Import these first-- I auto import them every time!:
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#! cat /Users/gully/.ipython/profile_default/startup/start.ipy
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import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
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import pandas as pd
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from astropy import units as u
from astropy.coordinates import SkyCoord
Generate/retrieve data file from here:
https://github.com/BrownDwarf/kinder
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d1 = pd.read_csv('../../kinder/analysis/K2C02_YSO_CLN_match.csv') #local version
d2 = pd.read_csv('../../kinder/analysis/K2C02_control_kplr_match.csv') #local version
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d2.k2_dec.isnull().sum()
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Fail, some don't have RA for some reason...
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d1 = d1[~d1.k2_ra.isnull()]
d2 = d2[~d2.k2_ra.isnull()]
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c1 = SkyCoord(d1.k2_ra.values, d1.k2_dec.values, unit=(u.deg, u.deg), frame='icrs')
c2 = SkyCoord(d2.k2_ra.values, d2.k2_dec.values, unit=(u.deg, u.deg), frame='icrs')
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df_list = []
This takes a finite amount of RAM, but should be fine for modern laptops.
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for i in range(16):
df_list.append(pd.read_csv('../data/TgasSource_000-000-{:03d}.csv'.format(i)))
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tt = pd.concat(df_list, ignore_index=True)
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plt.figure(figsize=(10,4))
ax = sns.jointplot(tt.ra, tt.dec, kind='hex', size=8)
ax.ax_joint.plot(c1.ra.deg, c1.dec.deg, '.', alpha=0.5)
ax.ax_joint.plot(c2.ra.deg, c2.dec.deg, '.', alpha=0.5)
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cg = SkyCoord(tt.ra.values, tt.dec.values, unit=(u.deg, u.deg), frame='icrs')
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idx, d2d, blah = c1.match_to_catalog_sky(cg)
idx2, d2d2, blah2 = c2.match_to_catalog_sky(cg)
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vec_units = d2d.to(u.arcsecond)
vec = vec_units.value
vec_units2 = d2d2.to(u.arcsecond)
vec2 = vec_units2.value
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bins = np.arange(0, 4, 0.2)
sns.distplot(vec, bins=bins, kde=False),
sns.distplot(vec2, bins=bins, kde=False),
plt.xlim(0,4)
plt.xlabel('match separation (arcsec)')
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Forced to match to nearest neighbor
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len(set(idx)), idx.shape[0]
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... yielding some redundancies in cross matching
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tt_sub = tt.iloc[idx]
tt_sub = tt_sub.reset_index()
tt_sub = tt_sub.drop('index', axis=1)
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d1 = d1.reset_index()
d1 = d1.drop('index', axis=1)
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x1 = pd.concat([d1, tt_sub], axis=1)
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tt_sub2 = tt.iloc[idx2]
tt_sub2 = tt_sub2.reset_index()
tt_sub2 = tt_sub2.drop('index', axis=1)
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d2 = d2.reset_index()
d2 = d2.drop('index', axis=1)
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x2 = pd.concat([d2, tt_sub2], axis=1)
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col_order = d1.columns.values.tolist() + tt_sub.columns.values.tolist()
x1 = x1[col_order]
x0 = x1.copy()
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col_order2 = d2.columns.values.tolist() + tt_sub2.columns.values.tolist()
x2 = x2[col_order2]
x0c = x2.copy()
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x0['xmatch_sep_as'] = vec
x0['Gaia_match'] = vec < 2.0 #Fairly liberal, 1.0 might be better.
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x0c['xmatch_sep_as'] = vec2
x0c['Gaia_match'] = vec2 < 2.0 #Fairly liberal, 1.0 might be better.
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plt.figure(figsize=(8,4))
bins = np.arange(2, 14, 0.5)
sns.distplot(x0c.parallax[x0c.Gaia_match], bins=bins, rug=False)
sns.distplot(x0.parallax[x0.Gaia_match], bins=bins, rug=True)
#sns.distplot(1.0/(x0.parallax[x0.Gaia_match]/1000.0))
plt.xlabel('Parallax (mas)')
plt.xlim(0,14)
plt.savefig('../results/k2cycle2_gaia.png', dpi=300)
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x0c.Gaia_match.sum(), len(d1)
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plt.figure(figsize=(10,4))
ax = sns.jointplot(tt.ra, tt.dec, kind='hex', size=8, xlim=(235,255), ylim=(-30,-15))
ax.ax_joint.plot(c1.ra.deg, c1.dec.deg, '.', alpha=1.0)
ax.ax_joint.plot(c2.ra.deg, c2.dec.deg, '.', alpha=1.0)
ax.ax_joint.scatter(x0.ra[x0.Gaia_match], x0.dec[x0.Gaia_match],
s=x0.parallax[x0.Gaia_match]**3*0.2, c='c',alpha=0.5)
ax.ax_joint.scatter(x0c.ra[x0c.Gaia_match], x0c.dec[x0c.Gaia_match],
s=x0c.parallax[x0c.Gaia_match]**3*0.2, c='y',alpha=0.5)
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xm = x0[x0.Gaia_match]
xmc = x0c[x0c.Gaia_match]
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xm.columns.values
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ax = sns.jointplot(xmc.parallax, xmc.logiqr, kind='kde', stat_func=None, joint_kws={"shade_lowest":False})
#ax.ax_joint.plot(xm.parallax, xm.logiqr, 'w.')
sns.kdeplot(xm.parallax, xm.logiqr, joint_kws={"shade":False}, ax=ax.ax_joint)
sns.kdeplot(xm.parallax.values,ax=ax.ax_marg_x, c='g')
sns.kdeplot(xm.logiqr.values,ax=ax.ax_marg_y, c='g', vertical=True)
sns.kdeplot(d1.logiqr.values,ax=ax.ax_marg_y, c='g', linestyle='--', vertical=True)
#ax.ax_joint.scatter(xm.parallax, xm.logiqr,
# s=2**(10-xm.phot_g_mean_mag)*30, c='y',alpha=0.5)
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qid = (xm.parallax > 5.0) & (xm.parallax > 5.0)
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xm.columns.values
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