关于在python中绘制二维数组的问题

我有一个关于使用matplotlib绘制二维阵列的问题。在我的代码中，我有一个名为z of len（z）=20的二维数组，z的值为：

   [[ 642.3774486   662.59980588  706.80142179  764.78786911  831.67963477
   904.67872269  982.01426528 1062.49208551 1145.27029231 1229.73549967
  1315.42936618 1402.00251422 1489.18433714 1576.7625077  1664.56866033
  1752.46813939 1840.35250424 1928.13395024 2015.74109019 2103.11572013]
 [ 554.60565024  560.31827232  591.87923587  638.51633542  695.03697015
   758.44479983  826.83191468  898.90395242  973.74278531 1050.67523901
  1129.19496311 1208.91328775 1289.52693752 1370.79606051 1452.52883572
  1534.57042218 1616.79485775 1699.09901217 1781.39800199 1863.6216653 ]
 [ 484.80770831  476.01059519  494.93090638  530.21865818  576.36816197
   630.18473341  689.62342052  753.28967576  820.18913475  889.58883479
   960.93441647 1033.79791772 1107.84339435 1182.80346976 1258.46286755
  1334.64656142 1411.2110677  1488.03793055 1565.02877024 1642.1014669 ]
 [ 432.98362283  409.67677451  415.95643334  439.89483737  475.67321023
   519.89852343  570.3887828   625.64925554  684.60934062  746.47628701
   810.64772628  876.65640413  944.13370762 1012.78473545 1082.37075581
  1152.6965571  1223.60113409 1294.95070536 1366.63339492 1438.55512495]
 [ 399.13339379  361.31681026  354.95581673  367.54487301  392.95211493
   427.58616989  469.12800152  515.98269176  567.00340294  621.33759567
   678.33489253  737.48874699  798.39787733  860.73985757  924.25250052
   988.72040921 1053.96505692 1119.83733661 1186.21187604 1252.98263943]
 [ 383.25702119  330.93070245  311.92905657  313.16876508  328.20487607
   353.24767279  385.84107667  424.28998442  467.37132169  514.17276077
   563.99591521  616.29494628  670.63590348  726.66883614  784.10810167
   842.71811777  902.30283619  962.6978243  1023.76421361 1085.38401036]
 [ 385.35450503  318.51845109  286.87615284  276.7665136   281.43149365
   296.88303213  320.52800827  350.57113352  385.71309689  424.98178231
   467.63079434  513.07500201  560.84778607  610.57167115  661.93755925
   714.68968276  768.6144719   823.53216843  879.29040761  935.75923772]
 [ 405.4258453   324.08005616  279.79710556  258.33811855  252.63196767
   258.49224791  273.18879631  294.82613906  322.02872853  353.76466029
   389.23952991  427.82891418  469.0335251   512.44836259  557.74087328
   604.6351042   652.89996405  702.340369    752.79045805  804.10832153]
 [ 443.47104202  347.61551768  290.69191471  257.88357994  241.80629812
   238.07532013  243.82344079  257.05500104  276.3182166   300.52139471
   328.82212191  360.55668279  395.19312056  432.29891048  471.51804375
   512.55438207  555.15931264  599.12242601  644.26436494  690.43126177]
 [ 499.49009518  389.12483563  319.56058031  275.40289778  248.95448502
   235.63224878  232.43194171  237.25771947  248.58156112  265.25198557
   286.37857036  311.25830784  339.32657247  370.12331481  403.26907065
   438.44751639  475.39251767  513.87833946  553.71212826  594.72805845]
 [ 573.48300477  448.60801002  366.40310234  310.89607205  274.07652836
   251.16303388  239.01429907  235.43429433  238.81876207  247.95643287
   261.90887525  279.93378933  301.43388082  325.92157557  352.993954
   382.31450714  413.59957914  446.60810935  481.13374802  516.99871158]
 [ 665.44977081  526.06504086  431.21948081  364.36310276  317.17242814
   284.66767542  263.57051287  251.58472563  247.02981947  248.63473661
   255.41303657  266.58312726  281.51504561  299.69369278  320.69269378
   344.15535434  369.78049705  397.31173568  426.52922422  457.24322114]
 [ 775.39039329  621.49592813  514.00971573  435.80398992  378.24218436
   336.1461734   306.10058311  285.70901337  273.2147333   267.28689679
   266.89105434  271.20632163  279.57006684  291.43966643  306.36529001
   323.97005797  343.9352714   365.98921845  389.89855687  415.46158715]
 [ 903.3048722   734.90067184  614.77380708  525.21873351  457.28579702
   405.59852782  366.60450978  337.80715755  317.37350358  303.91291341
   296.34292854  293.80337244  295.5989445   301.15949651  310.01174267
   321.75861805  336.06390219  352.64055766  371.24174595  391.65380959]
 [1049.19320756  866.27927199  733.51175488  632.60733354  554.30326611
   493.02473868  445.0822929   407.87915817  379.50613029  358.51278647
   343.76865919  334.37427969  329.60167861  328.85318304  331.63205178
   337.52103456  346.16638942  357.26575331  370.55879147  385.81988847]
 [1213.05539936 1015.63172859  870.22355911  757.96979001  669.29459165
   598.42480597  541.53393246  495.92501523  459.61261345  431.08651597
   409.16824628  392.91904338  381.57826916  374.520726    371.22621733
   371.25730752  374.24273309  379.8648054   387.84969343  397.9598238 ]
 [1394.89144759 1182.95804162 1024.90921978  901.30610293  802.25977363
   721.79872971  655.95942846  601.94472873  557.69295304  521.63410191
   492.5416898   469.43766351  451.52871614  438.16212541  428.79423931
   422.96743691  420.2929332   420.43771393  423.11445184  428.07361556]
 [1594.70135227 1368.25821109 1197.5687369  1062.61627228  953.19881205
   863.14650989  788.3587809   725.93829867  673.74714907  630.15554429
   593.88898977  563.93014008  539.45301957  519.77738125  504.33611774
   492.65142275  484.31698975  478.9844789   476.35306668  476.16126376]
 [1812.48511338 1571.532237   1388.20211045 1241.90029807 1122.11170691
  1022.46814651  938.73198977  867.90572504  807.77520155  756.65084311
   713.21014617  676.39647309  645.35117944  619.36649354  597.8518526
   580.30926502  566.31490274  555.50510031  547.56553796  542.22276841]
 [2048.24273094 1792.78011936 1596.80934044 1439.1581803  1308.9984582
  1199.76363956 1107.07905509 1027.84700786  959.77711046  901.11999837
   850.50515902  806.83666254  769.22319574  736.92946227  709.34144391
   685.94096373  666.28667217  649.99957816  636.75186568  626.25812949]]

我想绘制z的前20组数据，因此z[0]与我的另一个变量M相对。我做了以下操作：

M = np.arange(15.5,16.5, 0.05)
plt.plot(M, Z[0], label = r'$\chi^2$ for $\Omega_m[0] $ ')

它给了我下面的曲线图（忽略蓝色的标签，有两个相同的数据，只有一个标签）：

然后我尝试了下面的代码，这给了我另一张图片

plt.plot(M, Z[0:20], label = r'$\chi^2$ for$\Omege_m = 0$ ')

但我不明白为什么，在相同的数据下，两张图片的函数形状明显不同。有人能解释一下为什么第二张图片和第一张不同，它到底画了什么？matplotlib如何绘制二维阵列

---

如果我能解释一下z的背景，它是一个依赖于两个参数的函数，M和Omega_M，

Omega_M=np.arange（0.0,1.0,0.05）

（len（Omega_M）=20），z[0]对应于z函数中每个Omega_M值和M[0]的20个值，z对应于z函数的20个值，分别对应于ωm和m等的每个值，直到计算出每个参数的每个值为止。

首先，让我们解释为什么这两个图不同。因为在第一个图形中，您正在用

M

绘制

Z

的第一行。在第二个图形中，您正在使用

M

绘制

Z

的列。当我试图绘制

Z

的前三列时，这一点变得非常清楚：

plt.plot(M, Z[:, 0], label = r'$\chi^2$ for $\Omega_m[0] $ ')
plt.plot(M, Z[:, 1], label = r'$\chi^2$ for $\Omega_m[0] $ ')
plt.plot(M, Z[:, 2], label = r'$\chi^2$ for $\Omega_m[0] $ ')
plt.show()

这张图是由谁绘制的：

这是完全有道理的，因为当我用少一行通过

Z

时，它会抛出一个错误：

plt.plot(M, Z[0:19], label = r'$\chi^2$ for $\Omega_m[0] $ ')
ValueError: x and y must have same first dimension, but have shapes (20,) and (19, 20)

因此，要生成20条与

Z

的行相匹配的曲线，而不是与列相匹配的曲线，您需要使用

Z.T

符号转换

Z

数组，如下所示：

plt.plot(M, Z.T, label = r'$\chi^2$ for $\Omega_m[0] $ ')
plt.show()

这将得到以下图表：

---

当我们没有

z

和

M

时，回答您的问题并不容易。那么，你能发布他们的价值观吗？当然！我把这些值放在我的帖子里谢谢你的更新。。。现在我需要添加

M

pleaseIt的值，就在第一条绘图线上方，M=np.arange（15.5,16.5,0.05）是的。。。对不起：）非常感谢，这完全有道理！我很清楚，Z[：，0]，基本上是用M来绘制每列的第一个元素？谢谢！我很难理解2D数组的语法：））