSOHO = plt.subplot(111, projection=coronamap)
axSOHO.set_title("LASCO C3")
lasco = coronamap.plot(annotate=False, norm=colors.LogNorm(), clip_interval=(25.0, 99.5)*u.percent)
# lasco.set_clim(0,8000)
axSOHO.set_axis_off()
plt.colorbar()
plt.show()
# The black line in some of the images you see from LASCO are from the arm that holds a disc which blocks out the light from the solar disk.