First import all the modules such as healpy and astropy needed for analyzing the structure
In [1]:
import healpix_util as hu
import astropy as ap
import numpy as np
from astropy.io import fits
from astropy.table import Table
import astropy.io.ascii as ascii
from astropy.io import fits
from astropy.constants import c
import matplotlib.pyplot as plt
import math as m
from math import pi
#from scipy.constants import c
import scipy.special as sp
from astroML.decorators import pickle_results
from scipy import integrate
import warnings
from sklearn.neighbors import BallTree
import pickle
import multiprocessing as mp
import time
from aptestmetricdt import *
from aptestmetricdz import *
from scipy.spatial import distance as d
from apcat import *
from progressbar import *
from tqdm import *
from functools import partial
import pymangle
from apdz import *
from apdt import *
from scipy.optimize import curve_fit
#from astroML.datasets import fetch_sdss_specgals
#from astroML.correlation import bootstrap_two_point_angular
%matplotlib inline
Read the data file (taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html ) converted to ascii with comoving distance etc. in V01 reading from pkl files for faster read
In [2]:
# Getting back the objects:
with open('../output/DR12Qbin1.pkl') as f: # Python 3: open(..., 'rb')
dat = pickle.load(f)
dat
Out[2]:
array([[ 1.36035800e+00, 9.30000000e-05, -3.54870000e-02],
[ 1.13454400e+00, 6.82000000e-04, 2.43272000e-01],
[ 8.35325000e-01, 7.58000000e-04, -1.42169000e-01],
...,
[ 7.86509000e-01, 6.28274400e+00, 2.60439000e-01],
[ 8.35873000e-01, 6.28277000e+00, 1.59740000e-01],
[ 1.19725300e+00, 6.28307200e+00, 3.69546000e-01]])
In [3]:
dd2d=np.zeros((20,20))
In [4]:
len(dat)
Out[4]:
48309
In [5]:
rng = np.array([[0, 0.02], [0, 0.02]])
In [6]:
dd2d
Out[6]:
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.]])
In [7]:
%%time
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
dd2d+=np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
CPU times: user 421 ms, sys: 13 ms, total: 434 ms
Wall time: 425 ms
In [8]:
dd2d
Out[8]:
array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.]])
In [9]:
dd2d=np.zeros((20,20))
In [10]:
%%time
while(len(dat))>0:
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
dd2d+=np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
dat=np.delete(dat,0,axis=0)
if len(dat)%1000==0:
print len(dat)/1000
print dd2d
/Users/rohin/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py:804: RuntimeWarning: invalid value encountered in greater_equal
not_smaller_than_edge = (sample[:, i] >= edges[i][-1])
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[[ 4.58750000e+04 9.50000000e+01 1.12000000e+02 1.41000000e+02
1.43000000e+02 1.58000000e+02 1.79000000e+02 1.99000000e+02
1.68000000e+02 2.26000000e+02 2.06000000e+02 2.53000000e+02
2.96000000e+02 2.69000000e+02 2.81000000e+02 3.30000000e+02
3.42000000e+02 3.25000000e+02 3.15000000e+02 3.75000000e+02]
[ 4.00000000e+01 8.90000000e+01 1.12000000e+02 1.33000000e+02
1.26000000e+02 1.51000000e+02 1.80000000e+02 1.57000000e+02
1.86000000e+02 2.18000000e+02 2.02000000e+02 2.20000000e+02
2.67000000e+02 2.61000000e+02 3.11000000e+02 2.81000000e+02
3.10000000e+02 3.53000000e+02 3.67000000e+02 3.62000000e+02]
[ 2.90000000e+01 6.00000000e+01 8.80000000e+01 1.11000000e+02
9.50000000e+01 1.33000000e+02 1.70000000e+02 1.82000000e+02
1.91000000e+02 2.18000000e+02 2.28000000e+02 2.43000000e+02
2.56000000e+02 2.71000000e+02 2.91000000e+02 2.65000000e+02
3.06000000e+02 3.27000000e+02 3.66000000e+02 3.78000000e+02]
[ 1.90000000e+01 7.00000000e+01 8.80000000e+01 1.10000000e+02
1.25000000e+02 1.51000000e+02 1.62000000e+02 1.61000000e+02
2.11000000e+02 2.13000000e+02 2.32000000e+02 2.50000000e+02
2.60000000e+02 2.47000000e+02 2.73000000e+02 3.27000000e+02
3.05000000e+02 3.33000000e+02 3.53000000e+02 3.82000000e+02]
[ 2.20000000e+01 4.70000000e+01 6.80000000e+01 1.03000000e+02
1.09000000e+02 1.37000000e+02 1.48000000e+02 1.92000000e+02
1.77000000e+02 2.05000000e+02 2.18000000e+02 1.91000000e+02
2.42000000e+02 2.81000000e+02 2.84000000e+02 3.12000000e+02
3.16000000e+02 3.08000000e+02 3.87000000e+02 3.54000000e+02]
[ 1.90000000e+01 4.10000000e+01 7.10000000e+01 9.40000000e+01
1.08000000e+02 1.31000000e+02 1.54000000e+02 1.40000000e+02
1.88000000e+02 1.78000000e+02 1.86000000e+02 2.67000000e+02
2.59000000e+02 2.64000000e+02 2.62000000e+02 2.98000000e+02
3.01000000e+02 2.94000000e+02 3.29000000e+02 3.79000000e+02]
[ 1.40000000e+01 3.80000000e+01 6.30000000e+01 8.70000000e+01
1.21000000e+02 1.23000000e+02 1.54000000e+02 1.81000000e+02
1.77000000e+02 1.69000000e+02 2.22000000e+02 2.30000000e+02
2.44000000e+02 2.42000000e+02 3.11000000e+02 2.77000000e+02
2.87000000e+02 3.23000000e+02 3.84000000e+02 3.59000000e+02]
[ 1.00000000e+01 4.60000000e+01 6.40000000e+01 8.30000000e+01
8.90000000e+01 1.31000000e+02 1.38000000e+02 1.64000000e+02
1.58000000e+02 1.83000000e+02 2.14000000e+02 2.09000000e+02
2.41000000e+02 2.39000000e+02 3.05000000e+02 3.15000000e+02
3.09000000e+02 3.37000000e+02 3.17000000e+02 3.59000000e+02]
[ 9.00000000e+00 3.00000000e+01 5.40000000e+01 1.00000000e+02
9.10000000e+01 1.12000000e+02 1.20000000e+02 1.55000000e+02
1.57000000e+02 1.86000000e+02 1.97000000e+02 2.23000000e+02
2.43000000e+02 2.63000000e+02 2.82000000e+02 2.83000000e+02
2.97000000e+02 3.15000000e+02 3.17000000e+02 3.29000000e+02]
[ 1.40000000e+01 3.40000000e+01 4.30000000e+01 8.20000000e+01
8.80000000e+01 1.16000000e+02 1.37000000e+02 1.41000000e+02
1.73000000e+02 1.81000000e+02 2.09000000e+02 2.44000000e+02
2.45000000e+02 3.01000000e+02 2.56000000e+02 2.85000000e+02
2.85000000e+02 3.39000000e+02 3.25000000e+02 3.76000000e+02]
[ 1.40000000e+01 2.20000000e+01 6.40000000e+01 6.40000000e+01
9.90000000e+01 1.02000000e+02 1.51000000e+02 1.60000000e+02
1.81000000e+02 1.83000000e+02 2.35000000e+02 2.16000000e+02
2.45000000e+02 2.48000000e+02 2.90000000e+02 2.76000000e+02
2.97000000e+02 3.32000000e+02 3.59000000e+02 3.54000000e+02]
[ 1.00000000e+01 3.80000000e+01 4.90000000e+01 8.00000000e+01
8.50000000e+01 1.23000000e+02 1.23000000e+02 1.56000000e+02
1.61000000e+02 1.76000000e+02 2.01000000e+02 2.21000000e+02
2.35000000e+02 2.61000000e+02 2.89000000e+02 2.91000000e+02
3.01000000e+02 3.44000000e+02 3.46000000e+02 3.58000000e+02]
[ 1.30000000e+01 1.80000000e+01 6.10000000e+01 6.70000000e+01
9.30000000e+01 1.26000000e+02 1.20000000e+02 1.49000000e+02
1.42000000e+02 1.76000000e+02 2.06000000e+02 2.07000000e+02
2.18000000e+02 2.45000000e+02 2.95000000e+02 2.97000000e+02
3.16000000e+02 3.41000000e+02 3.47000000e+02 3.46000000e+02]
[ 1.20000000e+01 2.80000000e+01 5.50000000e+01 6.20000000e+01
9.00000000e+01 1.03000000e+02 1.27000000e+02 1.48000000e+02
1.90000000e+02 1.99000000e+02 2.04000000e+02 2.16000000e+02
2.58000000e+02 2.38000000e+02 2.79000000e+02 2.85000000e+02
3.03000000e+02 3.06000000e+02 3.37000000e+02 3.45000000e+02]
[ 8.00000000e+00 2.50000000e+01 5.10000000e+01 6.20000000e+01
1.00000000e+02 1.13000000e+02 1.42000000e+02 1.25000000e+02
1.59000000e+02 1.84000000e+02 1.88000000e+02 1.99000000e+02
2.39000000e+02 2.50000000e+02 2.90000000e+02 2.82000000e+02
2.84000000e+02 3.12000000e+02 3.80000000e+02 3.53000000e+02]
[ 1.10000000e+01 2.60000000e+01 5.10000000e+01 6.90000000e+01
9.10000000e+01 1.03000000e+02 1.21000000e+02 1.50000000e+02
1.55000000e+02 1.84000000e+02 1.93000000e+02 2.10000000e+02
2.25000000e+02 2.53000000e+02 2.75000000e+02 2.80000000e+02
2.98000000e+02 3.17000000e+02 3.16000000e+02 3.64000000e+02]
[ 1.20000000e+01 2.00000000e+01 4.80000000e+01 6.80000000e+01
7.80000000e+01 9.20000000e+01 1.28000000e+02 1.45000000e+02
1.50000000e+02 1.82000000e+02 1.98000000e+02 2.23000000e+02
2.26000000e+02 2.55000000e+02 2.58000000e+02 2.51000000e+02
2.81000000e+02 2.59000000e+02 3.32000000e+02 3.81000000e+02]
[ 7.00000000e+00 4.00000000e+01 5.30000000e+01 6.50000000e+01
9.10000000e+01 1.05000000e+02 1.25000000e+02 1.39000000e+02
1.77000000e+02 1.94000000e+02 2.20000000e+02 2.09000000e+02
2.14000000e+02 2.30000000e+02 2.88000000e+02 2.68000000e+02
2.66000000e+02 3.03000000e+02 3.12000000e+02 2.99000000e+02]
[ 5.00000000e+00 2.70000000e+01 3.50000000e+01 6.60000000e+01
7.70000000e+01 1.02000000e+02 1.20000000e+02 1.45000000e+02
1.57000000e+02 1.75000000e+02 1.83000000e+02 2.18000000e+02
2.31000000e+02 2.46000000e+02 2.60000000e+02 2.54000000e+02
2.97000000e+02 2.96000000e+02 3.31000000e+02 3.52000000e+02]
[ 1.00000000e+01 2.00000000e+01 3.90000000e+01 6.60000000e+01
8.60000000e+01 1.18000000e+02 1.04000000e+02 1.30000000e+02
1.70000000e+02 1.85000000e+02 2.11000000e+02 2.19000000e+02
2.10000000e+02 2.61000000e+02 2.52000000e+02 2.43000000e+02
2.94000000e+02 3.13000000e+02 3.30000000e+02 3.39000000e+02]]
CPU times: user 3h 47min 27s, sys: 2min 18s, total: 3h 49min 45s
Wall time: 3h 49min 51s
In [11]:
dd2d
Out[11]:
array([[ 4.58750000e+04, 9.50000000e+01, 1.12000000e+02,
1.41000000e+02, 1.43000000e+02, 1.58000000e+02,
1.79000000e+02, 1.99000000e+02, 1.68000000e+02,
2.26000000e+02, 2.06000000e+02, 2.53000000e+02,
2.96000000e+02, 2.69000000e+02, 2.81000000e+02,
3.30000000e+02, 3.42000000e+02, 3.25000000e+02,
3.15000000e+02, 3.75000000e+02],
[ 4.00000000e+01, 8.90000000e+01, 1.12000000e+02,
1.33000000e+02, 1.26000000e+02, 1.51000000e+02,
1.80000000e+02, 1.57000000e+02, 1.86000000e+02,
2.18000000e+02, 2.02000000e+02, 2.20000000e+02,
2.67000000e+02, 2.61000000e+02, 3.11000000e+02,
2.81000000e+02, 3.10000000e+02, 3.53000000e+02,
3.67000000e+02, 3.62000000e+02],
[ 2.90000000e+01, 6.00000000e+01, 8.80000000e+01,
1.11000000e+02, 9.50000000e+01, 1.33000000e+02,
1.70000000e+02, 1.82000000e+02, 1.91000000e+02,
2.18000000e+02, 2.28000000e+02, 2.43000000e+02,
2.56000000e+02, 2.71000000e+02, 2.91000000e+02,
2.65000000e+02, 3.06000000e+02, 3.27000000e+02,
3.66000000e+02, 3.78000000e+02],
[ 1.90000000e+01, 7.00000000e+01, 8.80000000e+01,
1.10000000e+02, 1.25000000e+02, 1.51000000e+02,
1.62000000e+02, 1.61000000e+02, 2.11000000e+02,
2.13000000e+02, 2.32000000e+02, 2.50000000e+02,
2.60000000e+02, 2.47000000e+02, 2.73000000e+02,
3.27000000e+02, 3.05000000e+02, 3.33000000e+02,
3.53000000e+02, 3.82000000e+02],
[ 2.20000000e+01, 4.70000000e+01, 6.80000000e+01,
1.03000000e+02, 1.09000000e+02, 1.37000000e+02,
1.48000000e+02, 1.92000000e+02, 1.77000000e+02,
2.05000000e+02, 2.18000000e+02, 1.91000000e+02,
2.42000000e+02, 2.81000000e+02, 2.84000000e+02,
3.12000000e+02, 3.16000000e+02, 3.08000000e+02,
3.87000000e+02, 3.54000000e+02],
[ 1.90000000e+01, 4.10000000e+01, 7.10000000e+01,
9.40000000e+01, 1.08000000e+02, 1.31000000e+02,
1.54000000e+02, 1.40000000e+02, 1.88000000e+02,
1.78000000e+02, 1.86000000e+02, 2.67000000e+02,
2.59000000e+02, 2.64000000e+02, 2.62000000e+02,
2.98000000e+02, 3.01000000e+02, 2.94000000e+02,
3.29000000e+02, 3.79000000e+02],
[ 1.40000000e+01, 3.80000000e+01, 6.30000000e+01,
8.70000000e+01, 1.21000000e+02, 1.23000000e+02,
1.54000000e+02, 1.81000000e+02, 1.77000000e+02,
1.69000000e+02, 2.22000000e+02, 2.30000000e+02,
2.44000000e+02, 2.42000000e+02, 3.11000000e+02,
2.77000000e+02, 2.87000000e+02, 3.23000000e+02,
3.84000000e+02, 3.59000000e+02],
[ 1.00000000e+01, 4.60000000e+01, 6.40000000e+01,
8.30000000e+01, 8.90000000e+01, 1.31000000e+02,
1.38000000e+02, 1.64000000e+02, 1.58000000e+02,
1.83000000e+02, 2.14000000e+02, 2.09000000e+02,
2.41000000e+02, 2.39000000e+02, 3.05000000e+02,
3.15000000e+02, 3.09000000e+02, 3.37000000e+02,
3.17000000e+02, 3.59000000e+02],
[ 9.00000000e+00, 3.00000000e+01, 5.40000000e+01,
1.00000000e+02, 9.10000000e+01, 1.12000000e+02,
1.20000000e+02, 1.55000000e+02, 1.57000000e+02,
1.86000000e+02, 1.97000000e+02, 2.23000000e+02,
2.43000000e+02, 2.63000000e+02, 2.82000000e+02,
2.83000000e+02, 2.97000000e+02, 3.15000000e+02,
3.17000000e+02, 3.29000000e+02],
[ 1.40000000e+01, 3.40000000e+01, 4.30000000e+01,
8.20000000e+01, 8.80000000e+01, 1.16000000e+02,
1.37000000e+02, 1.41000000e+02, 1.73000000e+02,
1.81000000e+02, 2.09000000e+02, 2.44000000e+02,
2.45000000e+02, 3.01000000e+02, 2.56000000e+02,
2.85000000e+02, 2.85000000e+02, 3.39000000e+02,
3.25000000e+02, 3.76000000e+02],
[ 1.40000000e+01, 2.20000000e+01, 6.40000000e+01,
6.40000000e+01, 9.90000000e+01, 1.02000000e+02,
1.51000000e+02, 1.60000000e+02, 1.81000000e+02,
1.83000000e+02, 2.35000000e+02, 2.16000000e+02,
2.45000000e+02, 2.48000000e+02, 2.90000000e+02,
2.76000000e+02, 2.97000000e+02, 3.32000000e+02,
3.59000000e+02, 3.54000000e+02],
[ 1.00000000e+01, 3.80000000e+01, 4.90000000e+01,
8.00000000e+01, 8.50000000e+01, 1.23000000e+02,
1.23000000e+02, 1.56000000e+02, 1.61000000e+02,
1.76000000e+02, 2.01000000e+02, 2.21000000e+02,
2.35000000e+02, 2.61000000e+02, 2.89000000e+02,
2.91000000e+02, 3.01000000e+02, 3.44000000e+02,
3.46000000e+02, 3.58000000e+02],
[ 1.30000000e+01, 1.80000000e+01, 6.10000000e+01,
6.70000000e+01, 9.30000000e+01, 1.26000000e+02,
1.20000000e+02, 1.49000000e+02, 1.42000000e+02,
1.76000000e+02, 2.06000000e+02, 2.07000000e+02,
2.18000000e+02, 2.45000000e+02, 2.95000000e+02,
2.97000000e+02, 3.16000000e+02, 3.41000000e+02,
3.47000000e+02, 3.46000000e+02],
[ 1.20000000e+01, 2.80000000e+01, 5.50000000e+01,
6.20000000e+01, 9.00000000e+01, 1.03000000e+02,
1.27000000e+02, 1.48000000e+02, 1.90000000e+02,
1.99000000e+02, 2.04000000e+02, 2.16000000e+02,
2.58000000e+02, 2.38000000e+02, 2.79000000e+02,
2.85000000e+02, 3.03000000e+02, 3.06000000e+02,
3.37000000e+02, 3.45000000e+02],
[ 8.00000000e+00, 2.50000000e+01, 5.10000000e+01,
6.20000000e+01, 1.00000000e+02, 1.13000000e+02,
1.42000000e+02, 1.25000000e+02, 1.59000000e+02,
1.84000000e+02, 1.88000000e+02, 1.99000000e+02,
2.39000000e+02, 2.50000000e+02, 2.90000000e+02,
2.82000000e+02, 2.84000000e+02, 3.12000000e+02,
3.80000000e+02, 3.53000000e+02],
[ 1.10000000e+01, 2.60000000e+01, 5.10000000e+01,
6.90000000e+01, 9.10000000e+01, 1.03000000e+02,
1.21000000e+02, 1.50000000e+02, 1.55000000e+02,
1.84000000e+02, 1.93000000e+02, 2.10000000e+02,
2.25000000e+02, 2.53000000e+02, 2.75000000e+02,
2.80000000e+02, 2.98000000e+02, 3.17000000e+02,
3.16000000e+02, 3.64000000e+02],
[ 1.20000000e+01, 2.00000000e+01, 4.80000000e+01,
6.80000000e+01, 7.80000000e+01, 9.20000000e+01,
1.28000000e+02, 1.45000000e+02, 1.50000000e+02,
1.82000000e+02, 1.98000000e+02, 2.23000000e+02,
2.26000000e+02, 2.55000000e+02, 2.58000000e+02,
2.51000000e+02, 2.81000000e+02, 2.59000000e+02,
3.32000000e+02, 3.81000000e+02],
[ 7.00000000e+00, 4.00000000e+01, 5.30000000e+01,
6.50000000e+01, 9.10000000e+01, 1.05000000e+02,
1.25000000e+02, 1.39000000e+02, 1.77000000e+02,
1.94000000e+02, 2.20000000e+02, 2.09000000e+02,
2.14000000e+02, 2.30000000e+02, 2.88000000e+02,
2.68000000e+02, 2.66000000e+02, 3.03000000e+02,
3.12000000e+02, 2.99000000e+02],
[ 5.00000000e+00, 2.70000000e+01, 3.50000000e+01,
6.60000000e+01, 7.70000000e+01, 1.02000000e+02,
1.20000000e+02, 1.45000000e+02, 1.57000000e+02,
1.75000000e+02, 1.83000000e+02, 2.18000000e+02,
2.31000000e+02, 2.46000000e+02, 2.60000000e+02,
2.54000000e+02, 2.97000000e+02, 2.96000000e+02,
3.31000000e+02, 3.52000000e+02],
[ 1.00000000e+01, 2.00000000e+01, 3.90000000e+01,
6.60000000e+01, 8.60000000e+01, 1.18000000e+02,
1.04000000e+02, 1.30000000e+02, 1.70000000e+02,
1.85000000e+02, 2.11000000e+02, 2.19000000e+02,
2.10000000e+02, 2.61000000e+02, 2.52000000e+02,
2.43000000e+02, 2.94000000e+02, 3.13000000e+02,
3.30000000e+02, 3.39000000e+02]])
In [12]:
with open('DR12QDDbin1.pkl','w') as f:
pickle.dump(dd2d,f)
dd2d
Out[12]:
array([[ 4.58750000e+04, 9.50000000e+01, 1.12000000e+02,
1.41000000e+02, 1.43000000e+02, 1.58000000e+02,
1.79000000e+02, 1.99000000e+02, 1.68000000e+02,
2.26000000e+02, 2.06000000e+02, 2.53000000e+02,
2.96000000e+02, 2.69000000e+02, 2.81000000e+02,
3.30000000e+02, 3.42000000e+02, 3.25000000e+02,
3.15000000e+02, 3.75000000e+02],
[ 4.00000000e+01, 8.90000000e+01, 1.12000000e+02,
1.33000000e+02, 1.26000000e+02, 1.51000000e+02,
1.80000000e+02, 1.57000000e+02, 1.86000000e+02,
2.18000000e+02, 2.02000000e+02, 2.20000000e+02,
2.67000000e+02, 2.61000000e+02, 3.11000000e+02,
2.81000000e+02, 3.10000000e+02, 3.53000000e+02,
3.67000000e+02, 3.62000000e+02],
[ 2.90000000e+01, 6.00000000e+01, 8.80000000e+01,
1.11000000e+02, 9.50000000e+01, 1.33000000e+02,
1.70000000e+02, 1.82000000e+02, 1.91000000e+02,
2.18000000e+02, 2.28000000e+02, 2.43000000e+02,
2.56000000e+02, 2.71000000e+02, 2.91000000e+02,
2.65000000e+02, 3.06000000e+02, 3.27000000e+02,
3.66000000e+02, 3.78000000e+02],
[ 1.90000000e+01, 7.00000000e+01, 8.80000000e+01,
1.10000000e+02, 1.25000000e+02, 1.51000000e+02,
1.62000000e+02, 1.61000000e+02, 2.11000000e+02,
2.13000000e+02, 2.32000000e+02, 2.50000000e+02,
2.60000000e+02, 2.47000000e+02, 2.73000000e+02,
3.27000000e+02, 3.05000000e+02, 3.33000000e+02,
3.53000000e+02, 3.82000000e+02],
[ 2.20000000e+01, 4.70000000e+01, 6.80000000e+01,
1.03000000e+02, 1.09000000e+02, 1.37000000e+02,
1.48000000e+02, 1.92000000e+02, 1.77000000e+02,
2.05000000e+02, 2.18000000e+02, 1.91000000e+02,
2.42000000e+02, 2.81000000e+02, 2.84000000e+02,
3.12000000e+02, 3.16000000e+02, 3.08000000e+02,
3.87000000e+02, 3.54000000e+02],
[ 1.90000000e+01, 4.10000000e+01, 7.10000000e+01,
9.40000000e+01, 1.08000000e+02, 1.31000000e+02,
1.54000000e+02, 1.40000000e+02, 1.88000000e+02,
1.78000000e+02, 1.86000000e+02, 2.67000000e+02,
2.59000000e+02, 2.64000000e+02, 2.62000000e+02,
2.98000000e+02, 3.01000000e+02, 2.94000000e+02,
3.29000000e+02, 3.79000000e+02],
[ 1.40000000e+01, 3.80000000e+01, 6.30000000e+01,
8.70000000e+01, 1.21000000e+02, 1.23000000e+02,
1.54000000e+02, 1.81000000e+02, 1.77000000e+02,
1.69000000e+02, 2.22000000e+02, 2.30000000e+02,
2.44000000e+02, 2.42000000e+02, 3.11000000e+02,
2.77000000e+02, 2.87000000e+02, 3.23000000e+02,
3.84000000e+02, 3.59000000e+02],
[ 1.00000000e+01, 4.60000000e+01, 6.40000000e+01,
8.30000000e+01, 8.90000000e+01, 1.31000000e+02,
1.38000000e+02, 1.64000000e+02, 1.58000000e+02,
1.83000000e+02, 2.14000000e+02, 2.09000000e+02,
2.41000000e+02, 2.39000000e+02, 3.05000000e+02,
3.15000000e+02, 3.09000000e+02, 3.37000000e+02,
3.17000000e+02, 3.59000000e+02],
[ 9.00000000e+00, 3.00000000e+01, 5.40000000e+01,
1.00000000e+02, 9.10000000e+01, 1.12000000e+02,
1.20000000e+02, 1.55000000e+02, 1.57000000e+02,
1.86000000e+02, 1.97000000e+02, 2.23000000e+02,
2.43000000e+02, 2.63000000e+02, 2.82000000e+02,
2.83000000e+02, 2.97000000e+02, 3.15000000e+02,
3.17000000e+02, 3.29000000e+02],
[ 1.40000000e+01, 3.40000000e+01, 4.30000000e+01,
8.20000000e+01, 8.80000000e+01, 1.16000000e+02,
1.37000000e+02, 1.41000000e+02, 1.73000000e+02,
1.81000000e+02, 2.09000000e+02, 2.44000000e+02,
2.45000000e+02, 3.01000000e+02, 2.56000000e+02,
2.85000000e+02, 2.85000000e+02, 3.39000000e+02,
3.25000000e+02, 3.76000000e+02],
[ 1.40000000e+01, 2.20000000e+01, 6.40000000e+01,
6.40000000e+01, 9.90000000e+01, 1.02000000e+02,
1.51000000e+02, 1.60000000e+02, 1.81000000e+02,
1.83000000e+02, 2.35000000e+02, 2.16000000e+02,
2.45000000e+02, 2.48000000e+02, 2.90000000e+02,
2.76000000e+02, 2.97000000e+02, 3.32000000e+02,
3.59000000e+02, 3.54000000e+02],
[ 1.00000000e+01, 3.80000000e+01, 4.90000000e+01,
8.00000000e+01, 8.50000000e+01, 1.23000000e+02,
1.23000000e+02, 1.56000000e+02, 1.61000000e+02,
1.76000000e+02, 2.01000000e+02, 2.21000000e+02,
2.35000000e+02, 2.61000000e+02, 2.89000000e+02,
2.91000000e+02, 3.01000000e+02, 3.44000000e+02,
3.46000000e+02, 3.58000000e+02],
[ 1.30000000e+01, 1.80000000e+01, 6.10000000e+01,
6.70000000e+01, 9.30000000e+01, 1.26000000e+02,
1.20000000e+02, 1.49000000e+02, 1.42000000e+02,
1.76000000e+02, 2.06000000e+02, 2.07000000e+02,
2.18000000e+02, 2.45000000e+02, 2.95000000e+02,
2.97000000e+02, 3.16000000e+02, 3.41000000e+02,
3.47000000e+02, 3.46000000e+02],
[ 1.20000000e+01, 2.80000000e+01, 5.50000000e+01,
6.20000000e+01, 9.00000000e+01, 1.03000000e+02,
1.27000000e+02, 1.48000000e+02, 1.90000000e+02,
1.99000000e+02, 2.04000000e+02, 2.16000000e+02,
2.58000000e+02, 2.38000000e+02, 2.79000000e+02,
2.85000000e+02, 3.03000000e+02, 3.06000000e+02,
3.37000000e+02, 3.45000000e+02],
[ 8.00000000e+00, 2.50000000e+01, 5.10000000e+01,
6.20000000e+01, 1.00000000e+02, 1.13000000e+02,
1.42000000e+02, 1.25000000e+02, 1.59000000e+02,
1.84000000e+02, 1.88000000e+02, 1.99000000e+02,
2.39000000e+02, 2.50000000e+02, 2.90000000e+02,
2.82000000e+02, 2.84000000e+02, 3.12000000e+02,
3.80000000e+02, 3.53000000e+02],
[ 1.10000000e+01, 2.60000000e+01, 5.10000000e+01,
6.90000000e+01, 9.10000000e+01, 1.03000000e+02,
1.21000000e+02, 1.50000000e+02, 1.55000000e+02,
1.84000000e+02, 1.93000000e+02, 2.10000000e+02,
2.25000000e+02, 2.53000000e+02, 2.75000000e+02,
2.80000000e+02, 2.98000000e+02, 3.17000000e+02,
3.16000000e+02, 3.64000000e+02],
[ 1.20000000e+01, 2.00000000e+01, 4.80000000e+01,
6.80000000e+01, 7.80000000e+01, 9.20000000e+01,
1.28000000e+02, 1.45000000e+02, 1.50000000e+02,
1.82000000e+02, 1.98000000e+02, 2.23000000e+02,
2.26000000e+02, 2.55000000e+02, 2.58000000e+02,
2.51000000e+02, 2.81000000e+02, 2.59000000e+02,
3.32000000e+02, 3.81000000e+02],
[ 7.00000000e+00, 4.00000000e+01, 5.30000000e+01,
6.50000000e+01, 9.10000000e+01, 1.05000000e+02,
1.25000000e+02, 1.39000000e+02, 1.77000000e+02,
1.94000000e+02, 2.20000000e+02, 2.09000000e+02,
2.14000000e+02, 2.30000000e+02, 2.88000000e+02,
2.68000000e+02, 2.66000000e+02, 3.03000000e+02,
3.12000000e+02, 2.99000000e+02],
[ 5.00000000e+00, 2.70000000e+01, 3.50000000e+01,
6.60000000e+01, 7.70000000e+01, 1.02000000e+02,
1.20000000e+02, 1.45000000e+02, 1.57000000e+02,
1.75000000e+02, 1.83000000e+02, 2.18000000e+02,
2.31000000e+02, 2.46000000e+02, 2.60000000e+02,
2.54000000e+02, 2.97000000e+02, 2.96000000e+02,
3.31000000e+02, 3.52000000e+02],
[ 1.00000000e+01, 2.00000000e+01, 3.90000000e+01,
6.60000000e+01, 8.60000000e+01, 1.18000000e+02,
1.04000000e+02, 1.30000000e+02, 1.70000000e+02,
1.85000000e+02, 2.11000000e+02, 2.19000000e+02,
2.10000000e+02, 2.61000000e+02, 2.52000000e+02,
2.43000000e+02, 2.94000000e+02, 3.13000000e+02,
3.30000000e+02, 3.39000000e+02]])
In [ ]:
In [13]:
# Getting back the objects:
with open('../output/DR12Qbin2.pkl') as f: # Python 3: open(..., 'rb')
dat = pickle.load(f)
dat
Out[13]:
array([[ 1.61884600e+00, 7.10000000e-05, 8.42960000e-02],
[ 1.57613400e+00, 1.49000000e-04, 6.05257000e-01],
[ 1.71010300e+00, 1.57000000e-04, 2.66244000e-01],
...,
[ 1.62190900e+00, 6.28264600e+00, -7.35650000e-02],
[ 1.56940900e+00, 6.28270300e+00, -7.70830000e-02],
[ 1.84922100e+00, 6.28282000e+00, 3.38480000e-02]])
In [14]:
dd2d=np.zeros((20,20))
In [15]:
len(dat)
Out[15]:
42650
In [16]:
dd2d
Out[16]:
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.]])
In [17]:
%%time
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
print np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
[[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]]
CPU times: user 415 ms, sys: 8.54 ms, total: 424 ms
Wall time: 489 ms
In [18]:
%%time
while(len(dat))>0:
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
dd2d+=np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
dat=np.delete(dat,0,axis=0)
if len(dat)%1000==0:
print len(dat)/1000
print dd2d
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20
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4
3
2
1
0
[[ 4.03410000e+04 1.70000000e+01 3.70000000e+01 5.30000000e+01
4.60000000e+01 5.70000000e+01 5.60000000e+01 6.40000000e+01
6.40000000e+01 6.40000000e+01 7.80000000e+01 7.60000000e+01
9.30000000e+01 7.50000000e+01 9.30000000e+01 1.04000000e+02
1.08000000e+02 1.05000000e+02 1.12000000e+02 1.22000000e+02]
[ 7.00000000e+00 3.00000000e+01 3.90000000e+01 3.40000000e+01
5.00000000e+01 3.80000000e+01 4.80000000e+01 6.20000000e+01
7.40000000e+01 7.10000000e+01 7.20000000e+01 6.90000000e+01
8.30000000e+01 1.03000000e+02 8.40000000e+01 7.70000000e+01
1.01000000e+02 1.18000000e+02 1.01000000e+02 1.28000000e+02]
[ 1.80000000e+01 3.20000000e+01 2.70000000e+01 4.10000000e+01
3.00000000e+01 5.50000000e+01 4.50000000e+01 5.70000000e+01
6.20000000e+01 5.90000000e+01 7.60000000e+01 7.90000000e+01
8.00000000e+01 7.90000000e+01 9.60000000e+01 8.70000000e+01
1.02000000e+02 1.13000000e+02 8.80000000e+01 1.12000000e+02]
[ 9.00000000e+00 2.00000000e+01 2.40000000e+01 4.00000000e+01
4.60000000e+01 4.60000000e+01 5.00000000e+01 5.10000000e+01
6.90000000e+01 8.10000000e+01 7.80000000e+01 8.90000000e+01
7.60000000e+01 8.00000000e+01 9.50000000e+01 1.09000000e+02
8.30000000e+01 1.28000000e+02 1.31000000e+02 1.24000000e+02]
[ 1.10000000e+01 2.10000000e+01 3.30000000e+01 4.50000000e+01
3.80000000e+01 4.10000000e+01 5.20000000e+01 5.00000000e+01
6.50000000e+01 7.70000000e+01 6.40000000e+01 8.70000000e+01
9.50000000e+01 8.60000000e+01 8.70000000e+01 8.10000000e+01
8.30000000e+01 1.15000000e+02 1.13000000e+02 1.19000000e+02]
[ 5.00000000e+00 1.90000000e+01 3.30000000e+01 3.40000000e+01
6.10000000e+01 4.70000000e+01 5.00000000e+01 6.20000000e+01
5.70000000e+01 6.50000000e+01 7.20000000e+01 8.10000000e+01
6.80000000e+01 9.10000000e+01 9.00000000e+01 9.90000000e+01
8.90000000e+01 1.15000000e+02 9.00000000e+01 1.23000000e+02]
[ 9.00000000e+00 1.90000000e+01 2.80000000e+01 2.80000000e+01
3.30000000e+01 4.30000000e+01 5.30000000e+01 6.80000000e+01
6.90000000e+01 6.50000000e+01 7.40000000e+01 8.50000000e+01
8.00000000e+01 9.00000000e+01 8.20000000e+01 1.06000000e+02
9.40000000e+01 1.19000000e+02 1.18000000e+02 1.18000000e+02]
[ 1.40000000e+01 2.20000000e+01 2.10000000e+01 3.50000000e+01
4.30000000e+01 4.10000000e+01 5.30000000e+01 5.30000000e+01
5.20000000e+01 5.90000000e+01 7.20000000e+01 9.20000000e+01
8.70000000e+01 1.07000000e+02 1.01000000e+02 9.30000000e+01
9.70000000e+01 8.60000000e+01 1.17000000e+02 1.29000000e+02]
[ 8.00000000e+00 1.30000000e+01 2.50000000e+01 2.90000000e+01
3.40000000e+01 5.20000000e+01 5.20000000e+01 6.00000000e+01
5.90000000e+01 7.10000000e+01 7.00000000e+01 6.60000000e+01
8.30000000e+01 9.10000000e+01 8.40000000e+01 8.80000000e+01
8.70000000e+01 1.17000000e+02 1.22000000e+02 9.10000000e+01]
[ 1.30000000e+01 1.60000000e+01 1.90000000e+01 4.80000000e+01
2.90000000e+01 3.90000000e+01 4.80000000e+01 5.70000000e+01
6.40000000e+01 4.60000000e+01 6.60000000e+01 7.80000000e+01
8.90000000e+01 9.50000000e+01 9.20000000e+01 7.80000000e+01
8.20000000e+01 1.08000000e+02 8.80000000e+01 1.15000000e+02]
[ 4.00000000e+00 2.50000000e+01 3.20000000e+01 2.10000000e+01
3.80000000e+01 5.70000000e+01 4.80000000e+01 5.20000000e+01
6.50000000e+01 5.90000000e+01 6.20000000e+01 8.70000000e+01
8.90000000e+01 7.80000000e+01 1.08000000e+02 8.70000000e+01
9.70000000e+01 9.40000000e+01 1.19000000e+02 1.20000000e+02]
[ 8.00000000e+00 9.00000000e+00 2.30000000e+01 3.10000000e+01
4.20000000e+01 4.70000000e+01 5.50000000e+01 5.60000000e+01
7.70000000e+01 7.50000000e+01 6.80000000e+01 6.70000000e+01
7.90000000e+01 9.60000000e+01 6.40000000e+01 9.20000000e+01
9.20000000e+01 1.00000000e+02 9.30000000e+01 1.15000000e+02]
[ 5.00000000e+00 1.10000000e+01 3.10000000e+01 2.50000000e+01
3.40000000e+01 4.60000000e+01 4.30000000e+01 7.00000000e+01
6.20000000e+01 6.20000000e+01 6.30000000e+01 6.30000000e+01
8.30000000e+01 8.30000000e+01 8.70000000e+01 8.10000000e+01
8.50000000e+01 1.11000000e+02 1.05000000e+02 1.15000000e+02]
[ 3.00000000e+00 1.00000000e+01 1.60000000e+01 2.80000000e+01
3.10000000e+01 4.50000000e+01 4.80000000e+01 4.80000000e+01
4.80000000e+01 5.30000000e+01 7.90000000e+01 7.40000000e+01
8.40000000e+01 9.20000000e+01 8.80000000e+01 8.70000000e+01
8.70000000e+01 1.05000000e+02 1.19000000e+02 1.11000000e+02]
[ 8.00000000e+00 1.60000000e+01 2.70000000e+01 3.50000000e+01
3.60000000e+01 2.30000000e+01 4.10000000e+01 6.30000000e+01
4.50000000e+01 5.40000000e+01 7.10000000e+01 7.60000000e+01
7.40000000e+01 8.70000000e+01 9.40000000e+01 9.40000000e+01
1.00000000e+02 1.02000000e+02 1.19000000e+02 8.90000000e+01]
[ 4.00000000e+00 7.00000000e+00 2.00000000e+01 2.60000000e+01
3.30000000e+01 3.00000000e+01 4.00000000e+01 4.00000000e+01
5.10000000e+01 4.20000000e+01 7.60000000e+01 7.10000000e+01
8.10000000e+01 8.40000000e+01 8.90000000e+01 8.60000000e+01
9.60000000e+01 9.20000000e+01 8.80000000e+01 1.22000000e+02]
[ 3.00000000e+00 1.00000000e+01 1.40000000e+01 2.40000000e+01
2.80000000e+01 5.10000000e+01 4.50000000e+01 2.90000000e+01
4.10000000e+01 5.70000000e+01 6.50000000e+01 7.60000000e+01
6.90000000e+01 8.80000000e+01 7.10000000e+01 9.70000000e+01
9.30000000e+01 1.00000000e+02 1.07000000e+02 1.15000000e+02]
[ 3.00000000e+00 1.30000000e+01 2.40000000e+01 2.80000000e+01
4.60000000e+01 3.00000000e+01 4.00000000e+01 4.80000000e+01
6.70000000e+01 5.80000000e+01 6.80000000e+01 7.40000000e+01
6.20000000e+01 8.60000000e+01 8.70000000e+01 9.30000000e+01
8.60000000e+01 1.00000000e+02 1.02000000e+02 1.14000000e+02]
[ 2.00000000e+00 1.70000000e+01 1.80000000e+01 2.70000000e+01
2.80000000e+01 3.30000000e+01 4.30000000e+01 4.60000000e+01
5.00000000e+01 6.10000000e+01 6.20000000e+01 6.90000000e+01
8.50000000e+01 9.30000000e+01 9.40000000e+01 8.00000000e+01
9.50000000e+01 1.12000000e+02 1.21000000e+02 9.90000000e+01]
[ 8.00000000e+00 1.60000000e+01 2.30000000e+01 2.00000000e+01
3.40000000e+01 3.70000000e+01 3.20000000e+01 4.60000000e+01
5.40000000e+01 5.20000000e+01 6.70000000e+01 7.20000000e+01
8.10000000e+01 8.50000000e+01 9.00000000e+01 8.90000000e+01
9.70000000e+01 9.70000000e+01 1.04000000e+02 1.18000000e+02]]
CPU times: user 2h 51min 49s, sys: 1min 33s, total: 2h 53min 23s
Wall time: 2h 53min 2s
In [19]:
dd2d
Out[19]:
array([[ 4.03410000e+04, 1.70000000e+01, 3.70000000e+01,
5.30000000e+01, 4.60000000e+01, 5.70000000e+01,
5.60000000e+01, 6.40000000e+01, 6.40000000e+01,
6.40000000e+01, 7.80000000e+01, 7.60000000e+01,
9.30000000e+01, 7.50000000e+01, 9.30000000e+01,
1.04000000e+02, 1.08000000e+02, 1.05000000e+02,
1.12000000e+02, 1.22000000e+02],
[ 7.00000000e+00, 3.00000000e+01, 3.90000000e+01,
3.40000000e+01, 5.00000000e+01, 3.80000000e+01,
4.80000000e+01, 6.20000000e+01, 7.40000000e+01,
7.10000000e+01, 7.20000000e+01, 6.90000000e+01,
8.30000000e+01, 1.03000000e+02, 8.40000000e+01,
7.70000000e+01, 1.01000000e+02, 1.18000000e+02,
1.01000000e+02, 1.28000000e+02],
[ 1.80000000e+01, 3.20000000e+01, 2.70000000e+01,
4.10000000e+01, 3.00000000e+01, 5.50000000e+01,
4.50000000e+01, 5.70000000e+01, 6.20000000e+01,
5.90000000e+01, 7.60000000e+01, 7.90000000e+01,
8.00000000e+01, 7.90000000e+01, 9.60000000e+01,
8.70000000e+01, 1.02000000e+02, 1.13000000e+02,
8.80000000e+01, 1.12000000e+02],
[ 9.00000000e+00, 2.00000000e+01, 2.40000000e+01,
4.00000000e+01, 4.60000000e+01, 4.60000000e+01,
5.00000000e+01, 5.10000000e+01, 6.90000000e+01,
8.10000000e+01, 7.80000000e+01, 8.90000000e+01,
7.60000000e+01, 8.00000000e+01, 9.50000000e+01,
1.09000000e+02, 8.30000000e+01, 1.28000000e+02,
1.31000000e+02, 1.24000000e+02],
[ 1.10000000e+01, 2.10000000e+01, 3.30000000e+01,
4.50000000e+01, 3.80000000e+01, 4.10000000e+01,
5.20000000e+01, 5.00000000e+01, 6.50000000e+01,
7.70000000e+01, 6.40000000e+01, 8.70000000e+01,
9.50000000e+01, 8.60000000e+01, 8.70000000e+01,
8.10000000e+01, 8.30000000e+01, 1.15000000e+02,
1.13000000e+02, 1.19000000e+02],
[ 5.00000000e+00, 1.90000000e+01, 3.30000000e+01,
3.40000000e+01, 6.10000000e+01, 4.70000000e+01,
5.00000000e+01, 6.20000000e+01, 5.70000000e+01,
6.50000000e+01, 7.20000000e+01, 8.10000000e+01,
6.80000000e+01, 9.10000000e+01, 9.00000000e+01,
9.90000000e+01, 8.90000000e+01, 1.15000000e+02,
9.00000000e+01, 1.23000000e+02],
[ 9.00000000e+00, 1.90000000e+01, 2.80000000e+01,
2.80000000e+01, 3.30000000e+01, 4.30000000e+01,
5.30000000e+01, 6.80000000e+01, 6.90000000e+01,
6.50000000e+01, 7.40000000e+01, 8.50000000e+01,
8.00000000e+01, 9.00000000e+01, 8.20000000e+01,
1.06000000e+02, 9.40000000e+01, 1.19000000e+02,
1.18000000e+02, 1.18000000e+02],
[ 1.40000000e+01, 2.20000000e+01, 2.10000000e+01,
3.50000000e+01, 4.30000000e+01, 4.10000000e+01,
5.30000000e+01, 5.30000000e+01, 5.20000000e+01,
5.90000000e+01, 7.20000000e+01, 9.20000000e+01,
8.70000000e+01, 1.07000000e+02, 1.01000000e+02,
9.30000000e+01, 9.70000000e+01, 8.60000000e+01,
1.17000000e+02, 1.29000000e+02],
[ 8.00000000e+00, 1.30000000e+01, 2.50000000e+01,
2.90000000e+01, 3.40000000e+01, 5.20000000e+01,
5.20000000e+01, 6.00000000e+01, 5.90000000e+01,
7.10000000e+01, 7.00000000e+01, 6.60000000e+01,
8.30000000e+01, 9.10000000e+01, 8.40000000e+01,
8.80000000e+01, 8.70000000e+01, 1.17000000e+02,
1.22000000e+02, 9.10000000e+01],
[ 1.30000000e+01, 1.60000000e+01, 1.90000000e+01,
4.80000000e+01, 2.90000000e+01, 3.90000000e+01,
4.80000000e+01, 5.70000000e+01, 6.40000000e+01,
4.60000000e+01, 6.60000000e+01, 7.80000000e+01,
8.90000000e+01, 9.50000000e+01, 9.20000000e+01,
7.80000000e+01, 8.20000000e+01, 1.08000000e+02,
8.80000000e+01, 1.15000000e+02],
[ 4.00000000e+00, 2.50000000e+01, 3.20000000e+01,
2.10000000e+01, 3.80000000e+01, 5.70000000e+01,
4.80000000e+01, 5.20000000e+01, 6.50000000e+01,
5.90000000e+01, 6.20000000e+01, 8.70000000e+01,
8.90000000e+01, 7.80000000e+01, 1.08000000e+02,
8.70000000e+01, 9.70000000e+01, 9.40000000e+01,
1.19000000e+02, 1.20000000e+02],
[ 8.00000000e+00, 9.00000000e+00, 2.30000000e+01,
3.10000000e+01, 4.20000000e+01, 4.70000000e+01,
5.50000000e+01, 5.60000000e+01, 7.70000000e+01,
7.50000000e+01, 6.80000000e+01, 6.70000000e+01,
7.90000000e+01, 9.60000000e+01, 6.40000000e+01,
9.20000000e+01, 9.20000000e+01, 1.00000000e+02,
9.30000000e+01, 1.15000000e+02],
[ 5.00000000e+00, 1.10000000e+01, 3.10000000e+01,
2.50000000e+01, 3.40000000e+01, 4.60000000e+01,
4.30000000e+01, 7.00000000e+01, 6.20000000e+01,
6.20000000e+01, 6.30000000e+01, 6.30000000e+01,
8.30000000e+01, 8.30000000e+01, 8.70000000e+01,
8.10000000e+01, 8.50000000e+01, 1.11000000e+02,
1.05000000e+02, 1.15000000e+02],
[ 3.00000000e+00, 1.00000000e+01, 1.60000000e+01,
2.80000000e+01, 3.10000000e+01, 4.50000000e+01,
4.80000000e+01, 4.80000000e+01, 4.80000000e+01,
5.30000000e+01, 7.90000000e+01, 7.40000000e+01,
8.40000000e+01, 9.20000000e+01, 8.80000000e+01,
8.70000000e+01, 8.70000000e+01, 1.05000000e+02,
1.19000000e+02, 1.11000000e+02],
[ 8.00000000e+00, 1.60000000e+01, 2.70000000e+01,
3.50000000e+01, 3.60000000e+01, 2.30000000e+01,
4.10000000e+01, 6.30000000e+01, 4.50000000e+01,
5.40000000e+01, 7.10000000e+01, 7.60000000e+01,
7.40000000e+01, 8.70000000e+01, 9.40000000e+01,
9.40000000e+01, 1.00000000e+02, 1.02000000e+02,
1.19000000e+02, 8.90000000e+01],
[ 4.00000000e+00, 7.00000000e+00, 2.00000000e+01,
2.60000000e+01, 3.30000000e+01, 3.00000000e+01,
4.00000000e+01, 4.00000000e+01, 5.10000000e+01,
4.20000000e+01, 7.60000000e+01, 7.10000000e+01,
8.10000000e+01, 8.40000000e+01, 8.90000000e+01,
8.60000000e+01, 9.60000000e+01, 9.20000000e+01,
8.80000000e+01, 1.22000000e+02],
[ 3.00000000e+00, 1.00000000e+01, 1.40000000e+01,
2.40000000e+01, 2.80000000e+01, 5.10000000e+01,
4.50000000e+01, 2.90000000e+01, 4.10000000e+01,
5.70000000e+01, 6.50000000e+01, 7.60000000e+01,
6.90000000e+01, 8.80000000e+01, 7.10000000e+01,
9.70000000e+01, 9.30000000e+01, 1.00000000e+02,
1.07000000e+02, 1.15000000e+02],
[ 3.00000000e+00, 1.30000000e+01, 2.40000000e+01,
2.80000000e+01, 4.60000000e+01, 3.00000000e+01,
4.00000000e+01, 4.80000000e+01, 6.70000000e+01,
5.80000000e+01, 6.80000000e+01, 7.40000000e+01,
6.20000000e+01, 8.60000000e+01, 8.70000000e+01,
9.30000000e+01, 8.60000000e+01, 1.00000000e+02,
1.02000000e+02, 1.14000000e+02],
[ 2.00000000e+00, 1.70000000e+01, 1.80000000e+01,
2.70000000e+01, 2.80000000e+01, 3.30000000e+01,
4.30000000e+01, 4.60000000e+01, 5.00000000e+01,
6.10000000e+01, 6.20000000e+01, 6.90000000e+01,
8.50000000e+01, 9.30000000e+01, 9.40000000e+01,
8.00000000e+01, 9.50000000e+01, 1.12000000e+02,
1.21000000e+02, 9.90000000e+01],
[ 8.00000000e+00, 1.60000000e+01, 2.30000000e+01,
2.00000000e+01, 3.40000000e+01, 3.70000000e+01,
3.20000000e+01, 4.60000000e+01, 5.40000000e+01,
5.20000000e+01, 6.70000000e+01, 7.20000000e+01,
8.10000000e+01, 8.50000000e+01, 9.00000000e+01,
8.90000000e+01, 9.70000000e+01, 9.70000000e+01,
1.04000000e+02, 1.18000000e+02]])
In [20]:
with open('DR12QDDbin2.pkl','w') as f:
pickle.dump(dd2d,f)
dd2d
Out[20]:
array([[ 4.03410000e+04, 1.70000000e+01, 3.70000000e+01,
5.30000000e+01, 4.60000000e+01, 5.70000000e+01,
5.60000000e+01, 6.40000000e+01, 6.40000000e+01,
6.40000000e+01, 7.80000000e+01, 7.60000000e+01,
9.30000000e+01, 7.50000000e+01, 9.30000000e+01,
1.04000000e+02, 1.08000000e+02, 1.05000000e+02,
1.12000000e+02, 1.22000000e+02],
[ 7.00000000e+00, 3.00000000e+01, 3.90000000e+01,
3.40000000e+01, 5.00000000e+01, 3.80000000e+01,
4.80000000e+01, 6.20000000e+01, 7.40000000e+01,
7.10000000e+01, 7.20000000e+01, 6.90000000e+01,
8.30000000e+01, 1.03000000e+02, 8.40000000e+01,
7.70000000e+01, 1.01000000e+02, 1.18000000e+02,
1.01000000e+02, 1.28000000e+02],
[ 1.80000000e+01, 3.20000000e+01, 2.70000000e+01,
4.10000000e+01, 3.00000000e+01, 5.50000000e+01,
4.50000000e+01, 5.70000000e+01, 6.20000000e+01,
5.90000000e+01, 7.60000000e+01, 7.90000000e+01,
8.00000000e+01, 7.90000000e+01, 9.60000000e+01,
8.70000000e+01, 1.02000000e+02, 1.13000000e+02,
8.80000000e+01, 1.12000000e+02],
[ 9.00000000e+00, 2.00000000e+01, 2.40000000e+01,
4.00000000e+01, 4.60000000e+01, 4.60000000e+01,
5.00000000e+01, 5.10000000e+01, 6.90000000e+01,
8.10000000e+01, 7.80000000e+01, 8.90000000e+01,
7.60000000e+01, 8.00000000e+01, 9.50000000e+01,
1.09000000e+02, 8.30000000e+01, 1.28000000e+02,
1.31000000e+02, 1.24000000e+02],
[ 1.10000000e+01, 2.10000000e+01, 3.30000000e+01,
4.50000000e+01, 3.80000000e+01, 4.10000000e+01,
5.20000000e+01, 5.00000000e+01, 6.50000000e+01,
7.70000000e+01, 6.40000000e+01, 8.70000000e+01,
9.50000000e+01, 8.60000000e+01, 8.70000000e+01,
8.10000000e+01, 8.30000000e+01, 1.15000000e+02,
1.13000000e+02, 1.19000000e+02],
[ 5.00000000e+00, 1.90000000e+01, 3.30000000e+01,
3.40000000e+01, 6.10000000e+01, 4.70000000e+01,
5.00000000e+01, 6.20000000e+01, 5.70000000e+01,
6.50000000e+01, 7.20000000e+01, 8.10000000e+01,
6.80000000e+01, 9.10000000e+01, 9.00000000e+01,
9.90000000e+01, 8.90000000e+01, 1.15000000e+02,
9.00000000e+01, 1.23000000e+02],
[ 9.00000000e+00, 1.90000000e+01, 2.80000000e+01,
2.80000000e+01, 3.30000000e+01, 4.30000000e+01,
5.30000000e+01, 6.80000000e+01, 6.90000000e+01,
6.50000000e+01, 7.40000000e+01, 8.50000000e+01,
8.00000000e+01, 9.00000000e+01, 8.20000000e+01,
1.06000000e+02, 9.40000000e+01, 1.19000000e+02,
1.18000000e+02, 1.18000000e+02],
[ 1.40000000e+01, 2.20000000e+01, 2.10000000e+01,
3.50000000e+01, 4.30000000e+01, 4.10000000e+01,
5.30000000e+01, 5.30000000e+01, 5.20000000e+01,
5.90000000e+01, 7.20000000e+01, 9.20000000e+01,
8.70000000e+01, 1.07000000e+02, 1.01000000e+02,
9.30000000e+01, 9.70000000e+01, 8.60000000e+01,
1.17000000e+02, 1.29000000e+02],
[ 8.00000000e+00, 1.30000000e+01, 2.50000000e+01,
2.90000000e+01, 3.40000000e+01, 5.20000000e+01,
5.20000000e+01, 6.00000000e+01, 5.90000000e+01,
7.10000000e+01, 7.00000000e+01, 6.60000000e+01,
8.30000000e+01, 9.10000000e+01, 8.40000000e+01,
8.80000000e+01, 8.70000000e+01, 1.17000000e+02,
1.22000000e+02, 9.10000000e+01],
[ 1.30000000e+01, 1.60000000e+01, 1.90000000e+01,
4.80000000e+01, 2.90000000e+01, 3.90000000e+01,
4.80000000e+01, 5.70000000e+01, 6.40000000e+01,
4.60000000e+01, 6.60000000e+01, 7.80000000e+01,
8.90000000e+01, 9.50000000e+01, 9.20000000e+01,
7.80000000e+01, 8.20000000e+01, 1.08000000e+02,
8.80000000e+01, 1.15000000e+02],
[ 4.00000000e+00, 2.50000000e+01, 3.20000000e+01,
2.10000000e+01, 3.80000000e+01, 5.70000000e+01,
4.80000000e+01, 5.20000000e+01, 6.50000000e+01,
5.90000000e+01, 6.20000000e+01, 8.70000000e+01,
8.90000000e+01, 7.80000000e+01, 1.08000000e+02,
8.70000000e+01, 9.70000000e+01, 9.40000000e+01,
1.19000000e+02, 1.20000000e+02],
[ 8.00000000e+00, 9.00000000e+00, 2.30000000e+01,
3.10000000e+01, 4.20000000e+01, 4.70000000e+01,
5.50000000e+01, 5.60000000e+01, 7.70000000e+01,
7.50000000e+01, 6.80000000e+01, 6.70000000e+01,
7.90000000e+01, 9.60000000e+01, 6.40000000e+01,
9.20000000e+01, 9.20000000e+01, 1.00000000e+02,
9.30000000e+01, 1.15000000e+02],
[ 5.00000000e+00, 1.10000000e+01, 3.10000000e+01,
2.50000000e+01, 3.40000000e+01, 4.60000000e+01,
4.30000000e+01, 7.00000000e+01, 6.20000000e+01,
6.20000000e+01, 6.30000000e+01, 6.30000000e+01,
8.30000000e+01, 8.30000000e+01, 8.70000000e+01,
8.10000000e+01, 8.50000000e+01, 1.11000000e+02,
1.05000000e+02, 1.15000000e+02],
[ 3.00000000e+00, 1.00000000e+01, 1.60000000e+01,
2.80000000e+01, 3.10000000e+01, 4.50000000e+01,
4.80000000e+01, 4.80000000e+01, 4.80000000e+01,
5.30000000e+01, 7.90000000e+01, 7.40000000e+01,
8.40000000e+01, 9.20000000e+01, 8.80000000e+01,
8.70000000e+01, 8.70000000e+01, 1.05000000e+02,
1.19000000e+02, 1.11000000e+02],
[ 8.00000000e+00, 1.60000000e+01, 2.70000000e+01,
3.50000000e+01, 3.60000000e+01, 2.30000000e+01,
4.10000000e+01, 6.30000000e+01, 4.50000000e+01,
5.40000000e+01, 7.10000000e+01, 7.60000000e+01,
7.40000000e+01, 8.70000000e+01, 9.40000000e+01,
9.40000000e+01, 1.00000000e+02, 1.02000000e+02,
1.19000000e+02, 8.90000000e+01],
[ 4.00000000e+00, 7.00000000e+00, 2.00000000e+01,
2.60000000e+01, 3.30000000e+01, 3.00000000e+01,
4.00000000e+01, 4.00000000e+01, 5.10000000e+01,
4.20000000e+01, 7.60000000e+01, 7.10000000e+01,
8.10000000e+01, 8.40000000e+01, 8.90000000e+01,
8.60000000e+01, 9.60000000e+01, 9.20000000e+01,
8.80000000e+01, 1.22000000e+02],
[ 3.00000000e+00, 1.00000000e+01, 1.40000000e+01,
2.40000000e+01, 2.80000000e+01, 5.10000000e+01,
4.50000000e+01, 2.90000000e+01, 4.10000000e+01,
5.70000000e+01, 6.50000000e+01, 7.60000000e+01,
6.90000000e+01, 8.80000000e+01, 7.10000000e+01,
9.70000000e+01, 9.30000000e+01, 1.00000000e+02,
1.07000000e+02, 1.15000000e+02],
[ 3.00000000e+00, 1.30000000e+01, 2.40000000e+01,
2.80000000e+01, 4.60000000e+01, 3.00000000e+01,
4.00000000e+01, 4.80000000e+01, 6.70000000e+01,
5.80000000e+01, 6.80000000e+01, 7.40000000e+01,
6.20000000e+01, 8.60000000e+01, 8.70000000e+01,
9.30000000e+01, 8.60000000e+01, 1.00000000e+02,
1.02000000e+02, 1.14000000e+02],
[ 2.00000000e+00, 1.70000000e+01, 1.80000000e+01,
2.70000000e+01, 2.80000000e+01, 3.30000000e+01,
4.30000000e+01, 4.60000000e+01, 5.00000000e+01,
6.10000000e+01, 6.20000000e+01, 6.90000000e+01,
8.50000000e+01, 9.30000000e+01, 9.40000000e+01,
8.00000000e+01, 9.50000000e+01, 1.12000000e+02,
1.21000000e+02, 9.90000000e+01],
[ 8.00000000e+00, 1.60000000e+01, 2.30000000e+01,
2.00000000e+01, 3.40000000e+01, 3.70000000e+01,
3.20000000e+01, 4.60000000e+01, 5.40000000e+01,
5.20000000e+01, 6.70000000e+01, 7.20000000e+01,
8.10000000e+01, 8.50000000e+01, 9.00000000e+01,
8.90000000e+01, 9.70000000e+01, 9.70000000e+01,
1.04000000e+02, 1.18000000e+02]])
In [ ]:
In [21]:
# Getting back the objects:
with open('../output/DR12Qbin3.pkl') as f: # Python 3: open(..., 'rb')
dat = pickle.load(f)
dat
Out[21]:
array([[ 2.30909700e+00, 3.30000000e-05, 3.10210000e-01],
[ 2.49794100e+00, 4.80000000e-05, 2.61357000e-01],
[ 2.33265500e+00, 1.00000000e-04, -2.31260000e-02],
...,
[ 2.41550800e+00, 6.28316600e+00, 1.76571000e-01],
[ 2.45101500e+00, 6.28317000e+00, 5.05355000e-01],
[ 2.39766700e+00, 6.28318400e+00, 6.06452000e-01]])
In [22]:
dd2d=np.zeros((20,20))
In [23]:
len(dat)
Out[23]:
139641
In [24]:
dd2d
Out[24]:
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.]])
In [25]:
%%time
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
print np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
[[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]]
CPU times: user 1.65 s, sys: 26.1 ms, total: 1.68 s
Wall time: 1.68 s
In [26]:
%%time
while(len(dat))>0:
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
dd2d+=np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
dat=np.delete(dat,0,axis=0)
if len(dat)%1000==0:
print len(dat)/1000
print dd2d
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0
[[ 1.32956000e+05 5.40000000e+01 6.30000000e+01 8.40000000e+01
8.20000000e+01 1.08000000e+02 1.04000000e+02 1.04000000e+02
1.20000000e+02 1.65000000e+02 1.63000000e+02 1.58000000e+02
1.78000000e+02 1.63000000e+02 1.58000000e+02 1.96000000e+02
1.93000000e+02 2.31000000e+02 2.42000000e+02 2.71000000e+02]
[ 2.00000000e+01 6.00000000e+01 8.10000000e+01 7.10000000e+01
1.07000000e+02 9.70000000e+01 9.40000000e+01 1.07000000e+02
1.54000000e+02 1.51000000e+02 1.29000000e+02 1.75000000e+02
1.57000000e+02 1.91000000e+02 1.85000000e+02 2.09000000e+02
2.07000000e+02 2.22000000e+02 2.60000000e+02 2.59000000e+02]
[ 1.70000000e+01 5.80000000e+01 6.80000000e+01 8.10000000e+01
9.80000000e+01 1.21000000e+02 8.90000000e+01 1.11000000e+02
1.42000000e+02 1.24000000e+02 1.54000000e+02 1.46000000e+02
1.93000000e+02 2.01000000e+02 1.79000000e+02 2.01000000e+02
2.11000000e+02 2.23000000e+02 2.36000000e+02 2.65000000e+02]
[ 2.40000000e+01 5.90000000e+01 6.90000000e+01 7.40000000e+01
7.90000000e+01 1.06000000e+02 1.07000000e+02 1.16000000e+02
1.25000000e+02 1.33000000e+02 1.60000000e+02 1.76000000e+02
1.75000000e+02 1.37000000e+02 1.79000000e+02 1.88000000e+02
2.04000000e+02 2.29000000e+02 2.46000000e+02 2.59000000e+02]
[ 1.60000000e+01 5.40000000e+01 6.00000000e+01 8.10000000e+01
8.60000000e+01 8.10000000e+01 1.01000000e+02 1.30000000e+02
1.15000000e+02 1.48000000e+02 1.58000000e+02 1.58000000e+02
1.63000000e+02 1.81000000e+02 1.85000000e+02 2.09000000e+02
2.06000000e+02 2.24000000e+02 2.14000000e+02 2.57000000e+02]
[ 2.70000000e+01 5.00000000e+01 6.30000000e+01 8.00000000e+01
7.90000000e+01 9.70000000e+01 1.11000000e+02 1.35000000e+02
1.26000000e+02 1.58000000e+02 1.46000000e+02 1.79000000e+02
1.69000000e+02 1.86000000e+02 1.78000000e+02 2.01000000e+02
2.22000000e+02 2.56000000e+02 2.29000000e+02 2.36000000e+02]
[ 2.20000000e+01 5.70000000e+01 7.50000000e+01 7.60000000e+01
1.00000000e+02 9.30000000e+01 1.05000000e+02 9.50000000e+01
1.14000000e+02 1.36000000e+02 1.42000000e+02 1.56000000e+02
1.65000000e+02 2.02000000e+02 1.63000000e+02 1.98000000e+02
2.25000000e+02 2.29000000e+02 2.16000000e+02 2.67000000e+02]
[ 1.80000000e+01 3.40000000e+01 6.20000000e+01 8.10000000e+01
9.40000000e+01 8.90000000e+01 1.13000000e+02 1.26000000e+02
1.22000000e+02 1.47000000e+02 1.45000000e+02 1.52000000e+02
1.65000000e+02 1.88000000e+02 1.94000000e+02 1.94000000e+02
2.17000000e+02 2.17000000e+02 2.12000000e+02 2.44000000e+02]
[ 1.40000000e+01 5.30000000e+01 5.60000000e+01 6.00000000e+01
7.50000000e+01 9.40000000e+01 9.40000000e+01 1.04000000e+02
1.24000000e+02 1.48000000e+02 1.58000000e+02 1.75000000e+02
1.68000000e+02 1.75000000e+02 1.89000000e+02 2.26000000e+02
2.09000000e+02 2.61000000e+02 2.15000000e+02 2.64000000e+02]
[ 2.10000000e+01 3.80000000e+01 6.00000000e+01 7.70000000e+01
7.50000000e+01 8.30000000e+01 1.19000000e+02 1.10000000e+02
1.14000000e+02 1.39000000e+02 1.53000000e+02 1.65000000e+02
1.59000000e+02 1.81000000e+02 1.98000000e+02 2.28000000e+02
2.09000000e+02 2.12000000e+02 2.38000000e+02 2.34000000e+02]
[ 2.10000000e+01 2.70000000e+01 7.00000000e+01 7.20000000e+01
8.60000000e+01 8.10000000e+01 1.09000000e+02 1.08000000e+02
1.19000000e+02 1.69000000e+02 1.59000000e+02 1.57000000e+02
1.63000000e+02 1.84000000e+02 2.00000000e+02 1.91000000e+02
2.08000000e+02 2.03000000e+02 2.17000000e+02 2.32000000e+02]
[ 1.80000000e+01 4.80000000e+01 4.50000000e+01 6.50000000e+01
8.70000000e+01 9.00000000e+01 1.14000000e+02 1.11000000e+02
1.34000000e+02 1.19000000e+02 1.43000000e+02 1.54000000e+02
1.76000000e+02 1.64000000e+02 1.88000000e+02 2.13000000e+02
1.99000000e+02 2.14000000e+02 2.49000000e+02 2.46000000e+02]
[ 1.00000000e+01 4.80000000e+01 4.80000000e+01 7.20000000e+01
8.10000000e+01 1.06000000e+02 1.14000000e+02 1.05000000e+02
1.32000000e+02 1.30000000e+02 1.61000000e+02 1.61000000e+02
1.87000000e+02 1.77000000e+02 1.91000000e+02 2.19000000e+02
2.10000000e+02 2.34000000e+02 2.15000000e+02 2.21000000e+02]
[ 1.40000000e+01 3.50000000e+01 4.50000000e+01 6.30000000e+01
7.70000000e+01 8.90000000e+01 9.10000000e+01 8.80000000e+01
1.23000000e+02 1.48000000e+02 1.56000000e+02 1.51000000e+02
1.54000000e+02 1.77000000e+02 1.88000000e+02 2.09000000e+02
1.87000000e+02 2.13000000e+02 2.28000000e+02 2.24000000e+02]
[ 1.30000000e+01 4.00000000e+01 5.30000000e+01 6.40000000e+01
7.50000000e+01 8.80000000e+01 1.04000000e+02 1.31000000e+02
1.16000000e+02 1.43000000e+02 1.39000000e+02 1.48000000e+02
1.78000000e+02 1.69000000e+02 1.88000000e+02 1.84000000e+02
2.02000000e+02 2.18000000e+02 2.21000000e+02 2.04000000e+02]
[ 1.30000000e+01 2.90000000e+01 5.10000000e+01 5.30000000e+01
6.10000000e+01 8.70000000e+01 1.07000000e+02 1.18000000e+02
1.20000000e+02 1.21000000e+02 1.39000000e+02 1.30000000e+02
1.71000000e+02 1.57000000e+02 1.86000000e+02 1.87000000e+02
2.06000000e+02 2.12000000e+02 1.96000000e+02 2.44000000e+02]
[ 1.00000000e+01 3.70000000e+01 5.10000000e+01 6.80000000e+01
8.80000000e+01 7.30000000e+01 6.90000000e+01 1.02000000e+02
1.09000000e+02 1.25000000e+02 1.32000000e+02 1.47000000e+02
1.93000000e+02 1.91000000e+02 1.72000000e+02 1.88000000e+02
1.96000000e+02 2.39000000e+02 2.39000000e+02 2.42000000e+02]
[ 1.50000000e+01 2.50000000e+01 5.20000000e+01 5.90000000e+01
7.30000000e+01 6.90000000e+01 8.30000000e+01 9.80000000e+01
1.03000000e+02 1.37000000e+02 1.78000000e+02 1.51000000e+02
1.73000000e+02 1.65000000e+02 1.88000000e+02 2.03000000e+02
1.97000000e+02 2.07000000e+02 2.31000000e+02 2.41000000e+02]
[ 1.60000000e+01 2.60000000e+01 5.40000000e+01 4.70000000e+01
6.00000000e+01 7.30000000e+01 1.06000000e+02 1.05000000e+02
1.14000000e+02 1.27000000e+02 1.45000000e+02 1.49000000e+02
1.59000000e+02 1.94000000e+02 1.68000000e+02 2.08000000e+02
2.32000000e+02 2.26000000e+02 2.04000000e+02 2.33000000e+02]
[ 3.00000000e+00 2.90000000e+01 4.20000000e+01 6.20000000e+01
6.50000000e+01 7.40000000e+01 9.00000000e+01 8.30000000e+01
1.28000000e+02 1.19000000e+02 1.50000000e+02 1.18000000e+02
1.71000000e+02 1.75000000e+02 1.76000000e+02 1.60000000e+02
2.24000000e+02 2.46000000e+02 2.28000000e+02 2.19000000e+02]]
CPU times: user 1d 3h 17min 46s, sys: 29min 13s, total: 1d 3h 46min 59s
Wall time: 1d 11h 1min 34s
In [27]:
dd2d
Out[27]:
array([[ 1.32956000e+05, 5.40000000e+01, 6.30000000e+01,
8.40000000e+01, 8.20000000e+01, 1.08000000e+02,
1.04000000e+02, 1.04000000e+02, 1.20000000e+02,
1.65000000e+02, 1.63000000e+02, 1.58000000e+02,
1.78000000e+02, 1.63000000e+02, 1.58000000e+02,
1.96000000e+02, 1.93000000e+02, 2.31000000e+02,
2.42000000e+02, 2.71000000e+02],
[ 2.00000000e+01, 6.00000000e+01, 8.10000000e+01,
7.10000000e+01, 1.07000000e+02, 9.70000000e+01,
9.40000000e+01, 1.07000000e+02, 1.54000000e+02,
1.51000000e+02, 1.29000000e+02, 1.75000000e+02,
1.57000000e+02, 1.91000000e+02, 1.85000000e+02,
2.09000000e+02, 2.07000000e+02, 2.22000000e+02,
2.60000000e+02, 2.59000000e+02],
[ 1.70000000e+01, 5.80000000e+01, 6.80000000e+01,
8.10000000e+01, 9.80000000e+01, 1.21000000e+02,
8.90000000e+01, 1.11000000e+02, 1.42000000e+02,
1.24000000e+02, 1.54000000e+02, 1.46000000e+02,
1.93000000e+02, 2.01000000e+02, 1.79000000e+02,
2.01000000e+02, 2.11000000e+02, 2.23000000e+02,
2.36000000e+02, 2.65000000e+02],
[ 2.40000000e+01, 5.90000000e+01, 6.90000000e+01,
7.40000000e+01, 7.90000000e+01, 1.06000000e+02,
1.07000000e+02, 1.16000000e+02, 1.25000000e+02,
1.33000000e+02, 1.60000000e+02, 1.76000000e+02,
1.75000000e+02, 1.37000000e+02, 1.79000000e+02,
1.88000000e+02, 2.04000000e+02, 2.29000000e+02,
2.46000000e+02, 2.59000000e+02],
[ 1.60000000e+01, 5.40000000e+01, 6.00000000e+01,
8.10000000e+01, 8.60000000e+01, 8.10000000e+01,
1.01000000e+02, 1.30000000e+02, 1.15000000e+02,
1.48000000e+02, 1.58000000e+02, 1.58000000e+02,
1.63000000e+02, 1.81000000e+02, 1.85000000e+02,
2.09000000e+02, 2.06000000e+02, 2.24000000e+02,
2.14000000e+02, 2.57000000e+02],
[ 2.70000000e+01, 5.00000000e+01, 6.30000000e+01,
8.00000000e+01, 7.90000000e+01, 9.70000000e+01,
1.11000000e+02, 1.35000000e+02, 1.26000000e+02,
1.58000000e+02, 1.46000000e+02, 1.79000000e+02,
1.69000000e+02, 1.86000000e+02, 1.78000000e+02,
2.01000000e+02, 2.22000000e+02, 2.56000000e+02,
2.29000000e+02, 2.36000000e+02],
[ 2.20000000e+01, 5.70000000e+01, 7.50000000e+01,
7.60000000e+01, 1.00000000e+02, 9.30000000e+01,
1.05000000e+02, 9.50000000e+01, 1.14000000e+02,
1.36000000e+02, 1.42000000e+02, 1.56000000e+02,
1.65000000e+02, 2.02000000e+02, 1.63000000e+02,
1.98000000e+02, 2.25000000e+02, 2.29000000e+02,
2.16000000e+02, 2.67000000e+02],
[ 1.80000000e+01, 3.40000000e+01, 6.20000000e+01,
8.10000000e+01, 9.40000000e+01, 8.90000000e+01,
1.13000000e+02, 1.26000000e+02, 1.22000000e+02,
1.47000000e+02, 1.45000000e+02, 1.52000000e+02,
1.65000000e+02, 1.88000000e+02, 1.94000000e+02,
1.94000000e+02, 2.17000000e+02, 2.17000000e+02,
2.12000000e+02, 2.44000000e+02],
[ 1.40000000e+01, 5.30000000e+01, 5.60000000e+01,
6.00000000e+01, 7.50000000e+01, 9.40000000e+01,
9.40000000e+01, 1.04000000e+02, 1.24000000e+02,
1.48000000e+02, 1.58000000e+02, 1.75000000e+02,
1.68000000e+02, 1.75000000e+02, 1.89000000e+02,
2.26000000e+02, 2.09000000e+02, 2.61000000e+02,
2.15000000e+02, 2.64000000e+02],
[ 2.10000000e+01, 3.80000000e+01, 6.00000000e+01,
7.70000000e+01, 7.50000000e+01, 8.30000000e+01,
1.19000000e+02, 1.10000000e+02, 1.14000000e+02,
1.39000000e+02, 1.53000000e+02, 1.65000000e+02,
1.59000000e+02, 1.81000000e+02, 1.98000000e+02,
2.28000000e+02, 2.09000000e+02, 2.12000000e+02,
2.38000000e+02, 2.34000000e+02],
[ 2.10000000e+01, 2.70000000e+01, 7.00000000e+01,
7.20000000e+01, 8.60000000e+01, 8.10000000e+01,
1.09000000e+02, 1.08000000e+02, 1.19000000e+02,
1.69000000e+02, 1.59000000e+02, 1.57000000e+02,
1.63000000e+02, 1.84000000e+02, 2.00000000e+02,
1.91000000e+02, 2.08000000e+02, 2.03000000e+02,
2.17000000e+02, 2.32000000e+02],
[ 1.80000000e+01, 4.80000000e+01, 4.50000000e+01,
6.50000000e+01, 8.70000000e+01, 9.00000000e+01,
1.14000000e+02, 1.11000000e+02, 1.34000000e+02,
1.19000000e+02, 1.43000000e+02, 1.54000000e+02,
1.76000000e+02, 1.64000000e+02, 1.88000000e+02,
2.13000000e+02, 1.99000000e+02, 2.14000000e+02,
2.49000000e+02, 2.46000000e+02],
[ 1.00000000e+01, 4.80000000e+01, 4.80000000e+01,
7.20000000e+01, 8.10000000e+01, 1.06000000e+02,
1.14000000e+02, 1.05000000e+02, 1.32000000e+02,
1.30000000e+02, 1.61000000e+02, 1.61000000e+02,
1.87000000e+02, 1.77000000e+02, 1.91000000e+02,
2.19000000e+02, 2.10000000e+02, 2.34000000e+02,
2.15000000e+02, 2.21000000e+02],
[ 1.40000000e+01, 3.50000000e+01, 4.50000000e+01,
6.30000000e+01, 7.70000000e+01, 8.90000000e+01,
9.10000000e+01, 8.80000000e+01, 1.23000000e+02,
1.48000000e+02, 1.56000000e+02, 1.51000000e+02,
1.54000000e+02, 1.77000000e+02, 1.88000000e+02,
2.09000000e+02, 1.87000000e+02, 2.13000000e+02,
2.28000000e+02, 2.24000000e+02],
[ 1.30000000e+01, 4.00000000e+01, 5.30000000e+01,
6.40000000e+01, 7.50000000e+01, 8.80000000e+01,
1.04000000e+02, 1.31000000e+02, 1.16000000e+02,
1.43000000e+02, 1.39000000e+02, 1.48000000e+02,
1.78000000e+02, 1.69000000e+02, 1.88000000e+02,
1.84000000e+02, 2.02000000e+02, 2.18000000e+02,
2.21000000e+02, 2.04000000e+02],
[ 1.30000000e+01, 2.90000000e+01, 5.10000000e+01,
5.30000000e+01, 6.10000000e+01, 8.70000000e+01,
1.07000000e+02, 1.18000000e+02, 1.20000000e+02,
1.21000000e+02, 1.39000000e+02, 1.30000000e+02,
1.71000000e+02, 1.57000000e+02, 1.86000000e+02,
1.87000000e+02, 2.06000000e+02, 2.12000000e+02,
1.96000000e+02, 2.44000000e+02],
[ 1.00000000e+01, 3.70000000e+01, 5.10000000e+01,
6.80000000e+01, 8.80000000e+01, 7.30000000e+01,
6.90000000e+01, 1.02000000e+02, 1.09000000e+02,
1.25000000e+02, 1.32000000e+02, 1.47000000e+02,
1.93000000e+02, 1.91000000e+02, 1.72000000e+02,
1.88000000e+02, 1.96000000e+02, 2.39000000e+02,
2.39000000e+02, 2.42000000e+02],
[ 1.50000000e+01, 2.50000000e+01, 5.20000000e+01,
5.90000000e+01, 7.30000000e+01, 6.90000000e+01,
8.30000000e+01, 9.80000000e+01, 1.03000000e+02,
1.37000000e+02, 1.78000000e+02, 1.51000000e+02,
1.73000000e+02, 1.65000000e+02, 1.88000000e+02,
2.03000000e+02, 1.97000000e+02, 2.07000000e+02,
2.31000000e+02, 2.41000000e+02],
[ 1.60000000e+01, 2.60000000e+01, 5.40000000e+01,
4.70000000e+01, 6.00000000e+01, 7.30000000e+01,
1.06000000e+02, 1.05000000e+02, 1.14000000e+02,
1.27000000e+02, 1.45000000e+02, 1.49000000e+02,
1.59000000e+02, 1.94000000e+02, 1.68000000e+02,
2.08000000e+02, 2.32000000e+02, 2.26000000e+02,
2.04000000e+02, 2.33000000e+02],
[ 3.00000000e+00, 2.90000000e+01, 4.20000000e+01,
6.20000000e+01, 6.50000000e+01, 7.40000000e+01,
9.00000000e+01, 8.30000000e+01, 1.28000000e+02,
1.19000000e+02, 1.50000000e+02, 1.18000000e+02,
1.71000000e+02, 1.75000000e+02, 1.76000000e+02,
1.60000000e+02, 2.24000000e+02, 2.46000000e+02,
2.28000000e+02, 2.19000000e+02]])
In [28]:
with open('DR12QDDbin3.pkl','w') as f:
pickle.dump(dd2d,f)
dd2d
Out[28]:
array([[ 1.32956000e+05, 5.40000000e+01, 6.30000000e+01,
8.40000000e+01, 8.20000000e+01, 1.08000000e+02,
1.04000000e+02, 1.04000000e+02, 1.20000000e+02,
1.65000000e+02, 1.63000000e+02, 1.58000000e+02,
1.78000000e+02, 1.63000000e+02, 1.58000000e+02,
1.96000000e+02, 1.93000000e+02, 2.31000000e+02,
2.42000000e+02, 2.71000000e+02],
[ 2.00000000e+01, 6.00000000e+01, 8.10000000e+01,
7.10000000e+01, 1.07000000e+02, 9.70000000e+01,
9.40000000e+01, 1.07000000e+02, 1.54000000e+02,
1.51000000e+02, 1.29000000e+02, 1.75000000e+02,
1.57000000e+02, 1.91000000e+02, 1.85000000e+02,
2.09000000e+02, 2.07000000e+02, 2.22000000e+02,
2.60000000e+02, 2.59000000e+02],
[ 1.70000000e+01, 5.80000000e+01, 6.80000000e+01,
8.10000000e+01, 9.80000000e+01, 1.21000000e+02,
8.90000000e+01, 1.11000000e+02, 1.42000000e+02,
1.24000000e+02, 1.54000000e+02, 1.46000000e+02,
1.93000000e+02, 2.01000000e+02, 1.79000000e+02,
2.01000000e+02, 2.11000000e+02, 2.23000000e+02,
2.36000000e+02, 2.65000000e+02],
[ 2.40000000e+01, 5.90000000e+01, 6.90000000e+01,
7.40000000e+01, 7.90000000e+01, 1.06000000e+02,
1.07000000e+02, 1.16000000e+02, 1.25000000e+02,
1.33000000e+02, 1.60000000e+02, 1.76000000e+02,
1.75000000e+02, 1.37000000e+02, 1.79000000e+02,
1.88000000e+02, 2.04000000e+02, 2.29000000e+02,
2.46000000e+02, 2.59000000e+02],
[ 1.60000000e+01, 5.40000000e+01, 6.00000000e+01,
8.10000000e+01, 8.60000000e+01, 8.10000000e+01,
1.01000000e+02, 1.30000000e+02, 1.15000000e+02,
1.48000000e+02, 1.58000000e+02, 1.58000000e+02,
1.63000000e+02, 1.81000000e+02, 1.85000000e+02,
2.09000000e+02, 2.06000000e+02, 2.24000000e+02,
2.14000000e+02, 2.57000000e+02],
[ 2.70000000e+01, 5.00000000e+01, 6.30000000e+01,
8.00000000e+01, 7.90000000e+01, 9.70000000e+01,
1.11000000e+02, 1.35000000e+02, 1.26000000e+02,
1.58000000e+02, 1.46000000e+02, 1.79000000e+02,
1.69000000e+02, 1.86000000e+02, 1.78000000e+02,
2.01000000e+02, 2.22000000e+02, 2.56000000e+02,
2.29000000e+02, 2.36000000e+02],
[ 2.20000000e+01, 5.70000000e+01, 7.50000000e+01,
7.60000000e+01, 1.00000000e+02, 9.30000000e+01,
1.05000000e+02, 9.50000000e+01, 1.14000000e+02,
1.36000000e+02, 1.42000000e+02, 1.56000000e+02,
1.65000000e+02, 2.02000000e+02, 1.63000000e+02,
1.98000000e+02, 2.25000000e+02, 2.29000000e+02,
2.16000000e+02, 2.67000000e+02],
[ 1.80000000e+01, 3.40000000e+01, 6.20000000e+01,
8.10000000e+01, 9.40000000e+01, 8.90000000e+01,
1.13000000e+02, 1.26000000e+02, 1.22000000e+02,
1.47000000e+02, 1.45000000e+02, 1.52000000e+02,
1.65000000e+02, 1.88000000e+02, 1.94000000e+02,
1.94000000e+02, 2.17000000e+02, 2.17000000e+02,
2.12000000e+02, 2.44000000e+02],
[ 1.40000000e+01, 5.30000000e+01, 5.60000000e+01,
6.00000000e+01, 7.50000000e+01, 9.40000000e+01,
9.40000000e+01, 1.04000000e+02, 1.24000000e+02,
1.48000000e+02, 1.58000000e+02, 1.75000000e+02,
1.68000000e+02, 1.75000000e+02, 1.89000000e+02,
2.26000000e+02, 2.09000000e+02, 2.61000000e+02,
2.15000000e+02, 2.64000000e+02],
[ 2.10000000e+01, 3.80000000e+01, 6.00000000e+01,
7.70000000e+01, 7.50000000e+01, 8.30000000e+01,
1.19000000e+02, 1.10000000e+02, 1.14000000e+02,
1.39000000e+02, 1.53000000e+02, 1.65000000e+02,
1.59000000e+02, 1.81000000e+02, 1.98000000e+02,
2.28000000e+02, 2.09000000e+02, 2.12000000e+02,
2.38000000e+02, 2.34000000e+02],
[ 2.10000000e+01, 2.70000000e+01, 7.00000000e+01,
7.20000000e+01, 8.60000000e+01, 8.10000000e+01,
1.09000000e+02, 1.08000000e+02, 1.19000000e+02,
1.69000000e+02, 1.59000000e+02, 1.57000000e+02,
1.63000000e+02, 1.84000000e+02, 2.00000000e+02,
1.91000000e+02, 2.08000000e+02, 2.03000000e+02,
2.17000000e+02, 2.32000000e+02],
[ 1.80000000e+01, 4.80000000e+01, 4.50000000e+01,
6.50000000e+01, 8.70000000e+01, 9.00000000e+01,
1.14000000e+02, 1.11000000e+02, 1.34000000e+02,
1.19000000e+02, 1.43000000e+02, 1.54000000e+02,
1.76000000e+02, 1.64000000e+02, 1.88000000e+02,
2.13000000e+02, 1.99000000e+02, 2.14000000e+02,
2.49000000e+02, 2.46000000e+02],
[ 1.00000000e+01, 4.80000000e+01, 4.80000000e+01,
7.20000000e+01, 8.10000000e+01, 1.06000000e+02,
1.14000000e+02, 1.05000000e+02, 1.32000000e+02,
1.30000000e+02, 1.61000000e+02, 1.61000000e+02,
1.87000000e+02, 1.77000000e+02, 1.91000000e+02,
2.19000000e+02, 2.10000000e+02, 2.34000000e+02,
2.15000000e+02, 2.21000000e+02],
[ 1.40000000e+01, 3.50000000e+01, 4.50000000e+01,
6.30000000e+01, 7.70000000e+01, 8.90000000e+01,
9.10000000e+01, 8.80000000e+01, 1.23000000e+02,
1.48000000e+02, 1.56000000e+02, 1.51000000e+02,
1.54000000e+02, 1.77000000e+02, 1.88000000e+02,
2.09000000e+02, 1.87000000e+02, 2.13000000e+02,
2.28000000e+02, 2.24000000e+02],
[ 1.30000000e+01, 4.00000000e+01, 5.30000000e+01,
6.40000000e+01, 7.50000000e+01, 8.80000000e+01,
1.04000000e+02, 1.31000000e+02, 1.16000000e+02,
1.43000000e+02, 1.39000000e+02, 1.48000000e+02,
1.78000000e+02, 1.69000000e+02, 1.88000000e+02,
1.84000000e+02, 2.02000000e+02, 2.18000000e+02,
2.21000000e+02, 2.04000000e+02],
[ 1.30000000e+01, 2.90000000e+01, 5.10000000e+01,
5.30000000e+01, 6.10000000e+01, 8.70000000e+01,
1.07000000e+02, 1.18000000e+02, 1.20000000e+02,
1.21000000e+02, 1.39000000e+02, 1.30000000e+02,
1.71000000e+02, 1.57000000e+02, 1.86000000e+02,
1.87000000e+02, 2.06000000e+02, 2.12000000e+02,
1.96000000e+02, 2.44000000e+02],
[ 1.00000000e+01, 3.70000000e+01, 5.10000000e+01,
6.80000000e+01, 8.80000000e+01, 7.30000000e+01,
6.90000000e+01, 1.02000000e+02, 1.09000000e+02,
1.25000000e+02, 1.32000000e+02, 1.47000000e+02,
1.93000000e+02, 1.91000000e+02, 1.72000000e+02,
1.88000000e+02, 1.96000000e+02, 2.39000000e+02,
2.39000000e+02, 2.42000000e+02],
[ 1.50000000e+01, 2.50000000e+01, 5.20000000e+01,
5.90000000e+01, 7.30000000e+01, 6.90000000e+01,
8.30000000e+01, 9.80000000e+01, 1.03000000e+02,
1.37000000e+02, 1.78000000e+02, 1.51000000e+02,
1.73000000e+02, 1.65000000e+02, 1.88000000e+02,
2.03000000e+02, 1.97000000e+02, 2.07000000e+02,
2.31000000e+02, 2.41000000e+02],
[ 1.60000000e+01, 2.60000000e+01, 5.40000000e+01,
4.70000000e+01, 6.00000000e+01, 7.30000000e+01,
1.06000000e+02, 1.05000000e+02, 1.14000000e+02,
1.27000000e+02, 1.45000000e+02, 1.49000000e+02,
1.59000000e+02, 1.94000000e+02, 1.68000000e+02,
2.08000000e+02, 2.32000000e+02, 2.26000000e+02,
2.04000000e+02, 2.33000000e+02],
[ 3.00000000e+00, 2.90000000e+01, 4.20000000e+01,
6.20000000e+01, 6.50000000e+01, 7.40000000e+01,
9.00000000e+01, 8.30000000e+01, 1.28000000e+02,
1.19000000e+02, 1.50000000e+02, 1.18000000e+02,
1.71000000e+02, 1.75000000e+02, 1.76000000e+02,
1.60000000e+02, 2.24000000e+02, 2.46000000e+02,
2.28000000e+02, 2.19000000e+02]])
In [ ]:
In [29]:
# Getting back the objects:
with open('../output/DR12Qbin4.pkl') as f: # Python 3: open(..., 'rb')
dat = pickle.load(f)
dat
Out[29]:
array([[ 3.08883900e+00, 1.03000000e-04, 3.49280000e-01],
[ 2.90644800e+00, 2.52000000e-04, 2.80095000e-01],
[ 2.87937200e+00, 3.61000000e-04, -4.85800000e-03],
...,
[ 3.16691100e+00, 6.28303300e+00, 6.24115000e-01],
[ 3.06474900e+00, 6.28308500e+00, 7.52100000e-03],
[ 3.11339100e+00, 6.28317300e+00, 6.05993000e-01]])
In [30]:
dd2d=np.zeros((20,20))
In [31]:
len(dat)
Out[31]:
42945
In [32]:
dd2d
Out[32]:
array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.]])
In [33]:
%%time
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
print np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
[[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.]]
CPU times: user 384 ms, sys: 26 ms, total: 410 ms
Wall time: 390 ms
In [34]:
%%time
while(len(dat))>0:
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdth)[0]
dd2d+=np.histogram2d(dist0, dist1, bins=20, range=rng)[0]
dat=np.delete(dat,0,axis=0)
if len(dat)%1000==0:
print len(dat)/1000
print dd2d
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0
[[ 4.09250000e+04 3.00000000e+00 7.00000000e+00 6.00000000e+00
1.00000000e+01 4.00000000e+00 4.00000000e+00 6.00000000e+00
9.00000000e+00 9.00000000e+00 9.00000000e+00 1.50000000e+01
1.40000000e+01 8.00000000e+00 1.30000000e+01 1.10000000e+01
1.40000000e+01 1.00000000e+01 1.50000000e+01 1.30000000e+01]
[ 0.00000000e+00 6.00000000e+00 7.00000000e+00 2.00000000e+00
6.00000000e+00 3.00000000e+00 1.20000000e+01 7.00000000e+00
5.00000000e+00 6.00000000e+00 1.10000000e+01 7.00000000e+00
1.20000000e+01 1.30000000e+01 1.20000000e+01 1.40000000e+01
1.60000000e+01 9.00000000e+00 1.10000000e+01 1.30000000e+01]
[ 3.00000000e+00 5.00000000e+00 5.00000000e+00 6.00000000e+00
7.00000000e+00 1.20000000e+01 9.00000000e+00 8.00000000e+00
1.10000000e+01 6.00000000e+00 9.00000000e+00 1.00000000e+01
1.00000000e+01 1.40000000e+01 1.40000000e+01 1.10000000e+01
1.30000000e+01 2.10000000e+01 2.10000000e+01 2.00000000e+01]
[ 1.00000000e+00 2.00000000e+00 3.00000000e+00 3.00000000e+00
3.00000000e+00 6.00000000e+00 8.00000000e+00 5.00000000e+00
5.00000000e+00 1.20000000e+01 9.00000000e+00 1.20000000e+01
1.10000000e+01 1.10000000e+01 1.70000000e+01 9.00000000e+00
1.20000000e+01 1.40000000e+01 1.10000000e+01 2.00000000e+01]
[ 1.00000000e+00 5.00000000e+00 3.00000000e+00 9.00000000e+00
5.00000000e+00 9.00000000e+00 5.00000000e+00 7.00000000e+00
8.00000000e+00 8.00000000e+00 7.00000000e+00 8.00000000e+00
1.10000000e+01 9.00000000e+00 1.40000000e+01 9.00000000e+00
1.30000000e+01 1.50000000e+01 1.40000000e+01 1.40000000e+01]
[ 3.00000000e+00 5.00000000e+00 4.00000000e+00 5.00000000e+00
3.00000000e+00 1.30000000e+01 1.10000000e+01 5.00000000e+00
5.00000000e+00 7.00000000e+00 1.00000000e+01 1.50000000e+01
1.30000000e+01 1.10000000e+01 1.70000000e+01 1.20000000e+01
1.50000000e+01 2.30000000e+01 1.50000000e+01 1.80000000e+01]
[ 1.00000000e+00 1.00000000e+00 6.00000000e+00 3.00000000e+00
8.00000000e+00 4.00000000e+00 7.00000000e+00 6.00000000e+00
1.30000000e+01 4.00000000e+00 1.20000000e+01 1.00000000e+01
1.00000000e+01 1.10000000e+01 8.00000000e+00 1.40000000e+01
1.50000000e+01 1.80000000e+01 1.00000000e+01 1.20000000e+01]
[ 2.00000000e+00 3.00000000e+00 1.20000000e+01 5.00000000e+00
8.00000000e+00 7.00000000e+00 8.00000000e+00 5.00000000e+00
8.00000000e+00 9.00000000e+00 6.00000000e+00 9.00000000e+00
1.40000000e+01 1.10000000e+01 1.20000000e+01 8.00000000e+00
1.10000000e+01 1.40000000e+01 1.30000000e+01 1.60000000e+01]
[ 0.00000000e+00 1.00000000e+00 6.00000000e+00 4.00000000e+00
4.00000000e+00 4.00000000e+00 7.00000000e+00 6.00000000e+00
1.00000000e+01 1.10000000e+01 1.00000000e+01 6.00000000e+00
1.00000000e+01 1.50000000e+01 1.20000000e+01 1.50000000e+01
1.80000000e+01 1.40000000e+01 2.00000000e+01 1.40000000e+01]
[ 2.00000000e+00 1.00000000e+00 4.00000000e+00 6.00000000e+00
5.00000000e+00 5.00000000e+00 7.00000000e+00 1.20000000e+01
1.00000000e+01 9.00000000e+00 1.60000000e+01 8.00000000e+00
8.00000000e+00 1.20000000e+01 5.00000000e+00 1.80000000e+01
1.20000000e+01 1.70000000e+01 7.00000000e+00 1.70000000e+01]
[ 1.00000000e+00 3.00000000e+00 5.00000000e+00 6.00000000e+00
3.00000000e+00 2.00000000e+00 1.00000000e+01 7.00000000e+00
5.00000000e+00 1.00000000e+01 1.10000000e+01 7.00000000e+00
1.00000000e+01 1.00000000e+01 9.00000000e+00 1.50000000e+01
1.50000000e+01 2.20000000e+01 1.00000000e+01 1.40000000e+01]
[ 2.00000000e+00 2.00000000e+00 8.00000000e+00 2.00000000e+00
3.00000000e+00 5.00000000e+00 7.00000000e+00 5.00000000e+00
6.00000000e+00 9.00000000e+00 1.70000000e+01 1.70000000e+01
8.00000000e+00 2.20000000e+01 1.30000000e+01 1.20000000e+01
1.50000000e+01 1.70000000e+01 1.20000000e+01 1.20000000e+01]
[ 2.00000000e+00 4.00000000e+00 6.00000000e+00 6.00000000e+00
5.00000000e+00 5.00000000e+00 9.00000000e+00 8.00000000e+00
6.00000000e+00 7.00000000e+00 7.00000000e+00 1.30000000e+01
1.40000000e+01 7.00000000e+00 1.10000000e+01 1.50000000e+01
1.30000000e+01 1.90000000e+01 1.50000000e+01 8.00000000e+00]
[ 2.00000000e+00 3.00000000e+00 8.00000000e+00 6.00000000e+00
4.00000000e+00 4.00000000e+00 4.00000000e+00 8.00000000e+00
6.00000000e+00 1.10000000e+01 3.00000000e+00 7.00000000e+00
9.00000000e+00 1.40000000e+01 2.60000000e+01 1.60000000e+01
1.30000000e+01 1.60000000e+01 1.30000000e+01 1.70000000e+01]
[ 0.00000000e+00 3.00000000e+00 3.00000000e+00 6.00000000e+00
6.00000000e+00 7.00000000e+00 5.00000000e+00 5.00000000e+00
8.00000000e+00 9.00000000e+00 9.00000000e+00 7.00000000e+00
7.00000000e+00 1.30000000e+01 1.10000000e+01 1.00000000e+01
1.30000000e+01 9.00000000e+00 1.10000000e+01 1.40000000e+01]
[ 0.00000000e+00 3.00000000e+00 5.00000000e+00 4.00000000e+00
3.00000000e+00 4.00000000e+00 3.00000000e+00 7.00000000e+00
8.00000000e+00 8.00000000e+00 8.00000000e+00 1.00000000e+01
7.00000000e+00 1.20000000e+01 1.00000000e+01 1.70000000e+01
1.00000000e+01 2.30000000e+01 1.00000000e+01 1.50000000e+01]
[ 1.00000000e+00 1.00000000e+00 4.00000000e+00 4.00000000e+00
5.00000000e+00 6.00000000e+00 3.00000000e+00 7.00000000e+00
1.00000000e+01 6.00000000e+00 6.00000000e+00 7.00000000e+00
8.00000000e+00 1.10000000e+01 1.40000000e+01 8.00000000e+00
9.00000000e+00 1.50000000e+01 8.00000000e+00 1.50000000e+01]
[ 0.00000000e+00 4.00000000e+00 2.00000000e+00 6.00000000e+00
4.00000000e+00 5.00000000e+00 5.00000000e+00 6.00000000e+00
8.00000000e+00 9.00000000e+00 9.00000000e+00 8.00000000e+00
1.20000000e+01 9.00000000e+00 1.30000000e+01 1.30000000e+01
1.10000000e+01 9.00000000e+00 9.00000000e+00 1.30000000e+01]
[ 1.00000000e+00 3.00000000e+00 3.00000000e+00 4.00000000e+00
5.00000000e+00 7.00000000e+00 1.00000000e+01 5.00000000e+00
7.00000000e+00 1.20000000e+01 1.20000000e+01 1.40000000e+01
6.00000000e+00 1.00000000e+01 8.00000000e+00 1.30000000e+01
1.70000000e+01 1.40000000e+01 1.00000000e+01 1.80000000e+01]
[ 1.00000000e+00 2.00000000e+00 3.00000000e+00 3.00000000e+00
6.00000000e+00 4.00000000e+00 5.00000000e+00 5.00000000e+00
5.00000000e+00 9.00000000e+00 1.50000000e+01 1.20000000e+01
1.10000000e+01 1.00000000e+01 1.30000000e+01 9.00000000e+00
1.40000000e+01 2.00000000e+01 1.20000000e+01 1.90000000e+01]]
CPU times: user 2h 4min 58s, sys: 53.1 s, total: 2h 5min 51s
Wall time: 2h 5min 26s
In [35]:
dd2d
Out[35]:
array([[ 4.09250000e+04, 3.00000000e+00, 7.00000000e+00,
6.00000000e+00, 1.00000000e+01, 4.00000000e+00,
4.00000000e+00, 6.00000000e+00, 9.00000000e+00,
9.00000000e+00, 9.00000000e+00, 1.50000000e+01,
1.40000000e+01, 8.00000000e+00, 1.30000000e+01,
1.10000000e+01, 1.40000000e+01, 1.00000000e+01,
1.50000000e+01, 1.30000000e+01],
[ 0.00000000e+00, 6.00000000e+00, 7.00000000e+00,
2.00000000e+00, 6.00000000e+00, 3.00000000e+00,
1.20000000e+01, 7.00000000e+00, 5.00000000e+00,
6.00000000e+00, 1.10000000e+01, 7.00000000e+00,
1.20000000e+01, 1.30000000e+01, 1.20000000e+01,
1.40000000e+01, 1.60000000e+01, 9.00000000e+00,
1.10000000e+01, 1.30000000e+01],
[ 3.00000000e+00, 5.00000000e+00, 5.00000000e+00,
6.00000000e+00, 7.00000000e+00, 1.20000000e+01,
9.00000000e+00, 8.00000000e+00, 1.10000000e+01,
6.00000000e+00, 9.00000000e+00, 1.00000000e+01,
1.00000000e+01, 1.40000000e+01, 1.40000000e+01,
1.10000000e+01, 1.30000000e+01, 2.10000000e+01,
2.10000000e+01, 2.00000000e+01],
[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00,
3.00000000e+00, 3.00000000e+00, 6.00000000e+00,
8.00000000e+00, 5.00000000e+00, 5.00000000e+00,
1.20000000e+01, 9.00000000e+00, 1.20000000e+01,
1.10000000e+01, 1.10000000e+01, 1.70000000e+01,
9.00000000e+00, 1.20000000e+01, 1.40000000e+01,
1.10000000e+01, 2.00000000e+01],
[ 1.00000000e+00, 5.00000000e+00, 3.00000000e+00,
9.00000000e+00, 5.00000000e+00, 9.00000000e+00,
5.00000000e+00, 7.00000000e+00, 8.00000000e+00,
8.00000000e+00, 7.00000000e+00, 8.00000000e+00,
1.10000000e+01, 9.00000000e+00, 1.40000000e+01,
9.00000000e+00, 1.30000000e+01, 1.50000000e+01,
1.40000000e+01, 1.40000000e+01],
[ 3.00000000e+00, 5.00000000e+00, 4.00000000e+00,
5.00000000e+00, 3.00000000e+00, 1.30000000e+01,
1.10000000e+01, 5.00000000e+00, 5.00000000e+00,
7.00000000e+00, 1.00000000e+01, 1.50000000e+01,
1.30000000e+01, 1.10000000e+01, 1.70000000e+01,
1.20000000e+01, 1.50000000e+01, 2.30000000e+01,
1.50000000e+01, 1.80000000e+01],
[ 1.00000000e+00, 1.00000000e+00, 6.00000000e+00,
3.00000000e+00, 8.00000000e+00, 4.00000000e+00,
7.00000000e+00, 6.00000000e+00, 1.30000000e+01,
4.00000000e+00, 1.20000000e+01, 1.00000000e+01,
1.00000000e+01, 1.10000000e+01, 8.00000000e+00,
1.40000000e+01, 1.50000000e+01, 1.80000000e+01,
1.00000000e+01, 1.20000000e+01],
[ 2.00000000e+00, 3.00000000e+00, 1.20000000e+01,
5.00000000e+00, 8.00000000e+00, 7.00000000e+00,
8.00000000e+00, 5.00000000e+00, 8.00000000e+00,
9.00000000e+00, 6.00000000e+00, 9.00000000e+00,
1.40000000e+01, 1.10000000e+01, 1.20000000e+01,
8.00000000e+00, 1.10000000e+01, 1.40000000e+01,
1.30000000e+01, 1.60000000e+01],
[ 0.00000000e+00, 1.00000000e+00, 6.00000000e+00,
4.00000000e+00, 4.00000000e+00, 4.00000000e+00,
7.00000000e+00, 6.00000000e+00, 1.00000000e+01,
1.10000000e+01, 1.00000000e+01, 6.00000000e+00,
1.00000000e+01, 1.50000000e+01, 1.20000000e+01,
1.50000000e+01, 1.80000000e+01, 1.40000000e+01,
2.00000000e+01, 1.40000000e+01],
[ 2.00000000e+00, 1.00000000e+00, 4.00000000e+00,
6.00000000e+00, 5.00000000e+00, 5.00000000e+00,
7.00000000e+00, 1.20000000e+01, 1.00000000e+01,
9.00000000e+00, 1.60000000e+01, 8.00000000e+00,
8.00000000e+00, 1.20000000e+01, 5.00000000e+00,
1.80000000e+01, 1.20000000e+01, 1.70000000e+01,
7.00000000e+00, 1.70000000e+01],
[ 1.00000000e+00, 3.00000000e+00, 5.00000000e+00,
6.00000000e+00, 3.00000000e+00, 2.00000000e+00,
1.00000000e+01, 7.00000000e+00, 5.00000000e+00,
1.00000000e+01, 1.10000000e+01, 7.00000000e+00,
1.00000000e+01, 1.00000000e+01, 9.00000000e+00,
1.50000000e+01, 1.50000000e+01, 2.20000000e+01,
1.00000000e+01, 1.40000000e+01],
[ 2.00000000e+00, 2.00000000e+00, 8.00000000e+00,
2.00000000e+00, 3.00000000e+00, 5.00000000e+00,
7.00000000e+00, 5.00000000e+00, 6.00000000e+00,
9.00000000e+00, 1.70000000e+01, 1.70000000e+01,
8.00000000e+00, 2.20000000e+01, 1.30000000e+01,
1.20000000e+01, 1.50000000e+01, 1.70000000e+01,
1.20000000e+01, 1.20000000e+01],
[ 2.00000000e+00, 4.00000000e+00, 6.00000000e+00,
6.00000000e+00, 5.00000000e+00, 5.00000000e+00,
9.00000000e+00, 8.00000000e+00, 6.00000000e+00,
7.00000000e+00, 7.00000000e+00, 1.30000000e+01,
1.40000000e+01, 7.00000000e+00, 1.10000000e+01,
1.50000000e+01, 1.30000000e+01, 1.90000000e+01,
1.50000000e+01, 8.00000000e+00],
[ 2.00000000e+00, 3.00000000e+00, 8.00000000e+00,
6.00000000e+00, 4.00000000e+00, 4.00000000e+00,
4.00000000e+00, 8.00000000e+00, 6.00000000e+00,
1.10000000e+01, 3.00000000e+00, 7.00000000e+00,
9.00000000e+00, 1.40000000e+01, 2.60000000e+01,
1.60000000e+01, 1.30000000e+01, 1.60000000e+01,
1.30000000e+01, 1.70000000e+01],
[ 0.00000000e+00, 3.00000000e+00, 3.00000000e+00,
6.00000000e+00, 6.00000000e+00, 7.00000000e+00,
5.00000000e+00, 5.00000000e+00, 8.00000000e+00,
9.00000000e+00, 9.00000000e+00, 7.00000000e+00,
7.00000000e+00, 1.30000000e+01, 1.10000000e+01,
1.00000000e+01, 1.30000000e+01, 9.00000000e+00,
1.10000000e+01, 1.40000000e+01],
[ 0.00000000e+00, 3.00000000e+00, 5.00000000e+00,
4.00000000e+00, 3.00000000e+00, 4.00000000e+00,
3.00000000e+00, 7.00000000e+00, 8.00000000e+00,
8.00000000e+00, 8.00000000e+00, 1.00000000e+01,
7.00000000e+00, 1.20000000e+01, 1.00000000e+01,
1.70000000e+01, 1.00000000e+01, 2.30000000e+01,
1.00000000e+01, 1.50000000e+01],
[ 1.00000000e+00, 1.00000000e+00, 4.00000000e+00,
4.00000000e+00, 5.00000000e+00, 6.00000000e+00,
3.00000000e+00, 7.00000000e+00, 1.00000000e+01,
6.00000000e+00, 6.00000000e+00, 7.00000000e+00,
8.00000000e+00, 1.10000000e+01, 1.40000000e+01,
8.00000000e+00, 9.00000000e+00, 1.50000000e+01,
8.00000000e+00, 1.50000000e+01],
[ 0.00000000e+00, 4.00000000e+00, 2.00000000e+00,
6.00000000e+00, 4.00000000e+00, 5.00000000e+00,
5.00000000e+00, 6.00000000e+00, 8.00000000e+00,
9.00000000e+00, 9.00000000e+00, 8.00000000e+00,
1.20000000e+01, 9.00000000e+00, 1.30000000e+01,
1.30000000e+01, 1.10000000e+01, 9.00000000e+00,
9.00000000e+00, 1.30000000e+01],
[ 1.00000000e+00, 3.00000000e+00, 3.00000000e+00,
4.00000000e+00, 5.00000000e+00, 7.00000000e+00,
1.00000000e+01, 5.00000000e+00, 7.00000000e+00,
1.20000000e+01, 1.20000000e+01, 1.40000000e+01,
6.00000000e+00, 1.00000000e+01, 8.00000000e+00,
1.30000000e+01, 1.70000000e+01, 1.40000000e+01,
1.00000000e+01, 1.80000000e+01],
[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00,
3.00000000e+00, 6.00000000e+00, 4.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
9.00000000e+00, 1.50000000e+01, 1.20000000e+01,
1.10000000e+01, 1.00000000e+01, 1.30000000e+01,
9.00000000e+00, 1.40000000e+01, 2.00000000e+01,
1.20000000e+01, 1.90000000e+01]])
In [36]:
with open('DR12QDDbin4.pkl','w') as f:
pickle.dump(dd2d,f)
dd2d
Out[36]:
array([[ 4.09250000e+04, 3.00000000e+00, 7.00000000e+00,
6.00000000e+00, 1.00000000e+01, 4.00000000e+00,
4.00000000e+00, 6.00000000e+00, 9.00000000e+00,
9.00000000e+00, 9.00000000e+00, 1.50000000e+01,
1.40000000e+01, 8.00000000e+00, 1.30000000e+01,
1.10000000e+01, 1.40000000e+01, 1.00000000e+01,
1.50000000e+01, 1.30000000e+01],
[ 0.00000000e+00, 6.00000000e+00, 7.00000000e+00,
2.00000000e+00, 6.00000000e+00, 3.00000000e+00,
1.20000000e+01, 7.00000000e+00, 5.00000000e+00,
6.00000000e+00, 1.10000000e+01, 7.00000000e+00,
1.20000000e+01, 1.30000000e+01, 1.20000000e+01,
1.40000000e+01, 1.60000000e+01, 9.00000000e+00,
1.10000000e+01, 1.30000000e+01],
[ 3.00000000e+00, 5.00000000e+00, 5.00000000e+00,
6.00000000e+00, 7.00000000e+00, 1.20000000e+01,
9.00000000e+00, 8.00000000e+00, 1.10000000e+01,
6.00000000e+00, 9.00000000e+00, 1.00000000e+01,
1.00000000e+01, 1.40000000e+01, 1.40000000e+01,
1.10000000e+01, 1.30000000e+01, 2.10000000e+01,
2.10000000e+01, 2.00000000e+01],
[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00,
3.00000000e+00, 3.00000000e+00, 6.00000000e+00,
8.00000000e+00, 5.00000000e+00, 5.00000000e+00,
1.20000000e+01, 9.00000000e+00, 1.20000000e+01,
1.10000000e+01, 1.10000000e+01, 1.70000000e+01,
9.00000000e+00, 1.20000000e+01, 1.40000000e+01,
1.10000000e+01, 2.00000000e+01],
[ 1.00000000e+00, 5.00000000e+00, 3.00000000e+00,
9.00000000e+00, 5.00000000e+00, 9.00000000e+00,
5.00000000e+00, 7.00000000e+00, 8.00000000e+00,
8.00000000e+00, 7.00000000e+00, 8.00000000e+00,
1.10000000e+01, 9.00000000e+00, 1.40000000e+01,
9.00000000e+00, 1.30000000e+01, 1.50000000e+01,
1.40000000e+01, 1.40000000e+01],
[ 3.00000000e+00, 5.00000000e+00, 4.00000000e+00,
5.00000000e+00, 3.00000000e+00, 1.30000000e+01,
1.10000000e+01, 5.00000000e+00, 5.00000000e+00,
7.00000000e+00, 1.00000000e+01, 1.50000000e+01,
1.30000000e+01, 1.10000000e+01, 1.70000000e+01,
1.20000000e+01, 1.50000000e+01, 2.30000000e+01,
1.50000000e+01, 1.80000000e+01],
[ 1.00000000e+00, 1.00000000e+00, 6.00000000e+00,
3.00000000e+00, 8.00000000e+00, 4.00000000e+00,
7.00000000e+00, 6.00000000e+00, 1.30000000e+01,
4.00000000e+00, 1.20000000e+01, 1.00000000e+01,
1.00000000e+01, 1.10000000e+01, 8.00000000e+00,
1.40000000e+01, 1.50000000e+01, 1.80000000e+01,
1.00000000e+01, 1.20000000e+01],
[ 2.00000000e+00, 3.00000000e+00, 1.20000000e+01,
5.00000000e+00, 8.00000000e+00, 7.00000000e+00,
8.00000000e+00, 5.00000000e+00, 8.00000000e+00,
9.00000000e+00, 6.00000000e+00, 9.00000000e+00,
1.40000000e+01, 1.10000000e+01, 1.20000000e+01,
8.00000000e+00, 1.10000000e+01, 1.40000000e+01,
1.30000000e+01, 1.60000000e+01],
[ 0.00000000e+00, 1.00000000e+00, 6.00000000e+00,
4.00000000e+00, 4.00000000e+00, 4.00000000e+00,
7.00000000e+00, 6.00000000e+00, 1.00000000e+01,
1.10000000e+01, 1.00000000e+01, 6.00000000e+00,
1.00000000e+01, 1.50000000e+01, 1.20000000e+01,
1.50000000e+01, 1.80000000e+01, 1.40000000e+01,
2.00000000e+01, 1.40000000e+01],
[ 2.00000000e+00, 1.00000000e+00, 4.00000000e+00,
6.00000000e+00, 5.00000000e+00, 5.00000000e+00,
7.00000000e+00, 1.20000000e+01, 1.00000000e+01,
9.00000000e+00, 1.60000000e+01, 8.00000000e+00,
8.00000000e+00, 1.20000000e+01, 5.00000000e+00,
1.80000000e+01, 1.20000000e+01, 1.70000000e+01,
7.00000000e+00, 1.70000000e+01],
[ 1.00000000e+00, 3.00000000e+00, 5.00000000e+00,
6.00000000e+00, 3.00000000e+00, 2.00000000e+00,
1.00000000e+01, 7.00000000e+00, 5.00000000e+00,
1.00000000e+01, 1.10000000e+01, 7.00000000e+00,
1.00000000e+01, 1.00000000e+01, 9.00000000e+00,
1.50000000e+01, 1.50000000e+01, 2.20000000e+01,
1.00000000e+01, 1.40000000e+01],
[ 2.00000000e+00, 2.00000000e+00, 8.00000000e+00,
2.00000000e+00, 3.00000000e+00, 5.00000000e+00,
7.00000000e+00, 5.00000000e+00, 6.00000000e+00,
9.00000000e+00, 1.70000000e+01, 1.70000000e+01,
8.00000000e+00, 2.20000000e+01, 1.30000000e+01,
1.20000000e+01, 1.50000000e+01, 1.70000000e+01,
1.20000000e+01, 1.20000000e+01],
[ 2.00000000e+00, 4.00000000e+00, 6.00000000e+00,
6.00000000e+00, 5.00000000e+00, 5.00000000e+00,
9.00000000e+00, 8.00000000e+00, 6.00000000e+00,
7.00000000e+00, 7.00000000e+00, 1.30000000e+01,
1.40000000e+01, 7.00000000e+00, 1.10000000e+01,
1.50000000e+01, 1.30000000e+01, 1.90000000e+01,
1.50000000e+01, 8.00000000e+00],
[ 2.00000000e+00, 3.00000000e+00, 8.00000000e+00,
6.00000000e+00, 4.00000000e+00, 4.00000000e+00,
4.00000000e+00, 8.00000000e+00, 6.00000000e+00,
1.10000000e+01, 3.00000000e+00, 7.00000000e+00,
9.00000000e+00, 1.40000000e+01, 2.60000000e+01,
1.60000000e+01, 1.30000000e+01, 1.60000000e+01,
1.30000000e+01, 1.70000000e+01],
[ 0.00000000e+00, 3.00000000e+00, 3.00000000e+00,
6.00000000e+00, 6.00000000e+00, 7.00000000e+00,
5.00000000e+00, 5.00000000e+00, 8.00000000e+00,
9.00000000e+00, 9.00000000e+00, 7.00000000e+00,
7.00000000e+00, 1.30000000e+01, 1.10000000e+01,
1.00000000e+01, 1.30000000e+01, 9.00000000e+00,
1.10000000e+01, 1.40000000e+01],
[ 0.00000000e+00, 3.00000000e+00, 5.00000000e+00,
4.00000000e+00, 3.00000000e+00, 4.00000000e+00,
3.00000000e+00, 7.00000000e+00, 8.00000000e+00,
8.00000000e+00, 8.00000000e+00, 1.00000000e+01,
7.00000000e+00, 1.20000000e+01, 1.00000000e+01,
1.70000000e+01, 1.00000000e+01, 2.30000000e+01,
1.00000000e+01, 1.50000000e+01],
[ 1.00000000e+00, 1.00000000e+00, 4.00000000e+00,
4.00000000e+00, 5.00000000e+00, 6.00000000e+00,
3.00000000e+00, 7.00000000e+00, 1.00000000e+01,
6.00000000e+00, 6.00000000e+00, 7.00000000e+00,
8.00000000e+00, 1.10000000e+01, 1.40000000e+01,
8.00000000e+00, 9.00000000e+00, 1.50000000e+01,
8.00000000e+00, 1.50000000e+01],
[ 0.00000000e+00, 4.00000000e+00, 2.00000000e+00,
6.00000000e+00, 4.00000000e+00, 5.00000000e+00,
5.00000000e+00, 6.00000000e+00, 8.00000000e+00,
9.00000000e+00, 9.00000000e+00, 8.00000000e+00,
1.20000000e+01, 9.00000000e+00, 1.30000000e+01,
1.30000000e+01, 1.10000000e+01, 9.00000000e+00,
9.00000000e+00, 1.30000000e+01],
[ 1.00000000e+00, 3.00000000e+00, 3.00000000e+00,
4.00000000e+00, 5.00000000e+00, 7.00000000e+00,
1.00000000e+01, 5.00000000e+00, 7.00000000e+00,
1.20000000e+01, 1.20000000e+01, 1.40000000e+01,
6.00000000e+00, 1.00000000e+01, 8.00000000e+00,
1.30000000e+01, 1.70000000e+01, 1.40000000e+01,
1.00000000e+01, 1.80000000e+01],
[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00,
3.00000000e+00, 6.00000000e+00, 4.00000000e+00,
5.00000000e+00, 5.00000000e+00, 5.00000000e+00,
9.00000000e+00, 1.50000000e+01, 1.20000000e+01,
1.10000000e+01, 1.00000000e+01, 1.30000000e+01,
9.00000000e+00, 1.40000000e+01, 2.00000000e+01,
1.20000000e+01, 1.90000000e+01]])
In [ ]:
Content source: rohinkumar/galsurveystudy
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