From f969c76e1375639ee7a8e535bb77d54529fa2191 Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Thu, 4 Dec 2025 16:24:54 -0500 Subject: [PATCH 1/6] fix: overflow issues from naive implementation of logistic function. now use tanh --- src/jaco/eos/H2_partition_function.py | 3 ++- src/jaco/models/wind_comparison/cooling.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/jaco/eos/H2_partition_function.py b/src/jaco/eos/H2_partition_function.py index 2847a86..3112fd6 100644 --- a/src/jaco/eos/H2_partition_function.py +++ b/src/jaco/eos/H2_partition_function.py @@ -1,4 +1,5 @@ import sympy as sp +from jaco.math import logistic def H2_energy_over_kbT(T): @@ -9,4 +10,4 @@ def H2_energy_over_kbT(T): p2 = ((0.102728060235312 * sp.log(T) - 2.42078687298443) * sp.log(T) + 19.4951634944834) * sp.log( T ) - 51.5605088374882 - return 1.5 + 1 / (1 + sp.exp(-p1)) + 1 / (1 + sp.exp(-p2)) + return 1.5 + logistic(p1) + logistic(p2) diff --git a/src/jaco/models/wind_comparison/cooling.py b/src/jaco/models/wind_comparison/cooling.py index cb279de..8dd09f8 100644 --- a/src/jaco/models/wind_comparison/cooling.py +++ b/src/jaco/models/wind_comparison/cooling.py @@ -2,6 +2,7 @@ from jaco.symbols import piecewise_powerlaw, T, n_ from jaco.processes import ThermalProcess import sympy as sp +from jaco.math import logistic T_cooling_curve = np.array( [0.99999999e1, 1.0e02, 6.0e03, 1.75e04, 4.0e04, 8.7e04, 2.30e05, 3.6e05, 1.5e06, 3.50e06, 2.6e07, 1.0e12] @@ -44,4 +45,4 @@ lambda_cooling = piecewise_powerlaw(T_cooling_curve, lambda_cooling_curve, T, extrapolate=True) cooling = ThermalProcess(-lambda_cooling * n_("H") ** 2, name="Cooling") -heating = ThermalProcess(2e-26 * n_("H") / (1 + sp.exp(sp.Min((T - 15000) / 1000, 100))), name="Heating") +heating = ThermalProcess(2e-26 * n_("H") * logistic((T - 15000) / 1000), name="Heating") From 38d5f5d8ad3b8a37c0d6c160f8df1acb19c02940 Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Thu, 4 Dec 2025 16:25:18 -0500 Subject: [PATCH 2/6] fix: missing math.py added for prev push --- src/jaco/math.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 src/jaco/math.py diff --git a/src/jaco/math.py b/src/jaco/math.py new file mode 100644 index 0000000..478912f --- /dev/null +++ b/src/jaco/math.py @@ -0,0 +1,13 @@ +"""Special mathematical functions""" + +import sympy as sp + + +def logistic(x): + """Symbolic implementation of the logistic function. + + f(x) = 1/(1+exp(-x)) + + Uses tanh which is better behaved numerically in extreme limits + """ + return 0.5 * (1 + sp.tanh(x / 2)) From 568ba4e80d9b76e9b83b4511b550d3d82cfc8083 Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Thu, 4 Dec 2025 16:41:00 -0500 Subject: [PATCH 3/6] fix: heating function in wind comparison model --- src/jaco/models/wind_comparison/cooling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/jaco/models/wind_comparison/cooling.py b/src/jaco/models/wind_comparison/cooling.py index 8dd09f8..a0f05f4 100644 --- a/src/jaco/models/wind_comparison/cooling.py +++ b/src/jaco/models/wind_comparison/cooling.py @@ -45,4 +45,4 @@ lambda_cooling = piecewise_powerlaw(T_cooling_curve, lambda_cooling_curve, T, extrapolate=True) cooling = ThermalProcess(-lambda_cooling * n_("H") ** 2, name="Cooling") -heating = ThermalProcess(2e-26 * n_("H") * logistic((T - 15000) / 1000), name="Heating") +heating = ThermalProcess(2e-26 * n_("H") * logistic(-(T - 15000) / 1000), name="Heating") From c9119b96b23112b99135bb42c1af4e9065f43ce3 Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Thu, 4 Dec 2025 18:35:17 -0500 Subject: [PATCH 4/6] fix: cie notebook --- examples/CIE.ipynb | 61 +-- experiments/windbubble_model.ipynb | 742 ++++++++++++++++------------- 2 files changed, 437 insertions(+), 366 deletions(-) diff --git a/examples/CIE.ipynb b/examples/CIE.ipynb index 469d310..0a3e7b3 100644 --- a/examples/CIE.ipynb +++ b/examples/CIE.ipynb @@ -134,8 +134,11 @@ "outputs": [ { "data": { + "text/latex": [ + "$\\displaystyle - \\frac{1.55 \\cdot 10^{-26} C_{2} n_{He+} n_{e-}}{T^{0.3647}} - \\frac{1.46719838641439 \\cdot 10^{-26} C_{2} \\sqrt{T} n_{H+} n_{e-}}{\\left(0.00119216696847702 \\sqrt{T} + 1.0\\right)^{1.748} \\left(0.563615123664978 \\sqrt{T} + 1.0\\right)^{0.252}} - \\frac{5.86879354565754 \\cdot 10^{-26} C_{2} \\sqrt{T} n_{He++} n_{e-}}{\\left(0.00119216696847702 \\sqrt{T} + 1.0\\right)^{1.748} \\left(0.563615123664978 \\sqrt{T} + 1.0\\right)^{0.252}} - \\frac{1.2746917300104 \\cdot 10^{-21} \\sqrt{T} n_{H} n_{e-} e^{- \\frac{157809.1}{T}}}{\\frac{\\sqrt{10} \\sqrt{T}}{1000} + 1} - \\frac{9.37661057635428 \\cdot 10^{-22} \\sqrt{T} n_{He} n_{e-} e^{- \\frac{285335.4}{T}}}{\\frac{\\sqrt{10} \\sqrt{T}}{1000} + 1} - \\frac{4.9524176975855 \\cdot 10^{-22} \\sqrt{T} n_{He+} n_{e-} e^{- \\frac{631515}{T}}}{\\frac{\\sqrt{10} \\sqrt{T}}{1000} + 1}$" + ], "text/plain": [ - "['1978ppim.book.....S']" + "-1.55e-26*C_2*n_He+*n_e-/T**0.3647 - 1.46719838641439e-26*C_2*sqrt(T)*n_H+*n_e-/((0.00119216696847702*sqrt(T) + 1.0)**1.748*(0.563615123664978*sqrt(T) + 1.0)**0.252) - 5.86879354565754e-26*C_2*sqrt(T)*n_He++*n_e-/((0.00119216696847702*sqrt(T) + 1.0)**1.748*(0.563615123664978*sqrt(T) + 1.0)**0.252) - 1.2746917300104e-21*sqrt(T)*n_H*n_e-*exp(-157809.1/T)/(sqrt(10)*sqrt(T)/1000 + 1) - 9.37661057635428e-22*sqrt(T)*n_He*n_e-*exp(-285335.4/T)/(sqrt(10)*sqrt(T)/1000 + 1) - 4.9524176975855e-22*sqrt(T)*n_He+*n_e-*exp(-631515/T)/(sqrt(10)*sqrt(T)/1000 + 1)" ] }, "execution_count": 4, @@ -144,9 +147,7 @@ } ], "source": [ - "from jaco.processes import FreeFreeEmission\n", - "system += FreeFreeEmission(\"H+\")\n", - "FreeFreeEmission(\"H+\").bibliography" + "system.heat" ] }, { @@ -206,21 +207,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Undetermined symbols: {C_2, y, T, x_He++, n_Htot, x_H+, x_He+}\n", - "C_2 not specified; assuming C_2=1.0.\n", + "Undetermined symbols: {x_He+, y, n_Htot, x_H+, T, C_2, x_He++}\n", "y not specified; assuming y=0.09254634923706946.\n", - "Free symbols: {x_He+, y, T, x_He++, n_Htot, x_H+, C_2}\n", + "C_2 not specified; assuming C_2=1.0.\n", + "Free symbols: {x_He+, y, n_Htot, x_H+, T, C_2, x_He++}\n", "Known values: ['T', 'n_Htot']\n", - "Assumed values: ['C_2', 'y']\n", - "Equations solved: ['He+', 'He++', 'H+']\n", - "It's solvin time. Solving for {'He+', 'H+', 'He++'} based on input {'n_Htot', 'T'} and assumptions about {'y', 'C_2'}\n", - "num_iter average=35.819149017333984 min=18 max=68\n", - "{'He+': Array([2.8003510e-16, 2.8003862e-16, 2.8003910e-16, ..., 7.6925389e-06,\n", - " 7.6924471e-06, 7.6923561e-06], dtype=float32), 'He++': Array([4.1297267e-17, 4.1297541e-17, 4.1297644e-17, ..., 9.2538655e-02,\n", - " 9.2538655e-02, 9.2538655e-02], dtype=float32), 'H+': Array([2.5647242e-16, 2.5647083e-16, 2.5647025e-16, ..., 9.9999940e-01,\n", - " 9.9999940e-01, 9.9999940e-01], dtype=float32), 'H': Array([1.0000000e+00, 1.0000000e+00, 1.0000000e+00, ..., 5.9604645e-07,\n", - " 5.9604645e-07, 5.9604645e-07], dtype=float32), 'He': Array([9.254635e-02, 9.254635e-02, 9.254635e-02, ..., 7.450581e-09,\n", - " 7.450581e-09, 7.450581e-09], dtype=float32), 'e-': Array([6.1910205e-16, 6.1910453e-16, 6.1910464e-16, ..., 1.1850843e+00,\n", + "Assumed values: ['y', 'C_2']\n", + "Equations solved: ['He++', 'H+', 'He+']\n", + "It's solvin time. Solving for {'He++', 'He+', 'H+'} based on input {'T', 'n_Htot'} and assumptions about {'y', 'C_2'}\n", + "num_iter average=35.86250305175781 min=18 max=68\n", + "{'He+': Array([8.259601e-17, 8.259580e-17, 8.259559e-17, ..., 7.692539e-06,\n", + " 7.692447e-06, 7.692356e-06], dtype=float32), 'H+': Array([4.1381906e-16, 4.1381869e-16, 4.1381975e-16, ..., 9.9999940e-01,\n", + " 9.9999940e-01, 9.9999940e-01], dtype=float32), 'He++': Array([1.4600168e-18, 1.4600135e-18, 1.4600135e-18, ..., 9.2538655e-02,\n", + " 9.2538655e-02, 9.2538655e-02], dtype=float32), 'He': Array([9.254635e-02, 9.254635e-02, 9.254635e-02, ..., 7.450581e-09,\n", + " 7.450581e-09, 7.450581e-09], dtype=float32), 'H': Array([1.0000000e+00, 1.0000000e+00, 1.0000000e+00, ..., 5.9604645e-07,\n", + " 5.9604645e-07, 5.9604645e-07], dtype=float32), 'e-': Array([4.9933508e-16, 4.9933450e-16, 4.9933535e-16, ..., 1.1850843e+00,\n", " 1.1850843e+00, 1.1850843e+00], dtype=float32)}\n" ] } @@ -248,7 +249,7 @@ }, { "data": { - 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", 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" ] @@ -297,11 +298,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "/* Computes the RHS function and Jacobian to solve for [x_He+, x_He++, x_H+]\n", + "/* Computes the RHS function and Jacobian to solve for [x_He+, x_H+, x_He++]\n", "\n", "This code was auto-generated by jaco v0.1.1 and is not intended to be modified or maintained by human beings.\n", "\n", - "INDEX CONVENTION: (0: x_He+) (1: x_He++) (2: x_H+)\n", + "INDEX CONVENTION: (0: x_He+) (1: x_H+) (2: x_He++)\n", "*/\n", "\n", "x0 = sqrt(T); \n", @@ -348,18 +349,18 @@ "x41 = -5.8500000000000005e-11*x0*x15*x28*x29*x4 + x26*x40;\n", "\n", "rhs_result[0] = 2.3800000000000001e-11*x0*x15*x20*x21*x4*x5 + x11 - x19 - x23*x5;\n", - "rhs_result[1] = -x11 + x19;\n", - "rhs_result[2] = 5.8500000000000005e-11*x0*x15*x28*x29*x4*x5 - x27*x_Hplus;\n", + "rhs_result[1] = 5.8500000000000005e-11*x0*x15*x28*x29*x4*x5 - x27*x_Hplus;\n", + "rhs_result[2] = -x11 + x19;\n", "\n", "jac_result[0] = -x22*x6 - x31 - x36 - x37;\n", - "jac_result[1] = 4.7600000000000002e-11*x0*x15*x20*x21*x4 + x10 - 2*x23 - x31 + x38 - x39;\n", - "jac_result[2] = -x35 - x37;\n", - "jac_result[3] = x36;\n", - "jac_result[4] = -x10 - x38 + x39;\n", - "jac_result[5] = x18 - x34;\n", - "jac_result[6] = -x41;\n", - "jac_result[7] = 1.1700000000000001e-10*x0*x15*x28*x29*x4 - 2.8324293174022895e-10*x24*x25*x40;\n", - "jac_result[8] = -x27 - 5.8500000000000005e-11*x29*x30 - x41;\n" + "jac_result[1] = -x35 - x37;\n", + "jac_result[2] = 4.7600000000000002e-11*x0*x15*x20*x21*x4 + x10 - 2*x23 - x31 + x38 - x39;\n", + "jac_result[3] = -x41;\n", + "jac_result[4] = -x27 - 5.8500000000000005e-11*x29*x30 - x41;\n", + "jac_result[5] = 1.1700000000000001e-10*x0*x15*x28*x29*x4 - 2.8324293174022895e-10*x24*x25*x40;\n", + "jac_result[6] = x36;\n", + "jac_result[7] = x18 - x34;\n", + "jac_result[8] = -x10 - x38 + x39;\n" ] } ], diff --git a/experiments/windbubble_model.ipynb b/experiments/windbubble_model.ipynb index ba915e3..f22de50 100644 --- a/experiments/windbubble_model.ipynb +++ b/experiments/windbubble_model.ipynb @@ -66,7 +66,7 @@ "Assumed values: []\n", "Equations solved: ['heat']\n", "It's solvin time. Solving for {'T'} based on input {'x_H', 'n_Htot'} and assumptions about set()\n", - "num_iter average=20.768869400024414 min=1 max=27\n" + "num_iter average=20.768810272216797 min=1 max=27\n" ] } ], @@ -90,7 +90,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -136,7 +136,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Free symbols: {u, T}\n", + "Free symbols: {T, u}\n", "Known values: ['T']\n", "Assumed values: []\n", "Equations solved: ['u']\n", @@ -421,606 +421,706 @@ "name": "stdout", "output_type": "stream", "text": [ - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "0\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=35.073001861572266 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=22.911001205444336 min=1 max=100\n", + "1\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=30.49700164794922 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=17.477001190185547 min=1 max=100\n", + "2\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=29.1510009765625 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=15.155000686645508 min=1 max=100\n", + "3\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=27.559001922607422 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=14.75200080871582 min=1 max=100\n", + "4\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=26.43000030517578 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=14.484001159667969 min=1 max=100\n", + "5\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=23.178001403808594 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=11.007000923156738 min=1 max=100\n", + "6\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=19.928001403808594 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=10.097000122070312 min=1 max=100\n", + "7\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=18.034000396728516 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=9.187000274658203 min=1 max=100\n", + "8\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=16.803001403808594 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.802000045776367 min=1 max=100\n", + "9\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=16.1820011138916 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.748000144958496 min=1 max=100\n", + "10\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=15.830000877380371 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.345000267028809 min=1 max=100\n", + "11\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=15.902000427246094 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.255000114440918 min=1 max=100\n", + "12\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=15.457000732421875 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.638999938964844 min=1 max=100\n", + "13\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=15.242000579833984 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.986000061035156 min=1 max=100\n", + "14\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=14.875000953674316 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=7.926000595092773 min=1 max=100\n", + "15\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=14.450000762939453 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=7.992000579833984 min=1 max=100\n", + "16\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=14.301000595092773 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=8.064000129699707 min=1 max=100\n", + "17\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=13.73900032043457 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=7.494000434875488 min=1 max=100\n", + "18\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=13.226000785827637 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=7.1670002937316895 min=1 max=100\n", + "19\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=12.533000946044922 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=7.0370001792907715 min=1 max=100\n", + "20\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=12.383000373840332 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=6.906000137329102 min=1 max=100\n", + "21\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=12.110000610351562 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=6.248000144958496 min=1 max=100\n", + "22\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=11.529000282287598 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=6.005000114440918 min=1 max=100\n", + "23\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=10.902000427246094 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=6.050000190734863 min=1 max=100\n", + "24\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=10.43600082397461 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=5.969000339508057 min=1 max=100\n", + "25\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=10.218000411987305 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=5.824000358581543 min=1 max=100\n", + "26\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=9.791000366210938 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=5.577000141143799 min=1 max=100\n", + "27\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=9.36400032043457 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=5.125000476837158 min=1 max=100\n", + "28\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=8.922000885009766 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=5.071000099182129 min=1 max=100\n", + "29\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=8.2160005569458 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=4.2260003089904785 min=1 max=100\n", + "30\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=7.803000450134277 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=4.134000301361084 min=1 max=100\n", + "31\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=7.457000255584717 min=1 max=25\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.92900013923645 min=1 max=100\n", + "32\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=7.242000579833984 min=1 max=25\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.7160000801086426 min=1 max=11\n", + "33\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=7.039000511169434 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.699000120162964 min=1 max=100\n", + "34\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=6.710000514984131 min=1 max=25\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.5870001316070557 min=1 max=100\n", + "35\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=6.4760003089904785 min=1 max=25\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.391000270843506 min=1 max=11\n", + "36\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=6.225000381469727 min=1 max=25\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.2930002212524414 min=1 max=11\n", + "37\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=5.999000072479248 min=1 max=25\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.2730002403259277 min=1 max=100\n", + "38\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=5.759000301361084 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=3.071000099182129 min=1 max=11\n", + "39\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=5.53000020980835 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.9740002155303955 min=1 max=11\n", + "40\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=5.328000068664551 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.9850001335144043 min=1 max=100\n", + "41\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=5.12000036239624 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.875000238418579 min=1 max=100\n", + "42\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=4.9160003662109375 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.6700000762939453 min=1 max=11\n", + "43\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=4.764000415802002 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.6030001640319824 min=1 max=11\n", + "44\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=4.559000015258789 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.5210001468658447 min=1 max=11\n", + "45\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=4.400000095367432 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.634000062942505 min=1 max=100\n", + "46\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=4.234000205993652 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.5850000381469727 min=1 max=100\n", + "47\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=4.098999977111816 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.4030001163482666 min=1 max=100\n", + "48\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.943000078201294 min=1 max=24\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.3370001316070557 min=1 max=100\n", + "49\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.8680002689361572 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.4130001068115234 min=1 max=100\n", + "50\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.9010002613067627 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.2920000553131104 min=1 max=100\n", + "51\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.744000196456909 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.1680002212524414 min=1 max=11\n", + "52\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.695000171661377 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.254000186920166 min=1 max=100\n", + "53\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.6750001907348633 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.2310001850128174 min=1 max=100\n", + "54\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.636000156402588 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.31000018119812 min=1 max=100\n", + "55\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.563000202178955 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.194000005722046 min=1 max=100\n", + "56\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.5420000553131104 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.190000057220459 min=1 max=100\n", + "57\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.4790000915527344 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.1700000762939453 min=1 max=100\n", + "58\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.4630000591278076 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.063000202178955 min=1 max=10\n", + "59\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.4220001697540283 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.1460001468658447 min=1 max=100\n", + "60\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.38100004196167 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.133000135421753 min=1 max=100\n", + "61\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.321000099182129 min=1 max=23\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.0160000324249268 min=1 max=10\n", + "62\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.2720000743865967 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.005000114440918 min=1 max=10\n", + "63\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.244000196456909 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.0820000171661377 min=1 max=100\n", + "64\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.2130000591278076 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9890000820159912 min=1 max=10\n", + "65\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.19700026512146 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9640001058578491 min=1 max=10\n", + "66\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.1570000648498535 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9450000524520874 min=1 max=10\n", + "67\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.129000186920166 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.0420000553131104 min=1 max=100\n", + "68\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.068000078201294 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.128000020980835 min=1 max=100\n", + "69\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.0530002117156982 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.009999990463257 min=1 max=100\n", + "70\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=3.0210001468658447 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9060001373291016 min=1 max=10\n", + "71\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.9830000400543213 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9930001497268677 min=1 max=100\n", + "72\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.949000120162964 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=2.1670000553131104 min=1 max=100\n", + "73\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.9090001583099365 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.967000126838684 min=1 max=100\n", + "74\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.892000198364258 min=1 max=22\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9550000429153442 min=1 max=100\n", + "75\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.8540000915527344 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.842000126838684 min=1 max=10\n", + "76\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.829000234603882 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.840000033378601 min=1 max=10\n", + "77\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.814000129699707 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.821000099182129 min=1 max=10\n", + "78\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.811000108718872 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.9130001068115234 min=1 max=100\n", + "79\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.7740001678466797 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.8010001182556152 min=1 max=10\n", + "80\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.744000196456909 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.8860000371932983 min=1 max=100\n", + "81\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.7370002269744873 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.971000075340271 min=1 max=100\n", + "82\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.705000162124634 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.7590000629425049 min=1 max=10\n", + "83\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.680000066757202 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.755000114440918 min=1 max=10\n", + "84\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.640000104904175 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.7410000562667847 min=1 max=10\n", + "85\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.6070001125335693 min=1 max=21\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.7310000658035278 min=1 max=10\n", + "86\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.690000057220459 min=1 max=100\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.721000075340271 min=1 max=10\n", + "87\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.562000036239624 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.8010001182556152 min=1 max=100\n", + "88\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.5530002117156982 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.8970000743865967 min=1 max=100\n", + "89\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.5370001792907715 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.874000072479248 min=1 max=100\n", + "90\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.497000217437744 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.8610000610351562 min=1 max=100\n", + "91\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.4860000610351562 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.6660001277923584 min=1 max=10\n", + "92\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.455000162124634 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.846000075340271 min=1 max=100\n", + "93\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.450000047683716 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.7420001029968262 min=1 max=100\n", + "94\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.4160001277923584 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.6370000839233398 min=1 max=9\n", + "95\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.392000198364258 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.627000093460083 min=1 max=9\n", + "96\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.375 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.621000051498413 min=1 max=9\n", + "97\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.3530001640319824 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.6160000562667847 min=1 max=9\n", + "98\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.3340001106262207 min=1 max=20\n", - "Free symbols: {x_H, T, Δt, n_Htot, u_0, u}\n", + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.6100001335144043 min=1 max=9\n", + "99\n", + "Free symbols: {n_Htot, u_0, x_H, Δt, T, u}\n", "Known values: ['n_Htot', 'u_0', 'x_H', 'Δt']\n", "Assumed values: []\n", "Equations solved: ['heat', 'u']\n", - "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'u_0', 'n_Htot'} and assumptions about set()\n", - "num_iter average=2.307000160217285 min=1 max=20\n" + "It's solvin time. Solving for {'u', 'T'} based on input {'Δt', 'x_H', 'n_Htot', 'u_0'} and assumptions about set()\n", + "num_iter average=1.6970000267028809 min=1 max=100\n" ] } ], @@ -1036,7 +1136,8 @@ "\n", "solutions = [guesses,]\n", "for i in range(100):\n", - " sol = system.solve(knowns, guesses, time_dependent=[\"T\",], dt=dt,tol=1e-6,careful_steps=40,verbose=True)\n", + " print(i)\n", + " sol = system.solve(knowns, guesses, time_dependent=[\"T\",], dt=dt,tol=1e-6,careful_steps=10,verbose=True)\n", " solutions.append(sol.copy())\n", " guesses = sol.copy() # let latest timestep be new initial guess\n", " knowns[\"u_0\"] = np.copy(sol[\"u\"]) # update initial internal energy for next timestep" @@ -1060,7 +1161,7 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -1087,37 +1188,6 @@ "ax.set_xlim(0.1,ngrid[-1])\n", "ax.set_ylim(1,2e4)" ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f420af2b", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'func' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m Tgrid \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlogspace(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m1000\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mloglog(Tgrid,np\u001b[38;5;241m.\u001b[39mabs(\u001b[43mfunc\u001b[49m(Tgrid,\u001b[38;5;241m1.\u001b[39m)))\n", - "\u001b[0;31mNameError\u001b[0m: name 'func' is not defined" - ] - } - ], - "source": [ - "Tgrid = np.logspace(1,6,1000)\n", - "plt.loglog(Tgrid,np.abs(func(Tgrid,1.)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b177a90e", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 0d8245d60250ea0dc0780040a50b24412ccdab7b Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Thu, 4 Dec 2025 18:50:53 -0500 Subject: [PATCH 5/6] notebook fix --- CIE_files/CIE_15_1.png | Bin 116170 -> 116386 bytes README.md | 56 +++++++++++++++++------------------ docs/source/CIE_15_1.png | Bin 116170 -> 116386 bytes docs/source/Quickstart.rst | 58 ++++++++++++++++++------------------- 4 files changed, 55 insertions(+), 59 deletions(-) diff --git a/CIE_files/CIE_15_1.png b/CIE_files/CIE_15_1.png index 882e1650b6837d82fbfe50df80035b86ecac8914..af43c694331e07e84f99c556af5e28cfb276a6bc 100644 GIT binary patch literal 116386 zcmeFZ2T)Yon=jf%&QXD5CSw8wNs==tlA}b)C<2mmj*5avl$?>Ao1Bx1WTDATYJ!9& zBcXvNzO}nKXa09)Ud`NkQ?Ke)T?(q3?zPwY_7{F%T5J0&%1aPlBD(~G!3d=!pDDp$ 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z;2?A)!tY)HdY9}H21n)MgYvAX1{)Bmb=B3?-@6_2O&m6ifd8L#2Y|BDyw|hfqPaOJ zG6Bap@d&hpSVD6+p|GFi*zXK&E1Z-j{aPhku`+u`5pTC!S1Ps%97xD00T!MhasdmM zRZMfkBfUQZiVHAD+A9XYS5hGrATqC1dqs&C8gv9)29Iig*m;dIvly&Yj0-Njfo4BM zv&$=oOzf%+R=_kuFnx;xsG#xnS9MXBh~Af_jbEI)EUxvj zmwWId7+N6QQw(yd|3X#$io>~Rfl&^_V!$E+5a?a0ha;#=gt~Td!Radx3Za4$*V(q{ z930|{W;+P@Q8pKqzJ(wX-~l+atA{Bn$P_#}0M2b=v=0Hmp|Uu%8-f5}R)VJwCPcy< zH>#@TU!DhS!`wX}et_!1*MPu_p11w2TB~CK41AvfO|l7PnYzAy6iT!f;ePn}|H3Lq p-gjLB{_i|^WG7Jnw>*|vHb-eS4~wlLzX!Rk*13xsnP)8T{TG4awDSM} diff --git a/README.md b/README.md index c7475fc..78bc509 100644 --- a/README.md +++ b/README.md @@ -98,15 +98,13 @@ Summed processes keep track of all subprocesses, e.g. the total net heating rate ```python -from jaco.processes import FreeFreeEmission -system += FreeFreeEmission("H+") -FreeFreeEmission("H+").bibliography +system.heat ``` - ['1978ppim.book.....S'] +$\displaystyle - \frac{1.55 \cdot 10^{-26} C_{2} n_{He+} n_{e-}}{T^{0.3647}} - \frac{1.46719838641439 \cdot 10^{-26} C_{2} \sqrt{T} n_{H+} n_{e-}}{\left(0.00119216696847702 \sqrt{T} + 1.0\right)^{1.748} \left(0.563615123664978 \sqrt{T} + 1.0\right)^{0.252}} - \frac{5.86879354565754 \cdot 10^{-26} C_{2} \sqrt{T} n_{He++} n_{e-}}{\left(0.00119216696847702 \sqrt{T} + 1.0\right)^{1.748} \left(0.563615123664978 \sqrt{T} + 1.0\right)^{0.252}} - \frac{1.2746917300104 \cdot 10^{-21} \sqrt{T} n_{H} n_{e-} e^{- \frac{157809.1}{T}}}{\frac{\sqrt{10} \sqrt{T}}{1000} + 1} - \frac{9.37661057635428 \cdot 10^{-22} \sqrt{T} n_{He} n_{e-} e^{- \frac{285335.4}{T}}}{\frac{\sqrt{10} \sqrt{T}}{1000} + 1} - \frac{4.9524176975855 \cdot 10^{-22} \sqrt{T} n_{He+} n_{e-} e^{- \frac{631515}{T}}}{\frac{\sqrt{10} \sqrt{T}}{1000} + 1}$ @@ -140,21 +138,21 @@ sol = system.solve(knowns, guesses,tol=1e-3, verbose=True,careful_steps=30) print(sol) ``` - Undetermined symbols: {C_2, y, T, x_He++, n_Htot, x_H+, x_He+} - C_2 not specified; assuming C_2=1.0. + Undetermined symbols: {x_He+, y, n_Htot, x_H+, T, C_2, x_He++} y not specified; assuming y=0.09254634923706946. - Free symbols: {x_He+, y, T, x_He++, n_Htot, x_H+, C_2} + C_2 not specified; assuming C_2=1.0. + Free symbols: {x_He+, y, n_Htot, x_H+, T, C_2, x_He++} Known values: ['T', 'n_Htot'] - Assumed values: ['C_2', 'y'] - Equations solved: ['He+', 'He++', 'H+'] - It's solvin time. Solving for {'He+', 'H+', 'He++'} based on input {'n_Htot', 'T'} and assumptions about {'y', 'C_2'} - num_iter average=35.819149017333984 min=18 max=68 - {'He+': Array([2.8003510e-16, 2.8003862e-16, 2.8003910e-16, ..., 7.6925389e-06, - 7.6924471e-06, 7.6923561e-06], dtype=float32), 'He++': Array([4.1297267e-17, 4.1297541e-17, 4.1297644e-17, ..., 9.2538655e-02, - 9.2538655e-02, 9.2538655e-02], dtype=float32), 'H+': Array([2.5647242e-16, 2.5647083e-16, 2.5647025e-16, ..., 9.9999940e-01, - 9.9999940e-01, 9.9999940e-01], dtype=float32), 'H': Array([1.0000000e+00, 1.0000000e+00, 1.0000000e+00, ..., 5.9604645e-07, - 5.9604645e-07, 5.9604645e-07], dtype=float32), 'He': Array([9.254635e-02, 9.254635e-02, 9.254635e-02, ..., 7.450581e-09, - 7.450581e-09, 7.450581e-09], dtype=float32), 'e-': Array([6.1910205e-16, 6.1910453e-16, 6.1910464e-16, ..., 1.1850843e+00, + Assumed values: ['y', 'C_2'] + Equations solved: ['He++', 'H+', 'He+'] + It's solvin time. Solving for {'He++', 'He+', 'H+'} based on input {'T', 'n_Htot'} and assumptions about {'y', 'C_2'} + num_iter average=35.86250305175781 min=18 max=68 + {'He+': Array([8.259601e-17, 8.259580e-17, 8.259559e-17, ..., 7.692539e-06, + 7.692447e-06, 7.692356e-06], dtype=float32), 'H+': Array([4.1381906e-16, 4.1381869e-16, 4.1381975e-16, ..., 9.9999940e-01, + 9.9999940e-01, 9.9999940e-01], dtype=float32), 'He++': Array([1.4600168e-18, 1.4600135e-18, 1.4600135e-18, ..., 9.2538655e-02, + 9.2538655e-02, 9.2538655e-02], dtype=float32), 'He': Array([9.254635e-02, 9.254635e-02, 9.254635e-02, ..., 7.450581e-09, + 7.450581e-09, 7.450581e-09], dtype=float32), 'H': Array([1.0000000e+00, 1.0000000e+00, 1.0000000e+00, ..., 5.9604645e-07, + 5.9604645e-07, 5.9604645e-07], dtype=float32), 'e-': Array([4.9933508e-16, 4.9933450e-16, 4.9933535e-16, ..., 1.1850843e+00, 1.1850843e+00, 1.1850843e+00], dtype=float32)} @@ -190,11 +188,11 @@ Suppose you just want the RHS of the system you're solving, or its Jacobian, bec print(system.generate_code(('H+','He+','He++'),language='c')) ``` - /* Computes the RHS function and Jacobian to solve for [x_He+, x_He++, x_H+] + /* Computes the RHS function and Jacobian to solve for [x_He+, x_H+, x_He++] This code was auto-generated by jaco v0.1.1 and is not intended to be modified or maintained by human beings. - INDEX CONVENTION: (0: x_He+) (1: x_He++) (2: x_H+) + INDEX CONVENTION: (0: x_He+) (1: x_H+) (2: x_He++) */ x0 = sqrt(T); @@ -241,18 +239,18 @@ print(system.generate_code(('H+','He+','He++'),language='c')) x41 = -5.8500000000000005e-11*x0*x15*x28*x29*x4 + x26*x40; rhs_result[0] = 2.3800000000000001e-11*x0*x15*x20*x21*x4*x5 + x11 - x19 - x23*x5; - rhs_result[1] = -x11 + x19; - rhs_result[2] = 5.8500000000000005e-11*x0*x15*x28*x29*x4*x5 - x27*x_Hplus; + rhs_result[1] = 5.8500000000000005e-11*x0*x15*x28*x29*x4*x5 - x27*x_Hplus; + rhs_result[2] = -x11 + x19; jac_result[0] = -x22*x6 - x31 - x36 - x37; - jac_result[1] = 4.7600000000000002e-11*x0*x15*x20*x21*x4 + x10 - 2*x23 - x31 + x38 - x39; - jac_result[2] = -x35 - x37; - jac_result[3] = x36; - jac_result[4] = -x10 - x38 + x39; - jac_result[5] = x18 - x34; - jac_result[6] = -x41; - jac_result[7] = 1.1700000000000001e-10*x0*x15*x28*x29*x4 - 2.8324293174022895e-10*x24*x25*x40; - jac_result[8] = -x27 - 5.8500000000000005e-11*x29*x30 - x41; + jac_result[1] = -x35 - x37; + jac_result[2] = 4.7600000000000002e-11*x0*x15*x20*x21*x4 + x10 - 2*x23 - x31 + x38 - x39; + jac_result[3] = -x41; + jac_result[4] = -x27 - 5.8500000000000005e-11*x29*x30 - x41; + jac_result[5] = 1.1700000000000001e-10*x0*x15*x28*x29*x4 - 2.8324293174022895e-10*x24*x25*x40; + jac_result[6] = x36; + jac_result[7] = x18 - x34; + jac_result[8] = -x10 - x38 + x39; Let's break down what happened there. 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z;2?A)!tY)HdY9}H21n)MgYvAX1{)Bmb=B3?-@6_2O&m6ifd8L#2Y|BDyw|hfqPaOJ zG6Bap@d&hpSVD6+p|GFi*zXK&E1Z-j{aPhku`+u`5pTC!S1Ps%97xD00T!MhasdmM zRZMfkBfUQZiVHAD+A9XYS5hGrATqC1dqs&C8gv9)29Iig*m;dIvly&Yj0-Njfo4BM zv&$=oOzf%+R=_kuFnx;xsG#xnS9MXBh~Af_jbEI)EUxvj zmwWId7+N6QQw(yd|3X#$io>~Rfl&^_V!$E+5a?a0ha;#=gt~Td!Radx3Za4$*V(q{ z930|{W;+P@Q8pKqzJ(wX-~l+atA{Bn$P_#}0M2b=v=0Hmp|Uu%8-f5}R)VJwCPcy< zH>#@TU!DhS!`wX}et_!1*MPu_p11w2TB~CK41AvfO|l7PnYzAy6iT!f;ePn}|H3Lq p-gjLB{_i|^WG7Jnw>*|vHb-eS4~wlLzX!Rk*13xsnP)8T{TG4awDSM} diff --git a/docs/source/Quickstart.rst b/docs/source/Quickstart.rst index c4f5326..02b5d31 100644 --- a/docs/source/Quickstart.rst +++ b/docs/source/Quickstart.rst @@ -78,16 +78,14 @@ heating rate is: .. code:: ipython3 - from jaco.processes import FreeFreeEmission - system += FreeFreeEmission("H+") - FreeFreeEmission("H+").bibliography + system.heat -.. parsed-literal:: +.. math:: - ['1978ppim.book.....S'] + \displaystyle - \frac{1.55 \cdot 10^{-26} C_{2} n_{He+} n_{e-}}{T^{0.3647}} - \frac{1.46719838641439 \cdot 10^{-26} C_{2} \sqrt{T} n_{H+} n_{e-}}{\left(0.00119216696847702 \sqrt{T} + 1.0\right)^{1.748} \left(0.563615123664978 \sqrt{T} + 1.0\right)^{0.252}} - \frac{5.86879354565754 \cdot 10^{-26} C_{2} \sqrt{T} n_{He++} n_{e-}}{\left(0.00119216696847702 \sqrt{T} + 1.0\right)^{1.748} \left(0.563615123664978 \sqrt{T} + 1.0\right)^{0.252}} - \frac{1.2746917300104 \cdot 10^{-21} \sqrt{T} n_{H} n_{e-} e^{- \frac{157809.1}{T}}}{\frac{\sqrt{10} \sqrt{T}}{1000} + 1} - \frac{9.37661057635428 \cdot 10^{-22} \sqrt{T} n_{He} n_{e-} e^{- \frac{285335.4}{T}}}{\frac{\sqrt{10} \sqrt{T}}{1000} + 1} - \frac{4.9524176975855 \cdot 10^{-22} \sqrt{T} n_{He+} n_{e-} e^{- \frac{631515}{T}}}{\frac{\sqrt{10} \sqrt{T}}{1000} + 1} @@ -132,21 +130,21 @@ The ``solve`` method calls the JAX solver and computes the solution: .. parsed-literal:: - Undetermined symbols: {C_2, y, T, x_He++, n_Htot, x_H+, x_He+} - C_2 not specified; assuming C_2=1.0. + Undetermined symbols: {x_He+, y, n_Htot, x_H+, T, C_2, x_He++} y not specified; assuming y=0.09254634923706946. - Free symbols: {x_He+, y, T, x_He++, n_Htot, x_H+, C_2} + C_2 not specified; assuming C_2=1.0. + Free symbols: {x_He+, y, n_Htot, x_H+, T, C_2, x_He++} Known values: ['T', 'n_Htot'] - Assumed values: ['C_2', 'y'] - Equations solved: ['He+', 'He++', 'H+'] - It's solvin time. Solving for {'He+', 'H+', 'He++'} based on input {'n_Htot', 'T'} and assumptions about {'y', 'C_2'} - num_iter average=35.819149017333984 min=18 max=68 - {'He+': Array([2.8003510e-16, 2.8003862e-16, 2.8003910e-16, ..., 7.6925389e-06, - 7.6924471e-06, 7.6923561e-06], dtype=float32), 'He++': Array([4.1297267e-17, 4.1297541e-17, 4.1297644e-17, ..., 9.2538655e-02, - 9.2538655e-02, 9.2538655e-02], dtype=float32), 'H+': Array([2.5647242e-16, 2.5647083e-16, 2.5647025e-16, ..., 9.9999940e-01, - 9.9999940e-01, 9.9999940e-01], dtype=float32), 'H': Array([1.0000000e+00, 1.0000000e+00, 1.0000000e+00, ..., 5.9604645e-07, - 5.9604645e-07, 5.9604645e-07], dtype=float32), 'He': Array([9.254635e-02, 9.254635e-02, 9.254635e-02, ..., 7.450581e-09, - 7.450581e-09, 7.450581e-09], dtype=float32), 'e-': Array([6.1910205e-16, 6.1910453e-16, 6.1910464e-16, ..., 1.1850843e+00, + Assumed values: ['y', 'C_2'] + Equations solved: ['He++', 'H+', 'He+'] + It's solvin time. Solving for {'He++', 'He+', 'H+'} based on input {'T', 'n_Htot'} and assumptions about {'y', 'C_2'} + num_iter average=35.86250305175781 min=18 max=68 + {'He+': Array([8.259601e-17, 8.259580e-17, 8.259559e-17, ..., 7.692539e-06, + 7.692447e-06, 7.692356e-06], dtype=float32), 'H+': Array([4.1381906e-16, 4.1381869e-16, 4.1381975e-16, ..., 9.9999940e-01, + 9.9999940e-01, 9.9999940e-01], dtype=float32), 'He++': Array([1.4600168e-18, 1.4600135e-18, 1.4600135e-18, ..., 9.2538655e-02, + 9.2538655e-02, 9.2538655e-02], dtype=float32), 'He': Array([9.254635e-02, 9.254635e-02, 9.254635e-02, ..., 7.450581e-09, + 7.450581e-09, 7.450581e-09], dtype=float32), 'H': Array([1.0000000e+00, 1.0000000e+00, 1.0000000e+00, ..., 5.9604645e-07, + 5.9604645e-07, 5.9604645e-07], dtype=float32), 'e-': Array([4.9933508e-16, 4.9933450e-16, 4.9933535e-16, ..., 1.1850843e+00, 1.1850843e+00, 1.1850843e+00], dtype=float32)} @@ -189,11 +187,11 @@ can do that too with ``generate_code``. .. parsed-literal:: - /* Computes the RHS function and Jacobian to solve for [x_He+, x_He++, x_H+] + /* Computes the RHS function and Jacobian to solve for [x_He+, x_H+, x_He++] This code was auto-generated by jaco v0.1.1 and is not intended to be modified or maintained by human beings. - INDEX CONVENTION: (0: x_He+) (1: x_He++) (2: x_H+) + INDEX CONVENTION: (0: x_He+) (1: x_H+) (2: x_He++) */ x0 = sqrt(T); @@ -240,18 +238,18 @@ can do that too with ``generate_code``. x41 = -5.8500000000000005e-11*x0*x15*x28*x29*x4 + x26*x40; rhs_result[0] = 2.3800000000000001e-11*x0*x15*x20*x21*x4*x5 + x11 - x19 - x23*x5; - rhs_result[1] = -x11 + x19; - rhs_result[2] = 5.8500000000000005e-11*x0*x15*x28*x29*x4*x5 - x27*x_Hplus; + rhs_result[1] = 5.8500000000000005e-11*x0*x15*x28*x29*x4*x5 - x27*x_Hplus; + rhs_result[2] = -x11 + x19; jac_result[0] = -x22*x6 - x31 - x36 - x37; - jac_result[1] = 4.7600000000000002e-11*x0*x15*x20*x21*x4 + x10 - 2*x23 - x31 + x38 - x39; - jac_result[2] = -x35 - x37; - jac_result[3] = x36; - jac_result[4] = -x10 - x38 + x39; - jac_result[5] = x18 - x34; - jac_result[6] = -x41; - jac_result[7] = 1.1700000000000001e-10*x0*x15*x28*x29*x4 - 2.8324293174022895e-10*x24*x25*x40; - jac_result[8] = -x27 - 5.8500000000000005e-11*x29*x30 - x41; + jac_result[1] = -x35 - x37; + jac_result[2] = 4.7600000000000002e-11*x0*x15*x20*x21*x4 + x10 - 2*x23 - x31 + x38 - x39; + jac_result[3] = -x41; + jac_result[4] = -x27 - 5.8500000000000005e-11*x29*x30 - x41; + jac_result[5] = 1.1700000000000001e-10*x0*x15*x28*x29*x4 - 2.8324293174022895e-10*x24*x25*x40; + jac_result[6] = x36; + jac_result[7] = x18 - x34; + jac_result[8] = -x10 - x38 + x39; Let’s break down what happened there. First, jaco is generating the From 1ca442797f6ef59dfa9db7495ac5384afe95adcb Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Fri, 5 Dec 2025 10:54:15 -0500 Subject: [PATCH 6/6] docs: missing periodictable requirement --- docs/requirements.txt | 1 + experiments/ccodegen.ipynb | 2 +- src/jaco/codegen/gizmo/gizmo.py | 2 +- src/jaco/symbols.py | 3 ++- 4 files changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index c0ce930..5c2a365 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -7,3 +7,4 @@ astropy jax sympy matplotlib +periodictable diff --git a/experiments/ccodegen.ipynb b/experiments/ccodegen.ipynb index d82be5e..45c2699 100644 --- a/experiments/ccodegen.ipynb +++ b/experiments/ccodegen.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "id": "561bc739", "metadata": {}, "outputs": [], diff --git a/src/jaco/codegen/gizmo/gizmo.py b/src/jaco/codegen/gizmo/gizmo.py index 4ce6b41..546eed6 100644 --- a/src/jaco/codegen/gizmo/gizmo.py +++ b/src/jaco/codegen/gizmo/gizmo.py @@ -29,7 +29,7 @@ def generate_funcjac_code(system, cse=True): for i, p in enumerate(paramsvars): funcjac = funcjac.subs(p, P[i]) assignments.append(Assignment(P[i], p)) - index_defs.append(f"#define IDX_{p} {i}") + index_defs.append(f"#define INDEX_{p} {i}") cg = C99CodeGen(cse=cse) routine = cg.routine("microphysics_func_jac", funcjac) # cg.routine("func",sp.Matrix(func + sp.flatten(jac))) cg.write([routine], "microphysics_func_jac", to_files=True) diff --git a/src/jaco/symbols.py b/src/jaco/symbols.py index 202b4c6..bcf4709 100644 --- a/src/jaco/symbols.py +++ b/src/jaco/symbols.py @@ -104,7 +104,8 @@ def piecewise_linear(X, Y, x, extrapolate=False): for i in range(len(X) - 1): cases.append((Y[i] + slopes[i] * (x - X[i]), (x >= X[i]) & (x < X[i + 1]))) - return sp.piecewise_exclusive(sp.Piecewise(*cases)) + # cases.append((sp.nan, True)) + return sp.Piecewise(*cases) def piecewise_powerlaw(X, Y, x, extrapolate=False):