Checking intermediate results stored in pickle

intermediate_check.md

import pandas as pd

%matplotlib inline
import matplotlib.pyplot as plt

df = pd.read_pickle('refit_results_round_2.pkl')

df.head()

	objectId	chisq	ndof	z	t0	x0	x1	c	z_model	t0_model	x0_model	x1_model	c_model
0	1034	19.024611	5	0.210104	60906.851726	0.000011	0.418726	-0.078705	0.2143	60909.606583	0.000112	-0.417935	-0.100454
1	2264	114.582635	32	0.183494	62784.000250	0.000010	-0.093271	0.080311	0.1701	62783.980442	0.000099	0.059946	0.103194
2	2339	179.975142	27	0.264409	60936.117872	0.000009	0.701409	-0.227959	0.2690	60936.061690	0.000095	0.709614	-0.232030
3	2339	366.869853	14	0.113918	61391.798655	0.000003	-1.762729	-0.251562	0.1901	61398.051322	0.000149	-0.494540	-0.134817
4	3245	67.600541	50	0.297779	60635.495350	0.000003	-0.054163	0.092550	0.3023	60635.484820	0.000032	-0.089033	0.078218

from desc.monitor import RefLightCurves

/Users/rbiswas/src/LSST/sims_catalogs_generation/python/lsst/sims/catalogs/generation/db/dbConnection.py:438: UserWarning: Duplicate object type id 25 specified: 
Output object ids may not be unique.
This may not be a problem if you do not want globally unique id values
  'want globally unique id values')
/Users/rbiswas/src/LSST/sims_catalogs_generation/python/lsst/sims/catalogs/generation/db/dbConnection.py:438: UserWarning: Duplicate object type id 40 specified: 
Output object ids may not be unique.
This may not be a problem if you do not want globally unique id values
  'want globally unique id values')

reflcTwink = RefLightCurves.fromTwinklesData(tableName='TwinkSN',
                                             idCol='snid',
                                             objectTypeID=42,
                                             dbHostName=None,
                                             columns=('snid', 'redshift', 'snra', 'sndec', 't0',
                                                      'x0', 'x1', 'c'),
                                             idSequence=None)

allParams = reflcTwink.get_params()

allParams.t0.describe()

count    776620.000000
mean       1825.093377
std        1053.525621
min           0.002604
25%         912.975108
50%        1823.963615
75%        2737.833141
max        3649.995311
Name: t0, dtype: float64

df.t0_model.describe()

count     1549.000000
mean     33609.185830
std      30617.327603
min          0.000000
25%          0.000000
50%      59910.570021
75%      61665.130372
max      63223.454712
Name: t0_model, dtype: float64

df.t0_model.apply(lambda x: x - 59580.).describe()

count     1549.000000
mean    -25970.814170
std      30617.327603
min     -59580.000000
25%     -59580.000000
50%        330.570021
75%       2085.130372
max       3643.454712
Name: t0_model, dtype: float64

df.t0_model.hist()

<matplotlib.axes._subplots.AxesSubplot at 0x117eb7b10>

df.query('t0_model < 10000').t0_model.unique()

array([ 0.])

print(df.query('t0_model < 10000').t0_model.size)

702

output_10_1.png

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