updating benchmark plots

examples/benchmark/benchmark_darwin.csv

@@ -8,121 +8,121 @@
 # N: [64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288]
 # J: [1, 2, 4, 8, 16, 32, 64, 128, 256]
 xi,yi,j,n,comp_time,ll_time,numpy_comp_time,numpy_ll_time
-0,0,1,64,6.334992e-06,2.992655e-06,9.328494e-04,8.357943e-05
-0,1,1,128,1.087542e-05,4.624758e-06,1.535088e-03,9.413288e-05
-0,2,1,256,1.985216e-05,7.815745e-06,3.991690e-03,2.031873e-04
-0,3,1,512,3.761453e-05,1.475251e-05,1.147796e-02,6.570248e-04
-0,4,1,1024,7.371156e-05,2.924849e-05,5.786537e-02,2.703399e-03
-0,5,1,2048,1.536541e-04,5.735398e-05,2.671258e-01,1.204519e-02
-0,6,1,4096,3.025256e-04,1.140196e-04,1.527710e+00,4.607124e-02
-0,7,1,8192,5.799457e-04,2.257456e-04,8.100574e+00,2.028296e-01
-0,8,1,16384,1.190857e-03,4.651333e-04,nan,nan
-0,9,1,32768,2.301351e-03,9.735332e-04,nan,nan
-0,10,1,65536,4.774633e-03,1.961774e-03,nan,nan
-0,11,1,131072,9.272054e-03,3.789488e-03,nan,nan
-0,12,1,262144,1.821571e-02,8.970265e-03,nan,nan
-0,13,1,524288,3.817102e-02,2.102397e-02,nan,nan
-1,0,2,64,1.049634e-05,3.288375e-06,nan,nan
-1,1,2,128,1.885964e-05,5.375341e-06,nan,nan
-1,2,2,256,3.588187e-05,9.363970e-06,nan,nan
-1,3,2,512,6.734447e-05,1.734544e-05,nan,nan
-1,4,2,1024,1.321654e-04,3.322624e-05,nan,nan
-1,5,2,2048,2.685402e-04,6.617328e-05,nan,nan
-1,6,2,4096,5.261614e-04,1.361689e-04,nan,nan
-1,7,2,8192,1.063245e-03,2.669255e-04,nan,nan
-1,8,2,16384,2.169521e-03,5.528607e-04,nan,nan
-1,9,2,32768,4.265595e-03,1.183981e-03,nan,nan
-1,10,2,65536,8.644078e-03,2.340150e-03,nan,nan
-1,11,2,131072,1.809590e-02,4.748116e-03,nan,nan
-1,12,2,262144,3.778002e-02,1.142767e-02,nan,nan
-1,13,2,524288,7.940600e-02,2.441632e-02,nan,nan
-2,0,4,64,1.945539e-05,3.933279e-06,nan,nan
-2,1,4,128,3.636973e-05,6.405042e-06,nan,nan
-2,2,4,256,7.093435e-05,1.165501e-05,nan,nan
-2,3,4,512,1.399097e-04,2.149535e-05,nan,nan
-2,4,4,1024,2.861469e-04,4.393334e-05,nan,nan
-2,5,4,2048,5.509736e-04,8.667897e-05,nan,nan
-2,6,4,4096,1.113151e-03,1.734018e-04,nan,nan
-2,7,4,8192,2.181403e-03,3.438241e-04,nan,nan
-2,8,4,16384,4.581249e-03,7.769871e-04,nan,nan
-2,9,4,32768,9.182717e-03,1.701084e-03,nan,nan
-2,10,4,65536,1.858784e-02,3.404966e-03,nan,nan
-2,11,4,131072,3.832323e-02,7.286026e-03,nan,nan
-2,12,4,262144,8.346409e-02,1.556144e-02,nan,nan
-2,13,4,524288,1.676245e-01,3.067417e-02,nan,nan
-3,0,8,64,4.336546e-05,4.499060e-06,nan,nan
-3,1,8,128,8.595545e-05,7.478590e-06,nan,nan
-3,2,8,256,1.707802e-04,1.380426e-05,nan,nan
-3,3,8,512,3.398846e-04,2.608405e-05,nan,nan
-3,4,8,1024,7.397264e-04,5.214543e-05,nan,nan
-3,5,8,2048,1.370483e-03,1.127825e-04,nan,nan
-3,6,8,4096,2.790764e-03,2.151295e-04,nan,nan
-3,7,8,8192,5.398292e-03,4.687905e-04,nan,nan
-3,8,8,16384,1.104516e-02,1.051238e-03,nan,nan
-3,9,8,32768,2.153527e-02,2.080902e-03,nan,nan
-3,10,8,65536,4.308973e-02,4.305421e-03,nan,nan
-3,11,8,131072,9.386923e-02,8.748968e-03,nan,nan
-3,12,8,262144,1.968908e-01,1.897646e-02,nan,nan
-3,13,8,524288,4.333720e-01,3.797175e-02,nan,nan
-4,0,16,64,1.304222e-04,5.505277e-06,nan,nan
-4,1,16,128,2.560694e-04,9.590909e-06,nan,nan
-4,2,16,256,5.242475e-04,1.852482e-05,nan,nan
-4,3,16,512,1.043219e-03,3.784846e-05,nan,nan
-4,4,16,1024,2.080994e-03,7.459113e-05,nan,nan
-4,5,16,2048,4.136264e-03,1.563531e-04,nan,nan
-4,6,16,4096,8.394605e-03,3.692075e-04,nan,nan
-4,7,16,8192,1.715442e-02,8.454938e-04,nan,nan
-4,8,16,16384,3.465469e-02,1.841871e-03,nan,nan
-4,9,16,32768,6.660785e-02,3.703638e-03,nan,nan
-4,10,16,65536,1.333879e-01,7.675381e-03,nan,nan
-4,11,16,131072,2.834785e-01,1.516717e-02,nan,nan
-4,12,16,262144,5.732740e-01,3.110564e-02,nan,nan
-4,13,16,524288,1.187769e+00,6.542890e-02,nan,nan
-5,0,32,64,3.775581e-04,7.435020e-06,nan,nan
-5,1,32,128,7.596689e-04,1.380619e-05,nan,nan
-5,2,32,256,1.531611e-03,2.798613e-05,nan,nan
-5,3,32,512,3.062034e-03,5.874724e-05,nan,nan
-5,4,32,1024,6.145771e-03,1.217583e-04,nan,nan
-5,5,32,2048,1.231989e-02,3.123654e-04,nan,nan
-5,6,32,4096,2.388909e-02,7.608342e-04,nan,nan
-5,7,32,8192,4.856137e-02,1.697829e-03,nan,nan
-5,8,32,16384,9.816428e-02,3.417132e-03,nan,nan
-5,9,32,32768,1.964770e-01,7.021585e-03,nan,nan
-5,10,32,65536,3.956519e-01,1.426110e-02,nan,nan
-5,11,32,131072,8.072472e-01,2.826493e-02,nan,nan
-5,12,32,262144,1.649863e+00,5.779528e-02,nan,nan
-5,13,32,524288,3.377349e+00,1.196027e-01,nan,nan
-6,0,64,64,1.251451e-03,1.132239e-05,nan,nan
-6,1,64,128,2.484044e-03,2.439597e-05,nan,nan
-6,2,64,256,5.080680e-03,5.212293e-05,nan,nan
-6,3,64,512,1.018979e-02,1.107488e-04,nan,nan
-6,4,64,1024,2.023534e-02,2.990403e-04,nan,nan
-6,5,64,2048,4.055909e-02,8.345576e-04,nan,nan
-6,6,64,4096,8.180027e-02,1.847838e-03,nan,nan
-6,7,64,8192,1.634347e-01,3.871062e-03,nan,nan
-6,8,64,16384,3.181112e-01,7.985017e-03,nan,nan
-6,9,64,32768,6.521143e-01,1.623677e-02,nan,nan
-6,10,64,65536,1.298247e+00,3.311197e-02,nan,nan
-6,11,64,131072,2.649350e+00,6.532674e-02,nan,nan
-6,12,64,262144,5.377152e+00,1.335311e-01,nan,nan
-7,0,128,64,4.658316e-03,2.213747e-05,nan,nan
-7,1,128,128,9.463541e-03,4.812729e-05,nan,nan
-7,2,128,256,1.896845e-02,1.045095e-04,nan,nan
-7,3,128,512,3.749778e-02,2.829894e-04,nan,nan
-7,4,128,1024,7.633184e-02,7.711139e-04,nan,nan
-7,5,128,2048,1.527041e-01,1.721815e-03,nan,nan
-7,6,128,4096,3.003500e-01,3.551063e-03,nan,nan
-7,7,128,8192,6.097458e-01,7.258322e-03,nan,nan
-7,8,128,16384,1.215167e+00,1.497034e-02,nan,nan
-7,9,128,32768,2.447352e+00,2.999724e-02,nan,nan
-7,10,128,65536,4.910393e+00,5.961896e-02,nan,nan
-7,11,128,131072,9.941676e+00,1.195227e-01,nan,nan
-8,0,256,64,1.896432e-02,4.773527e-05,nan,nan
-8,1,256,128,3.737244e-02,1.003953e-04,nan,nan
-8,2,256,256,7.933956e-02,2.678239e-04,nan,nan
-8,3,256,512,1.493796e-01,6.747265e-04,nan,nan
-8,4,256,1024,3.128242e-01,1.534219e-03,nan,nan
-8,5,256,2048,6.564304e-01,3.185390e-03,nan,nan
-8,6,256,4096,1.284293e+00,6.488313e-03,nan,nan
-8,7,256,8192,2.605619e+00,1.312096e-02,nan,nan
-8,8,256,16384,5.145701e+00,2.598901e-02,nan,nan
+0,0,1,64,5.855161e-06,1.349691e-06,4.198422e-04,5.779600e-05
+0,1,1,128,1.019260e-05,1.897329e-06,8.221716e-04,8.045054e-05
+0,2,1,256,1.876950e-05,2.924044e-06,2.376487e-03,1.473502e-04
+0,3,1,512,3.559915e-05,5.461031e-06,9.126340e-03,4.493248e-04
+0,4,1,1024,6.911599e-05,9.952119e-06,5.096506e-02,2.307139e-03
+0,5,1,2048,1.364628e-04,1.948417e-05,2.427336e-01,9.532317e-03
+0,6,1,4096,2.732136e-04,3.953774e-05,1.423487e+00,7.143976e-02
+0,7,1,8192,5.694557e-04,7.795968e-05,7.418048e+00,2.639497e-01
+0,8,1,16384,1.170248e-03,1.651425e-04,nan,nan
+0,9,1,32768,2.279199e-03,3.393653e-04,nan,nan
+0,10,1,65536,4.663637e-03,7.288130e-04,nan,nan
+0,11,1,131072,9.304186e-03,1.553972e-03,nan,nan
+0,12,1,262144,1.857281e-02,3.124351e-03,nan,nan
+0,13,1,524288,3.966839e-02,8.699879e-03,nan,nan
+1,0,2,64,9.943234e-06,1.528280e-06,nan,nan
+1,1,2,128,1.755426e-05,2.163412e-06,nan,nan
+1,2,2,256,3.329664e-05,3.676623e-06,nan,nan
+1,3,2,512,6.494777e-05,6.745844e-06,nan,nan
+1,4,2,1024,1.264852e-04,1.282706e-05,nan,nan
+1,5,2,2048,2.513889e-04,2.462332e-05,nan,nan
+1,6,2,4096,4.993138e-04,4.885034e-05,nan,nan
+1,7,2,8192,9.815870e-04,9.646177e-05,nan,nan
+1,8,2,16384,2.012083e-03,2.031092e-04,nan,nan
+1,9,2,32768,4.003998e-03,4.305592e-04,nan,nan
+1,10,2,65536,8.062377e-03,9.625250e-04,nan,nan
+1,11,2,131072,1.698113e-02,1.961750e-03,nan,nan
+1,12,2,262144,3.409768e-02,3.912394e-03,nan,nan
+1,13,2,524288,7.281794e-02,1.015448e-02,nan,nan
+2,0,4,64,1.820698e-05,1.779472e-06,nan,nan
+2,1,4,128,3.391324e-05,2.791287e-06,nan,nan
+2,2,4,256,6.579447e-05,4.722021e-06,nan,nan
+2,3,4,512,1.298298e-04,8.833689e-06,nan,nan
+2,4,4,1024,2.590992e-04,1.702148e-05,nan,nan
+2,5,4,2048,5.197286e-04,3.356770e-05,nan,nan
+2,6,4,4096,1.035121e-03,6.831900e-05,nan,nan
+2,7,4,8192,2.069318e-03,1.360213e-04,nan,nan
+2,8,4,16384,4.171909e-03,3.040023e-04,nan,nan
+2,9,4,32768,8.369792e-03,6.778368e-04,nan,nan
+2,10,4,65536,1.695572e-02,1.373243e-03,nan,nan
+2,11,4,131072,3.676732e-02,3.516425e-03,nan,nan
+2,12,4,262144,7.729726e-02,7.192878e-03,nan,nan
+2,13,4,524288,1.566899e-01,1.321425e-02,nan,nan
+3,0,8,64,4.165357e-05,2.582847e-06,nan,nan
+3,1,8,128,8.125428e-05,4.174173e-06,nan,nan
+3,2,8,256,1.596174e-04,7.346568e-06,nan,nan
+3,3,8,512,3.142076e-04,1.406583e-05,nan,nan
+3,4,8,1024,6.238835e-04,2.823262e-05,nan,nan
+3,5,8,2048,1.248195e-03,5.420768e-05,nan,nan
+3,6,8,4096,2.482264e-03,1.112410e-04,nan,nan
+3,7,8,8192,4.963543e-03,2.498813e-04,nan,nan
+3,8,8,16384,1.007802e-02,5.927390e-04,nan,nan
+3,9,8,32768,1.997960e-02,1.150807e-03,nan,nan
+3,10,8,65536,4.042038e-02,2.276115e-03,nan,nan
+3,11,8,131072,8.526601e-02,4.709720e-03,nan,nan
+3,12,8,262144,1.812908e-01,9.441901e-03,nan,nan
+3,13,8,524288,3.713472e-01,2.034090e-02,nan,nan
+4,0,16,64,1.260121e-04,3.258023e-06,nan,nan
+4,1,16,128,2.509821e-04,4.969400e-06,nan,nan
+4,2,16,256,4.851770e-04,9.330094e-06,nan,nan
+4,3,16,512,1.001025e-03,1.968473e-05,nan,nan
+4,4,16,1024,1.993652e-03,3.768410e-05,nan,nan
+4,5,16,2048,3.928572e-03,7.446236e-05,nan,nan
+4,6,16,4096,8.044287e-03,1.794952e-04,nan,nan
+4,7,16,8192,1.594977e-02,4.376606e-04,nan,nan
+4,8,16,16384,3.136313e-02,9.367638e-04,nan,nan
+4,9,16,32768,6.376707e-02,1.932791e-03,nan,nan
+4,10,16,65536,1.266301e-01,3.549018e-03,nan,nan
+4,11,16,131072,2.590124e-01,6.997053e-03,nan,nan
+4,12,16,262144,5.423299e-01,1.400457e-02,nan,nan
+4,13,16,524288,1.114153e+00,3.065537e-02,nan,nan
+5,0,32,64,3.703920e-04,3.997943e-06,nan,nan
+5,1,32,128,7.421155e-04,7.071983e-06,nan,nan
+5,2,32,256,1.497110e-03,1.579893e-05,nan,nan
+5,3,32,512,2.915613e-03,2.928054e-05,nan,nan
+5,4,32,1024,5.971183e-03,5.821652e-05,nan,nan
+5,5,32,2048,1.182068e-02,1.475403e-04,nan,nan
+5,6,32,4096,2.346944e-02,4.092698e-04,nan,nan
+5,7,32,8192,4.772717e-02,8.436647e-04,nan,nan
+5,8,32,16384,9.391333e-02,1.695362e-03,nan,nan
+5,9,32,32768,1.878763e-01,3.376284e-03,nan,nan
+5,10,32,65536,3.801047e-01,6.726964e-03,nan,nan
+5,11,32,131072,7.744202e-01,1.351999e-02,nan,nan
+5,12,32,262144,1.636594e+00,2.883604e-02,nan,nan
+5,13,32,524288,3.232203e+00,5.704919e-02,nan,nan
+6,0,64,64,1.211818e-03,5.953513e-06,nan,nan
+6,1,64,128,2.476220e-03,1.432509e-05,nan,nan
+6,2,64,256,5.083572e-03,2.861770e-05,nan,nan
+6,3,64,512,9.925830e-03,5.228675e-05,nan,nan
+6,4,64,1024,1.971004e-02,1.389814e-04,nan,nan
+6,5,64,2048,3.989185e-02,3.924648e-04,nan,nan
+6,6,64,4096,8.072951e-02,8.341166e-04,nan,nan
+6,7,64,8192,1.616469e-01,1.658299e-03,nan,nan
+6,8,64,16384,3.191911e-01,3.359952e-03,nan,nan
+6,9,64,32768,6.544070e-01,6.526748e-03,nan,nan
+6,10,64,65536,1.282471e+00,1.278669e-02,nan,nan
+6,11,64,131072,2.571583e+00,2.614448e-02,nan,nan
+6,12,64,262144,5.292798e+00,5.279268e-02,nan,nan
+7,0,128,64,4.770948e-03,1.268204e-05,nan,nan
+7,1,128,128,9.378568e-03,2.632192e-05,nan,nan
+7,2,128,256,1.880607e-02,5.268089e-05,nan,nan
+7,3,128,512,3.760313e-02,1.378490e-04,nan,nan
+7,4,128,1024,7.392583e-02,3.955761e-04,nan,nan
+7,5,128,2048,1.503593e-01,8.172414e-04,nan,nan
+7,6,128,4096,3.032073e-01,1.665678e-03,nan,nan
+7,7,128,8192,6.068055e-01,3.361522e-03,nan,nan
+7,8,128,16384,1.203398e+00,6.465751e-03,nan,nan
+7,9,128,32768,2.450309e+00,1.324081e-02,nan,nan
+7,10,128,65536,4.789869e+00,2.581596e-02,nan,nan
+7,11,128,131072,9.725475e+00,5.139872e-02,nan,nan
+8,0,256,64,1.811890e-02,2.574093e-05,nan,nan
+8,1,256,128,3.563876e-02,5.110026e-05,nan,nan
+8,2,256,256,7.420425e-02,1.375345e-04,nan,nan
+8,3,256,512,1.485017e-01,3.907748e-04,nan,nan
+8,4,256,1024,3.309539e-01,8.160103e-04,nan,nan
+8,5,256,2048,6.414313e-01,1.683626e-03,nan,nan
+8,6,256,4096,1.276484e+00,3.243768e-03,nan,nan
+8,7,256,8192,2.629699e+00,6.826351e-03,nan,nan
+8,8,256,16384,5.201035e+00,1.354860e-02,nan,nan

examples/benchmark/benchmark_darwin_george.csv

@@ -0,0 +1,93 @@
+# grad: False
+# george: True
+# minnpow: 6
+# maxnpow: 19
+# minjpow: 0
+# maxjpow: 8
+# platform: darwin
+# N: [64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288]
+# J: [1, 2, 4, 8, 16, 32, 64, 128, 256]
+xi,yi,j,n,comp_time,ll_time,numpy_comp_time,numpy_ll_time
+0,0,1,64,4.714659e-04,5.726274e-05,nan,nan
+0,1,1,128,1.254934e-03,7.085132e-05,nan,nan
+0,2,1,256,3.052886e-03,9.286582e-05,nan,nan
+0,3,1,512,7.118972e-03,1.403633e-04,nan,nan
+0,4,1,1024,1.779462e-02,2.317753e-04,nan,nan
+0,5,1,2048,3.824177e-02,4.317215e-04,nan,nan
+0,6,1,4096,9.041370e-02,1.341963e-03,nan,nan
+0,7,1,8192,2.099056e-01,3.403171e-03,nan,nan
+0,8,1,16384,4.624173e-01,4.782336e-03,nan,nan
+0,9,1,32768,9.673930e-01,9.345568e-03,nan,nan
+0,10,1,65536,2.087901e+00,1.783724e-02,nan,nan
+0,11,1,131072,4.620890e+00,3.084002e-02,nan,nan
+0,12,1,262144,1.002593e+01,6.217080e-02,nan,nan
+1,0,2,64,5.810900e-04,5.704270e-05,nan,nan
+1,1,2,128,1.682743e-03,7.187916e-05,nan,nan
+1,2,2,256,4.128010e-03,9.592676e-05,nan,nan
+1,3,2,512,1.015399e-02,1.431110e-04,nan,nan
+1,4,2,1024,2.469293e-02,2.397786e-04,nan,nan
+1,5,2,2048,5.663609e-02,4.410103e-04,nan,nan
+1,6,2,4096,1.302271e-01,1.018624e-03,nan,nan
+1,7,2,8192,2.943148e-01,2.045290e-03,nan,nan
+1,8,2,16384,6.440890e-01,4.002535e-03,nan,nan
+1,9,2,32768,1.438344e+00,7.983368e-03,nan,nan
+1,10,2,65536,3.162338e+00,1.598123e-02,nan,nan
+1,11,2,131072,6.920274e+00,3.282907e-02,nan,nan
+2,0,4,64,8.543970e-04,5.835047e-05,nan,nan
+2,1,4,128,2.414706e-03,7.337131e-05,nan,nan
+2,2,4,256,6.395152e-03,9.701234e-05,nan,nan
+2,3,4,512,1.594784e-02,1.455283e-04,nan,nan
+2,4,4,1024,3.877344e-02,2.412756e-04,nan,nan
+2,5,4,2048,9.245173e-02,4.375763e-04,nan,nan
+2,6,4,4096,2.033749e-01,9.910026e-04,nan,nan
+2,7,4,8192,4.674296e-01,2.009073e-03,nan,nan
+2,8,4,16384,1.055184e+00,3.948919e-03,nan,nan
+2,9,4,32768,2.325644e+00,8.107706e-03,nan,nan
+2,10,4,65536,5.132128e+00,1.601717e-02,nan,nan
+3,0,8,64,1.474106e-03,6.398810e-05,nan,nan
+3,1,8,128,4.559098e-03,7.931618e-05,nan,nan
+3,2,8,256,1.103453e-02,1.089455e-04,nan,nan
+3,3,8,512,3.091808e-02,2.385856e-04,nan,nan
+3,4,8,1024,7.048641e-02,2.495931e-04,nan,nan
+3,5,8,2048,1.698705e-01,4.515649e-04,nan,nan
+3,6,8,4096,3.642958e-01,1.014901e-03,nan,nan
+3,7,8,8192,8.542344e-01,2.041954e-03,nan,nan
+3,8,8,16384,1.875865e+00,4.083918e-03,nan,nan
+3,9,8,32768,4.266954e+00,8.633456e-03,nan,nan
+3,10,8,65536,9.329437e+00,1.718243e-02,nan,nan
+4,0,16,64,2.369534e-03,6.964307e-05,nan,nan
+4,1,16,128,7.332144e-03,8.714386e-05,nan,nan
+4,2,16,256,2.112689e-02,1.272405e-04,nan,nan
+4,3,16,512,5.261742e-02,1.555183e-04,nan,nan
+4,4,16,1024,1.392551e-01,2.454447e-04,nan,nan
+4,5,16,2048,3.050863e-01,4.437511e-04,nan,nan
+4,6,16,4096,7.190660e-01,1.039846e-03,nan,nan
+4,7,16,8192,1.703355e+00,2.071768e-03,nan,nan
+4,8,16,16384,3.624614e+00,3.999163e-03,nan,nan
+4,9,16,32768,7.971098e+00,8.427450e-03,nan,nan
+5,0,32,64,4.746907e-03,7.852435e-05,nan,nan
+5,1,32,128,1.574620e-02,9.658368e-05,nan,nan
+5,2,32,256,4.383329e-02,1.208859e-04,nan,nan
+5,3,32,512,1.151390e-01,1.758815e-04,nan,nan
+5,4,32,1024,2.852317e-01,2.777843e-04,nan,nan
+5,5,32,2048,6.698932e-01,4.626635e-04,nan,nan
+5,6,32,4096,1.499971e+00,1.014860e-03,nan,nan
+5,7,32,8192,3.429006e+00,2.100597e-03,nan,nan
+5,8,32,16384,7.767241e+00,4.119708e-03,nan,nan
+6,0,64,64,9.480887e-03,1.078095e-04,nan,nan
+6,1,64,128,3.184922e-02,1.126853e-04,nan,nan
+6,2,64,256,8.978670e-02,1.518115e-04,nan,nan
+6,3,64,512,2.480066e-01,2.052329e-04,nan,nan
+6,4,64,1024,5.570241e-01,2.994174e-04,nan,nan
+6,5,64,2048,1.348636e+00,5.136511e-04,nan,nan
+6,6,64,4096,3.060855e+00,1.140960e-03,nan,nan
+6,7,64,8192,7.153480e+00,2.084144e-03,nan,nan
+7,0,128,64,1.817328e-02,1.285581e-04,nan,nan
+7,1,128,128,3.483273e-02,1.432000e-04,nan,nan
+7,2,128,256,7.095558e-02,1.678428e-04,nan,nan
+7,3,128,512,1.501925e-01,2.169214e-04,nan,nan
+7,4,128,1024,3.024877e-01,3.154863e-04,nan,nan
+7,5,128,2048,6.428996e-01,5.506193e-04,nan,nan
+7,6,128,4096,1.351849e+00,1.177495e-03,nan,nan
+7,7,128,8192,2.791517e+00,2.262515e-03,nan,nan
+7,8,128,16384,5.795974e+00,4.534622e-03,nan,nan

examples/benchmark/plot.py

@@ -31,7 +31,10 @@
 data_matrix[:] = np.nan
 data_matrix[data.xi, data.yi] = data.comp_time + data.ll_time
 
-np_time = np.array(data.numpy_comp_time + data.numpy_ll_time)
+try:
+    np_time = np.array(data.numpy_comp_time + data.numpy_ll_time)
+except AttributeError:
+    np_time = np.nan + np.zeros(len(data))
 np_m = np.isfinite(np_time)
 np_time = np_time[np_m]
 np_n = np.array(data.n)[np_m]
@@ -55,7 +58,7 @@
 if suffix == "_george":
     f = N * np.log(N)**2
     ax1.plot(N, 4.0 * f / f[-1], ":k", label=r"$\mathcal{O}(N\,\log^2N)$")
-ax1.plot(N, 4e-2 * N / N[-1], "k", label=r"$\mathcal{O}(N)$")
+ax1.plot(N, 3e-2 * N / N[-1], "k", label=r"$\mathcal{O}(N)$")
 ax1.legend(loc="lower right", bbox_to_anchor=(1.05, 0), fontsize=8)
 
 for i, n in enumerate(N[::2]):
@@ -68,7 +71,7 @@
 if suffix == "_george":
     f = J
     ax2.plot(J, 0.1 * f / f[-1], ":k", label=r"$\mathcal{O}(J)$")
-ax2.plot(J, 2e-2 * J**2 / J[-1]**2, "k",
+ax2.plot(J, 1e-2 * J**2 / J[-1]**2, "k",
          label=r"$\mathcal{O}(J^2)$")
 ax2.legend(loc="lower right", bbox_to_anchor=(1.05, 0), fontsize=8)
 
@@ -78,7 +81,7 @@
 ax1.set_yscale("log")
 ax1.set_xlim(N.min(), N.max())
 ax2.set_xlim(1, J.max())
-ax2.set_ylim(6e-6, 9.0)
+ax2.set_ylim(6e-6, 6.0)
 
 ax1.set_ylabel("computational cost [seconds]")
 ax1.set_xlabel("number of data points [$N$]")