这个错误令我感到困惑 . 我正在尝试将curve_fit设置为某些数据，但只要我将线性函数设为负（-m * x b）或给出负启动参数，我就会得到一个值Error . 怎么会这样？
工作代码（但非常糟糕）：
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import curve_fit
import numpy as np
coeff = np.array([[[0.99686526094349259, 0.97938283953349337, 0.94863615936853296, 0.94854564124775342], [0.96827397528064774, 0.96540665305865092, 0.95427978197668806, 0.95856897574379896], [0.98179629099111465, 0.97186702693950267, 0.95159655933838905, 0.94914854300397633], [0.98405908534322395, 0.96303349070583055, 0.94049529092927575, 0.9388805223880119], [0.9823345791834085, 0.94988298322395981, 0.94432600638572839, 0.94690593392923761], [0.94749073253746852, 0.91026297490006691, 0.90355052302299765, 0.90594713815209094], [0.94666496539970868, 0.92991047096746327, 0.89794751156997354, 0.88308833222256322], [0.95086096977151513, 0.91492190545243046, 0.89878794025327369, 0.8977955699771023], [0.92085169316442461, 0.91020217046939911, 0.88784427604876237, 0.87496086561906938], [0.92584889400513992, 0.90085979379105019, 0.89133459679508531, 0.8616325150553189], [0.89310485081400282, 0.90595436374678995, 0.88127525591336353, 0.86326252690088379], [0.91840500742150677, 0.89454924632418276, 0.86408826887081058, 0.85566354989992877], [0.89798838704263029, 0.8832076831081086, 0.86506489826597432, 0.84337214054453236], [0.88889983668682515, 0.86980848536425781, 0.84371042174678323, 0.83957379021759648], [0.88736216219664954, 0.86926675475222814, 0.85277446531968648, 0.84865506267462365], [0.89663062070888311, 0.86607602443038323, 0.8659545150953607, 0.84029430834919172]], [[0.98470753576416092, 0.96971217002625243, 0.96004611886812241, 0.95478295609420716], [0.97857422516178882, 0.96736740927570075, 0.94170250223897756, 0.93502887988285932], [0.96108325241780046, 0.95765326691101016, 0.93539815321061726, 0.92604301825895496], [0.94587731435279332, 0.92768886024687802, 0.93282077530749263, 0.90535429731870432], [0.90302403377848528, 0.89266000623227681, 0.8692807234190687, 0.87544667565162571], [0.90842678587708314, 0.88666211356287894, 0.86398187968979789, 0.85504438852304376], [0.89752546995523119, 0.88267545460550534, 0.87234425766716139, 0.85876172135527762], [0.89822801432463506, 0.87655235478487536, 0.85584471851158772, 0.85658902361766176], [0.88601822394825691, 0.87395005190159714, 0.83627312926756237, 0.83330154332336259], [0.87884225037341357, 0.88065653329353177, 0.84056204405735102, 0.8401607849719015], [0.89075003483828041, 0.87267764041375551, 0.85244176713643305, 0.83568190241877782], [0.85959060915994512, 0.84803406989175478, 0.84371432488334785, 0.8367409774691672], [0.87852202454848527, 0.86726350147297349, 0.83818563899647958, 0.82938866147454338], [0.89804043662753275, 0.87937066125938534, 0.85151443998090559, 0.82187699186233953], [0.90321780317179479, 0.869062296367767, 0.83728089626607383, 0.83509314955439906], [0.86233285952566208, 0.849027598447326, 0.81563185958519957, 0.79413049278747028]], [[0.9614331304980952, 0.94276836763403404, 0.95224773173387689, 0.94435073432557415], [0.96646444638853402, 0.95169289512912125, 0.94337671204334916, 0.94803267888910381], [0.96611098757791991, 0.96360375171083434, 0.96470576514160444, 0.94484651240282402], [0.97475536616396286, 0.96246556353605883, 0.95381689117327517, 0.94139611761114761], [0.9687660923025867, 0.94859966696407028, 0.93134453209793255, 0.91523213974915074], [0.96375743022156513, 0.94270612998948089, 0.92535089023843731, 0.91900369430775275], [0.94887849034227589, 0.93238747357266238, 0.90554273021270026, 0.88665130565323924], [0.96854119457612831, 0.93254822994869568, 0.91757957859196093, 0.91072927943684312], [0.96094270419351024, 0.94039103897193355, 0.91576864754511167, 0.92371355506927211], [0.94084190749922514, 0.91115539435137249, 0.88412827862448162, 0.88618204421112579], [0.93003355897126139, 0.90939797810425416, 0.89104443138718059, 0.89132735010563013], [0.90555102340603832, 0.89271147136114004, 0.87690138866709455, 0.87137837587447309], [0.93200398659335737, 0.90591867247950508, 0.87354751734735214, 0.87814926743620247], [0.91365812945147296, 0.88981780409716227, 0.88190665467425855, 0.86826167757170658], [0.91141869862588043, 0.90635835796923025, 0.87233896204892503, 0.85353434262799799], [0.89195837829615954, 0.88300374082698796, 0.86748926159856432, 0.86449000226664907]], [[0.96879986856483347, 0.96078007490935335, 0.93950716344560459, 0.92488430078454453], [0.9752456609046477, 0.95774621246952218, 0.94184356900848631, 0.92293860209467038], [0.97065657373100589, 0.95795185233511859, 0.93859661118706295, 0.93202539768815995], [0.94804559142048028, 0.93006649637444039, 0.90771387112998492, 0.90258519884046184], [0.96077108006777256, 0.93048569573051476, 0.91133253827782301, 0.9148863958823249], [0.93860849723734407, 0.91677754219593677, 0.89153677099149176, 0.88976908460093151], [0.93265862156159263, 0.92866167470633953, 0.90696190909565666, 0.89277168267535201], [0.92296813671168709, 0.912224985039783, 0.87781537743834204, 0.87306411003486029], [0.91706712012105851, 0.90403722567106504, 0.8757354170079833, 0.87454171073055964], [0.92709726286042038, 0.91814685823696118, 0.88209201413565719, 0.87071552381815021], [0.91836653505437149, 0.91811646934434055, 0.9001814777254612, 0.89119177051694576], [0.9210885824786097, 0.91154006774147744, 0.89196779567392059, 0.87122730996452769], [0.92479730650932923, 0.90353180550680501, 0.89612926059255604, 0.8759153536185027], [0.93616171066757126, 0.91075016734463399, 0.88885447724252853, 0.88885626714321042], [0.92351264876398886, 0.90903710950245109, 0.88723620200477693, 0.87465600815320343], [0.92037888267316204, 0.89508057275264885, 0.87982783735445902, 0.87559393466446001]], [[0.97011550578127326, 0.95522581736604184, 0.93315192623145238, 0.93944424272962068], [0.94988019701622661, 0.94429908013578179, 0.92576004716017757, 0.91710357586537261], [0.94467022565392866, 0.9229482460189381, 0.92741253607795082, 0.90336279050519641], [0.97132626861718174, 0.94746596833131502, 0.9076094825815042, 0.91288527508638839], [0.95199902391784041, 0.95050488862555038, 0.92973643010482787, 0.90845366420624218], [0.95405326310867389, 0.93427868872098885, 0.91167876117244429, 0.89968868948164438], [0.93857427428164641, 0.93150605269708742, 0.90780371889218081, 0.91036705313234012], [0.93522870621459475, 0.91294797409007766, 0.89414533363003346, 0.88836341562806975], [0.94138132365257743, 0.90816250152409339, 0.88185250825262418, 0.86486337226106458], [0.91508625829121648, 0.90793015669518784, 0.89583048813425947, 0.87621198684565405], [0.90633297396449208, 0.89499118831037805, 0.87850550949117845, 0.86371480286029056], [0.90213998704102139, 0.88056609075844217, 0.85524694184658123, 0.84280735361069925], [0.89897461975500803, 0.87459703492299568, 0.8748773499757786, 0.85762482562168185], [0.89879779579602459, 0.87981668664918256, 0.86340696195704458, 0.86707561188353377], [0.88561185413957633, 0.87922278508857687, 0.86283072658593785, 0.8483337171568931], [0.91395479410421421, 0.89638471340143, 0.86875895133845749, 0.85659213749371366]], [[0.97500001219467169, 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0.89295089255515392], [0.9369025837438737, 0.9193931789007489, 0.9110731982857454, 0.91059413248461751], [0.93334790970257286, 0.92206668992859253, 0.92317543068719965, 0.90232362299032121], [0.9398654824049073, 0.91612744661546386, 0.89827645418861213, 0.88996033212717518], [0.92496298279783185, 0.90004617988357472, 0.88184540228383035, 0.87211014521464925]], [[0.96392327212364415, 0.97418561168314199, 0.9512302065349082, 0.93145462657387401], [0.97630040229891679, 0.94495179846314181, 0.9267551292699594, 0.9274361567109809], [0.964898746709276, 0.96313463284142475, 0.94211486984901494, 0.92641924230508965], [0.96533749293631588, 0.95631701333581365, 0.9331371055033808, 0.91902563034589746], [0.96405962017425517, 0.94648094474181099, 0.94009298295037413, 0.91149206935744942], [0.95510976751718646, 0.94511246213550326, 0.93529677151307777, 0.91556932092961396], [0.9543180987584382, 0.93918797237443519, 0.91280751903240898, 0.92915299202936608], [0.94805043080897777, 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0.86695649122175444], [0.89852161379486206, 0.88159969240740788, 0.84770784564505053, 0.85769611124739542]], [[0.96869482726760392, 0.96353897217046625, 0.93616658952012444, 0.91682688037294335], [0.95993615891078299, 0.94430960470579206, 0.90700786674962453, 0.90521642097712574], [0.94128351220688511, 0.93197529520037936, 0.91724766309672112, 0.90294000148796838], [0.93681377122867426, 0.92251302407975666, 0.89865362095896828, 0.87451439342885651], [0.9433534430357613, 0.92124418213207404, 0.90743224907774878, 0.90992390514606003], [0.93864374137934481, 0.92649899283294634, 0.90751598253809307, 0.89342546686470559], [0.92509838204204431, 0.92359795689788782, 0.9071665884850374, 0.88881858511536294], [0.9186528110896226, 0.90516803184113803, 0.89652012781541601, 0.88687511785284034], [0.91307623763837109, 0.89727397709859613, 0.89702405028793453, 0.89135331660806749], [0.916413904441718, 0.89682256171988728, 0.88390819413304844, 0.8907398141684657], [0.92015330488071723, 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0.96113772039458967, 0.94654705456907617, 0.93447603150314495], [0.97005393964194786, 0.95089713398313647, 0.94938577180946571, 0.94069492298542012], [0.95003405318298817, 0.93066410804286781, 0.94200831216004843, 0.92494701816436764], [0.96182554879689985, 0.94281654703535123, 0.92705597586145339, 0.93031328707137884], [0.93807943123336146, 0.92938051475328287, 0.90488874751990633, 0.90069596831080934], [0.93226464362035899, 0.93395961873233191, 0.91044668454707345, 0.89862578021469086], [0.92592514865144071, 0.9249579439660085, 0.91705130471309904, 0.89963303749296752], [0.94971097528067405, 0.92080056735161908, 0.90097748880507655, 0.90401963834449095], [0.94174371698415038, 0.92298503426915368, 0.91504856797725698, 0.89707418098941283], [0.94534599949029197, 0.9212818734165662, 0.90339447405147066, 0.91469628584989215], [0.93947719162376486, 0.92983114592091132, 0.90085073230452062, 0.8876632168361035], [0.95313065491690452, 0.91041199061012923, 0.88959793662960918, 0.90118023403235892], [0.935269872380733, 0.93321055929262442, 0.91872198114099235, 0.89460383867534654], [0.93193583237639099, 0.92584474498566616, 0.89550540121336775, 0.88277689841129969], [0.8915390291810289, 0.90381974357688288, 0.87586457325063538, 0.87600839259287067]]])
def linfunc(x, m, b):
return m*x+b
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = ["red", "green", "blue", "black", "purple","red", "green", "blue", "black", "purple","red", "green", "blue", "black", "purple", "orange"]
markers = ["o", "o", "o", "o", "o", "^", "^", "^", "^", "^", "x", "x", "x", "x", "x", "x"]
cX = []
cY = []
cZ = []
for obstacle in range(16):
time = 3
for molecule in range(16):
cX.append(obstacle)
cY.append(molecule)
cZ.append(coeff[obstacle][molecule][time])
popt, pcov = curve_fit(linfunc, cY, cZ, p0=[1, 1])
ax.plot(cX, cY, cZ)
ax.plot(cX, cY, linfunc(cY, *popt), ls="-")
cX = []
cY = []
cZ = []
ax.set_xlabel('obstacles')
ax.set_ylabel('molecules')
plt.show()
这个产生ValueError：操作数不能与形状（0）一起广播（16）
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import curve_fit
import numpy as np
coeff = same as above
def linfunc(x, m, b):
return m*x+b
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = ["red", "green", "blue", "black", "purple","red", "green", "blue", "black", "purple","red", "green", "blue", "black", "purple", "orange"]
markers = ["o", "o", "o", "o", "o", "^", "^", "^", "^", "^", "x", "x", "x", "x", "x", "x"]
cX = []
cY = []
cZ = []
for obstacle in range(16):
time = 3
for molecule in range(16):
cX.append(obstacle)
cY.append(molecule)
cZ.append(coeff[obstacle][molecule][time])
popt, pcov = curve_fit(linfunc, cY, cZ, p0=[-1, 1]) #note it's negative one here
ax.plot(cX, cY, cZ)
ax.plot(cX, cY, linfunc(cY, *popt), ls="-")
cX = []
cY = []
cZ = []
ax.set_xlabel('obstacles')
ax.set_ylabel('molecules')
plt.show()
编辑：完整的错误消息：
文件"coeff.py"，第32行，在popt中，pcov = curve_fit（linfunc，cY，cZ，p0 = [ - 11,1]）
文件"/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py"，第434行，在curve_fit中res = leastsq（func，p0，args = args，full_output = 1，** kw）文件"/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py"，第276行，至少为m = _check_func（'leastsq'，'func'，func，x0，args， n）[0]文件"/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py"，第13行，在_check_func res = atleast_1d（thefunc（（（x0 [：numinputs]，）args）））文件"/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py"，第346行，在_general_function返回函数中（xdata， params） - ydata ValueError：操作数无法与形状一起广播（0）（16）