notebook.community

Edit and run



In [78]:

    
import numpy as np
import pandas as pd
import scipy



In [30]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')









    



/usr/local/miniconda/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.
  "`IPython.html.widgets` has moved to `ipywidgets`.", ShimWarning)



In [22]:

    
fitResults = pd.DataFrame(pd.read_pickle('../data/snFits.p')).transpose()



In [137]:

    
1.5**2









    Out[137]:





2.25



In [147]:

    
fitResults.columns









    Out[147]:





Index([u'c', u'hostebv', u'hostr_v', u'inputParams', u'mu', u'mu_var',
       u'mwebv', u'mwr_v', u't0', u'x0', u'x1', u'z', u'mu_err'],
      dtype='object')



In [148]:

    
fig, ax = plt.subplots()
_ = ax.hist(fitResults.dropna().z.values, histtype='step', bins=np.arange(0., 1.4, 0.05), lw=2)
_ = ax.hist(fitResults.dropna().query('mu_var < 2.25').z, histtype='step',
            bins=np.arange(0., 1.4, 0.05), lw=2)



In [97]:

    
fitResults[['z', 'mu', 'mu_var']] = fitResults[['z', 'mu', 'mu_var']].astype(np.float)
fitResults['mu_err'] = fitResults.mu_var.apply(np.sqrt)
fitResults['mu'] += 29.7558
df = fitResults[['z', 'mu', 'mu_err']].query('mu_err < 1.5')



In [98]:

    
df.head()



In [99]:

    
fig, ax = plt.subplots()
ax.errorbar(df.z, df.mu, yerr=df.mu_err, fmt='ko')
ax.set_ylim(-5., 100.)









    Out[99]:





(-5.0, 100.0)



In [100]:

    
fig, ax = plt.subplots()
df['eff_mu_err']  = np.sqrt(df.mu_err**2 + 0.1**2) 
ax.plot(df.z, df.eff_mu_err**(-2),'ko')









    Out[100]:





[<matplotlib.lines.Line2D at 0x110a707d0>]



In [101]:

    
df['binIndex'] = df.z //0.1



In [102]:

    
grouped = df.groupby('binIndex')



In [103]:

    
newtable = grouped.agg()









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-103-fe60641113a6> in <module>()
----> 1 newtable = grouped.agg()

TypeError: aggregate() takes at least 2 arguments (1 given)



In [107]:

    
fig, ax = plt.subplots()
vals = np.arange(0., 4., 0.05)
#thresh = 1.50
grouped.get_group(10).mu_err.hist(bins=vals, histtype='step', lw=2, ax=ax, normed=1)
m = grouped.get_group(10).mu_err.mean()
s = grouped.get_group(10).mu_err.std()
ax.plot(vals, scipy.stats.norm.pdf(vals, m, s))
ax.axvline(0.15)









    Out[107]:





<matplotlib.lines.Line2D at 0x111794050>



In [105]:

    
grouped.get_group(10).mu_err.hist(histtype='step', lw=2,  normed=1)









    Out[105]:





<matplotlib.axes._subplots.AxesSubplot at 0x1117802d0>



In [106]:

    
x = np.random.uniform(size=100)
np.mean(x)









    Out[106]:





0.49848211966495048



In [115]:

    
x[[x > .5]]









    Out[115]:





array([ 0.89225505,  0.72589895,  0.61077439,  0.53851155,  0.80162991,
        0.94800473,  0.73418903,  0.90955367,  0.8355374 ,  0.90580072,
        0.54639376,  0.8929976 ,  0.90103991,  0.79048064,  0.92281019,
        0.75904057,  0.73961288,  0.55893867,  0.87708301,  0.94765174,
        0.6096643 ,  0.99225511,  0.85001206,  0.77581993,  0.59390156,
        0.96693297,  0.54857168,  0.99811029,  0.93903493,  0.69044033,
        0.87875709,  0.65385454,  0.67930871,  0.50007744,  0.83506021,
        0.57537595,  0.70676624,  0.75391403,  0.6987813 ,  0.73351539,
        0.50706712,  0.98156949,  0.50254658,  0.56037214,  0.62769137,
        0.63801157,  0.57604311,  0.50640514,  0.68448033,  0.53971395,
        0.9567777 ,  0.51806642,  0.54431154])



In [116]:

    
def clipped_mean(mu_err, outlier_thresh):
    return np.mean(mu_err[mu_err < outlier_thresh])



In [124]:

    
grouped.get_group(10).mu_err.mean()









    Out[124]:





0.70117617693592393



In [125]:

    
grouped.get_group(10).mu_err.std()









    Out[125]:





0.20875753414209755



In [121]:

    
grouped.agg(dict(mu_err=[np.mean, np.std]))



In [127]:

    
df.head()



In [132]:

    
df['mu_xxx'] = df['mu_err'].apply(np.sqrt)



In [133]:

    
df.head()



In [ ]:

	z	mu	mu_err
10005	0.968958	73.960111	0.503012
10024	0.861278	74.175364	0.526293
10034	0.820012	73.804557	0.548847
10059	1.040996	74.819783	0.732858
10087	0.981252	73.883489	0.627543

	mu_err
	mean	std
binIndex
0.0	0.373468	NaN
1.0	0.639079	0.453628
2.0	0.449529	0.205451
3.0	0.418621	0.261837
4.0	0.382548	0.165145
5.0	0.480845	0.219671
6.0	0.462202	0.166019
7.0	0.533473	0.190570
8.0	0.575415	0.191252
9.0	0.648154	0.212502
10.0	0.701176	0.208758
11.0	0.716599	0.198387

	z	mu	mu_err	eff_mu_err	binIndex
10005	0.968958	73.960111	0.503012	0.512856	9.0
10024	0.861278	74.175364	0.526293	0.535709	8.0
10034	0.820012	73.804557	0.548847	0.557883	8.0
10059	1.040996	74.819783	0.732858	0.739649	10.0
10087	0.981252	73.883489	0.627543	0.635461	9.0

	z	mu	mu_err	eff_mu_err	binIndex	mu_xxx
10005	0.968958	73.960111	0.503012	0.512856	9.0	0.709233
10024	0.861278	74.175364	0.526293	0.535709	8.0	0.725461
10034	0.820012	73.804557	0.548847	0.557883	8.0	0.740842
10059	1.040996	74.819783	0.732858	0.739649	10.0	0.856071
10087	0.981252	73.883489	0.627543	0.635461	9.0	0.792176