Выборка

Общее по выборке целиком.

**Name, NGC** **Name, UGC** **Type** $i,^{\circ}$ $\sigma$ source
NGC 338 UGC 624 Sab 64±4 Z
NGC 1167 UGC 2487 SA0 38±2 Z (CALIFA?)
NGC 2985 UGC 5253 (R)SA(r)ab 36±2 N08
NGC 3898 UGC 6787 SA(s)ab 61±8 N08 (ATLASS3D?)
NGC 4258 UGC 7353 SABb 65±5 H98 (ATLASS3D?)
NGC 4725 UGC 7989 SABa 50±6 H99 (ATLASS3D?)
NGC 5533 UGC 9133 SA(rs)ab 52±1 N08 (CALIFA?)

N08 - https://ui.adsabs.harvard.edu/#abs/2008MNRAS.388.1381N/abstract

Все картинки:


In [1]:
from IPython.display import Image, display
from prettypandas import PrettyPandas
import pandas as pd
import os
import sys
from PIL import Image as Im
%pylab
%matplotlib inline


Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib

In [2]:
summary_imgs_path = '..\\pics\\notebook_summary\\'

In [3]:
for img in os.listdir(summary_imgs_path):
    display(Image(summary_imgs_path + img))


Одна большая композитная картинка:


In [4]:
images = []
for img in os.listdir(summary_imgs_path):
    im = Im.open(summary_imgs_path + img)
    images.append(im)
    
widths, heights = zip(*(i.size for i in images))

total_height = sum(heights)
max_width = max(widths)

new_im = Im.new('RGB', (max_width, total_height))

offset = 0
for im in images:
    new_im.paste(im, (0,offset))
    offset += im.size[1]

new_im.save('summary_all.jpg')

In [5]:
# странное поведение - если тут поменять размер с 19 до 20, то картинку нельзя будет "открыть в новой вкладке", так что я просто ее сохранил 

# import matplotlib.pylab as plt

# fig = plt.figure(figsize=[19, 4*6])
# plt.imshow(new_im)
# plt.xticks([])
# plt.yticks([])
# plt.show()

Неустойчивость в далеких спиралях:


In [5]:
for img in os.listdir('..\\pics\\instab_spirals\\'):
    display(Image('..\\pics\\instab_spirals\\' + img))


Romeo-Falstad 2013 сравнение


In [7]:
images = []
imnames = []
for img in os.listdir('..\\pics\\RF13\\'):
    im = Im.open('..\\pics\\RF13\\' + img)
    images.append(im)
    imnames.append(img)
    
fig, axes = plt.subplots(7, 1, figsize=[10, 20])
for ind, im in enumerate(images):
    axes[ind].imshow(im)
    axes[ind].set_title(imnames[ind], fontsize=30)
plt.show()


Видно, что ситуация для всех похожая более-менее и все выглядит неплохо.

Таблица источников

Ссылка на таблицу на docs.google.

НЕ ПОСЛЕДНЯЯ ВЕРСИЯ


In [58]:
data_sources = pd.read_csv('sources_data.csv')

Без ссылок и замечаний (формат в ячейках - $i$, $D(Mpc)$, $PA$):


In [69]:
def highlights(s):
    return ['background-color: #D8D8D8' if i in [0, 1, 2, 3, 12, 18, 22,] else '' for i in range(len(s))]

s = data_sources[[c for c in data_sources.columns[:-2]]].style.apply(highlights)
s


Out[69]:
source N338 N1167 N2985 N3898 N4258 N4725 N5533
0 UGC 624 2487 5253 6787 7353 7989 9133
1 HYPERLEDA 75.6, 76.560 \pm 17.629, 107.3 49., -, 70. 37.9,-,177 56.3,-,107 68.3, 7.656 \pm 0.124,150 45.4,13.804 \pm 3.179,35.7 60.3,33.33 \pm 0.45,28.2
2 NED scale dist 62 66 19 18.4 8.97 20.5 56.1
3 Dispersions nan nan nan nan nan nan nan
4 Noordermeer 08 nan nan 37, 21.1, 356-340 69, 18.9, 107-118 nan nan 53, 54.3, 24-45
5 Zasov 08 nan 36, 67, 70 nan nan nan nan nan
6 Zasov 12 64, 65, 108 nan nan nan nan nan nan
7 Heraudeau 1998 nan nan nan nan 70.3, -480, 150 nan nan
8 Heraudeau 1999 nan nan 44.2, -1328, 0. 49.2, -1153, 107 nan 46.5, -1225,35 nan
9 CALIFA 2016 nan 40.5,-,62 nan nan nan nan nan
10 Dumas 07 SAURON nan nan 38,22.4,-2 nan nan nan nan
11 Pignatelli 01 nan nan 54., 17.1, 107 nan nan nan nan
12 Gas data nan nan nan nan nan nan nan
13 Noordermeer 05 HI 59, 65.1 36., 67.4 38, 21.1 67, 18.9 nan 51., 18.2 53, 54.3
14 Lavezzi 98 CO +' nan nan nan nan nan nan
15 Courteau 97 Vel_21cm 68, 71.22, - nan nan nan nan nan nan
16 Eymeren 11 Vel_HI nan nan nan nan 69.5, 7.8, 331.25 44.19,25.91,32.45 51.9,54.3,30.36
17 Yim 16 CO+HI nan nan nan nan 66., 8.0?, 44., 26.8 nan
18 SF nan nan nan nan nan nan nan
19 Epinat 08 GHASP nan nan 36\pm 5, 21.1, 176 53\pm 2, 18.9, 112 nan nan nan
20 Hameed 05 H_alpha nan nan 42., 22.4 46., 21.9 nan 43., 12.4 nan
21 SPITZER nan nan nan nan +' +' nan
22 Photometry nan nan nan nan nan nan nan
23 Noordermer, Hulst 07 BR (I) 64, -, -72 38, -, 70 36, -, -3 nan nan nan 52, -, 26
24 CALIFA 16 gri nan -,-,72 nan nan nan nan -,-,27.5
25 Heidt 01 JHK nan nan 32-40,28.,2-160 63-74,37.5,102-106 nan 63,13.0, 44.5 nan
26 Gutierrez 2012 R nan nan 36, 21.1, 178 53., 18.9, 107 nan nan nan
27 Mendez-Abreu 08 J nan nan 29.5, 22.4, 1.5 60., 21.9, 106.9 nan nan 50., 54., 26.3
28 S4G 15 nan nan 33.9, -,175 nan nan 56., -,34. nan
29 YOSHINO 08 VIJ nan nan nan nan 59.3,7.8 nan nan
30 Fisher 10 S4G nan nan nan nan -,8.17,- -,13.24,- nan
31 nan nan nan nan nan nan nan nan
32 HUBBLE nan nan +' nan nan +' nan

Ссылки:


In [65]:
PrettyPandas(data_sources[[c for c in (data_sources.columns[0], data_sources.columns[-2])]])


Out[65]:
source link
0 UGC nan
1 HYPERLEDA nan
2 NED scale dist nan
3 Dispersions nan
4 Noordermeer 08 https://ui.adsabs.harvard.edu/#abs/2008MNRAS.388.1381N/abstract https://ui.adsabs.harvard.edu/#abs/2007MNRAS.376.1513N/abstract https://ui.adsabs.harvard.edu/#abs/2007MNRAS.376.1480N/abstract
5 Zasov 08 https://ui.adsabs.harvard.edu/#abs/2008ARep...52...79Z/abstract
6 Zasov 12 https://ui.adsabs.harvard.edu/#abs/2012AstBu..67..362Z/abstract
7 Heraudeau 1998 http://adsabs.harvard.edu/cgi-bin/bib_query?1998A%26AS..133..317H
8 Heraudeau 1999 http://adsabs.harvard.edu/cgi-bin/bib_query?1999A%26AS..136..509H
9 CALIFA 2016 ftp://ftp.caha.es/CALIFA/dataproducts/Stellar_Kinematics_V1200/FalconBarroso_etal_2016.pdf
10 Dumas 07 SAURON https://ui.adsabs.harvard.edu/#abs/2007MNRAS.379.1249D/abstract
11 Pignatelli 01 https://ui.adsabs.harvard.edu/#abs/2001MNRAS.323..188P/abstract
12 Gas data nan
13 Noordermeer 05 HI https://ui.adsabs.harvard.edu/#abs/2005A&A...442..137N/abstract
14 Lavezzi 98 CO https://ui.adsabs.harvard.edu/#abs/1998AJ....115..405L/abstract
15 Courteau 97 Vel_21cm https://ui.adsabs.harvard.edu/#abs/1997AJ....114.2402C/abstract
16 Eymeren 11 Vel_HI https://arxiv.org/pdf/1103.4928v1.pdf
17 Yim 16 CO+HI https://arxiv.org/pdf/1608.06735v1.pdf
18 SF nan
19 Epinat 08 GHASP https://arxiv.org/pdf/0808.0132v1.pdf
20 Hameed 05 H_alpha http://iopscience.iop.org/article/10.1086/430211/pdf
21 SPITZER nan
22 Photometry nan
23 Noordermer, Hulst 07 BR (I) https://ui.adsabs.harvard.edu/#abs/2007MNRAS.376.1480N/abstract
24 CALIFA 16 gri http://adsabs.harvard.edu/abs/2016arXiv161005324M
25 Heidt 01 JHK http://www.aanda.org/articles/aa/pdf/2001/10/aa10227.pdf
26 Gutierrez 2012 R https://ui.adsabs.harvard.edu/#abs/2011AJ....142..145G/abstract
27 Mendez-Abreu 08 J https://ui.adsabs.harvard.edu/#abs/2008A&A...478..353M/abstract
28 S4G 15 http://adsabs.harvard.edu/abs/2015ApJS..219....4S
29 YOSHINO 08 VIJ http://pasj.oxfordjournals.org/content/60/3/493.full.pdf
30 Fisher 10 S4G http://iopscience.iop.org/article/10.1088/0004-637X/716/2/942/pdf
31 nan nan
32 HUBBLE nan

In [8]:
for img in os.listdir('..\\pics\\incl_summary\\'):
    display(Image('..\\pics\\incl_summary\\' + img))