================ by Jawad Haider
Chpt 4 - Visualization with Matplotlib¶
08 - Customizing Colorbars¶
Customizing Colorbars¶
Plot legends identify discrete labels of discrete points. For continuous labels based on the color of points, lines, or regions, a labeled colorbar can be a great tool. In Mat‐ plotlib, a colorbar is a separate axes that can provide a key for the meaning of colors in a plot.
array([[ 1.00000000e+00],
[ 9.99949900e-01],
[ 9.99799606e-01],
[ 9.99549133e-01],
[ 9.99198505e-01],
[ 9.98747758e-01],
[ 9.98196937e-01],
[ 9.97546097e-01],
[ 9.96795304e-01],
[ 9.95944632e-01],
[ 9.94994167e-01],
[ 9.93944004e-01],
[ 9.92794249e-01],
[ 9.91545016e-01],
[ 9.90196431e-01],
[ 9.88748629e-01],
[ 9.87201754e-01],
[ 9.85555963e-01],
[ 9.83811419e-01],
[ 9.81968298e-01],
[ 9.80026785e-01],
[ 9.77987073e-01],
[ 9.75849367e-01],
[ 9.73613882e-01],
[ 9.71280841e-01],
[ 9.68850478e-01],
[ 9.66323038e-01],
[ 9.63698772e-01],
[ 9.60977944e-01],
[ 9.58160826e-01],
[ 9.55247701e-01],
[ 9.52238861e-01],
[ 9.49134607e-01],
[ 9.45935251e-01],
[ 9.42641112e-01],
[ 9.39252521e-01],
[ 9.35769817e-01],
[ 9.32193350e-01],
[ 9.28523478e-01],
[ 9.24760567e-01],
[ 9.20904997e-01],
[ 9.16957152e-01],
[ 9.12917429e-01],
[ 9.08786232e-01],
[ 9.04563975e-01],
[ 9.00251081e-01],
[ 8.95847982e-01],
[ 8.91355120e-01],
[ 8.86772945e-01],
[ 8.82101915e-01],
[ 8.77342499e-01],
[ 8.72495174e-01],
[ 8.67560426e-01],
[ 8.62538748e-01],
[ 8.57430645e-01],
[ 8.52236627e-01],
[ 8.46957216e-01],
[ 8.41592940e-01],
[ 8.36144337e-01],
[ 8.30611953e-01],
[ 8.24996341e-01],
[ 8.19298066e-01],
[ 8.13517698e-01],
[ 8.07655815e-01],
[ 8.01713006e-01],
[ 7.95689865e-01],
[ 7.89586997e-01],
[ 7.83405013e-01],
[ 7.77144531e-01],
[ 7.70806181e-01],
[ 7.64390596e-01],
[ 7.57898419e-01],
[ 7.51330302e-01],
[ 7.44686901e-01],
[ 7.37968884e-01],
[ 7.31176922e-01],
[ 7.24311697e-01],
[ 7.17373896e-01],
[ 7.10364214e-01],
[ 7.03283355e-01],
[ 6.96132027e-01],
[ 6.88910947e-01],
[ 6.81620838e-01],
[ 6.74262431e-01],
[ 6.66836464e-01],
[ 6.59343679e-01],
[ 6.51784829e-01],
[ 6.44160671e-01],
[ 6.36471968e-01],
[ 6.28719491e-01],
[ 6.20904016e-01],
[ 6.13026327e-01],
[ 6.05087214e-01],
[ 5.97087471e-01],
[ 5.89027900e-01],
[ 5.80909308e-01],
[ 5.72732510e-01],
[ 5.64498325e-01],
[ 5.56207577e-01],
[ 5.47861097e-01],
[ 5.39459722e-01],
[ 5.31004293e-01],
[ 5.22495658e-01],
[ 5.13934670e-01],
[ 5.05322185e-01],
[ 4.96659067e-01],
[ 4.87946184e-01],
[ 4.79184410e-01],
[ 4.70374621e-01],
[ 4.61517701e-01],
[ 4.52614537e-01],
[ 4.43666022e-01],
[ 4.34673051e-01],
[ 4.25636526e-01],
[ 4.16557353e-01],
[ 4.07436441e-01],
[ 3.98274704e-01],
[ 3.89073060e-01],
[ 3.79832432e-01],
[ 3.70553744e-01],
[ 3.61237927e-01],
[ 3.51885914e-01],
[ 3.42498642e-01],
[ 3.33077052e-01],
[ 3.23622088e-01],
[ 3.14134698e-01],
[ 3.04615831e-01],
[ 2.95066442e-01],
[ 2.85487487e-01],
[ 2.75879926e-01],
[ 2.66244723e-01],
[ 2.56582842e-01],
[ 2.46895251e-01],
[ 2.37182922e-01],
[ 2.27446827e-01],
[ 2.17687942e-01],
[ 2.07907245e-01],
[ 1.98105716e-01],
[ 1.88284337e-01],
[ 1.78444091e-01],
[ 1.68585966e-01],
[ 1.58710948e-01],
[ 1.48820028e-01],
[ 1.38914196e-01],
[ 1.28994445e-01],
[ 1.19061768e-01],
[ 1.09117162e-01],
[ 9.91616222e-02],
[ 8.91961465e-02],
[ 7.92217334e-02],
[ 6.92393823e-02],
[ 5.92500934e-02],
[ 4.92548678e-02],
[ 3.92547068e-02],
[ 2.92506125e-02],
[ 1.92435873e-02],
[ 9.23463398e-03],
[-7.75244699e-04],
[-1.07850457e-02],
[-2.07937660e-02],
[-3.08004029e-02],
[-4.08039535e-02],
[-5.08034156e-02],
[-6.07977872e-02],
[-7.07860669e-02],
[-8.07672539e-02],
[-9.07403481e-02],
[-1.00704350e-01],
[-1.10658262e-01],
[-1.20601085e-01],
[-1.30531825e-01],
[-1.40449485e-01],
[-1.50353072e-01],
[-1.60241594e-01],
[-1.70114060e-01],
[-1.79969480e-01],
[-1.89806868e-01],
[-1.99625237e-01],
[-2.09423604e-01],
[-2.19200987e-01],
[-2.28956405e-01],
[-2.38688883e-01],
[-2.48397444e-01],
[-2.58081116e-01],
[-2.67738928e-01],
[-2.77369913e-01],
[-2.86973105e-01],
[-2.96547543e-01],
[-3.06092267e-01],
[-3.15606321e-01],
[-3.25088751e-01],
[-3.34538607e-01],
[-3.43954943e-01],
[-3.53336815e-01],
[-3.62683283e-01],
[-3.71993410e-01],
[-3.81266263e-01],
[-3.90500914e-01],
[-3.99696437e-01],
[-4.08851910e-01],
[-4.17966417e-01],
[-4.27039043e-01],
[-4.36068881e-01],
[-4.45055025e-01],
[-4.53996574e-01],
[-4.62892633e-01],
[-4.71742311e-01],
[-4.80544720e-01],
[-4.89298979e-01],
[-4.98004210e-01],
[-5.06659542e-01],
[-5.15264107e-01],
[-5.23817042e-01],
[-5.32317492e-01],
[-5.40764603e-01],
[-5.49157530e-01],
[-5.57495432e-01],
[-5.65777473e-01],
[-5.74002823e-01],
[-5.82170659e-01],
[-5.90280161e-01],
[-5.98330518e-01],
[-6.06320922e-01],
[-6.14250574e-01],
[-6.22118677e-01],
[-6.29924445e-01],
[-6.37667095e-01],
[-6.45345851e-01],
[-6.52959943e-01],
[-6.60508609e-01],
[-6.67991093e-01],
[-6.75406644e-01],
[-6.82754520e-01],
[-6.90033984e-01],
[-6.97244307e-01],
[-7.04384767e-01],
[-7.11454648e-01],
[-7.18453241e-01],
[-7.25379846e-01],
[-7.32233768e-01],
[-7.39014320e-01],
[-7.45720824e-01],
[-7.52352607e-01],
[-7.58909005e-01],
[-7.65389360e-01],
[-7.71793024e-01],
[-7.78119354e-01],
[-7.84367718e-01],
[-7.90537488e-01],
[-7.96628046e-01],
[-8.02638783e-01],
[-8.08569096e-01],
[-8.14418391e-01],
[-8.20186082e-01],
[-8.25871590e-01],
[-8.31474347e-01],
[-8.36993790e-01],
[-8.42429367e-01],
[-8.47780532e-01],
[-8.53046751e-01],
[-8.58227495e-01],
[-8.63322245e-01],
[-8.68330490e-01],
[-8.73251730e-01],
[-8.78085470e-01],
[-8.82831226e-01],
[-8.87488523e-01],
[-8.92056894e-01],
[-8.96535881e-01],
[-9.00925037e-01],
[-9.05223919e-01],
[-9.09432099e-01],
[-9.13549155e-01],
[-9.17574673e-01],
[-9.21508251e-01],
[-9.25349494e-01],
[-9.29098017e-01],
[-9.32753446e-01],
[-9.36315413e-01],
[-9.39783561e-01],
[-9.43157544e-01],
[-9.46437023e-01],
[-9.49621670e-01],
[-9.52711165e-01],
[-9.55705199e-01],
[-9.58603471e-01],
[-9.61405692e-01],
[-9.64111581e-01],
[-9.66720867e-01],
[-9.69233287e-01],
[-9.71648591e-01],
[-9.73966536e-01],
[-9.76186890e-01],
[-9.78309431e-01],
[-9.80333945e-01],
[-9.82260231e-01],
[-9.84088095e-01],
[-9.85817354e-01],
[-9.87447834e-01],
[-9.88979373e-01],
[-9.90411816e-01],
[-9.91745021e-01],
[-9.92978853e-01],
[-9.94113189e-01],
[-9.95147916e-01],
[-9.96082929e-01],
[-9.96918136e-01],
[-9.97653452e-01],
[-9.98288803e-01],
[-9.98824127e-01],
[-9.99259369e-01],
[-9.99594485e-01],
[-9.99829443e-01],
[-9.99964218e-01],
[-9.99998798e-01],
[-9.99933178e-01],
[-9.99767366e-01],
[-9.99501377e-01],
[-9.99135239e-01],
[-9.98668988e-01],
[-9.98102670e-01],
[-9.97436344e-01],
[-9.96670075e-01],
[-9.95803940e-01],
[-9.94838026e-01],
[-9.93772430e-01],
[-9.92607258e-01],
[-9.91342628e-01],
[-9.89978665e-01],
[-9.88515508e-01],
[-9.86953301e-01],
[-9.85292203e-01],
[-9.83532378e-01],
[-9.81674005e-01],
[-9.79717268e-01],
[-9.77662364e-01],
[-9.75509498e-01],
[-9.73258887e-01],
[-9.70910757e-01],
[-9.68465341e-01],
[-9.65922886e-01],
[-9.63283645e-01],
[-9.60547884e-01],
[-9.57715877e-01],
[-9.54787907e-01],
[-9.51764268e-01],
[-9.48645263e-01],
[-9.45431204e-01],
[-9.42122413e-01],
[-9.38719222e-01],
[-9.35221972e-01],
[-9.31631013e-01],
[-9.27946706e-01],
[-9.24169418e-01],
[-9.20299529e-01],
[-9.16337427e-01],
[-9.12283508e-01],
[-9.08138179e-01],
[-9.03901855e-01],
[-8.99574960e-01],
[-8.95157929e-01],
[-8.90651203e-01],
[-8.86055234e-01],
[-8.81370484e-01],
[-8.76597420e-01],
[-8.71736521e-01],
[-8.66788276e-01],
[-8.61753178e-01],
[-8.56631733e-01],
[-8.51424454e-01],
[-8.46131863e-01],
[-8.40754490e-01],
[-8.35292874e-01],
[-8.29747562e-01],
[-8.24119109e-01],
[-8.18408081e-01],
[-8.12615048e-01],
[-8.06740592e-01],
[-8.00785301e-01],
[-7.94749771e-01],
[-7.88634608e-01],
[-7.82440424e-01],
[-7.76167840e-01],
[-7.69817485e-01],
[-7.63389994e-01],
[-7.56886012e-01],
[-7.50306190e-01],
[-7.43651188e-01],
[-7.36921673e-01],
[-7.30118318e-01],
[-7.23241806e-01],
[-7.16292826e-01],
[-7.09272073e-01],
[-7.02180252e-01],
[-6.95018073e-01],
[-6.87786253e-01],
[-6.80485517e-01],
[-6.73116597e-01],
[-6.65680231e-01],
[-6.58177165e-01],
[-6.50608149e-01],
[-6.42973943e-01],
[-6.35275311e-01],
[-6.27513025e-01],
[-6.19687862e-01],
[-6.11800607e-01],
[-6.03852050e-01],
[-5.95842988e-01],
[-5.87774222e-01],
[-5.79646561e-01],
[-5.71460820e-01],
[-5.63217820e-01],
[-5.54918385e-01],
[-5.46563347e-01],
[-5.38153544e-01],
[-5.29689819e-01],
[-5.21173018e-01],
[-5.12603997e-01],
[-5.03983613e-01],
[-4.95312730e-01],
[-4.86592217e-01],
[-4.77822948e-01],
[-4.69005801e-01],
[-4.60141660e-01],
[-4.51231412e-01],
[-4.42275952e-01],
[-4.33276176e-01],
[-4.24232986e-01],
[-4.15147288e-01],
[-4.06019993e-01],
[-3.96852014e-01],
[-3.87644272e-01],
[-3.78397687e-01],
[-3.69113187e-01],
[-3.59791703e-01],
[-3.50434167e-01],
[-3.41041518e-01],
[-3.31614697e-01],
[-3.22154648e-01],
[-3.12662319e-01],
[-3.03138662e-01],
[-2.93584631e-01],
[-2.84001182e-01],
[-2.74389277e-01],
[-2.64749878e-01],
[-2.55083951e-01],
[-2.45392466e-01],
[-2.35676391e-01],
[-2.25936703e-01],
[-2.16174375e-01],
[-2.06390387e-01],
[-1.96585719e-01],
[-1.86761353e-01],
[-1.76918273e-01],
[-1.67057466e-01],
[-1.57179921e-01],
[-1.47286626e-01],
[-1.37378573e-01],
[-1.27456754e-01],
[-1.17522165e-01],
[-1.07575800e-01],
[-9.76186559e-02],
[-8.76517305e-02],
[-7.76760224e-02],
[-6.76925312e-02],
[-5.77022572e-02],
[-4.77062016e-02],
[-3.77053657e-02],
[-2.77007519e-02],
[-1.76933624e-02],
[-7.68420006e-03],
[ 2.32573223e-03],
[ 1.23354315e-02],
[ 2.23438947e-02],
[ 3.23501191e-02],
[ 4.23531021e-02],
[ 5.23518413e-02],
[ 6.23453348e-02],
[ 7.23325814e-02],
[ 8.23125803e-02],
[ 9.22843315e-02],
[ 1.02246836e-01],
[ 1.12199095e-01],
[ 1.22140112e-01],
[ 1.32068891e-01],
[ 1.41984436e-01],
[ 1.51885755e-01],
[ 1.61771855e-01],
[ 1.71641745e-01],
[ 1.81494437e-01],
[ 1.91328943e-01],
[ 2.01144278e-01],
[ 2.10939459e-01],
[ 2.20713504e-01],
[ 2.30465433e-01],
[ 2.40194270e-01],
[ 2.49899039e-01],
[ 2.59578769e-01],
[ 2.69232489e-01],
[ 2.78859232e-01],
[ 2.88458033e-01],
[ 2.98027932e-01],
[ 3.07567967e-01],
[ 3.17077185e-01],
[ 3.26554632e-01],
[ 3.35999358e-01],
[ 3.45410418e-01],
[ 3.54786867e-01],
[ 3.64127767e-01],
[ 3.73432181e-01],
[ 3.82699178e-01],
[ 3.91927829e-01],
[ 4.01117208e-01],
[ 4.10266396e-01],
[ 4.19374475e-01],
[ 4.28440534e-01],
[ 4.37463662e-01],
[ 4.46442957e-01],
[ 4.55377519e-01],
[ 4.64266452e-01],
[ 4.73108866e-01],
[ 4.81903875e-01],
[ 4.90650597e-01],
[ 4.99348156e-01],
[ 5.07995681e-01],
[ 5.16592305e-01],
[ 5.25137167e-01],
[ 5.33629410e-01],
[ 5.42068184e-01],
[ 5.50452643e-01],
[ 5.58781947e-01],
[ 5.67055261e-01],
[ 5.75271756e-01],
[ 5.83430610e-01],
[ 5.91531004e-01],
[ 5.99572127e-01],
[ 6.07553173e-01],
[ 6.15473343e-01],
[ 6.23331843e-01],
[ 6.31127885e-01],
[ 6.38860689e-01],
[ 6.46529479e-01],
[ 6.54133487e-01],
[ 6.61671951e-01],
[ 6.69144116e-01],
[ 6.76549233e-01],
[ 6.83886561e-01],
[ 6.91155363e-01],
[ 6.98354912e-01],
[ 7.05484486e-01],
[ 7.12543371e-01],
[ 7.19530859e-01],
[ 7.26446251e-01],
[ 7.33288853e-01],
[ 7.40057981e-01],
[ 7.46752954e-01],
[ 7.53373104e-01],
[ 7.59917766e-01],
[ 7.66386284e-01],
[ 7.72778011e-01],
[ 7.79092307e-01],
[ 7.85328537e-01],
[ 7.91486078e-01],
[ 7.97564313e-01],
[ 8.03562632e-01],
[ 8.09480434e-01],
[ 8.15317127e-01],
[ 8.21072126e-01],
[ 8.26744853e-01],
[ 8.32334742e-01],
[ 8.37841230e-01],
[ 8.43263768e-01],
[ 8.48601811e-01],
[ 8.53854824e-01],
[ 8.59022282e-01],
[ 8.64103666e-01],
[ 8.69098468e-01],
[ 8.74006186e-01],
[ 8.78826329e-01],
[ 8.83558414e-01],
[ 8.88201968e-01],
[ 8.92756523e-01],
[ 8.97221626e-01],
[ 9.01596827e-01],
[ 9.05881688e-01],
[ 9.10075781e-01],
[ 9.14178684e-01],
[ 9.18189988e-01],
[ 9.22109289e-01],
[ 9.25936195e-01],
[ 9.29670323e-01],
[ 9.33311299e-01],
[ 9.36858757e-01],
[ 9.40312343e-01],
[ 9.43671709e-01],
[ 9.46936521e-01],
[ 9.50106449e-01],
[ 9.53181178e-01],
[ 9.56160398e-01],
[ 9.59043812e-01],
[ 9.61831130e-01],
[ 9.64522073e-01],
[ 9.67116371e-01],
[ 9.69613765e-01],
[ 9.72014004e-01],
[ 9.74316848e-01],
[ 9.76522066e-01],
[ 9.78629437e-01],
[ 9.80638749e-01],
[ 9.82549803e-01],
[ 9.84362405e-01],
[ 9.86076374e-01],
[ 9.87691540e-01],
[ 9.89207739e-01],
[ 9.90624820e-01],
[ 9.91942641e-01],
[ 9.93161070e-01],
[ 9.94279984e-01],
[ 9.95299273e-01],
[ 9.96218833e-01],
[ 9.97038572e-01],
[ 9.97758408e-01],
[ 9.98378270e-01],
[ 9.98898095e-01],
[ 9.99317830e-01],
[ 9.99637435e-01],
[ 9.99856876e-01],
[ 9.99976133e-01],
[ 9.99995192e-01],
[ 9.99914052e-01],
[ 9.99732722e-01],
[ 9.99451218e-01],
[ 9.99069571e-01],
[ 9.98587817e-01],
[ 9.98006005e-01],
[ 9.97324193e-01],
[ 9.96542450e-01],
[ 9.95660854e-01],
[ 9.94679493e-01],
[ 9.93598466e-01],
[ 9.92417881e-01],
[ 9.91137856e-01],
[ 9.89758520e-01],
[ 9.88280011e-01],
[ 9.86702476e-01],
[ 9.85026074e-01],
[ 9.83250973e-01],
[ 9.81377351e-01],
[ 9.79405396e-01],
[ 9.77335304e-01],
[ 9.75167284e-01],
[ 9.72901553e-01],
[ 9.70538338e-01],
[ 9.68077875e-01],
[ 9.65520411e-01],
[ 9.62866203e-01],
[ 9.60115516e-01],
[ 9.57268626e-01],
[ 9.54325818e-01],
[ 9.51287388e-01],
[ 9.48153638e-01],
[ 9.44924885e-01],
[ 9.41601450e-01],
[ 9.38183667e-01],
[ 9.34671879e-01],
[ 9.31066437e-01],
[ 9.27367703e-01],
[ 9.23576047e-01],
[ 9.19691849e-01],
[ 9.15715499e-01],
[ 9.11647395e-01],
[ 9.07487943e-01],
[ 9.03237562e-01],
[ 8.98896678e-01],
[ 8.94465724e-01],
[ 8.89945145e-01],
[ 8.85335394e-01],
[ 8.80636933e-01],
[ 8.75850233e-01],
[ 8.70975773e-01],
[ 8.66014042e-01],
[ 8.60965536e-01],
[ 8.55830762e-01],
[ 8.50610235e-01],
[ 8.45304477e-01],
[ 8.39914019e-01],
[ 8.34439403e-01],
[ 8.28881176e-01],
[ 8.23239896e-01],
[ 8.17516128e-01],
[ 8.11710445e-01],
[ 8.05823429e-01],
[ 7.99855670e-01],
[ 7.93807766e-01],
[ 7.87680323e-01],
[ 7.81473955e-01],
[ 7.75189283e-01],
[ 7.68826938e-01],
[ 7.62387557e-01],
[ 7.55871785e-01],
[ 7.49280275e-01],
[ 7.42613687e-01],
[ 7.35872690e-01],
[ 7.29057959e-01],
[ 7.22170177e-01],
[ 7.15210034e-01],
[ 7.08178227e-01],
[ 7.01075461e-01],
[ 6.93902448e-01],
[ 6.86659906e-01],
[ 6.79348561e-01],
[ 6.71969145e-01],
[ 6.64522399e-01],
[ 6.57009068e-01],
[ 6.49429905e-01],
[ 6.41785669e-01],
[ 6.34077127e-01],
[ 6.26305051e-01],
[ 6.18470219e-01],
[ 6.10573417e-01],
[ 6.02615435e-01],
[ 5.94597072e-01],
[ 5.86519131e-01],
[ 5.78382421e-01],
[ 5.70187757e-01],
[ 5.61935960e-01],
[ 5.53627858e-01],
[ 5.45264283e-01],
[ 5.36846073e-01],
[ 5.28374071e-01],
[ 5.19849126e-01],
[ 5.11272092e-01],
[ 5.02643829e-01],
[ 4.93965202e-01],
[ 4.85237080e-01],
[ 4.76460337e-01],
[ 4.67635853e-01],
[ 4.58764512e-01],
[ 4.49847203e-01],
[ 4.40884820e-01],
[ 4.31878260e-01],
[ 4.22828426e-01],
[ 4.13736226e-01],
[ 4.04602569e-01],
[ 3.95428371e-01],
[ 3.86214551e-01],
[ 3.76962033e-01],
[ 3.67671743e-01],
[ 3.58344613e-01],
[ 3.48981577e-01],
[ 3.39583574e-01],
[ 3.30151544e-01],
[ 3.20686433e-01],
[ 3.11189189e-01],
[ 3.01660765e-01],
[ 2.92102114e-01],
[ 2.82514195e-01],
[ 2.72897968e-01],
[ 2.63254397e-01],
[ 2.53584448e-01],
[ 2.43889090e-01],
[ 2.34169294e-01],
[ 2.24426035e-01],
[ 2.14660288e-01],
[ 2.04873032e-01],
[ 1.95065249e-01],
[ 1.85237919e-01],
[ 1.75392030e-01],
[ 1.65528565e-01],
[ 1.55648516e-01],
[ 1.45752870e-01],
[ 1.35842619e-01],
[ 1.25918758e-01],
[ 1.15982279e-01],
[ 1.06034179e-01],
[ 9.60754549e-02],
[ 8.61071037e-02],
[ 7.61301246e-02],
[ 6.61455173e-02],
[ 5.61542823e-02],
[ 4.61574206e-02],
[ 3.61559340e-02],
[ 2.61508246e-02],
[ 1.61430949e-02],
[ 6.13374766e-03],
[-3.87621418e-03],
[-1.38857876e-02],
[-2.38939697e-02],
[-3.38997577e-02],
[-4.39021489e-02],
[-5.39001411e-02],
[-6.38927325e-02],
[-7.38789220e-02],
[-8.38577088e-02],
[-9.38280931e-02],
[-1.03789076e-01],
[-1.13739659e-01],
[-1.23678846e-01],
[-1.33605640e-01],
[-1.43519046e-01],
[-1.53418073e-01],
[-1.63301726e-01],
[-1.73169017e-01],
[-1.83018957e-01],
[-1.92850558e-01],
[-2.02662836e-01],
[-2.12454807e-01],
[-2.22225490e-01],
[-2.31973906e-01],
[-2.41699079e-01],
[-2.51400034e-01],
[-2.61075798e-01],
[-2.70725403e-01],
[-2.80347881e-01],
[-2.89942268e-01],
[-2.99507604e-01],
[-3.09042928e-01],
[-3.18547287e-01],
[-3.28019728e-01],
[-3.37459301e-01],
[-3.46865061e-01],
[-3.56236066e-01],
[-3.65571375e-01],
[-3.74870055e-01],
[-3.84131173e-01],
[-3.93353801e-01],
[-4.02537015e-01],
[-4.11679896e-01],
[-4.20781526e-01],
[-4.29840994e-01],
[-4.38857392e-01],
[-4.47829817e-01],
[-4.56757370e-01],
[-4.65639155e-01],
[-4.74474285e-01],
[-4.83261871e-01],
[-4.92001036e-01],
[-5.00690902e-01],
[-5.09330599e-01],
[-5.17919262e-01],
[-5.26456029e-01],
[-5.34940046e-01],
[-5.43370462e-01],
[-5.51746432e-01],
[-5.60067118e-01],
[-5.68331685e-01],
[-5.76539306e-01],
[-5.84689158e-01],
[-5.92780425e-01],
[-6.00812295e-01],
[-6.08783964e-01],
[-6.16694633e-01],
[-6.24543510e-01],
[-6.32329808e-01],
[-6.40052747e-01],
[-6.47711552e-01],
[-6.55305458e-01],
[-6.62833702e-01],
[-6.70295531e-01],
[-6.77690196e-01],
[-6.85016957e-01],
[-6.92275080e-01],
[-6.99463837e-01],
[-7.06582509e-01],
[-7.13630381e-01],
[-7.20606748e-01],
[-7.27510910e-01],
[-7.34342176e-01],
[-7.41099862e-01],
[-7.47783289e-01],
[-7.54391789e-01],
[-7.60924700e-01],
[-7.67381366e-01],
[-7.73761141e-01],
[-7.80063386e-01],
[-7.86287469e-01],
[-7.92432766e-01],
[-7.98498661e-01],
[-8.04484548e-01],
[-8.10389826e-01],
[-8.16213903e-01],
[-8.21956196e-01],
[-8.27616129e-01],
[-8.33193136e-01],
[-8.38686657e-01],
[-8.44096142e-01],
[-8.49421049e-01],
[-8.54660845e-01],
[-8.59815004e-01],
[-8.64883010e-01],
[-8.69864355e-01],
[-8.74758541e-01],
[-8.79565076e-01],
[-8.84283479e-01],
[-8.88913277e-01],
[-8.93454007e-01],
[-8.97905213e-01],
[-9.02266449e-01],
[-9.06537279e-01],
[-9.10717274e-01],
[-9.14806016e-01],
[-9.18803095e-01],
[-9.22708110e-01],
[-9.26520671e-01],
[-9.30240394e-01],
[-9.33866908e-01],
[-9.37399849e-01],
[-9.40838863e-01],
[-9.44183606e-01],
[-9.47433742e-01],
[-9.50588945e-01],
[-9.53648900e-01],
[-9.56613300e-01],
[-9.59481847e-01],
[-9.62254255e-01],
[-9.64930246e-01],
[-9.67509551e-01],
[-9.69991913e-01],
[-9.72377081e-01],
[-9.74664818e-01],
[-9.76854895e-01],
[-9.78947090e-01],
[-9.80941196e-01],
[-9.82837012e-01],
[-9.84634348e-01],
[-9.86333025e-01],
[-9.87932871e-01],
[-9.89433727e-01],
[-9.90835442e-01],
[-9.92137877e-01],
[-9.93340899e-01],
[-9.94444389e-01],
[-9.95448237e-01],
[-9.96352341e-01],
[-9.97156611e-01],
[-9.97860966e-01],
[-9.98465337e-01],
[-9.98969661e-01],
[-9.99373890e-01],
[-9.99677982e-01],
[-9.99881906e-01],
[-9.99985643e-01],
[-9.99989182e-01],
[-9.99892522e-01],
[-9.99695674e-01],
[-9.99398657e-01],
[-9.99001501e-01],
[-9.98504245e-01],
[-9.97906940e-01],
[-9.97209644e-01],
[-9.96412429e-01],
[-9.95515375e-01],
[-9.94518569e-01],
[-9.93422114e-01],
[-9.92226118e-01],
[-9.90930702e-01],
[-9.89535995e-01],
[-9.88042137e-01],
[-9.86449278e-01],
[-9.84757577e-01],
[-9.82967204e-01],
[-9.81078338e-01],
[-9.79091169e-01],
[-9.77005895e-01],
[-9.74822726e-01],
[-9.72541880e-01],
[-9.70163586e-01],
[-9.67688082e-01],
[-9.65115616e-01],
[-9.62446446e-01],
[-9.59680840e-01],
[-9.56819074e-01],
[-9.53861435e-01],
[-9.50808220e-01],
[-9.47659734e-01],
[-9.44416293e-01],
[-9.41078223e-01],
[-9.37645857e-01],
[-9.34119539e-01],
[-9.30499623e-01],
[-9.26786471e-01],
[-9.22980456e-01],
[-9.19081959e-01],
[-9.15091370e-01],
[-9.11009089e-01],
[-9.06835526e-01],
[-9.02571099e-01],
[-8.98216234e-01],
[-8.93771368e-01],
[-8.89236948e-01],
[-8.84613426e-01],
[-8.79901266e-01],
[-8.75100941e-01],
[-8.70212930e-01],
[-8.65237726e-01],
[-8.60175824e-01],
[-8.55027734e-01],
[-8.49793970e-01],
[-8.44475058e-01],
[-8.39071529e-01]])
<matplotlib.colorbar.Colorbar at 0x7f5d4c0728b0>
Customizing Colorbars¶
We can specify the colormap using the cmap argument to the plotting function that is creating the visualization
<matplotlib.image.AxesImage at 0x7f5d3a3e2490>
All the available colormaps are in the plt.cm namespace; using IPython’s tab- completion feature will give you a full list of built-in possibilities:
But being able to choose a colormap is just the first step: more important is how to decide among the possibilities! The choice turns out to be much more subtle than you might initially expect.
Choosing the colormap¶
A full treatment of color choice within visualization is beyond the scope of this book, but for entertaining reading on this subject and others,
Broadly, you should be aware of three different categories of
colormaps:
Sequential colormaps
These consist of one continuous sequence of colors (e.g., binary or
viridis).
Divergent colormaps These usually contain two distinct colors, which
show positive and negative deviations from a mean (e.g., RdBu or
PuOr).
Qualitative colormaps
These mix colors with no particular sequence (e.g., rainbow or jet).
The jet colormap, which was the default in Matplotlib prior to version 2.0, is an example of a qualitative colormap. Its status as the default was quite unfortunate, because qualitative maps are often a poor choice for representing quantitative data. Among the problems is the fact that qualitative maps usually do not display any uni‐ form progression in brightness as the scale increases.
from matplotlib.colors import LinearSegmentedColormap
def grayscale_cmap(cmap):
"""Return a grayscale version of the given colormap"""
cmap = plt.cm.get_cmap(cmap)
colors = cmap(np.arange(cmap.N))
# convert RGBA to perceived grayscale luminance
# cf. http://alienryderflex.com/hsp.html
RGB_weight = [0.299, 0.587, 0.114]
luminance = np.sqrt(np.dot(colors[:, :3] ** 2, RGB_weight))
colors[:, :3] = luminance[:, np.newaxis]
return LinearSegmentedColormap.from_list(cmap.name + "_gray", colors, cmap.N)
def view_colormap(cmap):
"""Plot a colormap with its grayscale equivalent"""
cmap = plt.cm.get_cmap(cmap)
colors = cmap(np.arange(cmap.N))
cmap = grayscale_cmap(cmap)
grayscale = cmap(np.arange(cmap.N))
fig, ax = plt.subplots(2, figsize=(6, 2),
subplot_kw=dict(xticks=[], yticks=[]))
ax[0].imshow([colors], extent=[0, 10, 0, 1])
ax[1].imshow([grayscale], extent=[0, 10, 0, 1])
Notice the bright stripes in the grayscale image. Even in full color, this uneven bright‐ ness means that the eye will be drawn to certain portions of the color range, which will potentially emphasize unimportant parts of the dataset. It’s better to use a color‐ map such as viridis (the default as of Matplotlib 2.0), which is specifically construc‐ ted to have an even brightness variation across the range. Thus, it not only plays well with our color perception, but also will translate well to grayscale printing
Color limits and extensions¶
Matplotlib allows for a large range of colorbar customization. The colorbar itself is simply an instance of plt.Axes, so all of the axes and tick formatting tricks we’ve learned are applicable. The colorbar has some interesting flexibility; for example, we can narrow the color limits and indicate the out-of-bounds values with a triangular arrow at the top and bottom by setting the extend property. This might come in handy, for example, if you’re displaying an image that is subject to noise
# make noise in 1% of the image pixel
speckles=(np.random.random(I.shape)<0.01)
I[speckles]=np.random.normal(0,3,np.count_nonzero(speckles))
plt.figure(figsize=(10,3.5))
plt.subplot(1,2,1)
plt.imshow(I, cmap='RdBu')
plt.colorbar()
plt.subplot(1,2,2)
plt.imshow(I, cmap='RdBu')
plt.colorbar(extend='both')
plt.clim(-1,1)
Discrete colorbars¶
Colormaps are by default continuous, but sometimes you’d like to represent discrete values. The easiest way to do this is to use the plt.cm.get_cmap() function, and pass the name of a suitable colormap along with the number of desired bins
