In [1]:

    

%run ../../../utils/load_notebook.py


    

In [2]:

    

return_back_path = '../../notebooks/2f/test_short/'


    

In [3]:

    

import datetime
start = datetime.datetime.now()
start


    

    
Out[3]:





datetime.datetime(2017, 5, 3, 23, 41, 37, 476000)

In [4]:

    

%%time
from n338 import *


    

    




importing Jupyter notebook from n338.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib
importing Jupyter notebook from photometry.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib
importing Jupyter notebook from instabilities.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib
importing Jupyter notebook from utils.ipynb






    















    




Wall time: 873 ms
I : 1.15; B : 1.06; R: 1.24.
+------+-----------+---------+----------+------+---------+----------+-------+-------------+-----------+
|      | Name      |   r_eff |   mu_eff |    n |   mu0_d |   h_disc |   M/L | M_d/M_sun   |   Sigma_0 |
|------+-----------+---------+----------+------+---------+----------+-------+-------------+-----------|
| 0.00 | Noorder R |   15.00 |    20.95 | 3.70 |   21.92 |    18.30 |  1.24 | 9.43E+09.   |        53 |
| 1.00 | Noorder B |   15.00 |    22.70 | 3.70 |   22.53 |    17.70 |  1.06 | 1.14E+10.   |        68 |
| 2.00 | Noorder I |   15.00 |    20.41 | 3.70 |   19.79 |    12.90 |  1.15 | 2.27E+10.   |       255 |
+------+-----------+---------+----------+------+---------+----------+-------+-------------+-----------+





    




C:\Anaconda\lib\site-packages\matplotlib\axes\_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.
  warnings.warn("No labelled objects found. "






    




Noorder R      : M/L was 1.24 and for max it equal 20.91, for submax equal 10.42
Noorder B      : M/L was 1.06 and for max it equal 14.02, for submax equal 6.99
Noorder I      : M/L was 1.15 and for max it equal 6.23, for submax equal 3.10






    




C:\Anaconda\lib\site-packages\matplotlib\pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




Wall time: 7min 24s






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [5]:

    

name


    

    
Out[5]:





'N0338'

In [6]:

    

n338dict = dict(globals(), **locals())


    

In [7]:

    

n338dict['name']


    

    
Out[7]:





'N0338'

In [8]:

    

os.chdir(return_back_path)


    

In [9]:

    

%%time
from n1167 import *


    

    




importing Jupyter notebook from n1167.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib






    















    




B : 3.80; R: 2.71.
+------+----------------+---------+----------+------+---------+----------+-------+-------------+-----------+
|      | Name           |   r_eff |   mu_eff |    n |   mu0_d |   h_disc |   M/L | M_d/M_sun   |   Sigma_0 |
|------+----------------+---------+----------+------+---------+----------+-------+-------------+-----------|
| 0.00 | Noorder R      |    6.70 |    19.40 | 1.70 |   20.12 |    24.20 |  2.71 | 2.30E+11.   |       605 |
| 1.00 | Noorder B      |    6.70 |    20.73 | 1.70 |   21.71 |    27.50 |  3.80 | 2.55E+11.   |       520 |
| 2.00 | Noorder R_max  |    6.70 |    19.40 | 1.70 |   20.12 |    24.20 |  4.00 | 3.39E+11.   |       893 |
| 3.00 | califa g (g-i) |    7.57 |    21.47 | 2.21 |   21.29 |    24.45 |  5.59 | 3.89E+11.   |      1006 |
| 4.00 | califa r (g-r) |    9.09 |    20.70 | 2.66 |   20.60 |    25.82 |  3.88 | 3.04E+11.   |       703 |
| 5.00 | califa i (r-i) |   11.43 |    20.61 | 3.21 |   20.32 |    27.51 |  2.95 | 2.85E+11.   |       581 |
+------+----------------+---------+----------+------+---------+----------+-------+-------------+-----------+
Noorder R      : M/L was 2.71 and for max it equal 5.53, for submax equal 2.76
Noorder B      : M/L was 3.80 and for max it equal 7.93, for submax equal 3.95
Noorder R_max  : M/L was 4.00 and for max it equal 5.53, for submax equal 2.76
califa g (g-i) : M/L was 5.59 and for max it equal 6.80, for submax equal 3.39
califa r (g-r) : M/L was 3.88 and for max it equal 6.39, for submax equal 3.18
califa i (r-i) : M/L was 2.95 and for max it equal 5.52, for submax equal 2.75
Wall time: 22min 15s






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [10]:

    

name


    

    
Out[10]:





'N1167'

In [11]:

    

n1167dict = dict(globals(), **locals())


    

In [12]:

    

n1167dict['name']


    

    
Out[12]:





'N1167'

In [13]:

    

del n1167dict['n338dict']


    

In [14]:

    

os.chdir(return_back_path)


    

In [15]:

    

%%time
from n2985 import *


    

    




importing Jupyter notebook from n2985.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib






    















    




Abs B : 0.47; R: 0.75.
Rel B : 0.53; R: 0.81.
0.58 0.63
+------+------------------+---------+----------+--------+----------------+----------------+-------+-------------+-----------+
|      | Name             |   r_eff |   mu_eff |      n | mu0_d          | h_disc         |   M/L | M_d/M_sun   |   Sigma_0 |
|------+------------------+---------+----------+--------+----------------+----------------+-------+-------------+-----------|
| 0.00 | b:Noorder R      |   25.10 |    20.41 |   3.90 | 21.32          | 52.2           |  0.81 | 8.47E+09.   |        60 |
| 1.00 | b:Noorder B      |   25.10 |    21.86 |   3.90 | 22.06          | 57.6           |  0.53 | 9.08E+09.   |        53 |
| 2.00 | b:Mendez-Abreu J |   13.20 |    17.94 |   2.92 | 18.22          | 25.8           |  0.76 | 1.66E+10.   |       479 |
| 3.00 | Gutierrez R 2d   |  nan    |   nan    | nan    | (18.57, 21.76) | (18.1, 81.0)   |  0.75 | 2.44E+10.   |       732 |
| 4.00 | Heidt J          |   12.08 |    17.73 |   2.86 | 18.05          | 30.63          |  0.76 | 2.73E+10.   |       560 |
| 5.00 | Heidt K          |   14.06 |    17.23 |   3.03 | 17.32          | 31.08          |  0.65 | 3.39E+10.   |       674 |
| 6.00 | Heidt H          |   12.81 |    17.03 |   2.86 | 17.27          | 26.1           |  0.70 | 2.76E+10.   |       779 |
| 7.00 | S4G 2d           |    6.32 |   nan    |   2.82 | (18.55, 20.84) | (12.78, 48.89) |  0.67 | 3.42E+10.   |      1630 |
| 8.00 | S4G_AM 2d        |  nan    |   nan    | nan    | (18.28, 20.74) | (11.71, 47.23) |  0.67 | 3.58E+10.   |      2067 |
| 9.00 | Noorder R_max    |   25.10 |    20.41 |   3.90 | 21.32          | 52.2           |  6.00 | 6.29E+10.   |       444 |
+------+------------------+---------+----------+--------+----------------+----------------+-------+-------------+-----------+
b:Noorder R    : M/L was 0.81 and for max it equal 12.08, for submax equal 6.02
b:Noorder B    : M/L was 0.53 and for max it equal 8.18, for submax equal 4.08
b:Mendez-Abreu J: M/L was 0.76 and for max it equal 2.80, for submax equal 1.40
Gutierrez R 2d : M/L was 0.75 and for max it equal 2.48, for submax equal 1.24
Heidt J        : M/L was 0.76 and for max it equal 2.03, for submax equal 1.01
Heidt K        : M/L was 0.65 and for max it equal 1.42, for submax equal 0.71
Heidt H        : M/L was 0.70 and for max it equal 1.55, for submax equal 0.77
S4G 2d         : M/L was 0.67 and for max it equal 1.35, for submax equal 0.67
S4G_AM 2d      : M/L was 0.67 and for max it equal 1.18, for submax equal 0.59
Noorder R_max  : M/L was 6.00 and for max it equal 12.08, for submax equal 6.02
Wall time: 9min 52s






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [16]:

    

n2985dict = dict(globals(), **locals())


    

In [17]:

    

n2985dict['name']


    

    
Out[17]:





'N2985'

In [18]:

    

del n2985dict['n338dict']
del n2985dict['n1167dict']


    

In [19]:

    

os.chdir(return_back_path)


    

In [20]:

    

%%time
from n3898 import *


    

    




importing Jupyter notebook from n3898.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib






    















    













    













    













    













    













    




Wall time: 207 ms






    













    













    













    













    













    













    




3.51965422525
+------+-------------------+---------+----------+--------+----------------+---------------+-------+-------------+-----------+
|      | Name              |   r_eff |   mu_eff |      n | mu0_d          | h_disc        |   M/L | M_d/M_sun   |   Sigma_0 |
|------+-------------------+---------+----------+--------+----------------+---------------+-------+-------------+-----------|
| 0.00 | Noorder R         |    8.80 |    18.37 |   2.30 | 20.49          | 36.2          |  1.95 | 2.16E+10.   |       310 |
| 1.00 | Noorder B         |    8.80 |    19.80 |   2.30 | 22.0           | 42.9          |  2.22 | 2.28E+10.   |       233 |
| 2.00 | Mendez-Abreu J    |   11.90 |    18.13 |   3.75 | 19.07          | 29.2          |  1.16 | 1.51E+10.   |       332 |
| 3.00 | Gutierrez R (new) |  nan    |   nan    | nan    | (19.03, 21.53) | (19.11, 59.9) |  1.95 | 4.58E+10.   |      1310 |
| 4.00 | Pignatelli V      |   18.90 |    20.60 |   4.00 | 20.4           | 29.0          |  3.52 | 3.96E+10.   |       886 |
+------+-------------------+---------+----------+--------+----------------+---------------+-------+-------------+-----------+
Noorder R      : M/L was 1.95 and for max it equal 7.80, for submax equal 3.89
Noorder B      : M/L was 2.22 and for max it equal 10.20, for submax equal 5.08
Mendez-Abreu J : M/L was 1.16 and for max it equal 4.96, for submax equal 2.47
Gutierrez R (new): M/L was 1.95 and for max it equal 2.93, for submax equal 1.46
Pignatelli V   : M/L was 3.52 and for max it equal 5.68, for submax equal 2.83
Wall time: 7min 54s






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [21]:

    

n3898dict = dict(globals(), **locals())


    

In [22]:

    

n3898dict['name']


    

    
Out[22]:





'N3898'

In [23]:

    

del n3898dict['n338dict']
del n3898dict['n1167dict']
del n3898dict['n2985dict']


    

In [24]:

    

os.chdir(return_back_path)


    

In [25]:

    

%%time
from n4258 import *


    

    




importing Jupyter notebook from n4258.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib






    















    




19.9002390653
+------+--------------------+---------+----------+--------+---------+----------+-------+-------------+-----------+
|      | Name               |   r_eff |   mu_eff |      n |   mu0_d |   h_disc |   M/L | M_d/M_sun   |   Sigma_0 |
|------+--------------------+---------+----------+--------+---------+----------+-------+-------------+-----------|
| 0.00 | YOSHINO I          |   14.89 |    18.48 |   1.50 |   18.26 |    74.24 |  0.97 | 5.62E+10.   |       878 |
| 1.00 | YOSHINO V          |   10.34 |    18.57 |   1.50 |   19.54 |    78.55 |  1.74 | 6.93E+10.   |       967 |
| 2.00 | YOSHINO J          |    7.31 |    16.43 |   1.50 |   16.37 |    38.95 |  0.95 | 5.76E+10.   |      3271 |
| 3.00 | SPITZER 3.6        |   15.03 |    17.41 |   2.80 |   18.82 |    80.74 |  0.65 | 8.45E+10.   |      1116 |
| 4.00 | SPITZER 3.6 faceon |   15.03 |    17.41 |   2.80 |   19.90 |    80.74 |  0.65 | 3.12E+10.   |       413 |
| 5.00 | GALFIT K           |    6.27 |   nan    |   3.26 |   17.80 |   146.00 |  0.79 | 1.30E+11.   |       523 |
| 6.00 | S4G 3.6            |  nan    |   nan    | nan    |   20.50 |   178.76 |  0.65 | 8.80E+10.   |       237 |
+------+--------------------+---------+----------+--------+---------+----------+-------+-------------+-----------+
r = 9.88; gas_d = 170.26; epicycl = 828.67; sig = 119.96; star_d = 768.96
	Qs = 8.91; Qg = 2.15; Qeff = 2.10
r = 28.64; gas_d = 90.39; epicycl = 272.92; sig = 109.81; star_d = 597.24
	Qs = 3.46; Qg = 1.33; Qeff = 1.28
r = 45.43; gas_d = 35.24; epicycl = 212.11; sig = 93.27; star_d = 476.37
	Qs = 2.86; Qg = 2.66; Qeff = 2.36
r = 63.20; gas_d = 16.97; epicycl = 91.21; sig = 81.75; star_d = 374.95
	Qs = 1.37; Qg = 2.38; Qeff = 1.19
r = 80.98; gas_d = 14.67; epicycl = 82.19; sig = 81.75; star_d = 295.11
	Qs = 1.57; Qg = 2.48; Qeff = 1.35
r = 98.75; gas_d = 10.43; epicycl = 75.31; sig = 81.75; star_d = 232.28
	Qs = 1.83; Qg = 3.19; Qeff = 1.58
r = 116.53; gas_d = 10.96; epicycl = 63.68; sig = 81.75; star_d = 182.82
	Qs = 1.96; Qg = 2.57; Qeff = 1.66
YOSHINO I                : M/L was 0.97 and for max it equal 0.82, for submax equal 0.41
YOSHINO V                : M/L was 1.74 and for max it equal 1.22, for submax equal 0.61
YOSHINO J                : M/L was 0.95 and for max it equal 0.53, for submax equal 0.27
SPITZER 3.6              : M/L was 0.65 and for max it equal 0.38, for submax equal 0.19
SPITZER 3.6 faceon       : M/L was 0.65 and for max it equal 1.03, for submax equal 0.51
GALFIT K                 : M/L was 0.79 and for max it equal 0.51, for submax equal 0.26
S4G 3.6                  : M/L was 0.65 and for max it equal 0.80, for submax equal 0.40
Wall time: 20min 32s






    




C:\Anaconda\lib\site-packages\matplotlib\axis.py:1015: UserWarning: Unable to find pixel distance along axis for interval padding of ticks; assuming no interval padding needed.
  warnings.warn("Unable to find pixel distance along axis "
C:\Anaconda\lib\site-packages\matplotlib\axis.py:1025: UserWarning: Unable to find pixel distance along axis for interval padding of ticks; assuming no interval padding needed.
  warnings.warn("Unable to find pixel distance along axis "






    




Error in callback <function post_execute at 0x000000000A48AF98> (for post_execute):






    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
C:\Anaconda\lib\site-packages\matplotlib\pyplot.pyc in post_execute()
    145             def post_execute():
    146                 if matplotlib.is_interactive():
--> 147                     draw_all()
    148 
    149             # IPython >= 2

C:\Anaconda\lib\site-packages\matplotlib\_pylab_helpers.pyc in draw_all(cls, force)
    148         for f_mgr in cls.get_all_fig_managers():
    149             if force or f_mgr.canvas.figure.stale:
--> 150                 f_mgr.canvas.draw_idle()
    151 
    152 atexit.register(Gcf.destroy_all)

C:\Anaconda\lib\site-packages\matplotlib\backend_bases.pyc in draw_idle(self, *args, **kwargs)
   2024         if not self._is_idle_drawing:
   2025             with self._idle_draw_cntx():
-> 2026                 self.draw(*args, **kwargs)
   2027 
   2028     def draw_cursor(self, event):

C:\Anaconda\lib\site-packages\matplotlib\backends\backend_agg.pyc in draw(self)
    472 
    473         try:
--> 474             self.figure.draw(self.renderer)
    475         finally:
    476             RendererAgg.lock.release()

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\figure.pyc in draw(self, renderer)
   1157         dsu.sort(key=itemgetter(0))
   1158         for zorder, a, func, args in dsu:
-> 1159             func(*args)
   1160 
   1161         renderer.close_group('figure')

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\axes\_base.pyc in draw(self, renderer, inframe)
   2322 
   2323         for zorder, a in dsu:
-> 2324             a.draw(renderer)
   2325 
   2326         renderer.close_group('axes')

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\axis.pyc in draw(self, renderer, *args, **kwargs)
   1118         self._update_label_position(ticklabelBoxes, ticklabelBoxes2)
   1119 
-> 1120         self.label.draw(renderer)
   1121 
   1122         self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2)

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\text.pyc in draw(self, renderer)
    755             posy = float(textobj.convert_yunits(textobj._y))
    756             if not np.isfinite(posx) or not np.isfinite(posy):
--> 757                 raise ValueError("posx and posy should be finite values")
    758             posx, posy = trans.transform_point((posx, posy))
    759             canvasw, canvash = renderer.get_canvas_width_height()

ValueError: posx and posy should be finite values





    













    













    













    













    













    













    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
C:\Anaconda\lib\site-packages\IPython\core\formatters.pyc in __call__(self, obj)
    305                 pass
    306             else:
--> 307                 return printer(obj)
    308             # Finally look for special method names
    309             method = get_real_method(obj, self.print_method)

C:\Anaconda\lib\site-packages\IPython\core\pylabtools.pyc in <lambda>(fig)
    225 
    226     if 'png' in formats:
--> 227         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    228     if 'retina' in formats or 'png2x' in formats:
    229         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

C:\Anaconda\lib\site-packages\IPython\core\pylabtools.pyc in print_figure(fig, fmt, bbox_inches, **kwargs)
    117 
    118     bytes_io = BytesIO()
--> 119     fig.canvas.print_figure(bytes_io, **kw)
    120     data = bytes_io.getvalue()
    121     if fmt == 'svg':

C:\Anaconda\lib\site-packages\matplotlib\backend_bases.pyc in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
   2178                     orientation=orientation,
   2179                     dryrun=True,
-> 2180                     **kwargs)
   2181                 renderer = self.figure._cachedRenderer
   2182                 bbox_inches = self.figure.get_tightbbox(renderer)

C:\Anaconda\lib\site-packages\matplotlib\backends\backend_agg.pyc in print_png(self, filename_or_obj, *args, **kwargs)
    525 
    526     def print_png(self, filename_or_obj, *args, **kwargs):
--> 527         FigureCanvasAgg.draw(self)
    528         renderer = self.get_renderer()
    529         original_dpi = renderer.dpi

C:\Anaconda\lib\site-packages\matplotlib\backends\backend_agg.pyc in draw(self)
    472 
    473         try:
--> 474             self.figure.draw(self.renderer)
    475         finally:
    476             RendererAgg.lock.release()

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\figure.pyc in draw(self, renderer)
   1157         dsu.sort(key=itemgetter(0))
   1158         for zorder, a, func, args in dsu:
-> 1159             func(*args)
   1160 
   1161         renderer.close_group('figure')

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\axes\_base.pyc in draw(self, renderer, inframe)
   2322 
   2323         for zorder, a in dsu:
-> 2324             a.draw(renderer)
   2325 
   2326         renderer.close_group('axes')

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\axis.pyc in draw(self, renderer, *args, **kwargs)
   1118         self._update_label_position(ticklabelBoxes, ticklabelBoxes2)
   1119 
-> 1120         self.label.draw(renderer)
   1121 
   1122         self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2)

C:\Anaconda\lib\site-packages\matplotlib\artist.pyc in draw_wrapper(artist, renderer, *args, **kwargs)
     59     def draw_wrapper(artist, renderer, *args, **kwargs):
     60         before(artist, renderer)
---> 61         draw(artist, renderer, *args, **kwargs)
     62         after(artist, renderer)
     63 

C:\Anaconda\lib\site-packages\matplotlib\text.pyc in draw(self, renderer)
    755             posy = float(textobj.convert_yunits(textobj._y))
    756             if not np.isfinite(posx) or not np.isfinite(posy):
--> 757                 raise ValueError("posx and posy should be finite values")
    758             posx, posy = trans.transform_point((posx, posy))
    759             canvasw, canvash = renderer.get_canvas_width_height()

ValueError: posx and posy should be finite values





    






<matplotlib.figure.Figure at 0x1d5ec4e0>






    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [26]:

    

n4258dict = dict(globals(), **locals())


    

In [27]:

    

n4258dict['name']


    

    
Out[27]:





'N4258'

In [28]:

    

del n4258dict['n338dict']
del n4258dict['n1167dict']
del n4258dict['n2985dict']
del n4258dict['n3898dict']


    

In [29]:

    

os.chdir(return_back_path)


    

In [30]:

    

%%time
from n4725 import *


    

    




importing Jupyter notebook from n4725.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib






    















    













    













    




+------+-------------------+---------+----------+--------+---------+----------+-------+-------------+-----------+
|      | Name              |   r_eff |   mu_eff |      n |   mu0_d |   h_disc |   M/L | M_d/M_sun   |   Sigma_0 |
|------+-------------------+---------+----------+--------+---------+----------+-------+-------------+-----------|
| 0.00 | S4G 3.6           |   10.14 |   nan    |   2.21 |   20.34 |    73.20 |  0.68 | 9.26E+10.   |       286 |
| 1.00 | Heidt J           |  nan    |   nan    | nan    |   17.78 |    49.99 |  0.75 | 1.06E+11.   |       703 |
| 2.00 | Heidt H           |  nan    |   nan    | nan    |   17.11 |    50.28 |  0.69 | 1.36E+11.   |       891 |
| 3.00 | Heidt K           |  nan    |   nan    | nan    |   17.01 |    54.66 |  0.65 | 1.60E+11.   |       889 |
| 4.00 | infra 3.6         |   15.72 |    17.49 |   3.61 |   19.65 |    71.86 |  0.68 | 1.68E+11.   |       539 |
| 5.00 | infra 3.6 face-on |   15.72 |    17.49 |   3.61 |   20.03 |    71.86 |  0.68 | 1.18E+11.   |       379 |
+------+-------------------+---------+----------+--------+---------+----------+-------+-------------+-----------+
316.386554622
41.1302521008
17.8888888889
2.16129032258
2.2905027933
S4G 3.6        : M/L was 0.68 and for max it equal 1.61, for submax equal 0.80
Heidt J        : M/L was 0.75 and for max it equal 0.94, for submax equal 0.47
Heidt H        : M/L was 0.69 and for max it equal 0.68, for submax equal 0.34
Heidt K        : M/L was 0.65 and for max it equal 0.61, for submax equal 0.31
infra 3.6      : M/L was 0.68 and for max it equal 0.87, for submax equal 0.43
infra 3.6 face-on: M/L was 0.68 and for max it equal 1.24, for submax equal 0.62
Wall time: 8min 59s






    













    













    













    













    













    













    













    













    













    













    













    













    

In [31]:

    

n4725dict = dict(globals(), **locals())


    

In [32]:

    

n4725dict['name']


    

    
Out[32]:





'N4725'

In [33]:

    

del n4725dict['n338dict']
del n4725dict['n1167dict']
del n4725dict['n2985dict']
del n4725dict['n3898dict']
del n4725dict['n4258dict']


    

In [34]:

    

os.chdir(return_back_path)


    

In [35]:

    

%%time
from n5533 import *


    

    




importing Jupyter notebook from n5533.ipynb
Using matplotlib backend: Qt4Agg
Populating the interactive namespace from numpy and matplotlib






    















    




Abs B : 2.34; R: 2.01.
Rel B : 2.52; R: 2.11.
1.21 1.24
+------+----------------+---------+----------+--------+---------+----------+-------+-------------+-----------+
|      | Name           |   r_eff |   mu_eff |      n |   mu0_d |   h_disc |   M/L | M_d/M_sun   |   Sigma_0 |
|------+----------------+---------+----------+--------+---------+----------+-------+-------------+-----------|
| 0.00 | Noorder R      |    9.90 |    19.75 |   2.70 |   21.27 |    34.40 |  2.11 | 8.52E+10.   |       163 |
| 1.00 | Noorder B      |    9.90 |    21.45 |   2.70 |   22.39 |    32.40 |  2.52 | 8.56E+10.   |       185 |
| 2.00 | Noorder R_max  |    9.90 |    19.75 |   2.70 |   21.27 |    34.40 |  5.00 | 2.02E+11.   |       387 |
| 3.00 | Mendez-Abreu J |    5.10 |    17.70 |   2.34 |   18.03 |    14.10 |  1.18 | 7.70E+10.   |       878 |
| 4.00 | 1991 R         |  nan    |    17.64 | nan    |   20.73 |    43.02 |  2.01 | 2.09E+11.   |       256 |
| 5.00 | 1998 V         |   19.60 |    21.62 |   4.00 |   22.43 |    40.70 |  2.26 | 6.41E+10.   |        88 |
| 6.00 | Silchenko V    |  nan    |   nan    | nan    |   20.20 |    18.00 |  2.26 | 9.78E+10.   |       684 |
| 7.00 | califa g (r-i) |    8.80 |    20.87 |   3.33 |   21.42 |    28.17 |  3.82 | 2.15E+11.   |       613 |
| 8.00 | califa r (g-i) |    8.88 |    20.01 |   3.17 |   20.72 |    28.01 |  2.91 | 1.63E+11.   |       472 |
| 9.00 | califa i (g-r) |    9.72 |    19.75 |   3.21 |   20.37 |    28.01 |  2.35 | 1.52E+11.   |       441 |
+------+----------------+---------+----------+--------+---------+----------+-------+-------------+-----------+
96.426
89.1
87.12
Noorder R      : M/L was 2.11 and for max it equal 7.15, for submax equal 3.56
Noorder B      : M/L was 2.52 and for max it equal 8.15, for submax equal 4.06
Noorder R_max  : M/L was 5.00 and for max it equal 7.15, for submax equal 3.56
Mendez-Abreu J : M/L was 1.18 and for max it equal 2.06, for submax equal 1.03
1991 R         : M/L was 2.01 and for max it equal 3.27, for submax equal 1.63
1998 V         : M/L was 2.26 and for max it equal 11.53, for submax equal 5.75
Silchenko V    : M/L was 2.26 and for max it equal 3.97, for submax equal 1.98
califa g (r-i) : M/L was 3.82 and for max it equal 4.42, for submax equal 2.20
califa r (g-i) : M/L was 2.91 and for max it equal 4.41, for submax equal 2.20
califa i (g-r) : M/L was 2.35 and for max it equal 3.81, for submax equal 1.90
[ 38.34400115]
[ 24.67098717]
Wall time: 1min 20s






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [36]:

    

n5533dict = dict(globals(), **locals())


    

In [37]:

    

n5533dict['name']


    

    
Out[37]:





'N5533'

In [38]:

    

del n5533dict['n338dict']
del n5533dict['n1167dict']
del n5533dict['n2985dict']
del n5533dict['n3898dict']
del n5533dict['n4258dict']
del n5533dict['n4725dict']


    

In [39]:

    

end = datetime.datetime.now()
end


    

    
Out[39]:





datetime.datetime(2017, 5, 4, 1, 2, 41, 26000)

In [40]:

    

finish = end-start


    

In [41]:

    

finish.total_seconds()/3600.


    

    
Out[41]:





1.3509861111111112

In [ ]:

    




    

In [ ]:

    




    

In [42]:

    

# %%time
# for ind, name1 in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
#     for name2 in ['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533'][ind+1:]:
#         dict1 = locals()[name1+'dict']
#         dict2 = locals()[name2+'dict']
#         for key in dict1.keys():
#             if key in dict2.keys():
#                 try:
#                     if type(dict1[key]) == type(dict2[key]) == np.ndarray:
#                         if (dict1[key] == dict2[key]).all():
#                             del dict2[key]
#                     elif dict1[key] == dict2[key]:
#                         del dict2[key]
#                 except Exception:
#                     print key, name1, name2


    

In [ ]:

    




    

In [43]:

    

n338dict['star_approx'] == n2985dict['star_approx']


    

    
Out[43]:





False

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [44]:

    

tex_imgs_dir = 'C:\\Users\\root\\Dropbox\\RotationCurves\\PhD\\paper2\\imgs'


    

In [45]:

    

os.chdir(tex_imgs_dir)

fig, axs = plt.subplots(nrows=7, ncols=3, sharex=False, sharey=False, figsize=[16,30])

names  = [r'$\rm{NGC\, 338}$', r'$\rm{NGC\, 1167}$', r'$\rm{NGC\, 2985}$', r'$\rm{NGC\, 3898}$', r'$\rm{NGC\, 4258}$', r'$\rm{NGC\, 4725}$', r'$\rm{NGC\, 5533}$',]

ax1 = axs[0,0]
ax2 = axs[1,0]
ax3 = axs[2,0]
ax4 = axs[3,0]
ax5 = axs[4,0]
ax6 = axs[5,0]
ax7 = axs[6,0]

axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7]

# ===================================================
# Дисперии
# ===================================================

for ind, name in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
    ax = axes[ind]
    try:
        ax.errorbar(map(abs, locals()[name+'dict']['r_sig_ma']), locals()[name+'dict']['sig_ma'], yerr=locals()[name+'dict']['e_sig_ma'], 
                    fmt='.', marker='.', mew=0, color='red', label='$\sigma_{los}^{maj}$')
        ax.plot(points, map(locals()[name+'dict']['sig_R_maj_min'], locals()[name+'dict']['points']))
        ax.plot(points, map(locals()[name+'dict']['sig_R_maj_max'], locals()[name+'dict']['points']))
#         ax.plot(points, map(locals()[name+'dict']['sig_R_maj_maxmaxtrue'], locals()[name+'dict']['points']))
    except Exception:
        print 'WARNING S:{}'.format(name)

    ax.xaxis.set_major_locator(MultipleLocator(10))
    ax.xaxis.set_minor_locator(MultipleLocator(2))
#     ax.plot([reb, reb],[0, (50 - 30*(ind/2))], color='black', lw=2)
#     ax.axvline(x=reb, ls='--', color='black')
    ax.text(0.8, 0.9, names[ind], fontsize=20, ha='center', va='center', transform=ax.transAxes)
    ax.yaxis.set_label_coords(-0.055, 0.5)
    ax.set_ylabel(r'$\sigma_{\rm{los}},\, \rm{km/s}$', fontsize=20)
    ax.set_xlabel(r'$R,\, \rm{arcsec}$', fontsize=20)
    ax.yaxis.set_major_locator(MultipleLocator(100))

    
# ===================================================
# Кривая вращения
# ===================================================

ax1 = axs[0,1]
ax2 = axs[1,1]
ax3 = axs[2,1]
ax4 = axs[3,1]
ax5 = axs[4,1]
ax6 = axs[5,1]
ax7 = axs[6,1]

axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7]

for ind, name in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
    ax = axes[ind]
    try:
        ax.plot(points, map(locals()[name+'dict']['spl_gas'], locals()[name+'dict']['points']), '--')
        ax.plot(locals()[name+'dict']['r_g_b'], locals()[name+'dict']['vel_g_b'], '.')
    except Exception:
        print 'WARNING V:{}'.format(name)
        
    try:
        ax.plot(locals()[name+'dict']['r_wsrt'], locals()[name+'dict']['vel_wsrt'], '.')
    except Exception:
        print 'WARNING V:{}'.format(name)
        
    try:
        ax.plot(locals()[name+'dict']['r_noord'], locals()[name+'dict']['vel_noord'], '.')
        ax.plot(locals()[name+'dict']['r_ma_n'], locals()[name+'dict']['vel_ma_n'], 's')
    except Exception:
        print 'WARNING V:{}'.format(name)    
        
    
    ax.text(0.8, 0.2, names[ind], fontsize=20, ha='center', va='center', transform=ax.transAxes)
    ax.yaxis.set_label_coords(-0.055, 0.5)
    ax.set_ylabel(r'$V,\, \rm{km/s}$', fontsize=20)
    ax.set_xlabel(r'$R,\, \rm{arcsec}$', fontsize=20)
    ax.set_ylim(0, 400)
    ax.xaxis.set_major_locator(MultipleLocator(10))
#     ax.xaxis.set_minor_locator(MultipleLocator(2))
    ax.yaxis.set_major_locator(MultipleLocator(100))
    
# ax2.set_ylim(0, 400)


# ===================================================
# Поверхностная плотность
# ===================================================
    
ax1 = axs[0,2]
ax2 = axs[1,2]
ax3 = axs[2,2]
ax4 = axs[3,2]
ax5 = axs[4,2]
ax6 = axs[5,2]
ax7 = axs[6,2]

axes = [ax1, ax2, ax3, ax4, ax5, ax6, ax7]



#     axes[3].plot(r_g_dens, gas_dens, 'd-')
#     axes[3].plot(r_g_dens, [He_coeff*(y_interp_(l[0], h_disc_R) + l[1]) for l in zip(r_g_dens, gas_dens)], '*-')
#     axes[3].plot(r_g_dens, [y_interp_(l, h_disc_R) for l in r_g_dens], '--', label='H2 (R-photom)')
#     axes[3].set_title('Gas')
#     axes[3].grid()
#     axes[3].set_xlim(0, 200)
#     axes[3].legend()
    
#     axes[3].plot(r_g_dens, gas_dens, 'd-')
#     axes[3].plot(r_g_dens, [He_coeff*(y_interp_(l[0], h_disc_I) + l[1]) for l in zip(r_g_dens, gas_dens)], '*-')
#     axes[3].plot(r_g_dens, [y_interp_(l, h_disc_I) for l in r_g_dens], '--', label='H2 (I-photom)')
#     axes[3].set_title('Gas')
#     axes[3].grid()
#     axes[3].set_xlim(0, 200)
#     axes[3].legend()
    
#     axes[3].plot(r_HI_dens, HI_dens, '--', label='HI')
#     axes[3].plot(zip(*total_gas_data)[0], zip(*total_gas_data)[1], '*-')
#     axes[3].plot(r_mol_dens, mol_dens, '--', label='mol')
#     axes[3].set_title('Gas')
#     axes[3].grid()
#     axes[3].set_xlim(0, 200)
#     axes[3].legend()


for ind, name in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
    ax = axes[ind]
    try:
        ax.plot(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'], 'o-')
    except Exception:
        print 'WARNING G:{}'.format(name)
        
    try:
        ax.plot(locals()[name+'dict']['r_HI_dens'], locals()[name+'dict']['HI_dens'], 'o-')
    except Exception:
        print 'WARNING G:{}'.format(name)
        
    try:
        ax.plot(zip(*locals()[name+'dict']['total_gas_data'])[0], zip(*locals()[name+'dict']['total_gas_data'])[1], 'o-')
    except Exception:
        print 'WARNING G:{}'.format(name)
        
    try:
        ax.plot(zip(*locals()[name+'dict']['total_gas_data_'])[0], zip(*locals()[name+'dict']['total_gas_data_'])[1], 'o-')
    except Exception:
        print 'WARNING G:{}'.format(name)

    ax.xaxis.set_major_locator(MultipleLocator(100))
    ax.xaxis.set_minor_locator(MultipleLocator(50))
#     ax.plot([reb, reb],[0, (50 - 30*(ind/2))], color='black', lw=2)
#     ax.axvline(x=reb, ls='--', color='black')
    ax.text(0.8, 0.9, names[ind], fontsize=20, ha='center', va='center', transform=ax.transAxes)
    ax.yaxis.set_label_coords(-0.055, 0.5)
    ax.set_ylabel(r'$\Sigma_{\rm{los}},\, \rm{M_{sun}/{pc}^2}$', fontsize=20)
    ax.set_xlabel(r'$R,\, \rm{arcsec}$', fontsize=20)
    ax.yaxis.set_major_locator(MultipleLocator(10))
    ax.yaxis.set_minor_locator(MultipleLocator(1))
    if name == 'n4258':
        ax.set_ylim(0, 30)

# ax1.set_xlim(0, 45)
# ax1.set_ylim(0)    

# ax2.set_ylim(0, 240)
# ax2.set_xlim(0, 45)
# # ax2.set_ylabel(r'$\sigma_{\rm{los}},\, \rm{km/s}$', fontsize=15)

# ax3.set_ylim(0, 225)
# ax3.set_xlim(0, 60)
# # ax3.axes.yaxis.set_ticklabels([])

# # ax4.set_ylim(0, 150)
# # ax4.set_xlim(0, 72)
# # ax4.set_ylabel(r'$\sigma_{\rm{los}},\, \rm{km/s}$', fontsize=10)
# # ax4.set_xlabel(r'$R,\, \rm{arcsec}$', fontsize=15)

# ax4.set_ylim(0, 110)
# # ax5.set_xlim(0, 50)
# # ax5.axes.yaxis.set_ticklabels([])
# # ax5.set_ylabel(r'$\sigma_{\rm{gas}},\, \rm{km/s}$', fontsize=20)
# # ax5.set_xlabel(r'$R,\, \rm{arcsec}$', fontsize=20)


fig.subplots_adjust(wspace=0.15, hspace=0.25)
# plt.savefig('observ_data.eps', format='eps')
# plt.savefig('observ_data.png', format='png')
# plt.savefig('observ_data.pdf', format='pdf', dpi=150)
plt.show()


    

    




WARNING V:n338
WARNING G:n338
WARNING G:n1167
WARNING G:n2985
WARNING G:n3898






    

In [ ]:

    




    

In [ ]:

    




    

In [46]:

    

def plot_kennicutt(ax=None, total_gas_data=None, epicycl=None, gas_approx=None, sound_vel=None, scale=None, sigma_max=None, sigma_min=None, star_density_max=None, 
                  star_density_min=None, data_lim=None, color=None, alpha=0.3, disk_scales=[], label=None, sfrange=None):

    invQg, invQs, invQeff_min = zip(*get_invQeff_from_data(gas_data=total_gas_data, 
                                    epicycl=epicycl, 
                                    gas_approx=gas_approx,
                                    sound_vel=sound_vel, 
                                    scale=scale,
                                    sigma=sigma_max,
                                    star_density=star_density_min))

    invQg, invQs, invQeff_max = zip(*get_invQeff_from_data(gas_data=total_gas_data, 
                                    epicycl=epicycl, 
                                    gas_approx=gas_approx,
                                    sound_vel=sound_vel, 
                                    scale=scale,
                                    sigma=sigma_min,
                                    star_density=star_density_max))
   
    rr = zip(*total_gas_data)[0]
    
#     ax.fill_between(rr, invQeff_min, invQeff_max, color=color, alpha=alpha, label=label)
    ax.plot(np.array(rr)/sfrange, invQeff_min, '.-', color=color, alpha=0.8)
    ax.plot(np.array(rr)/sfrange, invQeff_max, '--', color=color, alpha=0.8)
#     ax.plot(rr, invQg, 'v-', color='b')

#     ax.set_ylim(0., 1.5)
#     ax.set_xlim(0., data_lim+50.)
#     plot_SF(ax)
#     plot_data_lim(ax, data_lim)
#     for h, annot in disk_scales:
#         plot_disc_scale(h, ax, annot)
#     plot_Q_levels(ax, [1., 1.5, 2., 3.])
#     ax.legend()


    

In [47]:

    

kenn_photom={'n338' : ['mu0d_Ic', 'h_disc_I', 6.23, 'I', 60.],
            'n1167' : ['mu0d_Rc', 'h_disc_R', 5.53, 'R', 40.],
            'n2985' : ['mudK', 'hK', 1.42, 'K', 70.],
            'n3898' : ['mu0d_R', 'h_disc_R', 7.80, 'R', 80.],
            'n4258' : ['mu0d_I', 'h_disc_I', 0.82, 'I', 150.],
            'n4725' : ['mu0d_H', 'h_disc_H', 0.68, 'H', 55.],
            'n5533' : ['MU0_r', 'hi_r', 4.41, 'r', 100.]}

fig = plt.figure(figsize=[16, 6])

for ind, name in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
    ax = plt.gca()
    
    color = cm.rainbow(np.linspace(0, 1, 7))[ind]
    
    phot = kenn_photom[name]
    mud = locals()[name+'dict'][phot[0]]
    h = locals()[name+'dict'][phot[1]]
#     print name
#     print mud
#     print h
    
    if name in ['n4258', 'n4725']:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], map(lambda l: l[0], zip(locals()[name+'dict']['gas_dens'])))[1:15]
    elif name == 'n2985':
        total_gas_data=locals()[name+'dict']['total_gas_data_']
    else:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], [He_coeff*(locals()[name+'dict']['y_interp_'](l[0], h) + l[1]) for l in 
                                                                   zip(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'])])[1:15]
            
            
    if total_gas_data[0][0] < 0.1:
        total_gas_data = total_gas_data[1:]
    
#     print total_gas_data

    plot_kennicutt(ax=ax, total_gas_data=total_gas_data,
                      epicycl=locals()[name+'dict']['epicyclicFreq_real'], 
                      gas_approx=locals()[name+'dict']['spl_gas'], 
                      sound_vel=locals()[name+'dict']['sound_vel'], 
                      scale=locals()[name+'dict']['scale'], 
                      sigma_max=locals()[name+'dict']['sig_R_maj_max'], 
                      sigma_min=locals()[name+'dict']['sig_R_maj_min'],
                      star_density_max=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      star_density_min=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      data_lim=locals()[name+'dict']['data_lim'], 
                  color=color, alpha=0.2, 
                  label=name+' '+phot[3],
                  sfrange=phot[4])
    plt.plot([-1, -2], [-1, -2], '-', color=color, label=name+' '+phot[3])
    
plt.axvline(x=1, alpha=0.5)
plot_Q_levels(ax, [1.5, 2., 3.])
plt.legend()
plt.xlim(0, 3)
plt.ylim(0, 1.3)
plt.ylabel(r'$\Sigma_g/\Sigma_{cr}$', fontsize=20)
plt.xlabel(r'$R/R_{SF}$', fontsize=20)
plt.show()


    

In [54]:

    

def plot_kennicutt_half(ax=None, total_gas_data=None, epicycl=None, gas_approx=None, sound_vel=None, scale=None, sigma_max=None, sigma_min=None, star_density_max=None, 
                  star_density_min=None, data_lim=None, color=None, alpha=0.3, disk_scales=[], label=None, sfrange=None):

    invQg, invQs, invQeff_min = zip(*get_invQeff_from_data(gas_data=total_gas_data, 
                                    epicycl=epicycl, 
                                    gas_approx=gas_approx,
                                    sound_vel=sound_vel, 
                                    scale=scale,
                                    sigma=sigma_max,
                                    star_density=star_density_min))

    invQg, invQs, invQeff_max = zip(*get_invQeff_from_data(gas_data=total_gas_data, 
                                    epicycl=epicycl, 
                                    gas_approx=gas_approx,
                                    sound_vel=sound_vel, 
                                    scale=scale,
                                    sigma=sigma_min,
                                    star_density=star_density_max))
   
    rr = zip(*total_gas_data)[0]
    ax.plot(np.array(rr)/sfrange, (np.array(invQeff_min)+np.array(invQeff_max))/2., '.-', color=color, alpha=0.8)


    

In [55]:

    

kenn_photom={'n338' : ['mu0d_Ic', 'h_disc_I', 6.23, 'I', 60.],
            'n1167' : ['mu0d_Rc', 'h_disc_R', 5.53, 'R', 40.],
            'n2985' : ['mudK', 'hK', 1.42, 'K', 70.],
            'n3898' : ['mu0d_R', 'h_disc_R', 7.80, 'R', 80.],
            'n4258' : ['mu0d_I', 'h_disc_I', 0.82, 'I', 150.],
            'n4725' : ['mu0d_H', 'h_disc_H', 0.68, 'H', 55.],
            'n5533' : ['MU0_r', 'hi_r', 4.41, 'r', 100.]}

fig = plt.figure(figsize=[16, 6])

for ind, name in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
    ax = plt.gca()
    
    color = cm.rainbow(np.linspace(0, 1, 7))[ind]
    
    phot = kenn_photom[name]
    mud = locals()[name+'dict'][phot[0]]
    h = locals()[name+'dict'][phot[1]]
#     print name
#     print mud
#     print h
    
    if name in ['n4258', 'n4725']:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], map(lambda l: l[0], zip(locals()[name+'dict']['gas_dens'])))[1:15]
    elif name == 'n2985':
        total_gas_data=locals()[name+'dict']['total_gas_data_']
    else:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], [He_coeff*(locals()[name+'dict']['y_interp_'](l[0], h) + l[1]) for l in 
                                                                   zip(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'])])[1:15]
            
            
    if total_gas_data[0][0] < 0.1:
        total_gas_data = total_gas_data[1:]
    
#     print total_gas_data

    plot_kennicutt_half(ax=ax, total_gas_data=total_gas_data,
                      epicycl=locals()[name+'dict']['epicyclicFreq_real'], 
                      gas_approx=locals()[name+'dict']['spl_gas'], 
                      sound_vel=locals()[name+'dict']['sound_vel'], 
                      scale=locals()[name+'dict']['scale'], 
                      sigma_max=locals()[name+'dict']['sig_R_maj_max'], 
                      sigma_min=locals()[name+'dict']['sig_R_maj_min'],
                      star_density_max=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      star_density_min=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      data_lim=locals()[name+'dict']['data_lim'], 
                  color=color, alpha=0.2, 
                  label=name+' '+phot[3],
                  sfrange=phot[4])
    plt.plot([-1, -2], [-1, -2], '-', color=color, label=name+' '+phot[3])
    
plt.axvline(x=1, alpha=0.5)
plot_Q_levels(ax, [1.5, 2., 3.])
plt.legend()
plt.xlim(0, 3)
plt.ylim(0, 1.3)
plt.ylabel(r'$\Sigma_g/\Sigma_{cr}$', fontsize=20)
plt.xlabel(r'$R/R_{SF}$', fontsize=20)
plt.show()


    

In [58]:

    

kenn_photom={'n338' : ['mu0d_Ic', 'h_disc_I', 6.23, 'I', 60.],
            'n1167' : ['mu0d_Rc', 'h_disc_R', 5.53, 'R', 40.],
            'n2985' : ['mudK', 'hK', 1.42, 'K', 70.],
            'n3898' : ['mu0d_R', 'h_disc_R', 7.80, 'R', 80.],
            'n4258' : ['mu0d_I', 'h_disc_I', 0.82, 'I', 150.],
            'n4725' : ['mu0d_H', 'h_disc_H', 0.68, 'H', 55.],
            'n5533' : ['MU0_r', 'hi_r', 4.41, 'r', 100.]}

fig = plt.figure(figsize=[16, 6])

for ind, name in enumerate(['n2985', 'n4258', 'n4725']):
    ax = plt.gca()
    
    color = cm.rainbow(np.linspace(0, 1, 7))[ind]
    
    phot = kenn_photom[name]
    mud = locals()[name+'dict'][phot[0]]
    h = locals()[name+'dict'][phot[1]]
#     print name
#     print mud
#     print h
    
    if name in ['n4258', 'n4725']:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], map(lambda l: l[0], zip(locals()[name+'dict']['gas_dens'])))[1:15]
    elif name == 'n2985':
        total_gas_data=locals()[name+'dict']['total_gas_data_']
    else:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], [He_coeff*(locals()[name+'dict']['y_interp_'](l[0], h) + l[1]) for l in 
                                                                   zip(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'])])[1:15]
            
            
    if total_gas_data[0][0] < 0.1:
        total_gas_data = total_gas_data[1:]
    
#     print total_gas_data

    plot_kennicutt_half(ax=ax, total_gas_data=total_gas_data,
                      epicycl=locals()[name+'dict']['epicyclicFreq_real'], 
                      gas_approx=locals()[name+'dict']['spl_gas'], 
                      sound_vel=locals()[name+'dict']['sound_vel'], 
                      scale=locals()[name+'dict']['scale'], 
                      sigma_max=locals()[name+'dict']['sig_R_maj_max'], 
                      sigma_min=locals()[name+'dict']['sig_R_maj_min'],
                      star_density_max=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      star_density_min=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      data_lim=locals()[name+'dict']['data_lim'], 
                  color=color, alpha=0.2, 
                  label=name+' '+phot[3],
                  sfrange=phot[4])
    plt.plot([-1, -2], [-1, -2], '-', color=color, label=name+' '+phot[3])
    
plt.axvline(x=1, alpha=0.5)
plot_Q_levels(ax, [1.5, 2., 3.])
plt.legend()
plt.xlim(0, 3)
plt.ylim(0, 1.3)
plt.ylabel(r'$\Sigma_g/\Sigma_{cr}$', fontsize=20)
plt.xlabel(r'$R/R_{SF}$', fontsize=20)
plt.show()


    

In [59]:

    

kenn_photom={'n338' : ['mu0d_Ic', 'h_disc_I', 6.23, 'I', 60.],
            'n1167' : ['mu0d_Rc', 'h_disc_R', 5.53, 'R', 40.],
            'n2985' : ['mudK', 'hK', 1.42, 'K', 70.],
            'n3898' : ['mu0d_R', 'h_disc_R', 7.80, 'R', 80.],
            'n4258' : ['mu0d_I', 'h_disc_I', 0.82, 'I', 150.],
            'n4725' : ['mu0d_H', 'h_disc_H', 0.68, 'H', 100.],
            'n5533' : ['MU0_r', 'hi_r', 4.41, 'r', 100.]}

fig = plt.figure(figsize=[16, 6])

for ind, name in enumerate(['n2985', 'n4258', 'n4725']):
    ax = plt.gca()
    
    color = cm.rainbow(np.linspace(0, 1, 7))[ind]
    
    phot = kenn_photom[name]
    mud = locals()[name+'dict'][phot[0]]
    h = locals()[name+'dict'][phot[1]]
#     print name
#     print mud
#     print h
    
    if name in ['n4258', 'n4725']:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], map(lambda l: l[0], zip(locals()[name+'dict']['gas_dens'])))[1:15]
    elif name == 'n2985':
        total_gas_data=locals()[name+'dict']['total_gas_data_']
    else:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], [He_coeff*(locals()[name+'dict']['y_interp_'](l[0], h) + l[1]) for l in 
                                                                   zip(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'])])[1:15]
            
            
    if total_gas_data[0][0] < 0.1:
        total_gas_data = total_gas_data[1:]
    
#     print total_gas_data

    plot_kennicutt_half(ax=ax, total_gas_data=total_gas_data,
                      epicycl=locals()[name+'dict']['epicyclicFreq_real'], 
                      gas_approx=locals()[name+'dict']['spl_gas'], 
                      sound_vel=locals()[name+'dict']['sound_vel'], 
                      scale=locals()[name+'dict']['scale'], 
                      sigma_max=locals()[name+'dict']['sig_R_maj_max'], 
                      sigma_min=locals()[name+'dict']['sig_R_maj_min'],
                      star_density_max=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      star_density_min=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      data_lim=locals()[name+'dict']['data_lim'], 
                  color=color, alpha=0.2, 
                  label=name+' '+phot[3],
                  sfrange=phot[4])
    plt.plot([-1, -2], [-1, -2], '-', color=color, label=name+' '+phot[3])
    
plt.axvline(x=1, alpha=0.5)
plot_Q_levels(ax, [1.5, 2., 3.])
plt.legend()
plt.xlim(0, 3)
plt.ylim(0, 1.3)
plt.ylabel(r'$\Sigma_g/\Sigma_{cr}$', fontsize=20)
plt.xlabel(r'$R/R_{SF}$', fontsize=20)
plt.show()


    

In [60]:

    

kenn_photom={'n338' : ['mu0d_Ic', 'h_disc_I', 6.23, 'I', 60.],
            'n1167' : ['mu0d_Rc', 'h_disc_R', 5.53, 'R', 40.],
            'n2985' : ['mudK', 'hK', 1.42, 'K', 70.],
            'n3898' : ['mu0d_R', 'h_disc_R', 7.80, 'R', 80.],
            'n4258' : ['mu0d_I', 'h_disc_I', 0.82, 'I', 150.],
            'n4725' : ['mu0d_H', 'h_disc_H', 0.68, 'H', 55.],
            'n5533' : ['MU0_r', 'hi_r', 4.41, 'r', 100.]}

fig = plt.figure(figsize=[16, 6])

for ind, name in enumerate(['n2985', 'n4258', 'n4725']):
    ax = plt.gca()
    
    color = cm.rainbow(np.linspace(0, 1, 7))[ind]
    
    phot = kenn_photom[name]
    mud = locals()[name+'dict'][phot[0]]
    h = locals()[name+'dict'][phot[1]]
#     print name
#     print mud
#     print h
    
    if name in ['n4258', 'n4725']:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], map(lambda l: l[0], zip(locals()[name+'dict']['gas_dens'])))[1:15]
    elif name == 'n2985':
        total_gas_data=locals()[name+'dict']['total_gas_data_']
    else:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], [He_coeff*(locals()[name+'dict']['y_interp_'](l[0], h) + l[1]) for l in 
                                                                   zip(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'])])[1:15]
            
            
    if total_gas_data[0][0] < 0.1:
        total_gas_data = total_gas_data[1:]
    
#     print total_gas_data

    plot_kennicutt(ax=ax, total_gas_data=total_gas_data,
                      epicycl=locals()[name+'dict']['epicyclicFreq_real'], 
                      gas_approx=locals()[name+'dict']['spl_gas'], 
                      sound_vel=locals()[name+'dict']['sound_vel'], 
                      scale=locals()[name+'dict']['scale'], 
                      sigma_max=locals()[name+'dict']['sig_R_maj_max'], 
                      sigma_min=locals()[name+'dict']['sig_R_maj_min'],
                      star_density_max=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      star_density_min=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      data_lim=locals()[name+'dict']['data_lim'], 
                  color=color, alpha=0.2, 
                  label=name+' '+phot[3],
                  sfrange=phot[4])
    plt.plot([-1, -2], [-1, -2], '-', color=color, label=name+' '+phot[3])
    
plt.axvline(x=1, alpha=0.5)
plot_Q_levels(ax, [1.5, 2., 3.])
plt.legend()
plt.xlim(0, 3)
plt.ylim(0, 1.3)
plt.ylabel(r'$\Sigma_g/\Sigma_{cr}$', fontsize=20)
plt.xlabel(r'$R/R_{SF}$', fontsize=20)
plt.show()


    

In [64]:

    

def plot_1f(ax=None, total_gas_data=None, epicycl=None, gas_approx=None, sound_vel=None, scale=None, sigma_max=None, sigma_min=None, star_density_max=None, 
                  star_density_min=None, data_lim=None, color=None, alpha=0.3, disk_scales=[], label=None, sfrange=None):

    invQg, invQs, invQeff_min = zip(*get_invQeff_from_data(gas_data=total_gas_data, 
                                    epicycl=epicycl, 
                                    gas_approx=gas_approx,
                                    sound_vel=sound_vel, 
                                    scale=scale,
                                    sigma=sigma_max,
                                    star_density=star_density_min))
   
    rr = zip(*total_gas_data)[0]
    ax.plot(np.array(rr)/sfrange, invQg, '--', color=color, alpha=0.8)


    

In [65]:

    

kenn_photom={'n338' : ['mu0d_Ic', 'h_disc_I', 6.23, 'I', 60.],
            'n1167' : ['mu0d_Rc', 'h_disc_R', 5.53, 'R', 40.],
            'n2985' : ['mudK', 'hK', 1.42, 'K', 70.],
            'n3898' : ['mu0d_R', 'h_disc_R', 7.80, 'R', 80.],
            'n4258' : ['mu0d_I', 'h_disc_I', 0.82, 'I', 150.],
            'n4725' : ['mu0d_H', 'h_disc_H', 0.68, 'H', 55.],
            'n5533' : ['MU0_r', 'hi_r', 4.41, 'r', 100.]}

fig = plt.figure(figsize=[16, 6])

for ind, name in enumerate(['n338', 'n1167', 'n2985', 'n3898', 'n4258', 'n4725', 'n5533']):
    ax = plt.gca()
    
    color = cm.rainbow(np.linspace(0, 1, 7))[ind]
    
    phot = kenn_photom[name]
    mud = locals()[name+'dict'][phot[0]]
    h = locals()[name+'dict'][phot[1]]
#     print name
#     print mud
#     print h
    
    if name in ['n4258', 'n4725']:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], map(lambda l: l[0], zip(locals()[name+'dict']['gas_dens'])))[1:15]
    elif name == 'n2985':
        total_gas_data=locals()[name+'dict']['total_gas_data_']
    else:
        total_gas_data=zip(locals()[name+'dict']['r_g_dens'], [He_coeff*(locals()[name+'dict']['y_interp_'](l[0], h) + l[1]) for l in 
                                                                   zip(locals()[name+'dict']['r_g_dens'], locals()[name+'dict']['gas_dens'])])[1:15]
            
            
    if total_gas_data[0][0] < 0.1:
        total_gas_data = total_gas_data[1:]
    
#     print total_gas_data

    plot_1f(ax=ax, total_gas_data=total_gas_data,
                      epicycl=locals()[name+'dict']['epicyclicFreq_real'], 
                      gas_approx=locals()[name+'dict']['spl_gas'], 
                      sound_vel=locals()[name+'dict']['sound_vel'], 
                      scale=locals()[name+'dict']['scale'], 
                      sigma_max=locals()[name+'dict']['sig_R_maj_max'], 
                      sigma_min=locals()[name+'dict']['sig_R_maj_min'],
                      star_density_max=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      star_density_min=lambda l: surf_density(mu=mu_disc(l, mu0=mud, h=h), M_to_L=phot[2], band=phot[3]), 
                      data_lim=locals()[name+'dict']['data_lim'], 
                  color=color, alpha=0.2, 
                  label=name+' '+phot[3],
                  sfrange=phot[4])
    plt.plot([-1, -2], [-1, -2], '-', color=color, label=name+' '+phot[3])
    
plt.axvline(x=1, alpha=0.5)
plot_Q_levels(ax, [1.5, 2., 3.])
plt.legend()
plt.xlim(0, 3)
plt.ylim(0, 1.3)
plt.ylabel(r'$\Sigma_g/\Sigma_{cr}$', fontsize=20)
plt.xlabel(r'$R/R_{SF}$', fontsize=20)
plt.show()