In [1]:
from astropy.io import ascii, fits
import pylab as plt
%matplotlib inline
from astropy import wcs
import numpy as np
import xidplus
from xidplus import moc_routines
import pickle
import pandas as pd
import seaborn as sns
In [3]:
# load up run
priors,posterior=xidplus.load(filename='./test_SPM_mrr.pkl')
In [4]:
from xidplus import plots
orig_map=plots.plot_map(priors);
for i in range(0,priors[0].nsrc):
orig_map[0][0].add_label(priors[0].sra[i], priors[0].sdec[i]+0.0005, np.arange(0,priors[0].nsrc)[i], relative=False)
for i in range(0,6):
orig_map[0][i].show_markers(priors[0].sra, priors[0].sdec, edgecolor='black', facecolor='black',
marker='o', s=50, alpha=0.5)
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
In [5]:
orig_map[0][0].show_markers(priors[0].sra, priors[0].sdec, edgecolor='black', facecolor='black',
marker='o', s=50, alpha=0.5)
In [6]:
movie=plots.replicated_map_movie(priors,posterior, 5)
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
In [7]:
movie
Out[7]:
In [8]:
plots.plot_Bayes_pval_map(priors, posterior)
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
WARNING: Cannot determine equinox. Assuming J2000. [aplpy.wcs_util]
Out[8]:
([<aplpy.core.FITSFigure at 0x1111a46d8>,
<aplpy.core.FITSFigure at 0x119610f98>,
<aplpy.core.FITSFigure at 0x119610c88>,
<aplpy.core.FITSFigure at 0x11a8d07b8>,
<aplpy.core.FITSFigure at 0x123712630>,
<aplpy.core.FITSFigure at 0x110128588>],
<matplotlib.figure.Figure at 0x119531518>)
In [9]:
s1=138
In [10]:
df = pd.DataFrame(posterior.samples['src_f'][0:1500,:,s1],columns=[ '24','100', '160','250', '350', '500'])
g = sns.PairGrid(df,size=5)
g.map_diag(plt.hist, alpha=0.5)
g.map_lower(sns.kdeplot, cmap="Reds_d",alpha=0.5,n_levels=10,normed=True, shade=True,shade_lowest=False)
Out[10]:
<seaborn.axisgrid.PairGrid at 0x11c89e518>
In [33]:
from xidplus import sed
SEDs,df =sed.mrr_templates()
0.0001
0.0001
0.0001
0.0001
In [35]:
df['wave']
Out[35]:
0 1450.106836
1 1200.107086
2 1000.000000
3 831.955314
4 691.990289
5 575.042575
6 478.960832
7 398.015514
8 330.978665
9 274.979299
10 228.981291
11 190.989724
12 158.019251
13 132.010963
14 109.999320
15 100.000000
16 91.201084
17 75.892699
18 63.095734
19 60.006735
20 52.504920
21 43.701868
22 36.298610
23 30.200213
24 24.998273
25 20.897773
26 17.398027
27 14.501068
28 12.001071
29 11.601122
30 11.301081
31 10.999932
32 10.899084
33 9.709570
34 9.399397
35 8.990834
36 8.590135
37 8.390735
38 8.199738
39 7.689534
40 7.000032
41 6.609978
42 6.369422
43 6.190133
44 6.029760
45 5.850595
46 4.799544
47 3.400165
48 3.339566
49 3.299894
50 3.269565
51 3.160094
52 3.069729
53 2.690296
54 2.511886
55 1.258925
Name: wave, dtype: float64
In [15]:
sns.set_style("white")
plt.figure(figsize=(6,6))
from astropy.cosmology import Planck13
violin_parts=plt.violinplot(posterior.samples['src_f'][:,3:6,s1],[250,350,500], points=60, widths=100,
showmeans=True, showextrema=True, showmedians=True,bw_method=0.5)
# Make all the violin statistics marks red:
for partname in ('cbars','cmins','cmaxes','cmeans','cmedians'):
vp = violin_parts[partname]
vp.set_edgecolor('green')
vp.set_linewidth(1)
for pc in violin_parts['bodies']:
pc.set_facecolor('green')
violin_parts=plt.violinplot(posterior.samples['src_f'][:,0:3,s1],[24,100,160], points=60, widths=20,showmeans=True, showextrema=True, showmedians=True,bw_method=0.5)
# Make all the violin statistics marks red:
for partname in ('cbars','cmins','cmaxes','cmeans','cmedians'):
vp = violin_parts[partname]
vp.set_edgecolor('green')
vp.set_linewidth(1)
for pc in violin_parts['bodies']:
pc.set_facecolor('green')
import astropy.units as u
for s in range(0,1500,20):
z= posterior.samples['z'][s,s1]
div=(4.0*np.pi * np.square(Planck13.luminosity_distance(z).cgs))
div=div.value
plt.loglog((z+1.0)*df['wave'],np.power(10.0,posterior.samples['Nbb'][s,s1])*(1.0+z)*df[df.columns[np.arange(1,posterior.samples['p'].shape[2]+1)
[np.random.multinomial(1, posterior.samples['p'][s,s1,:])==1]]]/div,alpha=0.05,c='b',zorder=0)
#plt.plot([250,350,500, 24,100,160],posterior_IR.samples['src_f'][s,0:6,s1], 'ko', alpha=0.1, ms=10)
#plt.plot([250,350,500],posterior.samples['src_f'][s,0:3,s1], 'ro', alpha=0.1, ms=10)
plt.ylim(10E-7,10E2)
plt.xlim(5,5E3)
#plt.plot([3.6,4.5,5.7,7.9],[2.91E-3,2.38E-3,2.12E-3,9.6E-3], 'ro')
plt.xlabel('Wavelength (microns)')
plt.ylabel('Flux (mJy)')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-13722b4fee44> in <module>()
33
34 plt.loglog((z+1.0)*df['wave'],np.power(10.0,posterior.samples['Nbb'][s,s1])*(1.0+z)*df[df.columns[np.arange(1,posterior.samples['p'].shape[2]+1)
---> 35 [np.random.multinomial(1, posterior.samples['p'][s,s1,:])==1]]]/div,alpha=0.05,c='b',zorder=0)
36
37 #plt.plot([250,350,500, 24,100,160],posterior_IR.samples['src_f'][s,0:6,s1], 'ko', alpha=0.1, ms=10)
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/pyplot.py in loglog(*args, **kwargs)
3176 mplDeprecation)
3177 try:
-> 3178 ret = ax.loglog(*args, **kwargs)
3179 finally:
3180 ax._hold = washold
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/axes/_axes.py in loglog(self, *args, **kwargs)
1541 b = self._hold
1542 self._hold = True # we've already processed the hold
-> 1543 l = self.plot(*args, **kwargs)
1544 self._hold = b # restore the hold
1545
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1896 warnings.warn(msg % (label_namer, func.__name__),
1897 RuntimeWarning, stacklevel=2)
-> 1898 return func(ax, *args, **kwargs)
1899 pre_doc = inner.__doc__
1900 if pre_doc is None:
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/axes/_axes.py in plot(self, *args, **kwargs)
1404 kwargs = cbook.normalize_kwargs(kwargs, _alias_map)
1405
-> 1406 for line in self._get_lines(*args, **kwargs):
1407 self.add_line(line)
1408 lines.append(line)
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/axes/_base.py in _grab_next_args(self, *args, **kwargs)
405 return
406 if len(remaining) <= 3:
--> 407 for seg in self._plot_args(remaining, kwargs):
408 yield seg
409 return
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs)
355 ret = []
356 if len(tup) > 1 and is_string_like(tup[-1]):
--> 357 linestyle, marker, color = _process_plot_format(tup[-1])
358 tup = tup[:-1]
359 elif len(tup) == 3:
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/matplotlib/axes/_base.py in _process_plot_format(fmt)
92 # handle the multi char special cases and strip them from the
93 # string
---> 94 if fmt.find('--') >= 0:
95 linestyle = '--'
96 fmt = fmt.replace('--', '')
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)
3079 if name in self._info_axis:
3080 return self[name]
-> 3081 return object.__getattribute__(self, name)
3082
3083 def __setattr__(self, name, value):
AttributeError: 'DataFrame' object has no attribute 'find'
In [16]:
df=pd.DataFrame(np.log10(posterior.samples['p'][:,s1,:]),columns=df.columns.tolist()[1:])
f, ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df,annot=False, ax=ax)
ax.set_yticklabels('None')
ax.set_ylabel('Iteration')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-05692f6c1916> in <module>()
1 df=pd.DataFrame(np.log10(posterior.samples['p'][:,s1,:]),columns=df.columns.tolist()[1:])
2 f, ax = plt.subplots(figsize=(20, 20))
----> 3 sns.heatmap(df,annot=False, ax=ax)
4 ax.set_yticklabels('None')
5 ax.set_ylabel('Iteration')
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/seaborn/matrix.py in heatmap(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, ax, xticklabels, yticklabels, mask, **kwargs)
483 plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
484 annot_kws, cbar, cbar_kws, xticklabels,
--> 485 yticklabels, mask)
486
487 # Add the pcolormesh kwargs here
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/seaborn/matrix.py in __init__(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)
165 # Determine good default values for the colormapping
166 self._determine_cmap_params(plot_data, vmin, vmax,
--> 167 cmap, center, robust)
168
169 # Sort out the annotations
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/seaborn/matrix.py in _determine_cmap_params(self, plot_data, vmin, vmax, cmap, center, robust)
204 calc_data = plot_data.data[~np.isnan(plot_data.data)]
205 if vmin is None:
--> 206 vmin = np.percentile(calc_data, 2) if robust else calc_data.min()
207 if vmax is None:
208 vmax = np.percentile(calc_data, 98) if robust else calc_data.max()
/Users/pdh21/anaconda3/envs/new/lib/python3.6/site-packages/numpy/core/_methods.py in _amin(a, axis, out, keepdims)
27
28 def _amin(a, axis=None, out=None, keepdims=False):
---> 29 return umr_minimum(a, axis, None, out, keepdims)
30
31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
ValueError: zero-size array to reduction operation minimum which has no identity
In [17]:
plt.figure(figsize=(6,6))
plt.hist(posterior.samples['Nbb'][:,s1], bins=np.arange(8,14,0.1),normed=True, alpha=0.4);
plt.xlabel(r'$\log_{10}L_{IR}\odot$')
plt.ylabel(r'p($L_{IR}$)')
Out[17]:
<matplotlib.text.Text at 0x1246686d8>
In [18]:
plt.figure(figsize=(6,6))
plt.hist(np.random.normal(priors[0].z_median[s1],priors[0].z_sig[s1],size=1000),bins=np.arange(0,7, 0.1),normed=True,color='red', alpha=0.5);
plt.hist(posterior.samples['z'][0:1500,s1],bins=np.arange(0,7, 0.1),normed=True, alpha=0.5, color='green');
plt.xlabel('redshift')
plt.ylabel('p(z)')
Out[18]:
<matplotlib.text.Text at 0x126bd4b38>
In [25]:
plt.plot(posterior.samples['src_f'][:,0,s1])
Out[25]:
[<matplotlib.lines.Line2D at 0x126d24b00>]
In [67]:
plt.semilogy(np.abs(posterior.samples['lp__']))
Out[67]:
[<matplotlib.lines.Line2D at 0x128ee7198>]
In [26]:
posterior.n_eff
Out[26]:
{'Nbb': array([ 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2000., 2., 2., 2.,
2000., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2000.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2000., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2000., 2., 2., 2., 2., 2.,
2., 2000., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2000., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2.]),
'bkg': array([ 2., 2., 2000., 2., 2., 2.]),
'sigma_conf': array([ 2000., 2000., 2000., 2000., 2000., 2000.]),
'src_f': array([[ 2., 2., 2., 2., 2., 2.],
[ 2., 2., 2., 2., 2., 2.],
[ 2., 2., 2., 2., 2., 2.],
...,
[ 2., 2., 2., 2., 2., 2.],
[ 2., 2., 2., 2., 2., 2.],
[ 2., 2., 2., 2., 2., 2.]]),
'z': array([ 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2000., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2000., 2., 2.,
2., 2., 2., 2., 2000., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2000., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2000.,
2., 2., 2000., 2000., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2000.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2000., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2000., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2000., 2., 2., 2000., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2.])}
In [55]:
red=np.arange(0,8,0.01)
red[0]=0.000001
plt.semilogy(red,SEDs[0,0,:]*np.power(10.0,12))
plt.semilogy(red,SEDs[0,1,:]*np.power(10.0,12),c='g')
plt.semilogy(red,SEDs[0,2,:]*np.power(10.0,12),c='r')
plt.semilogy(red,SEDs[0,3,:]*np.power(10.0,12),c='m')
plt.semilogy(red,SEDsb[0,0,:]*np.power(10.0,12),c='k')
Out[55]:
[<matplotlib.lines.Line2D at 0x125b72080>]
In [54]:
div_test=(4.0*np.pi * np.square(Planck13.luminosity_distance(0.1).cgs))
div_test=div_test.value
plt.loglog(df['wave'],np.power(10.0,8)*(1.0+0.1)*df['0']/div_test)
plt.loglog(dfb['wave'],np.power(10.0,8)*(1.0+0.1)*dfb['Blue_SF_glx.norm_LIR']/div_test)
plt.ylim(0.01,10)
Out[54]:
(0.01, 10)
In [48]:
dfb
Out[48]:
wave
Blue_SF_glx.norm_LIR
BroadFIR_SF_glx.norm_LIR
Cold_glx.norm_LIR
Elliptical.norm_LIR
Ly_break.norm_LIR
MIR_powlaw_SF_glx.norm_LIR
MIRex_SF_glx.norm_LIR
Mod_SF_glx.norm_LIR
Obs_SF_glx.norm_LIR
...
Si_break.norm_LIR
Spiral.norm_LIR
Torus.norm_LIR
Type1_AGN_1.norm_LIR
Type2_AGN_1.norm_LIR
Type2_AGN_2.norm_LIR
Warm_SF_glx.norm_LIR
WeakPAH_SF_glx_1.norm_LIR
WeakPAH_SF_glx_2.norm_LIR
Young_SF_glx.norm_LIR
0
0.009100
1.302995e+31
2.205580e+31
6.456699e+33
1.477389e+35
1.539932e+36
1.079182e+32
1.613976e+31
1.009116e+32
5.289167e+31
...
4.706273e+41
6.939039e+14
1.844869e+40
1.949440e+42
8.995525e+27
1.528648e+34
4.808136e+21
1.217179e+31
1.011173e+31
1.116179e+33
1
0.009400
2.410548e+31
4.137099e+31
1.067047e+34
2.347837e+35
2.714404e+36
8.497038e+31
2.938124e+31
1.959359e+32
1.055748e+32
...
4.952793e+41
3.165719e+15
1.897311e+40
2.094512e+42
6.701925e+27
1.331927e+34
1.545787e+22
2.429558e+31
2.023013e+31
2.017934e+33
2
0.009600
3.617449e+31
6.265887e+31
1.497857e+34
3.205934e+35
3.944221e+36
7.253610e+31
4.358698e+31
3.015787e+32
1.655187e+32
...
5.118431e+41
8.428669e+15
1.933860e+40
2.189624e+42
5.501565e+27
1.218336e+34
3.284252e+22
3.817807e+31
3.157077e+31
2.972991e+33
3
0.009800
5.499361e+31
9.416562e+31
2.120216e+34
4.353759e+35
5.832718e+36
6.172541e+31
6.550380e+31
4.734895e+32
2.634855e+32
...
5.285019e+41
2.231870e+16
1.969404e+40
2.292341e+42
4.512279e+27
1.116033e+34
7.020147e+22
6.091494e+31
4.911283e+31
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0.017000
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
10975
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10990
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11005 rows × 33 columns
In [44]:
SEDs
Out[44]:
array([[[ 1.08510874e+00, 1.06459148e-08, 2.61169972e-09, ...,
1.63365948e-17, 1.62167322e-17, 1.60974837e-17],
[ 3.93440774e+01, 3.86333356e-07, 9.48589185e-08, ...,
7.45085594e-14, 7.04240490e-14, 6.63626272e-14],
[ 3.82574507e+01, 3.84410443e-07, 9.64401676e-08, ...,
2.00136215e-14, 1.99571217e-14, 1.99008562e-14],
[ 1.87573040e+01, 1.90571477e-07, 4.83699909e-08, ...,
3.59535413e-14, 3.58260940e-14, 3.56992167e-14],
[ 8.07471570e+00, 8.22522598e-08, 2.09280667e-08, ...,
6.87298149e-14, 6.84194576e-14, 6.81105724e-14],
[ 2.75951266e+00, 2.81450781e-08, 7.16963998e-09, ...,
2.20674796e-13, 2.19325749e-13, 2.17983451e-13]],
[[ 1.31541958e+00, 1.26371034e-08, 3.03383362e-09, ...,
7.66583650e-17, 7.64802913e-17, 7.63028955e-17],
[ 3.33053134e+01, 3.31370685e-07, 8.24342220e-08, ...,
3.40600489e-15, 3.25773226e-15, 3.11029463e-15],
[ 2.11539626e+01, 2.13360285e-07, 5.39725478e-08, ...,
1.18441339e-14, 1.17767085e-14, 1.17096167e-14],
[ 8.59539110e+00, 8.75571457e-08, 2.22780861e-08, ...,
6.07021755e-14, 6.03648994e-14, 6.00292857e-14],
[ 3.40443946e+00, 3.47208291e-08, 8.84425141e-09, ...,
1.89544244e-13, 1.88516863e-13, 1.87494525e-13],
[ 1.10564503e+00, 1.12832087e-08, 2.87579439e-09, ...,
4.97278128e-13, 4.95216168e-13, 4.93163807e-13]],
[[ 3.64186771e+00, 3.53647809e-08, 8.58551891e-09, ...,
2.21902480e-16, 2.19802008e-16, 2.17712589e-16],
[ 1.86728636e+01, 1.87220247e-07, 4.69241155e-08, ...,
2.99085591e-14, 2.87241445e-14, 2.75463879e-14],
[ 9.34058201e+00, 9.44250151e-08, 2.40079518e-08, ...,
4.51860556e-14, 4.49738946e-14, 4.47627479e-14],
[ 3.32577051e+00, 3.39504358e-08, 8.65560677e-09, ...,
1.39160761e-13, 1.38544535e-13, 1.37931220e-13],
[ 1.22613822e+00, 1.25186952e-08, 3.19207623e-09, ...,
2.92531037e-13, 2.91397615e-13, 2.90269382e-13],
[ 3.78283029e-01, 3.86300986e-09, 9.85197311e-10, ...,
4.86918514e-13, 4.85582727e-13, 4.84252419e-13]],
[[ 9.19925611e+00, 9.14980729e-08, 2.27544771e-08, ...,
1.09451264e-14, 1.08896409e-14, 1.08344246e-14],
[ 2.51743087e-03, 2.88704978e-11, 8.11416510e-12, ...,
1.51265413e-13, 1.50734148e-13, 1.50205252e-13],
[ 1.03906425e-04, 1.07502244e-12, 3.19101074e-13, ...,
2.01869337e-13, 2.01228053e-13, 2.00589543e-13],
[ 4.13321818e-06, 4.92121955e-14, 1.42144229e-14, ...,
2.14104823e-13, 2.14026566e-13, 2.13947800e-13],
[ 4.28645511e-07, 4.49202524e-15, 1.17286602e-15, ...,
2.82886303e-14, 2.86199670e-14, 2.89492769e-14],
[ 1.92066574e-08, 2.34952828e-16, 6.91407191e-17, ...,
2.27108806e-15, 2.27765302e-15, 2.28417016e-15]]])
In [42]:
plt.loglog(df['wave'],df['0'])
Out[42]:
[<matplotlib.lines.Line2D at 0x124a415c0>]
In [ ]:
Content source: H-E-L-P/XID_plus
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