Carpyncho RR-Lyrae V.1.0 Catalogs(cp_rr_v1)

This notebook give some insights about the data stored in all the features types catalogs.

[1]:
# import the module and instance the client
import carpyncho
client = carpyncho.Carpyncho()

Now we download te Carpyncho RR-Lyrae V1 Catalog

[2]:
df = client.get_catalog("others", "cpy_rr_v1")
df
[2]:
id tile cnt ra_k dec_k prob tsample
6799063 33960000211620 b396 131 267.549917 -18.699892 0.852000 b206, b214, b216, b220, b228, b234, b247, b248...
7153972 33960000942530 b396 130 268.118017 -17.763556 0.849467 b206, b214, b216, b220, b228, b234, b247, b248...
6971905 33960000566886 b396 131 267.536346 -18.093889 0.837733 b206, b214, b216, b220, b228, b234, b247, b248...
6801853 33960000220135 b396 131 267.637892 -18.734767 0.837333 b206, b214, b216, b220, b228, b234, b247, b248...
6747151 33960000105195 b396 131 267.487762 -18.848939 0.828400 b206, b214, b216, b220, b228, b234, b247, b248...
... ... ... ... ... ... ... ...
6457949 33600000787886 b360 139 263.635417 -29.303825 0.462667 b206, b214, b216, b220, b228, b234, b247, b248...
6926060 33960000470380 b396 131 267.525504 -18.250553 0.462000 b206, b214, b216, b220, b228, b234, b247, b248...
6874859 33960000359818 b396 131 267.490592 -18.416206 0.460933 b206, b214, b216, b220, b228, b234, b247, b248...
682776 32200000656825 b220 123 274.725521 -34.229878 0.460933 b206, b214, b216, b228, b234, b247, b248, b261...
6617136 33600000965623 b360 204 263.473292 -28.911094 0.460533 b206, b214, b216, b220, b228, b234, b247, b248...

242 rows × 7 columns

The columns of this catalog are

[3]:
print(list(df.columns))
['id', 'tile', 'cnt', 'ra_k', 'dec_k', 'prob', 'tsample']

Where

  • id (ID): This is the unique identifier of every light curve. If you want to access all the points of the lightcurve of a source wiht any id, you can search for the same value of a bm_src_idin the lc, or id in the features catalog of the same tile indicated in the column tile.
  • tile: The name of the tile where the candidate is located.
  • cnt (Count): How many epochs has the lightcurve.
  • ra_k: Right Ascension in band \(K_s\) of the source in the first epoch.
  • dec_k: Declination in band \(K_s\) of the source in the first epoch.
  • prob (Probability): The probability of this source to be a RR-Lyrae star [1].
  • tsample (Tiles-Sample): Which tiles was used to create the ensemble select this source as a candidate [1].

[1] To more insights about this feature please chek our work

Not ready

Well lets play with a candidate

[4]:
rr = df.iloc[0]
rr
[4]:
id                                            33960000211620
tile                                                    b396
cnt                                                      131
ra_k                                                  267.55
dec_k                                               -18.6999
prob                                                   0.852
tsample    b206, b214, b216, b220, b228, b234, b247, b248...
Name: 6799063, dtype: object

We can check their mean of magnitudes to check if the source is not saturated or diffuse.

Now we know id from the tile b396 we can retrieve the entire collection of features and the light-curve

[5]:
feats = client.get_catalog("b396", "features")
lc = client.get_catalog("b396", "lc")

# retrieve the features of the selected source
feats = feats[feats.id == rr.id]
lc = lc[lc.bm_src_id == rr.id]

No we have the features

[6]:
feats
[6]:
id cnt ra_k dec_k vs_type vs_catalog Amplitude Autocor_length Beyond1Std Con ... c89_jk_color c89_m2 c89_m4 n09_c3 n09_hk_color n09_jh_color n09_jk_color n09_m2 n09_m4 ppmb
144267 33960000211620 131 267.549917 -18.699892 0.178 2.0 0.274809 0.0 ... 0.139307 13.758278 13.748731 0.039034 0.037559 0.103765 0.141323 13.8797 13.880858 1.49232

1 rows × 73 columns

and the entire lc

[7]:
lc
[7]:
bm_src_id pwp_id pwp_stack_src_id pwp_stack_src_hjd pwp_stack_src_mag3 pwp_stack_src_mag_err3
752483 33960000211620 2753 3000275300031172 56152.044089 14.230 0.028
926175 33960000211620 2754 3000275400026402 56152.044623 14.198 0.030
1745826 33960000211620 2759 3000275900028136 56156.014732 14.282 0.030
1922566 33960000211620 2760 3000276000029792 56156.015266 14.342 0.032
2959474 33960000211620 2765 3000276500036874 56167.994914 14.304 0.029
... ... ... ... ... ... ...
82226182 33960000211620 2711 3000271100035211 56075.305061 14.265 0.031
82456417 33960000211620 2712 3000271200042427 56075.305558 14.242 0.027
83636862 33960000211620 2717 3000271700036927 56078.297314 14.206 0.029
83884497 33960000211620 2718 3000271800042962 56078.297828 14.205 0.026
85016389 33960000211620 2723 3000272300029811 56099.295536 14.295 0.031

131 rows × 6 columns

We can phase the light curve now

For make our code simple we can use to folde the light curve the PyAstronomy and numpy library

[8]:
from PyAstronomy.pyasl import foldAt
import numpy as np
[9]:
%matplotlib inline
import matplotlib.pyplot as plt

now we can plot the folded and unfolded lightcurves

[10]:
lc = lc.sort_values("pwp_stack_src_hjd")

time, mag, err = (
        lc.pwp_stack_src_hjd.values,
        lc.pwp_stack_src_mag3.values,
        lc.pwp_stack_src_mag_err3.values)

t0 = time[0]

phases = foldAt(time, feats.PeriodLS.values, T0=t0)
sort = np.argsort(phases)
phases, pmag, perr = phases[sort], mag[sort], err[sort]

phases = np.hstack((phases, phases + 1))
pmag = np.hstack((pmag, pmag))
perr = np.hstack((perr, perr))


fig, axes = plt.subplots(2, 1, figsize=(12, 6))

ax = axes[0]
ax.errorbar(time, mag, err, ls="", marker="o", ecolor="red")
ax.set_title(f"Light Curve of source {rr.id} (Prob: ~{rr.prob:.2f})")
ax.set_ylabel("Magnitude")
ax.set_xlabel("HJD")
ax.invert_yaxis()

ax = axes[1]
ax.errorbar(phases, pmag, perr, ls="", marker="o", ecolor="blue", color="red")
ax.set_title(f"Folded Light Curve of source {rr.id} (Prob: ~{rr.prob:.2f})")
ax.set_ylabel("Magnitude")
ax.set_xlabel("Phase")
ax.invert_yaxis()

fig.tight_layout()
../../_images/tutorials_catalogs_02_cpy_rr_v1_20_0.png
[11]:
import datetime as dt
dt.datetime.now()
[11]:
datetime.datetime(2020, 4, 30, 21, 57, 11, 124782)