#obj = ["3C 454.3", 343.49062, 16.14821, 1.0]
obj = ["PKS J0006-0623", 1.55789, -6.39315, 1.0]
#obj = ["M87", 187.705930, 12.391123, 1.0]
#### name, ra, dec, radius of cone

obj_name = obj[0]
obj_ra   = obj[1]
obj_dec  = obj[2]
cone_radius  = obj[3]

obj_coord = coordinates.SkyCoord(ra=obj_ra, dec=obj_dec, unit=(u.deg, u.deg), frame="icrs")

# Query data
data_2mass = Irsa.query_region(obj_coord, catalog="fp_psc", radius=cone_radius * u.deg)
data_wise  = Irsa.query_region(obj_coord, catalog="allwise_p3as_psd", radius=cone_radius * u.deg)
__data_galex = Vizier.query_region(obj_coord, catalog='II/335', radius=cone_radius * u.deg)
data_galex = __data_galex[0]

num_2mass = len(data_2mass)
num_wise  = len(data_wise)
num_galex = len(data_galex)

print("Number of object in (2MASS, WISE, GALEX): ", num_2mass, num_wise, num_galex)

"""
Explanation: Get the data
2MASS => J, H K, angular resolution ~4"
WISE => 3.4, 4.6, 12, and 22 μm (W1, W2, W3, W4) with an angular resolution of 6.1", 6.4", 6.5", & 12.0" 
GALEX imaging => Five imaging surveys in a Far UV band (1350-1750Å) and Near UV band (1750-2800Å) with 6-8 arcsecond resolution (80% encircled energy) and 1 arcsecond astrometry, and a cosmic UV background map.
End of explanation
"""


# use only coordinate columns
ra_2mass = data_2mass['ra']
dec_2mass = data_2mass['dec']
c_2mass = coordinates.SkyCoord(ra=ra_2mass, dec=dec_2mass, unit=(u.deg, u.deg), frame="icrs")

ra_wise  = data_wise['ra']
dec_wise = data_wise['dec']
c_wise = coordinates.SkyCoord(ra=ra_wise, dec=dec_wise, unit=(u.deg, u.deg), frame="icrs")

ra_galex  = data_galex['RAJ2000']
dec_galex = data_galex['DEJ2000']
c_galex = coordinates.SkyCoord(ra=ra_galex, dec=dec_galex, unit=(u.deg, u.deg), frame="icrs")

####
sep_min = 1.0 * u.arcsec # minimum separation in arcsec

# Only 2MASS and WISE matching 
#
idx_2mass, idx_wise, d2d, d3d = c_wise.search_around_sky(c_2mass, sep_min)
# select only one nearest if there are more in the search reagion (minimum seperation parameter)!

print("Only 2MASS and WISE: ", len(idx_2mass))

"""
Explanation: Matching coordinates
End of explanation
"""


# from matching of 2 cats (2MASS and WISE) coordinate
data_2mass_matchwith_wise = data_2mass[idx_2mass]
data_wise_matchwith_2mass = data_wise[idx_wise] # WISE dataset

w1 = data_wise_matchwith_2mass['w1mpro']
j = data_2mass_matchwith_wise['j_m']
w1j = w1-j

cutw1j = -1.7 # https://academic.oup.com/mnras/article/448/2/1305/1055284

# WISE galaxy data -> from cut
galaxy = data_wise_matchwith_2mass[w1j < cutw1j] 
print("Number of galaxy from cut W1-J:", len(galaxy))

w1j_galaxy = w1j[w1j<cutw1j]
w1_galaxy = w1[w1j<cutw1j]

plt.scatter(w1j, w1, marker='o', color='blue')
plt.scatter(w1j_galaxy, w1_galaxy, marker='.', color="red")
          
plt.axvline(x=cutw1j) # https://academic.oup.com/mnras/article/448/2/1305/1055284

"""
Explanation: Plot $W_1-J$ vs $W_1$
End of explanation
"""


# GALEX
###
# coord of object in 2mass which match wise (first objet/nearest in sep_min region)
c_2mass_matchwith_wise = c_2mass[idx_2mass]
c_wise_matchwith_2mass = c_wise[idx_wise]

#Check with 2mass cut
idx_2mass_wise_galex, idx_galex1, d2d, d3d = c_galex.search_around_sky(c_2mass_matchwith_wise, sep_min)
num_galex1 = len(idx_galex1)
#Check with wise cut
idx_wise_2mass_galex, idx_galex2, d2d, d3d = c_galex.search_around_sky(c_wise_matchwith_2mass, sep_min)
num_galex2 = len(idx_galex2)

print("Number of GALEX match in 2MASS cut (with WISE): ", num_galex1)
print("Number of GALEX match in WISE cut (with 2MASS): ", num_galex2)

# diff/average
print("Confusion level: ", abs(num_galex1 - num_galex2)/np.mean([num_galex1, num_galex2])*100, "%")

# Choose which one is smaller!
if num_galex1 < num_galex2:
    select_from_galex = idx_galex1
    
    match_galex = data_galex[select_from_galex]
    c_selected_galex = c_galex[select_from_galex]
    
    # 2MASS from GALEX_selected
    _idx_galex1, _idx_2mass, d2d, d3d = c_2mass.search_around_sky(c_selected_galex, sep_min)
    match_2mass = data_2mass[_idx_2mass]
    
    # WISE from 2MASS_selected
    _ra_match_2mass = match_2mass['ra']
    _dec_match_2mass = match_2mass['dec']
    _c_match_2mass = coordinates.SkyCoord(ra=_ra_match_2mass, dec=_dec_match_2mass, unit=(u.deg, u.deg), frame="icrs")

    _idx, _idx_wise, d2d, d3d = c_wise.search_around_sky(_c_match_2mass, sep_min)
    match_wise = data_wise[_idx_wise]
    
else:
    select_from_galex = idx_galex2

    match_galex = data_galex[select_from_galex]
    c_selected_galex = c_galex[select_from_galex]
    
    # WISE from GALEX_selected
    _idx_galex1, _idx_wise, d2d, d3d = c_wise.search_around_sky(c_selected_galex, sep_min)
    match_wise = data_wise[_idx_wise]
    
    # 2MASS from WISE_selected
    _ra_match_wise = match_wise['ra']
    _dec_match_wise = match_wise['dec']
    _c_match_wise = coordinates.SkyCoord(ra=_ra_match_wise, dec=_dec_match_wise, unit=(u.deg, u.deg), frame="icrs")

    _idx, _idx_2mass, d2d, d3d = c_2mass.search_around_sky(_c_match_wise, sep_min)
    match_2mass = data_2mass[_idx_2mass]

print("Number of match in GALEX: ", len(match_galex))
print("Number of match in 2MASS: ", len(match_2mass))
print("Number of match in WISE : ", len(match_wise))

"""
Explanation: W1-J < -1.7 => galaxy
W1-J > -1.7 => stars

Filter all Cats
End of explanation
"""


joindata = np.array([match_2mass['j_m'], match_2mass['h_m'], match_2mass['k_m'], 
                     match_wise['w1mpro'], match_wise['w2mpro'], match_wise['w3mpro'], match_wise['w4mpro'],
                     match_galex['NUVmag']])

joindata = joindata.T

"""
Explanation: Collect relevant data
End of explanation
"""


from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale

X = joindata

pca = PCA(n_components=4)
X_r = pca.fit(X).transform(X)

print(pca.components_)
print(pca.explained_variance_)

# plot PCA result
# Plot data using PC1 vs PC2
plt.scatter(X_r[:,0], X_r[:,1], marker='o', color='blue')

# overplot galaxy selected using cut W1-J
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,0], X_r[i,1], marker=".", color="red")

# plot PCA result
# Plot data using PC1 vs PC2
plt.scatter(X_r[:,0], X_r[:,2], marker='o', color='blue')

# overplot galaxy selected using cut W1-J
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,0], X_r[i,2], marker=".", color="red")

# plot PCA result
# Plot data using PC1 vs PC2
plt.scatter(X_r[:,0], X_r[:,3], marker='o', color='blue')

# overplot galaxy selected using cut W1-J
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,0], X_r[i,3], marker=".", color="red")

# plot PCA result
# Plot data using PC1 vs PC2
plt.scatter(X_r[:,1], X_r[:,2], marker='o', color='blue')

# overplot galaxy selected using cut W1-J
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,1], X_r[i,2], marker=".", color="red")

# plot PCA result
# Plot data using PC1 vs PC2
plt.scatter(X_r[:,1], X_r[:,3], marker='o', color='blue')

# overplot galaxy selected using cut W1-J
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,1], X_r[i,3], marker=".", color="red")

# plot PCA result
# Plot data using PC1 vs PC2
plt.scatter(X_r[:,2], X_r[:,3], marker='o', color='blue')

# overplot galaxy selected using cut W1-J
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,2], X_r[i,3], marker=".", color="red")

"""
Explanation: Analysis
we can try: 
- dimensionality reduction
- clustering 
- classification
- data embedding
PCA
End of explanation
"""


from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

X = joindata

db = DBSCAN(eps=1, min_samples=3).fit(X)

core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

print('Estimated number of clusters: %d' % n_clusters_)
#print(labels)

"""
Explanation: DBSCAN
End of explanation
"""


# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 1]

    class_member_mask = (labels == k)

    
    ## J vs J-W1
    
    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 3], xy[:, 0], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=14)

    xy = X[class_member_mask & ~core_samples_mask]
    plt.plot(xy[:, 3], xy[:, 0], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=8)
    
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.plot(X[i,3], X[i,0], marker="X", markerfacecolor='red', markeredgecolor='none', markersize=8)  

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

"""
Explanation: Plot $W_1$ vs $J$
End of explanation
"""


from sklearn.manifold import TSNE
X = joindata #scale(joindata)
X_r = TSNE(n_components=2).fit_transform(X)

plt.scatter(X_r[:,0], X_r[:,1], marker='o', color="blue")
for i, name in enumerate(match_wise['designation']):
    for galaxyname in galaxy['designation']:
        if name == galaxyname:
            plt.scatter(X_r[i,0], X_r[i,1], marker='.', color="red")  

"""
Explanation: t-SNE
End of explanation
"""


