{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.8vfm3NRc/b1/lmfit-py_1.0.1-6_i386.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.8vfm3NRc/b2/lmfit-py_1.0.1-6_i386.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,3 +1,3 @@\n \n- 43c7ec51a09d6739e429ef03596b698f 1516068 doc optional python-lmfit-doc_1.0.1-6_all.deb\n+ b360675ddb3556bf9235bc225c541b52 1515780 doc optional python-lmfit-doc_1.0.1-6_all.deb\n ede7fa9b9581eeaa87d24e7c642c46bf 68932 python optional python3-lmfit_1.0.1-6_all.deb\n"}, {"source1": "python-lmfit-doc_1.0.1-6_all.deb", "source2": "python-lmfit-doc_1.0.1-6_all.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r-- 0 0 0 4 2021-05-06 08:32:40.000000 debian-binary\n--rw-r--r-- 0 0 0 6184 2021-05-06 08:32:40.000000 control.tar.xz\n--rw-r--r-- 0 0 0 1509692 2021-05-06 08:32:40.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 6180 2021-05-06 08:32:40.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 1509408 2021-05-06 08:32:40.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: python-lmfit-doc\n Source: lmfit-py\n Version: 1.0.1-6\n Architecture: all\n Maintainer: Debian Science Maintainers
We can immediately see the best-fit values:
\nprint(result.values)\n
Out:
\n-{'N': 6.984064048780478, 'tau': 3.0113542941657188}\n+{'N': 6.984064048745731, 'tau': 3.0113542942000042}\n
\n \n and use these best-fit parameters for plotting with the plot
function:
\n result.plot()\n
\n \n \n@@ -350,15 +350,15 @@\n [[Variables]]\n N: 7.46253423 +/- 0.24975620 (3.35%) (init = 10)\n tau: 2.79210670 +/- 0.12172740 (4.36%) (init = 1)\n [[Correlations]] (unreported correlations are < 0.100)\n C(N, tau) = -0.932\n
\n \n-Total running time of the script: ( 0 minutes 0.248 seconds)
\n+Total running time of the script: ( 0 minutes 0.665 seconds)
\n \n
We can see that this fit is already very good, which is what we should expect\n since our brute
force grid is sampled rather finely and encompasses the\n \u201ccorrect\u201d values.
In a more realistic, complicated example the brute
method will be used\n to get reasonable values for the parameters and perform another minimization\n@@ -543,15 +543,15 @@\n plt.plot(x, data + fcn2min(best_result.params, x, data), 'g--',\n label='brute followed by leastsq')\n plt.legend()\n
Out:
\n-<matplotlib.legend.Legend object at 0xee386508>\n+<matplotlib.legend.Legend object at 0xaf1d01a8>\n
\n \n Finally, the results from the brute
force grid-search can be visualized\n using the rather lengthy Python function below (which might get incorporated\n in lmfit at some point).
\n def plot_results_brute(result, best_vals=True, varlabels=None,\n output=None):\n@@ -657,15 +657,15 @@\n
\n \n and finally, to generated the figure:
\n plot_results_brute(result_brute, best_vals=True, varlabels=None)\n
\n \n \n-Total running time of the script: ( 0 minutes 40.749 seconds)
\n+Total running time of the script: ( 1 minutes 32.872 seconds)
\n \n \n-Total running time of the script: ( 0 minutes 33.637 seconds)
\n+Total running time of the script: ( 1 minutes 4.569 seconds)
\n \n
Out:
\n-<matplotlib.legend.Legend object at 0xee63c6b8>\n+<matplotlib.legend.Legend object at 0xaf151190>\n
\n \n Plot the influence on the parameter value and error of each point
\n fig, axs = plt.subplots(4, figsize=(4, 7), sharex='col')\n axs[0].plot(x, best_vals['a'])\n axs[0].scatter(x[idx], best_vals['a'][idx], color='r', label='outlier')\n axs[0].set_ylabel('best a')\n@@ -250,15 +250,15 @@\n
\n \n \n Out:
\n Text(0.5, 37.499999999999936, 'x')\n
\n \n-Total running time of the script: ( 0 minutes 0.942 seconds)
\n+Total running time of the script: ( 0 minutes 2.232 seconds)
\n \n
Total running time of the script: ( 0 minutes 0.316 seconds)
\n+Total running time of the script: ( 0 minutes 0.801 seconds)
\n \n \n-Total running time of the script: ( 0 minutes 0.149 seconds)
\n+Total running time of the script: ( 0 minutes 0.407 seconds)
\n \nTotal running time of the script: ( 0 minutes 0.119 seconds)
\n+Total running time of the script: ( 0 minutes 0.322 seconds)
\n