\n
\n \n
\n \n
\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-t_adwsmf 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-bwq0xkt6 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 0x7f3b2caf5100>\n+<matplotlib.legend.Legend at 0x7f466c9db4a0>\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-t_adwsmf\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-bwq0xkt6\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 -2.3593505171408347\n+num value -2.3532773507667026\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 -2.3519104613566135\n+num value -2.3519104613380346\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-krnigjla 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-m3nk5ksj 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@@ -146,27 +146,27 @@\n
[2]:\n \n
\n \n
\n \n
\n \n \n Parameter Value Standard Deviation\n-k1_f 9.540423e-02 4.440700e-03\n-k1_r 1.065123e-01 7.165733e-02\n-k2_f 2.706130e-01 5.305070e-02\n-k2_r 2.633628e-01 5.647256e-02\n+k1_f 9.540425e-02 4.440697e-03\n+k1_r 1.065138e-01 7.165712e-02\n+k2_f 2.706132e-01 5.305062e-02\n+k2_r 2.633627e-01 5.647246e-02\n Status message CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n Number of iterations 30\n-Objective <symfit.core.objectives.LeastSquares object at 0x7f56a680e6c0>\n-Minimizer <symfit.core.minimizers.LBFGSB object at 0x7f56a688b500>\n+Objective <symfit.core.objectives.LeastSquares object at 0x7f345555b350>\n+Minimizer <symfit.core.minimizers.LBFGSB object at 0x7f345557fe60>\n \n Goodness of fit qualifiers:\n-chi_squared 33.985494535052595\n-objective_value 16.992747267526298\n-r_squared 0.9936568366400244\n+chi_squared 33.98549453673938\n+objective_value 16.99274726836969\n+r_squared 0.9936568366397095\n \n \n \n \n \n
[5]:\n taxis = np.linspace(tdata.min(), tdata.max(), 1000)\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-krnigjla\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-m3nk5ksj\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@@ -70,28 +70,28 @@\n fit = Fit(model, t=tdata, MM=data[MM], F=data[F],\n FMMF=None, FMM=None,\n sigma_F=sigma_data, sigma_MM=sigma_data)\n fit_result = fit.execute()\n print(fit_result)\n \n Parameter Value Standard Deviation\n-k1_f 9.540423e-02 4.440700e-03\n-k1_r 1.065123e-01 7.165733e-02\n-k2_f 2.706130e-01 5.305070e-02\n-k2_r 2.633628e-01 5.647256e-02\n+k1_f 9.540425e-02 4.440697e-03\n+k1_r 1.065138e-01 7.165712e-02\n+k2_f 2.706132e-01 5.305062e-02\n+k2_r 2.633627e-01 5.647246e-02\n Status message CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n Number of iterations 30\n Objective \n-Minimizer \n+0x7f345555b350>\n+Minimizer \n \n Goodness of fit qualifiers:\n-chi_squared 33.985494535052595\n-objective_value 16.992747267526298\n-r_squared 0.9936568366400244\n+chi_squared 33.98549453673938\n+objective_value 16.99274726836969\n+r_squared 0.9936568366397095\n [5]:\n taxis = np.linspace(tdata.min(), tdata.max(), 1000)\n model_fit = model(t=taxis, **fit_result.params)._asdict()\n for var in data:\n plt.scatter(tdata, data[var], label='[{}]'.format(var.name))\n plt.plot(taxis, model_fit[var], label='[{}]'.format(var.name))\n plt.legend()\n"}]}, {"source1": "./usr/share/doc/python3-symfit/html/examples/ex_ode_system.ipynb.gz", "source2": "./usr/share/doc/python3-symfit/html/examples/ex_ode_system.ipynb.gz", "unified_diff": null, "details": [{"source1": "ex_ode_system.ipynb", "source2": "ex_ode_system.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9988541666666666%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2025-10-15T13:26:18.002576Z', \"", " \"'iopub.status.busy': '2025-10-15T13:26:18.002075Z', 'iopub.status.idle': \"", " \"'2025-10-15T13:26:19.437304Z', 'shell.execute_reply': \"", " \"'2025-10-15T13:26:19.436523Z'}}, 'outputs': {0: {'text': ['Matplotlib created a \"", " 'temporary cache directory at /tmp/matplotlib-m3nk5ksj because the default path '", " '(/nonexistent/second-build/.config/matplotlib) is no [\u2026]"], "unified_diff": "@@ -13,29 +13,29 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-09-12T06:53:34.807663Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:34.807238Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:53:36.776053Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:53:36.775072Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:18.002576Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:18.002075Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:19.437304Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:19.436523Z\"\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-krnigjla 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-m3nk5ksj 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, ODEModel, D, Fit\\n\",\n \") \\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\": \"2024-09-12T06:53:36.784388Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:36.783366Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:53:36.860949Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:53:36.860168Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:19.441021Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:19.440379Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:19.484617Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:19.483709Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -118,18 +118,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-09-12T06:53:36.866556Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:36.866143Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:53:37.876227Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:53:37.874924Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:19.488877Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:19.488202Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:19.848556Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:19.847751Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n@@ -177,45 +177,45 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-09-12T06:53:37.892467Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:37.891900Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:53:53.861655Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:53:53.859419Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:19.851977Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:19.851216Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:27.486590Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:27.485559Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Parameter Value Standard Deviation\\n\",\n- \"k1_f 9.540423e-02 4.440700e-03\\n\",\n- \"k1_r 1.065123e-01 7.165733e-02\\n\",\n- \"k2_f 2.706130e-01 5.305070e-02\\n\",\n- \"k2_r 2.633628e-01 5.647256e-02\\n\",\n+ \"k1_f 9.540425e-02 4.440697e-03\\n\",\n+ \"k1_r 1.065138e-01 7.165712e-02\\n\",\n+ \"k2_f 2.706132e-01 5.305062e-02\\n\",\n+ \"k2_r 2.633627e-01 5.647246e-02\\n\",\n \"Status message CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\\n\",\n \"Number of iterations 30\\n\",\n- \"Objective \\n\",\n- \"Minimizer \\n\",\n+ \"Objective \\n\",\n+ \"Minimizer \\n\",\n \"\\n\",\n \"Goodness of fit qualifiers:\\n\",\n- \"chi_squared 33.985494535052595\\n\",\n- \"objective_value 16.992747267526298\\n\",\n- \"r_squared 0.9936568366400244\\n\"\n+ \"chi_squared 33.98549453673938\\n\",\n+ \"objective_value 16.99274726836969\\n\",\n+ \"r_squared 0.9936568366397095\\n\"\n ]\n }\n ],\n \"source\": [\n \"k1_f.min, k1_f.max = 0, 1\\n\",\n \"k1_r.min, k1_r.max = 0, 1\\n\",\n \"k2_f.min, k2_f.max = 0, 1\\n\",\n@@ -229,18 +229,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-09-12T06:53:53.871226Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:53.870735Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:53:54.394104Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:53:54.386871Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:27.496759Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:27.496247Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:27.989241Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:27.988306Z\"\n },\n \"pycharm\": {\n \"is_executing\": false,\n \"metadata\": false,\n \"name\": \"#%%\\n\"\n }\n },\n"}]}]}, {"source1": "./usr/share/doc/python3-symfit/html/examples/ex_tikhonov.html", "source2": "./usr/share/doc/python3-symfit/html/examples/ex_tikhonov.html", "unified_diff": "@@ -53,15 +53,15 @@\n \n
\n \n
\n \n
\n \n-Matplotlib created a temporary cache directory at /tmp/matplotlib-ows7pygk 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-dpynu0_0 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 0x7efc479942f0>\n+<matplotlib.legend.Legend at 0x7f20ef1a3e30>\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 0x7efc47a7b2c0>\n-Minimizer <symfit.core.minimizers.BFGS object at 0x7efc47a79280>\n+Objective <symfit.core.objectives.LeastSquares object at 0x7f20eee18740>\n+Minimizer <symfit.core.minimizers.BFGS object at 0x7f20eee2c230>\n \n Goodness of fit qualifiers:\n chi_squared 3.2864985083579836e-19\n objective_value 1.6432492541789918e-19\n r_squared -inf\n \n \n \n
\n [8]:\n \n
\n \n-<matplotlib.legend.Legend at 0x7efc4782bb30>\n+<matplotlib.legend.Legend at 0x7f20ef136300>\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 0x7efc477e11c0>\n+<matplotlib.legend.Legend at 0x7f20eee181a0>\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-ows7pygk\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-dpynu0_0\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)$')\n plt.xlim(0, None)\n plt.legend()\n \n [3]:\n-\n+\n [../_images/examples_ex_tikhonov_5_1.png]\n We will now invert this data, using the procedure outlined in \\cite{}.\n [4]:\n N_s = symbols('N_s', integer=True) # Number of s_i points\n \n M = MatrixSymbol('M', N_s, N_s)\n W = MatrixSymbol('W', N_s, N_s)\n@@ -100,16 +100,16 @@\n \n \n Parameter Value Standard Deviation\n a 5.449374e-02 None\n Status message Optimization terminated successfully.\n Number of iterations 14\n Objective \n-Minimizer \n+0x7f20eee18740>\n+Minimizer \n \n Goodness of fit qualifiers:\n chi_squared 3.2864985083579836e-19\n objective_value 1.6432492541789918e-19\n r_squared -inf\n /PKGBUILDDIR/symfit/core/fit_results.py:279: RuntimeWarning: divide by zero\n encountered in scalar divide\n@@ -124,15 +124,15 @@\n plt.plot(s_data[1:], F_re, label=r'$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': '2025-10-15T13:26:31.022395Z', \"", " \"'iopub.status.busy': '2025-10-15T13:26:31.021418Z', 'iopub.status.idle': \"", " \"'2025-10-15T13:26:32.516512Z', 'shell.execute_reply': \"", " \"'2025-10-15T13:26:32.515522Z'}}, 'outputs': {0: {'text': ['Matplotlib created a \"", " 'temporary cache directory at /tmp/matplotlib-dpynu0_0 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\": \"2024-09-12T06:53:57.838933Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:57.838501Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:53:59.986357Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:53:59.985229Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:31.022395Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:31.021418Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:32.516512Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:32.515522Z\"\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-ows7pygk 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-dpynu0_0 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\": \"2024-09-12T06:53:59.991367Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:53:59.990536Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:00.652907Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:00.651941Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:32.521819Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:32.520959Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:32.911964Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:32.911320Z\"\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\": \"2024-09-12T06:54:00.662994Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:00.662636Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.061875Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.060905Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:32.915807Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:32.915215Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.181299Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.180672Z\"\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\": \"2024-09-12T06:54:01.065711Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:01.065334Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.082620Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.081616Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:33.185856Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:33.185388Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.196473Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.195888Z\"\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\": \"2024-09-12T06:54:01.086357Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:01.086046Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.092921Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.092009Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:33.200106Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:33.199636Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.204486Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.203919Z\"\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\": \"2024-09-12T06:54:01.096764Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:01.096435Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.101341Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.100474Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:33.207794Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:33.207332Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.210595Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.210020Z\"\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\": \"2024-09-12T06:54:01.104815Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:01.104499Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.208171Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.207122Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:33.213846Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:33.213378Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.275911Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.275264Z\"\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 3.2864985083579836e-19\\n\",\n \"objective_value 1.6432492541789918e-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\": \"2024-09-12T06:54:01.212077Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:01.211748Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.498428Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.497496Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:33.280176Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:33.279643Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.480241Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.479593Z\"\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\": \"2024-09-12T06:54:01.502456Z\",\n- \"iopub.status.busy\": \"2024-09-12T06:54:01.502141Z\",\n- \"iopub.status.idle\": \"2024-09-12T06:54:01.791050Z\",\n- \"shell.execute_reply\": \"2024-09-12T06:54:01.788450Z\"\n+ \"iopub.execute_input\": \"2025-10-15T13:26:33.484062Z\",\n+ \"iopub.status.busy\": \"2025-10-15T13:26:33.483449Z\",\n+ \"iopub.status.idle\": \"2025-10-15T13:26:33.698573Z\",\n+ \"shell.execute_reply\": \"2025-10-15T13:26:33.697942Z\"\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\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n"}]}]}, {"source1": "./usr/share/doc/python3-symfit/html/searchindex.js", "source2": "./usr/share/doc/python3-symfit/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -1358,35 +1358,35 @@\n \"0324\": [4, 7],\n \"0393\": [4, 7],\n \"04\": 5,\n \"05\": [5, 14],\n \"051163e\": 13,\n \"053\": 22,\n \"0546\": [4, 7],\n- \"065123e\": 12,\n+ \"065138e\": 12,\n \"0697\": [4, 7],\n \"075395e\": 6,\n \"08\": 22,\n \"080979e\": 6,\n \"086\": [4, 7],\n \"0875\": [4, 7],\n \"09\": 22,\n \"0939\": [4, 7],\n \"09866205647752\": 10,\n \"09866205722533\": 10,\n- \"0x7efc477e11c0\": 15,\n- \"0x7efc4782bb30\": 15,\n- \"0x7efc479942f0\": 15,\n- \"0x7efc47a79280\": 15,\n- \"0x7efc47a7b2c0\": 15,\n- \"0x7f133ef03c80\": 5,\n- \"0x7f133f0111f0\": 5,\n- \"0x7f3b2caf5100\": 10,\n- \"0x7f56a680e6c0\": 12,\n- \"0x7f56a688b500\": 12,\n+ \"0x7f114a36aab0\": 5,\n+ \"0x7f114a6f9d30\": 5,\n+ \"0x7f20eee181a0\": 15,\n+ \"0x7f20eee18740\": 15,\n+ \"0x7f20eee2c230\": 15,\n+ \"0x7f20ef136300\": 15,\n+ \"0x7f20ef1a3e30\": 15,\n+ \"0x7f345555b350\": 12,\n+ \"0x7f345557fe60\": 12,\n+ \"0x7f466c9db4a0\": 10,\n \"1\": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, 21, 22, 23, 26],\n \"10\": [2, 3, 8, 10, 12, 14, 15, 17, 22, 23],\n \"100\": [5, 10, 15, 17, 26],\n \"1000\": [3, 9, 11, 12, 17],\n \"10000\": 23,\n \"100000\": 5,\n \"100898e\": 5,\n@@ -1404,15 +1404,15 @@\n \"132108e\": 6,\n \"14\": [15, 17, 22, 26],\n \"1495\": [4, 7],\n \"15\": [17, 26],\n \"150\": 17,\n \"150556e\": 5,\n \"16\": 12,\n- \"165733e\": 12,\n+ \"165712e\": 12,\n \"17\": [4, 7],\n \"1761\": [4, 7],\n \"18\": 13,\n \"180\": 17,\n \"19\": [15, 26],\n \"197889e\": 6,\n \"1e\": 22,\n@@ -1447,83 +1447,81 @@\n \"2d\": 14,\n \"2x\": 17,\n \"2xy\": 17,\n \"2y\": 17,\n \"3\": [2, 3, 5, 6, 8, 10, 12, 13, 14, 15, 17, 20, 22, 24, 26],\n \"30\": 12,\n \"303400e\": 5,\n- \"305070e\": 12,\n+ \"305062e\": 12,\n \"31\": [4, 7],\n \"31e29e235407e4913dc0ec403af7ed524372b612\": 23,\n \"325068e\": 6,\n \"33\": 12,\n \"3367571\": 26,\n \"34\": 17,\n \"35\": [4, 7],\n \"35191046133532\": 10,\n- \"3519104613566135\": 10,\n- \"3593505171408347\": 10,\n+ \"3519104613380346\": 10,\n+ \"3532773507667026\": 10,\n \"3718\": [4, 7],\n \"39\": 5,\n \"3922\": [4, 7],\n \"3rd\": 19,\n \"4\": [2, 5, 6, 10, 12, 13, 15, 17, 22, 26],\n \"40\": 22,\n \"400000e\": 13,\n \"42\": [4, 5, 7, 11, 12, 17],\n \"423\": 22,\n \"44\": [6, 17],\n- \"440700e\": 12,\n+ \"440697e\": 12,\n \"449374e\": 15,\n \"459\": 5,\n \"46\": [4, 7],\n \"461684e\": 5,\n \"4747\": [4, 7],\n \"481346e\": 5,\n \"485\": 5,\n \"49172\": 26,\n- \"4u7ue4i5\": 5,\n \"5\": [0, 2, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, 22, 23, 26],\n \"50\": [4, 7, 8, 9, 13, 22],\n \"500\": 17,\n \"5014170\": 23,\n \"5156\": [4, 7],\n \"5255\": [4, 7],\n \"54\": 17,\n- \"540423e\": 12,\n+ \"540425e\": 12,\n \"546980e\": 6,\n \"55\": [4, 7],\n \"555\": 22,\n \"5622\": [4, 7],\n \"59\": 5,\n \"5999999737968128\": 3,\n \"6\": [3, 5, 6, 10, 11, 13, 14, 15, 17, 22, 26],\n \"60\": 26,\n \"61\": [4, 7],\n- \"633628e\": 12,\n+ \"633627e\": 12,\n \"637273e\": 6,\n \"6432492541789918e\": 15,\n- \"647256e\": 12,\n+ \"647246e\": 12,\n \"65\": [4, 7],\n \"6555\": [4, 7],\n \"6558\": 26,\n \"6603\": [4, 7],\n \"66946\": 26,\n \"671471e\": 6,\n \"69\": 17,\n \"7\": [5, 10, 12, 15, 17, 22],\n \"70\": [4, 7, 17],\n- \"706130e\": 12,\n+ \"706132e\": 12,\n \"71\": 17,\n \"725019e\": 6,\n \"73\": 17,\n \"736484e\": 14,\n \"780338e\": 13,\n \"7949\": [4, 7],\n- \"7u62gyb2\": 3,\n \"8\": [5, 6, 7, 8, 9, 13, 15, 17],\n \"80\": 4,\n \"827\": [4, 7],\n \"846406e\": 6,\n \"8558226069368662\": 13,\n \"857033e\": 6,\n \"8739\": [4, 7],\n@@ -1538,19 +1536,19 @@\n \"9401712713086535\": 6,\n \"95\": 22,\n \"950890866472\": 26,\n \"973\": [4, 7],\n \"973489e\": 14,\n \"9783\": [4, 7],\n \"9849188499599985\": 13,\n- \"985494535052595\": 12,\n+ \"98549453673938\": 12,\n \"986491e\": 6,\n \"99\": 17,\n- \"992747267526298\": 12,\n- \"9936568366400244\": 12,\n+ \"99274726836969\": 12,\n+ \"9936568366397095\": 12,\n \"9952824293713467\": 14,\n \"996901e\": 14,\n \"9993\": [4, 7],\n \"A\": [4, 7, 14, 15, 17, 20, 22, 26],\n \"And\": [0, 20, 22, 23],\n \"As\": [0, 6, 13, 17, 20, 22, 23],\n \"At\": 0,\n@@ -1765,14 +1763,15 @@\n \"bool\": 22,\n \"both\": [0, 11, 17, 20, 22],\n \"bound\": [0, 1, 13, 17, 20, 22],\n \"boundedminim\": 22,\n \"buffer\": 22,\n \"build\": [0, 3, 5, 10, 12, 15, 17, 22, 23],\n \"bullet\": 23,\n+ \"bwq0xkt6\": 10,\n \"c\": [12, 15, 17, 19],\n \"c1\": [14, 17],\n \"c2\": [14, 17],\n \"c_1\": 17,\n \"c_2\": 17,\n \"c_i\": 17,\n \"cach\": [3, 5, 10, 12, 15, 22],\n@@ -1971,14 +1970,16 @@\n \"doe\": [0, 10, 13, 17, 20, 22, 23, 26],\n \"doesn\": 17,\n \"domain\": [1, 17],\n \"don\": [1, 17, 22],\n \"done\": [2, 17, 22],\n \"dot\": 15,\n \"download\": 19,\n+ \"dpynu0_0\": 15,\n+ \"dqxywuk2\": 3,\n \"draw\": [5, 11, 17, 22],\n \"drawn\": 22,\n \"drop\": 22,\n \"dt\": 17,\n \"ducktyp\": 22,\n \"due\": [1, 3, 17, 23],\n \"dummymodel\": 22,\n@@ -2079,15 +2080,15 @@\n \"figur\": 22,\n \"filecopyrighttext\": [7, 8, 9],\n \"fill\": 22,\n \"final\": [5, 20],\n \"find\": [10, 15, 16, 17, 22],\n \"finit\": 22,\n \"finite_differ\": 22,\n- \"first\": [3, 4, 5, 7, 10, 11, 12, 13, 15, 17, 22],\n+ \"first\": [4, 7, 10, 11, 12, 13, 15, 17, 22],\n \"firstli\": 17,\n \"fit\": [2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 15, 16, 18, 20, 23, 24, 25, 26],\n \"fit_no_sigma\": 23,\n \"fit_result\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, 22, 23, 26],\n \"fit_result_no_sigma\": 23,\n \"fitmodelswithmeasurementerror\": 23,\n \"fitresult\": [0, 17, 22, 26],\n@@ -2292,15 +2293,14 @@\n \"key2str\": [12, 22],\n \"keyword\": [0, 17, 22, 26],\n \"kind\": [22, 26],\n \"kinet\": [7, 16, 17],\n \"know\": [0, 15, 17, 22],\n \"knowledg\": [17, 22],\n \"known\": [15, 23],\n- \"krnigjla\": 12,\n \"kwarg\": [0, 22],\n \"l\": [6, 15, 17, 21, 22],\n \"label\": [2, 4, 10, 12, 15, 17],\n \"lack\": 12,\n \"lambda\": [3, 15, 17, 22],\n \"languag\": 23,\n \"laplac\": 15,\n@@ -2352,14 +2352,15 @@\n \"lot\": [0, 13, 22],\n \"lower\": 22,\n \"lsoda\": 22,\n \"lt\": [5, 10, 12, 15],\n \"luck\": 26,\n \"luckili\": 17,\n \"m\": [4, 15, 17, 22],\n+ \"m3nk5ksj\": 12,\n \"m_mat\": 15,\n \"made\": [12, 13, 17, 22],\n \"magic\": [2, 26],\n \"mai\": [17, 22],\n \"mail\": 17,\n \"make\": [0, 1, 6, 13, 14, 16, 17, 20, 22, 26],\n \"man\": 23,\n@@ -2541,15 +2542,14 @@\n \"outlin\": [15, 23],\n \"output\": 22,\n \"outsid\": 0,\n \"over\": [17, 20, 22],\n \"overal\": 17,\n \"overflow\": 5,\n \"own\": [0, 17, 20, 22],\n- \"ows7pygk\": 15,\n \"p\": [17, 21, 22, 26],\n \"packag\": [20, 22],\n \"page\": [18, 22],\n \"pai\": 1,\n \"pair\": 22,\n \"paper\": 16,\n \"param\": [2, 4, 6, 12, 13, 14, 15, 17, 20, 22, 26],\n@@ -2731,15 +2731,15 @@\n \"scipyhessianminim\": 22,\n \"scipyminim\": [0, 22],\n \"screenshot\": [7, 8, 9],\n \"script\": 13,\n \"seaborn\": [1, 14],\n \"seamlessli\": 22,\n \"search\": 18,\n- \"second\": 22,\n+ \"second\": [3, 5, 10, 12, 15, 22],\n \"secondli\": 17,\n \"secretli\": 15,\n \"section\": 17,\n \"see\": [5, 8, 10, 11, 12, 17, 19, 22, 23, 26],\n \"seed\": [5, 11, 12, 13, 15, 22, 23],\n \"seem\": 17,\n \"select\": [17, 22, 23],\n@@ -2876,15 +2876,14 @@\n \"symfit\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 22, 23],\n \"sympi\": [1, 10, 15, 16, 18, 20, 22],\n \"sympy_to_pi\": 22,\n \"sympy_to_scipi\": 22,\n \"syntax\": [17, 26],\n \"system\": [12, 17, 22],\n \"t\": [1, 3, 4, 5, 7, 10, 12, 15, 17, 22],\n- \"t_adwsmf\": 10,\n \"t_axi\": 4,\n \"t_data\": 15,\n \"t_i\": 15,\n \"tabl\": 22,\n \"take\": [0, 6, 17, 22],\n \"taken\": [4, 7, 8, 22],\n \"takesdata\": 22,\n@@ -2897,14 +2896,15 @@\n \"tell\": [10, 12, 17, 22],\n \"temperatur\": 17,\n \"temporari\": [3, 5, 10, 12, 15],\n \"term\": [6, 17, 22],\n \"termin\": [6, 13, 14, 15, 19],\n \"test\": [22, 23],\n \"text\": [4, 6, 13],\n+ \"tg8csqc2\": 5,\n \"th\": 22,\n \"tha\": 22,\n \"than\": [8, 22, 24],\n \"the_rate_law\": [4, 7],\n \"thei\": [0, 12, 16, 17, 22],\n \"them\": [12, 17, 20, 22],\n \"themselv\": 22,\n"}]}]}]}]}]}
\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-ows7pygk\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-dpynu0_0\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-