\n
\n \n
\n \n
\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-g4f360wd because the default path (/nonexistent/first-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-r76hj9d3 because the default path (/nonexistent/second-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n First we define a model for the skewed mexican hat.
\n\n \n \n@@ -99,15 +99,15 @@\n
\n [2]:\n \n \n \n
\n [3]:\n \n
\n \n-<matplotlib.legend.Legend at 0xffff75b87170>\n+<matplotlib.legend.Legend at 0xffff806fcfb0>\n \n
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -5,17 +5,17 @@\n then use DifferentialEvolution to find the global minimum.\n [1]:\n from symfit import Parameter, Variable, Model, Fit, solve, diff, N, re\n from symfit.core.minimizers import DifferentialEvolution, BFGS\n import numpy as np\n import matplotlib.pyplot as plt\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-g4f360wd\n-because the default path (/nonexistent/first-build/.config/matplotlib) is not a\n-writable directory; it is highly recommended to set the MPLCONFIGDIR\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-r76hj9d3\n+because the default path (/nonexistent/second-build/.config/matplotlib) is not\n+a writable directory; it is highly recommended to set the MPLCONFIGDIR\n environment variable to a writable directory, in particular to speed up the\n import of Matplotlib and to better support multiprocessing.\n First we define a model for the skewed mexican hat.\n [2]:\n x = Parameter('x')\n x.min, x.max = -100, 100\n y = Variable('y')\n@@ -33,15 +33,15 @@\n plt.plot(xdata, ydata, label=r'$f(x)$')\n plt.xlabel('x')\n plt.ylabel('f(x)')\n plt.ylim(1.1 * ydata.min(), 1.1 * ydata.max())\n plt.legend()\n \n [3]:\n-\n
\n \n ![\"../_images/examples_ex_mexican_hat_5_1.png\"](\"../_images/examples_ex_mexican_hat_5_1.png\")
\n@@ -169,15 +169,15 @@\n
\n \n@@ -169,15 +169,15 @@\n \n
\n \n
\n \n
\n \n exact value -2.35191046133532\n-num value -2.3491487551237533\n+num value -2.350518594704376\n Using DifferentialEvolution, we find the correct global minimum. However, it is not exactly the same as the analytical solution. This is because DifferentialEvolution is expensive to perform, and therefore does not solve to high precision by default. We could demand a higher precission from DifferentialEvolution, but this isn\u2019t worth the high computational cost. Instead, we will just tell symfit to perform DifferentialEvolution, followed by BFGS.
\n \n \n@@ -190,15 +190,15 @@\n
\n [7]:\n \n
\n \n
\n \n
\n \n exact value -2.35191046133532\n-num value -2.351910461335323\n+num value -2.351910461335324\n We see that now the proper solution has been found to much higher precision.
\n \n \n \n\n
\n \n
\n \n
\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-3aquxxfx because the default path (/nonexistent/first-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-bbsuyljs because the default path (/nonexistent/second-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n First we build a model representing the system of equations.
\n\n \n \n@@ -152,16 +152,16 @@\n Parameter Value Standard Deviation\n k1_f 9.540413e-02 4.440705e-03\n k1_r 1.065094e-01 7.165728e-02\n k2_f 2.706121e-01 5.305036e-02\n k2_r 2.633617e-01 5.647221e-02\n Status message CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n Number of iterations 30\n-Objective <symfit.core.objectives.LeastSquares object at 0xffff60b84110>\n-Minimizer <symfit.core.minimizers.LBFGSB object at 0xffff60b857f0>\n+Objective <symfit.core.objectives.LeastSquares object at 0xffff80bb1190>\n+Minimizer <symfit.core.minimizers.LBFGSB object at 0xffff81c4d820>\n \n Goodness of fit qualifiers:\n chi_squared 33.98549454195023\n objective_value 16.992747270975116\n r_squared 0.9936568366387369\n
\n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -6,17 +6,17 @@\n from symfit import (\n variables, parameters, ODEModel, D, Fit\n )\n from symfit.core.support import key2str\n import numpy as np\n import matplotlib.pyplot as plt\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-3aquxxfx\n-because the default path (/nonexistent/first-build/.config/matplotlib) is not a\n-writable directory; it is highly recommended to set the MPLCONFIGDIR\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-bbsuyljs\n+because the default path (/nonexistent/second-build/.config/matplotlib) is not\n+a writable directory; it is highly recommended to set the MPLCONFIGDIR\n environment variable to a writable directory, in particular to speed up the\n import of Matplotlib and to better support multiprocessing.\n First we build a model representing the system of equations.\n [2]:\n t, F, MM, FMM, FMMF = variables('t, F, MM, FMM, FMMF')\n k1_f, k1_r, k2_f, k2_r = parameters('k1_f, k1_r, k2_f, k2_r')\n \n@@ -77,16 +77,16 @@\n k1_f 9.540413e-02 4.440705e-03\n k1_r 1.065094e-01 7.165728e-02\n k2_f 2.706121e-01 5.305036e-02\n k2_r 2.633617e-01 5.647221e-02\n Status message CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n Number of iterations 30\n Objective [2]:\n \n
\n \n
\n \n
\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-7i3pdkcj because the default path (/nonexistent/first-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-guufbfms because the default path (/nonexistent/second-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n Say \\(f(t) = t * exp(- t)\\), and \\(F(s)\\) is the Laplace transform of \\(f(t)\\). Let us first evaluate this transform using sympy.
\n \n \n@@ -105,15 +105,15 @@\n
\n [2]:\n \n \n \n
\n [3]:\n \n
\n \n-<matplotlib.legend.Legend at 0xffff547afec0>\n+<matplotlib.legend.Legend at 0xffff861efec0>\n \n
\n \n
\n \n ![\"../_images/examples_ex_tikhonov_5_1.png\"](\"../_images/examples_ex_tikhonov_5_1.png\")
\n@@ -206,16 +206,16 @@\n
\n@@ -256,15 +256,15 @@\n \n@@ -206,16 +206,16 @@\n \n
\n \n \n Parameter Value Standard Deviation\n a 5.449374e-02 None\n Status message Optimization terminated successfully.\n Number of iterations 14\n-Objective <symfit.core.objectives.LeastSquares object at 0xffff5482d310>\n-Minimizer <symfit.core.minimizers.BFGS object at 0xffff54858200>\n+Objective <symfit.core.objectives.LeastSquares object at 0xffff86018650>\n+Minimizer <symfit.core.minimizers.BFGS object at 0xffff85e9c7d0>\n \n Goodness of fit qualifiers:\n chi_squared 3.2950323615883767e-19\n objective_value 1.6475161807941883e-19\n r_squared -inf\n \n \n \n
\n [8]:\n \n
\n \n-<matplotlib.legend.Legend at 0xffff54467c20>\n+<matplotlib.legend.Legend at 0xffff85e9cc50>\n \n
\n
\n \n ![\"../_images/examples_ex_tikhonov_15_2.png\"](\"../_images/examples_ex_tikhonov_15_2.png\")
\n@@ -292,15 +292,15 @@\n
\n \n@@ -292,15 +292,15 @@\n \n \n \n
\n [9]:\n \n
\n \n-<matplotlib.legend.Legend at 0xffff544b0dd0>\n+<matplotlib.legend.Legend at 0xffff85f2acc0>\n \n
\n
\n \n ![\"../_images/examples_ex_tikhonov_17_1.png\"](\"../_images/examples_ex_tikhonov_17_1.png\")
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -7,17 +7,17 @@\n from symfit import (\n variables, parameters, Model, Fit, exp, laplace_transform, symbols,\n MatrixSymbol, sqrt, Inverse, CallableModel\n )\n import numpy as np\n import matplotlib.pyplot as plt\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-7i3pdkcj\n-because the default path (/nonexistent/first-build/.config/matplotlib) is not a\n-writable directory; it is highly recommended to set the MPLCONFIGDIR\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-guufbfms\n+because the default path (/nonexistent/second-build/.config/matplotlib) is not\n+a writable directory; it is highly recommended to set the MPLCONFIGDIR\n environment variable to a writable directory, in particular to speed up the\n import of Matplotlib and to better support multiprocessing.\n Say \\(f(t) = t * exp(- t)\\), and \\(F(s)\\) is the Laplace transform of \\(f(t)\\).\n Let us first evaluate this transform using sympy.\n [2]:\n t, f, s, F = variables('t, f, s, F')\n model = Model({f: t * exp(- t)})\n@@ -45,15 +45,15 @@\n = F(s)$')\n plt.xlabel(r'$s_i$')\n plt.ylabel(r'$F(s_i)$')\n plt.xlim(0, None)\n plt.legend()\n \n [3]:\n-\n+\n [../_images/examples_ex_tikhonov_5_1.png]\n We will now invert this data, using the procedure outlined in \\cite{}.\n [4]:\n N_s = symbols('N_s', integer=True) # Number of s_i points\n \n M = MatrixSymbol('M', N_s, N_s)\n W = MatrixSymbol('W', N_s, N_s)\n@@ -100,16 +100,16 @@\n \n \n Parameter Value Standard Deviation\n a 5.449374e-02 None\n Status message Optimization terminated successfully.\n Number of iterations 14\n Objective \n-Minimizer \n+0xffff86018650>\n+Minimizer \n \n Goodness of fit qualifiers:\n chi_squared 3.2950323615883767e-19\n objective_value 1.6475161807941883e-19\n r_squared -inf\n /PKGBUILDDIR/symfit/core/fit_results.py:279: RuntimeWarning: divide by zero\n encountered in scalar divide\n@@ -124,15 +124,15 @@\n plt.plot(s_data[1:], F_re, label=r'$F_{re}(s)$')\n plt.xlabel(r'$x$')\n plt.xlabel(r'$F(s)$')\n plt.xlim(0, None)\n plt.legend()\n (100,) (100,)\n [8]:\n-\n+\n [../_images/examples_ex_tikhonov_15_2.png]\n Reconstruct \\(f(t)\\) and compare with the known original.\n [9]:\n t_data = np.linspace(0, 10, 101)\n f_data = model(t=t_data).f\n f_re_func = lambda x: - np.exp(- x * s_data[1:]).dot(ans.c) / fit_result.value\n (a)**2\n@@ -142,15 +142,15 @@\n plt.axvline(0, color='black')\n plt.plot(t_data, f_data, label=r'$f(t)$')\n plt.plot(t_data, f_re, label=r'$f_{re}(t)$')\n plt.xlabel(r'$t$')\n plt.xlabel(r'$f(t)$')\n plt.legend()\n [9]:\n-\n+\n [../_images/examples_ex_tikhonov_17_1.png]\n Not bad, for an ill-defined problem.\n *\b**\b**\b**\b**\b**\b* _\bs\bs_\by\by_\bm\bm_\bf\bf_\bi\bi_\bt\bt *\b**\b**\b**\b**\b**\b*\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bI_\bn_\bt_\br_\bo_\bd_\bu_\bc_\bt_\bi_\bo_\bn\n * _\bI_\bn_\bs_\bt_\ba_\bl_\bl_\ba_\bt_\bi_\bo_\bn\n * _\bT_\bu_\bt_\bo_\br_\bi_\ba_\bl\n"}]}, {"source1": "./usr/share/doc/python3-symfit/html/examples/ex_tikhonov.ipynb.gz", "source2": "./usr/share/doc/python3-symfit/html/examples/ex_tikhonov.ipynb.gz", "unified_diff": null, "details": [{"source1": "ex_tikhonov.ipynb", "source2": "ex_tikhonov.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9990797305764412%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-08-28T13:12:57.733780Z', \"", " \"'iopub.status.busy': '2024-08-28T13:12:57.732715Z', 'iopub.status.idle': \"", " \"'2024-08-28T13:12:58.933669Z', 'shell.execute_reply': \"", " \"'2024-08-28T13:12:58.932892Z'}}, 'outputs': {0: {'text': ['Matplotlib created a \"", " 'temporary cache directory at /tmp/matplotlib-guufbfms because the default path '", " '(/nonexistent/second-build/.config/matplotlib) is no [\u2026]"], "unified_diff": "@@ -14,29 +14,29 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:13.003598Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:13.002453Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.189410Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.188510Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:57.733780Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:57.732715Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:58.933669Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:58.932892Z\"\n },\n \"pycharm\": {\n \"is_executing\": false\n }\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n- \"Matplotlib created a temporary cache directory at /tmp/matplotlib-7i3pdkcj because the default path (/nonexistent/first-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\\n\"\n+ \"Matplotlib created a temporary cache directory at /tmp/matplotlib-guufbfms because the default path (/nonexistent/second-build/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from symfit import (\\n\",\n \"\\tvariables, parameters, Model, Fit, exp, laplace_transform, symbols, \\n\",\n \"\\tMatrixSymbol, sqrt, Inverse, CallableModel\\n\",\n@@ -58,18 +58,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.195638Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.194312Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.548129Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.547184Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:58.938803Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:58.938099Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.235394Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.234484Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -107,30 +107,30 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.552533Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.552259Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.782417Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.781557Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:59.239765Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:59.239477Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.469266Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.468458Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -173,18 +173,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.786821Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.786548Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.797639Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.796742Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:59.474301Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:59.473733Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.485976Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.484673Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -244,18 +244,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.801766Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.801493Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.807513Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.806591Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:59.490923Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:59.490632Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.497310Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.496495Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -289,18 +289,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.811792Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.811519Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.815718Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.814771Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:59.501905Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:59.501644Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.507131Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.505727Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -315,18 +315,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.819949Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.819678Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:14.878742Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:14.877898Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:59.511789Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:59.511497Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.614539Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.613499Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -336,16 +336,16 @@\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Parameter Value Standard Deviation\\n\",\n \"a 5.449374e-02 None\\n\",\n \"Status message Optimization terminated successfully.\\n\",\n \"Number of iterations 14\\n\",\n- \"Objective \\n\",\n- \"Minimizer \\n\",\n+ \"Objective \\n\",\n+ \"Minimizer \\n\",\n \"\\n\",\n \"Goodness of fit qualifiers:\\n\",\n \"chi_squared 3.2950323615883767e-19\\n\",\n \"objective_value 1.6475161807941883e-19\\n\",\n \"r_squared -inf\\n\"\n ]\n },\n@@ -378,18 +378,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:14.883405Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:14.883109Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:15.053425Z\",\n- \"shell.execute_reply\": \"2024-08-28T13:08:15.052566Z\"\n+ \"iopub.execute_input\": \"2024-08-28T13:12:59.620206Z\",\n+ \"iopub.status.busy\": \"2024-08-28T13:12:59.619793Z\",\n+ \"iopub.status.idle\": \"2024-08-28T13:12:59.835408Z\",\n+ \"shell.execute_reply\": \"2024-08-28T13:12:59.834412Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -400,15 +400,15 @@\n \"text\": [\n \"(100,) (100,)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -449,30 +449,30 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-08-28T13:08:15.057844Z\",\n- \"iopub.status.busy\": \"2024-08-28T13:08:15.057569Z\",\n- \"iopub.status.idle\": \"2024-08-28T13:08:15.236497Z\",\n- 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\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -7,17 +7,17 @@\n from symfit import (\n variables, parameters, Model, Fit, exp, laplace_transform, symbols,\n MatrixSymbol, sqrt, Inverse, CallableModel\n )\n import numpy as np\n import matplotlib.pyplot as plt\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-7i3pdkcj\n-because the default path (/nonexistent/first-build/.config/matplotlib) is not a\n-writable directory; it is highly recommended to set the MPLCONFIGDIR\n+Matplotlib created a temporary cache directory at /tmp/matplotlib-guufbfms\n+because the default path (/nonexistent/second-build/.config/matplotlib) is not\n+a writable directory; it is highly recommended to set the MPLCONFIGDIR\n environment variable to a writable directory, in particular to speed up the\n import of Matplotlib and to better support multiprocessing.\n Say \\(f(t) = t * exp(- t)\\), and \\(F(s)\\) is the Laplace transform of \\(f(t)\\).\n Let us first evaluate this transform using sympy.\n [2]:\n t, f, s, F = variables('t, f, s, F')\n model = Model({f: t * exp(- t)})\n@@ -45,15 +45,15 @@\n = F(s)$')\n plt.xlabel(r'$s_i$')\n plt.ylabel(r'$F(s_i)$')\n plt.xlim(0, None)\n plt.legend()\n \n [3]:\n-