def test_SkyServer_objectSearch(self):
#object search
object = SkyServer.objectSearch(ra=258.25,
dec=64.05,
dataRelease=SkyServer_DataRelease)
self.maxDiff = None
self.assertEqual(SkyServer_ObjectSearchResultObjID,
object[0]["Rows"][0]["id"])
def test_SkyServer_radialSearch(self):
# radial search
df = SkyServer.radialSearch(ra=258.25,
dec=64.05,
radius=0.1,
dataRelease=SkyServer_DataRelease)
self.maxDiff = None
self.assertEqual(SkyServer_RadialSearchResultCSV,
df.to_csv(index=False, float_format="%.6f"))
def test_SkyServer_rectangularSearch(self):
#rectangular search
df = SkyServer.rectangularSearch(min_ra=258.3,
max_ra=258.31,
min_dec=64,
max_dec=64.01,
dataRelease=SkyServer_DataRelease)
self.maxDiff = None
self.assertEqual(SkyServer_RectangularSearchResultCSV,
df.to_csv(index=False, float_format="%.6f"))
def test_SkyServer_getJpegImgCutout(self):
#image cutout
img = SkyServer.getJpegImgCutout(
ra=197.614455642896,
dec=18.438168853724,
width=512,
height=512,
scale=0.4,
dataRelease=SkyServer_DataRelease,
opt="OG",
query=
"SELECT TOP 100 p.objID, p.ra, p.dec, p.r FROM fGetObjFromRectEq(197.6,18.4,197.7,18.5) n, PhotoPrimary p WHERE n.objID=p.objID"
)
im = skimage.io.imread("./TestGalaxy.jpeg")
self.assertEqual(img.tobytes(), im.tobytes())
def get_images(df, width=424, height=424, scale=.02):
"""Get images for objects present in pandas dataframe.
Downloads JPEG images of objects from SDSS to disk storage, naming
images according to their :attr:`specobjid`. Filepaths are
additionally stored in the dataframe, such that the dataframe can
be used with :class:`keras.preprocessing.image.ImageDataGenerator`
"""
catalog_path = os.path.join(ARGS.DATA, ARGS.CATALOG + '.h5')
df = df.assign(imgpath=None)
with tqdm(total=len(df.index), unit='object') as pbar:
for index, obj in df.iterrows():
# allows for restarting incomplete downloads by
# checking for all entries if the path already exists
# XXX: tqdm includes better ways to approach this
if obj['imgpath'] is not None:
pbar.update(1)
continue
img = None
img = SkyServer.getJpegImgCutout(ra=obj['ra'],
dec=obj['dec'],
width=width,
height=height,
scale=(scale * obj['petroR90_r']),
dataRelease='DR15')
if img is not None:
imgpath = os.path.join(ARGS.IMG,
'{0}.jpeg'.format(obj['specobjid']))
Image.fromarray(img).save(imgpath)
df.at[index, 'imgpath'] = imgpath
df.to_hdf(catalog_path,
key=ARGS.CATALOG,
append=False,
mode='w')
pbar.update(1)
return df.dropna(axis=0, subset=['imgpath'], inplace=True)
def get_catalog():
"""Downloads GZ2 object catalog.
Retrieve a dataframe of the objects present in the GalaxyZoo2
catalog, as well as weighted fraction task answers for each object.
"""
sql = 'SELECT gz2.specobjid, gz2.ra, gz2.dec, dr7.petroR90_r, ' \
'gz2.t01_smooth_or_features_a01_smooth_weighted_fraction' \
' AS t01a01, ' \
'gz2.t01_smooth_or_features_a02_features_or_disk_weighted_fraction' \
' AS t01a02, ' \
'gz2.t01_smooth_or_features_a03_star_or_artifact_weighted_fraction' \
' AS t01a03, ' \
'gz2.t02_edgeon_a04_yes_weighted_fraction' \
' AS t02a04, ' \
'gz2.t02_edgeon_a05_no_weighted_fraction' \
' AS t02a05, ' \
'gz2.t03_bar_a06_bar_weighted_fraction' \
' AS t03a06, ' \
'gz2.t03_bar_a07_no_bar_weighted_fraction' \
' AS t03a07, ' \
'gz2.t04_spiral_a08_spiral_weighted_fraction' \
' AS t04a08, ' \
'gz2.t04_spiral_a09_no_spiral_weighted_fraction' \
' AS t04a09, ' \
'gz2.t05_bulge_prominence_a10_no_bulge_weighted_fraction' \
' AS t05a10, ' \
'gz2.t05_bulge_prominence_a11_just_noticeable_weighted_fraction' \
' AS t05a11, ' \
'gz2.t05_bulge_prominence_a12_obvious_weighted_fraction' \
' AS t05a12, ' \
'gz2.t05_bulge_prominence_a13_dominant_weighted_fraction' \
' AS t05a13, ' \
'gz2.t06_odd_a14_yes_weighted_fraction' \
' AS t06a14, ' \
'gz2.t06_odd_a15_no_weighted_fraction' \
' AS t06a15, ' \
'gz2.t07_rounded_a16_completely_round_weighted_fraction' \
' AS t07a16, ' \
'gz2.t07_rounded_a17_in_between_weighted_fraction' \
' AS t07a17, ' \
'gz2.t07_rounded_a18_cigar_shaped_weighted_fraction' \
' AS t07a18, ' \
'gz2.t08_odd_feature_a19_ring_weighted_fraction' \
' AS t08a19, ' \
'gz2.t08_odd_feature_a20_lens_or_arc_weighted_fraction' \
' AS t08a20, ' \
'gz2.t08_odd_feature_a21_disturbed_weighted_fraction' \
' AS t08a21, ' \
'gz2.t08_odd_feature_a22_irregular_weighted_fraction' \
' AS t08a22, ' \
'gz2.t08_odd_feature_a23_other_weighted_fraction' \
' AS t08a23, ' \
'gz2.t08_odd_feature_a24_merger_weighted_fraction' \
' AS t08a24, ' \
'gz2.t08_odd_feature_a38_dust_lane_weighted_fraction' \
' AS t08a38, ' \
'gz2.t09_bulge_shape_a25_rounded_weighted_fraction' \
' AS t09a25, ' \
'gz2.t09_bulge_shape_a26_boxy_weighted_fraction' \
' AS t09a26, ' \
'gz2.t09_bulge_shape_a27_no_bulge_weighted_fraction' \
' AS t09a27, ' \
'gz2.t10_arms_winding_a28_tight_weighted_fraction' \
' AS t10a28, ' \
'gz2.t10_arms_winding_a29_medium_weighted_fraction' \
' AS t10a29, ' \
'gz2.t10_arms_winding_a30_loose_weighted_fraction' \
' AS t10a30, ' \
'gz2.t11_arms_number_a31_1_weighted_fraction' \
' AS t11a31, ' \
'gz2.t11_arms_number_a32_2_weighted_fraction' \
' AS t11a32, ' \
'gz2.t11_arms_number_a33_3_weighted_fraction' \
' AS t11a33, ' \
'gz2.t11_arms_number_a34_4_weighted_fraction' \
' AS t11a34, ' \
'gz2.t11_arms_number_a36_more_than_4_weighted_fraction' \
' AS t11a36, ' \
'gz2.t11_arms_number_a37_cant_tell_weighted_fraction' \
' AS t11a37 ' \
'FROM zoo2MainSpecz as gz2 JOIN SpecDR7 AS dr7 ' \
'ON gz2.dr7objid=dr7.dr7objid'
return SkyServer.sqlSearch(sql=sql, dataRelease='DR15')
def test_SkyServer_sqlSearch(self):
#sql search
df = SkyServer.sqlSearch(sql=SkyServer_TestQuery,
dataRelease=SkyServer_DataRelease)
self.assertEqual(SkyServer_QueryResultCSV, df.to_csv(index=False))
#help(SkyServer)
# In[ ]:
#defining sql query and SDSS data relelease:
SkyServer_TestQuery = "select top 1 specobjid, ra, dec from specobj order by specobjid"
SkyServer_DataRelease = "DR13"
# In[ ]:
#Exectute sql query
df = SkyServer.sqlSearch(sql=SkyServer_TestQuery, dataRelease=SkyServer_DataRelease)
print(df)
# In[ ]:
#get an image cutout
img = SkyServer.getJpegImgCutout(ra=197.614455642896, dec=18.438168853724, width=2, height=2, scale=0.4,
dataRelease=SkyServer_DataRelease,opt="OG",
query="SELECT TOP 100 p.objID, p.ra, p.dec, p.r FROM fGetObjFromRectEq(197.6,18.4,197.7,18.5) n, PhotoPrimary p WHERE n.objID=p.objID")
plt.imshow(img)
# In[ ]:
imgName = str(galaxies.iloc[x][0]) + ".jpeg"
maxValue = 0
shapeIndex = ellipticIndex
for y in xrange(ellipticIndex,combinedIndex + 1):
if galaxies.iloc[x][y] > maxValue:
maxValue = galaxies.iloc[x][y]
shapeIndex = y
hd2 = galaxies.iloc[x][1] + " " + galaxies.iloc[x][2]
# Obtain decimal representation
ra1, dec1 = pyasl.coordsSexaToDeg(hd2)
img = SkyServer.getJpegImgCutout(ra=ra1, dec=dec1, width=512, height=512, scale=0.1,
dataRelease="DR13",opt="I",
query="SELECT TOP 1 g.objID, g.ra, g.dec, g.r FROM fGetObjFromRectEq(ra1-0.5,dec1-0.5,ra1+0.5,dec1+0.5) n, Galaxy g WHERE n.objID=g.objID")
im = Image.fromarray(img)
im.save(imgName)
with open('shapes.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow([str(galaxies.iloc[x][0]), str(shapeIndex)])
csv_reader = csv.reader(csv_file, delimiter=',')
line_count = 0
for x in range(0, num_lines):
row = next(csv_reader)
if line_count == 0:
line_count += 1
else:
ra_sx, dec_sx = row[1], row[2]
c = SkyCoord(ra_sx, dec_sx, unit=u.degree)
print(f'object {row[0]}', c)
line_count += 1
img = SkyServer.getJpegImgCutout(ra=c.ra.deg,
dec=c.dec.deg,
width=IMG_WIDTH,
height=IMG_HEIGHT,
scale=0.1,
dataRelease='DR13')
Image.fromarray(img).save(f"skyserver_fits/{row[0]}.png")
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
red = fits.PrimaryHDU(data=r)
red.writeto(f"skyserver_fits/{row[0]}r.fits")
green = fits.PrimaryHDU(data=g)
green.writeto(f"skyserver_fits/{row[0]}g.fits")
blue = fits.PrimaryHDU(data=b)
blue.writeto(f"skyserver_fits/{row[0]}b.fits")