` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py: Manipulate a time series ======================== .. GENERATED FROM PYTHON SOURCE LINES 6-17 The objective here is to create and manipulate a time series. A time series is a particular field where the mesh :math:`\mathcal{M}` 1-d and regular, eg a time grid :math:`(t_0, \dots, t_{N-1})`. It is possible to draw a time series, using interpolation between the values: see the use case on the Field. A time series can be obtained as a realization of a multivariate stochastic process :math:`X: \Omega \times [0,T] \rightarrow \mathbb{R}^d` of dimension :math:`d` where :math:`[0,T]` is discretized according to the regular grid :math:`(t_0, \dots, t_{N-1})` . The values :math:`(\underline{x}_0, \dots, \underline{x}_{N-1})` of the time series are defined by: .. math:: \forall i \in [0, N-1],\quad \underline{x}_i= X(\omega)(t_i) A time series is stored in the **TimeSeries** object that stores the regular time grid and the associated values. .. GENERATED FROM PYTHON SOURCE LINES 19-25 .. code-block:: default import openturns as ot import openturns.viewer as viewer from matplotlib import pylab as plt ot.Log.Show(ot.Log.NONE) .. GENERATED FROM PYTHON SOURCE LINES 26-27 Create the RegularGrid .. GENERATED FROM PYTHON SOURCE LINES 27-32 .. code-block:: default tMin = 0.0 timeStep = 0.1 N = 100 myTimeGrid = ot.RegularGrid(tMin, timeStep, N) .. GENERATED FROM PYTHON SOURCE LINES 33-35 Case 1: Create a time series from a time grid and values Care! The number of steps of the time grid must correspond to the size of the values .. GENERATED FROM PYTHON SOURCE LINES 35-39 .. code-block:: default myValues = ot.Normal(3).getSample(myTimeGrid.getVertices().getSize()) myTimeSeries = ot.TimeSeries(myTimeGrid, myValues) myTimeSeries .. raw:: html

[ t X0 X1 X2 ]
0 : [ 0 -0.304826 -1.00361 0.483947 ]
1 : [ 0.1 0.624004 0.0395438 0.66043 ]
2 : [ 0.2 1.10081 0.585758 0.328136 ]
3 : [ 0.3 -0.178412 0.0833079 0.897776 ]
4 : [ 0.4 -1.40973 0.711103 -1.75418 ]
5 : [ 0.5 -1.33661 0.269893 1.41368 ]
6 : [ 0.6 0.499241 1.15854 -0.098967 ]
7 : [ 0.7 -0.650467 1.46114 0.61372 ]
8 : [ 0.8 2.14201 -1.66772 -0.151473 ]
9 : [ 0.9 2.4158 0.137289 -0.969704 ]
10 : [ 1 -0.292688 -1.02522 -0.354939 ]
11 : [ 1.1 -0.869302 0.266897 0.296524 ]
12 : [ 1.2 -2.24114 -1.5607 -2.272 ]
13 : [ 1.3 0.722451 -2.16128 -0.32861 ]
14 : [ 1.4 1.06975 -1.77994 -0.832708 ]
15 : [ 1.5 -0.245372 -0.0205006 -0.170101 ]
16 : [ 1.6 0.529296 -0.725104 -1.16247 ]
17 : [ 1.7 0.199523 0.727148 -0.260688 ]
18 : [ 1.8 -0.136772 0.52023 -0.659133 ]
19 : [ 1.9 -0.180673 -1.04885 0.512371 ]
20 : [ 2 0.20648 -0.960832 0.414682 ]
21 : [ 2.1 -1.22871 2.57497 -0.00804901 ]
22 : [ 2.2 -1.8859 0.830757 -0.378346 ]
23 : [ 2.3 0.479046 1.60938 -0.570841 ]
24 : [ 2.4 0.269096 0.803503 0.583218 ]
25 : [ 2.5 0.449756 -0.693556 1.89666 ]
26 : [ 2.6 0.0270818 -0.258272 -0.37012 ]
27 : [ 2.7 0.0456596 -0.343048 -0.392484 ]
28 : [ 2.8 -2.41093 1.93921 -0.590044 ]
29 : [ 2.9 0.22705 -0.141765 0.855507 ]
30 : [ 3 0.286761 0.564812 -0.509701 ]
31 : [ 3.1 1.40334 -1.37852 0.434035 ]
32 : [ 3.2 0.0342518 0.896116 -0.870577 ]
33 : [ 3.3 1.36995 0.272597 0.579223 ]
34 : [ 3.4 -1.5321 0.957065 0.427663 ]
35 : [ 3.5 -0.36668 0.648699 -0.00464944 ]
36 : [ 3.6 0.171548 -0.0795761 0.455389 ]
37 : [ 3.7 -2.14009 0.933245 0.818686 ]
38 : [ 3.8 -1.54826 0.370246 -0.773089 ]
39 : [ 3.9 -0.0129833 0.187309 -2.13145 ]
40 : [ 4 -1.19768 -0.00500185 -0.125673 ]
41 : [ 4.1 -1.89201 3.40565 -0.103576 ]
42 : [ 4.2 0.415448 0.727255 0.978855 ]
43 : [ 4.3 1.15808 0.295275 0.283934 ]
44 : [ 4.4 1.29426 0.200773 0.342265 ]
45 : [ 4.5 0.164085 -0.608383 0.144346 ]
46 : [ 4.6 0.537733 0.696557 1.18791 ]
47 : [ 4.7 2.18097 -0.194809 0.628316 ]
48 : [ 4.8 0.230866 -0.648071 -0.0280203 ]
49 : [ 4.9 0.871005 1.24473 -0.106358 ]
50 : [ 5 -0.234489 -2.0102 0.121701 ]
51 : [ 5.1 -1.33163 -0.825457 -1.21658 ]
52 : [ 5.2 -1.02579 -1.22486 -0.735057 ]
53 : [ 5.3 0.267431 -0.313967 0.328403 ]
54 : [ 5.4 -1.18542 0.272577 -0.537997 ]
55 : [ 5.5 -0.154628 0.0348939 0.357208 ]
56 : [ 5.6 0.87381 -1.4897 -1.60323 ]
57 : [ 5.7 0.276884 -0.205279 0.313591 ]
58 : [ 5.8 1.52063 2.12789 0.15741 ]
59 : [ 5.9 0.056432 1.05201 -1.06929 ]
60 : [ 6 0.0389696 0.108862 1.56022 ]
61 : [ 6.1 0.897858 0.0713179 0.329058 ]
62 : [ 6.2 0.768345 -0.201722 0.148307 ]
63 : [ 6.3 0.498826 -0.540609 0.202215 ]
64 : [ 6.4 1.52964 -1.19218 0.524954 ]
65 : [ 6.5 -0.127176 1.00122 0.299567 ]
66 : [ 6.6 -0.0732479 -0.592801 0.509773 ]
67 : [ 6.7 1.56808 0.369343 0.687346 ]
68 : [ 6.8 0.26022 1.5601 0.68388 ]
69 : [ 6.9 -0.260408 0.169652 -1.01657 ]
70 : [ 7 0.810285 -0.934548 0.440233 ]
71 : [ 7.1 0.102655 0.16255 0.977606 ]
72 : [ 7.2 -0.685128 -0.0411968 -0.161531 ]
73 : [ 7.3 0.00948899 -0.699237 0.835643 ]
74 : [ 7.4 0.961209 -0.395342 0.250509 ]
75 : [ 7.5 -1.71279 -0.303372 1.71343 ]
76 : [ 7.6 0.287997 -0.346204 -1.24308 ]
77 : [ 7.7 -0.661934 -0.539626 0.78918 ]
78 : [ 7.8 0.525199 0.265505 -0.615353 ]
79 : [ 7.9 0.667728 -0.320656 -0.00603524 ]
80 : [ 8 -1.44043 0.0706512 0.400517 ]
81 : [ 8.1 -0.537003 -2.13043 0.186229 ]
82 : [ 8.2 -1.32629 0.242601 -0.897333 ]
83 : [ 8.3 -0.957364 1.58824 -0.238077 ]
84 : [ 8.4 -0.654398 1.49892 -0.713136 ]
85 : [ 8.5 -1.33516 0.567629 0.640198 ]
86 : [ 8.6 -0.259729 0.192286 -1.40222 ]
87 : [ 8.7 0.560018 -1.35624 1.03452 ]
88 : [ 8.8 -0.378793 -0.125727 -0.587836 ]
89 : [ 8.9 1.07894 -1.66939 1.70834 ]
90 : [ 9 -0.845941 -0.178621 -0.195884 ]
91 : [ 9.1 1.81133 0.400036 1.10812 ]
92 : [ 9.2 -0.455236 -0.793417 2.28383 ]
93 : [ 9.3 0.351885 -0.0608221 1.18257 ]
94 : [ 9.4 2.05724 2.0836 -1.10946 ]
95 : [ 9.5 0.646117 0.314088 -1.25919 ]
96 : [ 9.6 2.51347 1.10677 -1.23708 ]
97 : [ 9.7 -0.405063 1.24478 0.258866 ]
98 : [ 9.8 -0.1138 0.3815 0.155791 ]
99 : [ 9.9 0.402412 1.33272 -0.805619 ]

.. GENERATED FROM PYTHON SOURCE LINES 40-41 Case 2: Get a time series from a Process .. GENERATED FROM PYTHON SOURCE LINES 41-45 .. code-block:: default myProcess = ot.WhiteNoise(ot.Normal(3), myTimeGrid) myTimeSeries2 = myProcess.getRealization() myTimeSeries2 .. raw:: html

	t	X0	X1	X2
0	0	0.3854209	-1.61086	-0.6874292
1	0.1	-0.02107395	-1.405266	-0.6029087
2	0.2	-0.07453712	-0.287633	-0.4026233
3	0.3	-0.4894317	-0.5803388	1.196489
4	0.4	1.004556	0.5372572	-0.08770909
5	0.5	1.423935	0.6820146	2.884055
6	0.6	0.2796988	-1.178997	-0.143892
7	0.7	0.6813079	0.01437919	0.5099701
8	0.8	-1.060234	0.04483657	0.2499197
9	0.9	1.24773	-0.3856004	-0.2880728
10	1	-0.5890517	0.4995753	1.132313
11	1.1	-0.8437811	1.43619	-0.1876503
12	1.2	0.940522	0.7151117	-1.439318
13	1.3	-0.1429401	-0.1765888	0.9054335
14	1.4	0.6688361	-0.1848348	-0.2056171
15	1.5	0.8539061	1.082717	0.7860448
16	1.6	-1.839514	-0.4807376	-0.7431111
17	1.7	0.2583894	0.06498678	0.8220976
18	1.8	-0.2202976	-1.267407	0.06548754
19	1.9	-2.506485	0.2182682	-0.3734256
20	2	-0.3483342	-1.020392	-0.9373684
21	2.1	0.793814	-0.983334	-0.4151898
22	2.2	0.1049272	-0.4991656	0.3643877
23	2.3	-0.1627931	0.4925782	0.3548167
24	2.4	-0.8811936	-0.819895	-2.106536
25	2.5	0.1773956	-0.04881701	-0.9867962
26	2.6	-0.8862132	1.219161	0.266691
27	2.7	0.188304	0.8090514	1.619885
28	2.8	-0.5646788	-0.9921044	0.7245245
29	2.9	0.3057475	-0.4119946	2.759856
30	3	0.4088039	1.121707	-0.6501654
31	3.1	-1.034288	1.150379	0.5587453
32	3.2	1.332409	-0.3225148	0.4750779
33	3.3	-0.1536095	1.035535	1.381175
34	3.4	1.225896	-0.1056646	0.3069166
35	3.5	0.4924758	0.4262604	-0.5698308
36	3.6	-0.4156163	-2.609303	-2.173168
37	3.7	-1.324497	-1.45585	0.1801837
38	3.8	1.421198	1.866039	-0.1742316
39	3.9	-1.55547	1.4884	1.303924
40	4	-1.061323	-1.305955	-1.629615
41	4.1	-0.2962869	0.8739792	0.1051378
42	4.2	-0.02998592	-1.516032	1.474471
43	4.3	-1.03669	-1.534651	0.8259901
44	4.4	0.457382	-0.3865615	1.28411
45	4.5	-0.3259461	1.637177	-0.8420178
46	4.6	-0.2924097	0.3615991	0.4570965
47	4.7	0.237978	1.020826	1.699262
48	4.8	-0.5438809	0.4973056	-1.469904
49	4.9	-2.294773	-0.2623551	-1.554523
50	5	-2.82731	0.5825531	0.4139608
51	5.1	-0.9302437	0.549059	-0.69065
52	5.2	-0.6021352	-0.7677184	1.285077
53	5.3	-0.22259	1.221741	0.4439343
54	5.4	-0.7078664	-1.056912	0.5648551
55	5.5	0.2980986	1.342418	1.085837
56	5.6	0.8239627	-0.6283856	-0.8834576
57	5.7	0.8607533	1.456264	0.1421699
58	5.8	-0.3323323	0.8952978	0.1655028
59	5.9	0.02714461	0.1645807	0.2626963
60	6	1.638611	0.1818056	-0.1240066
61	6.1	1.56386	-0.5471615	0.4136208
62	6.2	-0.5009097	-1.561814	-2.157897
63	6.3	-0.8845609	-0.03278067	-0.4371368
64	6.4	0.9263022	0.3640217	1.127778
65	6.5	-0.2958129	0.521623	-0.5048369
66	6.6	-1.126024	-0.1538759	0.9138794
67	6.7	-2.058274	1.093646	0.353957
68	6.8	-0.5708488	1.521397	0.2852253
69	6.9	-1.835236	-0.3044852	0.9165636
70	7	0.9140664	0.1075705	0.06927429
71	7.1	-0.6650488	1.951216	0.7997068
72	7.2	-0.8125796	-0.5797791	0.1117721
73	7.3	-0.2133026	-1.116885	-0.872058
74	7.4	1.629164	3.399959	-0.9405087
75	7.5	0.8080016	-0.5450092	1.626903
76	7.6	-0.06128802	0.308256	-0.9618253
77	7.7	-1.255094	0.4358796	-0.7273887
78	7.8	-0.3513546	-1.318261	-0.47417
79	7.9	-0.1005602	1.643525	-0.4139103
80	8	0.8686027	-0.4322521	1.012874
81	8.1	-1.114927	0.469528	0.9161205
82	8.2	-0.356955	1.022334	-2.00257
83	8.3	-1.71516	0.6274581	-1.352094
84	8.4	-0.03491598	-0.03793251	0.05596954
85	8.5	-0.2810947	0.144073	-2.171863
86	8.6	-0.3389453	0.5843859	-0.8390798
87	8.7	-1.04138	0.3519497	1.069267
88	8.8	-2.866462	1.182504	0.2067203
89	8.9	-0.6907754	-0.7425984	1.164752
90	9	-0.09003073	-1.209451	0.7730654
91	9.1	-0.8069562	-1.046643	0.1396704
92	9.2	1.067365	0.1232827	-0.776005
93	9.3	-0.882326	-0.0145659	0.2200673
94	9.4	0.4727389	-0.3159074	1.723677
95	9.5	0.5338985	0.4875888	-0.5419431
96	9.6	0.7959215	-0.9714537	-0.3666259
97	9.7	0.1363355	1.229809	-0.4606246
98	9.8	0.5330227	-0.9875807	0.2573491
99	9.9	0.415046	-0.7534109	0.07963906

.. GENERATED FROM PYTHON SOURCE LINES 46-47 Get the number of values of the time series .. GENERATED FROM PYTHON SOURCE LINES 47-49 .. code-block:: default myTimeSeries.getSize() .. rst-class:: sphx-glr-script-out .. code-block:: none 100 .. GENERATED FROM PYTHON SOURCE LINES 50-51 Get the dimension of the values observed at each time .. GENERATED FROM PYTHON SOURCE LINES 51-53 .. code-block:: default myTimeSeries.getMesh().getDimension() .. rst-class:: sphx-glr-script-out .. code-block:: none 1 .. GENERATED FROM PYTHON SOURCE LINES 54-55 Get the value Xi at index i .. GENERATED FROM PYTHON SOURCE LINES 55-58 .. code-block:: default i = 37 print("Xi = ", myTimeSeries.getValueAtIndex(i)) .. rst-class:: sphx-glr-script-out .. code-block:: none Xi = [-2.14009,0.933245,0.818686] .. GENERATED FROM PYTHON SOURCE LINES 59-60 Get the time series at index i : Xi .. GENERATED FROM PYTHON SOURCE LINES 60-63 .. code-block:: default i = 37 print("Xi = ", myTimeSeries[i]) .. rst-class:: sphx-glr-script-out .. code-block:: none Xi = [-2.14009,0.933245,0.818686] .. GENERATED FROM PYTHON SOURCE LINES 64-65 Get a the marginal value at index i of the time series .. GENERATED FROM PYTHON SOURCE LINES 65-71 .. code-block:: default i = 37 # get the time stamp: print("ti = ", myTimeSeries.getTimeGrid().getValue(i)) # get the first component of the corresponding value : print("Xi1 = ", myTimeSeries[i, 0]) .. rst-class:: sphx-glr-script-out .. code-block:: none ti = 3.7 Xi1 = -2.1400929826096444 .. GENERATED FROM PYTHON SOURCE LINES 72-73 Get all the values (X1, .., Xn) of the time series .. GENERATED FROM PYTHON SOURCE LINES 73-75 .. code-block:: default myTimeSeries.getValues() .. raw:: html

	X0	X1	X2
0	-0.3048259	-1.003608	0.4839469
1	0.624004	0.03954378	0.6604305
2	1.100812	0.5857581	0.3281357
3	-0.1784119	0.08330789	0.8977757
4	-1.409734	0.7111029	-1.754176
5	-1.336608	0.2698927	1.413675
6	0.4992414	1.158536	-0.09896703
7	-0.6504669	1.461145	0.6137203
8	2.142012	-1.667722	-0.1514732
9	2.4158	0.1372886	-0.9697043
10	-0.292688	-1.025222	-0.3549386
11	-0.8693017	0.266897	0.296524
12	-2.241144	-1.560704	-2.271998
13	0.7224505	-2.161275	-0.3286104
14	1.069747	-1.779941	-0.8327076
15	-0.2453716	-0.0205006	-0.1701006
16	0.5292955	-0.7251038	-1.162473
17	0.1995235	0.7271477	-0.2606875
18	-0.1367718	0.5202298	-0.6591333
19	-0.1806734	-1.048847	0.5123711
20	0.2064803	-0.960832	0.4146824
21	-1.228714	2.57497	-0.008049008
22	-1.885899	0.830757	-0.3783459
23	0.4790463	1.609382	-0.5708413
24	0.2690964	0.8035033	0.5832181
25	0.4497564	-0.6935559	1.896662
26	0.02708176	-0.258272	-0.37012
27	0.04565963	-0.3430478	-0.3924844
28	-2.410929	1.939206	-0.5900438
29	0.2270499	-0.1417654	0.8555065
30	0.286761	0.5648119	-0.5097008
31	1.403344	-1.378522	0.4340351
32	0.03425181	0.8961165	-0.8705775
33	1.369953	0.2725969	0.5792226
34	-1.532103	0.957065	0.4276634
35	-0.3666802	0.6486989	-0.004649441
36	0.1715484	-0.07957611	0.4553892
37	-2.140093	0.9332446	0.8186856
38	-1.548256	0.370246	-0.773089
39	-0.01298333	0.1873089	-2.131449
40	-1.197682	-0.005001849	-0.1256726
41	-1.892007	3.40565	-0.1035762
42	0.4154477	0.7272545	0.9788553
43	1.158081	0.2952752	0.2839339
44	1.294258	0.2007735	0.342265
45	0.1640854	-0.6083832	0.1443463
46	0.5377329	0.6965567	1.187906
47	2.180975	-0.1948093	0.6283156
48	0.2308662	-0.6480712	-0.02802031
49	0.8710046	1.244731	-0.1063582
50	-0.2344887	-2.010204	0.1217012
51	-1.331632	-0.8254575	-1.216578
52	-1.025789	-1.224865	-0.7350567
53	0.2674311	-0.3139666	0.3284034
54	-1.185418	0.2725766	-0.5379969
55	-0.1546276	0.03489387	0.3572081
56	0.8738098	-1.489697	-1.603233
57	0.2768838	-0.2052791	0.3135911
58	1.520626	2.127892	0.1574096
59	0.05643199	1.05201	-1.069286
60	0.03896958	0.1088619	1.560223
61	0.8978581	0.07131786	0.3290581
62	0.7683447	-0.2017215	0.1483074
63	0.4988259	-0.5406089	0.202215
64	1.52964	-1.192179	0.5249542
65	-0.1271758	1.001217	0.2995675
66	-0.07324792	-0.5928008	0.509773
67	1.568079	0.3693428	0.6873462
68	0.2602205	1.560101	0.6838802
69	-0.2604079	0.1696515	-1.016573
70	0.8102853	-0.9345477	0.4402335
71	0.1026545	0.1625502	0.9776058
72	-0.6851276	-0.04119683	-0.1615313
73	0.009488993	-0.6992373	0.8356431
74	0.9612086	-0.3953424	0.2505092
75	-1.712787	-0.3033722	1.713433
76	0.2879968	-0.3462038	-1.243077
77	-0.6619336	-0.5396257	0.7891796
78	0.525199	0.2655049	-0.6153533
79	0.6677281	-0.3206562	-0.00603524
80	-1.440427	0.07065125	0.4005165
81	-0.5370034	-2.130432	0.1862285
82	-1.326288	0.2426011	-0.8973327
83	-0.9573643	1.588237	-0.2380769
84	-0.6543979	1.498919	-0.7131357
85	-1.335157	0.5676285	0.640198
86	-0.259729	0.1922855	-1.402221
87	0.5600177	-1.356244	1.034522
88	-0.3787931	-0.1257271	-0.5878356
89	1.078941	-1.669386	1.708344
90	-0.8459409	-0.1786205	-0.1958844
91	1.811325	0.4000363	1.108118
92	-0.4552358	-0.7934174	2.283829
93	0.351885	-0.06082214	1.182574
94	2.057236	2.083603	-1.109457
95	0.6461174	0.3140881	-1.259195
96	2.51347	1.106768	-1.237082
97	-0.4050629	1.244775	0.2588656
98	-0.1137998	0.3814998	0.1557911
99	0.4024124	1.332716	-0.8056192

.. GENERATED FROM PYTHON SOURCE LINES 76-78 Compute the temporal Mean It corresponds to the mean of the values of the time series .. GENERATED FROM PYTHON SOURCE LINES 78-80 .. code-block:: default myTimeSeries.getInputMean() .. raw:: html

[0.0282591,0.0564162,0.0199248]

.. GENERATED FROM PYTHON SOURCE LINES 81-82 Draw the marginal i of the time series using linear interpolation .. GENERATED FROM PYTHON SOURCE LINES 82-85 .. code-block:: default graph = myTimeSeries.drawMarginal(0) view = viewer.View(graph) .. image-sg:: /auto_probabilistic_modeling/stochastic_processes/images/sphx_glr_plot_timeseries_manipulation_001.png :alt: Unnamed - 0 marginal :srcset: /auto_probabilistic_modeling/stochastic_processes/images/sphx_glr_plot_timeseries_manipulation_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 86-87 with no interpolation .. GENERATED FROM PYTHON SOURCE LINES 87-90 .. code-block:: default graph = myTimeSeries.drawMarginal(0, False) view = viewer.View(graph) plt.show() .. image-sg:: /auto_probabilistic_modeling/stochastic_processes/images/sphx_glr_plot_timeseries_manipulation_002.png :alt: Unnamed - 0 marginal :srcset: /auto_probabilistic_modeling/stochastic_processes/images/sphx_glr_plot_timeseries_manipulation_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.121 seconds) .. _sphx_glr_download_auto_probabilistic_modeling_stochastic_processes_plot_timeseries_manipulation.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_timeseries_manipulation.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_timeseries_manipulation.ipynb `