In [1]:

    

import numpy as np
import wisps
import wisps.simulations as wispsim
import wisps.simulations.effective_numbers as eff
import matplotlib.pyplot as plt
from astropy.coordinates import SkyCoord
import astropy.units as u
from tqdm import tqdm 
import pandas as pd
import matplotlib as mpl
%matplotlib inline


    

In [2]:

    

#wispsim.make_pointings()


    

    




535it [01:33,  5.75it/s]

In [3]:

    

from wisps import drop_nan


    

In [4]:

    

baraffe_data=eff.simulation_outputs()["baraffe2003"]
saumon_data=eff.simulation_outputs()["saumon2008"]


    

In [5]:

    

import seaborn as sns


    

In [6]:

    

cmap_teff=sns.diverging_palette(100, 300, s=80, l=55, n=19, as_cmap=True)


    

In [7]:

    

pnts=wisps.OBSERVED_POINTINGS


    

In [8]:

    

volumes=[]
dlimits=[]
for pnt in pnts:
    vs=[]
    dls=[]
    for g in wispsim.SPGRID:
        vsx=[]
        for h in wispsim.HS:
            vsx.append((pnt.volumes[h])[g])
        dls.append(pnt.dist_limits[g])
        vs.append(vsx)
    volumes.append(vs)
    dlimits.append(dls)
volumes=np.array(volumes)
dlimits=np.array(dlimits)


    

In [9]:

    

galc=SkyCoord([x.coord.galactic for x in pnts])


    

In [10]:

    

vls250=volumes[:,0, :,]


    

In [11]:

    

fig = plt.figure(figsize=(8,5))
ax = fig.add_subplot(111, projection="mollweide")
c=ax.scatter(galc.l.wrap_at(180*u.degree).radian,galc.b.wrap_at(90*u.degree).radian, marker='+', cmap='viridis')
ax.set_xlabel('l (deg)', fontsize=18)
ax.set_ylabel('b (deg)', fontsize=18)
plt.grid()
plt.savefig(wisps.OUTPUT_FIGURES+'/fields_skymap.pdf', bbox_inches='tight')


    

In [12]:

    

from matplotlib.colors import Normalize


    

In [13]:

    

from scipy import integrate


    

In [14]:

    

volume_fx=np.vectorize(wispsim.custom_volume)


    

In [16]:

    

import seaborn as sns
import matplotlib
#cmap= sns.color_palette("coolwarm", 8, as_cmap=True)
cmap=matplotlib.cm.get_cmap('coolwarm')
cnorm=Normalize(wispsim.HS[0]/100, (wispsim.HS[-1])/100)


    

In [17]:

    

ds=np.logspace(0, 3.5, 1000)
fig, ax=plt.subplots(figsize=(8, 5))
for idx, h in tqdm(enumerate(wispsim.HS)):
    plt.plot(ds, np.log10(volume_fx(0.,np.pi/4, 0, ds,h)), color=cmap(cnorm(h/100)), label=r'h ={}'.format(h))
plt.plot(ds, np.log10(ds**3), c='k', label=r'd$^3$')
plt.ylabel(r'Log Veff (pc$^3$)', fontsize=18)
plt.xlabel('d (pc)', fontsize=18)
plt.legend(fontsize=14)


    

    




7it [00:01,  6.68it/s]






    
Out[17]:





<matplotlib.legend.Legend at 0x1a7d189b90>






    

In [18]:

    

vsunif=np.nansum((dlimits[:, :,0])**3-(dlimits[:, :,1])**3, axis=0)*4.1*(u.arcmin**2).to(u.radian**2)


    

In [19]:

    

VOLUMES=np.nansum(volumes.T, axis=2)*4.1*(u.arcmin**2).to(u.radian**2)


    

In [20]:

    

fig, ax=plt.subplots(figsize=(8, 5))
for idx, h in enumerate(wispsim.HS):
    plt.plot(wispsim.SPGRID, np.log10(VOLUMES[idx]), color=cmap(cnorm(h/100)),
             label=r'h ={}'.format(h), linewidth=3)

#plt.plot(wispsim.SPGRID, np.log10(vsunif), label=r'd$^3$' )
plt.ylabel(r'Log Veff (pc$^3$)', fontsize=18)
plt.xlabel('SpT', fontsize=18)
plt.legend(fontsize=18)

plt.minorticks_on()

plt.tight_layout()
ax.set_xticks([20, 25, 30, 35, 40])
ax.set_xticklabels(['L0', 'L5', 'T0', 'T5', 'Y0'])

plt.savefig(wisps.OUTPUT_FIGURES+'/simulation_volumes.pdf', bbox_inches='tight')


    

In [21]:

    

cum_volumes=np.cumsum(volumes, axis=1)
cum_volumes.shape


    

    
Out[21]:





(533, 25, 7)

In [22]:

    

np.nansum((cum_volumes[:, :, 0]), axis=1).shape


    

    
Out[22]:





(533,)

In [23]:

    

steps=np.arange(533)


    

In [81]:

    

AREA=4.1*(u.arcmin**2).to(u.radian**2)


    

In [25]:

    

import splat


    

In [82]:

    

fig, ((ax, ax1), (ax2, ax3))=plt.subplots(figsize=(10, 8), ncols=2, nrows=2)
for idx, h in enumerate(wispsim.HS):
    ax.step(steps, np.log10(np.cumsum(volumes[:, 0, idx])*AREA), color=cmap(cnorm(h/100)), linewidth=3)
    ax1.step(steps, np.log10(np.cumsum(volumes[:, 9, idx])*AREA), color=cmap(cnorm(h/100)), linewidth=3)
    ax2.step(steps, np.log10(np.cumsum(volumes[:,-6 , idx])*AREA), color=cmap(cnorm(h/100)), linewidth=3)
    ax3.step(steps, np.log10(np.cumsum(volumes[:, -1, idx])*AREA), color=cmap(cnorm(h/100)), linewidth=3, label=r'h ={}'.format(h))


ax.set_ylabel(r'Log Cumulative Veff (pc$^3$)', fontsize=18)
ax2.set_ylabel(r'Log Cumulative Veff (pc$^3$)', fontsize=18)

for a in [ax, ax1, ax2, ax3]:
    a.set_xlabel('Number of Pointings', fontsize=18)
    a.minorticks_on()
    
ax.set_title('{} UCDs'.format(splat.typeToNum(wispsim.SPGRID[0])), fontsize=18)
ax1.set_title('{} UCDs'.format(splat.typeToNum(wispsim.SPGRID[9])), fontsize=18)
ax2.set_title('{} UCDs'.format(splat.typeToNum(wispsim.SPGRID[-6])), fontsize=18)
ax3.set_title('{} UCDs'.format(splat.typeToNum(wispsim.SPGRID[-1])), fontsize=18)

ax3.legend(fontsize=15)


plt.tight_layout()


plt.savefig(wisps.OUTPUT_FIGURES+'/cumulative_volumes.pdf', bbox_inches='tight')


    

In [27]:

    

import numba


    

In [28]:

    

@numba.jit
def get_kde(r, z):
    kde=wisps.kernel_density([r, z])
    dens=kde.pdf([r, z])
    return dens


    

In [29]:

    

data=eff.simulation_outputs()['baraffe2003']


    

In [30]:

    

r100=data[150]['r'].flatten()
z100=data[150]['z'].flatten()
d100=data[150]['d'].flatten()


    

In [31]:

    

r600=data[400]['r'].flatten()
z600=data[400]['z'].flatten()
d600=data[400]['d'].flatten()


    

In [32]:

    

data[200].keys()


    

    
Out[32]:





dict_keys(['f110', 'f140', 'f160', 'd', 'r', 'z', 'appf140', 'appf110', 'appf160', 'snrj', 'sl', 'pnt', 'spts', 'teff'])

In [33]:

    

dens1=wispsim.density_function(r100, z100)
dens2=wispsim.density_function(r600, z600)

#dens1=data[150]['sl']
#dens2=data[400]['sl']


    

In [34]:

    

fig, ax=plt.subplots()
ax.scatter(data[400]['spts'], np.log10(data[400]['snrj']), c=data[400]['sl'], cmap='viridis')


    

    
Out[34]:





<matplotlib.collections.PathCollection at 0x1a7cd7fb50>






    

In [35]:

    

fig, (ax, ax1)=plt.subplots( figsize=(10, 10), ncols=2, nrows=2)

h=ax[0].hist(np.log10(d100.flatten()), color='#FF4136',
          bins='auto', histtype='step', label="h = 150 pc")

h=ax[0].hist(np.log10(d600.flatten()),  color='#0074D9', bins='auto', 
          histtype='step', label="h = 400 pc")


h=ax[1].hist(z100.flatten(), color='#FF4136',
          bins='auto', histtype='step', label="h = 150 pc")

h=ax[1].hist(z600.flatten(),  color='#0074D9', bins='auto', 
          histtype='step', label="h = 400 pc")

c1=ax1[0].scatter(r100, z100, s=1.,  c=np.log10(dens1), cmap='viridis', alpha=0.1)

ax1[0].set_title("h = 150 pc", fontsize=18)
ax1[1].set_title("h = 400 pc", fontsize=18)

c=ax1[1].scatter(r600, z600,s=1.,  c=np.log10(dens2), cmap='viridis', alpha=0.1)

cbar0=plt.colorbar(c, ax=ax1[1],    orientation='horizontal')
cbar1=plt.colorbar(c1, ax=ax1[0],    orientation='horizontal')


cbar0.ax.set_xlabel(r'Log $ \rho/ \rho_0$ ', fontsize=18)
cbar1.ax.set_xlabel(r'Log $ \rho/ \rho_0$ ', fontsize=18)

ax[0].legend(fontsize=15, loc='lower left')
ax[0].set_xlabel('Log Distance (pc)', fontsize=18)
ax[0].set_ylabel('N', fontsize=18)

ax[1].set_xlabel('Z (pc)', fontsize=18)
ax[1].set_ylabel('N', fontsize=18)
ax[1].set_yscale('log')

#ax1[0].legend(fontsize=15, loc='lower left')
ax1[0].set_xlabel('R (pc)', fontsize=18)
ax1[0].set_ylabel('Z (pc)', fontsize=18)

#ax1[1].legend(fontsize=15, loc='lower left')
ax1[1].set_xlabel('R (pc)', fontsize=18)
ax1[1].set_ylabel('Z (pc)', fontsize=18)


for a in np.concatenate([ax, ax1]):
    a.minorticks_on()
    
    
plt.tight_layout()
plt.savefig(wisps.OUTPUT_FIGURES+'/simulation_distances.jpeg', bbox_inches='tight', rasterized=True)


    

In [77]:

    

maglimits=pd.DataFrame([x.mag_limits for x in pnts])
exposure_times=[np.nanmean(x.exposure_time) for x in pnts]
maglimits['pointing']=[x.name for x in pnts]
maglimits['survey']=[x.survey for x in pnts]
maglimits['exp']=np.log10(exposure_times)

nsrcs=pd.DataFrame([x.number_of_sources for x in pnts])
number_of_sources=nsrcs.rename(columns={'F140': 'nF140', 'F160': 'nF160', 'F110':'nF110'})
number_of_sources['pointing']=[x.name for x in pnts]


    

In [78]:

    

mag_lts_df=maglimits.merge(number_of_sources, on='pointing')


    

In [ ]:

    

#split into fitted and unfitted


    

In [ ]:

    

for k in ['F110', 'F140', '']


    

In [38]:

    

#magpolw=wispsim.MAG_LIMITS[survey][][0]
#magpolh=wispsim.MAG_LIMITS[survey][key][0]

#magsctt=MAG_LIMITS[survey][key][1]

#maglt=np.nanmean(np.random.normal(magpol(np.log10(pnt.exposure_time)), magsctt, 100))


    

In [39]:

    

import seaborn as sns


    

In [40]:

    

wisps_pnts=[x for x in pnts if x.name.startswith('par')]


    

In [41]:

    

hst3d_pnts=[x for x in pnts if not x.name.startswith('par')]


    

In [42]:

    

pols={'wisps':{}, 'hst3d':{}}


    

In [43]:

    

wisps.MAG_LIMITS


    

    
Out[43]:





{'wisps': {'F110': (poly1d([ 1.58332072, 16.55679476]), 0.5038943667884174),
  'F140': (poly1d([ 1.55165028, 15.97456775]), 0.7495636144809823),
  'F160': (poly1d([ 0.32381253, 20.32498832]), 1.4165037712823727)},
 'hst3d': {'F110': (None, nan),
  'F140': (poly1d([ 0.3353958 , 21.64429535]), 0.18353856592257553),
  'F160': (poly1d([ 0.38991196, 21.33841198]), 0.1840187995673484)},
 'ncutoff': 50}

In [44]:

    

pnt_dicts={'wisps':wisps_pnts, 'hst3d':hst3d_pnts}


    

In [55]:

    

fig, ax=plt.subplots(ncols=3, figsize=(12, 4))

sns.scatterplot(x='F110', y='exp', hue='survey', s=100., ax=ax[0], palette=['#FF851B', '#0074D9'], data=maglimits)
sns.scatterplot(x='F140', y='exp', hue='survey', s=100., ax=ax[1],palette=['#FF851B', '#0074D9'] , data=maglimits)
sns.scatterplot(x='F160', y='exp', hue='survey', s=100., ax=ax[2], palette=['#FF851B', '#0074D9'],data=maglimits)

(ax[0]).plot(((wispsim.MAG_LIMITS['wisps'])['F110'][0])(maglimits.exp),maglimits.exp, c='k')
(ax[1]).plot(((wispsim.MAG_LIMITS['wisps'])['F140'][0])(maglimits.exp),maglimits.exp, c='k')
(ax[2]).plot(((wispsim.MAG_LIMITS['wisps'])['F160'][0])(maglimits.exp),maglimits.exp, c='k')



(ax[1]).plot(((wispsim.MAG_LIMITS['hst3d'])['F140'][0])(maglimits.exp),maglimits.exp, c='k')
(ax[2]).plot(((wispsim.MAG_LIMITS['hst3d'])['F160'][0])(maglimits.exp),maglimits.exp, c='k')


for a,k in zip(ax, maglimits.columns):
    #a.set_xlim([20, 25])
    #a.set_yscale('log')
    a.minorticks_on()
    a.set_xlabel('PS Limiting '+ k+'W', fontsize=18)
    a.set_ylabel('Log Grism Exposure Time (s)', fontsize=18)
plt.tight_layout()
plt.savefig(wisps.OUTPUT_FIGURES+'/mag_limit.pdf', bbox_inches='tight')


    

In [ ]:

    




    

In [46]:

    

asfbfgn


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-46-cd9d2a25da7b> in <module>
----> 1 asfbfgn

NameError: name 'asfbfgn' is not defined

In [ ]:

    

maglimits[maglimits.F160<15.]


    

In [ ]:

    

p=pnts[np.random.randint(533)]


    

In [ ]:

    

p.name


    

In [ ]:

    

import scipy


    

In [ ]:

    

vals=p.mags['F160']
vals=vals[~np.isnan(vals)]


    

In [ ]:

    

len(vals)


    

In [ ]:

    

kde=scipy.stats.gaussian_kde(vals)


    

In [ ]:

    

frq, edges=np.histogram(vals, bins=int(np.ptp(vals)/0.3), normed=True)


    

In [ ]:

    

firstd=np.diff(frq, n=1, prepend=frq[0])
sencd=np.diff(firstd, n=1, prepend=firstd[0])


    

In [ ]:

    

len(edges[: -1]), len(np.gradient(frq)), len(frq), len(firstd)


    

In [ ]:

    

fig, ax=plt.subplots(figsize=(10, 6))
plt.plot(np.sort(vals), kde(np.sort(vals)), c='b')

plt.bar(edges[:-1], frq, width=np.diff(edges), align="edge", label='0.5 hist', color='#7FDBFF', 
        edgecolor='#7FDBFF', fill=True)


plt.axvline(edges[:-1][firstd==0][0], c='k')
plt.axvline(edges[:-1][firstd==0][-3], c='k')
plt.axvline(edges[:-1][firstd==0][-2], c='k')
plt.axvline(edges[:-1][firstd==0][-1], c='k')

#plt.axvline(edges[:-1][np.argmin(sencd)], c='#2ECC40', label='sec')
plt.axvline(vals[np.argmax(kde(vals))], c='#2ECC40', linestyle='--', label='kde')
plt.legend(fontsize=18)
plt.xlabel('F160W', fontsize=18)

plt.minorticks_on()
plt.tight_layout()
plt.savefig(wisps.OUTPUT_FIGURES+'/mag_limit_illustration.pdf')


    

In [ ]:

    

x=np.arange(10).astype(int)


    

In [ ]:

    

ys=np.random.random(10)


    

In [ ]:

    

bs=(x==3)


    

In [ ]:

    

x[bs], ys[bs]


    

In [ ]:

    

a=np.empty(23)
a[:]=np.nan


    

In [ ]:

    

a


    

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