从python绘图创建视频，使其看起来像移动的概率分布

Question

我使用以下代码生成i图：

for i in range(len(hlist)):

        p1 = np.array(hlist[i])

        kde = gaussian_kde(p1[:,0], bw_method=.4, weights=p1[:,1])
        x = np.linspace(0, 100, 1000)
        plt.plot(x, kde(x), color='crimson')

        plt.ylim([minmin, maxmax])

        plt.show()

所有绘图的x轴和y轴相同

所以我想从所有的情节中制作一个视频，如果可能的话，在每个情节之间进行转换，看起来像一个移动的概率分布

完整列表：

hlist = np.array([[[0.0, 0.9003079895267837],
[10.0, 0.000654274453636099],
[15.0, 4.8062543883990662e-05],
[20.0, 0.0078540048399245721],
[25.0, 0.00030775066073687057],
[30.0, 0.0069461270425554486],
[35.0, 0.00038747043199781708],
[40.0, 0.0040570022317705538],
[45.0, 0.01242208947573863],
[50.0, 0.00042730445196067818],
[55.0, 9.2094566445921082e-05],
[60.0, 2.3533619494819096e-17],
[65.0, 6.8535505965721361e-15],
[70.0, 2.3163309687227612e-12],
[75.0, 4.7244481305730235e-14],
[80.0, 3.946900273532476e-15],
[85.0, 1.387482842660183e-12],
[90.0, 1.169953999346581e-10],
[95.0, 9.405541221977168e-06],
[100.0, 0.00020815494180476587]],
[[0.0, 0.093092478749998001],
[10.0, 1.4053195886715078e-07],
[15.0, 2.4433977582117203e-05],
[20.0, 0.00082219857285037044],
[25.0, 2.6948063300386935e-05],
[30.0, 0.093959576222220995],
[35.0, 0.0001656191073324495],
[40.0, 0.0029118221893314833],
[45.0, 0.01564108273403109],
[50.0, 1.6648778174359671e-05],
[55.0, 2.8100410030581244e-07],
[60.0, 6.0753841480616879e-17],
[65.0, 9.8080692203972863e-15],
[70.0, 6.9694730549144334e-17],
[75.0, 5.8326312613461052e-15],
[80.0, 2.4139075497471945e-16],
[85.0, 5.9084558298527006e-13],
[90.0, 1.6711579730178305e-10],
[95.0, 1.5864098433225844e-08],
[100.0, 2.2572886039178069e-08]],
[[0.0, 4.7418107222805056e-05],
[10.0, 0.72289205649751387],
[15.0, 3.308651897500352e-07],
[20.0, 0.076673642863552008],
[25.0, 0.0001501767582823212],
[30.0, 0.03027984877348755],
[35.0, 0.00011292381988867554],
[40.0, 0.00016702076039256828],
[45.0, 0.00081579620071354846],
[50.0, 1.0458962769769357e-05],
[55.0, 5.5802811319507686e-08],
[60.0, 4.1253613398572781e-19],
[65.0, 9.4051858824518925e-18],
[70.0, 1.3368786287741004e-14],
[75.0, 5.9931111040137393e-14],
[80.0, 3.2539929633588511e-15],
[85.0, 1.047255336132784e-15],
[90.0, 1.4017813965595768e-12],
[95.0, 2.8511569193363785e-09],
[100.0, 1.0183773063292469e-10]],
[[0.0, 0.0045609821258978814],
[10.0, 0.0001917787950273313],
[15.0, 0.99771505193318455],
[20.0, 0.0055938427174407005],
[25.0, 0.013201240944903663],
[30.0, 0.018570016510629072],
[35.0, 0.012700626279918454],
[40.0, 0.019933344753975846],
[45.0, 0.15619609747558014],
[50.0, 0.031265415297255877],
[55.0, 0.42528804082092103],
[60.0, 1.3054822476679266e-18],
[65.0, 8.152405640596557e-18],
[70.0, 1.4328231368499457e-17],
[75.0, 1.3158371543509022e-15],
[80.0, 2.0369465623715189e-18],
[85.0, 1.1462339774892004e-13],
[90.0, 4.7664892870840778e-13],
[95.0, 7.2181814960747638e-06],
[100.0, 0.0011762750538733889]],
[[0.0, 1.4364539307817039e-05],
[10.0, 0.0002382809395285733],
[15.0, 1.1335942314786911e-05],
[20.0, 0.52821149082545871],
[25.0, 0.1355189607672023],
[30.0, 0.0013765391762318477],
[35.0, 0.01044957461521267],
[40.0, 0.00056647346484793787],
[45.0, 0.00076442899303849577],
[50.0, 0.032497831121074787],
[55.0, 0.00032304336212375635],
[60.0, 8.7317718742465198e-16],
[65.0, 6.539726198592529e-12],
[70.0, 9.2846001586370999e-08],
[75.0, 1.1275715667447136e-16],
[80.0, 9.3449145004368205e-16],
[85.0, 7.6700176188571133e-13],
[90.0, 3.6113266426134806e-09],
[95.0, 8.8657633159259348e-07],
[100.0, 1.0088131613242799e-08]],
[[0.0, 0.00010259026745556786],
[10.0, 2.8416604131363134e-05],
[15.0, 1.5265724585429075e-08],
[20.0, 0.00035572734341432835],
[25.0, 0.74774814405543055],
[30.0, 0.20170120434943195],
[35.0, 0.31688948032690523],
[40.0, 0.016495873067229414],
[45.0, 0.019452385467953772],
[50.0, 0.0063096344918544449],
[55.0, 1.4586177114635856e-07],
[60.0, 1.2622740425026807e-15],
[65.0, 1.6230117141451513e-13],
[70.0, 2.7328268483555935e-15],
[75.0, 5.9099377394502244e-17],
[80.0, 1.0947896137035609e-17],
[85.0, 2.2319783866749845e-12],
[90.0, 7.3069777072490827e-10],
[95.0, 1.2982219252486507e-07],
[100.0, 9.2703590554295812e-09]],
[[0.0, 5.6467274191670147e-06],
[10.0, 1.9311914301356571e-15],
[15.0, 0.00030373592016130291],
[20.0, 0.37222022990454917],
[25.0, 7.1237862206873578e-07],
[30.0, 0.016227799829528728],
[35.0, 0.33096651567634761],
[40.0, 0.00014104696271763757],
[45.0, 0.001730151916132917],
[50.0, 0.00019647094586097279],
[55.0, 1.61437439554684e-14],
[60.0, 7.1139359284635841e-65],
[65.0, 8.3871906097721264e-63],
[70.0, 1.2336993844192023e-53],
[75.0, 5.0051598531858931e-64],
[80.0, 2.9758606051614185e-62],
[85.0, 2.8460978574131379e-51],
[90.0, 2.8677742500204446e-37],
[95.0, 3.7194535849027315e-27],
[100.0, 2.3439805162091607e-24]],
[[0.0, 0.0001430560406790707],
[10.0, 6.2797052001508247e-13],
[15.0, 4.8114669550502021e-06],
[20.0, 0.0007443231772534647],
[25.0, 0.00061070912573869406],
[30.0, 0.48116582167944905],
[35.0, 0.24698643991977953],
[40.0, 0.016407283121225951],
[45.0, 0.2557158314329116],
[50.0, 1.1252231121357235e-05],
[55.0, 0.064666668633158647],
[60.0, 1.7631447655837744e-17],
[65.0, 1.1294722466816786e-14],
[70.0, 2.9419020411134367e-16],
[75.0, 3.0887653014525822e-17],
[80.0, 4.4973693062706866e-17],
[85.0, 9.0975358174005147e-15],
[90.0, 1.0758266454985257e-10],
[95.0, 7.2923752473657924e-08],
[100.0, 1.8065366882584036e-08]],
[[0.0, 8.4950625072720679e-07],
[10.0, 1.1421138441091606e-09],
[15.0, 4.5866299179754217e-05],
[20.0, 1.5172948619703533e-06],
[25.0, 0.00052404930089682738],
[30.0, 0.0013085043037665261],
[35.0, 0.0011664239519834339],
[40.0, 0.82750281205997822],
[45.0, 0.10132991062898908],
[50.0, 0.0085433968529198406],
[55.0, 1.4344253354768514e-09],
[60.0, 4.1465759737420333e-63],
[65.0, 8.8542318068147446e-61],
[70.0, 6.5220929006588264e-57],
[75.0, 4.8147063172017478e-62],
[80.0, 1.6649888561929801e-54],
[85.0, 5.8101309745184615e-51],
[90.0, 1.0489469511522901e-34],
[95.0, 1.4172891722901137e-26],
[100.0, 7.7737342215176175e-21]],
[[0.0, 3.1632028979839915e-05],
[10.0, 0.22111326362785799],
[15.0, 5.0749184816502276e-05],
[20.0, 0.0037647084552139601],
[25.0, 0.06595171605012351],
[30.0, 0.020353996100110065],
[35.0, 0.064862755938758426],
[40.0, 0.063464515818315706],
[45.0, 0.28662321979050187],
[50.0, 0.29871027240136533],
[55.0, 0.0080037249999781925],
[60.0, 4.7989170718538567e-08],
[65.0, 7.1337119197448125e-13],
[70.0, 2.1156334756020827e-14],
[75.0, 5.2206045634456785e-13],
[80.0, 3.4493997391226592e-14],
[85.0, 1.0500710333351341e-12],
[90.0, 3.6230960591527929e-10],
[95.0, 5.4508034466239748e-07],
[100.0, 1.113734841284331e-08]],
[[0.0, 2.1072826422141414e-05],
[10.0, 5.2005089026009156e-06],
[15.0, 0.00035632479168291582],
[20.0, 0.00020117324964893726],
[25.0, 0.025321662490265816],
[30.0, 0.019375041125499575],
[35.0, 0.0072489358231882196],
[40.0, 0.027646703948782582],
[45.0, 0.061781297188577035],
[50.0, 0.61650443678873623],
[55.0, 6.2548100028575296e-10],
[60.0, 4.3624321149810504e-14],
[65.0, 2.1028732991018519e-15],
[70.0, 7.8258766740408378e-09],
[75.0, 2.3541542464960154e-15],
[80.0, 7.1311782140494435e-18],
[85.0, 7.9730756880702952e-14],
[90.0, 8.2168425453267622e-12],
[95.0, 2.7060634831745863e-07],
[100.0, 2.5445845858147043e-06]],
[[0.0, 5.2908114997246604e-05],
[10.0, 0.01017193185840104],
[15.0, 1.9357672782766133e-07],
[20.0, 0.00033902736145621922],
[25.0, 2.6389276490704271e-05],
[30.0, 0.018976734061801625],
[35.0, 0.00019847993445041843],
[40.0, 0.0003987651813445142],
[45.0, 0.00077280097799644102],
[50.0, 7.0623545425863526e-06],
[55.0, 0.49987468099653093],
[60.0, 2.2781104904754321e-10],
[65.0, 3.6325273589160318e-08],
[70.0, 1.3537824953350784e-07],
[75.0, 1.6173509221250697e-06],
[80.0, 1.4416343176395238e-08],
[85.0, 4.1563319348145668e-08],
[90.0, 3.889471307678879e-07],
[95.0, 5.5104492670400779e-05],
[100.0, 2.7151502910109198e-05]],
[[0.0, 6.7973224793022393e-05],
[10.0, 2.4433844497448081e-05],
[15.0, 3.1104921306578064e-06],
[20.0, 0.00034408444849371377],
[25.0, 0.00023308492218191118],
[30.0, 0.011131477320772989],
[35.0, 0.00065907967432373565],
[40.0, 0.0021975971207933783],
[45.0, 0.0064663180932303744],
[50.0, 0.00022182805626473468],
[55.0, 3.0364808672527216e-05],
[60.0, 0.5088867111766856],
[65.0, 0.022878262885332335],
[70.0, 4.5920971324139236e-06],
[75.0, 0.0022218172448998821],
[80.0, 6.2738973247353222e-07],
[85.0, 0.00029845009911656876],
[90.0, 0.00010489918264551884],
[95.0, 0.00060753276898116491],
[100.0, 0.00022654293893088778]],
[[0.0, 3.9706684562645418e-06],
[10.0, 5.1270388737533639e-06],
[15.0, 5.262507305695827e-08],
[20.0, 2.5999077691490454e-05],
[25.0, 2.7813624200874083e-05],
[30.0, 0.0031991625978026266],
[35.0, 7.9249804143628584e-05],
[40.0, 0.00041177406702603777],
[45.0, 0.0011392701023576817],
[50.0, 8.0323465327795955e-06],
[55.0, 4.8154250431354482e-08],
[60.0, 0.49105543193823659],
[65.0, 0.7287168056069141],
[70.0, 0.001517224119720642],
[75.0, 0.011333832180200976],
[80.0, 1.72411085405819e-06],
[85.0, 0.0024773347817556406],
[90.0, 0.0051538392104878905],
[95.0, 0.0012157783163112316],
[100.0, 6.9627245730982824e-06]],
[[0.0, 0.0002912083712195997],
[10.0, 0.00010000827323660753],
[15.0, 1.3045847050921363e-05],
[20.0, 0.00076214254175277312],
[25.0, 0.00069053586992726265],
[30.0, 0.019496501720614564],
[35.0, 0.0015575117915095839],
[40.0, 0.0061672295672820353],
[45.0, 0.024249792592964857],
[50.0, 0.00032644214446402552],
[55.0, 7.7266514916862301e-06],
[60.0, 7.7205272501591181e-06],
[65.0, 0.017897081479520592],
[70.0, 0.99798778384755971],
[75.0, 1.8624562046616415e-05],
[80.0, 0.0011212071784075478],
[85.0, 1.4365766090813676e-05],
[90.0, 0.0032064950461695454],
[95.0, 0.0012746996733909491],
[100.0, 0.002184674454638652]],
[[0.0, 1.4875831960944082e-05],
[10.0, 0.044356141901610378],
[15.0, 6.2599114973306784e-07],
[20.0, 0.0001137512258409283],
[25.0, 0.00074658171939148493],
[30.0, 0.0026392679644018989],
[35.0, 0.00016209963633384973],
[40.0, 0.00078042928094971609],
[45.0, 0.0036289420011120584],
[50.0, 0.0015766136061278139],
[55.0, 1.0905709141111546e-05],
[60.0, 2.7323842104187985e-06],
[65.0, 3.3133452546150147e-05],
[70.0, 9.4042250154063172e-07],
[75.0, 0.97971055505602644],
[80.0, 0.072870311800762033],
[85.0, 0.00053021457261792214],
[90.0, 0.022423571257812192],
[95.0, 0.00070111679082283159],
[100.0, 3.6497263003914267e-05]],
[[0.0, 3.5864031562522839e-05],
[10.0, 8.3611261574776709e-07],
[15.0, 3.1900347488304889e-06],
[20.0, 0.00018123337202990332],
[25.0, 0.0002136705754060659],
[30.0, 0.0057251370200655845],
[35.0, 0.0012906924788080578],
[40.0, 0.0025502956186607153],
[45.0, 0.01424309136060831],
[50.0, 6.8216937833192329e-05],
[55.0, 5.6413951413567533e-06],
[60.0, 3.395689693128361e-05],
[65.0, 0.0032457520420234795],
[70.0, 8.3915675399849457e-09],
[75.0, 0.00013330080558625364],
[80.0, 0.91501828269209085],
[85.0, 0.0020937770133090659],
[90.0, 0.0012871409503378462],
[95.0, 0.00014758805743257718],
[100.0, 0.00086787665688324784]],
[[0.0, 4.1652247577331996e-06],
[10.0, 1.2212829713673957e-06],
[15.0, 6.5906857192417344e-08],
[20.0, 0.00016745946587138236],
[25.0, 0.0054431111796765554],
[30.0, 0.0067575214586160616],
[35.0, 0.00011856110316632124],
[40.0, 0.00032181662132509944],
[45.0, 0.001397981055516994],
[50.0, 0.0027058954834684062],
[55.0, 2.553142406703067e-06],
[60.0, 1.1514033594755017e-08],
[65.0, 0.21961568282994792],
[70.0, 2.4658349829099807e-08],
[75.0, 0.0022850986575076743],
[80.0, 3.5603047823624507e-06],
[85.0, 0.99406392082894734],
[90.0, 0.24399923235645221],
[95.0, 0.0013470125217945798],
[100.0, 0.042582366972883985]],
[[0.0, 5.5827483149344828e-06],
[10.0, 4.2094534384536198e-07],
[15.0, 5.723201102401161e-08],
[20.0, 4.1809740042541515e-05],
[25.0, 0.00049057556903029373],
[30.0, 0.0073861106939193556],
[35.0, 0.00025382321327730041],
[40.0, 0.0013088484764716428],
[45.0, 0.0026974167667276621],
[50.0, 3.069464890989831e-05],
[55.0, 5.8148299885777761e-06],
[60.0, 7.4621165800794304e-07],
[65.0, 0.002374761247356731],
[70.0, 7.989880181871685e-05],
[75.0, 0.0042580794657908335],
[80.0, 0.010965726532792501],
[85.0, 0.00032799281228739028],
[90.0, 0.72335894920682331],
[95.0, 0.0028560935416300647],
[100.0, 0.00023222597525614469]],
[[0.0, 1.1150563179140846e-05],
[10.0, 3.9137754880943888e-06],
[15.0, 2.6540828280675093e-07],
[20.0, 0.00030085951063170473],
[25.0, 0.00099029668428149789],
[30.0, 0.0040087490203686207],
[35.0, 0.00086288885669378408],
[40.0, 0.0012383336640490626],
[45.0, 0.0024569535179785368],
[50.0, 5.3078640889812177e-05],
[55.0, 3.655855476404959e-05],
[60.0, 1.135303263695363e-05],
[65.0, 0.0052337161196729541],
[70.0, 0.00038799120104653003],
[75.0, 6.6611573170762462e-07],
[80.0, 1.551926769812043e-05],
[85.0, 3.0383118936603649e-05],
[90.0, 0.00045596510625991982],
[95.0, 0.97142691361436007],
[100.0, 0.10797398637691005]],
[[0.0, 0.0011842207743420531],
[10.0, 0.00021255186566113124],
[15.0, 0.0014186746952926585],
[20.0, 0.0012807740120210007],
[25.0, 0.0017758699839103857],
[30.0, 0.02941486302872582],
[35.0, 0.0028808476159808511],
[40.0, 0.0053310120235299015],
[45.0, 0.030475142227338822],
[50.0, 0.00050901345787313437],
[55.0, 0.0016516486463798513],
[60.0, 1.288101329664901e-06],
[65.0, 4.7680039665889616e-06],
[70.0, 2.1300407821392148e-05],
[75.0, 3.6408560648746925e-05],
[80.0, 3.0263064939939249e-06],
[85.0, 0.00016351943738745334],
[90.0, 9.5136297575506478e-06],
[95.0, 0.020349612775663113],
[100.0, 0.84447466931781623]]])

Answer 1

使用plt.savefig可以从绘图创建单个.png文件。要获得平滑过渡，可以分多个步骤对曲线图进行插值。要将.png文件转换为动画，^{}是一个非常有用的工具

以下是从your previous question的数据开始的一些代码：

from matplotlib import pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde

p1 = np.array([[5.0, 0.0001430560406790707], [10.0, 6.2797052001508247e-13], [15.0, 4.8114669550502021e-06],
               [20.0, 0.0007443231772534647], [25.0, 0.00061070912573869406], [30.0, 0.48116582167944905],
               [35.0, 0.24698643991977953], [40.0, 0.016407283121225951], [45.0, 0.2557158314329116],
               [50.0, 1.1252231121357235e-05], [55.0, 0.064666668633158647], [60.0, 1.7631447655837744e-17],
               [65.0, 1.1294722466816786e-14], [70.0, 2.9419020411134367e-16], [75.0, 3.0887653014525822e-17],
               [80.0, 4.4973693062706866e-17], [85.0, 9.0975358174005147e-15], [90.0, 1.0758266454985257e-10],
               [95.0, 7.2923752473657924e-08], [100.0, 1.8065366882584036e-08]])
p2 = np.array([[5.0, 4.1652247577331996e-06], [10.0, 1.2212829713673957e-06], [15.0, 6.5906857192417344e-08],
               [20.0, 0.00016745946587138236], [25.0, 0.0054431111796765554], [30.0, 0.0067575214586160616],
               [35.0, 0.00011856110316632124], [40.0, 0.00032181662132509944], [45.0, 0.001397981055516994],
               [50.0, 0.0027058954834684062], [55.0, 2.553142406703067e-06], [60.0, 1.1514033594755017e-08],
               [65.0, 0.21961568282994792], [70.0, 2.4658349829099807e-08], [75.0, 0.0022850986575076743],
               [80.0, 3.5603047823624507e-06], [85.0, 0.99406392082894734], [90.0, 0.24399923235645221],
               [95.0, 0.0013470125217945798], [100.0, 0.042582366972883985]])
x = np.linspace(0, 100, 1000)

hlist = [p1, p2, p1]
kdelist = [gaussian_kde(p[:, 0], bw_method=.4, weights=p[:, 1]) for p in hlist]

steps = 20

fig, ax = plt.subplots()
for i in range(len(kdelist) - 1):
    for s in range(steps + 1):
        plt.plot(x, kdelist[i](x) * s / steps + kdelist[i + 1](x) * (1 - s / steps), color='crimson')
        plt.ylim(0, 0.065)
        plt.savefig(f'kde_{i*(steps+1)+s:04d}.png')
        plt.cla() # needed to remove the plot because savefig doesn't clear it

将.png文件转换为.mp4动画，然后再转换为动画gif的示例方法：

ffmpeg -i kde_%04d.png -c:v libx264 -r 25 -pix_fmt yuv420p out.mp4 
ffmpeg -i out.mp4 out.gif