Kernel: Python 3 (Anaconda 2020)
In [138]:
1.1016051191333829
In [34]:
No handles with labels found to put in legend.
In [37]:
No handles with labels found to put in legend.
In [49]:
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:21: RuntimeWarning: overflow encountered in exp
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:21: RuntimeWarning: invalid value encountered in double_scalars
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:23: RuntimeWarning: overflow encountered in exp
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:23: RuntimeWarning: invalid value encountered in double_scalars
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:25: RuntimeWarning: divide by zero encountered in double_scalars
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:26: RuntimeWarning: overflow encountered in exp
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:26: RuntimeWarning: invalid value encountered in double_scalars
/ext/anaconda2020.02/lib/python3.7/site-packages/ipykernel/__main__.py:27: RuntimeWarning: overflow encountered in exp
No handles with labels found to put in legend.
On constate que plus la valeur de eta est grande, plus l'est aussi.
In [52]:
No handles with labels found to put in legend.
On constate que pour différentes valeurs de eta, la solution reste constante.
In [135]:
array([0.55046708, 0.68754378, 0.77539625, 0.78949102, 0.71264108,
0.53671772, 0.26346017, 0.0956976 , 0.52076578, 0.98518972,
1.45849484, 1.90932198, 2.30855084, 2.63220041, 2.86381759,
2.9961159 , 3.03170126, 2.98281242, 2.87010197, 2.72057929,
2.89531353, 3.09011672, 3.25344255, 3.41044491, 3.57678943,
3.76107252, 3.96637059, 4.19138184, 4.43141827, 4.67937012,
4.92667439, 5.16425756, 5.38338174, 5.57629525, 5.73656631,
5.85895448, 5.9386351 , 5.96952162, 5.94129933, 5.83455001])
In [133]:
No handles with labels found to put in legend.
In [122]:
5.969521624575377
On obtient donc la valeur de pour x dans
In [120]:
array([ 0.65046708, 0.94600532, 1.19231933, 1.36487564, 1.44648723,
1.42902541, 1.3142294 , 1.11353317, 0.84692653, 0.54096413,
0.22612054, -0.06624506, -0.30701238, -0.47220041, -0.54535605,
-0.51919282, -0.39631664, -0.18896627, 0.08220572, 0.39018994,
0.37391724, 0.33757559, 0.3327113 , 0.33417047, 0.32628749,
0.30046594, 0.25362941, 0.1870797 , 0.10550481, 0.0160145 ,
-0.07282824, -0.15194987, -0.21261251, -0.24706448, -0.24887401,
-0.21280063, -0.13401972, -0.0064447 , 0.18023913, 0.44544999])
In [140]:
1.0778788811872724
In [4]:
-0.4161468365471424
In [73]:
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0.38468481 0.44879895 0.51291309 0.57702722 0.64114136 0.70525549
0.76936963 0.83348377 0.8975979 0.96171204 1.02582617 1.08994031
1.15405444 1.21816858 1.28228272 1.34639685 1.41051099 1.47462512
1.53873926 1.60285339 1.66696753 1.73108167 1.7951958 1.85930994
1.92342407 1.98753821 2.05165235 2.11576648 2.17988062 2.24399475
2.30810889 2.37222302 2.43633716 2.5004513 2.56456543 2.62867957
2.6927937 2.75690784 2.82102197 2.88513611 2.94925025 3.01336438
3.07747852 3.14159265]
array([ 0.98006658, 0.86934393, 0.67203258, 0.40778519, 0.10292144,
-0.21219353, -0.50617352, -0.74973744, -0.91862574, -0.99601675,
-0.97420214, -0.85535469, -0.65131191, -0.38239692, -0.07539429,
0.23911778, 0.52981316, 0.7677379 , 0.92919413, 0.99810043,
0.96759358, 0.84071212, 0.63009375, 0.35671656, 0.04780956,
-0.26585939, -0.55304812, -0.78515195, -0.93905279, -0.99942176,
-0.96024596, -0.8254274 , -0.60839432, -0.33076374, -0.0201883 ,
0.29239794, 0.57586065, 0.80196629, 0.94819419, 0.99997971])
In [110]:
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0.38468481 0.44879895 0.51291309 0.57702722 0.64114136 0.70525549
0.76936963 0.83348377 0.8975979 0.96171204 1.02582617 1.08994031
1.15405444 1.21816858 1.28228272 1.34639685 1.41051099 1.47462512
1.53873926 1.60285339 1.66696753 1.73108167 1.7951958 1.85930994
1.92342407 1.98753821 2.05165235 2.11576648 2.17988062 2.24399475
2.30810889 2.37222302 2.43633716 2.5004513 2.56456543 2.62867957
2.6927937 2.75690784 2.82102197 2.88513611 2.94925025 3.01336438
3.07747852 3.14159265]
No handles with labels found to put in legend.
In [111]:
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0.99208189 1.15742887 1.32277585 1.48812284 1.65346982 1.8188168
1.98416378 2.14951076 2.31485774 2.48020473 2.64555171 2.81089869
2.97624567 3.14159265]
0.2426866721574766
In [112]:
In [113]:
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0.99208189 1.15742887 1.32277585 1.48812284 1.65346982 1.8188168
1.98416378 2.14951076 2.31485774 2.48020473 2.64555171 2.81089869
2.97624567 3.14159265]
1.0223800553312685
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0.99208189 1.15742887 1.32277585 1.48812284 1.65346982 1.8188168
1.98416378 2.14951076 2.31485774 2.48020473 2.64555171 2.81089869
2.97624567 3.14159265]
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0.64998469 0.75831547 0.86664625 0.97497703 1.08330781 1.19163859
1.29996937 1.40830016 1.51663094 1.62496172 1.7332925 1.84162328
1.94995406 2.05828484 2.16661562 2.2749464 2.38327719 2.49160797
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0.48332195 0.5638756 0.64442926 0.72498292 0.80553658 0.88609024
0.96664389 1.04719755 1.12775121 1.20830487 1.28885852 1.36941218
1.44996584 1.5305195 1.61107316 1.69162681 1.77218047 1.85273413
1.93328779 2.01384144 2.0943951 2.17494876 2.25550242 2.33605608
2.41660973 2.49716339 2.57771705 2.65827071 2.73882436 2.81937802
2.89993168 2.98048534 3.061039 3.14159265]
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0.38468481 0.44879895 0.51291309 0.57702722 0.64114136 0.70525549
0.76936963 0.83348377 0.8975979 0.96171204 1.02582617 1.08994031
1.15405444 1.21816858 1.28228272 1.34639685 1.41051099 1.47462512
1.53873926 1.60285339 1.66696753 1.73108167 1.7951958 1.85930994
1.92342407 1.98753821 2.05165235 2.11576648 2.17988062 2.24399475
2.30810889 2.37222302 2.43633716 2.5004513 2.56456543 2.62867957
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2.55587199 2.60911932 2.66236666 2.71561399 2.76886132 2.82210865
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0.81954591 0.86507624 0.91060657 0.95613689 1.00166722 1.04719755
1.09272788 1.13825821 1.18378854 1.22931886 1.27484919 1.32037952
1.36590985 1.41144018 1.45697051 1.50250083 1.54803116 1.59356149
1.63909182 1.68462215 1.73015248 1.7756828 1.82121313 1.86674346
1.91227379 1.95780412 2.00333445 2.04886477 2.0943951 2.13992543
2.18545576 2.23098609 2.27651642 2.32204674 2.36757707 2.4131074
2.45863773 2.50416806 2.54969839 2.59522871 2.64075904 2.68628937
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2.32972039 2.36501919 2.40031798 2.43561678 2.47091557 2.50621436
2.54151316 2.57681195 2.61211075 2.64740954 2.68270833 2.71800713
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In [115]:
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1.14239733 1.17413059 1.20586385 1.23759711 1.26933037 1.30106362
1.33279688 1.36453014 1.3962634 1.42799666 1.45972992 1.49146318
1.52319644 1.5549297 1.58666296 1.61839622 1.65012947 1.68186273
1.71359599 1.74532925 1.77706251 1.80879577 1.84052903 1.87226229
1.90399555 1.93572881 1.96746207 1.99919533 2.03092858 2.06266184
2.0943951 2.12612836 2.15786162 2.18959488 2.22132814 2.2530614
2.28479466 2.31652792 2.34826118 2.37999443 2.41172769 2.44346095
2.47519421 2.50692747 2.53866073 2.57039399 2.60212725 2.63386051
2.66559377 2.69732703 2.72906028 2.76079354 2.7925268 2.82426006
2.85599332 2.88772658 2.91945984 2.9511931 2.98292636 3.01465962
3.04639288 3.07812614 3.10985939 3.14159265]
No handles with labels found to put in legend.
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[<matplotlib.lines.Line2D at 0x7ff11b18ce50>]
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---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-120-fdaa63998d4d> in <module>
4 plt.plot(Y,Ubar_p)
5
----> 6 trace_pen_dir(50,1,2,1,1)
<ipython-input-120-fdaa63998d4d> in trace_pen_dir(N, test, m, c, eta)
1 def trace_pen_dir(N,test,m,c,eta):
----> 2 Y,A,U_p,Ubar_p,e_p=norm_pen_dir(N,test,m,c,eta)
3 plt.plot(Y,U_p,'-*')
4 plt.plot(Y,Ubar_p)
5
<ipython-input-119-db74452ca002> in norm_pen_dir(N, test, m, c, eta)
6 for i in range(len(Y)):
7 b.append((f(Y[i], test, m) - (1/eta)*Q(Y[i])))
----> 8 Ubar_p=ue_pen_dir(Y,eta,m)
9 U_p=np.linalg.solve(A,b)
10 e_p=np.linalg.norm(Ubar_p-U_p,np.infty)
<ipython-input-107-7c8a4a981002> in ue_pen_dir(x, eta, m)
27
28
---> 29 if 0 < x and x < np.pi:
30 return np.sin(m*x)+A1_d*x+A2_d
31 elif np.pi < x and x < 2*np.pi:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
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---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-126-fbd33c67edd6> in <module>
4
5 plt.plot(l,ue_npen(l,2,2,0))
----> 6 plt.plot(l,conv_neu(0.1,1,1,0))
7 plt.xlabel('valeurs de eta')
8 plt.legend(loc = 'upper right')
/ext/anaconda2020.02/lib/python3.7/site-packages/matplotlib/pyplot.py in plot(scalex, scaley, data, *args, **kwargs)
2840 return gca().plot(
2841 *args, scalex=scalex, scaley=scaley,
-> 2842 **({"data": data} if data is not None else {}), **kwargs)
2843
2844
/ext/anaconda2020.02/lib/python3.7/site-packages/matplotlib/axes/_axes.py in plot(self, scalex, scaley, data, *args, **kwargs)
1741 """
1742 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-> 1743 lines = [*self._get_lines(*args, data=data, **kwargs)]
1744 for line in lines:
1745 self.add_line(line)
/ext/anaconda2020.02/lib/python3.7/site-packages/matplotlib/axes/_base.py in __call__(self, data, *args, **kwargs)
271 this += args[0],
272 args = args[1:]
--> 273 yield from self._plot_args(this, kwargs)
274
275 def get_next_color(self):
/ext/anaconda2020.02/lib/python3.7/site-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs)
397
398 if x.shape[0] != y.shape[0]:
--> 399 raise ValueError(f"x and y must have same first dimension, but "
400 f"have shapes {x.shape} and {y.shape}")
401 if x.ndim > 2 or y.ndim > 2:
ValueError: x and y must have same first dimension, but have shapes (20,) and (1,)
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