Merge pull request #38 from jverzani/v0.9

V0.9
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john verzani authored and GitHub committed 2022-09-14 14:47:06 -04:00
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@@ -26,16 +26,16 @@ jobs:
# setup Quarto
- uses: quarto-dev/quarto-actions/setup@v2
# setup Julia
- uses: julia-actions/setup-julia@v1
- run: julia --project -e 'using Pkg; ENV["PYTHON"]=""; Pkg.add("PyCall"); Pkg.build("PyCall"); Pkg.instantiate(); '
# setup Python / Jupyter
- uses: actions/setup-python@v4
with:
python-version: '3.x' # Version range or exact version of a Python version to use, using SemVer's version range syntax
- run: pip install jupyter
# setup Julia
- uses: julia-actions/setup-julia@v1
- run: julia --project -e 'using Pkg; Pkg.instantiate()'
# render and push to the gh-pages branch
- name: Publish to GitHub Pages (and render)
uses: quarto-dev/quarto-actions/publish@v2
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/_book/
/_freeze/
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"markdown": "# The `DifferentialEquations` suite\n\n\n\nThis section uses these add-on packages:\n\n``` {.julia .cell-code}\nusing OrdinaryDiffEq\nusing Plots\nusing ModelingToolkit\n```\n\n\n\n\n---\n\n\nThe [`DifferentialEquations`](https://github.com/SciML/DifferentialEquations.jl) suite of packages contains solvers for a wide range of various differential equations. This section just briefly touches touch on ordinary differential equations (ODEs), and so relies only on `OrdinaryDiffEq` part of the suite. For more detail on this type and many others covered by the suite of packages, there are many other resources, including the [documentation](https://diffeq.sciml.ai/stable/) and accompanying [tutorials](https://github.com/SciML/SciMLTutorials.jl).\n\n\n## SIR Model\n\n\nWe follow along with an introduction to the SIR model for the spread of disease by [Smith and Moore](https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-introduction). This model received a workout due to the COVID-19 pandemic.\n\n\nThe basic model breaks a population into three cohorts: The **susceptible** individuals, the **infected** individuals, and the **recovered** individuals. These add to the population size, $N$, which is fixed, but the cohort sizes vary in time. We name these cohort sizes $S(t)$, $I(t)$, and $R(t)$ and define $s(t)=S(t)/N$, $i(t) = I(t)/N$ and $r(t) = R(t)/N$ to be the respective proportions.\n\n\nThe following *assumptions* are made about these cohorts by Smith and Moore:\n\n\n> No one is added to the susceptible group, since we are ignoring births and immigration. The only way an individual leaves the susceptible group is by becoming infected.\n\n\n\nThis implies the rate of change in time of $S(t)$ depends on the current number of susceptibles, and the amount of interaction with the infected cohorts. The model *assumes* each infected person has $b$ contacts per day that are sufficient to spread the disease. Not all contacts will be with susceptible people, but if people are assumed to mix within the cohorts, then there will be on average $b \\cdot S(t)/N$ contacts with susceptible people per infected person. As each infected person is modeled identically, the time rate of change of $S(t)$ is:\n\n\n\n$$\n\\frac{dS}{dt} = - b \\cdot \\frac{S(t)}{N} \\cdot I(t) = -b \\cdot s(t) \\cdot I(t)\n$$\n\n\nIt is negative, as no one is added, only taken off. After dividing by $N$, this can also be expressed as $s'(t) = -b s(t) i(t)$.\n\n\n> assume that a fixed fraction $k$ of the infected group will recover during any given day.\n\n\n\nThis means the change in time of the recovered depends on $k$ and the number infected, giving rise to the equation\n\n\n\n$$\n\\frac{dR}{dt} = k \\cdot I(t)\n$$\n\n\nwhich can also be expressed in proportions as $r'(t) = k \\cdot i(t)$.\n\n\nFinally, from $S(t) + I(T) + R(t) = N$ we have $S'(T) + I'(t) + R'(t) = 0$ or $s'(t) + i'(t) + r'(t) = 0$.\n\n\nCombining, it is possible to express the rate of change of the infected population through:\n\n\n\n$$\n\\frac{di}{dt} = b \\cdot s(t) \\cdot i(t) - k \\cdot i(t)\n$$\n\n\nThe author's apply this model to flu statistics from Hong Kong where:\n\n\n\n$$\n\\begin{align*}\nS(0) &= 7,900,000\\\\\nI(0) &= 10\\\\\nR(0) &= 0\\\\\n\\end{align*}\n$$\n\n\nIn `Julia` we define these, `N` to model the total population, and `u0` to be the proportions.\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nS0, I0, R0 = 7_900_000, 10, 0\nN = S0 + I0 + R0\nu0 = [S0, I0, R0]/N # initial proportions\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\n3-element Vector{Float64}:\n 0.9999987341788175\n 1.2658211825048323e-6\n 0.0\n```\n:::\n:::\n\n\nAn *estimated* set of values for $k$ and $b$ are $k=1/3$, coming from the average period of infectiousness being estimated at three days and $b=1/2$, which seems low in normal times, but not for an infected person who may be feeling quite ill and staying at home. (The model for COVID would certainly have a larger $b$ value).\n\n\nOkay, the mathematical modeling is done; now we try to solve for the unknown functions using `DifferentialEquations`.\n\n\nTo warm up, if $b=0$ then $i'(t) = -k \\cdot i(t)$ describes the infected. (There is no circulation of people in this case.) The solution would be achieved through:\n\n::: {.cell hold='true' execution_count=5}\n``` {.julia .cell-code}\nk = 1/3\n\nf(u,p,t) = -k * u # solving u′(t) = - k u(t)\ntime_span = (0.0, 20.0)\n\nprob = ODEProblem(f, I0/N, time_span)\nsol = solve(prob, Tsit5(), reltol=1e-8, abstol=1e-8)\n\nplot(sol)\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n![](differential_equations_files/figure-html/cell-6-output-1.svg){}\n:::\n:::\n\n\nThe `sol` object is a set of numbers with a convenient `plot` method. As may have been expected, this graph shows exponential decay.\n\n\nA few comments are in order. The problem we want to solve is\n\n\n\n$$\n\\frac{di}{dt}Line truncated
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410.019,1686.36 412.473,1686.36 414.927,1686.36 417.381,1686.35 419.835,1686.35 422.289,1686.35 424.743,1686.35 427.197,1686.35 429.651,1686.35 432.106,1686.35
434.56,1686.35 437.014,1686.35 439.468,1686.35 441.922,1686.35 444.376,1686.35 446.83,1686.35 449.284,1686.35 451.738,1686.35 454.192,1686.35 456.646,1686.34
459.1,1686.34 461.554,1686.34 464.008,1686.34 466.462,1686.34 468.916,1686.34 471.37,1686.34 473.824,1686.34 476.279,1686.34 478.733,1686.34 481.187,1686.34
483.641,1686.33 486.095,1686.33 488.549,1686.33 491.003,1686.33 493.457,1686.33 495.911,1686.33 498.365,1686.33 500.819,1686.33 503.273,1686.33 505.727,1686.32
508.181,1686.32 510.635,1686.32 513.089,1686.32 515.543,1686.32 517.998,1686.32 520.452,1686.32 522.906,1686.31 525.36,1686.31 527.814,1686.31 530.268,1686.31
532.722,1686.31 535.176,1686.31 537.63,1686.31 540.084,1686.3 542.538,1686.3 544.992,1686.3 547.446,1686.3 549.9,1686.3 552.354,1686.29 554.808,1686.29
557.262,1686.29 559.716,1686.29 562.171,1686.29 564.625,1686.28 567.079,1686.28 569.533,1686.28 571.987,1686.28 574.441,1686.27 576.895,1686.27 579.349,1686.27
581.803,1686.27 584.257,1686.26 586.711,1686.26 589.165,1686.26 591.619,1686.25 594.073,1686.25 596.527,1686.25 598.981,1686.24 601.435,1686.24 603.889,1686.24
606.344,1686.23 608.798,1686.23 611.252,1686.23 613.706,1686.22 616.16,1686.22 618.614,1686.21 621.068,1686.21 623.522,1686.21 625.976,1686.2 628.43,1686.2
630.884,1686.19 633.338,1686.19 635.792,1686.18 638.246,1686.18 640.7,1686.17 643.154,1686.17 645.608,1686.16 648.062,1686.16 650.517,1686.15 652.971,1686.15
655.425,1686.14 657.879,1686.14 660.333,1686.13 662.787,1686.12 665.241,1686.12 667.695,1686.11 670.149,1686.1 672.603,1686.1 675.057,1686.09 677.511,1686.08
679.965,1686.08 682.419,1686.07 684.873,1686.06 687.327,1686.05 689.781,1686.04 692.235,1686.04 694.69,1686.03 697.144,1686.02 699.598,1686.01 702.052,1686
704.506,1685.99 706.96,1685.98 709.414,1685.97 711.868,1685.96 714.322,1685.95 716.776,1685.94 719.23,1685.93 721.684,1685.92 724.138,1685.91 726.592,1685.89
729.046,1685.88 731.5,1685.87 733.954,1685.86 736.408,1685.84 738.863,1685.83 741.317,1685.82 743.771,1685.8 746.225,1685.79 748.679,1685.77 751.133,1685.76
753.587,1685.74 756.041,1685.73 758.495,1685.71 760.949,1685.69 763.403,1685.68 765.857,1685.66 768.311,1685.64 770.765,1685.62 773.219,1685.6 775.673,1685.58
778.127,1685.56 780.581,1685.54 783.036,1685.52 785.49,1685.5 787.944,1685.48 790.398,1685.45 792.852,1685.43 795.306,1685.41 797.76,1685.38 800.214,1685.36
802.668,1685.33 805.122,1685.31 807.576,1685.28 810.03,1685.25 812.484,1685.22 814.938,1685.19 817.392,1685.17 819.846,1685.13 822.3,1685.1 824.754,1685.07
827.209,1685.04 829.663,1685 832.117,1684.97 834.571,1684.93 837.025,1684.9 839.479,1684.86 841.933,1684.82 844.387,1684.78 846.841,1684.74 849.295,1684.7
851.749,1684.66 854.203,1684.62 856.657,1684.57 859.111,1684.53 861.565,1684.48 864.019,1684.44 866.473,1684.39 868.928,1684.34 871.382,1684.29 873.836,1684.23
876.29,1684.18 878.744,1684.13 881.198,1684.07 883.652,1684.01 886.106,1683.95 888.56,1683.89 891.014,1683.83 893.468,1683.77 895.922,1683.7 898.376,1683.64
900.83,1683.57 903.284,1683.5 905.738,1683.43 908.192,1683.35 910.646,1683.28 913.101,1683.2 915.555,1683.12 918.009,1683.04 920.463,1682.96 922.917,1682.87
925.371,1682.79 927.825,1682.7 930.279,1682.61 932.733,1682.51 935.187,1682.42 937.641,1682.32 940.095,1682.22 942.549,1682.11 945.003,1682.01 947.457,1681.9
949.911,1681.79 952.365,1681.68 954.819,1681.56 957.274,1681.44 959.728,1681.32 962.182,1681.2 964.636,1681.07 967.09,1680.94 969.544,1680.81 971.998,1680.67
974.452,1680.53 976.906,1680.39 979.36,1680.24 981.814,1680.09 984.268,1679.94 986.722,1679.78 989.176,1679.62 991.63,1679.46 994.084,1679.29 996.538,1679.12
998.992,1678.94 1001.45,1678.76 1003.9,1678.58 1006.35,1678.39 1008.81,1678.2 1011.26,1678 1013.72,1677.8 1016.17,1677.59 1018.62,1677.38 1021.08,1677.16
1023.53,1676.94 1025.99,1676.72 1028.44,1676.49 1030.9,1676.25 1033.35,1676.01 1035.8,1675.76 1038.26,1675.51 1040.71,1675.25 1043.17,1674.99 1045.62,1674.72
1048.07,1674.44 1050.53,1674.16 1052.98,1673.88 1055.44,1673.58 1057.89,1673.28 1060.34,1672.98 1062.8,1672.66 1065.25,1672.34 1067.71,1672.02 1070.16,1671.69
1072.61,1671.35 1075.07,1671 1077.52,1670.65 1079.98,1670.28 1082.43,1669.92 1084.88,1669.54 1087.34,1669.16 1089.79,1668.77 1092.25,1668.37 1094.7,1667.96
1097.15,1667.55 1099.61,1667.13 1102.06,1666.7 1104.52,1666.26 1106.97,1665.81 1109.42,1665.35 1111.88,1664.89 1114.33,1664.42 1116.79,1663.93 1119.24,1663.44
1121.7,1662.94 1124.15,1662.43 1126.6,1661.91 1129.06,1661.38 1131.51,1660.85 1133.97,1660.3 1136.42,1659.74 1138.87,1659.18 1141.33,1658.6 1143.78,1658.02
1146.24,1657.42 1148.69,1656.82 1151.14,1656.2 1153.6,1655.58 1156.05,1654.95 1158.51,1654.3 1160.96,1653.65 1163.41,1652.99 1165.87,1652.32 1168.32,1651.64
1170.78,1650.95 1173.23,1650.25 1175.68,1649.54 1178.14,1648.82 1180.59,1648.09 1183.05,1647.36 1185.5,1646.61 1187.95,1645.86 1190.41,1645.1 1192.86,1644.32
1195.32,1643.54 1197.77,1642.76 1200.23,1641.96 1202.68,1641.15 1205.13,1640.34 1207.59,1639.52 1210.04,1638.69 1212.5,1637.85 1214.95,1637.01 1217.4,1636.16
1219.86,1635.3 1222.31,1634.43 1224.77,1633.56 1227.22,1632.68 1229.67,1631.8 1232.13,1630.92 1234.58,1630.02 1237.04,1629.13 1239.49,1628.23 1241.94,1627.33
1244.4,1626.42 1246.85,1625.52 1249.31,1624.61 1251.76,1623.7 1254.21,1622.79 1256.67,1621.87 1259.12,1620.96 1261.58,1620.05 1264.03,1619.14 1266.48,1618.23
1268.94,1617.33 1271.39,1616.42 1273.85,1615.52 1276.3,1614.62 1278.75,1613.73 1281.21,1612.84 1283.66,1611.95 1286.12,1611.07 1288.57,1610.2 1291.03,1609.33
1293.48,1608.47 1295.93,1607.62 1298.39,1606.78 1300.84,1605.95 1303.3,1605.12 1305.75,1604.31 1308.2,1603.5 1310.66,1602.71 1313.11,1601.93 1315.57,1601.16
1318.02,1600.41 1320.47,1599.67 1322.93,1598.94 1325.38,1598.23 1327.84,1597.54 1330.29,1596.86 1332.74,1596.19 1335.2,1595.55 1337.65,1594.92 1340.11,1594.32
1342.56,1593.73 1345.01,1593.16 1347.47,1592.61 1349.92,1592.07 1352.38,1591.56 1354.83,1591.07 1357.28,1590.6 1359.74,1590.16 1362.19,1589.73 1364.65,1589.33
1367.1,1588.94 1369.55,1588.58 1372.01,1588.25 1374.46,1587.93 1376.92,1587.64 1379.37,1587.37 1381.83,1587.13 1384.28,1586.91 1386.73,1586.71 1389.19,1586.54
1391.64,1586.39 1394.1,1586.26 1396.55,1586.16 1399,1586.08 1401.46,1586.02 1403.91,1585.99 1406.37,1585.98 1408.82,1586 1411.27,1586.04 1413.73,1586.1
1416.18,1586.19 1418.64,1586.3 1421.09,1586.43 1423.54,1586.59 1426,1586.77 1428.45,1586.97 1430.91,1587.19 1433.36,1587.44 1435.81,1587.7 1438.27,1587.99
1440.72,1588.3 1443.18,1588.63 1445.63,1588.98 1448.08,1589.35 1450.54,1589.74 1452.99,1590.15 1455.45,1590.58 1457.9,1591.02 1460.35,1591.49 1462.81,1591.97
1465.26,1592.47 1467.72,1592.98 1470.17,1593.51 1472.63,1594.05 1475.08,1594.61 1477.53,1595.18 1479.99,1595.77 1482.44,1596.37 1484.9,1596.98 1487.35,1597.6
1489.8,1598.23 1492.26,1598.88 1494.71,1599.53 1497.17,1600.2 1499.62,1600.88 1502.07,1601.56 1504.53,1602.25 1506.98,1602.95 1509.44,1603.66 1511.89,1604.38
1514.34,1605.1 1516.8,1605.83 1519.25,1606.57 1521.71,1607.31 1524.16,1608.05 1526.61,1608.8 1529.07,1609.56 1531.52,1610.31 1533.98,1611.07 1536.43,1611.84
1538.88,1612.6 1541.34,1613.37 1543.79,1614.14 1546.25,1614.92 1548.7,1615.69 1551.16,1616.46 1553.61,1617.24 1556.06,1618.01 1558.52,1618.79 1560.97,1619.56
1563.43,1620.33 1565.88,1621.1 1568.33,1621.87 1570.79,1622.64 1573.24,1623.41 1575.7,1624.17 1578.15,1624.94 1580.6,1625.7 1583.06,1626.45 1585.51,1627.21
1587.97,1627.96 1590.42,1628.7 1592.87,1629.44 1595.33,1630.18 1597.78,1630.92 1600.24,1631.65 1602.69,1632.37 1605.14,1633.09 1607.6,1633.81 1610.05,1634.52
1612.51,1635.23 1614.96,1635.93 1617.41,1636.62 1619.87,1637.31 1622.32,1638 1624.78,1638.67 1627.23,1639.35 1629.68,1640.01 1632.14,1640.67 1634.59,1641.33
1637.05,1641.97 1639.5,1642.62 1641.96,1643.25 1644.41,1643.88 1646.86,1644.51 1649.32,1645.13 1651.77,1645.74 1654.23,1646.34 1656.68,1646.94 1659.13,1647.53
1661.59,1648.12 1664.04,1648.7 1666.5,1649.27 1668.95,1649.84 1671.4,1650.4 1673.86,1650.96 1676.31,1651.5 1678.77,1652.04 1681.22,1652.58 1683.67,1653.11
1686.13,1653.63 1688.58,1654.15 1691.04,1654.65 1693.49,1655.16 1695.94,1655.65 1698.4,1656.14 1700.85,1656.63 1703.31,1657.1 1705.76,1657.58 1708.21,1658.04
1710.67,1658.5 1713.12,1658.95 1715.58,1659.4 1718.03,1659.84 1720.48,1660.27 1722.94,1660.7 1725.39,1661.12 1727.85,1661.53 1730.3,1661.94 1732.76,1662.35
1735.21,1662.74 1737.66,1663.14 1740.12,1663.52 1742.57,1663.9 1745.03,1664.28 1747.48,1664.65 1749.93,1665.01 1752.39,1665.37 1754.84,1665.72 1757.3,1666.07
1759.75,1666.42 1762.2,1666.75 1764.66,1667.09 1767.11,1667.42 1769.57,1667.74 1772.02,1668.06 1774.47,1668.37 1776.93,1668.68 1779.38,1668.98 1781.84,1669.28
1784.29,1669.58 1786.74,1669.87 1789.2,1670.15 1791.65,1670.43 1794.11,1670.71 1796.56,1670.98 1799.01,1671.25 1801.47,1671.51 1803.92,1671.77 1806.38,1672.03
1808.83,1672.28 1811.28,1672.53 1813.74,1672.77 1816.19,1673.01 1818.65,1673.24 1821.1,1673.47 1823.56,1673.7 1826.01,1673.92 1828.46,1674.14 1830.92,1674.36
1833.37,1674.57 1835.83,1674.78 1838.28,1674.98 1840.73,1675.18 1843.19,1675.38 1845.64,1675.57 1848.1,1675.77 1850.55,1675.95 1853,1676.14 1855.46,1676.32
1857.91,1676.5 1860.37,1676.67 1862.82,1676.84 1865.27,1677.01 1867.73,1677.18 1870.18,1677.34 1872.64,1677.5 1875.09,1677.66 1877.54,1677.81 1880,1677.97
1882.45,1678.12 1884.91,1678.26 1887.36,1678.41 1889.81,1678.55 1892.27,1678.69 1894.72,1678.83 1897.18,1678.96 1899.63,1679.09 1902.09,1679.22 1904.54,1679.35
1906.99,1679.48 1909.45,1679.6 1911.9,1679.73 1914.36,1679.85 1916.81,1679.96 1919.26,1680.08 1921.72,1680.19 1924.17,1680.31 1926.63,1680.42 1929.08,1680.52
1931.53,1680.63 1933.99,1680.73 1936.44,1680.84 1938.9,1680.94 1941.35,1681.04 1943.8,1681.13 1946.26,1681.23 1948.71,1681.32 1951.17,1681.41 1953.62,1681.5
1956.07,1681.59 1958.53,1681.68 1960.98,1681.77 1963.44,1681.85 1965.89,1681.93 1968.34,1682.01 1970.8,1682.09 1973.25,1682.17 1975.71,1682.25 1978.16,1682.32
1980.61,1682.4 1983.07,1682.47 1985.52,1682.54 1987.98,1682.61 1990.43,1682.68 1992.89,1682.75 1995.34,1682.81 1997.79,1682.88 2000.25,1682.94 2002.7,1683
2005.16,1683.07 2007.61,1683.13 2010.06,1683.19 2012.52,1683.24 2014.97,1683.3 2017.43,1683.36 2019.88,1683.41 2022.33,1683.47 2024.79,1683.52 2027.24,1683.57
2029.7,1683.62 2032.15,1683.67 2034.6,1683.72 2037.06,1683.77 2039.51,1683.82 2041.97,1683.87 2044.42,1683.91 2046.87,1683.96 2049.33,1684 2051.78,1684.05
2054.24,1684.09 2056.69,1684.13 2059.14,1684.17 2061.6,1684.21 2064.05,1684.25 2066.51,1684.29 2068.96,1684.33 2071.41,1684.37 2073.87,1684.41 2076.32,1684.44
2078.78,1684.48 2081.23,1684.51 2083.69,1684.55 2086.14,1684.58 2088.59,1684.61 2091.05,1684.65 2093.5,1684.68 2095.96,1684.71 2098.41,1684.74 2100.86,1684.77
2103.32,1684.8 2105.77,1684.83 2108.23,1684.86 2110.68,1684.89 2113.13,1684.91 2115.59,1684.94 2118.04,1684.97 2120.5,1684.99 2122.95,1685.02 2125.4,1685.04
2127.86,1685.07 2130.31,1685.09 2132.77,1685.12 2135.22,1685.14 2137.67,1685.16 2140.13,1685.18 2142.58,1685.21 2145.04,1685.23 2147.49,1685.25 2149.94,1685.27
2152.4,1685.29 2154.85,1685.31 2157.31,1685.33 2159.76,1685.35 2162.21,1685.37 2164.67,1685.39 2167.12,1685.4 2169.58,1685.42 2172.03,1685.44 2174.49,1685.46
2176.94,1685.47 2179.39,1685.49 2181.85,1685.51 2184.3,1685.52 2186.76,1685.54 2189.21,1685.55 2191.66,1685.57 2194.12,1685.58 2196.57,1685.6 2199.03,1685.61
2201.48,1685.63 2203.93,1685.64 2206.39,1685.66 2208.84,1685.67 2211.3,1685.68 2213.75,1685.69 2216.2,1685.71 2218.66,1685.72 2221.11,1685.73 2223.57,1685.74
2226.02,1685.76 2228.47,1685.77 2230.93,1685.78 2233.38,1685.79 2235.84,1685.8 2238.29,1685.81 2240.74,1685.82 2243.2,1685.83 2245.65,1685.84 2248.11,1685.85
2250.56,1685.86 2253.02,1685.87 2255.47,1685.88 2257.92,1685.89 2260.38,1685.9 2262.83,1685.91 2265.29,1685.92 2267.74,1685.92 2270.19,1685.93 2272.65,1685.94
2275.1,1685.95 2277.56,1685.96 2280.01,1685.96 2282.46,1685.97 2284.92,1685.98 2287.37,1685.99 2289.83,1685.99 2292.28,1686 2294.73,1686.01 2297.19,1686.01
2299.64,1686.02 2302.1,1686.03 2304.55,1686.03 2307,1686.04 2309.46,1686.05 2311.91,1686.05 2314.37,1686.06 2316.82,1686.06 2319.27,1686.07 2321.73,1686.08
2324.18,1686.08 2326.64,1686.09 2329.09,1686.09 2331.54,1686.1 2334,1686.1 2336.45,1686.11 2338.91,1686.11 2341.36,1686.12 2343.82,1686.12 2346.27,1686.13
2348.72,1686.13 2351.18,1686.14 2353.63,1686.14 2356.09,1686.15 2358.54,1686.15 2360.99,1686.15 2363.45,1686.16 2365.9,1686.16 2368.36,1686.17 2370.81,1686.17
2373.26,1686.17 2375.72,1686.18 2378.17,1686.18 2380.63,1686.18 2383.08,1686.19 2385.53,1686.19 2387.99,1686.19 2390.44,1686.2 2392.9,1686.2 2395.35,1686.2
2397.8,1686.21 2400.26,1686.21 2402.71,1686.21 2405.17,1686.22 2407.62,1686.22 2410.07,1686.22 2412.53,1686.23 2414.98,1686.23 2417.44,1686.23 2419.89,1686.23
2422.34,1686.24 2424.8,1686.24 2427.25,1686.24 2429.71,1686.24 2432.16,1686.25 2434.62,1686.25 2437.07,1686.25 2439.52,1686.25 2441.98,1686.26 2444.43,1686.26
2446.89,1686.26 2449.34,1686.26 2451.79,1686.26 2454.25,1686.27 2456.7,1686.27 2459.16,1686.27 2461.61,1686.27 2464.06,1686.27 2466.52,1686.28 2468.97,1686.28
2471.43,1686.28 2473.88,1686.28 2476.33,1686.28 2478.79,1686.29 2481.24,1686.29 2483.7,1686.29 2486.15,1686.29 2488.6,1686.29 2491.06,1686.29 2493.51,1686.29
2495.97,1686.3 2498.42,1686.3 2500.87,1686.3 2503.33,1686.3 2505.78,1686.3 2508.24,1686.3 2510.69,1686.3 2513.14,1686.31 2515.6,1686.31 2518.05,1686.31
2520.51,1686.31 2522.96,1686.31 2525.42,1686.31 2527.87,1686.31 2530.32,1686.31 2532.78,1686.32 2535.23,1686.32 2537.69,1686.32 2540.14,1686.32 2542.59,1686.32
2545.05,1686.32 2547.5,1686.32 2549.96,1686.32 2552.41,1686.32 2554.86,1686.32 2557.32,1686.33 2559.77,1686.33 2562.23,1686.33 2564.68,1686.33 2567.13,1686.33
2569.59,1686.33 2572.04,1686.33 2574.5,1686.33 2576.95,1686.33 2579.4,1686.33 2581.86,1686.33 2584.31,1686.33 2586.77,1686.34 2589.22,1686.34 2591.67,1686.34
2594.13,1686.34 2596.58,1686.34 2599.04,1686.34 2601.49,1686.34 2603.95,1686.34 2606.4,1686.34 2608.85,1686.34 2611.31,1686.34 2613.76,1686.34 2616.22,1686.34
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"markdown": "# Euler's method\n\n\n\nThis section uses these add-on packages:\n\n``` {.julia .cell-code}\nusing CalculusWithJulia\nusing Plots\nusing SymPy\nusing Roots\n```\n\n\n\n\n---\n\n\nThe following section takes up the task of numerically approximating solutions to differential equations. `Julia` has a huge set of state-of-the-art tools for this task starting with the [DifferentialEquations](https://github.com/SciML/DifferentialEquations.jl) package. We don't use that package in this section, focusing on simpler methods and implementations for pedagogical purposes, but any further exploration should utilize the tools provided therein. A brief introduction to the package follows in an upcoming [section](./differential_equations.html).\n\n\n---\n\nConsider the differential equation:\n\n\n\n$$\ny'(x) = y(x) \\cdot x, \\quad y(1)=1,\n$$\n\n\nwhich can be solved with `SymPy`:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\n@syms x, y, u()\nD = Differential(x)\nx0, y0 = 1, 1\nF(y,x) = y*x\n\ndsolve(D(u)(x) - F(u(x), x))\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```{=html}\n<span class=\"math-left-align\" style=\"padding-left: 4px; width:0; float:left;\"> \n\\[\nu{\\left(x \\right)} = C_{1} e^{\\frac{x^{2}}{2}}\n\\]\n</span>\n```\n:::\n:::\n\n\nWith the given initial condition, the solution becomes:\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nout = dsolve(D(u)(x) - F(u(x),x), u(x), ics=Dict(u(x0) => y0))\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```{=html}\n<span class=\"math-left-align\" style=\"padding-left: 4px; width:0; float:left;\"> \n\\[\nu{\\left(x \\right)} = \\frac{e^{\\frac{x^{2}}{2}}}{e^{\\frac{1}{2}}}\n\\]\n</span>\n```\n:::\n:::\n\n\nPlotting this solution over the slope field\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\np = plot(legend=false)\nvectorfieldplot!((x,y) -> [1, F(x,y)], xlims=(0, 2.5), ylims=(0, 10))\nplot!(rhs(out), linewidth=5)\n```\n\n::: {.cell-output .cell-output-display execution_count=7}\n![](euler_files/figure-html/cell-7-output-1.svg){}\n:::\n:::\n\n\nwe see that the vectors that are drawn seem to be tangent to the graph of the solution. This is no coincidence, the tangent lines to integral curves are in the direction of the slope field.\n\n\nWhat if the graph of the solution were not there, could we use this fact to *approximately* reconstruct the solution?\n\n\nThat is, if we stitched together pieces of the slope field, would we get a curve that was close to the actual answer?\n\n::: {.cell cache='true' hold='true' execution_count=7}\n\n::: {.cell-output .cell-output-display execution_count=8}\n```{=html}\n<div class=\"d-flex justify-content-center\"> <figure> <img src=\"data:image/gif;base64,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 truncated
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"markdown": "# ODEs\n\n\n\nThis section uses these add-on packages:\n\n``` {.julia .cell-code}\nusing CalculusWithJulia\nusing Plots\nusing SymPy\n```\n\n\n\n\n---\n\n\nSome relationships are easiest to describe in terms of rates or derivatives. For example:\n\n\n * Knowing the speed of a car and how long it has been driving can summarize the car's location.\n * One of Newton's famous laws, $F=ma$, describes the force on an object of mass $m$ in terms of the acceleration. The acceleration is the derivative of velocity, which in turn is the derivative of position. So if we know the rates of change of $v(t)$ or $x(t)$, we can differentiate to find $F$.\n * Newton's law of [cooling](http://tinyurl.com/z4lmetp). This describes the temperature change in an object due to a difference in temperature with the object's surroundings. The formula being, $T'(t) = -r \\left(T(t) - T_a \\right)$, where $T(t)$ is temperature at time $t$ and $T_a$ the ambient temperature.\n * [Hooke's law](http://tinyurl.com/kbz7r8l) relates force on an object to the position on the object, through $F = k x$. This is appropriate for many systems involving springs. Combined with Newton's law $F=ma$, this leads to an equation that $x$ must satisfy: $m x''(t) = k x(t)$.\n\n\n## Motion with constant acceleration\n\n\nLet's consider the case of constant acceleration. This describes how nearby objects fall to earth, as the force due to gravity is assumed to be a constant, so the acceleration is the constant force divided by the constant mass.\n\n\nWith constant acceleration, what is the velocity?\n\n\nAs mentioned, we have $dv/dt = a$ for any velocity function $v(t)$, but in this case, the right hand side is assumed to be constant. How does this restrict the possible functions, $v(t)$, that the velocity can be?\n\n\nHere we can integrate to find that any answer must look like the following for some constant of integration:\n\n\n\n$$\nv(t) = \\int \\frac{dv}{dt} dt = \\int a dt = at + C.\n$$\n\n\nIf we are given the velocity at a fixed time, say $v(t_0) = v_0$, then we can use the definite integral to get:\n\n\n\n$$\nv(t) - v(t_0) = \\int_{t_0}^t a dt = at - a t_0.\n$$\n\n\nSolving, gives:\n\n\n\n$$\nv(t) = v_0 + a (t - t_0).\n$$\n\n\nThis expresses the velocity at time $t$ in terms of the initial velocity, the constant acceleration and the time duration.\n\n\nA natural question might be, is this the *only* possible answer? There are a few useful ways to think about this.\n\n\nFirst, suppose there were another, say $u(t)$. Then define $w(t)$ to be the difference: $w(t) = v(t) - u(t)$. We would have that $w'(t) = v'(t) - u'(t) = a - a = 0$. But from the mean value theorem, a function whose derivative is *continuously* $0$, will necessarily be a constant. So at most, $v$ and $u$ will differ by a constant, but if both are equal at $t_0$, they will be equal for all $t$.\n\n\nSecond, since the derivative of any solution is a continuous function, it is true by the fundamental theorem of calculus that it *must* satisfy the form for the antiderivative. The initial condition makes the answer unique, as the indeterminate $C$ can take only one value.\n\n\nSummarizing, we have\n\n\n> If $v(t)$ satisfies the equation: $v'(t) = a$, $v(t_0) = v_0,$ then the unique solution will be $v(t) = v_0 + a (t - t_0)$.\n\n\n\nNext, what about position? Here we know that the time derivative of position yields the velocity, so we should have that the unknown position function satisfies this equation and initial condition:\n\n\n\n$$\nx'(t) = v(t) = v_0 + a (t - t_0), \\quad x(t_0) = x_0.\n$$\n\n\nAgain, we can integrate to get an answer for any value $t$:\n\n\n\n$$\n\\begin{align*}\nx(t) - x(t_0) &= \\int_{t_0}^t \\frac{dv}{dt} dt \\\\\n&= (v_0t + \\frac{1}{2}a t^2 - at_0 t) |_{t_0}^t \\\\\n&= (v_0 - at_0)(t - t_0) + \\frac{1}{2} a (t^2 - t_0^2).\n\\end{align*}\n$$\n\n\nThere are three constants: the initial value for the independent variable, $t_0$, and the two initial values for the velocity and position, $v_0, x_0$. Assuming $t_0 = 0$, we can simplify the above to get a formula familiar from introductory physics:\n\n\n\n$$\nx(t) = x_0 + v_0 t + \\frac{1}{2} at^2.\n$$\n\n\nAgain, the mean value theorem can show that with the initial value specified this is the only possible solution.\n\n\n## First-order initial-value problems\n\n\nThe two problems just looked at can be summarized by the following. We are looking for solutions to an equation of the form (taking $y$ and $x$ as the variables, in place of $x$ and $t$):\n\n\n\n$$\ny'(x) = f(x), \\quad y(x_0) = y_0.\n$$\n\n\nThis is called an *ordinary differential equation* (ODE), as it is an equation involving the ordinary derivative of an unknown function, $y$.\n\n\nThis is called a first-order, ordinary differential equation, as there is only the first derivative involved.\n\n\nThis is called an initial-value problem, as the value at the initial point $x_0$ is specified as part of the problem.\n\n\n#### Line truncated
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"markdown": "# The problem-algorithm-solve interface\n\n\n\nThis section uses these add-on packages:\n\n``` {.julia .cell-code}\nusing Plots\nusing MonteCarloMeasurements\n```\n\n\n\n\n---\n\n\nThe [DifferentialEquations.jl](https://github.com/SciML) package is an entry point to a suite of `Julia` packages for numerically solving differential equations in `Julia` and other languages. A common interface is implemented that flexibly adjusts to the many different problems and algorithms covered by this suite of packages. In this section, we review a very informative [post](https://discourse.julialang.org/t/function-depending-on-the-global-variable-inside-module/64322/10) by discourse user `@genkuroki` which very nicely demonstrates the usefulness of the problem-algorithm-solve approach used with `DifferentialEquations.jl`. We slightly modify the presentation below for our needs, but suggest a perusal of the original post.\n\n\n##### Example: FreeFall\n\n\nThe motion of an object under a uniform gravitational field is of interest.\n\n\nThe parameters that govern the equation of motions are the gravitational constant, `g`; the initial height, `y0`; and the initial velocity, `v0`. The time span for which a solution is sought is `tspan`.\n\n\nA problem consists of these parameters. Typical `Julia` usage would be to create a structure to hold the parameters, which may be done as follows:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nstruct Problem{G, Y0, V0, TS}\n g::G\n y0::Y0\n v0::V0\n tspan::TS\nend\n\nProblem(;g=9.80665, y0=0.0, v0=30.0, tspan=(0.0,8.0)) = Problem(g, y0, v0, tspan)\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\nProblem\n```\n:::\n:::\n\n\nThe above creates a type, `Problem`, *and* a default constructor with default values. (The original uses a more sophisticated setup that allows the two things above to be combined.)\n\n\nJust calling `Problem()` will create a problem suitable for the earth, passing different values for `g` would be possible for other planets.\n\n\nTo solve differential equations there are many different possible algorithms. Here is the construction of two types to indicate two algorithms:\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nstruct EulerMethod{T}\n dt::T\nend\nEulerMethod(; dt=0.1) = EulerMethod(dt)\n\nstruct ExactFormula{T}\n dt::T\nend\nExactFormula(; dt=0.1) = ExactFormula(dt)\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\nExactFormula\n```\n:::\n:::\n\n\nThe above just specifies a type for dispatch –- the directions indicating what code to use to solve the problem. As seen, each specifies a size for a time step with default of `0.1`.\n\n\nA type for solutions is useful for different `show` methods or other methods. One can be created through:\n\n``` {.julia .cell-code}\nstruct Solution{Y, V, T, P<:Problem, A}\n y::Y\n v::V\n t::T\n prob::P\n alg::A\nend\n```\n\n\nThe different algorithms then can be implemented as part of a generic `solve` function. Following the post we have:\n\n::: {.cell execution_count=7}\n``` {.julia .cell-code}\nsolve(prob::Problem) = solve(prob, default_algorithm(prob))\ndefault_algorithm(prob::Problem) = EulerMethod()\n\nfunction solve(prob::Problem, alg::ExactFormula)\n g, y0, v0, tspan = prob.g, prob.y0, prob.v0, prob.tspan\n dt = alg.dt\n t0, t1 = tspan\n t = range(t0, t1 + dt/2; step = dt)\n\n y(t) = y0 + v0*(t - t0) - g*(t - t0)^2/2\n v(t) = v0 - g*(t - t0)\n\n Solution(y.(t), v.(t), t, prob, alg)\nend\n\nfunction solve(prob::Problem, alg::EulerMethod)\n g, y0, v0, tspan = prob.g, prob.y0, prob.v0, prob.tspan\n dt = alg.dt\n t0, t1 = tspan\n t = range(t0, t1 + dt/2; step = dt)\n\n n = length(t)\n y = Vector{typeof(y0)}(undef, n)\n v = Vector{typeof(v0)}(undef, n)\n y[1] = y0\n v[1] = v0\n\n for i in 1:n-1\n v[i+1] = v[i] - g*dt # F*h step of Euler\n y[i+1] = y[i] + v[i]*dt # F*h step of Euler\n end\n\n Solution(y, v, t, prob, alg)\nend\n```\n\n::: {.cell-output .cell-output-display execution_count=8}\n```\nsolve (generic function with 3 methods)\n```\n:::\n:::\n\n\nThe post has a more elegant means to unpack the parameters from the structures, but for each of the above, the parameters are unpacked, and then the corresponding algorithm employed. As of version `v1.7` of `Julia`, the syntax `(;g,y0,v0,tspan) = prob` could also be employed.\n\n\nThe exact formulas, `y(t) = y0 + v0*(t - t0) - g*(t - t0)^2/2` and `v(t) = v0 - g*(t - t0)`, follow from well-known physics formulas. Each answer is wrapped in a `Solution` type so that the answers found can be easily extracted in a uniform manner.\n\n\nFor example, plots of each can be obtained through:\n\n::: {.cell execution_count=8}\n``` {.julia .cell-code}\nearth = Problem()\nsol_euler = solve(earth)\nsol_exact = solve(earth, ExactFormula())\n\nplot(sol_euler.t, sol_euler.y;\n label=\"Euler's method (dtLine truncated
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"markdown": "# The SciML suite of packages\n\n\n\nThe `Julia` ecosystem advances rapidly. For much of it, the driving force is the [SciML](https://github.com/SciML) organization (Scientific Machine Learning).\n\n\nIn this section we describe some packages provided by this organization that could be used as alternatives to the ones utilized in these notes. Members of this organization created many packages for solving different types of differential equations, and have branched out from there. Many newer efforts of this organization have been to write uniform interfaces to other packages in the ecosystem, some of which are discussed below. We don't discuss this key promise of SciML: \"Performance is considered a priority, and performance issues are considered bugs,\" as we don't pursue features like in-place modification, sparsity, etc. Interested readers should consult the relevant packages documentation.\n\n\nThe basic structure to use these packages is the \"problem-algorithm-solve\" interface described in [The problem-algorithm-solve interface](../ODEs/solve.html) sectino. We also discussed this interface a bit in [ODEs](../ODEs/differential_equations.html).\n\n\n:::{.callout-note}\n## Note\nThese packages are in a process of rapid development and change to them is expected.\n:::\n\n\n## Symbolic math (`Symbolics`)\n\n\nThe `Symbolics`, `SymbolicUtils`, and `ModelingToolkit` packages are provided by this organization. These can be viewed as an alternative to `SymPy`, which is used throughout this set of notes. See the section on [Symbolics](./symbolics.html) for some additional details, the package [documentation](https://symbolics.juliasymbolics.org/stable/), or the documentation for [SymbolicsUtils](https://github.com/JuliaSymbolics/SymbolicUtils.jl).\n\n\n## Solving equations\n\n\nSolving one or more equations (simultaneously) is different in the linear case (where solutions are readily found – though performance can distinguish approaches) and the nonlinear case – where for most situations, numeric approaches are required.\n\n\n### `LinearSolve`\n\n\nThe `LinearSolve` package aims to generalize the solving of linear equations. For many cases these are simply represented as matrix equations of the form `Ax=b`, from which `Julia` (borrowing from MATLAB) offers the interface `A \\ b` to yield `x`. There are scenarios that don't naturally fit this structure and perhaps problems where different tolerances need to be specified, and the `LinearSolve` package aims to provide a common interface to handle these scenarios. As this set of notes doesn't bump into such, this package is not described here. In the symbolic case, the `Symbolics.solve_for` function was described in [Symbolics](./symbolics.html).\n\n\n### `NonlinearSolve`\n\n\nThe `NonlinearSolve` package can be seen as an alternative to the use of the `Roots` package in this set of notes. The package presents itself as \"Fast implementations of root finding algorithms in Julia that satisfy the SciML common interface.\"\n\n\nThe package is loaded through the following command:\n\n``` {.julia .cell-code}\nusing NonlinearSolve\n```\n\n\nUnlike `Roots`, the package handles problems beyond the univariate case, as such, the simplest problems have a little extra setup required.\n\n\nFor example, suppose we want to use this package to solve for zeros of $f(x) = x^5 - x - 1$. 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"markdown": "# Calculus plots with Makie\n\n\n\nThe [Makie.jl webpage](https://github.com/JuliaPlots/Makie.jl) says\n\n\n> From the Japanese word Maki-e, which is a technique to sprinkle lacquer with gold and silver powder. Data is basically the gold and silver of our age, so let's spread it out beautifully on the screen!\n\n\n\n`Makie` itself is a metapackage for a rich ecosystem. We show how to use the interface provided by the `GLMakie` backend to produce the familiar graphics of calculus.\n\n\n:::{.callout-note}\n## Examples and tutorials\n`Makie` is a sophisticated plotting package, and capable of an enormous range of plots (cf. [examples](https://makie.juliaplots.org/stable/examples/plotting_functions/).) `Makie` also has numerous [tutorials](https://makie.juliaplots.org/stable/tutorials/) to learn from. These are far more extensive that what is described herein, as this section focuses just on the graphics from calculus.\n\n:::\n\n## Figures\n\n\nMakie draws graphics onto a canvas termed a \"scene\" in the Makie documentation. A scene is an implementation detail, the basic (non-mutating) plotting commands described below return a `FigureAxisPlot` object, a compound object that combines a figure, an axes, and a plot object. The `show` method for these objects display the figure.\n\n\nFor `Makie` there are the `GLMakie`, `WGLMakie`, and `CairoMakie` backends for different types of canvases. In the following, we have used `GLMakie`. `WGLMakie` is useful for incorporating `Makie` plots into web-based technologies.\n\n\nWe begin by loading the main package and the `norm` function from the standard `LinearAlgebra` package:\n\n``` {.julia .cell-code}\nusing GLMakie\nimport LinearAlgebra: norm\n```\n\n\nThe `Makie` developers have workarounds for the delayed time to first plot, but without utilizing these the time to load the package is lengthy.\n\n\n## Points (`scatter`)\n\n\nThe task of plotting the points, say $(1,2)$, $(2,3)$, $(3,2)$ can be done different ways. Most plotting packages, and `Makie` is no exception, allow the following: form vectors of the $x$ and $y$ values then plot those with `scatter`:\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nxs = [1,2,3]\nys = [2,3,2]\nscatter(xs, ys)\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n![](makie_plotting_files/figure-html/cell-4-output-1.png){}\n:::\n:::\n\n\nThe `scatter` function creates and returns an object, which when displayed shows the plot.\n\n\n### `Point2`, `Point3`\n\n\nWhen learning about points on the Cartesian plane, a \"`t`\"-chart is often produced:\n\n\n```\nx | y\n-----\n1 | 2\n2 | 3\n3 | 2\n```\n\nThe `scatter` usage above used the columns. The rows are associated with the points, and these too can be used to produce the same graphic. Rather than make vectors of $x$ and $y$ (and optionally $z$) coordinates, it is more idiomatic to create a vector of \"points.\" `Makie` utilizes a `Point` type to store a 2 or 3 dimensional point. The `Point2` and `Point3` constructors will be utilized.\n\n\n`Makie` uses a GPU, when present, to accelerate the graphic rendering. GPUs employ 32-bit numbers. Julia uses an `f0` to indicate 32-bit floating points. Hence the alternate types `Point2f0` to store 2D points as 32-bit numbers and `Points3f0` to store 3D points as 32-bit numbers are seen in the documentation for Makie.\n\n\nWe can plot a vector of points in as direct manner as vectors of their coordinates:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\npts = [Point2(1,2), Point2(2,3), Point2(3,2)]\nscatter(pts)\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n![](makie_plotting_files/figure-html/cell-5-output-1.png){}\n:::\n:::\n\n\nA typical usage is to generate points from some vector-valued function. Say we have a parameterized function `r` taking $R$ into $R^2$ defined by:\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nr(t) = [sin(t), cos(t)]\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\nr (generic function with 1 method)\n```\n:::\n:::\n\n\nThen broadcasting values gives a vector of vectors, each identified with a point:\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\nts = [1,2,3]\nr.(ts)\n```\n\n::: {.cell-output .cell-output-display execution_count=7}\n```\n3-element Vector{Vector{Float64}}:\n [0.8414709848078965, 0.5403023058681398]\n [0.9092974268256817, -0.4161468365471424]\n [0.1411200080598672, -0.9899924966004454]\n```\n:::\n:::\n\n\nWe can broadcast `Point2` over this to create a vector of `Point` objects:\n\n::: {.cell execution_count=7}\n``` {.julia .cell-code}\npts = Point2.(r.(ts))\n```\n\n::: {.cell-output .cell-output-display execution_count=8}\n```\n3-element Vector{Point2{Float64}}:\n [0.8414709848078965, 0.5403023058681398]\n [0.9092974268256817, -0.4161468365471424]\n [0.1411200080598672, -0.9899924966004454]\n```\n:::\n:::\n\n\nThese then can be plotted directly:\n\n::: {.cell execution_count=8}\n``` {.juliLine truncated
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"markdown": "---\nformat:\n html:\n header-includes: |\n <script src=\"https://cdn.plot.ly/plotly-2.11.0.min.js\"></script>\n---\n\n# JavaScript based plotting libraries\n\n\n\n:::{.callout-note}\n## Plotly and Quarto\nThere are some oddities working with `Plotly`, `Julia`, and Quarto that require some hand editing of an HTML file. If the images are not shown below, there was an oversight. A reported issue would be welcome.\n\n:::\n\nThis section uses this add-on package:\n\n``` {.julia .cell-code}\nusing PlotlyLight\n```\n\n\n\n\nTo avoid a dependence on the `CalculusWithJulia` package, we load two utility packages:\n\n``` {.julia .cell-code}\nusing PlotUtils\nusing SplitApplyCombine\n```\n\n\n---\n\n\n`Julia` has different interfaces to a few JavaScript plotting libraries, notably the [vega](https://vega.github.io/vega/) and [vega-lite](https://vega.github.io/vega-lite/) through the [VegaLite.jl](https://github.com/queryverse/VegaLite.jl) package, and [plotly](https://plotly.com/javascript/) through several interfaces: `Plots.jl`, `PlotlyJS.jl`, and `PlotlyLight.jl`. These all make web-based graphics, for display through a web browser.\n\n\nThe `Plots.jl` interface is a backend for the familiar `Plots` package, making the calling syntax familiar, as is used throughout these notes. The `plotly()` command, from `Plots`, switches to this backend.\n\n\nThe `PlotlyJS.jl` interface offers direct translation from `Julia` structures to the underlying `JSON` structures needed by plotly, and has mechanisms to call back into `Julia` from `JavaScript`. This allows complicated interfaces to be produced.\n\n\nHere we discuss `PlotlyLight` which conveniently provides the translation from `Julia` structures to the `JSON` structures needed in a light-weight package, which plots quickly, without the delays due to compilation of the more complicated interfaces. Minor modifications would be needed to adjust the examples to work with `PlotlyJS` or `PlotlyBase`. The documentation for the `JavaScript` [library](https://plotly.com/javascript/) provides numerous examples which can easily be translated. The [one-page-reference](https://plotly.com/javascript/reference/) gives specific details, and is quoted from below, at times.\n\n\nThis discussion covers the basic of graphing for calculus purposes. It does not cover, for example, the faceting common in statistical usages, or the chart types common in business and statistics uses. The `plotly` library is much more extensive than what is reviewed below.\n\n\n## Julia dictionaries to JSON\n\n\n`PlotlyLight` uses the `JavaScript` interface for the `plotly` libraries. Unlike more developed interfaces, like the one for `Python`, `PlotlyLight` only manages the translation from `Julia` structures to `JavaScript` structures and the display of the results.\n\n\nThe key to translation is the mapping for `Julia`'s dictionaries to the nested `JSON` structures needed by the `JavaScript` library.\n\n\nFor example, an introductory [example](https://plotly.com/javascript/line-and-scatter/) for a scatter plot includes this `JSON` structure:\n\n``` {.julia .cell-code}\nvar trace1 = {\n x: [1, 2, 3, 4],\n y: [10, 15, 13, 17],\n mode: 'markers',\n type: 'scatter'\n};\n```\n\n\nThe `{}` create a list, the `[]` an Array (or vector, as it does with `Julia`), the `name:` are keys. The above is simply translated via:\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\nConfig(x = [1,2,3,4],\n y = [10, 15, 13, 17],\n mode = \"markers\",\n type = \"scatter\"\n )\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\nConfig with 4 entries:\n :x => [1, 2, 3, 4]\n :y => [10, 15, 13, 17]\n :mode => \"markers\"\n :type => \"scatter\"\n```\n:::\n:::\n\n\nThe `Config` constructor (from the `EasyConfig` package loaded with `PlotlyLight`) is an interface for a dictionary whose keys are symbols, which are produced by the named arguments passed to `Config`. By nesting `Config` statements, nested `JavaScript` structures can be built up. As well, these can be built on the fly using `.` notation, as in:\n\n::: {.cell hold='true' execution_count=7}\n``` {.julia .cell-code}\ncfg = Config()\ncfg.key1.key2.key3 = \"value\"\ncfg\n```\n\n::: {.cell-output .cell-output-display execution_count=7}\n```\nConfig with 1 entry:\n :key1 => Config(:key2=>Config(:key3=>\"value\"))\n```\n:::\n:::\n\n\nTo produce a figure with `PlotlyLight` then is fairly straightforward: data and, optionally, a layout are created using `Config`, then passed along to the `Plot` command producing a `Plot` object which has `display` methods defined for it. This will be illustrated through the examples.\n\n\n## Scatter plot\n\n\nA basic scatter plot of points $(x,y)$ is created as follows:\n\n::: {.cell hold='true' execution_count=8}\n``` {.julia .cell-code}\nxs = 1:5\nys = rand(5)\ndata = Config(x = xs,\n y = ys,\n type=\"scatter\",\n mode=\"mLine truncated
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"markdown": "# Symbolics.jl\n\n\n\n\nThere are a few options in `Julia` for symbolic math, for example, the `SymPy` package which wraps a Python library. This section describes a collection of native `Julia` packages providing many features of symbolic math.\n\n\n## About\n\n\nThe `Symbolics` package bills itself as a \"fast and modern Computer Algebra System (CAS) for a fast and modern programming language.\" This package relies on the `SymbolicUtils` package and is built upon by the `ModelingToolkit` package, which we don't describe here.\n\n\nWe begin by loading the `Symbolics` package which when loaded re-exports the `SymbolicUtils` package.\n\n``` {.julia .cell-code}\nusing Symbolics\n```\n\n\n## Symbolic variables\n\n\nSymbolic math at its core involves symbolic variables, which essentially defer evaluation until requested. The creation of symbolic variables differs between the two packages discussed here.\n\n\n`SymbolicUtils` creates variables which carry `Julia` type information (e.g. `Int`, `Float64`, ...). This type information carries through operations involving these variables. Symbolic variables can be created with the `@syms` macro. For example:\n\n::: {.cell execution_count=2}\n``` {.julia .cell-code}\n@syms x y::Int f(x::Real)::Real\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n(x, y, f)\n```\n:::\n:::\n\n\nThis creates `x` a symbolic value with symbolic type `Number`, `y` a symbolic variable holding integer values, and `f` a symbolic function of a single real variable outputting a real variable.\n\n\nThe non-exported `symtype` function reveals the underlying type:\n\n::: {.cell execution_count=3}\n``` {.julia .cell-code}\nimport Symbolics.SymbolicUtils: symtype\n\nsymtype(x), symtype(y)\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\n(Number, Int64)\n```\n:::\n:::\n\n\nFor `y`, the symbolic type being real does not imply the type of `y` is a subtype of `Real`:\n\n::: {.cell execution_count=4}\n``` {.julia .cell-code}\nisa(y, Real)\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\nfalse\n```\n:::\n:::\n\n\nWe see that the function `f` when called with `y` would return a value of (symbolic) type `Real`:\n\n::: {.cell execution_count=5}\n``` {.julia .cell-code}\nf(y) |> symtype\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\nReal\n```\n:::\n:::\n\n\nAs the symbolic type of `x` is `Number` – which is not a subtype of `Real` – the following will error:\n\n::: {.cell execution_count=6}\n``` {.julia .cell-code}\nf(x)\n```\n\n::: {.cell-output .cell-output-error}\n```\nLoadError: Tuple{Number} is not a subtype of Tuple{Real}.\n```\n:::\n:::\n\n\n:::{.callout-note}\n## Note\nThe `SymPy` package also has an `@syms` macro to create variables. Though their names agree, they do different things. Using both packages together would require qualifying many shared method names. For `SymbolicUtils`, the `@syms` macro uses `Julia` types to parameterize the variables. In `SymPy` it is possible to specify *assumptions* on the variables, but that is different and not useful for dispatch without some extra effort.\n\n:::\n\nFor `Symbolics`, symbolic variables are created using a wrapper around an underlying `SymbolicUtils` object. This wrapper, `Num`, is a subtype of `Real` (the underlying `SymbolicUtils` object may have symbolic type `Real`, but it won't be a subtype of `Real`.)\n\n\nSymbolic values are created with the `@variables` macro. For example:\n\n::: {.cell execution_count=7}\n``` {.julia .cell-code}\n@variables x y::Int z[1:3]::Int f(..)::Int\n```\n\n::: {.cell-output .cell-output-display execution_count=8}\n```\n4-element Vector{Any}:\n x\n y\n z[1:3]\n f⋆\n```\n:::\n:::\n\n\nThis creates\n\n\n * a symbolic value `x` of `symtype` `Real`\n * a symbolic value `y` of `symtype` `Int`\n * a vector of symbolic values each of `symtype` `Int`\n * a symbolic function `f` returning an object of `symtype` `Int`\n\n\nThe symbolic type reflects that of the underlying object behind the `Num` wrapper:\n\n::: {.cell execution_count=8}\n``` {.julia .cell-code}\ntypeof(x), symtype(x), typeof(Symbolics.value(x))\n```\n\n::: {.cell-output .cell-output-display execution_count=9}\n```\n(Num, Real, SymbolicUtils.Sym{Real, Base.ImmutableDict{DataType, Any}})\n```\n:::\n:::\n\n\n(The `value` method unwraps the `Num` wrapper.)\n\n\n## Symbolic expressions\n\n\nSymbolic expressions are built up from symbolic variables through natural `Julia` idioms. `SymbolicUtils` privileges a few key operations: `Add`, `Mul`, `Pow`, and `Div`. For example:\n\n::: {.cell execution_count=9}\n``` {.julia .cell-code}\n@syms x y\ntypeof(x + y) # `Add`\n```\n\n::: {.cell-output .cell-output-display execution_count=10}\n```\nSymbolicUtils.Add{Number, Int64, Dict{Any, Number}, Nothing}\n```\n:::\n:::\n\n\n::: {.cell execution_count=10}\n``` {.julia .cell-code}\ntypeof(x * y) # `Mul`\n```\n\n::: {.cell-output .cell-output-display executioLine truncated
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