.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_representation.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_representation.py: Representation of functional data ================================= Explores the different representations of functional data. .. GENERATED FROM PYTHON SOURCE LINES 7-17 .. code-block:: Python # Author: Carlos Ramos Carreño # License: MIT import skfda from skfda.representation.interpolation import SplineInterpolation import skfda.representation.basis as basis .. GENERATED FROM PYTHON SOURCE LINES 18-25 In this example we are going to show the different representations of functional data available in scikit-fda. First we are going to fetch a functional data dataset, such as the Berkeley Growth Study. This dataset correspond to the height of several boys and girls measured until the 18 years of age. The number and times of the measurements are the same for each individual. .. GENERATED FROM PYTHON SOURCE LINES 25-33 .. code-block:: Python dataset = skfda.datasets.fetch_growth() fd = dataset['data'] y = dataset['target'] print(repr(fd)) fd.plot(group=y, group_colors=['red', 'blue']) .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_001.png :alt: Berkeley Growth Study :srcset: /auto_examples/images/sphx_glr_plot_representation_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none FDataGrid( array([[[ 81.3], [ 84.2], [ 86.4], ..., [193.8], [194.3], [195.1]], [[ 76.2], [ 80.4], [ 83.2], ..., [176.1], [177.4], [178.7]], [[ 76.8], [ 79.8], [ 82.6], ..., [170.9], [171.2], [171.5]], ..., [[ 68.6], [ 73.6], [ 78.6], ..., [166. ], [166.3], [166.8]], [[ 79.9], [ 82.6], [ 84.8], ..., [168.3], [168.4], [168.6]], [[ 76.1], [ 78.4], [ 82.3], ..., [168.6], [168.9], [169.2]]]), grid_points=(array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. , 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 , 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. , 16.5 , 17. , 17.5 , 18. ]),), domain_range=((1.0, 18.0),), dataset_name='Berkeley Growth Study', argument_names=('age',), coordinate_names=('height',), extrapolation=None, interpolation=SplineInterpolation(interpolation_order=1, monotone=False))
.. GENERATED FROM PYTHON SOURCE LINES 34-36 This kind of representation is a discretized representation, in which the measurement points are shared between samples. .. GENERATED FROM PYTHON SOURCE LINES 36-38 .. code-block:: Python print(fd.grid_points) .. rst-class:: sphx-glr-script-out .. code-block:: none (array([ 1. , 1.25, 1.5 , 1.75, 2. , 3. , 4. , 5. , 6. , 7. , 8. , 8.5 , 9. , 9.5 , 10. , 10.5 , 11. , 11.5 , 12. , 12.5 , 13. , 13.5 , 14. , 14.5 , 15. , 15.5 , 16. , 16.5 , 17. , 17.5 , 18. ]),) .. GENERATED FROM PYTHON SOURCE LINES 39-40 In this representation, the data can be arranged as a matrix. .. GENERATED FROM PYTHON SOURCE LINES 40-42 .. code-block:: Python print(fd.data_matrix) .. rst-class:: sphx-glr-script-out .. code-block:: none [[[ 81.3] [ 84.2] [ 86.4] ... [193.8] [194.3] [195.1]] [[ 76.2] [ 80.4] [ 83.2] ... [176.1] [177.4] [178.7]] [[ 76.8] [ 79.8] [ 82.6] ... [170.9] [171.2] [171.5]] ... [[ 68.6] [ 73.6] [ 78.6] ... [166. ] [166.3] [166.8]] [[ 79.9] [ 82.6] [ 84.8] ... [168.3] [168.4] [168.6]] [[ 76.1] [ 78.4] [ 82.3] ... [168.6] [168.9] [169.2]]] .. GENERATED FROM PYTHON SOURCE LINES 43-45 By default, the data points are interpolated using a linear interpolation, but this is configurable. .. GENERATED FROM PYTHON SOURCE LINES 45-51 .. code-block:: Python dataset = skfda.datasets.fetch_medflies() fd = dataset['data'] first_curve = fd[0] first_curve.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_002.png :alt: Medflies :srcset: /auto_examples/images/sphx_glr_plot_representation_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 52-54 The interpolation used can however be changed. Here, we will use an interpolation with degree 3 splines. .. GENERATED FROM PYTHON SOURCE LINES 54-57 .. code-block:: Python first_curve.interpolation = SplineInterpolation(3) first_curve.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_003.png :alt: Medflies :srcset: /auto_examples/images/sphx_glr_plot_representation_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 58-60 This representation allows also functions with arbitrary dimensions of the domain and codomain. .. GENERATED FROM PYTHON SOURCE LINES 60-68 .. code-block:: Python fd = skfda.datasets.make_multimodal_samples(n_samples=1, dim_domain=2, dim_codomain=2) print(fd.dim_domain) print(fd.dim_codomain) fd.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_004.png :alt: plot representation :srcset: /auto_examples/images/sphx_glr_plot_representation_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none 2 2
.. GENERATED FROM PYTHON SOURCE LINES 69-75 Another possible representation is a decomposition in a basis of functions. $$ f(t) = \\sum_{i=1}^N a_i \\phi_i(t) $$ It is possible to transform between both representations. Let us use again the Berkeley Growth dataset. .. GENERATED FROM PYTHON SOURCE LINES 75-81 .. code-block:: Python dataset = skfda.datasets.fetch_growth() fd = dataset['data'] y = dataset['target'] fd.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_005.png :alt: Berkeley Growth Study :srcset: /auto_examples/images/sphx_glr_plot_representation_005.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 82-83 We will represent it using a basis of B-splines. .. GENERATED FROM PYTHON SOURCE LINES 83-87 .. code-block:: Python fd_basis = fd.to_basis(basis.BSplineBasis(n_basis=4)) fd_basis.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_006.png :alt: Berkeley Growth Study :srcset: /auto_examples/images/sphx_glr_plot_representation_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 88-90 We can increase the number of elements in the basis to try to reproduce the original data with more fidelity. .. GENERATED FROM PYTHON SOURCE LINES 90-94 .. code-block:: Python fd_basis_big = fd.to_basis(basis.BSplineBasis(n_basis=7)) fd_basis_big.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_007.png :alt: Berkeley Growth Study :srcset: /auto_examples/images/sphx_glr_plot_representation_007.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 95-97 Lets compare the diferent representations in the same plot, for the same curve .. GENERATED FROM PYTHON SOURCE LINES 97-103 .. code-block:: Python fig = fd[0].plot() fd_basis[0].plot(fig=fig) fd_basis_big[0].plot(fig=fig) fig.axes[0].legend(['Original', '4 elements', '7 elements']) .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_008.png :alt: Berkeley Growth Study :srcset: /auto_examples/images/sphx_glr_plot_representation_008.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 104-108 We can also see the effect of changing the basis. For example, in the Fourier basis the functions start and end at the same points if the period is equal to the domain range, so this basis is clearly non suitable for the Growth dataset. .. GENERATED FROM PYTHON SOURCE LINES 108-112 .. code-block:: Python fd_basis = fd.to_basis(basis.FourierBasis(n_basis=7)) fd_basis.plot() .. image-sg:: /auto_examples/images/sphx_glr_plot_representation_009.png :alt: Berkeley Growth Study :srcset: /auto_examples/images/sphx_glr_plot_representation_009.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 113-114 The data is now represented as the coefficients in the basis expansion. .. GENERATED FROM PYTHON SOURCE LINES 114-115 .. code-block:: Python print(fd_basis) .. rst-class:: sphx-glr-script-out .. code-block:: none FDataBasis( _basis=FourierBasis(domain_range=((1.0, 18.0),), n_basis=7, period=17.0), coefficients=[[ 5.99308923e+02 -1.14873764e+02 2.75173660e+01 -5.99461262e+01 3.00407655e+01 -2.42099297e+01 3.06116238e+01] [ 5.48897873e+02 -8.54081358e+01 1.29613544e+01 -4.69373290e+01 3.05139003e+01 -2.11534333e+01 3.43876128e+01] [ 5.34606133e+02 -8.92928005e+01 2.19564051e+01 -4.88308206e+01 2.67601831e+01 -2.05310410e+01 2.75094852e+01] [ 5.60189583e+02 -9.81740138e+01 2.27565968e+01 -5.28604478e+01 2.95873338e+01 -2.57220403e+01 3.05609992e+01] [ 5.31961222e+02 -9.84166135e+01 2.29692209e+01 -5.08047387e+01 2.83861058e+01 -2.25286667e+01 3.14426375e+01] [ 5.44375259e+02 -9.14228813e+01 1.79086286e+01 -5.05486070e+01 2.74899404e+01 -2.13354242e+01 2.92148972e+01] [ 5.38760220e+02 -8.78443853e+01 1.11447104e+01 -4.92415698e+01 2.93566074e+01 -2.19018332e+01 3.18418770e+01] [ 5.74075270e+02 -1.06735394e+02 2.22520372e+01 -5.59996432e+01 2.64575787e+01 -2.38079035e+01 3.16454287e+01] [ 5.36487114e+02 -8.32523968e+01 1.86725048e+01 -4.37373810e+01 2.81948114e+01 -2.11428475e+01 2.94069450e+01] [ 5.95983096e+02 -1.09237516e+02 1.88789354e+01 -5.74865300e+01 2.49614186e+01 -2.22693486e+01 3.12035720e+01] [ 5.57080765e+02 -9.14783686e+01 1.82083758e+01 -4.87224084e+01 3.08468578e+01 -2.28748170e+01 3.35785799e+01] [ 5.79295854e+02 -1.05128952e+02 2.54881738e+01 -5.38268512e+01 3.16513011e+01 -2.32330489e+01 3.24641734e+01] [ 5.61698906e+02 -9.87617375e+01 2.20129447e+01 -5.21161085e+01 3.09546822e+01 -2.34860283e+01 3.15000308e+01] [ 5.80002600e+02 -1.06359425e+02 2.05832708e+01 -5.53528032e+01 2.68848174e+01 -2.24624360e+01 2.95663744e+01] [ 5.76586883e+02 -9.77828548e+01 1.56075226e+01 -5.36391220e+01 2.13316411e+01 -2.14705636e+01 2.70132444e+01] [ 5.76900135e+02 -1.04095143e+02 1.87699541e+01 -5.59296938e+01 2.53397993e+01 -2.32538634e+01 3.00921699e+01] [ 5.65926245e+02 -1.07146597e+02 2.09271432e+01 -5.52081521e+01 2.96883433e+01 -2.27857514e+01 3.08455764e+01] [ 5.72282791e+02 -1.06730159e+02 1.49370759e+01 -5.19543484e+01 2.18480779e+01 -1.79838600e+01 2.83431984e+01] [ 5.46281407e+02 -9.64583335e+01 1.63314484e+01 -5.21077025e+01 2.74183538e+01 -2.39873506e+01 2.97883139e+01] [ 5.54718097e+02 -1.02314585e+02 1.97618668e+01 -5.37439238e+01 2.43879308e+01 -2.17599864e+01 2.87428586e+01] [ 5.40664946e+02 -8.55292523e+01 1.59158395e+01 -4.69104985e+01 2.93651736e+01 -2.09169457e+01 3.26516204e+01] [ 5.43190236e+02 -9.31881751e+01 2.20087944e+01 -4.78145679e+01 2.67885059e+01 -2.22787786e+01 2.73298127e+01] [ 5.48101974e+02 -9.42605483e+01 1.97440108e+01 -5.07283821e+01 3.01148080e+01 -2.51827512e+01 3.46146301e+01] [ 5.35974379e+02 -9.31891966e+01 2.08102241e+01 -4.98979283e+01 2.41687758e+01 -1.97785861e+01 2.66652135e+01] [ 5.45080584e+02 -9.07019215e+01 2.24437932e+01 -4.74386437e+01 2.69158000e+01 -2.15649788e+01 2.75707272e+01] [ 5.56071285e+02 -1.00970119e+02 1.83444576e+01 -5.09149329e+01 3.20481885e+01 -2.30340659e+01 3.65994803e+01] [ 5.65317909e+02 -8.85619621e+01 1.20622371e+01 -4.56513244e+01 2.98196483e+01 -2.13220966e+01 3.39207202e+01] [ 5.32437188e+02 -8.40135179e+01 1.80058244e+01 -4.55500693e+01 2.62772466e+01 -2.08909021e+01 2.89143539e+01] [ 6.06168308e+02 -1.13998299e+02 2.14636268e+01 -6.00612720e+01 2.80212325e+01 -2.45679365e+01 3.17706005e+01] [ 5.54652665e+02 -9.59141822e+01 1.83963898e+01 -5.08625195e+01 3.26084310e+01 -2.42618221e+01 3.34156902e+01] [ 5.70570928e+02 -1.07306210e+02 2.03934395e+01 -5.51306638e+01 2.38801393e+01 -2.14537819e+01 2.86923361e+01] [ 5.98736539e+02 -1.09836265e+02 2.31587725e+01 -5.88578930e+01 2.81480280e+01 -2.32859370e+01 3.11203998e+01] [ 5.52219352e+02 -8.68132817e+01 1.73874116e+01 -4.62856832e+01 2.80210530e+01 -2.22090130e+01 3.02535201e+01] [ 5.45150662e+02 -9.57236717e+01 2.57514324e+01 -5.02866832e+01 2.51052795e+01 -2.12797644e+01 2.68490104e+01] [ 5.97211431e+02 -1.06850021e+02 1.32554219e+01 -5.17107091e+01 2.55641465e+01 -2.51713173e+01 3.23104670e+01] [ 5.61518039e+02 -1.04507220e+02 1.73437109e+01 -5.74511173e+01 2.73371579e+01 -2.51743884e+01 3.12306629e+01] [ 5.89181128e+02 -1.01765056e+02 1.96490191e+01 -5.13798171e+01 2.26424568e+01 -1.98818676e+01 2.74849815e+01] [ 5.79382454e+02 -1.05791220e+02 1.39335819e+01 -5.26716093e+01 2.36948942e+01 -1.96474940e+01 2.87840205e+01] [ 5.51407740e+02 -9.57356066e+01 1.92393603e+01 -5.04749182e+01 2.68379989e+01 -2.28241092e+01 3.07542560e+01] [ 5.39135250e+02 -8.88513179e+01 2.55144104e+00 -3.97960747e+01 1.40580870e+01 -1.54917823e+01 2.59400829e+01] [ 5.46244322e+02 -9.56132391e+01 9.23417894e+00 -4.42620585e+01 1.93539566e+01 -1.76435571e+01 2.56146984e+01] [ 5.60313637e+02 -9.15648753e+01 -5.82003275e+00 -3.83271136e+01 1.49012021e+01 -1.80998766e+01 2.79783174e+01] [ 5.56978023e+02 -9.28112207e+01 7.18894247e+00 -4.58625098e+01 1.76981238e+01 -1.82840459e+01 2.64319644e+01] [ 5.52572545e+02 -9.71360646e+01 1.15055655e+01 -4.70741375e+01 2.16358629e+01 -1.79707499e+01 2.58851254e+01] [ 5.36934255e+02 -9.76897659e+01 1.28613949e+01 -4.54412544e+01 1.75414041e+01 -1.65134308e+01 2.65471784e+01] [ 5.19927942e+02 -8.57299537e+01 1.08433283e+01 -4.67556614e+01 2.32981647e+01 -2.16429969e+01 2.87503736e+01] [ 6.12204934e+02 -1.09601555e+02 1.31527081e+00 -4.55721145e+01 1.83217318e+01 -2.11128813e+01 3.23763995e+01] [ 5.50480200e+02 -9.51255290e+01 5.75751181e+00 -4.36021818e+01 1.56118505e+01 -1.61905251e+01 2.67642244e+01] [ 5.75273565e+02 -1.03785710e+02 2.87894403e+00 -5.00606070e+01 1.77107011e+01 -1.89021366e+01 3.12706087e+01] [ 5.39181810e+02 -8.74897261e+01 9.63381503e+00 -4.73582040e+01 2.34098312e+01 -2.21053642e+01 2.72025837e+01] [ 5.38321252e+02 -9.25717805e+01 6.44366835e+00 -4.74686455e+01 1.75843207e+01 -1.83972642e+01 2.62746131e+01] [ 5.00140286e+02 -7.79292834e+01 9.20713731e+00 -4.22066082e+01 2.13577811e+01 -1.96794009e+01 2.51025814e+01] [ 5.52970660e+02 -9.28711825e+01 1.12721168e+01 -4.76036637e+01 2.26887239e+01 -1.95380353e+01 2.69499961e+01] [ 5.61345841e+02 -1.07003573e+02 4.54667968e+00 -4.58593784e+01 2.11905849e+01 -1.47219200e+01 2.90074639e+01] [ 5.10558942e+02 -8.11616379e+01 1.06513156e+01 -4.21917638e+01 1.70349388e+01 -1.58033350e+01 2.46077602e+01] [ 5.43627855e+02 -9.68349026e+01 -1.22287745e+00 -3.90608220e+01 1.43854996e+01 -1.79044988e+01 2.70921791e+01] [ 5.77161078e+02 -9.85333632e+01 1.20014603e-01 -4.39834039e+01 1.71144994e+01 -1.66659377e+01 2.73868569e+01] [ 5.49485081e+02 -1.00664731e+02 4.94702320e+00 -4.37712750e+01 1.49573258e+01 -1.70420508e+01 3.01066654e+01] [ 5.59108004e+02 -9.87706028e+01 8.26596278e+00 -4.62067956e+01 1.77503711e+01 -1.87062333e+01 2.84358540e+01] [ 5.65003297e+02 -1.06435308e+02 5.49420620e+00 -4.85466595e+01 1.88716609e+01 -1.73355026e+01 2.98685083e+01] [ 5.43868630e+02 -1.00345329e+02 4.90381879e+00 -4.21452697e+01 1.76339644e+01 -1.53534156e+01 2.81154045e+01] [ 5.38525284e+02 -9.59331252e+01 6.52639178e+00 -4.40901713e+01 1.62142161e+01 -1.56457079e+01 2.69396619e+01] [ 5.23370065e+02 -8.95406540e+01 6.30223624e+00 -4.30799760e+01 1.66905202e+01 -1.42799947e+01 2.53341322e+01] [ 5.71121926e+02 -1.07954730e+02 1.84983994e+01 -5.26238120e+01 2.62347728e+01 -2.18001889e+01 2.92125040e+01] [ 5.13426363e+02 -8.90791956e+01 1.19825429e+01 -4.38593857e+01 1.85655885e+01 -1.65421271e+01 2.41213457e+01] [ 5.52962188e+02 -9.21345904e+01 3.28902527e+00 -4.58454141e+01 1.69308410e+01 -1.71481129e+01 2.72311887e+01] [ 5.49964443e+02 -9.27854148e+01 9.48112465e+00 -4.75482728e+01 1.43935455e+01 -1.76735609e+01 2.67026063e+01] [ 5.00705442e+02 -8.53092980e+01 1.43470009e+01 -4.40308172e+01 2.08833472e+01 -1.78026303e+01 2.47683865e+01] [ 5.46098900e+02 -9.39441571e+01 1.30284787e+01 -4.79309436e+01 1.93794660e+01 -1.78586939e+01 2.61801955e+01] [ 5.46913499e+02 -8.93870530e+01 8.52549272e+00 -4.52328860e+01 1.86808044e+01 -1.62772943e+01 2.63048681e+01] [ 5.53660092e+02 -9.99403236e+01 -5.25265019e+00 -4.46349558e+01 1.51694965e+01 -1.85739527e+01 2.97632580e+01] [ 5.59813837e+02 -9.49280033e+01 1.15486892e+01 -4.95937257e+01 2.21884722e+01 -2.07278596e+01 2.73240069e+01] [ 5.42647879e+02 -9.36782615e+01 7.39169942e+00 -4.38218071e+01 1.56916538e+01 -1.69626153e+01 2.65022153e+01] [ 5.57556323e+02 -1.00060524e+02 5.46702364e+00 -4.72428033e+01 1.97932991e+01 -1.72037376e+01 2.79946719e+01] [ 5.40417474e+02 -9.45361518e+01 1.06497154e+01 -4.67932237e+01 1.93909626e+01 -1.80517830e+01 2.58176113e+01] [ 5.26776001e+02 -9.26407816e+01 1.11481148e+01 -4.81163405e+01 2.17720423e+01 -2.04315426e+01 2.69207214e+01] [ 5.80189078e+02 -1.07654945e+02 3.55013740e+00 -4.93143073e+01 2.04402466e+01 -1.98081935e+01 3.06725691e+01] [ 5.62261092e+02 -1.02538949e+02 7.24425492e+00 -4.73770964e+01 1.71637520e+01 -1.93611070e+01 2.82762388e+01] [ 5.64264106e+02 -1.00658789e+02 5.01547652e+00 -4.34512632e+01 1.84211522e+01 -1.66923320e+01 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2.92127438e+01] [ 5.52913103e+02 -9.69469680e+01 9.33037775e+00 -4.82863136e+01 1.90800553e+01 -1.91917087e+01 2.77800981e+01] [ 5.59491093e+02 -9.70216275e+01 1.52571651e+01 -4.86860660e+01 2.02550233e+01 -1.86072260e+01 2.71144889e+01] [ 5.46770790e+02 -9.20803174e+01 5.24920693e+00 -4.77334391e+01 1.78731892e+01 -1.91157009e+01 2.60562549e+01] [ 5.69999363e+02 -9.51923077e+01 -1.04845608e-01 -4.36245509e+01 1.73427670e+01 -1.59510974e+01 2.77084573e+01] [ 5.66546168e+02 -9.96135076e+01 6.61223309e-01 -4.53432193e+01 1.74256050e+01 -1.78759776e+01 2.86847824e+01]]) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.132 seconds) .. _sphx_glr_download_auto_examples_plot_representation.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/GAA-UAM/scikit-fda/develop?filepath=examples/plot_representation.py :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_representation.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_representation.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_