How to plot galaxy visible light data using OpenNGC database in python

What is the Open NGC Catalog?

(OpenNGC) [https://github.com/mattiaverga/OpenNGC] is a database that organizes NGC (New General Catalog) and IC (Index Catalog).

PyOngc in python interface

You can easily get the data using https://github.com/mattiaverga/PyOngc. It can be installed with pip install pyongc on python3 series.

python

import pyongc
name="NGC4051"                                                                                         
DSOobject = pyongc.Dso(name) 
gtype = DSOobject.getType() 
mag = DSOobject.getMagnitudes() 
surf = DSOobject.getSurfaceBrightness() 
hubble = DSOobject.getHubble()                                                                 
# print(gtype,mag,surf,hubble)
# ('Galaxy', (11.08, 12.92, 8.58, 8.06, 7.67), 22.87, 'SABb')

When you install pyongc, command line tools are also installed, and you can see how many stars and galaxies are included with the following command.

python

> ongc stats

PyONGC version: 0.5.1
Database version: 20191019
Total number of objects in database: 13978
Object types statistics:
	Star                         -> 546
	Double star                  -> 246
	Association of stars         -> 62
	Star cluster + Nebula        -> 66
	Dark Nebula                  -> 2
	Duplicated record            -> 636
	Emission Nebula              -> 8
	Galaxy                       -> 10490
	Globular Cluster             -> 205
	Group of galaxies            -> 16
	Galaxy Pair                  -> 212
	Galaxy Triplet               -> 23
	HII Ionized region           -> 83
	Nebula                       -> 93
	Nonexistent object           -> 14
	Nova star                    -> 3
	Open Cluster                 -> 661
	Object of other/unknown type -> 434
	Planetary Nebula             -> 129
	Reflection Nebula            -> 38
	Supernova remnant            -> 11

When analyzing the entire catalog

If you want to analyze the entire catalog, download the original csv file. If wget is installed, you should be able to download it in one shot below.

python

wget https://raw.githubusercontent.com/mattiaverga/OpenNGC/master/NGC.csv .

NGC.csv plot example

Code example

python

import pandas as pd
df = pd.read_table('NGC.csv', header=0,delimiter=";")

import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plt.rcParams['font.family'] = 'serif'
import matplotlib.colors as colors

fig = plt.figure(figsize=(14,7.))
ax = fig.add_subplot(1, 2, 1)

num=len(set(df["Hubble"]))
usercmap = plt.get_cmap('jet')
cNorm  = colors.Normalize(vmin=0, vmax=num)
scalarMap = cm.ScalarMappable(norm=cNorm, cmap=usercmap)

for i, sp in enumerate(set(df["Hubble"])):
    c = scalarMap.to_rgba(i)
    df_species = df[df['Hubble'] == sp]
    ax.scatter(data=df_species, x='B-Mag', y='K-Mag', label=sp, color=c, s=1)

plt.xlabel("B-Mag")
plt.ylabel("K-Mag")
plt.legend(bbox_to_anchor=(0., 1.01, 1., 0.01), loc='lower left',ncol=8, mode="expand", borderaxespad=0.,fontsize=8)

ax = fig.add_subplot(1, 2, 2)


for i, sp in enumerate(set(df["Hubble"])):
    c = scalarMap.to_rgba(i)
    df_species = df[df['Hubble'] == sp]
    ax.scatter(data=df_species, x='B-Mag', y='SurfBr', label=sp, color=c, s=1)

plt.xlabel("B-Mag")
plt.ylabel(r"Surface brigness within 25 mag isophoto (B-band) [mag/arcsec$^2$]")
plt.legend(bbox_to_anchor=(0., 1.01, 1., 0.01), loc='lower left',ncol=8, mode="expand", borderaxespad=0.,fontsize=8)

plt.savefig("ngc_bk_bs.png ")
plt.show()

import seaborn as sns
sns.pairplot(df[df["Type"]=="G"],vars=["B-Mag","V-Mag","K-Mag"])
plt.savefig("ngc_bvk_galaxy.png ")

matplotlib scatter plot diagram

seaborn sns.pairplot diagram

Database description

There is an explanation at https://github.com/mattiaverga/OpenNGC/blob/master/NGC_guide.txt.

• Name: Object name composed by catalog + number NGC: New General Catalogue IC: Index Catalogue

• Type: Object type *: Star **: Double star *Ass: Association of stars OCl: Open Cluster GCl: Globular Cluster Cl+N: Star cluster + Nebula G: Galaxy GPair: Galaxy Pair GTrpl: Galaxy Triplet GGroup: Group of galaxies PN: Planetary Nebula HII: HII Ionized region DrkN: Dark Nebula EmN: Emission Nebula Neb: Nebula RfN: Reflection Nebula SNR: Supernova remnant Nova: Nova star NonEx: Nonexistent object Dup: Duplicated object (see NGC or IC columns to find the master object) Other: Other classification (see object notes)

• RA: Right Ascension in J2000 Epoch (HH:MM:SS.SS)

• Dec: Declination in J2000 Epoch (+/-DD:MM:SS.SS)

• Const: Constellation where the object is located

• MajAx: Major axis, expressed in arcmin

• MinAx: Minor axis, expressed in arcmin

• PosAng: Major axis position angle (North Eastwards)

• B-Mag: Apparent total magnitude in B filter

• V-Mag: Apparent total magnitude in V filter

• J-Mag: Apparent total magnitude in J filter

• H-Mag: Apparent total magnitude in H filter

• K-Mag: Apparent total magnitude in K filter

• SurfBr (only Galaxies): Mean surface brigthness within 25 mag isophot (B-band), expressed in mag/arcsec2

• Hubble (only Galaxies): Morphological type (for galaxies)

• Cstar U-Mag (only Planetary Nebulae): Apparent magnitude of central star in U filter

• Cstar B-Mag (only Planetary Nebulae): Apparent magnitude of central star in B filter

• Cstar V-Mag (only Planetary Nebulae): Apparent magnitude of central star in V filter

• M: cross reference Messier number

• NGC: other NGC identification, if the object is listed twice in the catalog

• IC: cross reference IC number, if the object is also listed with that identification

• Cstar Names (only Planetary Nebulae): central star identifications

• Identifiers: cross reference with other catalogs

• Common names: Common names of the object if any

• NED Notes: notes about object exported from NED

• OpenNGC Notes: notes about the object data from OpenNGC catalog

How to simplify the Hubble type, remove Nan and then plot

For example, the Hubble type is so numerous that you may want to simplify it. In that case,

python

set(df["Hubble"])

As, check the type of Hubble type. Create a dictionary to convert it and create a new column to record the result of the conversion.

python

htype={'E':"Etype", 'E-S0':"Etype",  'E?':"Etype",  'I':"Etype",  'IAB':"Etype",  'IB':"Etype",  \
'S0':"Etype", 'S0-a':"Etype",  'S?':"Etype",  'SABa':"Stype",   'SABb':"Stype",  'SABc':"Stype",  'SABd':"Stype",  'SABm':"Stype",  'SBa':"Stype", \
'SBab':"Stype", 'SBb':"Stype", 'SBbc':"Stype", 'SBc':"Stype", 'SBcd':"Stype", 'SBd':"Stype", 'SBm':"Stype", 'Sa':"Stype", 'Sab':"Stype", 'Sb':"Stype",  \
'Sbc':"Stype", 'Sc':"Stype", 'Scd':"Stype", 'Sd':"Stype", 'Sm':"Stype", "nan":"Nan"}
df["htype"] = df["Hubble"]
df = df.replace({"htype":htype})

Here, for example, an example of simply dividing into three parts, Etype, Stype, and Nan, is shown. Converting E to E is recursive and can be crazy, so avoid converting to the same character.

How to delete Nan

Since it is a problem if Nan is in the data, extract only the necessary columns and execute dropna for it to delete Nan.

python

    ds = df_species.loc[:,["B-Mag","K-Mag","J-Mag"]] 
    ds = ds.dropna(how='any')

Sample code

python

import pandas as pd
df = pd.read_table('NGC.csv', header=0,delimiter=";")

htype={'E':"Etype", 'E-S0':"Etype",  'E?':"Etype",  'I':"Etype",  'IAB':"Etype",  'IB':"Etype",  \
'S0':"Etype", 'S0-a':"Etype",  'S?':"Etype",  'SABa':"Stype",   'SABb':"Stype",  'SABc':"Stype",  'SABd':"Stype",  'SABm':"Stype",  'SBa':"Stype", \
'SBab':"Stype", 'SBb':"Stype", 'SBbc':"Stype", 'SBc':"Stype", 'SBcd':"Stype", 'SBd':"Stype", 'SBm':"Stype", 'Sa':"Stype", 'Sab':"Stype", 'Sb':"Stype",  \
'Sbc':"Stype", 'Sc':"Stype", 'Scd':"Stype", 'Sd':"Stype", 'Sm':"Stype", "nan":"Nan"}

df["htype"] = df["Hubble"]
df = df.replace({"htype":htype})

import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plt.rcParams['font.family'] = 'serif'
import matplotlib.colors as colors

fig = plt.figure(figsize=(14,7.))

ax = fig.add_subplot(1, 2, 1)
for i, sp in enumerate(set(df["htype"])):
    df_species = df[df['htype'] == sp]
    ds = df_species.loc[:,["B-Mag","K-Mag","J-Mag"]] 
    ds = ds.dropna(how='any')
    bk = ds["B-Mag"] - ds["K-Mag"]
    bj = ds["B-Mag"] - ds["J-Mag"]   
    ax.scatter(bk,bj,label=sp, s=2)
plt.xlabel("B-K")
plt.ylabel("B-J")
plt.legend(bbox_to_anchor=(0., 1.01, 1., 0.01), loc='lower left',borderaxespad=0.,fontsize=8)

ax = fig.add_subplot(1, 2, 2)
for i, sp in enumerate(set(df["htype"])):
    df_species = df[df['htype'] == sp]
    ds = df_species.loc[:,["B-Mag","K-Mag","J-Mag"]] 
    ds = ds.dropna(how='any')
    bj = ds["B-Mag"] - ds["J-Mag"]   
    ax.scatter(ds["B-Mag"],bj,label=sp, s=2)
plt.xlabel("B")
plt.ylabel("B-J")
plt.legend(bbox_to_anchor=(0., 1.01, 1., 0.01), loc='lower left',borderaxespad=0.,fontsize=8)

plt.savefig("ngc_bk_bj.png ")
plt.show()

Generated figure